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ASME 2024 International mechanical engineering Congress and Exposition, IMECE 2024

作者： Guo, Qi Everage, Daxton Zhang, Qinhe Department of Engineering & Computer Science McNeese State University Lake CharlesLA United States School of Mechanical Engineering Shandong University Jinan China

ISBN: (纸本)9780791888650

Thermodynamics is a fundamental subject in engineering education, yet it often poses challenges for students due to its abstract nature and complex concepts. One particularly difficult topic within thermodynamics is refrigeration cycles, which require a deep understanding of thermodynamic principles and their practical applications. This paper explores the implementation and impact of hands-on refrigeration labs in an engineering curriculum to enhance students' understanding of thermodynamics, specifically focusing on refrigeration systems. The hands-on labs utilize a refrigeration cycle hands-on trainer, providing students with practical experience in measuring temperature and pressure, analyzing heat transfer and thermodynamic work with enthalpy and entropy data, computing refrigerant mass flow rate and Coefficient of Performance (COP), and applying thermodynamic principles to real-world scenarios. By conducting these labs, students not only deepen their theoretical knowledge but also develop critical skills such as problem-solving, critical thinking, and practical application of engineering principles. The hands-on refrigeration labs demonstrate the effectiveness of experiential learning methodologies in enhancing students' understanding of thermodynamics. Through the lab activities, many students expressed that the labs significantly improved their understanding of refrigeration systems and their ability to apply thermodynamic concepts in engineering practice. This experiential learning approach bridges the gap between theory and practice, preparing students for future engineering challenges and enhancing their readiness for the workforce. Copyright © 2024 by ASME.

关键词： Students

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Graph Convolutional Networks for Lithium-Ion Battery Health Estimation 10

Graph Convolutional Networks for Lithium-Ion Battery Health ...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： He, Yuxin Deng, Zhongwei Nie, Wenhao Jiang, Chunlin University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

ISBN: (纸本)9798350351330

Assessing the state of health (SOH) is essential for guaranteeing the safety and maintenance of lithium-ion batteries (LIBs). Nevertheless, due to the complex operating conditions, accurately estimating the capacity of LIBs is challenging. This study proposes a data-driven approach leveraging graph convolutional networks (GCN) to estimate the SOH of LIBs by utilizing multiple voltage segments. Then, the extracted voltage sequence is input into a graph generation layer(GGL), which transforms traditional sequential data into graph structured data. Finally, aggregating features from multiple nodes, and battery SOH estimation is achieved through a multi-layer perceptron. The efficacy of this approach in estimating battery SOH is validated using the MIT-Stanford dataset. Based on the results, the GCN method is able to achieve a high accuracy of 0.41% MAE and 0.63% RMSE, representing a substantial improvement over alternative methods. © 2024 IEEE.

关键词： Lithium-ion batteries

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A Multi-Objective Approach for Optimizing Technical, Environmental, and Economic Impact of Energy Communities: A Case Study of a Residential Community in Tarragona 37

A Multi-Objective Approach for Optimizing Technical, Environ...

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37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024

作者： Elomari, Youssef Mateu, Carles Marín-Genescà, M. Boer, Dieter Department of Mechanical Engineering Universitat Rovira i Virgili Tarragona Spain Department of Computer Science and Industrial Engineering University of Lleida Lleida25001 Spain

ISBN: (纸本)9798331307660

In this paper, we introduce a novel framework tailored for the design and optimization of renewable energy communities (RECs) in residential areas, emphasizing a balance between techno-economic feasibility and environmental impact. Our approach focuses on determining the optimal design of configuration and sizing of renewable energy technologies, including photovoltaic systems, wind turbines, and battery electrical energy systems, guided by comprehensive criteria encompassing energy efficiency, cost-effectiveness, and environmental impact. We have developed a data-driven framework that merges the capabilities of Homer Pro with an in-house-developed Python tool to perform the calculations of environmental and economic factors. This framework integrates an advanced machine-learning algorithm and incorporates both life cycle cost (LCC) and life cycle assessment (LCA) in evaluating the REC model. Our model lies in establishing a multi-objective optimization model that not only strives to minimize LCC and LCA parameters but also aims to maximize the utilization of green energy. Additionally, this study is further reinforced by the incorporation of a Multi-Criteria Decision-Making (MCDM) approach through the Weighted Sum Model (WSM), which enables stakeholders to weigh their preferences regarding LCC, LCA, thereby facilitating the selection of a REC scenario that best aligns with their specific objectives. This represents an advancement in REC system planning, providing a nuanced and customizable tool for the residential sector to adopt sustainable energy solutions effectively. A case study of a residential community of 100 buildings in Tarragona, Spain, is used to demonstrate the framework application. The findings of our case study highlight significant economic and environmental benefits in REC design, showcasing an optimal solution that dramatically reduces the levelized cost of energy (LCOE) by 85% in comparison to the base case scenario with a payback period of 7.1

关键词： Clean energy

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Analyzing Muscle Synergies for Finger Movement Recognition using sEMG Signals 11

Analyzing Muscle Synergies for Finger Movement Recognition u...

