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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: Hashemi, Hebatollah | Ezzati, Reza | Mikaeilvand, Naser | Nazari, Mojtaba
Article Type: Research Article
Abstract: This research paper presents an innovative approach for modeling and analyzing complex systems with uncertain data. Our strategy leverages fuzzy calculus and time-fractional differential equations to achieve this goal. Specifically, we propose the utilization of the fuzzy Atangana-Baleanu time-fractional derivative, which incorporates non-singular kernels for fuzzy functions. This derivative type is particularly suitable for qualitative analysis of fractional differential equations in fuzzy space. We establish the existence and uniqueness of solutions for fuzzy linear time-fractional problems based on this differentiability concept. Additionally, we introduce a numerical solution method, namely the fuzzy homotopy perturbation transform method (FHPTM), to solve these problems. …To demonstrate the effectiveness and practical applicability of our approach, we provide concrete examples such as the fuzzy time-fractional Advection-Dispersion equation, the fuzzy time-fractional Diffusion equation, and the fuzzy time-fractional Black-Scholes European option pricing problem. These examples not only illustrate the solution steps involved but also showcase the potential of our method in addressing real-world problems. The outcomes of our research underscore the significance of considering fuzzy calculus and time-fractional differential equations when modeling and analyzing intricate systems with uncertain data. Show more
Keywords: Fuzzy atangana-baleanu time-fractional derivative, fuzzy homotopy perturbation transform method, fuzzy time-fractional black-scholes european option pricing problem, fuzzy time-fractional advection-dispersion equation, fuzzy time-fractional diffusion equation
DOI: 10.3233/JIFS-232094
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8567-8582, 2023
Authors: Guo, Liang | Zhang, Junzhao | Dong, Peiyi | Wan, Yuanzheng | Li, Wenhui
Article Type: Research Article
Abstract: To solve the problem of inaccurate user phase identification, the paper proposes a new algorithm based on improved cloud model and adaptive segmented voltage algorithm. Firstly, the new algorithm uses improved cloud model to calculate the digital features of station area and users’ voltage sequences quickly. Secondly, the paper uses the adaptive segmentation voltage algorithm to divide the full voltage sequences into three parts automatically to add local features into phase identification. Finally, the paper calculates cosine similarity between each segmented voltage cloud model to identify users’ voltage phase. The analysis based on station data and field verification shows that …the new algorithm has not only improved the calculation efficiency by 41% compared with traditional user phase identification algorithm, but also increased the difference in identification results between different phases by 1000 times. In the final result, the accuracy of the new algorithm is 95%. The new algorithm has more obvious differentiation and higher accuracy. The analysis results based on the actual engineering data also prove the feasibility and effectiveness of the new user phase identification algorithm. Show more
Keywords: Phase identification, adaptive segmentation voltage, improved cloud model, cosine similarity
DOI: 10.3233/JIFS-232415
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8583-8594, 2023
Authors: Wang, Haochen | Zhang, Changlun | Chen, Shuang | Wang, Hengyou | He, Qiang | Mu, Haibing
Article Type: Research Article
Abstract: Point cloud upsampling can improve the resolutions of point clouds and maintain the forms of point clouds, which has attracted more and more attention in recent years. However, upsampling networks sometimes generate point clouds with unclear contours and deficient topological structures, i.e., the problem of insufficient form fidelity of upsampled point clouds. This paper focuses on the above problem. Firstly, we manage to find the points located at contours or sparse positions of point clouds, i.e., the form describers, and make them multiply correctly. To this end, 3 statistics of points, i.e., local coordinate difference, local normal difference and describing …index, are designed to estimate the form describers of the point clouds and rectify the feature aggregation of them with reliable neighboring features. Secondly, we divide points into disjoint levels according to the above statistics and apply K nearest neighbors algorithm to the points of different levels respectively to build an accurate graph. Finally, cascaded networks and graph information are fused and added to the feature aggregation so that the network can learn the topology of objects deeply, enhancing the perception of model toward graph information. Our upsampling model PU-FPG is obtained by combining these 3 parts with upsampling networks. We conduct abundant experiments on PU1K dataset and Semantic3D dataset, comparing the upsampling effects of PU-FPG and previous works in multiple metrics. Compared with the baseline model, the Chamfer distance, the Hausdorff distance and the point-to-surface distance of PU-FPG are reduced by 0.159 × 10-3 , 2.892 × 10-3 and 0.852 × 10-3 , respectively. This shows that PU-FPG can improve the form fidelity and raise the quality of upsampled point clouds effectively. Our code is publicly available at https://github.com/SATURN2021/PU-FPG . Show more
Keywords: Point cloud, upsampling, convolutional networks, completion
DOI: 10.3233/JIFS-232490
