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Decisioning-Based Approach for Optimising Control engineering Tools Using Digital Twin Capabilities and Other Cyber-Physical Metaverse Manufacturing System Components

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3212-3221页

作者： Mourad, Nahia Alsattar, Hassan A. Qahtan, Sarah Zaidan, Aws Alaa Deveci, Muhammet Sangaiah, Arun Kumar Pedrycz, Witold British University in Dubai Faculty of Engineering and IT Dubai United Arab Emirates University of Mashreq College of Administrative Science Department of Business Administration Baghdad10067 Iraq Middle Technical University College of Health and Medical Technology-Baghdad Department of Computer Center Baghdad10047 Iraq SP Jain School of Global Management SydneyNSW2141 Australia National Defence University Turkish Naval Academy Department of Industrial Engineering Istanbul34040 Turkey Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin64002 Taiwan University of Alberta Faculty of Engineering Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul34010 Turkey

The optimisation of control engineering tools based on digital twin capabilities and other cyber-physical metaverse manufacturing system (CPMMS) components are crucial for the successful performance. This study proposes a model for optimising control engineering tools using digital twin capabilities and other CPMMS components to solve the open issues. The main contributions and novelty aspects of the methodological process are outlined as follows: Formulated and developed is a decision matrix based on a utility procedure for 10 control engineering tools with digital twin capabilities and other three CPMMS components (Programmable-Logic-Controller and Human-Machine-Interface, Internet of Things connectivity and cybersecurity features). This matrix accounts for the uncertainty associated with tool assessment and transformation evaluation issue;formulated and develop an integrating fuzzy weighted with zero-inconsistency-interval-valued spherical fuzzy rough sets (IvSFRS-FWZIC) and combined compromise solution (CoCoSo) methods. The IvSFRS-FWZIC method is utilised to assign importance degrees to the digital twin capabilities and other CPMMS components. The applicability and robustness of the proposed approach are validated and evaluated through conducting sensitivity, correlation, and comparative analyses. The proposed approach can assist managers in analysing and selecting the most suitable tool for developing CPMMS. © 1975-2011 IEEE.

关键词： Internet of things

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Multi-Agent Path Finding Using Conflict-Based Search and Structural-Semantic Topometric Maps

arXiv

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arXiv 2025年

作者： Fredriksson, Scott Bai, Yifan Saradagi, Akshit Nikolakopoulos, George Robotics and AI group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

As industries increasingly adopt large robotic fleets, there is a pressing need for computationally efficient, practical, and optimal conflict-free path planning for multiple robots. Conflict-Based Search (CBS) is a popular method for multi-agent path finding (MAPF) due to its completeness and optimality;however, it is often impractical for real-world applications, as it is computationally intensive to solve and relies on assumptions about agents and operating environments that are difficult to realize. This article proposes a solution to overcome computational challenges and practicality issues of CBS by utilizing structural-semantic topometric maps. Instead of running CBS over large grid-based maps, the proposed solution runs CBS over a sparse topometric map containing structural-semantic cells representing intersections, pathways, and dead ends. This approach significantly accelerates the MAPF process and reduces the number of conflict resolutions handled by CBS while operating in continuous time. In the proposed method, robots are assigned time ranges to move between topometric regions, departing from the traditional CBS assumption that a robot can move to any connected cell in a single time step. The approach is validated through real-world multi-robot path-finding experiments and benchmarking simulations. The results demonstrate that the proposed MAPF method can be applied to real-world non-holonomic robots and yields significant improvement in computational efficiency compared to traditional CBS methods while improving conflict detection and resolution in cases of corridor symmetries. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Plant Leaf Disease Detection using Novel Machine Learning Approach

Plant Leaf Disease Detection using Novel Machine Learning Ap...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Yogananda, G.S. Ananda Babu, J. Ramya, R. Maheswari, Mamilla Sundaram, S. Meenakshi Kalpataru Institute of Technology Department of Electronics and Communication Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Malla Reddy Engineering College for Women India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

Plant leaf disease detection is a critical component of modern agriculture, aimed at early and accurate identification of diseases that can impact crop yields and food security. But the existing detection models attain insufficient and low-quality when utilizing the large amount of data. To overcomes this issue, a novel Machine Learning (ml) algorithm namely, Logistic Regression based Ensemble Classifier (LREC) is proposed for detecting the plant leaf disease. The data are gathered from the leaf image dataset and collected images are pre-processed to remove noise and Histogram equalization is utilized to enhance the quality of the image. Principal Component Analysis (PCA) is performed in feature extraction process and classified through Logistic regression integrated with ensemble classifier. When compared with existing method, the proposed method attains the better performance in detection of plant leaf diseases. The results are outperformed with evaluation metrics such as precision, recall, accuracy and f-measure as respectively. © 2023 IEEE.

