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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Abarna, P.S. Priya, S. Padma Pavalarajan, S. Sivatharani, S. Manshi, M. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. The comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. The system initiates a multi-tiered response upon detecting a gas leak;it sounds an alarm, notifies user-specified The mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. The need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. The results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure. © 2025 IEEE.

关键词： Solenoid valves

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Topology, Size, and Shape Optimization in Civil engineering Structures: A Review

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computer Modeling in engineering & Sciences 2025年第2期142卷 933-971页

作者： Ahmed Manguri Hogr Hassan Najmadeen Saeed Robert Jankowski Faculty of Civil and Environmental Engineering Gdansk University of TechnologyGdansk80-223Poland Department of Computer Science ModelingElectronics and Systems EngineeringUniversity of CalabriaCosenza87036Italy Civil Engineering Department University of RaparinRania46012Iraq Civil Engineering Department Tishk International UniversityErbil44001Iraq

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering *** optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural *** design approaches aim to reduce the built environment’s energy use and carbon *** comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these *** trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were *** review also discusses emerging technologies,such as machine learning applications with different optimization *** of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and *** techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure *** and non-linear structures,including geometric and material nonlinearity,are *** role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

关键词： Structural optimization topology optimization size optimization shape optimization multi-objective optimization

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A New Model of Trapped Charge Sources in Switching Transient Studies in the Presence of Shunt Reactors

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IEEE Access 2025年 13卷 37118-37127页

作者： Akafi-Mobarakeh, Mohsen Shariatinasab, Reza Siano, Pierluigi Vahidi, Behrooz Reza Najafi, Hamid University of Birjand Department of Electrical and Computer Engineering Birjand*** Iran University of Salerno Department of Management and Innovation Systems Fisciano84084 Italy National University of Science and Technology POLITEHNICA Bucharest Department of Power Systems Bucharest060042 Romania Tehran*** Iran

Insulation coordination studies are of great importance in power grid reliability. In this paper, a new method is proposed for modeling trapped charge sources (TCS) in switching transient studies. The TCS is used to take into account the voltage stored in line capacitors during reclosing operation after fault occurrence. The proposed model is designed based on the active filter concept, thus overcoming the limitations of conventional TCS for simulating transient states in EMTP/ATPDraw. Given the natural frequencies of a transmission line to which the proposed TCS (PTCS) is connected, it injects the appropriate frequencies and eliminates voltage oscillations which limit the use of TCS. To verify the efficiency of the PTCS, it is implemented in a real system with a shunt reactor, and the results are then compared with field measurements. A comparison of the results shows that the PTCS eliminates the voltage oscillations in the simulation before closing and provides a smooth voltage with the desired amplitude. Using the proposed model, the maximum line switching overvoltage is correctly calculated;this, in turn, results in a more accurate transmission line insulation design, which is technically and economically beneficial. © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://***/licenses/by/4.0/

关键词： Electromagnetic transients

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FunGrasp: Functional Grasping for Diverse Dexterous Hands

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IEEE Robotics and Automation Letters 2025年第6期10卷 6175-6182页

作者： Huang, Linyi Zhang, Hui Wu, Zijian Christen, Sammy Song, Jie Thrust of Robotics and Autonomous Systems Guangzhou China ETH Zürich Department of Computer Science Zürich Switzerland The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Functional grasping is essential for humans to perform specific tasks, such as grasping scissors by the finger holes to cut materials or by the blade to safely hand them over. Enabling dexterous robot hands with functional grasping capabilities is crucial for their deployment to accomplish diverse real-world tasks. Recent research in dexterous grasping, however, often focuses on power grasps while overlooking task- and object-specific functional grasping poses. In this paper, we introduce FunGrasp, a system that enables functional dexterous grasping across various robot hands and performs one-shot transfer to unseen objects. Given a single RGBD image of functional human grasping, our system estimates the hand pose and transfers it to different robotic hands via a human-to-robot (H2R) grasp retargeting module. Guided by the retargeted grasping poses, a policy is trained through reinforcement learning in simulation for dynamic grasping control. To achieve robust sim-to-real transfer, we employ several techniques including privileged learning, system identification, domain randomization, and gravity compensation. In our experiments, we demonstrate that our system enables diverse functional grasping of unseen objects using single RGBD images, and can be successfully deployed across various dexterous robot hands. The significance of the components is validated through comprehensive ablation studies. © 2016 IEEE.

