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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 91655 条记录，以下是741-750 订阅

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A Method for Optimizing Human-Computer Interaction Interfaces Based on Heuristic algorithms 4

A Method for Optimizing Human-Computer Interaction Interface...

引用

4th international conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Lu, Yongchao Li, Dong Wei, Wencheng Kang, Peiqi Northwest Institute of Mechanical & Electrical Engineering Xianyang China

ISBN: (纸本)9798331531409

This paper addresses the inefficiencies in human-computer interaction (HCI) within manned ground equipment interfaces, which stem from empirical and subjective design approaches that lead to suboptimal observation and complicated operations. We propose an HCI layout optimization method that integrates human factors engineering with advanced optimization algorithms. Initially, we identify four critical factors influencing HCI in manned ground systems and develop corresponding sub-models tailored to these factors. A comprehensive optimization model with weighted factors is then constructed. Using human factors engineering analysis software, we assess the visibility levels of various visual regions within the current HCI, creating a field-of-view grid map. The simulated annealing algorithm is applied to solve the layout optimization model. Experimental simulations confirm the feasibility and effectiveness of this method, resulting in an optimized layout design. © 2024 IEEE.

关键词： Simulated annealing

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Dynamic Modeling and Control Research of 6-DOF Parallel Mechanisms 7

Dynamic Modeling and Control Research of 6-DOF Parallel Mech...

引用

7th international conference on Advanced algorithms and Control engineering, ICAACE 2024

作者： Li, Qinghong Xu, Yuanyuan Zhao, Bin College Of Logistics Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350361445

Ships in ocean navigation are affected by wind, waves, currents, and surges, resulting in six degrees of freedom of movement, which can reduce the efficiency of offshore operations and endanger the safety of operators. In order to maintain the stability of ships during navigation and consider the comprehensive impact of various degrees of freedom on ships, this paper designs a wave compensation platform based on a 6-DOF parallel mechanism. Firstly, based on SolidWorks a 6-DOF parallel mechanism model is designed, and the assembly of the 6-DOF parallel mechanism is imported into the SIMULINK environment for joint simulation, constructing a six degree of freedom parallel active wave compensation device;Secondly, based on the theory of coordinate transformation, kinematic analysis is conducted to establish an inverse mathematical model for the relationship between spatial pose changes and the extension and contraction motion of the support chain;Finally, use PID control to track and control. The correctness of the kinematic model and the feasibility of the motion mechanism have been verified through experimental simulation, which can be used in ship wave compensation systems. © 2024 IEEE.

关键词： Three term control systems

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Origami Infrastructure: A Viable Solution to Construction for Challenging Environments 19

Origami Infrastructure: A Viable Solution to Construction fo...

引用

19th Biennial international conference on engineering, Science, Construction, and Operations in Challenging Environments, Earth and Space 2024

作者： Müller, Claudia Calle Bordo, Alvaro Ballón Elzomor, Mohamed Dept. of Civil and Environmental Engineering Florida International Univ United States Toronto ON Canada Moss School of Construction Infrastructure and Sustainability College of Engineering and Computing Florida International Univ. MiamiFL United States

ISBN: (数字)9780784485736

ISBN: (纸本)9780784485736

Infrastructure built in challenging environments should be able to withstand unforeseen climate and environmental challenges, including extreme temperatures, high winds, severe rainfall, and natural disasters. Most importantly, dwellings should provide humans with a safe, livable, and resilient shelter. To this end, construction materials, design solutions, and construction processes must be considered to ensure safe and livable conditions. Furthermore, technology and innovative means of construction must be explored to reinforce infrastructure resilience and sustainable performance. However, previous literature has not addressed which features and materials are most adequate for resilient, deployable, and cost-effective origami shelter structures. This research aims to fill this gap by (1) identifying the suitable materials and features of structures that have been proven to be effectively deployable in such challenging environments through literature review;and (2) proposing and analyzing the viability of origami infrastructure as a solution to construction obstacles in challenging environments. Origami is rapidly emerging in science, engineering, and construction applications as deployable and reconfigurable engineering systems of all scales that can be fabricated with a wide variety of materials. Origami structures are geometrically versatile, adaptable, and can be easily and quickly assembled. Moreover, they are lightweight and foldable, which facilitates their transportation into challenging environments where access can be difficult and limited. Therefore, origami structures present an innovative, sustainable, resilient, and feasible solution to construction obstacles in challenging environments. The findings of this study serve researchers and construction stakeholders who will be designing and building infrastructure systems in challenging environments. © ASCE.

关键词： Structural Mechanics

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AI-Driven Retention: A Hybrid Approach to Employee Turnover Prediction 9

AI-Driven Retention: A Hybrid Approach to Employee Turnover ...

