Journal of Intelligent & Fuzzy Systems - Volume 44, issue 1

Authors: Kang, Xinhui | Nagasawa, Shin’ya

Article Type: Research Article

Abstract: To show the unique charm of Jiangxi’s traditional culture, it is of great importance to apply Jiangxi’s unique red culture to products’ creative designs. This paper aims to apply Kansei Engineering (KE) and interactive genetic algorithm (IGA) to extract the apparent symbol elements of Jiangxi red culture and then transform them into the creative watch design with modern culture. First of all, KE is used to extract customers’ emotional resonance to red culture, and 16 pairs of Kansei image vocabulary pairs are preliminarily collected. The theory of semiotics is used to extract symbols such as shapes, colors, and patterns from …the perspective of Jiangxi’s red architecture. Secondly, through the designers’ subjective aesthetic thinking, these cultural symbols are broken up and reconstructed, thus forming the morphological deconstruction table combined with the case of the watch. Finally, IGA is implemented to code and decode the cultural symbols, thus building a product form’s evolutionary design system. Through biological genetic manipulation, cultural symbols of refinement, particularity, and regionality are retained. Then these superior cultural genes are integrated into the innovation of the watch to get creative products with the characteristics of Jiangxi red culture. The model proposed in this paper optimizes the decision-making process of cultural creative product design, and also explores a sustainable development path of culture. Show more

Keywords: Kansei engineering, interactive genetic algorithm, cultural and creative product design, jiangxi red culture

DOI: 10.3233/JIFS-221737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 647-660, 2023

Authors: Liang, Meishe | Mi, Jusheng | Zhang, Shaopu | Jin, Chenxia

Article Type: Research Article

Abstract: Ranking intuitionistic fuzzy numbers is an important issue in the practical application of intuitionistic fuzzy sets. Many scholars rank intuitionistic fuzzy numbers by defining different measures. These measures do not comprehensively consider the fuzzy semantics expressed by membership degree, nonmembership degree, and hesitancy degree. As a result, the ranking results are often counterintuitive, such as the indifference problems, the non-robustness problems, etc. In this paper, according to geometrical representation, a novel measure for intuitionistic fuzzy numbers is defined, which is called the ideal measure. After that, a new ranking approach is proposed. It’s proved that the ideal measure satisfies the …properties of weak admissibility, membership degree robustness, nonmembership degree robustness, and determinism. A numerical example is applied to illustrate the effectiveness and feasibility of this method. Finally, using the presented approach, the optimal alternative can be acquired in multi-attribute decision-making problems. Comparison analysis shows that the ideal measure is more effective and simple than other existing methods. Show more

Keywords: Intuitionistic fuzzy number, intuitionistic fuzzy set, ideal measure, multi-attribute decision making

DOI: 10.3233/JIFS-221041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 661-672, 2023

Authors: Yu, Yang | He, Kun | Yan, Gang | Cen, Shixin | Li, Yang | Yu, Ming

Article Type: Research Article

Abstract: Vehicle Re-Identification (Re-ID) aims to discover and match target vehicles in different cameras of road surveillance. The high similarity between vehicle appearances and the dramatic variations in viewpoints and illumination cause great challenges for vehicle Re-ID. Meanwhile, in safety supervision and intelligent traffic systems, one needs a quick efficient method of identifying target vehicles. In this paper, we propose a Multi-Attention Guided Feature Enhancement Network (MAFEN) to extract robust vehicle appearance features. Specifically, the Fusing Spatial-Channel information multi-receptive fields Feature Enhancement module (FSCFE) is first proposed to aggregate richer and more representative multi-receptive fields features at different receptive fields sizes. …It also learned the spatial structure information and channel dependencies of the multi-receptive fields features and embedded them to enhance the feature. Then, we construct the Spatial Attention-Guided Adaptive Feature Erasure (SAAFE) module, which uses spatial attention to erase the most distinguishing features. The network’s attention is shifted to potentially salient features to strengthen the ability of the network to extract salient features. In addition, a multi-loss knowledge distillation (MLKD) method using MAFEN as a teacher network is designed to improve computational efficiency. It uses multiple loss functions to jointly supervise the student network. Experimental results on three public datasets demonstrate the merits of the proposed method over the state-of-the-art methods. Show more

Keywords: Vehicle re-identification, deep learning, multi-receptive fields, feature erasure, knowledge distillation

DOI: 10.3233/JIFS-221468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 673-690, 2023

Authors: Karthika, J. | Rajkumar, M. | Vishnupriyan, J.