关键词： Reinforcement learning

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural Computing and Applications 2025年 1-19页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Generalized Critical Observability of Labeled Petri Nets Under Nondeterministic Observations

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14633-14645页

作者： Zhang, Yuling Hadjicostis, Christoforos N. Li, Zhiwu Xidian University School of Electro-Mechanical Engineering Xi’an710071 China University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus Macau University of Science and Technology Institute of Systems Engineering China

This paper addresses the property of critical observability in labeled Petri nets. A system is deemed critically observable if its state estimate (formed by observing the system output) can be determined to belong to a set of either critical markings or non-critical markings. More generally, for a critically observable system, there may be multiple types of critical markings and the operator may desire to know the type to which the current state estimate belongs. We propose a generalized version of critical observability, namely generalized critical observability, and provide two necessary and sufficient conditions to check it based on basis markings and integer linear programs, effectively circumventing the need for an exhaustive enumeration of all reachable markings of the net. In addition, we investigate (generalized) critical observability of Petri nets under nondeterministic observations, which may arise due to the loss of transmission signals or due to interference with sensor measurement outputs. To handle this, an augmented system is constructed from the original Petri net by adding new transitions that capture the effect of transitions associated with nondeterministic labels. Thus, we transform the verification of (generalized) critical observability of a Petri net under nondeterministic observations to that of the augmented model. Two real-world examples illustrate the applicability of the presented notion and methodology. © 2004-2012 IEEE.

关键词： Petri nets

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Attention-Driven Emotion Recognition in EEG: A Transformer-Based Approach With Cross-Dataset Fine-Tuning

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IEEE Access 2025年 13卷 69369-69394页

作者： Ghous, Ghulam Najam, Shaheryar Alshehri, Mohammed Alshahrani, Abdulmonem AlQahtani, Yahya Jalal, Ahmad Liu, Hui Riphah International University Department of Electrical and Computer Engineering Islamabad44000 Pakistan Bahria University Department of Electrical Engineering Islamabad44000 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia King Khalid University Department of Informatics and Computer Systems Abha61421 Saudi Arabia Air University Faculty of Computer Science and AI Islamabad44000 Pakistan Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of University of Bremen Cognitive Systems Laboratory Bremen28359 Germany Guodian Nanjing Automation Company Ltd. Nanjing210032 China

Emotion recognition from EEG (electroencephalogram) signals is crucial in mental health diagnostics and human-computer interaction but is often hindered by high dimensionality, noise, and complex temporal dependencies in the data. This paper presents a novel approach that integrates transformer models, attention mechanisms, and transfer learning to enhance emotion recognition accuracy from EEG signals. The proposed methodology consists of two phases: Attention Enhanced Base Model Development (AE-BMD) and Cross-Dataset Fine Tuning Adaptation (CD-FTA). In the AE-BMD phase, the base model is developed and trained on the SEED-IV dataset (15 participants, 62 EEG channels), achieving an accuracy of 84%, with an average precision of 84.75%, recall of 84% and F1-score of 84%. This phase employs optimized feature extraction from key EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) using techniques such as MFCC, GFCC, power spectral density, and Hjorth parameters. A transformer encoder with integrated spectral and temporal attention mechanisms captures intricate patterns and long-range dependencies within the EEG signals. In the CD-FTA phase, the model undergoes fine-tuning on the SEED-V dataset (20 participants, 62 channels) leading to an improved accuracy of 90%, with an average precision of 90.6%, recall of 90.6%, and F1-score of 90.6%. The model’s generalization is further validated on the MPED dataset (23 participants, 62 channels, seven emotion classes), achieving 79%, with an average precision of 79.3%, recall of 79.3% and F1-score of 79.1% across diverse emotional states. This cross-dataset adaptation leverages transfer learning to enhance the model’s generalization across different emotional states and EEG datasets. Experimental results show that the proposed approach outperforms traditional methods, achieving superior accuracy and robustness in emotion recognition tasks. This work advances emotion recognition systems by addressing challenges in EEG signal proc

