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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Zhang, Xiang | Huang, Jianhua
Article Type: Research Article
Abstract: The occurrence of safety incidents for existing glass curtain walls (EGCWs) pronounced menace to the security of both lives and property. Undertaking safety assessment for EGCWs carries essential practical significance. However, current fuzzy evaluation methods overlook the uncertainty of indicator weights and the intricacies of rank attribution. In response, this paper proposes a novel approach to the safety assessment of EGCWs. This research establishes a framework of evaluation indicators for EGCWs and divides the safety ranks of each indicator into four tiers: Safe, Mild risk, Moderate risk, and High risk. Quantitative and qualitative indicators are quantified via the variable fuzzy …cloud algorithm and cloud model. The information cloud combination weighting method is introduced to determine the weight clouds of indicators. Finally, a two-dimensional assessment result is derived using an improved fuzzy comprehensive evaluation method and fuzzy entropy. The exemplified outcomes demonstrate that this approach captures the safety status of evaluation subjects based on risk ranks, and fuzzy entropy addresses two issues: inconsistent level attribution and the comparison of identical risk ranks. The appraisal method further unveils the safety details of EGCWs, with findings that align consistently with the actual situation. Show more
Keywords: Safety evaluation, existing glass curtain wall, variable fuzzy cloud algorithm, cloud model, fuzzy entropy
DOI: 10.3233/JIFS-237414
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5125-5137, 2024
Authors: Fu, Zhiyu | Fu, Zhihui
Article Type: Research Article
Abstract: Textural translations from diverse foreign languages require reference corpora for providing error-free readability. This applies to any foreign language example English or any other language translation to Japanese. Therefore Japanese corpus repository verifies the consistency of the translated texts, words, and sentences for its readability. This article introduces a Semantic Translation Model (STM) using Fuzzy Control (FC) for supporting the aforementioned fact. The proposed model analyzes the translated word semantics based on its sentence occurrence and meaning. These two factors are analyzed using two-level fuzzy control; the first level identifies the word placement/occurrence-based readability and the second level identifies the …meaning retention. If any inconsistency is observed in the first level, the second is not carried out preventing readability errors. The fuzzy control process relies on near-to-same Japanese corpus inputs for improving readability. If the case fails then a new word replacement or displacement of the semantics is enforced. Therefore the fuzzy control levels are expanded based on different word occurrences, preventing time complexity. Show more
Keywords: Japanese translation, fuzzy control, readability, semantic analysis
DOI: 10.3233/JIFS-234575
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5139-5153, 2024
Authors: Zhao, Weisen
Article Type: Research Article
Abstract: Electrochemical energy storage battery fault prediction and diagnosis can provide timely feedback and accurate judgment for the battery management system(BMS), so that this enables timely adoption of appropriate measures to rectify the faults, thereby ensuring the long-term operation and high efficiency of the energy storage battery system. Based on the idea of data driven, this paper applies the Long-Short Term Memory(LSTM) algorithm in the field of artificial intelligence to establish the fault prediction model of energy storage battery, which can realize the prediction of the voltage difference over-limit fault according to the operation data of the energy storage battery, and …introduce the parameter of the difference between maximum voltage and minimum voltage(DMM) at the cluster level to quantitatively determine whether the battery cluster has a fault. It provides powerful guidance and effective methods for the safe and stable operation of electrochemical energy storage power stations. Show more
Keywords: Fault prediction, data driven, LSTM, artificial intelligence
DOI: 10.3233/JIFS-235726
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5155-5164, 2024
Authors: Zheng, Guangyuan | Cheng, Chen | Dong, Xinling | Liu, Yi
Article Type: Research Article
Abstract: Acceleration and deceleration control, as one of the key technologies in high-speed CNC system, directly affects the machining efficiency, the stability of machining process and the error of machining follow. Therefore, it is necessary to study and explore new acceleration and deceleration control methods in high-speed CNC system to ensure the smooth feed and improve the machining accuracy. Therefore, the acceleration and deceleration control algorithm of NC system based on deep reinforcement learning and single chip microcomputer is studied. The theoretical basis of deep reinforcement learning is analyzed, and the acceleration of acceleration and deceleration is calculated based on the …linear acceleration and deceleration control. The whole integer operation of single chip microcomputer is used to estimate the value range of each step. The variation characteristics of trajectory motion are predicted so that acceleration and deceleration can be processed across program segments. The velocity of transfer point is calculated by the rate of change of feed velocity vector, and the speed of multi-program is smoothed by adjusting the allowable contour error. Based on the proximal strategy optimization algorithm in deep reinforcement learning, the acceleration and deceleration control model of CNC system is established to realize the acceleration and deceleration control. The experimental results show that the proposed algorithm has better control effect, shorter time and smaller interpolation error, which can ensure the NC system to feed smoothly at high speed. Show more
Keywords: Deep reinforcement learning, single chip microcomputer, near-end strategy optimization algorithm, CNC system, acceleration and deceleration control, Smooth processing
DOI: 10.3233/JIFS-238195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5165-5174, 2024
Authors: Yi, Yangyang | Yu, Long | Tian, Shengwei | Gao, Xuezhuang | Li, Jie | Zhao, Xingang
Article Type: Research Article
Abstract: In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing …BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model’s flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN’s superior accuracy compared to other methods, affirming its efficacy in 3D object detection. Show more
Keywords: 3D object detection, Point clouds, deep learning
DOI: 10.3233/JIFS-234024
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5175-5189, 2024
