Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Venkataramanan, K. | Arun, M. | Jha, Shankaranand | Sharma, Aditi

Article Type: Research Article

Abstract: This study delves into the development and analysis of a novel Embedded Fuzzy Type 2 PID Controller for Robot Manipulators, motivated by the increasing need for enhanced control systems in robotic applications to improve precision and stability. In the background section, the limitations of conventional PID controllers in addressing uncertainties and disturbances, especially in complex tasks performed by robot manipulators, are presented. The concept of fuzzy logic and the Type 2 fuzzy system is introduced, highlighting their potential to manage imprecise and uncertain information. Through rigorous analysis and simulation, the superior performance of the Embedded Fuzzy Type 2 PID Controller …is demonstrated when compared to traditional PID controllers and even Type 1 fuzzy controllers. The results showcase enhanced tracking accuracy, disturbance rejection, and adaptability, making it a promising solution for advanced robotic applications. In conclusion, this research provides a robust solution for improving the control of robot manipulators in uncertain and dynamic environments. The Embedded Fuzzy Type 2 PID Controller offers a new paradigm in control theory, ensuring stability and precision even in the face of unpredictable factors. This innovation holds great promise for advancing the capabilities of robotic systems and underlines the potential for further research in embedded fuzzy control systems. Show more

Keywords: Fuzzy type 2 PID controller, robot manipulator, embedded control, stability analysis, precision control

DOI: 10.3233/JIFS-235338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1429-1442, 2024

Authors: Peng, Bo | Zhang, Tao | Han, Kundong | Zhang, Zhe | Ma, Yuquan | Ma, Mengnan

Article Type: Research Article

Abstract: Text classification is an important tasks in natural language processing. Multilayer attention networks have achieved excellent performance in text classification tasks, but they also face challenges such as high temporal and spatial complexity levels and low-rank bottleneck problems. This paper incorporates spatial attention into a neural network architecture that utilizes fewer encoder layers. The proposed model aims to enhance the spatial information of semantic features while addressing the high temporal and spatial demands of traditional multilayer attention networks. This approach utilizes spatial attention to selectively weigh the relevance of the spatial locations in the input feature maps, thereby enabling the …model to focus on the most informative regions while ignoring the less important regions. By incorporating spatial attention into a shallower encoder network, the proposed model achieves improved performance on spatially oriented tasks while reducing the computational overhead associated with deeper attention-based models. To alleviate the low-rank bottleneck problem of multihead attention, this paper proposes a variable multihead attention mechanism, which changes the number of attention heads in a layer-by-layer manner with the encoder, achieving a balance between expression power and computational efficiency. We use two Chinese text classification datasets and an English sentiment classification dataset to verify the effectiveness of the proposed model. Show more

Keywords: Text classification, BERT, Spatial attention, Multihead attention mechanism

DOI: 10.3233/JIFS-231368

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1443-1454, 2024

Authors: Sun, Xu | Zou, Qingyu

Article Type: Research Article

Abstract: Modern information technology has been constantly evolving, transforming the traditional power grid into a network that couples both power and information layers. Understanding the cascade failure behavior of such power communication interdependent networks is essential for effectively controlling catastrophic network failures, preventing system collapse, and ensuring normal network operation. This research can contribute to the development of tools to predict and prevent such failures, and restore normal network functions in a timely manner. This paper focuses on the modeling method and cascading fault analysis of the power-information double-layer coupling network. We construct power information interdependent networks based on IEEE30 system …and England39 system, and evaluate the cascade failure results using load distribution cascade failure model and HITS algorithm. The evaluation criteria include network efficiency, residual network size, and residual network load. By analyzing these parameters, we can gain insights into the performance of the power-information interdependent networks during cascade failures. Through simulation results, we demonstrate that the type i attack proposed in this paper renders the network structure unstable and less robust compared to the degree attack, intermediate attack, and random attack. These findings provide valuable references for developing strategies to mitigate the cascading failure of power-information interdependent networks. Show more

Keywords: Power network, information network, interdependent network, cascade failure, critical nodes

DOI: 10.3233/JIFS-232016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1455-1467, 2024

Authors: Li, Wenying | Guo, Qinghong | Wen, Ming | Zhang, Yun | Pan, Xin | Xiao, Zhenfeng | Yang, Shuzhi

Article Type: Research Article

Abstract: This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method …is validated by an IEEE 30-node simulation distributed system. Show more

Keywords: Active distribution network, distributed power sources, demand-side response, dynamic reconfiguration, backtracking search algorithm

DOI: 10.3233/JIFS-232993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1469-1480, 2024

Authors: Amsaprabhaa, M.

