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A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

Intelligent Automation & Soft Computing 2024年第2期39卷 233-254页

作者： Chao-Lung Yang Atinkut Atinafu Yilma Bereket Haile Woldegiorgis Hendrik Tampubolon Hendri Sutrisno Department of Industrial Management National Taiwan University of Science and TechnologyTaipeiTaiwanChina Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology(BiT)Bahir DarUniversityBahir Dar6000Ethiopia Department of Computer Science and Information Engineering National Taiwan University of Science and TechnologyTaipeiTaiwanChina Department of Information System Faculty of Engineering and Computer ScienceKridaWacana Christian UniversityJakartaIndonesia Department of Information Management National Dong Hwa UniversityHualienTaiwanChina

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing *** real-time production process monitoring is critical in today’s smart *** more robust the monitoring model,the more reliable a process is to be under *** the past,many researchers have developed real-time monitoring methods to detect process shifts ***,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and *** this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts *** effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution ***,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal *** results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal *** experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring *** result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

关键词： Smart manufacturing process monitoring quality control gated recurrent unit neural network random forest

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining 23

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Optimizing Multi-Agent system Swarm Performance Through Explainable AI 11

Optimizing Multi-Agent System Swarm Performance Through Expl...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Mahmud, Shekhar Kutlu, Mustafa Alan, Alper Turan Military Technological College System Engineering Department Muscat Oman Sakarya University of Applied Sciences Mechatronics Engineering Department Sakarya Turkey International University of Kuwait Computer Science & Cybersecurity Department Kuwait City Kuwait

ISBN: (纸本)9798331509293

Advancements in multi-agent systems (MAS) have enabled swarm-based systems to perform decentralized decision-making and autonomous tasks. However, optimizing their performance while ensuring transparency and interpretability remains a challenge. This paper introduces a framework that combines Bayesian optimization with Explainable Artificial Intelligence (XAI) techniques to enhance both the efficiency and transparency of MAS swarms. The Bayesian optimization framework fine-tunes agent parameters to improve swarm metrics such as energy efficiency, task completion time, and coordination success. The experimental results show significant improvements: a 25% increase in the coordination success rate, a 15% increase in energy efficiency, and a 20 % reduction in task completion time. XAI techniques, including SHAP values, provide interpretable explanations for optimization decisions, improving user trust and understanding. This study demonstrates the efficacy of integrating Bayesian optimization with XAI to create transparent, efficient, and reliable MAS swarms. Future work should address scalability and implications in dynamic environments. © 2025 IEEE.

关键词： Multi agent systems

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Lax-net: Freezing of Gait Detection in Parkinson's Disease Using LSTM with Attention and XGBoost

Lax-net: Freezing of Gait Detection in Parkinson's Disease U...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Bouslah Ayoub Khaoucha Aicha Taleb Nora Messaoud Bouthaina Computer Science Department LRI Laboratory Badji Mokhtar University Annaba Algeria Computer Science Department Complex System Engineering Laboratory (LISCO) Badji Mokhtar University Annaba Algeria

Parkinson's disease (PD) is a neurodegenerative disorder marked by motor symptoms like gait freezing (FoG), which notably affects patients' mobility and overall quality of life. Early detection of FOG episodes is crucial for timely intervention and reducing the risk of falls and injuries. In this paper, we present Lax-net, a deep learning-based approach for the early detection of FOG using wearable sensor data with time series information. These data often exhibit complex temporal dependencies, making it challenging to extract meaningful features and accurately classify them. To address this challenge, our proposed method combines the strengths of LSTM with attention mechanisms to capture temporal relationships and identify important features in the data. The derived features are then fed into the XGBoost model, a robust classifier with the ability to grasp non-linear relationships. We evaluate the performance of our proposed method on a benchmark dataset, comparing it with other state-of-the-art methods. The results demonstrate that our combined approach achieves superior accuracy and outperforms individual models such as LSTM and XGBoost.

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An Improved YOLO-v3Algorithm for Ship Detection in SAR Image Based on K-means++ with Focal Loss 3

An Improved YOLO-v3Algorithm for Ship Detection in SAR Image...

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3rd China International SAR Symposium, CISS 2022

作者： Wang, Haonan Wu, Baolong Wu, Yanni Zhang, Shuangxi Mei, Shaohui Liu, Yanyang Northwestern Polytechnical University School of Electronics and Information Xi'an China Shanxi Normal University School of Computer Science Xi'an China Shanghai Institute of Satellite Engineering Department of Radar System Engineering Shanghai China

ISBN: (数字)9798350398717

ISBN: (纸本)9798350398717

In recent years, practical industrial production application have put forward extremely high requirements for its detection accuracy and detection efficiency in the aspect of synthetic aperture radar (SAR) image ship detection. Among the solutions to this problem, the YOLO has received more and more attention due to its advantages such as high speed. In this paper, the K-means++ is used to obtain the Anchor Box, the Focal loss is introduced to balance the proportion of positive and negative samples, and an improved image detection algorithm based on YOLO-v3 is proposed to solve the low detection efficiency and detection accuracy of ship images. The experimental results show that the improved algorithm in this paper can get rid of the local optimum well, shorten the convergence time, improve the training efficiency and the accuracy of ship image detection. © 2022 IEEE.

关键词： Ships

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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation systems

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IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

On the Approximation of the Quotient of Two Gaussian Densiti...

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26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions

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User’s Position-Dependent Strategies in Consumer-Generated Media with Monetary Rewards

arXiv

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arXiv 2023年

作者： Ueki, Shintaro Toriumi, Fujio Sugawara, Toshiharu Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Department of System Innovation The University of Tokyo Tokyo Japan

Numerous forms of consumer-generated media (CGM), such as social networking services (SNS), are widely used. Their success relies on users’ voluntary participation, often driven by psychological rewards like recognition and connection from reactions by other users. Furthermore, a few CGM platforms offer monetary rewards to users, serving as incentives for sharing items such as articles, images, and videos. However, users have varying preferences for monetary and psychological rewards, and the impact of monetary rewards on user behaviors and the quality of the content they post remains unclear. Hence, we propose a model that integrates some monetary reward schemes into the SNS-norms game, which is an abstraction of CGM. Subsequently, we investigate the effect of each monetary reward scheme on individual agents (users), particularly in terms of their proactivity in posting items and their quality, depending on agents’ positions in a CGM network. Our experimental results suggest that these factors distinctly affect the number of postings and their quality. We believe that our findings will help CGM platformers in designing better monetary reward schemes. © 2023, CC BY-NC-ND.

关键词： Behavioral research

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information system, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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