Journal of Intelligent & Fuzzy Systems - Volume 35, issue 4

Authors: Wang, Bo | Xu, Jing | Sidoravicius, B.H.

Article Type: Research Article

Abstract: Due to the poor image quality and complex enhancement process in the current image contrast fuzzy enhancement algorithm, a multilevel image contrast fuzzy enhancement algorithm in multimedia network based on homogeneity measurement was put forward. This algorithm used the minimum fuzzy entropy to detect noise in multilevel image and remove noise through improved Shannon entropy, so as to achieve restoration of multilevel image. According to the membership degree of restored image, the local feature of image was determined to realize and the homogeneity expression of image. Then, the nonlinear transformation was introduced to optimize the image homogeneity. Thus, the multilevel …image contrast fuzzy enhancement in multimedia network was realized. Experimental results show that the proposed algorithm can effectively guarantee the image quality after the contrast enhancement and reduce the computational complexity. Show more

Keywords: Multimedia network, multilevel image, contrast, fuzzy enhancement, algorithm

DOI: 10.3233/JIFS-169755

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4351-4360, 2018

Authors: Ran, Li | He, Yizhou | Ludwig, P.A.

Article Type: Research Article

Abstract: At present, network abnormal data detection algorithm has low efficiency and accuracy, and the false negative rate is very high. Therefore, the location accuracy of abnormal data is not ideal. An intelligent detection method of network abnormal data based on space-time nearest neighbor and likelihood ratio test was proposed. The time interval adjustment algorithm based on the change smoothness judgement strategy and the adaptive data change rule was used to adaptively adjust data acquisition time interval according to network performance parameters and achieve network data acquisition. The grid partition was used to convert source data points into appropriate granularity to …complete the data preprocessing. Based on the maximum a posteriori probability, we selected the measured values of data to be detected at several moments as the time nearest neighbor points. The abnormal degree of data was quantified. Meanwhile, the likelihood ratio test was used to determine whether the data was abnormal. The abnormal alarm information was aggregated. All alarm information was arranged according to the size. The two alarm times with maximum difference value are used as the boundary, and the multi-point dislocation combined abnormal location method was used to locate the detection result. Experiment results show that the average detection time of proposed algorithm is 0.21 s. The average false negative rate is 2.8%. The accuracy of abnormal data detection and the positioning accuracy are high. The proposed algorithm can detect network abnormal data efficiently, which lays a foundation for the development of this field. Show more

Keywords: Dynamic data, network abnormal data, intelligent detection, likelihood, ratio test

DOI: 10.3233/JIFS-169756

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4361-4371, 2018

Authors: Yang, Jianzhong | Li, Xianyang | Jiang, Yu | Qiu, Guihua | Buckdahn, S.

Article Type: Research Article

Abstract: Using the current recognition system to recognize dynamic scene cannot effectively speed up the target recognition. When target recognition increases, the accuracy of target recognition is relatively low. In order to solve this problem, a target recognition system of dynamic scene based on DSP was designed. Combined with the idea of DSP system design, the design process and composition of target recognition system was expounded. The recognition algorithm based on spatial-temporal condition information was used to realize the designed recognition system. By introducing the visual attention mechanism, the spatial-temporal domain model based on visual significance was built. The pixel neighborhood …weighted condition information was used as classification features to enhance the linear separability for target and background and improve the recognition accuracy of dynamic scene moving target. Finally, combined with image block modeling strategy, the efficient and real-time recognition of moving target in dynamic scene was realized. Experimental results show that the proposed target recognition system can effectively improve the accuracy of target recognition. Show more

Keywords: Artificial intelligence vision, dynamic scene, target recognition, recognition system

DOI: 10.3233/JIFS-169757

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4373-4383, 2018

Authors: Zhou, Gaiyun | Ma, Li | Li, Zhanguo | Zhang, Guoping | Kim, C.