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11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Sarmah, Nilavjyoti Khargharia, Panchal Prakrit Bora, Rajdeep Hazarika, Shyamanta M. Indian Institute of Technology Guwahati Mechanical Engineering Guwahati India Tezpur University Computer Science and Engineering Tezpur India Indian Institute of Technology Guwahati Mechanical Engineering and M F School of DS & AI Guwahati India

ISBN: (纸本)9798350308433

The study focuses on extracting muscle synergy from electromyographic (EMG) signals for recognition of finger movement. The process involves using a non-negative matrix factorization (NNMF) technique to decompose the EMG signal matrix into muscle synergies and their corresponding activation coefficients. The synergy obtained from this process reveals the underlying patterns in the EMG data that help in the classification of finger movements. Other steps include hyperparameter tuning using grid search, feature selection using sequential forward selection (SFS), and classification using support vector machine (SVM). The proposed method shows the effectiveness of NNMF in detecting muscle synergy for accurate classification of finger movements. A classification accuracy of 97.5±1.67% is obtained. © 2024 IEEE.

关键词： Support vector machines

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Rucklidge-based memristive chaotic system:Dynamic analysis and image encryption

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Chinese Physics B 2023年第10期32卷 298-309页

作者：蹇璨岭田泽安梁波胡晨阳王桥陈靖翕 College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China College of Mathematics and Big Data Guizhou Education UniversityGuiyang 550018China College of Mechanical Engineering Guizhou UniversityGuiyang 550025China

A new four-dimensional(4D)memristive chaotic system is obtained by introducing a memristor into the Rucklidge chaotic system,and a detailed dynamic analysis of the system is *** sensitivity of the system to parameters allows it obtains 16 different attractors by changing only one *** various transient behaviors and excellent spectral entropy and C0 complexity values of the system can also reflect the high complexity of the system.A circuit is designed and verified the feasibility of the system from the physical ***,the system is applied to image encryption,and the security of the encryption system is analyzed from multiple aspects,providing a reference for the application of such memristive chaotic systems.

关键词： chaotic system memristor intermittent chaos multi-transient behavior image encryption

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Designing Optimal Parameters for Single-Axis Solar-Tracker through Analysis of Sun-Path and Tracking Vector Dynamics 6

Designing Optimal Parameters for Single-Axis Solar-Tracker t...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Akhtaruzzaman, M. Islam, Md Shofiqul Shahryer, Fahim Arif, Muammer Din Mollik, Md. Sazib Rahman, Muhammad Towfiqur Rahman, Md. Mahbubur Hasan, Mohammad Kamrul Ahmed, Shugata Ashrafuzzaman, Md. Department of Computer Science and Engineering Dhaka1216 Bangladesh Department of Mechanical Engineering Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Faculty of Information Science and Technology Bangi Malaysia Department of Robotics and Mechatronics Engineering Bangladesh Sdn. Bhd. Cyberjaya Malaysia

ISBN: (纸本)9798350385779

The persistent challenges faced by sun-tracking systems include inefficient power production, wastage of energy, ineffective control, and high costs. Most of the existing systems are either static or dual-axis tracking which lead to inefficient output or increasing energy wastage and cost overheads. Though, single-axis tracking system has some benefits over the static or dual-axis tracking, most of the systems do not analyze the selection of optimal sun-path which again leads to inefficient and inadequate absorption of sunlight. Addressing the issues, this article presents a design of optimal parameters for single-axis solar-tracking by analyzing the sun-path and tracking vector dynamics. The study mainly focuses on analyzing solar-tracking dynamics, variations in sunray angles, Azimuth and Elevation dynamics throughout the day for the twelve months, Sun-vector calculation for local coordinates, joint rotational-axis, and joint motion control using a Proportional-Derivative controller. Additionally, the article outlines a conceptual architecture for automated control, monitoring, and power distribution for the potential development of a sustainable smart city. The proposed strategy addresses the issues of the optimal design of a single-axis solar-tracker, thus ensuring the reduction of cost overhead and energy wastage. © 2024 IEEE.

