Journal of Intelligent & Fuzzy Systems - Volume 38, issue 4

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3527-3527, 2020

Authors: Yang, Xinfeng | Hu, Qiping | Li, Shuaihao

Article Type: Research Article

Abstract: With the development of technology, fingerprint identification has become an effective means of personal identification, and has been widely used in the fields of public security, custom, banking, network security and other areas requiring identification. Nowadays, many effective methods have been proposed for fingerprint identification, but these methods are not effective in identifying damaged fingerprints, and the correct recognition rate is low. In order to effectively solve the problem of identification and classification of damaged fingerprints, this paper proposes a method for classification of broken fingerprints based on deep learning fuzzy theory. Firstly, after pre-processing the fingerprint, using the bifurcation …point and the endpoint in the broken fingerprint image as the minutiae, the feature extraction ability of the deep convolutional neural network is utilized to extract the feature of the damaged fingerprint minutiae. Secondly, the fuzzy rough set is used to reduce the feature. Finally, using the reduced feature uses the Softmax classifier to classify the damaged fingerprint image. The simulation results show that, after preprocessing the damaged fingerprint image, using OPTA algorithm to refine the damaged fingerprint image, the features of the fingerprint image can be extracted effectively by deep convolutional neural network, and then the classification accuracy can be improved by using Softmax classifier to reduce the features. Show more

Keywords: Damaged fingerprint recognition, deep learning, OPTA algorithm, deep convolutional neural network, softmax classifier

DOI: 10.3233/JIFS-179575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3529-3537, 2020

Authors: Yang, Xinfeng | Hu, Qiping | Li, Shuaihao

Article Type: Research Article

Abstract: With the development of society, health has attracted more and more attention. Heart disease is a common and frequently occurring disease, and it is fatal. Rapid and timely diagnosis and treatment of heart disease is very important. Electrocardiogram (ECG) reflects human heart health and is widely used in heart disease examination. Existing methods depending on doctors’ personal experience and diagnostic level are time-consuming and inefficient. Therefore, a classification method that can automatically analyze ECG is required. Aiming at the classification of 12-lead ECG, based on the good performance of convolution neural network, this paper proposes a method of ECG classification …based on lead convolution neural network, which can effectively and accurately detect, recognize and classify ECG. First, the image features are extracted after the ECG is preprocessed, and then using the fuzzy set reduces the extracted ECG image features. Then, residual learning is used to optimize the convolutional neural network, and in order to ensure that the network is easy to train and fast convergence, a random parameter initialization method is introduced to achieve better classification results. The simulation results show that the proposed multi-lead filtering algorithm reduces the loss of useful information while eliminating noise; at the same time, the convolution neural network can effectively and accurately classify ECG images; and the introduction of residual network can improve the classification effect. Show more

Keywords: Electrocardiogram, 12 lead, convolutional neural network, multi lead filter, residual learning

DOI: 10.3233/JIFS-179576

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3539-3548, 2020

Authors: He, Tao | He, Miao

Article Type: Research Article

Abstract:  With the establishment and development of technologies and theories such as computer technology, image processing, pattern recognition and artificial intelligence, image analysis systems have gradually become one of the methods of automatic quantitative analysis and testing in the medical field. However, the current technology is limited to the objectivity and comprehensiveness of blood edge detection, and no detection method with high accuracy can be found. In order to accurately and effectively detect the blood color of the color ultrasound image, this paper classifies the image feature extraction method, and simulates the classical differential algorithm, mathematical morphology algorithm and fuzzy Pal.King. …In the following, the classical canny algorithm and its improved algorithm and the improved fuzzy Pal.King algorithm are introduced in detail. Finally, the simulation results are obtained. Among the indicators tested, DD has the highest accuracy. In the curve analysis, the FI value of FIB was > 0.05, the area under the curve of FDP was 67.9%, the sensitivity was 64%, and the specificity was 59%. In this paper, the quantitative analysis of the image feature extraction effect is given by the calculation results, and the subjective and objective unity is achieved. At the same time, the improved algorithm proposed in this paper is applied to the evaluation system for analysis and summary, and the results obtained are consistent with the theoretical analysis. Show more

Keywords: Fuzzy algorithm, color ultrasound image

DOI: 10.3233/JIFS-179577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3549-3556, 2020

Authors: Wang, Shuqiang | Liang, Shuo | Peng, Fei

Article Type: Research Article

Abstract: Edge detection is image processing, analysis and one of the most important areas of research in the field of computer vision; it is the basic tools of pattern recognition and image information extraction. Actual image processing is generally mixed with noise. How to eliminate the false edge caused by noise interference and ensure the accuracy of edge positioning, it becomes an important problem to be solved in edge detection and is also the purpose of this paper. Firstly, a histogram matching image enhancement algorithm based on maximum fuzzy entropy dynamic improvement is proposed. The algorithm first maps gray scale images …from the spatial domain to the fuzzy domain, the target image is divided into several gray layers based on maximum fuzzy entropy. And then, for the characteristics of different gray levels, histogram matching method is used to design corresponding matching function for each gray layer. These matching functions are used to enhance the corresponding gray layer to obtain the enhanced image. Image enhancement method combines fuzzy entropy and histogram matching algorithm, it can effectively suppress noise and improve image contrast ratio. Secondly, an image edge detection algorithm based on improved fuzzy theory is proposed. This algorithm uses the improved fuzzy enhancement algorithm to enhance the original image. The non-maximum suppression algorithm is used to process the enhanced image; the optimal threshold value is obtained by fuzzy extraction and maximum inter-group variance method. This algorithm is used for edge detection of image. Experiments show that the algorithm is feasible and effective, and has some advantages. Show more

Keywords: Edge detection, fuzzy set, histogram algorithm, image processing

DOI: 10.3233/JIFS-179578

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3557-3566, 2020

Authors: Yu, Changqing | Wang, Liguang | Zhao, Jiong | Hao, Li | Shen, Yafeng

Article Type: Research Article

Abstract: With the development of modern remote sensing technology, remote sensing images have become one of the powerful tools for people to understand the Earth and its surroundings. However, there is currently no good classification algorithm that can accurately classify images. In order to accurately classify remote sensing images, this paper studies the content of the article by using fuzzy C-means clustering algorithm and radial basis neural network (RBF). The classification accuracy of SIRI-WHU dataset was analyzed by using the classification accuracy evaluation index such as overall accuracy and Kappa coefficient. The Kappa coefficient of vegetation classification in SIRI-WHU dataset was …0.9678, and the overall accuracy reached 97.18%. According to the classification problem of remote sensing image, according to the characteristics of remote sensing image, the improved model Alex Net-10-FCM is used to classify the remote sensing image dataset, and very high classification accuracy is obtained. Show more

Keywords: Remote sensing image classification, fuzzy C-means clustering algorithm, Kappa coefficient, data set, RBP neural network

DOI: 10.3233/JIFS-179579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3567-3574, 2020

Authors: Zhang, Le | Wang, Jinsong | An, Zhiyong

Article Type: Research Article

Abstract: The purpose of image segmentation is to select the target region from the existing image, which is the core technology for image understanding, description and analysis. When faced with some complicated problems, the image segmentation effect of the traditional method is often unsatisfactory. As a branch of the swarm intelligence optimization algorithm, Particle Swarm Optimization (PSO) provides a new power and direction for the development of image segmentation. However, the algorithm has a large probability of loss of particle diversity in the late stage, which makes the algorithm converge prematurely. Therefore, the purpose of this paper is to improve the …problem existing in the PSO algorithm and apply the improved algorithm in image segmentation. In this paper, the whole population of PSO algorithm is divided into multiple sub-populations and co-evolution. The mutation operation from the genetic algorithm is introduced at the same time. The worst sub-population is mutated according to the mutation probability. The larger inertia factor is selected to speed the particles. Update, and then carry out simulation experiments on some classical test functions. Finally, combined with the improved PSO algorithm and fuzzy C-means clustering algorithm (FCM), the fuzzy clustering validity index is introduced, and the blood cell image is segmented by the algorithm. The experimental results show that the algorithm can find a reasonable number of cluster center segmentation categories and efficiently perform adaptive segmentation of images. Show more