Article Type: Research Article

Abstract: The increased use of DC microgrid for complex application leads to the need for advanced control design for stable operation of the system. Loads connected to a DC microgrid are controlled by power electronic devices and exhibit constant power load (CPL) behavior, which is a serious challenge for stability as it enhances nonlinearity and reduces effective damping. This manuscript proposes an effective hybrid approach based on DC micro grid (MG) connected constant power loads. The proposed control approach is the consolidation of Marine Predators Algorithm (MPA) and mayfly optimization algorithm (MOA), hence it is named as hybrid MPA-MOA approach. The …DC microgrid system contains the sources, like two photovoltaic (PV), two wind turbine (WT), grid, battery. The major objective of the proposed approach is “to find the problems while interfacing the sources of the microgrid and increase the security of the system”. The proposed approach contains two controllers, they are primary and secondary. The primary controller is based on droop controller that shares the current and limits the oscillations because of the constant power loads (CPL). The secondary controller is used to regulate the voltage of the system from a single area. The secondary control is executed using the proposed MPA-MOA method. The proposed method is executed on MATLAB/Simulink platform; its performance is analyzed with the existing methods. The THD (%), efficiency (%) and Eigen value of the proposed technique achieves 1.4%, 92% and -9.3541±j2.4209. Show more

Keywords: Microgrid, primary controller, secondary controller, stability, marine predators algorithm, mayfly optimization algorithm

DOI: 10.3233/JIFS-221632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 691-712, 2023

Authors: Xu, Zhiyun | Hu, Zhaoyan | Zheng, Xiaoyao | Zhang, Haiyan | Luo, Yonglong

Article Type: Research Article

Abstract: Adding noise to user history data helps to protect user privacy in recommendation systems but affects the recommendation performance. To solve this problem, a matrix factorization tourism point of interest recommendation model based on interest offset and differential privacy is proposed in this paper. The recommendation performance of the model is improved by analyzing user interest preferences. Specifically, user interest offsets are extracted from user tags and user ratings under time-series factors to calculate user interest drift. Then, similar neighbors are found to train user feature preferences which are integrated into the matrix model in the form of regular terms. …Meanwhile, based on the differential privacy theory, a privacy neighbor selection algorithm combining the K-Medoides clustering algorithm and index mechanism is designed to effectively protect the identity of neighbors and prevent KNN attacks. Besides, the Laplace mechanism is used to implement differential privacy protection for the model’s gradient descent process. Finally, the feasibility of the proposed recommendation model is verified through experiments, and the experimental results indicate that this model has advantages in recommendation accuracy and privacy protection. Show more

Keywords: Matrix factorization, recommendation system, differential privacy, interest shift, clustering

DOI: 10.3233/JIFS-211542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 713-727, 2023

Authors: Hussain, Zahid | Abbas, Sahar | Rahman, Shams ur | Hussain, Rashid | Sharif, Razia

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 729-743, 2023

Authors: Bansal, Kanishk | Singh, Amar

Article Type: Research Article

Abstract: Computer Vision (CV) is constantly inundated with massive volumes of data. One of the most challenging types of data for an Artificial Intelligence (AI) system is imagery data. Convolutional neural networks (CNNs) are utilized to cope with Big Data of such type, but progress is gradual. The 3 Parent Genetic Algorithm (3PGA), an evolutionary computation method, is employed to evolve a default CNN in this study. 3PGA is an extension of GA which has been developed further for better optimization. We observed from the literature that 3PGA is giving excellent results on standard benchmark functions as compared to other recent …soft-computing-based approaches. The accuracy of the evolved CNN increased from 53% to 75%, resulting in a net improvement of more than 40%. Furthermore, it was noted that the hyperparametric combinations or features of a CNN, which are very distinct from those commonly utilized, appear to perform better. A geographical landmarks dataset from Google was used for testing purposes. Landmark recognition is one of the most time-consuming jobs for an AI system, and the optimization of a network on a landmarks dataset shows that evolutionary computation can be substantially used in the future for the evolution of Artificial Neural Networks (ANNs). Show more