关键词： Ensemble classifier Feature extraction Leaf disease detection Machine Learning Principal Component Analysis

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Development of Telugu Treebank Using Panini Karaka Relations for Dependency Parsing 15th

Development of Telugu Treebank Using Panini Karaka Relations...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagaraju, G. Ramesh, G.S. Subhash, P. Srinivas, M. Pulipati, Venkateswararao VNRVJIET Hyderabad India Telangana Hyderabad India and AI&DS VNRVJIET Hyderabad India Department of CSE GCET Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad India

ISBN: (纸本)9783031889912

A Treebank is an annotated corpus and it is a very useful linguistic resource for building dependency parsers, word sense disambiguation systems, and question answering systems. Most of the treebanks are now available in the English language, and a very limited number of Treebanks are available in Indian languages. English and other languages follows western grammar formalism, but these grammar rules are not applicable to Indian languages. Telugu is one of the south Indian languages and the most predominant spoken language in India. This paper describes the steps for developing Telugu Treebank using Panini karaka and non-karaka relations. The developed Telugu treebank is applied to various dependency parsers. This paper also discusses the results achieved with the various dependency parsers using Telugu Treebank. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Karaka relations Panini Telugu Treebank

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An Enhanced Prediction Logic to Forecast Bitcoin Price Variations Using Elevated Machine Learning Principles 9

An Enhanced Prediction Logic to Forecast Bitcoin Price Varia...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Bhuvaneswari, J. Nair, M. Saranya Devi, S. Durga Santhiya, J. Juliet, N. Rmk College of Engineering And Technology Department of Ai&ds Chennai Puduvoyal India Vellore Institute of Technology Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai India Dr.Mahalingam College of Engineering And Technology Department of Computer Science And Engineering Pollachi India Computer Applications S.A.Engineering College Chennai India

ISBN: (纸本)9798350365092

The volatility of Bitcoin prices presents both challenges and opportunities for investors and researchers seeking to understand and forecast cryptocurrency market dynamics. In this study, we propose a Hybrid Recurrent Neural Network and Autoregressive Integrated Moving Average (RNN-ARIMA) model to predict Bitcoin price variations. Leveraging minute-to-minute Bitcoin price data sourced from Bitstamp exchange, our methodology integrates the capabilities of RNNs and ARIMA models to capture both temporal dependencies and linear trends in the data. Through rigorous experimentation, we evaluate the performance of the hybrid model against baseline models and alternative configurations. Our results demonstrate the superiority of the Hybrid RNN-ARIMA model, with a Mean Absolute Error (MAE) of 120.54, Mean Squared Error (MSE) of 25098.23, Root Mean Squared Error (RMSE) of 158.45, R-squared (R2) of 0.87, and Mean Absolute Percentage Error (MAPE) of 3.8%. These findings underscore the effectiveness of the hybrid modeling approach in accurately predicting Bitcoin price variations and provide valuable insights for stakeholders navigating the volatile cryptocurrency market. © 2024 IEEE.

关键词： Recurrent neural networks

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Retrieval-Augmented Generation with Hierarchical Knowledge

arXiv

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arXiv 2025年

作者： Huang, Haoyu Huang, Yongfeng Yang, Junjie Pan, Zhenyu Chen, Yongqiang Ma, Kaili Chen, Hongzhi Cheng, James KASMA.ai Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. In this paper, we introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems in the indexing and retrieval processes. Our extensive experiments demonstrate that HiRAG achieves significant performance improvements over the state-of-the-art baseline methods. The code of our proposed method is available at https://***/hhy-huang/HiRAG. © 2025, CC BY.

关键词： Semantics

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ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary 31

ChatCite: LLM Agent with Human Workflow Guidance for Compara...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Li, Yutong Chen, Lu Liu, aiwei Yu, Kai Wen, Lijie Tsinghua University Beijing China X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761964

The literature review is an indispensable step in the research process. It provides the benefit of comprehending the research problem and understanding the current research situation while conducting a comparative analysis of prior works. However, literature summary is challenging and time consuming. The previous LLM-based studies on literature review mainly focused on the complete process, including literature retrieval, screening, and summarization. However, for the summarization step, simple CoT method often lacks the ability to provide extensive comparative summary. In this work, we firstly focus on the independent literature summarization step and introduce ChatCite, an LLM agent with human workflow guidance for comparative literature summary. This agent, by mimicking the human workflow, first extracts key elements from relevant literature and then generates summaries using a Reflective Incremental Mechanism. In order to better evaluate the quality of the generated summaries, we devised a LLM-based automatic evaluation metric, G-Score, in refer to the human evaluation criteria. The ChatCite agent outperformed other models in various dimensions in the experiments. The literature summaries generated by ChatCite can also be directly used for drafting literature *** code is available. © 2025 Association for Computational Linguistics.