Keywords: Fractional particle swarm, maximum entropy, c-means clustering algorithm, image segmentation

DOI: 10.3233/JIFS-179580

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3575-3584, 2020

Authors: Lu, Chunfeng

Article Type: Research Article

Abstract: The moving target tracking is a very important in computer vision research topic and is widely used, it is of great research significance, has been widely used, and involved in image recognition, production automation, intelligent pattern recognition, artificial intelligence, weather information, and other fields, but in some high speed movement, such as target under complex background target trajectory when not much target tracking is still more difficult. This paper mainly studies the Kalman tracking algorithm of ping-pong robot based on fuzzy real-time image. For table tennis high-speed motion blurred images, air resistance and the camera imaging distortion caused by factors …such as the error problem, puts forward an adaptive measurement covariance discrete Kalman trajectory estimation algorithm. The algorithm with dynamic adjustment of measuring the size of the covariance, has realized the accurate tracking of the target motion trajectory, and further laid the groundwork for table tennis balls prediction and hitting arm. The experimental results show that the algorithm can effectively overcome the interference of measurement noise and data loss and give excellent tracking results when the image acquisition rate is higher than 70 frames /s and the table tennis speed is higher than 5 m/s. Show more

Keywords: Fuzzy ecognition, ping-pong robot, real-time tracking, image recognition, Kalman filtering

DOI: 10.3233/JIFS-179581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3585-3594, 2020

Authors: Yin, Deshuai | Hou, Rui | Du, Junchao | Chang, Liang | Yue, Hongxuan | Wang, Liusheng | Liu, Jiayue

Article Type: Research Article

Abstract: OBJECTIVE: The purpose of this study is to realize the precise detection of Synthetic Aperture Radar (SAR) image changes. METHODS: In this study, an intuitionistic fuzzy C-means clustering algorithm is used to accurately detect the target changes in SAR images. The change of SAR image is detected by the constructed intuitionistic fuzzy C-means clustering algorithm. Then, the effect of intuitionistic fuzzy C-means clustering algorithm, block principal component analysis (PCA) and logarithmic ratio method is compared and analyzed in the aspects of stability, accuracy, image extraction, restoration, error and work efficiency of the algorithm. RESULTS: Compared with …block PCA and logarithmic ratio methods, intuitionistic fuzzy C-means clustering algorithm has obvious advantages in stability, with standard deviation of 0.010 and other two algorithms of 0.014 and 0.017. In terms of detection accuracy and error, the algorithm in this study also has a good performance, and the detection accuracy can reach 92.4%. In addition, the intuitionistic fuzzy C-means clustering algorithm is clear and efficient for SAR image target extraction and restoration. Compared with the other two algorithms, the algorithm in this study improves by at least 20% in operation speed. There is no significant difference in the detection results of the proposed algorithm for SAR images with different targets, such as objects, people, geographical environment, etc. CONCLUSION: In this study, based on intuitionistic fuzzy C-means clustering algorithm, target changes in SAR images are detected, and the operation of the algorithm is studied. The algorithm used in this study shows a relatively comprehensive and good result, and also shows that the algorithm is a comprehensive result, which requires a good operation at many levels. This research greatly improves the recognition of intuitionistic fuzzy C-means clustering algorithm and SAR image. Show more

Keywords: Algorithms, models, images, SAR, change

DOI: 10.3233/JIFS-179582

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3595-3604, 2020

Authors: Xu, Huan Chun | Hou, Rui | Liu, Lan | Cai, Jiao Yong | Chen, Ji Gang | Liu, Jia Yue

Article Type: Research Article

Abstract: The conventional image segmentation algorithm of the colorimetric sensor array is inefficient and vulnerable to the interferences of the environment. Therefore, in order to improve the conventional algorithm, an image segmentation algorithm based on fuzzy C-means clustering (FCM) algorithm is proposed in this study. Through the information of the gray-scale distribution histogram, the proposed algorithm divides the different wave-peak regions, where the pixels are relatively concentrated, into different clusters to determine the number of clusters. In addition, the gray values of these clusters are calculated to determine the initial cluster center. Next, the calculation results are used as the input …of the FCM algorithm to complete the clustering segmentation of FCM. The research results show that the algorithm proposed in this study avoids the human participations of the traditional FCM algorithm. Also, based on the original algorithm, the proposed algorithm can reduce the calculation iterations, thereby improving the computational efficiency and obtaining the number of clusters with reference significance. As the results indicate, the proposed algorithm can better describe the fuzzy information in the image, thereby avoiding the problem of classifying the pixels into one category. Besides, the exponential function is used to control the influence weight of the neighboring pixels, and the adaptive weighting of the pixel grayscale is realized to improve the calculation accuracy of pixel grayscale and realize the image segmentation. Show more

Keywords: Fuzzy C-means clustering, image segmentation, colorimetric sensor array

DOI: 10.3233/JIFS-179583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3605-3613, 2020

Authors: Yue, Changwu | Li, Xiaoqian | Zhao, Wen | Cui, Xiangyi | Wang, Yinyin

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219320 .

DOI: 10.3233/JIFS-179584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3615-3624, 2020

Authors: Li, Hui

Article Type: Research Article

Abstract: The purpose is to use medical image processing technology to avoid the influence of subjective factors through the mutual penetration and development of clinical medicine and computer science. Can diagnose the degree of malignancy of ischemic optic neuropathy as quickly as possible, and can take an effective treatment plan for the patient early.Therefore, image segmentation of ischemic optic neuropathy based on fuzzy clustering theory is particularly important for the diagnosis of disease in patients. This paper analyzes the research status of medical image segmentation at home and abroad and the development trend in this aspect in China. Discussed the fuzzy …C-means clustering (FCM) image segmentation algorithm in depth, studied the effects of iterative cutoff error, initial clustering center, number of clustering categories and fuzzy weighted index on the practical application of the algorithm. At the same time, the traditional algorithm is not sensitive to the spatial information of the image, making the algorithm sensitive to noise. Firstly, introduced the spatial information of the image, and introduced the algorithm based on spatial information constraint, Based on the above description and based on the neighborhood properties described by the two-dimensional histogram, studied and proposed a relatively easy to understand multidimensional distance measurement method. That is, the two-dimensional pixel value and the neighborhood pixel value viewpoint that can be updated in the two-dimensional direction, by setting a clustering objective function, a clustering measurement method includes neighborhood information. Through the above two-dimensional image segmentation algorithm based on neighborhood spatial information, proposed an image segmentation algorithm for ischemic optic neuropathy of fuzzy kernel clustering theory combined with spatial information. The experimental results show that the proposed algorithm can show excellent results in ischemic neuropathy image segmentation, and the algorithm has faster convergence speed and higher classification accuracy. Experimental results of artificial images and actual images show that the algorithm has strong noise immunity and practicability. Show more

Keywords: Medical image segmentation, fuzzy c-means, kernel method, fuzzy clustering algorithm, spatial information

DOI: 10.3233/JIFS-179585

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3625-3633, 2020

Authors: Ning, Yuwen | Shi, Xiaoyuan | Yin, Jingong | Xie, Duowen

Article Type: Research Article

Abstract: Medical image processing is an interdisciplinary subject of integrated medical imaging, mathematics, computer science and other disciplines. With high spatial resolution, high signal-to-noise ratio and high resolution of soft tissue, the technology can accurately locate the target areas of interest in medical images, thus providing useful information for clinicians to formulate disease treatment plans. These techniques include digital subtraction angiography, magnetic resonance imaging, computed tomography, ultrasound imaging and positron emission tomography. The purpose of this paper is to study the application of fuzzy C-means clustering in image analysis of critical medicine. This paper discusses the classification effect, clustering process, iteration …times and running time of different algorithms, and the segmentation effect of different algorithms. By designing parameters and carrying out simulation experiments, the traditional clustering algorithm and improved local adaptive method are compared, and the problem of long coding time of traditional image compression algorithm is solved. The simulation results under the same working environment show that the coding speed of the algorithm is about five times faster than that of the traditional image compression algorithm without affecting the signal-to-noise ratio and compression rate, which proves the superiority of the algorithm. Show more