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8595-8612, 2023
Authors: Călin, Mariana Floricica | Flaut, Cristina | Piciu, Dana
Article Type: Research Article
Abstract: Algebras of Logic deal with some algebraic structures, often bounded lattices, considered as models of certain logics, including logic as a domain of order theory. There are well known their importance and applications in social life to advance useful concepts, as for example computer algebra. Starting from results obtained by Di Nolla and Lettieri in [1 ], in which they analyzed the structure of finite BL-algebras, in this paper we find properties and give examples of commutative unitary rings R with its set of ideals Id (R ) to be a BL-algebra of a given type. Moreover, we …present properties of finite rings or rings with a finite number of ideals in their connections with BL-rings. Show more
Keywords: Algebras of Logic, BL-algebras, BL-rings
DOI: 10.3233/JIFS-232815
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8613-8622, 2023
Authors: Li, Yuejie | Liu, Chang’an | Li, Shijun
Article Type: Research Article
Abstract: Text detection and recognition are widely used in daily life. Although it is a very rich market, it has very difficult challenges in practical application. The complex and changeable natural scenes lead to the complex background of the text in the image, which also reflects the research value of text information recognition and extraction in natural scenes. To solve the many problems faced by the recognition of Chinese characters, such as complex shapes and diverse structures, this paper uses VGG16 to extract features and introduces a two-layer bidirectional LSTM network. It improves Faster R-CNN by using a RPN to extract …candidate boxes and adjust the position of candidate regions. In this paper, the improved model Faster BLSTM-CNN is tested, and the effectiveness experiment of feature extraction, the difference comparison before and after the improvement of the algorithm, and the comparison experiment with the traditional recognition algorithm are carried out respectively. And it finally carried out an experimental comparison of the combination of text recognition and positioning, and obtained the results. The algorithm Faster BLSTM-CNN in this paper is better in the localization and recognition of Chinese characters in the dataset. In the natural scene, the recognition rate of Faster BLSTM-CNN in this paper is 81.54%, the positioning accuracy is 88.14%, and the detection speed is 86 ms, which has improved performance. Therefore, the improvement of Faster R-CNN is effective. It can effectively locate and recognize Chinese characters in natural scenes. Show more
Keywords: Text recognition, deep learning, algorithm optimization, Faster R-CNN
DOI: 10.3233/JIFS-233700
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8623-8636, 2023
Authors: Gokila, R.G. | Kannan, S.
Article Type: Research Article
Abstract: In the internet era, billions of devices are connected to the network generates large volume of data and the generation rate increases exponentially every day. As the data increases, the chances for cyber attackers to exploit the data increases which results into numerous security threats to organizations and network. Fast and accurate detection of attacks in big data environment is difficult due to its volume and variety and velocity. Over a decade, numerous attack detection systems are developed using machine learning. However, most of the traditional detection systems cannot recognize the attack types specifically which reduces the detection performances and …network performances. Thus, the intrusion detection model presented in this research which incorporates deep variational auto-encoder and convolutional neural network to detect intrusions. Experimentations using benchmark dataset validated the proposed model better performances over existing machine learning techniques like logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, and self-scalable heuristic artificial neural network algorithms using accuracy, recall, precision, and F1-score. The proposed model outperforms with a maximum precision of 97.48%, Recall of 99.52%, F1-score of 98.49% and accuracy of 98.65% over conventional intrusion detection algorithms. Show more
Keywords: Big data, intrusions, denial of service, intrusion detection system, deep learning, auto encoder, convolutional neural network
DOI: 10.3233/JIFS-234311
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8637-8649, 2023
Authors: Chen, Junfen | Han, Jie | Xie, Bojun | Li, Nana
Article Type: Research Article
Abstract: Contrastive learning is a powerful technique for learning feature representations without manual annotation. The K-nearest neighbor (KNN) method is commonly used to construct positive sample pairs to calculate the contrastive loss. However, it is challenging to distinguish positive sample pairs, reducing clustering performance. We propose a novel D eep C ontrastive C lustering method based on a G rapH convolutional network called GHDCC. It uses an instance-level contrastive loss with mean square error (MSE) regularization and a cluster-level contrastive loss to incorporate semantic features and perform cluster assignments. The method utilizes a graph convolutional network (GCN) to improve the …semantic consistency of features and linear interpolation data augmentation to improve the representation ability of the model. To minimize the occurrence of false positive sample pairs, we select only samples whose similarity exceeds a predefined threshold to construct the adjacency matrix. The experimental results on six public datasets demonstrate that the GHDCC significantly outperforms contrastive clustering (CC, 500) by a large margin except on CIFAR-10. The GHDCC performs well compared to other deep contrastive clustering methods and achieves the highest clustering accuracy of 0.913 on ImageNet-10. Show more