关键词： Chatbots

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Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine Learning

Circuit Design and Efficient Simulation of Quantum Inner Pro...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Xiong Yehui Tang Xinyu Ye Junchi Yan Department of Computer Science and Engineering MoE Key Lab of AI Shanghai Jiao Tong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

For the essential operation, namely inner product (IP) as widely adopted in classic computing e.g. matrix multi-plication, its quantum counterpart: quantum inner product (QIP), has also been recently theoretically explored with a verifiable lower complexity on quantum computers. How-ever, it remains unclear for the embodiment of the quantum circuits (QC) for QIP, let alone a (thorough) evaluation of the QIP circuits, especially in a practical context in the NISQ era by applying QIP to ml via hybrid quantum-classic pipelines. In this paper, we carefully design the QIP circuits from scratch, whose complexity is in accordance with the theoretical complexity. To make the simulation tractable on classic computers, especially when it is integrated in the gradient-based hybrid ml pipelines, we further devise a highly-efficient simulation scheme by directly simulates the output state. Experiments show that the scheme acceler-ates the simulation for more than 68k times compared with the previous circuit simulator. This allows our empirical evaluation on typical machine learning tasks, ranging from supervised and self-supervised learning via neural nets, to K-Means clustering. The results show that the calculation error brought by typical quantum mechanisms would incur in general little influence on the final numerical results given sufficient qubits. However, certain tasks e.g. ranking in K-Means could be more sensitive to quantum noise.

关键词： computers Pipelines Qubit Quantum mechanics Machine learning Self-supervised learning Complexity theory

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Multi-Step Hindsight Experience Replay with Bias Reduction for Efficient Multi-Goal Reinforcement Learning

Multi-Step Hindsight Experience Replay with Bias Reduction f...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： Yang, Yu Yang, Rui Lyu, Jiafei Yan, Jiangpeng Luo, Feng Luo, Dijun Li, Xiu Li, Lanqing Tsinghua University Shenzhen International Graduate School Shenzhen China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Ai Lab Tencent Inc. Shenzhen China

ISBN: (纸本)9798350301113

Multi-goal reinforcement learning has emerged as a powerful approach for planning and robot manipulation tasks, but it faces challenges such as sparse rewards and sample inefficiency. Hindsight Experience Replay (HER) has been proposed as a solution to these challenges by relabeling goals, but it still requires a large number of samples and significant computation. To address these issues, we propose Multi-step Hindsight Experience Replay (MHER), which incorporates multi-step relabeling to improve sample efficiency. Despite the advantages of n -step relabeling, we theoretically and experimentally prove the off-policy n -step bias introduced by n -step relabeling may lead to poor performance in many environments. To address this issue, two bias-reduced MHER algorithms, MHER ( λ ) and Model-based MHER (MMHER) are presented. MHER ( λ ) exploits the λ return while MMHER benefits from model-based value expansions. Experimental results on numerous multi-goal robotic tasks show that our solutions can successfully alleviate the off-policy n -step bias and achieve significantly higher sample efficiency than previous multi-goal RL baselines with little additional computation beyond HER. © 2023 IEEE.

关键词： Reinforcement learning

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Model Adaptation for Enhanced Liveness Face Detection: Experimental Findings on SiWMv2 Dataset 8

Model Adaptation for Enhanced Liveness Face Detection: Exper...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Khairnar, Smita Gite, Shilpa Thepade, Sudeep D. Mahajan, Kashish University Symbiosis Institute of Technology Computer Science and Information Technology Department Pune412115 India Pimpri Chinchwad College of Engineering Pune411044 India Pune Symbiosis Centre for Applied Ai Symbiosis Institute of Technology Aiml Department Pune412115 India PCET's Pimpri Chinchwad University Pimpri Chinchwad College of Engineering Pune411044 India Pimpri Chinchwad College of Engineering and Research Computer Science Department Pune412101 India

ISBN: (纸本)9798350391770

The field of liveness face detection, an essential component of security and biometrics, has seen notable advancements through developments in computer vision and deep learning. It's crucial for accurately distinguishing between genuine and spoofed data samples. This study aims to elevate the performance of automated liveness face detection by focusing on model adaptation, utilizing the SiWMv2 dataset. This dataset is particularly valuable as it encompasses 14 kinds of spoofing attacks, that include print, replay attacks, obfuscation makeup, paper glasses, and various others, which significantly enhances the robustness of the detection systems against a wide array of spoofing techniques. By fine-tuning and adapting seven pre-trained architectures - VGG16, DenseNet201, InceptionV3, VGG19, ResNet50, MobileNetV2, and Xception - paper aims to push all boundaries of current automated liveness face detection capabilities. Through systematic experimentation and analysis, this study aims to contribute to the improvement and effectiveness of liveness face detection systems across various applications, ensuring they are more resilient and capable of handling diverse spoofing scenarios. © 2024 IEEE.

关键词： Data obfuscation

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