Article Type: Research Article

Abstract: Currently, the method was not applicable to the requirement of feature extraction of different types of grayscale images, resulting in the feature extraction results with low accuracy, long time consumption, low clarity and poor flexibility. In this article, a method of extracting feature of gray image based on fuzzy clustering algorithm was proposed. The grayscale, the median filtering, the edge detection and mathematical morphology processing were carried out for the color image of CCD camera collected by acquisition card. Then, sample feature object of target object gray level image and object of target feature were obtained. The similarity between sample …feature object of target object gray level image and object of target feature was obtained through calculation. Moreover, the feature conforming to the set threshold was selected. Meanwhile, the grayscale image feature extraction results with different requirements were obtained through adjusting gray level image matrix and similarity parameters. From comparison and analysis of experimental result, we can see that the correctness, effectiveness and flexibility of proposed method are proved for different types of gray level image feature extraction. The extraction result has high definition and short running time. Show more

Keywords: Fuzzy clustering algorithm, gray level image, feature extraction, similarity

DOI: 10.3233/JIFS-169758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4385-4397, 2018

Authors: Ma, Xiangfen | Lee, A.

Article Type: Research Article

Abstract: At present, obstacle avoidance systems of robots cannot avoid obstacles with high efficiency, high stability and high precision. Thus, a self-adaptive obstacle avoidance fuzzy system for mobile robots based on ultrasonic range measurement is proposed and designed. An upper computer, a motor drive module, an ultrasonic ranging sensor module, an infrared sensor module, an electronic compass module, a communication module, a power supply module and peripheral circuits are connected to form the system hardware. After the system is initialized, the robot starts to work according to instructions of the upper computer and enters the self-adaptive obstacle avoidance subroutine. In the …subroutine, the ultrasonic sensor scans the infrared sensor output at the corresponding position. After receiving reflection information of the ultrasonic wave, the counter is stopped, and reflection time of the ultrasonic wave is simply calculated and cached into the buffer, so as to determine whether there is an obstacle in front, and the result is fed back to the upper computer through the RS485 bus. If there is an obstacle, then the interrupt program will be called, and the electronic compass program is utilized to determine the direction to avoid the obstacle; if there is no obstacle, the robot will continue to move following instructions of the upper computer to complete the system software design. Experiments show that the average time to avoid obstacles using this system is 0.40 s, and the obstacle avoidance accuracy is high and the stability is good. Under the data comparison and analysis, the proposed system is obviously superior to current systems in the time-consuming and accuracy of obstacle avoidance, and has great reliability. Show more

Keywords: Mobile robots, self-adaptive, obstacle avoidance, system

DOI: 10.3233/JIFS-169759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4399-4409, 2018

Authors: Sun, Yuan

Article Type: Research Article

Abstract: When using the current authentication code recognition system to identify the character authentication code, there are the problems of low integrity and low recognition accuracy. In this regard, a design method of artificial intelligence recognition system for cracking character type authentication code is proposed in this paper. The denoising algorithm based on the connected domain is used to remove the noise in the character type authentication code, and the character authentication code after the denoising is normalized. The feature extraction module is used to extract color moments, color correlation diagrams and LBP texture features of character authentication codes, and complete …the feature extraction of character authentication codes. The similarity matching module is used to match the characters of the character authentication code. In the recognition module, the character authentication code is classified by the classification algorithm based on multi-feature SVM, and the recognition of the character authentication code is completed. The experimental results show that the proposed method has high information integrity and high recognition accuracy. Show more

Keywords: Character authentication code, artificial intelligence, recognition system, feature extraction

DOI: 10.3233/JIFS-169760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4411-4420, 2018

Authors: Xiao, Ling | Elsawah, A.

Article Type: Research Article

Abstract: In order to solve the problem of storing large amounts of data in the wireless sensor network space, the design method of data storage system of wireless sensor network space should be studied. When the current method is used to design a data storage system for wireless sensor network space, there are problems of low storage efficiency and low data storage quality. We propose a design method of data storage system for wireless sensor network space based on fuzzy control. The C/S mode is used to design the client module, transmission module and server module in the data storage system …of wireless sensor network space according to the concept of level and modularity. The flow control method based on module control is used to forward or discard data in the network space to complete the design of data storage system of wireless sensor network space. Experimental results show that the proposed method has high data transmission rate and high accuracy of the decision function. It is verified that the proposed method has extraordinary storage efficiency and great data storage quality. Show more

Keywords: Fuzzy control, wireless sensor, network space, data storage system

DOI: 10.3233/JIFS-169761

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4421-4431, 2018

Authors: Lv, Jinwen | Chen, Xianqiao | Salah, M.