Keywords: Medical image processing, fuzzy C-means algorithm, clustering algorithm, medical image analysis

DOI: 10.3233/JIFS-179586

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3635-3645, 2020

Authors: Zhi, Hui | Liu, Sanyang

Article Type: Research Article

Abstract: The regions obtained by image segmentation need to satisfy both the requirements of uniformity and connectivity. Image segmentation is the process of dividing an image into several specific regions. The result of image segmentation is a set of combinations covering the main feature areas of the whole image. The pixels in an area are similar to some or calculated characteristics, but there are obvious differences between adjacent areas. In this paper, a gray image segmentation algorithm based on fuzzy C-means combined with bee colony algorithm is proposed, which has strong optimization ability for multi-objective problems. By using the fuzzy membership …function of the fuzzy C-means algorithm, the optimal clustering centers in the artificial bee colony optimization algorithm can be quickly calculated. It makes image segmentation faster and more accurate. The bee colony search algorithm is optimized and an effective local search algorithm is designed, it makes the bee colony converge to the optimal solution efficiently. Finally, the improved fuzzy C-means and artificial bee colony optimization algorithm are used to improve and optimize the seed region growth method. The multi-criteria are taken as the multi-objective optimization problem, and the segmentation results are finally obtained. Benefiting from our local search program and feature extraction in multi-color space, it makes the stability; efficiency and accuracy of image segmentation are higher. Show more

Keywords: Fuzzy clustering, c-means clustering, artificial bee colony, gray image segmentation

DOI: 10.3233/JIFS-179587

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3647-3655, 2020

Authors: Huo, Jiaofei | Lin, Dong | Qi, Wanqiang

Article Type: Research Article

Abstract: With the rapid development of modern industry and science and technology, mechanical equipment has become larger, faster and more intelligent. In real life, there is no absolutely safe and reliable equipment, so it is impossible to require mechanical equipment not to break down in the operation process, and the working environment of mechanical equipment is complex and harsh, aging is serious, and breakdowns occur frequently. Research on effective intelligent fault detection methods has become a theoretical hot spot of current discipline research. Intelligent fault diagnosis of mechanical equipment is based on the algorithm to analyze the problems of equipment fault. …In this paper, a fault detection model of mechanical equipment is proposed based on the method of fuzzy pattern recognition, and the fault detection is classified by the method of Fuzzy C-Means clustering. In this paper, the method of mechanical equipment fault detection based on Convolutional Neural Network is compared with the method proposed in this paper. The experimental results show that the model has good performance in fault detection and has strong practicability. Show more

Keywords: Fault diagnosis of mechanical equipment, fuzzy pattern recognition, convolutional neural network, fuzzy c-means clustering

DOI: 10.3233/JIFS-179588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3657-3664, 2020

Authors: Xiao, Fuyuan

Article Type: Research Article

Abstract:  The complex-value-based generalized Dempster–Shafer evidence theory, also called complex evidence theory is a useful methodology to handle uncertainty problems of decision-making on the framework of complex plane. In this paper, we propose a new concept of belief function in complex evidence theory. Furthermore, we analyze the axioms of the proposed belief function, then define a plausibility function in complex evidence theory. The newly defined belief and plausibility functions are the generalizations of the traditional ones in Dempster–Shafer (DS) evidence theory, respectively. In particular, when the complex basic belief assignments are degenerated from complex numbers to classical basic belief assignments (BBAs), …the generalized belief and plausibility functions in complex evidence theory degenerate into the traditional belief and plausibility functions in DS evidence theory, respectively. Some special types of the generalized belief function are further discussed as well as their characteristics. In addition, an interval constructed by the generalized belief and plausibility functions can be utilized for fuzzy measure, which provides a promising way to express and model the uncertainty in decision theory. Show more

Keywords: Complex evidence theory, generalized dempster–Shafer evidence theory, generalized belief function, generalized plausibility function, complex basic belief assignment, complex mass function, uncertainty modelling, fuzzy measure, decision theory

DOI: 10.3233/JIFS-179589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3665-3673, 2020

Authors: Zhou, Mu | Li, Xinyue | Wang, Yong | Yang, Xiaolong | Tian, Zengshan

Article Type: Research Article

Abstract: In this paper, we derive out an analytical result towards fuzzy error bound of indoor Neighbor Matching based Positioning Algorithm (NMPA). First of all, in a typical Line-of-sight (LOS) environment, we utilize the fuzzy comprehensive evaluation approach to obtain the fuzzy membership of the target in fingerprint matching. Second, we present an analysis of the theoretical relationship between the positioning error of NMPA and different environmental parameters. Third, we work out the fuzzy closed form solution to the positioning error of NMPA concerning the size of environment, the spacing of Reference Points (RPs), number of neighbors, and positions of Access …Points (APs). Finally, relevant experiments verify that the fuzzy error bound proposed in this paper can reflect the influence of different factors of the environment on the performance of the positioning system, thereby, the positioning accuracy can be improved and the deployment costs can be reduced by optimizing the environmental parameters. Show more

Keywords: Indoor localization, fuzzy membership, environmental parameters, error bound, neighbor matching

DOI: 10.3233/JIFS-179590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3675-3686, 2020

Authors: Liu, Zhu | Zhou, Mu | Nie, Wei | Xie, Liangbo | Tian, Zengshan

Article Type: Research Article

Abstract: At present, indoor intrusion detection technologies based on WLAN are widely applied to protect the privacy of users and have a robust anti-interference ability under the condition of the Non-line-of-sight (NLOS), which become the mainstream topics of domestic and foreign studies. Most of the existing researches rely on the signal strength to train heuristic models, while the relationship between intrusion targets and signal fluctuations is not explored fully. In this circumstance, this paper proposes a novel indoor intrusion detection method based on fuzzy membership degree and Dempster-Shafer Theory (DST). First of all, the correlation between WLAN signal fluctuation features and …locations of intrusion targets are converted into DST mass function by fuzzy membership. Second, the reliability values from each MP are combined to select reliable reference positions by using the reliability combination rules in DST. Finally, the positions of the intrusion target are calculated based on the weighted maximum likelihood and centroid method. Finally, the related experimental results show that the proposed approach can not only ensure the high accuracy of intrusion detection but also obtain ideally accurate locations of the intrusion target. Show more

Keywords: Passive intrusion detection, indoor WLAN, fuzzy membership, Dempster-Shafer theory, weighted maximum likelihood and centroid method

DOI: 10.3233/JIFS-179591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3687-3696, 2020

Authors: Liu, Shi | Li, Tingting

Article Type: Research Article

Abstract: The digital media technology major is a multi-disciplinary and multi-disciplinary discipline. The cultivated talents should be based on applied talents. According to the traditional practice teaching methods, the disciplines are no longer suitable for the discipline. Colleges and universities strengthen the practice teaching reform of the digital media technology profession. It is a very important and necessary means. The purpose of this paper is to realize the evaluation of the reform effect of AVG comprehensive practice in digital media art through fuzzy theory. In this paper, the fuzzy comprehensive evaluation method is used to comprehensively evaluate the AVG practice of …digital media art. Because the fuzzy comprehensive evaluation method is very restrictive to the index weight, it cannot be well adapted to the system. In this paper, the processing scheme of the index weight value of the fuzzy comprehensive evaluation method is interval data, so that the algorithm can be better applied in the system. In the determination of weight, this paper uses the improved entropy weight method to determine the weight of the index. By comparing with other algorithms that obtain the weight of the index, it can be concluded that using the improved entropy weight method to obtain the weight value can not only effectively reduce the external interference. Moreover, the weight value obtained can well reflect the importance of the indicator. The experimental results show that the dynamic variability of fuzzy set theory can satisfactorily meet the comprehensiveness and reliability of AVG comprehensive practice reform evaluation results. Therefore, the application of fuzzy set theory in the field of AVG comprehensive practice reform effect evaluation is reasonable and accurate. Show more

Keywords: Fuzzy theory, digital media art, AVG comprehensive practice, education reform

DOI: 10.3233/JIFS-179592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3697-3706, 2020