Keywords: Self-supervised clustering, graph convolutional network, linear interpolation data augmentation, contrastive learning
DOI: 10.3233/JIFS-230208
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8651-8661, 2023
Authors: Cao, Mengmeng | Hu, Jian | Wang, Zeming | Yao, Jianyong
Article Type: Research Article
Abstract: In this paper, the high accuracy motion output feedback control of a kind of launching platforms driven by motors is focused. The launching platform is used to launch kinetic load to hit the target so it is susceptible to external disturbance. In addition, significant issues arise due to limitations on the plant inputs, such as actuator energy limits and velocity state is usually unavailable due to the limitation of system cost and volume. A new adaptive fuzzy output feedback controller based on dual observers is proposed for solving these problems. A smooth and continuous model is established for input saturation …to compensate it. A sliding mode observer and a fuzzy observer with proper membership function are combined to estimate the unmeasured system states more accurately. An adaptive robust controller and the fuzzy observer are combined to realize a motion control with disturbance rejection, which allows correct adaptation while the plant input is saturated. Lyapunov theorem proves the bounded stability of the proposed controller when there exists observation error. Extensive comparative simulation and experiment results verify the effectiveness and practicability of the proposed controller and show that the control accuracy can be improved by an order of magnitude compared with the traditional PID controller and better than some other nonlinear controllers. Show more
Keywords: Launching platform, fuzzy observer, output feedback control, adaptive robust control, input saturation
DOI: 10.3233/JIFS-230688
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8663-8678, 2023
Authors: Chen, Dewang | Zhou, Jiali | Tong, Wenlin | Kong, Lingkun | Chen, Yuandong
Article Type: Research Article
Abstract: As a model for reasoning and decision-making based on fuzzy rules, fuzzy systems have high interpretability. However, when the data dimension increases, the fuzzy system will face the problem of “rule explosion”, making it difficult to learn and predict effectively. In this paper, the fuzzy system trained by the FLOWFS (Fast-Learning with Optimal Weights for Fuzzy Systems) algorithm is used as sub-module in the deep fuzzy system, and the deep fuzzy system DFLOWFS (Deep FLOWFS) is constructed from the bottom-up hierarchical structure as the following three steps. 1) The FLOWFS algorithm assigns weight attributions to each fuzzy rule, and the …rule weights are trained by the least square method with regularization terms to shorten training time and improve accuracy. 2) Three strategies of dividing high-dimensional inputs into multiple low-dimensional inputs are proposed as sequential division, random division and correlation division. Then, it is verified by experiments that the correlation division has the best performance. 3) The sub-module discarding method is proposed to discard the sub-modules with poor performance to have a maximum improvement of 13.8% compared to the DFLOWFS without using the sub-module discarding method. Then, the optimized DFLOWFS is verified and compared with the other three classic regression models on the three UCI datasets. Experiments show that with the increase of the data dimension, DFLOWFS not only have good interpretability but also have good accuracy. Furthermore, DFLOWFS performs best among all models in comprehensive scores, with good learning ability and generalization ability. Therefore, the proposed strategies with hierarchical structure for optimal shallow fuzzy systems are effective, which give a new insight for fuzzy system research. Show more
Keywords: Correlation division, fuzzy system, interpretability, rule weights, submodule discarding method
DOI: 10.3233/JIFS-231050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8679-8690, 2023
Authors: Qiu, Guangying | Tao, Dan | Su, Housheng
Article Type: Research Article
Abstract: The complexity of relationships between bridge inspection presents a significant challenge for the answer system. Sparse knowledge graph (KG) due to limited triples further compounds the issue. To overcome this, this paper proposes a dynamic reasoning strategy for bridge inspection question answering. The framework comprises a teacher-student network and dynamic reasoning strategy. The teacher network, based on the neural state machine (NSM), acquires auxiliary intermediate supervision signals. Its output provides probability distribution and entity embedding as input for the student network. The student network, also NSM-based, provides accurate answers with the aid of intermediate supervision signals. To handle incomplete KG, …the dynamic reasoning strategy incorporates knowledge embedding, updating the KG by capturing contextual information of each related entity node in relation to the question. Experiments on the bridge inspection dataset demonstrated the effectiveness of this method, outperforming other approaches. Show more
Keywords: Knowledge base question answering, Subgraph embedding, Bridge
DOI: 10.3233/JIFS-232846
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8691-8701, 2023
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