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Optimal control for unreliable manufacturing and remanufacturing system under environmental subsidy and overrun penalty mechanism

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IEEE Transactions on Engineering Management 2025年 72卷 1983-2002页

作者： Turki, Sadok Jemmali, Mahdi Hidri, Lotfi University of Lorraine Department of Logistic and Maintenance UFR MIM at Metz Metz17 France University of Sharjah Computer Science Department College of Computing and Informatics Sharjah United Arab Emirates King Saud University Industrial Engineering Department College of Engineering Riyadh11421 Saudi Arabia

Because of environmental concerns, remanufacturing has become an integral process for many production companies. Most published papers dealing with manufacturing and remanufacturing systems (MRSs) overlook some industrial features. These include machine unreliability, the distinction between newly manufactured and remanufactured products, and uncertainty about the returned used products (RUPs). As a result, these models do not reflect the reality of industrial systems. This study aims to bridge the gap in research on MRSs by elaborating an optimal and dynamic control of manufacturing, remanufacturing, and recovery of RUPs. The goal is to develop a model based on manufacturing remanufacturing emissions hedging point control policy (MREHP) to adjust the production of new and remanufactured products, and recovery of RUPs according to economic and environmental thresholds. To reduce carbon emissions, a new method is proposed based on the environmental subsidy and overrun penalty mechanism (ESOPM), and that is combined with MREHP to control in parallel the MRS and generated emissions. A robust optimization algorithm is developed to determine the optimal economic and environmental thresholds in MREHP and ESOPM. A comparative study is provided to evaluate our proposed ESOPM in comparison with traditional reduction policies: carbon cap and trade (CCT) and carbon tax regulation (CTR). The obtained results reveal that machines' reliability, rate of RUPs, emissions subsidy, and carbon emissions limit have notable impacts on economic and environmental thresholds. Additionally, results show that the proposed ESOPM performs higher profit than CTR and CCT while reducing emissions when the carbon price is below a certain value. © 1988-2012 IEEE.

关键词： Fabrication

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A Semi-decentralized Congestion-Free Multi-path Energy Routing for P2P Energy Trading Systems 8th

A Semi-decentralized Congestion-Free Multi-path Energy Routi...

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8th International Symposium on Modelling and Implementation of Complex Systems, MISC 2024

作者： Hebal, Sara Harous, Saad Mechta, Djamila Louail, Lemia LRSD Laboratory Department of Computer Science Sétif 1 University Ferhat Abbas Sétif19000 Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Université de Lorraine CNRS CRAN Nancy54000 France

ISBN: (纸本)9783031821110

The increased emphasis on clean energy has accelerated the integration of renewable energy sources into electrical grids, resulting in the rise of peer-to-peer energy trading systems. For optimal power dispatch and management the Energy Internet concept advocates using energy routers. Efficient energy routing algorithms are crucial for successful energy transfer between trading pairs in peer-to-peer energy trading across the complex Energy Internet. This research treats the energy routing problem as a non-convex, non-linear optimization problem using a combination technique of graph theory and non-convex nonlinear programming. It efficiently generates a multi-path solution for a trading pair, minimizing energy transmission loss while considering power flow direction constraints and accurately computing power losses during transmission which has not been considered in existing energy routing algorithms. A semi-decentralized approach with a priority concept is proposed for managing simultaneous power transmissions from multiple energy trading pairs, to avoid congestion and shift computational workloads to individual routers and the network system operator. The approach’s performance is assessed through numerical simulations and comparisons with existing energy routing algorithms to showcase its novelty in power loss reduction, congestion management, and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph theory

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Private Computation of Boolean Functions Using Single Qubits 15th

Private Computation of Boolean Functions Using Single Qubit...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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Neuro-Symbolic Classification of Basic Human Values 2nd

Neuro-Symbolic Classification of Basic Human Values

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2nd International Workshop on Value Engineering in Artificial Intelligence, VALE 2024

作者： De Giorgis, Stefano Lazzari, Nicolas Institute of Cognitive Sciences and Technologies National Research Council Rome Italy Department of Computer Science University of Pisa Pisa Italy Department of Modern Languages Literature and Cultures University of Bologna Bologna Italy