Article Type: Research Article

Abstract: Vision-based Human Activity Recognition (HAR) is a challenging research task in sports. This paper aims to track the player’s movements and recognize the different types of sports activities in videos. The proposed work aims in developing Hybrid Optimized Multimodal SpatioTemporal Feature Fusion (HOM-STFF) model using skeletal information for vision-based sports activity recognition. The proposed HOM-STFF model presents a deep multimodal feature fusion approach that combines the features that are generated from the multichannel-1DCNN and 2D-CNN network model using a concatenative feature fusion process. The fused features are fed into the 2-GRU model that generates temporal features for activity recognition. Nature-inspired …Bald Eagle Search Optimizer (BESO) is applied to optimize the network weights during training. Finally, performance of the classification model is evaluated and compared for identifying different activities in sports videos. Experimentation was carried out with the three vision-based sports datasets namely, Sports Videos in the Wild (SVW), UCF50 sports action and Self-build dataset, which achieved accuracy rate of 0.9813, 0.9506 and 0.9733, respectively. The results indicate that the proposed HOM-STFF model outperforms the other state-of-the-art methods in terms of activity detection capability. Show more

Keywords: Bald eagle search optimizer, Gated recurrent unit, human activity recognition, multichannel-1DCNN, 2D-CNN

DOI: 10.3233/JIFS-233498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1481-1501, 2024

Authors: Sun, Xianshan | Sheng, Yuefeng | Mao, Hongfei | Qian, Qingfeng | Cai, Qingnan

Article Type: Research Article

Abstract: In order to solve the problems of tedious, insufficient manpower, low efficiency, and easy to cause human errors in the verification of relay protection equipment settings with the development of the power grid, an automatic verification method of relay protection equipment settings combining cell image gray enhancement and AI recognition is studied. In this method, Gaussian mixture and particle swarm algorithm are used to enhance the gray level of the original image captured, and the binary method is used to further denoise the image; The histogram is used to segment the cells in the denoised constant value image one by …one; The OCR technology in AI technology uses the maximum width backtracking segmentation algorithm to segment a coherent text in a cell into multiple single words, and collects the 13 dimensional characteristics of the text to be detected to compare with the text in the database. The text with the smallest error is the detected text, which completes the text extraction in the cell; Store the extracted text data in the database, check the data in the notification constant value sheet and the device constant value sheet, and give an abnormal prompt of different data. The experimental results show that the image pre processed by this method is clear, the fixed value single cell segmentation is accurate, and the OCR text extraction efficiency is high. Through a large number of data experiments, the final verification accuracy can reach 99.8%. Show more

Keywords: Gray enhancement, OCR text extraction, cell segmentation, equipment constant value sheet, notify the fixed value sheet, automatic detection

DOI: 10.3233/JIFS-234457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1503-1515, 2024

Authors: Yang, Huailei

Article Type: Research Article

Abstract: The grid connected inverter is the core component of the photovoltaic grid connected power generation system, which mainly converts the direct current of the photovoltaic matrix into alternating current that meets the grid connected requirements, playing a key role in the efficient and stable operation of the photovoltaic grid connected power generation system.This paper uses fuzzy PI control model which to improve the performance of intelligent photovoltaic grid-connected inverter to simulate the intelligent photovoltaic inverter system, using mathematical analysis and reasoning methods for model analysis,adopts two-stage three-phase LCL grid-connected inverter, establishes mathematical models in two-phase synchronous rotating and two-phase static …coordinate systems, and adopts an active damping strategy based on grid-connected current. Based on existing research and empirical analysis,aiming at the disadvantage of poor dynamic response of repetitive control, an improved repetitive control strategy is adopted, and the controller is analyzed from two aspects of stability and dynamic performance, and the simulation model of photovoltaic grid-connected power generation system is built. Use experimental analysis method to verify the effectiveness of the model in this article,The experimental results show that the simulation system of intelligent photovoltaic grid-connected inverter considering fuzzy PI control proposed in this paper has certain effects. Show more

Keywords: Fuzzy PI control, intelligence, photovoltaic, grid connection, inverter

DOI: 10.3233/JIFS-234491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1517-1529, 2024