Authors: Wang, Pin | Fan, En | Wang, Peng

Article Type: Research Article

Abstract: All along, the identification of night-driving safety car features is a major research direction in the field of intelligent traffic management, with a wide range of applications and development space, and these identification technologies include theoretical knowledge and important theoretical research in many fields. Due to the interference of lights and other light sources, the gray value of the background area also changes frequently. A common method during the day is to detect these background areas as moving vehicles, which greatly reduces the detection accuracy. The most reliable information at night is the headlights. If the light can be accurately …detected and other sources are excluded, accurate detection can be performed and tracking accuracy can be guaranteed. Vehicle safety assisted driving technology is one of the main research directions of intelligent transportation systems. It mainly uses computer technology, sensor technology and communication technology to collect and analyze the state information of roads, other vehicles and drivers. Provide advice and warnings to the driver before reaching the danger, determine current traffic conditions and avoid traffic accidents in advance. This paper studies some problems of night vehicle target recognition and detection, mainly the division of target and background, and the classification and recognition of target extraction. To solve these problems, a particle filter algorithm is introduced to introduce nonlinear statistics. The fuzzy theory is introduced to classify the video processed by the particle filter algorithm. The target recognition is realized by the method, and the purpose of identifying the night vehicle target is achieved. Comparative experimental analysis shows that this method is more accurate and powerful than the common target recognition algorithm and can be applied to real scenes. Show more

Keywords: Night vehicle recognition, particle filter algorithm, nonlinear statistics, fuzzy clustering

DOI: 10.3233/JIFS-179593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3707-3716, 2020

Authors: Zhou, Jingyong | Guo, Yuan | Sun, Yu | Wu, Kai

Article Type: Research Article

Abstract: With the rapid development of database and Internet technologies, data collection and storage is possible. It is often impossible to correctly analyze the valuable information contained in the data, and it becomes more difficult to obtain valuable information. Therefore, it faces the status of “rich data and scarce knowledge”. Traditional information processing technology can no longer meet the needs of reality. There is an urgent need for more capable and effective information processing skills to help us analyze the information we need from big data and guide us to make the right decisions. Data mining technology is born in the …background. Data mining technology is one of the effective methods to solve rich data and improve lack of knowledge. It is also one of the main research topics in the field of information science. Related research and applications have greatly improved people’s decision-making ability. It has been recognized as one of the extremes of data research and has a very broad application prospect. Large databases often contain redundant and unnecessary attributes for many search rules, so the ability to remove duplicate attributes can greatly improve the clarity of potential system knowledge and reduce the time complexity of finding rules. At the same time, it enables efficient operation and improved adaptability. Because the structure of the neural network is variable, it has strong self-organization, self-learning, nonlinearity and high fault tolerance, but the ability to express and interpret knowledge is very poor. The network parameters lack physical meaning and learning. Therefore, it has become an inevitable trend to form a fuzzy neural network combining the characteristics of the two. Therefore, exploring the organic combination between rough sets and fuzzy neural networks is undoubtedly of great significance for data mining technology research. This paper proposes a data mining method based on the combination of rough set and fuzzy neural network technology. Using the approximate set to discover the rules of the database rules, the initial structure of the fuzzy neural network is determined, and the training data is used to train the network. Since the fuzzy neural network thus constructed has a good topology of data distribution features from the beginning, the network scale can be greatly reduced and the network training speed can be improved. Show more

Keywords: Data mining, rough set, fuzzy logic, fuzzy neural network

DOI: 10.3233/JIFS-179594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3717-3725, 2020

Authors: Zhao, Wei | Luo, Zeju

Article Type: Research Article

Abstract: With the advent of Web 3.0 era, the number and complexity of Web pages in Bayesian networks have shown an explosive growth trend. Accompanying this is the geometric growth of information contained in Web pages. Web text data in Bayesian networks usually hide rich knowledge and rules of user value. However, due to the semi-structured, real-time and discrete characteristics of Web text data, it is difficult for users to obtain the knowledge they need directly from such complex data sets. The emergence of fuzzy mathematics provides a good research idea and method for solving such problems. It can use the …idea of fuzzy mathematics to analyze the practical problems in text data. Therefore, how to effectively mine the Web text data information and knowledge that users really care about from Bayesian network, and present it in a way that users can understand, it is a very popular research topic at present. In this paper, we select the text of Bayesian network: microblog data for experiments. User data model of microblog is established by using relevant knowledge of fuzzy theory. The concept of fuzzy measure is introduced to calculate the non-additive measure value under the interaction relationship between the detection indicators. Determine the membership function relationship between the detection user and the text data, calculate the integral values of Choquet integral, Sugeno integral and Wang integral of the membership function under the non-additive measure, the final valuable web text data is judged by integral value. On the basis of the above research contents, the research results of Web text mining technology and fuzzy arithmetic mathematics are combined, design and implement information acquisition and analysis for Bayesian network community. The recall rate obtained by the experimental method in this paper is as low as 4%, and tends to be more stable. Show more

Keywords: Fuzzy algorithm, Bayesian network, data mining, web text data mining

DOI: 10.3233/JIFS-179595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3727-3735, 2020

Authors: Man, Na | Wang, Kechao | Liu, Lin

Article Type: Research Article

Abstract: A fuzzy system is a system that defines input, output, and state variables on a fuzzy set and is a generalization of a deterministic system. The fuzzy system begins at the macro level and covers the fuzzy features of human brain thinking. It has advantages in describing advanced knowledge. Fuzzy sets can mimic the comprehensive conclusions of people solving fuzzy information problems, which are difficult to solve by conventional mathematical methods, so computer applications can be extended to humanities, social sciences and complex systems. In this way, it can better solve nonlinear problems and is widely used in automatic control, …decision analysis, time series signal processing, economic information systems, medical diagnostic systems, and earthquake prediction systems. This paper aims to study the data mining algorithm of fuzzy systems based on fuzzy sets. By using the powerful fuzzy data modeling function of fuzzy theory, it combines with other intelligent processing methods, and makes practical use in industrial life. By comparing the application of fuzzy set data mining and algorithm, it is found that in the application state, the economic benefits of the factory are improved by 36% and the production efficiency is improved by 25% under the application of data mining and algorithm. The research data shows that the data mining and recommendation algorithms of fuzzy sets are beneficial to the development and operation of the factory. The research results show that compared with the conventional production and processing plan, the technology uses fuzzy set theory to transform the fuzzy attributes, which is more advantageous in scientific and technical systems and algorithms with its scientificity, accuracy, innovation and extensiveness. Show more

Keywords: Data mining, recommendation algorithm, fuzzy system, fuzzy set

DOI: 10.3233/JIFS-179596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3737-3745, 2020

Authors: Zhang, Jing

Article Type: Research Article

Abstract: In recent years, with the rapid increase in the number of electric vehicles in China, the accidents caused by electrical system failures are also increasing year by year. Therefore, it is necessary to carry out reliability analysis on the electrical systems of electric vehicles. Traditional reliability analysis cannot be quantitatively evaluated and it is not possible to accurately diagnose multiple fault conditions of the system. Aiming at this problem, this paper combines fuzzy mathematics theory with fault tree analysis, and establishes a multi-state fuzzy fault tree to analyze the reliability of pure electric bus high-voltage electrical system, including qualitative analysis …and quantitative analysis. The results show that the multi-state fuzzy fault tree reliability analysis method can accurately describe the various fault states of the high-voltage electric system of pure electric passenger car, and can quantitatively evaluate the reliability, which has great practical significance. Show more

Keywords: Polymorphic fuzzy fault tree, eliability analysis

DOI: 10.3233/JIFS-179597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3747-3754, 2020