关键词： Dynamics

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Enhanced Rainfall Prediction: Leveraging Ensembling Models for Maximum Forecasting Performance 1

Enhanced Rainfall Prediction: Leveraging Ensembling Models f...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dasari, Krishna Kumar Nuthanakanti, Bhaskar Raju, K.Srujan Madhusudhan, Mudimela Nazare, Sanjana S Raji Reddy, Avala Cmr Technical Campus Dept. Computer Science and Engineering Telangana Hyderabad India Cmr Dept. of Mechanical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350367720

Many important fields rely on accurate rainfall predictions, including agriculture, water resource management, and emergency preparation. The many nonlinear interactions included in weather data are notoriously difficult for traditional approaches to grasp. Agriculture, water resource management, and disaster preparation are just a few of the many sectors that might benefit greatly from accurate rainfall predictions. Because meteorological data is both complicated and dynamic, traditional methods often get it wrong. Recent years have seen encouraging results in the use of machine learning (ML) approaches, especially ensemble models, to enhance the precision and consistency of precipitation forecasts. This research provides a new method for predicting precipitation using an ensemble of ML models. The ensemble model EERP algorithm combines the predictions of numerous base learners to create a more reliable and accurate forecast. Decision trees, random forests, gradient boosting machines, neural networks, and other ML approaches serve as the ensemble's base learners. Assess the effectiveness of the proposed approach and then compare it with some existing classifiers in which the proposed classifier gave the best accuracy when compared with the other i.e., 96 %. © 2024 IEEE.

关键词： Prediction models

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Design and Exploration of Digital Media Comprehensive Experiment Based on 3E 18th

Design and Exploration of Digital Media Comprehensive Experi...

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18th International Conference on computer science and Education, ICCSE 2023

作者： Pan, Huawei Li, Li College of Computer Science and Electronic Engineering Hunan University Hunan Changsha China College of Mechanical and Vehicle Engineering Hunan University Hunan Changsha China

ISBN: (纸本)9789819707362

In order to enable college students to integrate the knowledge of various professional courses and break the experimental barriers between professional courses, and under the background of promoting the construction of Emerging engineering Education (3E) by the Ministry of Education of China, this paper studies and explores the teaching contents and methods of comprehensive experiment of digital media courses, aiming at cultivating students’ ability of innovative thinking, analyzing and solving complex problems, and improving students’ ability of mutual cooperation and engineering practice. Based on Outcomes Based Education (OBE), a comprehensive experimental case of interaction between virtual objects and hand posture is designed. The case puts forward an assessment method focusing on the evaluation of students’ comprehensive experimental practice ability. The experiment case can broaden students’ horizons, cultivate students’ innovative spirit, improve students’ engineering practice ability. We achieved good teaching results in practice. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Road Crack Segmentation Based on Dual Branch Attention Mechanism and Multi-Scale Features 2

Road Crack Segmentation Based on Dual Branch Attention Mecha...

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2nd IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2024

作者： Chen, Zhaoxuan Jia, Haixin Peng, Jianbo Liu, Huilin School of Computer Science and Engineering Northeastern University Shenyang China School of Mechanical and Electrical Engineering China University of Mining and Technology-Beijing Beijing China

ISBN: (纸本)9798350380958

In road surface crack segmentation algorithms, obtaining comprehensive contextual information is crucial. While many solutions use the Transformer architecture for global information, its computational complexity requires combining it with CNN for feature extraction, making multi-scale contextual information extraction challenging. Although UNet-based networks are popular for fine-grained features, their decoder introduces redundant contents. To address above-mentioned issues, The paper proposes DBAUNet, incorporating SVD-ASPP (Same Value Dense ASPP) for global information extraction and DBAM (Dual Branch Attention Mechanism) to enhance crack information while suppressing background information. SVD-ASPP prevents crack disconnections, ensuring more accurate segmentation results. © 2024 IEEE.

关键词： Information retrieval

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Generating Assembly Instructions Using Reinforcement Learning in Combination with Large Language Models 22

Generating Assembly Instructions Using Reinforcement Learnin...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Widulle, Niklas Meyer, Frederic Niggemann, Oliver Computer Science in Mechanical Engineering Helmut Schmidt University Hamburg Germany Industrial Engineering Laboratory OWL University of Applied Sciences and Arts Lemgo Germany

ISBN: (纸本)9798331527471

The efficiency of manual assembly can be significantly improved by utilizing assistance systems that display assembly instructions. However, generating and maintaining these instructions require substantial effort, especially for low-volume or highly customized products. If neglected, this can lead to outdated instructions, frustrated operators, and the abandonment of the assistance platform. Recent advancements in Large Language Models (LLMs) have made it possible to generate high-quality assembly instructions given the right input. However, relying solely on LLMs to plan the assembly process risks producing unfeasible assembly sequences due to potential hallucinations by the models. To address this, Reinforcement Learning (RL) can be used in simulations to plan the assembly process, imposing restrictions on impractical movements. We propose a framework that integrates RL and LLMs to generate practical and accurate assembly instructions. In our framework, RL is used within a simulation to generate feasible assembly sequences. These sequences are then transformed into detailed assembly instructions by an LLM. We evaluate our framework on real-world product assemblies, generating comprehensive assembly instructions from corresponding CAD files. Our results demonstrate the potential of combining RL and LLMs to automate the generation of assembly instructions, thereby overcoming the limitations of current assistance systems and enhancing the efficiency of manual assembly processes. © 2024 IEEE.

关键词： Reinforcement learning

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