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Human-Centered Coordination for Robot-Assisted Equipment Transport

ASME Letters in Dynamic Systems and Control 2025年第2期5卷 021002页

作者： Fernandez, Joshua Cosby, Austin Mazumdar, Anirban Department of Mechanical Engineering Georgia Institute of Technology AtlantaGA30363 United States Department of Computer Science Mississippi State University MS39762 United States

This work explores how to use an unmanned ground vehicle (UGV) to offload the physical burdens of equipment from humans. This work formulates dynamic alignment following and compares it to position-based following techniques. We describe the control strategies of both following methods and implement them in a dynamic simulation and a physical prototype. We test the performance of the two following methods and show that dynamic alignment following can reduce robot positional error and interaction force between the human and the robot. We then analyze the energetics and the performance of the human-UGV team for candidate transportation tasks. The presence of the robot can make some tasks take longer to perform. Nonetheless, the results show that for the candidate tasks, the robot can reduce human average metabolic power and average overall task energy. © 2024 by ASME.

关键词： Ground vehicles

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Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

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Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 213-227页

作者： Shirley, C.P. Pattar, Jagannath Kavitha Rani, P. Saini, Sumit Ranga, Jarabala Elangovan, D. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Mechanical Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electrical Engineering School of Engineering & Technology Central University of Haryana Mahendergarh India Detartment of Electrical and Electronics Engineering Ramachandra College of Engineering Eluru India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi institute of technology Hyderabad India

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). ©, Engineers Australia.

关键词： Energy utilization

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Exploring EMG Signals for Prosthetic Control and Gesture Recognition: Advancements, Applications, and Future Directions

Exploring EMG Signals for Prosthetic Control and Gesture Rec...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Tushir, Sweety Verma, Neetu Kumar, Krishan Dcrust Department Of Computer Science And Engineering Sonepat Murthal India Jcbust Department Of Mechanical Engineering Faridabad India

ISBN: (纸本)9798350306446

EMG signals and machine learning enable the automation of gesture recognition for addressing challenges faced by amputees in daily life. This review analyses diverse approaches within EMG-based gesture recognition and hand prosthesis control. Shifting muscular intention into natural control offers numerous applications which include helping amputees regain their functioning to completely changing the way we interact with digital surroundings. It highlights advancements in this dynamic field, emphasizing its applications in human-computer interaction, prosthetics and assistive communication. The review focuses on techniques that improve accuracy and robustness which are essential for wider accessibility. Additionally, it explores the potential impact on industries beyond healthcare such as AR/VR, prosthetic hand control, human-machine interaction and sign language recognition. The performance of hybrid approaches and convolutional neural networks (CNNs) in gesture recognition tests demonstrates a high degree of accuracy. Overall, EMG-based gesture recognition offers significant promise in driving innovations that enhance quality of life and promote inclusivity. This exploration delves into the recent strides, diverse applications, and future directions of EMG-based prosthetic control and gesture recognition, offering insights into the groundbreaking research shaping this domain. © 2024 IEEE.

关键词： Machine learning

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Recommendation Algorithm Based on Social Influence Diffusion 17

Recommendation Algorithm Based on Social Influence Diffusion

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Jin, Xin Chen, Shiyu Nanjing University of Science and Technology School of Mechanical Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Social influence is everywhere in people's lives. It controls users' activities in social networks. Understanding the mechanism of interactions between users in social networks can benefit various applications, such as recommendations. Traditional recommendation systems based on collaborative filtering suppose that the behaviors of users are independent of each other and ignore the influence relationships among users. However, different historical behaviors and social relationships of users result in different influences among users. First, the complete user behavior sequences are fed into a recurrent neural network to capture long term patterns of user interest in items. Secondly, a self-attention network is used to obtain short lived interests of users. Then the outputs of the recurrent neural network as queries are fed into another self-attention network to obtain user representations that combine transient effect of and long term patterns of users' interests, thereby predicting the ratings of users to items. Finally, the proposed recommendation system is used to recommend products that need to be promoted to the maximum number of users. The proposed method is verified to outperform the state-of-the-art algorithms on three real-world datasets. © 2024 IEEE.

关键词： Chatbots

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest X-rays

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Multimedia Tools and Applications 2025年第15期84卷 14885-14913页

作者： Asham, Mohammed A. Al-Shargabi, Asma A. Al-Sabri, Raeed Meftah, Ibrahim School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Information Technology Collage of Computer Qassim University Buraydah51452 Saudi Arabia College of Mechanical and Electrical Engineering Central South University Changsha410017 China

Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often require extensive computing resources and complex procedures, rendering them impractical. This study focuses on the development of a lightweight deep-learning model for the detection of pulmonary diseases. Leveraging the benefits of knowledge distillation (KD) and the integration of the ConvMixer block, we propose a novel lightweight student model based on the MobileNet architecture. The methodology begins with training multiple teacher model candidates to identify the most suitable teacher model. Subsequently, KD is employed, utilizing the insights of this robust teacher model to enhance the performance of the student model. The objective is to reduce the student model's parameter size and computational complexity while preserving its diagnostic accuracy. We perform an in-depth analysis of our proposed model's performance compared to various well-established pre-trained student models, including MobileNetV2, ResNet50, InceptionV3, Xception, and NasNetMobile. Through extensive experimentation and evaluation across diverse datasets, including chest X-rays of different pulmonary diseases such as pneumonia, COVID-19, tuberculosis, and pneumothorax, we demonstrate the robustness and effectiveness of our proposed model in diagnosing various chest infections. Our model showcases superior performance, achieving an impressive classification accuracy of 97.92%. We emphasize the significant reduction in model complexity, with 0.63 million parameters, allowing for efficient inference and rapid prediction times, rendering it ideal for resource-constrained environments. Outperforming various pre-trained student models in terms of overall performance and computation cost, our findings underscore the effectiveness of the proposed KD strategy and the integration of the Conv