Keywords: Convolutional neural network, 3 parent genetic algorithm, optimization, geographical landmark recognition, hyperparametric features

DOI: 10.3233/JIFS-221473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 745-756, 2023

Authors: Souidi, Mohammed El Habib | Haouassi, Hichem | Ledmi, Makhlouf | Maarouk, Toufik Messaoud | Ledmi, Abdeldjalil

Article Type: Research Article

Abstract: Multi-Pursuers Multi-Evader Game (MPMEG) is considered as a multi-agent complex problem in which the pursuers must perform the capture of the detected evaders according to the temporal constraints. In this paper, we propose a metaheuristic approach based on a Discrete Particle Swarm Optimization in order to allow a dynamic coalition formation of the pursuers during the pursuit game. A pursuit coalition can be considered as the role definition of each pursuer during the game. In this work, each possible coalition is represented by a feasible particle’s position, which changes the concerned coalition according to its velocity during the pursuit game. …With the aim of showcasing the performance of the new approach, we propose a comparison study in relation to recent approaches processing the MPMEG in term of capturing time and payoff acquisition. Moreover, we have studied the pursuit capturing time according to the number of used particles as well as the dynamism of the pursuit coalitions formed during the game. The obtained results note that the proposed approach outperforms the compared approaches in relation to the capturing time by only using eight particles. Moreover, this approach improves the pursuers’ payoff acquisition, which represents the pursuers’ learning rate during the task execution. Show more

Keywords: Multi-agent systems, coalition formation algorithm, discrete particle swarm optimization, pursuit-evasion game

DOI: 10.3233/JIFS-221767

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 757-773, 2023

Authors: Peng, Weishi | Fang, Yangwang

Article Type: Research Article

Abstract: In performance evaluation, the widely used root-mean-square error is easily affected by large error terms and is also an incomprehensive measure. Therefore, the error spectrum as a comprehensive measure was proposed for parameter estimation. However, error spectrum (ES) is a three-dimension plot (among ES, r axis and time t axis) in the whole time horizon in dynamic evaluation system, which is not intuitive and easy to be analyzed. To smooth this, a new dynamic error spectrum (NDES) is proposed in dynamic evaluation system in this paper. Firstly, the NDES is defined for EPE in dynamic systems. Secondly, the …computation method is proposed to calculate the NDES. Thirdly, several nice properties of NDES are presented for dynamic system performance evaluation. Finally, the effectiveness of the proposed new dynamic error spectrum is verified by a numerical example. Show more

Keywords: Performance evaluation, decision support systems, parameter estimation, new dynamic error spectrum

DOI: 10.3233/JIFS-221958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 775-782, 2023

Authors: Luo, Wei | Feng, Tao | Liang, Hong

Article Type: Research Article

Abstract: Change detection in synthetic aperture radar (SAR) images is an important part of remote sensing (RS) image analysis. Contemporary researchers have concentrated on the spatial and deep-layer semantic information while giving little attention to the extraction of multidimensional and shallow-layer feature representations. Furthermore, change detection relies on patch-wise training and pixel-to-pixel prediction while the accuracy of change detection is sensitive to the introduction of edge noise and the availability of original position information. To address these challenges, we propose a new neural network structure that enables spatial-frequency-temporal feature extraction through end-to-end training for change detection between SAR images from two …different points in time. Our method uses image patches fed into three parallel network structures: a densely connected convolutional neural network (CNN), a frequency domain processing network based on a discrete cosine transform (DCT), and a recurrent neural network (RNN). Multi-dimensional feature representations alleviate speckle noise and provide comprehensive consideration of semantic information. We also propose an ensemble multi-region-channel module (MRCM) to emphasize the central region of each feature map, with the most critical information in each channel employed for binary classification. We validate our proposed method on four benchmark SAR datasets. Experimental results demonstrate the competitive performance of our method. Show more

Keywords: Change detection, SFTNet, feature extraction, synthetic aperture radar (SAR) images, deep learning, neural network

DOI: 10.3233/JIFS-220689

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 783-800, 2023