Authors: Chen, Ying | Qi, Pengyuan | Liu, Songqing

Article Type: Research Article

Abstract: In order to effectively avoid the violent vibration in the process of mechanical processing and to achieve high efficiency and high precision machining of mechanical parts, the improved algorithm of adaptive neuro-fuzzy inference system is used to study the optimization of parameters in the process of side milling of mechanical parts, and an adaptive network structure is formed. It has the learning ability of artificial neural network and the expression ability of “if-then” of fuzzy reasoning system, which is a new prediction and control method. The results validate the applicability of the stability. The machined surface topography is measured and …the effect of flutter on the surface topography is analyzed. The three-dimensional stability of milling provides a theoretical basis for the rational selection of milling parameters of mechanical parts, the realization of stable milling and the improvement of processing efficiency. Thus, the relationship between the radial depth of cut, the axial depth of cut and the spindle speed is established, and the contour of material removal rate is obtained. The corresponding spindle speed and radial shear depth are obtained when the material removal rate is maximum. The reasonable selection of machining parameters is carried out in the region near the maximum spindle speed with stability. Show more

Keywords: Adaptive neuro-fuzzy reasoning system, machining, parameter optimization, machining error

DOI: 10.3233/JIFS-179598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3755-3764, 2020

Authors: Wang, Hanxu | Yao, Yubing

Article Type: Research Article

Abstract:  With the development and popularization of electronic computers and the Internet, the problem of language barriers has once again become prominent in the new era, and people are more in need of machine translation. However, there is currently no suitable method for effective semantic ordering of English machine translation. In order to better perform semantic ordering on English machine translation, the article combines fuzzy theory to construct an algorithm model, and analyzes the experimental results through evaluation indicators. The results show that with the increase of training concentration training examples, the semantic parser can learn more natural language sentence analysis …methods from the training examples, and the natural language sentences that can be correctly parsed gradually increase, so with the training examples increased recall rate and F value gradually increased. The experimental results also show that the use of higher precision syntax analyzers can effectively improve the performance of statistical machine translation systems, whether in phrase-based or machine-based translation methods. Show more

Keywords: Semantic ordering, machine translation, fuzzy theory, semantic parser, system performance

DOI: 10.3233/JIFS-179599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3765-3772, 2020

Authors: Gao, Huanbing | Lu, Shouyin | Wang, Tao

Article Type: Research Article

Abstract: The fuzzy control algorithm is used to establish the relation between the functions of motion of 6-DOF (degree of freedom) industrial robots on the rotation angle of each joint. The algorithm optimizes the motion path of the robot, thereby accelerating the increase in industrial productivity and promoting the development of industrial production. During the motions, the 6-DOF industrial robots have weak avoidance ability toward the encountered obstacles, which is not conducive to the safe production and will reduce industrial efficiency. Therefore, by analyzing and summarizing the previous researches, the fuzzy control algorithm is used to construct and optimize the kinematics …model, thereby proposing a method of robot motion path planning. Also, based on the unstructured operating environment, a multi-functional motion navigation system for 6-DOF industrial robots is proposed. The experimental results show that the fuzzy control algorithm can optimize the robot motion path, shorten the time of motion, and make the robots reach the destinations smoothly. The algorithm can avoid safety accidents in industrial production effectively, reduce casualties, improve industrial productivity, and promote the optimized allocation of human resources. The motion system of 6-DOF industrial robot based on fuzzy control algorithm has excellent practicability, which can promote the development of industrial production effectively and be widely applied to industrial production. Show more

Keywords: Fuzzy control, motion path, robot

DOI: 10.3233/JIFS-179600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3773-3782, 2020

Authors: Xu, Huanchun | Hou, Rui | Fan, Jinfeng | Zhou, Liang | Yue, Hongxuan | Wang, Liusheng | Liu, Jiayue

Article Type: Research Article

Abstract: The data of time series are massive in quantity and not conducive to subsequent processing. Therefore, the unordered time series fuzzy clustering algorithm of adaptive incremental learning has been utilized to explore the segmentation of time series in further. The research results show that the emergence of incremental learning technology can solve such problems. Also, it can continuously accumulate and increase the data, as well as improving the learning accuracy. Incremental learning technology correctly processes, retains, and utilizes the historical results, thereby reducing the training time of new samples by using historical results. Therefore, the clustering algorithm mostly clusters the …cluster-liked shape of discrete datasets and uses the hierarchical clustering algorithm, which is more suitable for measuring the similarity of time series, to replace the Euclidean distance for distance metric and hierarchical clustering. The distance matrix update method is improved to reduce the computational complexity, which proves that the algorithm has higher clustering validity and reduces the operating time of the algorithm. Show more

Keywords: Time series, incremental learning, fuzzy clustering

DOI: 10.3233/JIFS-179601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3783-3791, 2020

Authors: Liu, Yunpeng | Dong, Xinling

Article Type: Research Article

Abstract: Network virtualization technology releases human resources to some extent through network and cloud computing technology, reducing the workload of staff. The application of network virtualization technology in cloud computing data centers is based on this condition to improve work quality and efficiency. The purpose of this paper is to use the fuzzy algorithm to realize network virtualization of cloud computing data center. In this paper, we study the adaptive fuzzy control in depth, and conduct the practical application based on the basic knowledge of adaptive fuzzy control we learned, achieved “learn to use”. Apply the design of adaptive fuzzy control …to the load balancing algorithm of the network virtual cloud computing data center, realized the load balancing algorithm of the network virtual cloud computing data center based on adaptive fuzzy control. According to the load balancing algorithm based on adaptive fuzzy control to achieve this algorithm by using Internet knowledge, and designed the load balancing system of the whole network virtual cloud computing data center. Test the whole load balancing system which has been achieved, and obtained the performance variance curve of the system under different algorithms. Then obtained advantages and disadvantages of the algorithm by analyzing the experimental data. The experimental results show that the proposed method can effectively improve the execution performance of communication-intensive applications and ensure the stable execution of the application. At the same time, the algorithm inherits the advantages of the general fuzzy control load balancing algorithm. The stability is strong and the variance curve does not appear pulsed fluctuation. There is also no divergence phenomenon with time increased. Show more

Keywords: Fuzzy Algorithm, Network Virtualization, Cloud Computing, Network Load

DOI: 10.3233/JIFS-179602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3793-3801, 2020

Authors: Chen, Guobin | Chen, Zhongsheng

Article Type: Research Article

Abstract: With the rapid development of the economy, the demand for urban land resources is also growing. How to make more rational use of land resources and make more rational planning of cities become a major problem in current economic development. At present, the use of remote sensing images to classify urban land use areas has become a research hot spot. However, the traditional classification accuracy rate using the maximum likelihood classification method needs to be improved. How to improve the classification accuracy rate of urban land use area of remote sensing image has become the focus and key of the …research. Both rough sets and fuzzy sets are mathematical methods for dealing with uncertain problems. The rough fuzzy sets generated by the combination of the two can solve the problem of information loss due to the rough set discretization process. Based on the advantages of fuzzy rough sets, this paper applies fuzzy rough sets to the study of urban land use area classification of remote sensing images, so as to improve the accuracy of urban land use area classification of remote sensing images. Firstly, the spectral features and texture features of the remote sensing image are extracted after preprocessing the remote sensing image. Secondly, using the domain relationship fuzzy rough set reduces the extracted features. Finally, the support vector machine is used to classify the reduced feature set, and the classification of urban land use area is realized. In the simulation experiment, the classification accuracy is evaluated by the overall classification accuracy, Kappa coefficient, and single class classification success index. The evaluation data shows that the fuzzy rough set is applied to the remote sensing image urban land use area classification, which has a good application effect. Show more

Keywords: Urban land use area, remote sensing image, fuzzy rough set, support vector machine

DOI: 10.3233/JIFS-179603

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3803-3812, 2020

Authors: Wang, Zhe | Zhu, Hong

Article Type: Research Article

Abstract: In order to achieve the improvement and optimization of e-commerce logistics, a fuzzy algorithm was built to optimize the e-commerce logistics in the marine economy. Through the construction of the fuzzy algorithm, a comparative analysis was made on the effects of logistics before the optimization of marine e-commerce, such as customer satisfaction, product arrival rate on time, logistics cost, number of routes, safety rate, work efficiency, and other aspects. The research shows that the fuzzy algorithm has a better performance in logistics cost than before. After optimization, the monthly mileage of logistics has also been significantly reduced, and the economic …benefit is very significant. The algorithm in this paper has an excellent effect in customer satisfaction and product arrival rate on time, which has obvious improvement effect on the logistics network. The improvement of the customer satisfaction is of great value and significance to the follow-up development of marine e-commerce. The improvement and optimization of logistics path based on fuzzy algorithm has reference value for the development of logistics in other fields. Based on the fuzzy algorithm, the function of the algorithm is studied by analyzing the logistics optimization of marine e-commerce. The fuzzy algorithm used shows a comprehensive and excellent result in improving the marine logistics situation, and shows that the optimization and improvement of the algorithm is a process that needs to be improved in many ways. The research greatly improves the understanding of the fuzzy algorithm and the e-commerce logistics in the marine economy environment. Show more

Keywords: Fuzzy algorithm, marine economy, e-commerce logistics optimization

DOI: 10.3233/JIFS-179604

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3813-3821, 2020

Authors: Lyu, Yuhong | Li, Xiaoqian | Wang, Xuemei | Zhao, Wen | Cui, Xiangyi | Yue, Changwu

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219321 .

