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

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Abstract: This article has been retracted.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