Authors: Li, Yunzhi | Lei, Jingsheng | Shi, Wenbin | Yang, Shengying

Article Type: Research Article

Abstract: PCB defect detection aims to identify the presence of gaps, open circuits, short circuits, and other defects in the PCB boards produced in the industry. Designing effective deep learning algorithms is crucial to finding a solution. Previously proposed PCB defect detection algorithms are limited in detecting tiny objects in high-density. Directly applying previous models to tackle PCB defect detection tasks will cause serious issues, such as missed detection and false detection. In this paper, we present a detection algorithm for tiny PCB defect targets in high-density regions to solve the above-mentioned problems. We firstly propose a detection head to detect …tiny objects. Then, we design a four-channel feature fusion mechanism to fuse four different scale features and add an attention mechanism to find the attention region in scenarios with dense objects. Finally, we achieved accurate detection of tiny targets in high-density areas. Experiments were performed on the publicly available PCB defect dataset from Peking University. Our mAP@.5:.95 achieves 48.6%, while mAP@0.5 exceeds 90%. Compared with YOLOX and YOLOv5, our improved model can better localize tiny objects in high-density scenes. The experimental results certify that our model can obtain higher performance in comparison with the baseline and the state of the art. Show more

Keywords: defect detection, tiny objects, high density, detection head, feature fusion, print circuit board

DOI: 10.3233/JIFS-230150

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1531-1541, 2024

Authors: Pashikanti, Rajesh | Patil, C.Y. | Shinde, Amita

Article Type: Research Article

Abstract: Arrhythmia is the medical term for any irregularities in the normal functioning of the heart. Due to their ease of use and non-invasive nature, electrocardiograms (ECGs) are frequently used to identify heart problems. Analyzing a huge number of ECG data manually by medical professionals uses excessive medical resources. Consequently, identifying ECG characteristics based on machine learning has become increasingly popular. However, these conventional methods have some limitations, including the need for manual feature recognition, complex models, and lengthy training periods. This research offers a unique hybrid POA-F3DCNN method for arrhythmia classification that combines the Pelican Optimisation algorithm with fuzzy-based 3D-CNN …(F3DCNN) to alleviate the shortcomings of the existing methods. The POA is applied to hyper-tune the parameters of 3DCNN and determine the ideal parameters of the Gaussian Membership Functions used for FLSs. The experimental results were obtained by testing the performance of five and thirteen categories of arrhythmia classification, respectively, on UCI-arrhythmia and the MIT-BIH Arrhythmia datasets. Standard measures such as F1-score, Precision, Accuracy, Specificity, and Recall enabled the classification results to be expressed appropriately. The outcomes of the novel framework achieved testing average accuracies after ten-fold cross-validation are 98.96 % on the MIT-BIH dataset and 99.4% on the UCI arrhythmia datasets compared to state-of-the-art approaches. Show more

Keywords: Deep learning, optimization algorithm, ECG classification, cardiac arrhythmia, feature extraction, 3D-CNN, Pelican optimization algorithm

DOI: 10.3233/JIFS-230359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1543-1566, 2024

Authors: Saranya, D. | Bharathi, A.

Article Type: Research Article

Abstract: The interpretation of the electroencephalogram (EEG) signal is one method that can be utilized to diagnose epilepsy, which is one of the most prevalent brain illnesses. The length of an EEG signal is typically quite long, making it difficult to interpret manually. Extreme Learning Machine (ELM) is used to detection of Epilepsy and Seizure. But in ELM Storage space and training time is high. In order to reduce training time and storage space African Buffalo Optimization (ABO) algorithm is used. ABO is combined with Sparse ELM to improve the speed, accuracy of detection and reduce the storage space. First, Wavelet …transform is used to extract relevant features. Due to their high dimensionality, these features are then reduced by using linear discriminant analysis (LDA). The proposed Hybrid Sparse ELM technique is successfully implemented for diagnosing epileptic seizure disease. For classification, the Sparse ELM-ABO classifier is applied to the UCI Epileptic Seizure Recognition Data Set training dataset, and the experimental findings are compared to those of the SVM, Sparse ELM, and ELM classifiers applied to the same database. The proposed model was tested in two scenarios: binary classification and multi-label classification. Seizure identification is the only factor in binary classification. Seizure and epilepsy identification are part of multi-label classification. It is observed that the proposed method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as prediction accuracy, specificity, precision, recall and F-score. Binary classification scores 96.08%, while multi-label classification achieves 90.89%. Show more

Keywords: Extreme learning machine, african buffalo optimization, epilepsy and seizure detection, sigmoid activation, cost function optimization

DOI: 10.3233/JIFS-237054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1567-1582, 2024