Article Type: Research Article

Abstract: The traditional re-recognition algorithm needs to find or design the characteristics with better robustness to light, scale, and deformation. The quality of the feature directly affects the recognition performance and the uncertainty is high. In addition, it needs supervision and training, and has the higher training time and space complexity. To address this problem, a new intelligent re-recognition algorithm for specific ship target in busy waters under the actual scene is proposed in this paper. Combining the existing feature extraction model and graph model, the graph structure is used to describe the identity relationship between the samples. Two points with …side connections have the same identity label. Then the multi-layer graph structure is built. After obtaining the block of the divided area, the similarity between the two samples of the link is calculated and the weight of the edge is obtained. Labeled samples are built according to the selected initial area. The energy loss of the graph model is obtained by estimating the pixel likelihood energy function with different labels of pixels and areas. A graph structure is obtained by minimizing the energy loss, which is the intelligent recognition result of specific ship target. For the large-scale data, the problem of incremental processing is solved by incremental maintenance. Experimental results show that the proposed algorithm has high recognition precision. Show more

Keywords: Actual scene, busy waters, specific ship target, intelligence, re-recognition

DOI: 10.3233/JIFS-169762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4433-4443, 2018

Authors: Wu, Minghui | Hassan, Nasruddin

Article Type: Research Article

Abstract: Aiming at the problem that the active queue management algorithm can not explicitly control the queue length and the relationship between throughput and delay, a fuzzy information control algorithm based on Active Queue Management for digital substation communication network congestion is proposed. After packet grouping is entered into the router cache queue, the packets that first enter the cache area of the router are preprocessed, and the data packet is processed fairly by using the geometric distribution function. The combination of Smith predictive control and adaptive fuzzy control is used to compensate the network delay of packets, eliminate the negative …impact of time delay on active queue jitter and delay jitter, and control congestion according to fuzzy rules intelligently. The experimental results showed that the proposed algorithm can maintain smaller queue oscillations, especially when the network conditions change. It can effectively eliminate the impact of time delay on queue jitter and delay jitter, and improve the overall performance of the network. Show more

Keywords: Digital substation, communication network, congestion information, fuzzy control algorithm

DOI: 10.3233/JIFS-169763

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4445-4454, 2018

Authors: Qin, Chunbin | Zheng, Yanjun | Basu, M.A.

Article Type: Research Article

Abstract: At present, the intelligent control system of robots is closed, which has the disadvantages of poor fault tolerance, unstable operation and low positioning accuracy. Aiming at these deficiencies, a Petri net model of the intelligent control system for open architecture robots based on PMAC is designed. Starting from the kinematics of robots, the forward and inverse kinematics model of open architecture robots are established according to DH method; then the trajectory planning is performed from Cartesian space linear interpolation algorithm and circular interpolation algorithm respectively, and the basic function of robot path planning is constructed. Finally, a PMAC-based open architecture …robot intelligent control system is established. The control system adopts dual-microcomputer hierarchical control mode and modular structure design. Real-time communication between the upper computer and the lower computer can be realized by calling the Pcomm32 dynamic link library; based on the robot’s forward and inverse kinematics model and trajectory interpolation algorithm, the modular control software for the robot system is developed. The control software realizes functions such as security check, parameters setting, kinematics analysis, and teaching reproduction. Combined with the principle of hierarchical Petri nets, various modules of open architecture robot control system based on PMAC are modeled. Experiments show that the designed system runs smoothly, has high positioning accuracy, good openness and scalability. Show more

Keywords: Open architecture robot, intelligent control system, mathematical model, PMAC, petri net model

DOI: 10.3233/JIFS-169764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4455-4464, 2018