ISBN: (纸本)9783031854620

This work explores the integration of ontology-based reasoning and Machine Learning techniques for explainable classification in the domain of moral and cultural values. By relying on an ontological formalization of moral values as in the Basic Human Values theory, which is based on the Description and Situation Ontology Design Pattern, the sandra neuro-symbolic reasoner is used to infer values (formalized as descriptions) that are satisfied by a certain sentence. Sentences, alongside their structured representation, are automatically generated using an open-source Large Language Model. The inferred descriptions are used to automatically detect the value associated with a sentence. We show that only relying on the reasoner’s inference results in explainable classification comparable to other more complex approaches. Moreover, we test how the LLMs tacit knowledge can be exploited to obtain novel formalizations of the domain. We show that combining the reasoner’s inferences with simple distributional semantics methods largely outperforms all the baselines, including complex models based on neural network architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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Silent Speech Interfaces: Non-Invasive Neuromuscular Signal Processing for Assistive Communication 4

Silent Speech Interfaces: Non-Invasive Neuromuscular Signal ...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Khan, Sallar Ali, Khawaja Usman Qadri, Ali Raza Ali Rizvi, Maisam Technological University Dublin School of Informatics and Cybersecurity Dublin Ireland Sir Syed University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan

ISBN: (纸本)9798331518882

Our research demonstrates a discrete speech system that empowers those afflicted with speech impairments to convey ideas by internally articulating words without producing audible sound. This method harnesses the minimal movements involved in articulation, making it less arduous than conventional vocalization, especially for individuals facing communication challenges. Silent Speech Interfaces (SSIs) form the foundation of our research, enabling the decoding of neuromuscular signals associated with internal speech articulation to facilitate non-invasive and effective communication for individuals with speech impairments. This research entails the utilization of a multi-electrode configuration to capture subtle, low-voltage electrical signals positioned on the surface area of the subject's neck. These signals emerge as a consequence of neural activation within the speech articulator muscles, signifying the internal articulation of words within the oral cavity, accompanied by minimal physical movement. Subsequent to signal acquisition, a comprehensive analysis of the data is conducted using a biosensing board (Cyton), facilitated by the implementation of the OpenBCI GUI interface and visualization via Python plotting tools. © 2025 IEEE.

关键词： Digital signal processing

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VLC positioning in low data rate for V2V communication 2024

VLC positioning in low data rate for V2V communication

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12th Euro-American Conference on Telematics and Information Systems, EATIS 2024

作者： Giraldo, Luis Miguel Soler, Joaquin Perez Botella-Mascarell, Carmen Juan, Vicent Girbes Varea, Sandra Roger Torres, Julio Martos Olcina, Raimundo Garcia Electronical Engineering Department Universitat de València Burjassot Valencia46100 Spain Computer Science Department Informatics Department Universitat de València Burjassot Valencia46100 Spain

ISBN: (纸本)9798400717338

The creation and implementation of a system that bridges visible light communication (VLC) with selected identifiers, envisioned as informational beacons, are outlined. This system is adept at controlling LED panels and orchestrating communication between LED lights and the receiving unit, laying the groundwork for vehicle-to-vehicle (V2V) communication systems tailored for precise positioning. The design and assessment of test-bed requirements cater to the anticipation of future V2V optical wireless communication scenarios. Through analysis, it is demonstrated that commercial outdoor lighting can be effectively utilized for V2V positioning, capable of operating under daylight with a data transmission rate of 100 Kbps. Crucially, the system's ability to accurately detect positioning beacons facilitates the localization of vehicles within designated areas of interest, paving the way for the advancement of V2V communication technologies. This comprehensive approach not only validates the feasibility of using VLC for vehicle positioning but also sets a precedent for future developments in V2V communication systems. © 2024 Owner/Author.