DOI: 10.3233/JIFS-179605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3823-3829, 2020

Authors: Zhu, Wenjun | Shi, Huisheng

Article Type: Research Article

Abstract: With the wide application of fuzzy mathematics in various fields of life, fuzzy mathematics has shown its strong vitality in many disciplines, such as civil architecture, environmental protection, medical science, operations management, chemical industry and so on. With the increasing depth of research issues, various disciplines are also constantly intersecting. Fuzzy mathematics is gradually being combined with other analytical methods. The purpose of this paper is to discuss the application of fuzzy mathematics in cartography. Through the quota selection model and the structure selection model, the membership function, the equal ratio sequence method, and other fuzzy mathematics methods are applied …to the river map making. Through the above methods, it is concluded that they can solve many kinds of problems, including the study of the geographical distribution of cartographic objects, cartographic selection, mutual relations, and evaluation and prediction models. Therefore, it can be concluded that the concept of fuzzy mathematics is applied to cartographic generalization. Fuzzy mathematics can deal with this kind of fuzziness better, which makes cartographic generalization possible to use more map information. It also provides a new means for the study of cartographic generalization. At the same time, it also provides a new research way for map database and automatic mapping. The accuracy of the experimental method in this paper is 5% higher than that of traditional mathematical cartography; it tends to restore the truth. Show more

Keywords: Fuzzy mathematics, mapping, equal ratio series, comprehensive evaluation

DOI: 10.3233/JIFS-179606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3831-3840, 2020

Authors: Feng, Yanping

Article Type: Research Article

Abstract: It is a common disease of abnormal early pregnancy in obstetrics and gynecology. Inside them, abortion and hydatidiform mole are two common types, which seriously threaten the life safety of pregnant women. To evaluate the diagnostic value of transvaginal ultrasound and serum-HCG in hydatidiform mole and missed abortion. This article introduces the application of transvaginal ultrasound in disease diagnosis. An edge detection algorithm based on multiplicative gradient was proposed: normal pregnant women and abnormal pregnant women were selected as the research objects. According to the pathological examination results of abnormal pregnancy, all pregnant women were divided into two groups: hydatidiform …mole group and abortion group. The level of serum β -HCG was measured and its diagnostic value was analyzed. Transvaginal ultrasound combined with serum beta-hcg confirmed the diagnosis of β -HCG. Show more

Keywords: Vaginal ultrasound, hydatidiform mole, missed abortion, edge detection algorithm, multiplicative gradient

DOI: 10.3233/JIFS-179607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3841-3847, 2020

Authors: Xu, Hui | Li, Yuanhai | Zhang, Jingjun | Cheng, Liang | Pan, Song

Article Type: Research Article

Abstract: The traditional epidural anesthesia often relies on blind touch method, which has high technical requirements and low efficiency for doctors. But the ultrasonic image localization method also affects the doctor’s use because of the insufficient definition, the big spot noise and so on. Based on this, a new lumbar ultrasound processing system is proposed. Anisotropic diffusion filter can enhance contrast and make image smoother. The new adaptive threshold binarization algorithm can not only remove speckle noise, but also get clear ultrasound image. The template matching algorithm is used to locate the epidural anesthesia automatically. Through several ultrasound images were examined, …The success rate was 98.12%. This technology was used in the control experiment of obese patients, which showed that this technology can better solve the problem of low epidural anesthesia operation. Show more

Keywords: Ultrasound image, epidural anesthesia, ultrasound location guidance

DOI: 10.3233/JIFS-179608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3849-3857, 2020

Authors: Zhang, Yihong

Article Type: Research Article

Abstract: Pathological diagnosis is the most common and reliable method of cancer diagnosis, but the technology of pathological diagnosis is relatively backward. It is an urgent problem to identify and classify the pathological pictures of cancer cells. Based on this, the digital image processing and recognition technology are analyzed for the classification and recognition of hydrothorax cancer cells. There is a big difference in the morphology of pleural effusion cancer cells, and uncertainty, so the edge detection algorithm is improved, with the simulated edge detection method used to extract information. After image segmentation, feature extraction is of vital importance for cell …image classification. A method of block statistics based on Gabor coefficient is proposed. Firstly, the cell image is filtered by multi-scale and multi-directional filtering, then the average and variance are calculated, and the image is divided into several blocks to solve the problem of large amount of data. Finally, BP neural network is established to input the morphological characteristics of hydrothorax cells, and the results are classified directly. After the experiment, the proposed classification method can improve the classification effectiveness; the design model can accurately identify the breast water cancer cells, and can be effectively applied to the early diagnosis of breast water cancer cells. Show more

Keywords: Hydrothorax cancer cell, image segmentation, image recognition, feature extraction

DOI: 10.3233/JIFS-179609

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3859-3866, 2020

Authors: Liu, Xiaopei | Teng, Jianfu

Article Type: Research Article

Abstract: In China, moyamoya disease is one of the main causes of stroke events in children and adults. Early diagnosis and early treatment can prevent irreversible damage to nerve function and greatly improve the quality of life of patients. Based on the improved sparse Bayesian low-dose CT image blind restoration algorithm, moyamoya disease diagnosis and treatment has important application value guidance and evaluation Cheng, through the retrospective analysis of the clinical characteristics, imaging characteristics and treatment of 105 patients with moyamoya disease diagnosed by Blind image restoration algorithm based on improved sparse Bayesian low dose CT from April 2012 to November …2015 in a hospital in Hefei City, we think that CT angiography has important application value in the diagnosis and treatment of moyamoya disease, and we can draw a conclusion. Among these 105 patients, women are the majority. The ratio of male to female was 1:1.188, and the peak period was 30– 40 years old. There were 41 cases of ischemic stroke and 64 cases of hemorrhagic stroke. 29 patients underwent STA-MCA bypass, temporal muscle compression and dural reversal. Blind image restoration algorithm based on improved sparse Bayesian low dose CT was reexamined. The results of Blind image restoration algorithm based on improved sparse Bayesian low dose CT and DSA were the same. It can be seen that Blind image restoration algorithm based on improved sparse Bayesian low dose CT is a reliable method to diagnose moyamoya disease, which can be used as a preoperative guidance and postoperative evaluation of bypass vessel patency and collateral circulation formation. Show more

Keywords: CT angiography, moyamoya disease, artery bypass

DOI: 10.3233/JIFS-179610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3867-3875, 2020

Authors: Liu, Na

Article Type: Research Article

Abstract: Medical image segmentation is an important step of medical image processing, which divides medical image into thousands of regions and extracts the regions of tissues and organs of interest. The accuracy of segmentation is very important for the follow-up processing of medical image and doctors’ judgment of the real situation of diseases. Medical image segmentation is a classic problem in the field of image segmentation. 3D image reconstruction technology is to obtain 3D structure information from 2D images of objects, to provide users with realistic 3D graphics, and to restore the prototype of objects, so that users can observe and …analyze from multiple perspectives, which greatly improves the accuracy of measurement and the scientific accuracy of medical diagnosis, and plays a very important role in assisting doctors in clinical diagnosis. Based on the three-dimensional image model of MRI, the load variation of the internal oblique muscle can be applied to the finite element analysis of the near end of the patellar tendon. Show more