关键词： Diagnosis

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Machine Learning-Based Fault-Tolerant Inertial Navigation System for Micro Tunnel Boring Machines

Machine Learning-Based Fault-Tolerant Inertial Navigation Sy...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Roudra, Nafis Ashraf Saha, Neelavro Sakib, Saadman Al Mamun, Rahat Shithil, Shaekh Mohammad Iqbal, M Shahriar Tasnim, Rumana BRAC University Department of Computer Science and Engineering Bangladesh Independent University Department of Computer Science and Engineering Bangladesh School of Mechanical Engineering Universiti Teknologi Malaysia Malaysia Rajshahi University of Engineering & Technology Department of Mechanical Engineering Bangladesh World University of Bangladesh Department of Mechatronics Engineering Bangladesh

ISBN: (纸本)9798350369106

Underground tunneling poses a navigation challenge as Micro Tunnel Boring Machines (mTBMs) operate in GPS denied environments. In such settings, Inertial Measurement Units (IMUs) offer an alternative navigation solution. However, faults in mTBM operation caused by various environmental and operational factors lead to inaccuracies in IMU data. This paper presents a machine learning-based fault tolerance system for IMU based mTBM navigation. IMU data corresponding to four specific mTBM faults: vibration, high temperature, magnetic interference and mTBM precession were collected under rigorous testing in a lab environment. Support Vector Machine (SVM) was applied to classify these faults and predict the mTBM's operational state based on the IMU readings. The results demonstrate the effectiveness of the SVM model in identifying and classifying the faults, thereby enhancing the reliability and safety of mTBM operations in challenging environments. © 2024 IEEE.

关键词： Inertial navigation systems

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Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

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Intelligent Automation & Soft Computing 2023年第3期35卷 3403-3413页

作者： S.Ayshwarya Lakshmi K.Anandavelu Department of Computer Science and Engineering University College of Engineering Panruti607106TamilnaduIndia Department of Mechanical Engineering MRK Institute of TechnologyCuddalore608301TamilnaduIndia

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer *** suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation *** time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difﬁ*** of the most signiﬁcant requirements is to design optimal solutions to optimize their *** is no particular technique that can answer all optimization *** proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized *** accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization *** total sensitivity and speciﬁcity attained in the proposed system are 99.56%and 99.73%*** proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional *** proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefﬁ*** model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

关键词： Cukoo search optimization technique ﬁtness function cancer

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Enhancement of mechanical properties in AA5083 aluminum metal matrix composites through alumina and titanium carbide reinforcement

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Interactions 2024年第1期245卷 1-12页

作者： Vadivel, M. S., Seenivasan P., Satishkumar S., Gomkale R., Saminathan Department of Civil Engineering Nehru Institute of Technology Tamilnadu Coimbatore India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Applied Chemistry Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

As a result of its remarkable resistance to corrosion, strong wear resistance, high strength, and low weight, aluminum metal matrix composites (AMMC’s) have garnered tremendous widespread recognition for structural and aeronautical applications. Several mechanical properties of aluminum alloy (AA5083) are examined in this experimental study, which focuses on the impact of reinforcing particles (alumina and titanium carbide). The stir casting method was used to manufacture AMMC. Fabrication was carried out using the less difficult ex-situ approach. Reinforcement particle weight percentages ranging from 4 to 12% were used in the MMC preparation process. Machine operations were performed on the ingots following manufacturing in order to prepare test samples. To investigate the mechanical and tribological properties and effects of different reinforcement weight percentages, tests were conducted including tensile, compression, impact, hardness and wear tests. Adding alumina and titanium carbide resulted in a gradual rise of the tensile and compression strengths. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Aluminum corrosion

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A PAC-Bayesian Framework for Optimal Control with Stability Guarantees 63

A PAC-Bayesian Framework for Optimal Control with Stability ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Boroujeni, Mahrokh Ghoddousi Galimberti, Clara LucIa Krause, Andreas Ferrari-Trecate, Giancarlo Institute of Mechanical Engineering Epfl Switzerland Eth Zϋrich Department of Computer Science Switzerland

ISBN: (纸本)9798350316339

Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically addressed by minimizing an empirical cost, which represents the average cost across a finite dataset of sampled disturbances. However, this approach raises the challenge of quantifying the control performance against out-of-sample uncertainties. Particularly, in scenarios where the training dataset is small, SNOC policies are prone to overfitting, resulting in significant discrepancies between the empirical cost and the true cost, i.e., the average SNOC cost incurred during control deployment. Therefore, establishing generalization bounds on the true cost is crucial for ensuring reliability in real-world applications. In this paper, we introduce a novel approach that leverages PAC-Bayes theory to provide rigorous generalization bounds for SNOC. Based on these bounds, we propose a new method for designing optimal controllers, offering a principled way to incorporate prior knowledge into the synthesis process, which aids in improving the control policy and mitigating overfitting. Furthermore, by leveraging recent parametrizations of stabilizing controllers for nonlinear systems, our framework inherently ensures closedloop stability. The effectiveness of our proposed method in incorporating prior knowledge and combating overfitting is shown by designing neural network controllers for tasks in cooperative robotics. © 2024 IEEE.

关键词： Stochastic systems

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