关键词： Electroencephalography

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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OptiMobileX: Optimizing Deep Transfer Learning Model for Accurate Human Posture Recognition Using a Deep Feature Fusion Technique

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IEEE Sensors Journal 2025年第6期25卷 9759-9766页

作者： Ogundokun, Roseline Oluwaseun Damasevicius, Robertas Maskeliunas, Rytis Kaunas University of Technology Department of Software Engineering Kaunas44249 Lithuania Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania

Human posture recognition (HPR) has garnered growing interest given the possibility of its use in various applications, including healthcare and sports fitness. Interestingly, achieving accurate pose recognition on mobile devices with very little computing power is still tricky. The precise identification of human posture on mobile devices with constrained computational resources is of utmost importance to bring about significant advancements in healthcare, fitness, and technology engagement. By facilitating real-time monitoring and providing personalized solutions, this technology improves accessibility and well-being, revolutionizing our interaction with digital applications. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using two modern pretrained DTL models, MobileNetV2 and Xception, which led to our proposed OptiMobileX: A deep transfer learning (DTL) model based on a DFC of the MobileNetV2 and Xception models for the overall improvement of the identification capability of this posture. The classification accuracy of 96.42% is achieved by the OptiMobileX model, demonstrating the superiority of the proposed model over MobileNetV2, Xception, AlexNet, and InceptionV3, which only manage to obtain 88.17%, 92.47%, 90.32%, and 73.48% accuracy, respectively. The OptiMobileX model exhibits notable capabilities but faces significant limitations. Inadequate details on detecting inappropriate postures and a lack of information on required computing resources hinder the study's comprehensiveness and practical implementation. The absence of insufficient discussion on dataset biases and a lack of information on the model's generalization capacity create obstacles, raising concerns about its fairness, applicability, and real-world utility. © 2001-2012 IEEE.

关键词： Deep learning

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Remote Sensing Surveillance Using Multilevel Feature Fusion and Deep Neural Network

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IEEE Access 2025年 13卷 38282-38300页

作者： Zahoor, Laiba Alhasson, Haifa F. Alnusayri, Mohammed Alatiyyah, Mohammed Alhammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science Air University Islamabad44000 Pakistan Department of Information Technology College of Computer Qassim University Buraydah52571 Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka72388 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj16278 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Cognitive Systems Laboratory University of Bremen Bremen28359 Germany

Human action recognition from aerial imagery poses significant challenges due to the dynamic nature of the scenes and the complexity of human movements. In this paper, we present an enhanced system that combines YOLO for human detection with a complete multilevel feature fusion approach to improve recognition of human actions in drone-captured photos. Our system presents a reliable drone-based human action system through the integration of state-of-the-art methods for multilevel feature extraction and object detection. Initially, frames are extracted individually from drone footage sequences. Preprocessing techniques, which include Gaussian blur, grayscale conversion, and background removal, are used for every frame to improve image quality and feature reliability. For object detection, we effectively locate and recognize human subjects in these aerial frames using YOLO approach. Afterward, the framework extracts 14 body landmarks that represent the shape of the human body by keypoint extraction. Four significant features are employed to capture the complexity of human movement effectively: the incorporation of 3D point cloud data adds depth to the image and makes it feasible to construct a more detailed three-dimensional representation;measuring the angles between keypoints provides significant details on joint orientations which are essential for posture analysis;and geodesic distance measure the shortest paths along the surface of the body to provide useful insight into the spatial relationships between keypoints. The extracted features are optimized by using quadratic discriminant analysis. In the end, a deep neural network was trained to perform the action classification. Three benchmark datasets, the UAV Gesture, UAV Human, and UCF-ARG datasets, were used for our experiments and system testing. Our model achieved corresponding action recognition accuracy values of 90.15%, 72.37%, and 76.50% on each of these datasets. © 2013 IEEE.

关键词： Drones

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