Keywords: Three-dimensional reconstruction technology, medical image, MRI

DOI: 10.3233/JIFS-179611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3877-3883, 2020

Authors: Nong, Zhonghai

Article Type: Research Article

Abstract: Image recognitionan is an important part of pattern recognition, It has been widely used in modern production and life. Using computer technology and modern information processing technology to complete human visual cognition and understanding. Thus, It have become a hot topic about how to study image recognition based on medical color feature extraction. This study firstly studied the existing literature, explored and analyzed relevant theories and technologies, and studied several algorithms related to current image recognition. Then the image recognition algorithm based on wavelet moment and support vector machine is combined with the artificial intelligence technology based on image feature …extraction theory to establish the color medical image recognition algorithm based on wavelet moment and support vector machine. In order to verify the feasibility and advancedness of the new algorithm, practical experiments are carried out, and the experimental results are compared and analyzed by statistical method. The concrete chart proves the correctness of the conclusion. The final of the new algorithm is proved to be successful. Show more

Keywords: Feature extraction, image recognition, color characteristics, medical image database

DOI: 10.3233/JIFS-179612

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3885-3891, 2020

Authors: Wang, Kailing | Yu, Qinglian

Article Type: Research Article

Abstract: Three-dimensional reconstruction technology can display the three-dimensional graphical data of medical images to diagnostic personnel, so as to facilitate multi-dimensional and multi-level observation of patient data, and assist doctors in qualitative and quantitative analysis of pathological tissue. A surface reconstruction algorithm for three-dimensional medical images based on segmentation is proposed. It combines image segmentation with MC (marching cubes) algorithm organically, which can be based on the characteristics of different medical images. An appropriate segmentation method is used to segment different tissues accurately, and the result of segmentation is used to extract the isosurface accurately, which avoids the limitation that MC …is only suitable for threshold segmentation. After the medical image is segmented by combining threshold and region growth, the segmentation results are input as the reconstructed data, and the improved algorithm of three-dimensional reconstruction is realized. The medical image is rendered on three-dimensional surface, and the debugging results of the software are displayed. At the same time, a cube detection method based on region growing is adopted to improve the efficiency of surface tracking. Experiments show that this algorithm can improve the reconstruction speed and display effect. Show more

Keywords: Three dimensional reconstruction, image processing, segmentation

DOI: 10.3233/JIFS-179613

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3893-3902, 2020

Authors: Guo, Rui | Shen, Xuanjing | Kang, Hui

Article Type: Research Article

Abstract: Image segmentation is very important for various fields. With the development of computer technology, computer technology has become more and more effective for image segmentation, and it is studied on the basis of partial differential equations. The curve representation method in plane differential geometry is expounded, with the SegNet-v2 segmentation model analyzed and tested in medical image segmentation. The test results show that the partial differential equation image segmentation algorithm can achieve more accurate segmentation, especially in medical image segmentation, which can achieve good results, and it is worth in practice to further promote.

Keywords: Partial differential equation, image segmentation, algorithm analysis

DOI: 10.3233/JIFS-179614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3903-3909, 2020

Authors: Kung, Wang

Article Type: Research Article

Abstract: In order to improve the signal processing technology of contact image sensor, the optimization of image processing technology is carried out from the aspects of feature extraction, fine-grained semantic feature generation and semantic analysis matching optimization. To solve the problem of inaccurate feature extraction, a multi-scale feature representation algorithm for food images is proposed. By extracting the features of multi-scale convolution layer, and according to the food image, the feature extraction process is dispersed into each convolution process. By comparing the features of the layers with the image library, the most accurate features are selected for transmission. To solve the …problem of conservative vocabulary and poor generalization performance of generated sentences, a fine-grained image semantic analysis algorithm based on subword segmentation is proposed. The results show that compared with the mainstream methods, the proposed method has improved in varying degrees on the four evaluation indicators. The research provides a reference for the optimization of image sensor signal processing technology and the wide application of BP neural network algorithm. Show more

Keywords: BP neural network algorithms, contact image, sensors, signal processing technology

DOI: 10.3233/JIFS-179616

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3911-3919, 2020

Authors: Yi, Bo | Cao, Yuan Ping | Song, Ying

Article Type: Research Article

Abstract: With the rapid development of information science and technology, network security has occupied a very important position in people’s lives. Since the network security situation problem does not form a unified optimal solution in the model and algorithm, it is still necessary for researchers to continue to explore. In order to better evaluate the network security risk, based on fuzzy theory, particle swarm optimization and RBF neural network, this paper proposes a network security risk assessment model based on fuzzy theory. By mining the rules in the historical data of the network security situation and combining with the current network …status, the assessment of the current network security situation is realized, and the objectivity and comprehensibility of the evaluation results are improved. The experimental comparison shows that the fuzzy theory prediction model with PSO-RBF neural network has more rapid and effective evaluation and prediction results than the fuzzy theory prediction model with RBF neural network only. Show more

Keywords: Cyber security risk assessment, fuzzy theory

DOI: 10.3233/JIFS-179617

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3921-3928, 2020

Authors: Wang, Jing

Article Type: Research Article

Abstract: In recent years, technology of face recognition has developed rapidly, more and more face recognition technologies have been integrated into our work and life. In practical applications, due to influence of various factors, the resolution of the face image is low, the noise interference is large, and the illumination changes sharply during the imaging process, which brings difficulties to the face recognition, which seriously affects the accuracy of the face recognition method. This paper aims to introduce two-type fuzzy theory into face recognition and study its extraction and recognition methods of face feature. Firstly, it introduce the face recognition technology …simply. Face recognition is a technique that uses a computer to analyze a face image and extract valid identification information to identify the identity. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two methods for extracting features from face recognition. Principal Component Analysis (PCA) is a data analysis method that uses a small number of characterizations to reduce the number of dimensions, which reduces computational complexity greatly. The purpose of linear discriminant analysis is to extract data from high-dimensional feature spaces. Extracted the low-dimensional features with recognition ability, and studied the two-type fuzzy system based on fuzzy sets deeply. Obtained the output function of the two-type fuzzy system by studying the structure of each layer of the two-type fuzzy system. Introduce two types of fuzzy ideas into linear discriminant analysis. Discussed the construction of fuzzy membership functions, the selection of kernel functions and the determination of clustering rules. Finally, the ORL face database of the trained fuzzy face recognition model. As a result, the face recognition method based on the type 2 fuzzy has certain feasibility. The experimental results show that face recognition based on interval two-type fuzzy neural network has good recognition rate and anti-noise ability. Show more

Keywords: Type 2 fuzzy rules, linear identification, face recognition, feature extraction

DOI: 10.3233/JIFS-179618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3929-3938, 2020

Authors: Lyu, Yi | Jiang, YiJie

Article Type: Research Article

Abstract: The purpose of this paper is to accurately locate the fault prediction and diagnosis technology, to have a high degree of automation, and to handle it quickly, for the large aircraft avionics system failure presents the feature of multiple coupling, complex impact and rapid spread. At the same time, the fault prediction diagnosis technology is one of the most important contents of the avionics system equipment prediction, so how to quickly and effectively predict the failure of key system parts of avionics is the core essential to ensure the complete operation of the whole system. This paper through establishing the …gray neural network model, combining the advantages of gray model to deal with poor information and the characteristics of artificial neural network processing nonlinear data, to realize the fault prediction of avionics system, At the same time, At the same time, through the fuzzy recognition method based on the deterioration degree, established the bridge between the two, in turn, to achieve the health prediction management of system. The method mainly includes: Firstly, by combining gray theory and artificial neural network algorithm with fuzzy recognition to establish a network model that contains gray neural network models and can reflect the excellent characteristics of fuzzy recognition and conduct experimental analysis; Second, on this basis, improve the weight update strategy of the gray neural network by using additional learning rate method which based on momentum and improve the accuracy of the algorithm. Therefore, it can be concluded that the predictions presented in this paper should not be directly imitated when the system disturbance factor is too large or the system is abnormally caused by a serious disturbance suddenly appearing at a certain point in time, but should properly processed the data firstly according to the actual situation. According to the time series of the actual situation, several models are established, and the data correction is explained from the model prediction effect, and the gray model and description are improved. The improved combination of gray neural network and gray neural network can not only improve the prediction accuracy, but also provide a feasible method for such time series prediction, which provides a practical and effective technical method for avionics system fault prediction. Show more