Authors: he, Jia-long | zhang, Xiao-Lin | wang, Yong-Ping | gu, Rui-Chun | liu, Li-xin | xu, En-Hui
Article Type: Research Article
Abstract: Although deep learning models show powerful performance, they are still easily deceived by adversarial samples. Some methods for generating adversarial samples have the drawback of high time loss, which is problematic for adversarial training, and the existing adversarial training methods are difficult to adapt to the dynamic nature of the model, so it is still challenging to study an efficient adversarial training method. In this paper, we propose an adversarial training method, the core of which is the improved adversarial sample generation method AGFAT for adversarial training and the improved dynamic adversarial training method AGFAT-DAT. AGFAT uses a word frequency-based …approach to identify significant words, filter replacement candidates, and use an efficient semantic constraint module as a means to reduce the time of adversarial sample generation; AGFAT-DAT is a dynamic adversarial training approach that uses a cyclic attack on the model after adversarial training and generates adversarial samples for adversarial training again. It is demonstrated that the proposed method can significantly reduce the generation time of adversarial samples, and the adversarial-trained model can also effectively defend against other types of word-level adversarial attacks. Show more
Keywords: Text classification, adversarial samples, adversarial training
DOI: 10.3233/JIFS-234034
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5191-5202, 2024
Authors: Fan, Kun | Zhang, Dingran | Lv, Yuanyuan | Zhou, Lang | Qu, Hua
Article Type: Research Article
Abstract: In order to solve the problem of discrete manufacturing customization and personalized production scheduling, considering the influence of manual labor on processing time, we propose a multi-objective Hybrid Job-shop Scheduling with Multiprocessor Task(HJSMT) problem with cooperative effect model. Based on the actual production, two optimization objectives are set, i. e. minimizing the maximum completion time and the total tardiness. Firstly, considering the situation where workers’ cooperation reduces job processing time, the cooperative effect of workers co-processing is considered by referring to the learning effect curve in the model. Subsequently, we develop an Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) to solve …the multi-objective HJSMT problem by improving Precedence Operation Crossover (POX) and Multiple Mutations (MM) operations. Finally, the scheduling results and the C values are compared with other algorithms to verify the effectiveness of the algorithm. Simultaneously, the multi-objective HJSMT problem with the cooperative effect is solved by the INSGA-II algorithm, and the experimental results also demonstrate the superior performance of the algorithm. Show more
Keywords: Hybrid job-shop scheduling, multiprocessor task, cooperative effect, multi-objective optimization, improved non-dominated sorting genetic algorithm-II
DOI: 10.3233/JIFS-235047
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5203-5217, 2024
Authors: Guo, Lixin
Article Type: Research Article
Abstract: We analyze the properties and characteristics of the information structure in incomplete lattice-valued information system (ILIS), we redefine the information structure and the dependence and information distance between the two information structures. In addition, in order to evaluate the uncertainty of ILIS, the concepts of granular measure and entropy measure are expounded, including information granulation, information quantity, rough entropy and information entropy. Finally, we carry out numerical experiments to verify the feasibility of the method, and carry out effective statistical analysis. These results are conducive to the establishment of granular computing framework in ILIS.
Keywords: Granular computing, incomplete lattice-valued information system, information distance, information granule, information structure
DOI: 10.3233/JIFS-235777
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5219-5237, 2024
Authors: Li, Chunhua | Zhu, Ying | Zhan, Xiaoqin | Huang, Huawei
Article Type: Research Article
Abstract: Type B semigroups are described as the generalized inverse semigroups in the range of abundant semigroup. Motivated by studying fuzzy congruences in inverse semigroups, and as a continuation of N. Kuroki’s work in inverse semigroups and our work in abundant semigroups in terms of fuzzy subsets, this paper considers fuzzy admissible congruences on some classes of type B semigroups. Our main purpose is to show when a fuzzy admissible congruence on a type B semigroup with E -properties is E -properties preserving. In particular, we get some sufficient and necessary conditions for some classes of type B semigroups to be …primitive, E -unitary and E -reflexive, respectively. As an application, we extend our results to the cases of inverse semigroups. Show more
Keywords: Fuzzy admissible congruences, type B semigroups, E-unitary, primitive, E-reflexive
DOI: 10.3233/JIFS-230383
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5239-5247, 2024
Authors: You, Xingye | Mao, Jian | Liu, Jingming | Huang, Kai
Article Type: Research Article
Abstract: Conducted electromagnetic emissions from interconnecting cables in computer systems can lead to internal information leakage and cause information security problems. However, unintentionally leaked EM signals are characterized by low signal-to-noise ratio and random noise, making it difficult to recover the original signal. In this paper, we propose a denoising model (S-DnCNN) based on an improved DnCNN to better recover the original signal. The network structure consists of three parts: feature mapping generation, low-dimensional feature extraction, and original reconstruction. To improve the noise extraction capability, we use Leaky ReLU as the activation function of the CNN, and introduce a residual structure …and a convolutional attention module. The residual structure uses residual hopping to implicitly remove potentially clean images by hidden layer operations, thus training noisy data to recover clean data. We construct a one-dimensional selective convolution kernel (SKConv1d) and fuse it with local paths to form a feature extraction network, which improves the performance of the network. The experimental results show that our proposed method can preserve the details in the effective signal during denoising and shows good generalization to complex SNR data. Show more
Keywords: Information security, electromagnetic information leakage, feature extraction, low signal-to-noise ratio, denoising
DOI: 10.3233/JIFS-232371
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5249-5261, 2024
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