Journal of Intelligent & Fuzzy Systems - Volume 46, issue 4

Authors: Duan, Chunyan | Zhu, Mengshan | Wang, Kangfan

Article Type: Research Article

Abstract: Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an …improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems. Show more

Keywords: Failure mode and effects analysis, reliability analysis, intelligent manufacturing systems, machine learning

DOI: 10.3233/JIFS-232712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10375-10392, 2024

Authors: Ren, Yonghui | Shi, Yan | Li, Chenglin | Jin, Yanxu

Article Type: Research Article

Abstract: Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot …control system in perception and decision-making experiments was 94.62%. The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology. Show more

Keywords: Robot control system, Robotic Process Automation (RPA), Convolutional Neural Network (CNN), Deep Learning (DL)

DOI: 10.3233/JIFS-233056

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10393-10403, 2024

Authors: Maddali, Deepika

Article Type: Research Article

Abstract: A rising number of edge devices, like controllers, sensors, and robots, are crucial for Industrial Internet of Things (IIoT) networks for collecting data for communication, storage, and processing. The security of the IIoT could be compromised by any malicious or unusual behavior on the part of any of these devices. They may also make it possible for malicious software placed on end nodes to enter the network and perform unauthorized activities. Existing anomaly detection techniques are less effective due to the increasing diversity of the network and the complexity of cyberattacks. In addition, most strategies are ineffective for devices with …limited resources. Therefore, this work presents an effective deep learning based Malware Detection framework to make the edge based IIoT network more secure. This multi-stage system begins with the Deep Convolutional Generative Adversarial Networks (DCGAN) based data augmentation method to overcome the issue of data imbalance. Next, a ConvNeXt-based method extracts the features from the input data. Finally, an optimized Enhanced Elman Spike Neural Network (EESNN) based deep learning is utilized for malware recognition and classification. Using two distinct datasets— MaleVis and Malimg— the generalizability of the suggested model is clearly demonstrated. With an accuracy of 99.24% for MaleVis and 99.31% for the Malimg dataset, the suggested strategy demonstrated excellent results and surpassed all other existing methods. It illustrates how the suggested strategy outperforms alternative models and offers numerous benefits. Show more

Keywords: IIoT, deep learning, ConvNeXt, Malimg, EESNN, DCGAN, MaleVis

DOI: 10.3233/JIFS-234897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10405-10421, 2024

Authors: Yi, Lingzhi | Peng, Xinlong | Fan, Chaodong | Wang, Yahui | Li, Yunfan | Liu, Jiangyong

Article Type: Research Article

Abstract: Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in …this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. Show more

Keywords: Short-term residential load forecasting, gate recurrent unit, multi-objective optimization algorithm, deep learning

DOI: 10.3233/JIFS-237189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10423-10440, 2024

Authors: Yang, Jingling | Chen, Liren | Chen, Huayou | Liu, Jinpei | Han, Bing

Article Type: Research Article

Abstract: The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of ‘high coverage and narrow width’. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of ‘high coverage, narrow width, and small bias’ in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework …is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. Show more

Keywords: Prediction interval, uncertainty prediction, deep neural networks, carbon price

DOI: 10.3233/JIFS-237524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10441-10456, 2024

Authors: Zou, Yu | Fu, Deyu | Mo, Honghuai | Chen, Henglong | Wang, Deyin

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-237868

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10457-10470, 2024

Authors: Wang, Lai-Wang | Hung, Chen-Chih

Article Type: Research Article

Abstract: In response to the low efficiency and poor quality of current seed optimization algorithms for multi-threshold image segmentation, this paper proposes the utilization of the normal distribution in the cluster distribution mathematical model, the Levy flight mechanism, and the differential evolution algorithm to address the deficiencies of the seed optimization algorithm. The main innovation lies in applying the BBO algorithm to image multi threshold segmentation, providing a new perspective and method for image segmentation tasks. The second significant progress is the combination of Levy flight dynamics and differential evolution algorithm (DEA) to improve the BBO algorithm, thereby enhancing its performance …and image segmentation quality. Therefore, a multi-threshold image segmentation model based on the optimized seed optimization algorithm is developed. The experimental results showed that on the function f1, the iteration of the improved seed optimization algorithm was 53, the Generational Distance value was 0.0020, the Inverted Generational Distance value was 0.098, and the Spacing value was 0.051. Compared with the other two algorithms, the improved seed optimization algorithm has better image segmentation performance and clearer image segmentation details. In summary, compared with existing multi-threshold image segmentation methods, the proposed multi-threshold image segmentation model based on the improved seed optimization algorithm has a better image segmentation effect and higher efficiency, can significantly improve the quality of image segmentation, has positive significance for the development of image processing technology, and also provides references for the improvement and application of optimization algorithms. Show more

Keywords: Seed optimization algorithm, differential evolution algorithm, image segmentation, levy flight mechanism

DOI: 10.3233/JIFS-237994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10471-10492, 2024

Authors: Zhang, Bei | Cao, Yuan | Wang, Changqing | Wang, Meng

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-238575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10493-10505, 2024

Authors: Kaur, Gaganpreet | Shobana, M. | Kavin, F. | Sellakumar, S. | Meenakshi, D. | Bharathiraja, N.

Article Type: Research Article

Abstract: The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, …this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture. Show more

Keywords: Intelligent Transport System, vulnerabilities, security, Big Data, business intelligence development

DOI: 10.3233/JIFS-230831

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10507-10521, 2024

Authors: Lan, Zhiqiang | Wu, Guoyao | Wu, Jiacheng | Li, Jiaqi | Pan, Fan

Article Type: Research Article

Abstract: In the application of new energy consumption system engineering, in order to evaluate the contribution of electric power industry expansion, an evaluation model of electric power industry expansion contribution considering the influencing factors of new energy consumption is constructed. In the process of power industry expansion, the growth of new energy installed capacity, power system regulation ability, power grid interconnection and electricity demand are the core factors that affect the change of power contribution to power industry expansion. Using the characteristic extraction method of power consumption behavior of users with industrial expansion, after extracting two characteristics, namely, the utilization hours …of user’s industrial expansion capacity and the proportion of new energy load put into operation under the change of four major factors, the monthly industrial expansion power consumption of typical users is predicted by the monthly industrial expansion power consumption forecasting method of users considering industrial expansion capacity, and then the growth curve of user’s industrial expansion power consumption is drawn. Based on the forecast method of monthly industry expansion electricity generated by industry expansion quantity, the industry expansion quantity of typical individual users is calculated, and the industry expansion quantity is input into RBF network model trained by particle swarm optimization algorithm to complete the forecast of monthly industry expansion electricity; Finally, the contribution ratio of each influencing factor is calculated, and the evaluation of power industry expansion contribution considering the influencing factors of new energy consumption is completed. After testing, this model can be used as an available model for evaluating the contribution of electric power industry under the condition of considering the influencing factors of new energy consumption. Show more

Keywords: New energy consumption, influencing factors, power industry expansion, contribute electricity, evaluation model, industry expansion capacity

DOI: 10.3233/JIFS-236907

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10523-10534, 2024