关键词： Optical communication

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis

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IEEE Access 2025年 13卷 85473-85492页

作者： Ba Mahel, Abduljabbar S. Al-Gaashani, Mehdhar S. A. M. Alkanhel, Reem Ibrahim Hassan, Dina S. M. Muthanna, Mohammed Saleh Ali Muthanna, Ammar Aziz, Ahmed University of Electronic Science and Technology of China School of Life Science and Technology Chengdu610054 China University of Electronic Science and Technology of China School of Resources and Environment Chengdu610056 China Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Tashkent State University of Economics Department of International Business Management Tashkent Uzbekistan RUDN University Department of Applied Probability and Informatics Peoples’Friendship University of Russia 6 Miklukho-Maklaya St Moscow117198 Russia Faculty of Computer and Artificial Intelligence Benha University Department of Computer Science Egypt Central Asian University Engineering school Tashkent Uzbekistan

Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular diseases (CVD), especially atrial fibrillation (AF), a prevalent cardiac rhythm abnormality. However, the variability and complexity of ECG signals make AF classification challenging, highlighting the need for more accurate and reliable methods. This paper introduces a novel deep learning (DL) architecture designed to enhance the processing, feature extraction, and analysis of ECG signals. The proposed model utilizes a hybrid framework that combines standard and dilated convolutional networks, advanced attention mechanisms, and temporal sequence learning to address the complexities of ECG data. A parallel architecture integrates a MobileViT block for efficient spatial feature extraction and an Efficient Channel Attention (ECA) module to refine feature representations. Simultaneously, a Long Short-Term Memory (LSTM) network captures temporal dependencies in the data. The concatenation of the outputs from both the MobileViT-ECA block and the LSTM network allows for the extraction of both local and global features. Fully connected layers, along with dropout regularization, ensure robust performance and effective classification. We analyzed ECG signals of three different lengths (1, 2, and 3 seconds) to investigate how segment length affects model performance. Additionally, we conducted various classification scenarios and an ablation study to simulate real-world conditions and identify key components of the architecture. Our experimental findings show that the model attains high accuracy in ECG classification and AF identification, with average metrics including accuracy of 87.80%, recall of 87.80%, F1-score of 87.45%, and specificity of 95.66%. The proposed model outperforms existing methods on the same AF dataset, demonstrating its superior performance. This method accurately classifies ECG signals and detects AF, highlighting its potential for clinical application. The results help move forwar

关键词： Electrocardiography

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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Leveraging Peripheral Visual Stimuli for Enhanced SSVEP-Based BCIs in Fast Calibration Scenario

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IEEE Sensors Journal 2025年第10期25卷 17683-17695页

作者： He, Xinjie Allison, Brendan Z. Liang, Wei Chen, Weijie Wang, Xingyu Cichocki, Andrzej Jin, Jing East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China University of California San Diego Department of Cognitive Science La Jolla CA92093 United States Nicolaus Copernicus University Department of Informatics Toruń87-100 Poland Systems Research Institute Polish Academy of Science Warsaw01-447 Poland RIKEN Brain Science Institute Laboratory for Advanced Brain Signal Processing Wako351-010 Japan East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education School of Mathematics Shanghai200237 China

The decoding method based on training sample is utilized to effectively improve the recognition ability for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, these methods are not adapted to fast calibration scenarios because their performance degrades as the number of calibration trials decreases. For fast calibration scenarios, we proposed a multivisual source aliasing matrix estimation task-related component analysis (MVSAME-TRCA) method, which can simultaneously exploit the common information of the source aliasing matrix and the spatial filter across a peripheral visual stimulus (PVS). Experiment results show that the MVSAME-TRCA method outperforms the state-of-the art (SOTA) method such as ensemble TRCA approach (eTRCA), ePRCA, and same-eTRCA in a fast calibration scenario in the two public datasets. Notably, our approach only needs 28 s to calibrate 40 targets in the two public datasets and achieves an averaged information transfer rates (ITRs) of 248.78 ± 86.47 bits/min and 186.86 ± 100.32 bits/min, respectively. This research greatly reduces the need for individualized calibration for SSVEP system and is conducive to the development of practical plug and play SSVEP-BCI. © 2001-2012 IEEE.

关键词： Brain computer interface

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