Keywords: Ashy neural network, avionics system, fuzzy recognition, fault prediction, combined forecast

DOI: 10.3233/JIFS-179619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3939-3947, 2020

Authors: Liu, Yanbing | Dhakal, Sanjev | Hao, Binyao

Article Type: Research Article

Abstract: The coal-rock interface identification function enables the shearer to automatically identify the coal-rock interface and demonstrates outstanding advantages in improving economic efficiency and safe operation. It can improve the recovery rate of coal seam, reduce the content of rock, ash and sulfur in coal, improve the efficiency of coal mining operation and reduce equipment wear. It is one of the key equipments to realize coal mining automation. At present, there are more and more researchers on the research of coal rock interface identification technology. A common method is to use a single sensor to establish a coal rock identification system, …and use the neural network algorithm as the core algorithm of the system. Therefore, this paper proposes a recognition system based on wavelet packet decomposition and fuzzy neural network. A variety of sensors are used to collect the response signal of the shearer, and then the multi-signal feature extraction and data fusion of the coal-rock interface identification method are realized, thereby improving the recognition rate. On the basis of the physical simulation system of coal and rock interface, a large number of tests were carried out, and a large amount of test data was collected through experiments. In view of the many advantages of wavelet analysis, this paper uses wavelet packet technology to extract signal features. An energy allocation method based on wavelet packet decomposition can determine the sensitive frequency band of each sensor signal and extract each feature value. The wavelet packet energy method is used for feature extraction, which completes the conversion from mode space to feature space, and provides reliable and accurate feature level data for data fusion. The results show that neural networks and genetic neural networks can be trained and simulated using experimental data. Data fusion based on genetic neural network can perform state recognition and has high recognition accuracy. Multi-sensor data fusion technology based on genetic neural network is feasible in coal-rock interface identification. Show more

Keywords: Multi-sensor, coal-rock interface identification, fuzzy neural network, wavelet packet decomposition

DOI: 10.3233/JIFS-179620

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3949-3959, 2020

Authors: Fan, Linyuan

Article Type: Research Article

Abstract: Multiple sensor information fusion technology originated from the military. It has developed into a very active and popular field of defense research. It is also a high-level universal key technology, which has attracted attention in many disciplines and fields. Many countries, including China, have listed them on the research list. The purpose of this paper is to study multiple sensor data fusion through fuzzy sets and statistical theory. According to the scholars’ research results at home and abroad, proposed a new evidence synthesis algorithm. The algorithm combines the advantages of modifying the original evidence and modifying the comprehensive rules. By …comparing the accuracy of 100 sensor data in multiple sensors and single sensors, the consistency information and conflict information between the evidences are, mined and comprehensively considered the consistency information and conflict information between the evidences,, and analyzed the evidence empirically. Considered ENCE in the weight distribution of conflict evidence fully. By comparing the results of multiple sets of experiments, established multiple sensor data fusion algorithm based on fuzzy sets and statistical theory. Record experimental data and analyze the experimental results. The experimental results show that compared with other methods, the method can reflect the credibility of the evidence more objectively, the convergence speed is faster, and the fusion result is more in line with the actual situation. The experimental results show that the fusion algorithm based on the best fusion set and the new integrated method of conflict evidence can be used as the core algorithm of local fusion center and global fusion center in the fusion system respectively, and can also be used as the secondary fusion model of the fusion system. Show more

Keywords: Multiple sensor data fusion, weight distribution, fusion precision, convergence speed

DOI: 10.3233/JIFS-179621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3961-3970, 2020

Authors: Yuan, Yana | Chai, Huaqi

Article Type: Research Article

Abstract: The discussion of knowledge management and technological innovation has never stopped, and the discussion of the relationship between the two has not only important practical significance but also profound theoretical significance. The purpose of this paper is to study a new method of knowledge fusion from the perspective of science and technology philosophy. From a new perspective, this paper analyzes defects of subjective tendencies, decision-dependent partial attributes of knowledge source existed in practical applications of management field and proposed a knowledge fusion method based on fuzzy set theory. This paper firstly explains the characteristics of knowledge sources, and transforms knowledge …into a new knowledge layer through combining multi-source knowledge, then improves the connotation, level and self-confidence of knowledge, finally improves the ability of the system to accomplish tasks and goals. Then combine the fuzzy set theory with the knowledge fusion algorithm reasonably and effectively, and obtain the results of knowledge fusion by using evidence synthesis and decision rules, so as to make up for the lack and defects in the knowledge fusion process and solve the uncertainty problem in knowledge reasoning. Finally, through the practical example, merged the fuzzy set theory proposed in this paper into knowledge fusion to deal it, obtain a kind of processing of fuzzy set theory, forming a knowledge fusion method based on fuzzy set theory. Based on fuzzy set theory, obtain the observation results of knowledge fusion algorithm combined with the various warning models, then to discuss and analyze the enterprise warning problem deeply. Therefore, the examples and simulation results show that the advantages in practicality and versatility of knowledge fusion method proposed through fuzzy set theory is higher than the common knowledge fusion method. The method used in the production of manufacturing products can help manufacturing companies improve development quality of product and shorten development cycle of product. Moreover, in the product design industry, it has verified that knowledge fusion can promote the dissemination of knowledge in the field of knowledge management, which helps to share and reuse design knowledge, reduce difficulty of development and improve efficiency of development. Show more

Keywords: Knowledge management, information fusion, fuzzy set theory, fuzzy petri net, knowledge update

DOI: 10.3233/JIFS-179622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3971-3979, 2020

Authors: Wang, Renqiang | Miao, Keyin | Sun, Jianming

Article Type: Research Article

Abstract: In view of the objectively ambiguous feature of infrared image of unmanned autonomous ship, this paper presents a quantitative method to deal with the ambiguity problem in infrared image by using the fuzzy mathematical model to realize the purpose of intelligent recognition of infrared imaging target. In order to simplify the computation of target recognition and improve the response time and accuracy in the selection of target features in infrared images, three features of target location, radiation distribution and shape are selected for analysis in this paper. The membership functions of these three features are weighted to calculate the confidence, …and the classification and recognition are realized according to the confidence. Finally, the simulation results show that the recognition method proposed in this paper can effectively identify the target, and the recognition rate is very high. Compared with the recognition methods based on neural network and SVM, the recognition distance of this method is longer than that of the latter two methods. Show more

Keywords: Fuzzy mathematical model, unmanned autonomous ship, infrared imaging, intelligent target recognition

DOI: 10.3233/JIFS-179623

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3981-3989, 2020

Authors: Wang, Wei | Hu, Xiaohui | Wang, Mingye

Article Type: Research Article

Abstract: With the development of Internet technology, the growth of network services is accelerating. For more and more network service requests, how to ensure the response speed and query accuracy required by users is a huge challenge. In order to realize fast clustering of large data business request data and improve the accuracy of clustering. This paper presents a data fuzzy clustering algorithm based on Adaptive Incremental learning time series. The algorithm defines large data clustering in time series, and the incremental time series clustering method is used. Firstly, the complexity of network data is reduced by data compression, and then …time series data clustering based on service time similarity is carried out. In this paper, the time series fuzzy clustering algorithm based on Adaptive Incremental Learning inherits the clustering structure information obtained by previous clustering. Initialize the current clustering process, and then search the outlier samples in the current data block adaptively without setting parameters. Automatically create new clusters from outlier samples, and finally check empty cluster recognition. Identification determines whether certain clusters need to be deleted to ensure the efficiency of subsequent cluster processes. The experimental results show that the algorithm has good clustering accuracy and efficiency for isochronous and unequal time series. Show more

Keywords: Network data, adaptive incremental learning, time series, fuzzy clustering algorithm

DOI: 10.3233/JIFS-179624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3991-3998, 2020