引用

9th international conference on Communication and Electronics systems, ICCES 2024

作者： Ponmalar, S. Affrin Fowmiya, N. Nandhini, C. Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798350377972

Predictive analytics by combining ensemble learning and deep learning techniques. The first step normalizes the feature space with MinMaxScaler, which improves the performance of the algorithms. To tackle class imbalances, the Synthetic Minority Over-sampling Technique (SMOTE) is used, ensuring strong predictive modeling. An empirical study shows the effectiveness of various algorithms, including ensemble methods like CatBoost, AdaBoost, LightGBM, and Gradient Boosting Machine (GBM), whicheffectively capture complex interactions and nonlinear relationships in employee data. Furthermore, Long Short- Term Memory (LSTM) networks are employed to identify temporal dependencies in longitudinal records. The findings emphasize the approach's capability to detect subtle signals of potential turnover. By integrating these methodologies, this research provides HR professionals with valuable tools for proactive retention strategies. Ultimately, it advances HR analytics, assisting organizations in reducing attrition risks and promoting an engaged workforce. © 2024 IEEE.

关键词： Predictive analytics

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Hybrid Cipher:AES-RC4 Fusion 4

Hybrid Cipher:AES-RC4 Fusion

引用

4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Baskaran, M. Santhosh, P. Sree Tapas Ranjan, D.S. Thirumurugan, S. Abishakan, M. Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This paper presents the novel hybrid encryption algorithm based on the combination of strength of Advanced Encryption Standard and Rivest Cipher 4. The substitution- permutation network of AES provides a strong basis for secure encryption and decryption, while the key schedule of RC4 adds simplicity and efficiency to exchange keys. As a result, the hybrid method provides compromise between computational costs and encryption strength. The solution is equipped with design rationale and implementation details, while the security-wise description contains its resistance to known attacks. The performance analysis reveals multiple benefits of the proposed solution, in terms of computational overhead and throughput when compared to other hybrid algorithms. It is a universal solution for secure communication and data protection. This approach is highly innovative and can open further ways for cryptographic techniques, including enhancements to the existing approach. © 2024 IEEE.

关键词： Encryption algorithms

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A Quantum Variational Classifier for Predictive Maintenance and Monitoring of Battery Health in Electric Vehicles

A Quantum Variational Classifier for Predictive Maintenance ...

引用

2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Grandhi, Sri Harsha Al-Jawahry, Hassan M. Divya, S. Kumar, Bura Vijay Padhi, Manoj Kumar Intel FolsomCA United States The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Centurion University of Technology and Management School of Engineering and Technology Paralakhemundi India

ISBN: (纸本)9798350360660

In order to ensure the dependability and durability of Electric Vehicles (EVs), there is an increasing requirement for improved maintenance systems. This research combines optical and quantum approaches to present a unique methodology for the predictive maintenance of EV components. High-resolution, real-time data on vital components including the battery, electric motor, and power electronics are captured by the Fiber Bragg Grating (FBG) sensors. These sensors are useful because of their high sensitivity, ability to multiplex, and resilience to electromagnetic interference. A Quantum Variational Classifier (QVC) with quantum computing is proposed to manage massive datasets and improve prediction accuracy. The AI model is trained to anticipate possible failures and identify early indicators of component degradation, allowing for proactive maintenance and reducing downtime. The experimental results prove the efficacy of proposed method in providing accurate, appropriate predictions and improving the durability and performance of EV components. The proposed method achieved 0.0035 of MAE and 0.0097 of RMSE when compared with the existing methods. © 2024 IEEE.

关键词： Quantum computers

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Natural Language Processing algorithms for Academic Content Generation 4

Natural Language Processing Algorithms for Academic Content ...

引用

4th IEEE international conference on Advanced Learning Technologies for Education and Research, ICALTER 2024

作者： Paredes-Vargas, Alfredo Salinas-Llorca, Piero Santisteban, Jose Information Systems Engineering Universidad Peruana de ciencias Aplicadas UPC Lima Peru Information Systems Engineering Universidad Peruana de Ciencias Aplicadas Lima Peru

ISBN: (纸本)9798331510244

This study has conducted several activities to analyze and compare the performance of five Natural Language Processing (NLP) models: GPT-3, T5, ERNIE, BERT, and XLNet for generating academic content in universities located in Lima, Peru. Some NLP models, including GPT and BERT, were assessed based on accuracy, quality, speed, and customization capabilities. The research examined the use of various algorithms for academic tasks like essay and summary creation. The findings show that all models perform adequately;however, some excel in generating coherent content tailored to different academic styles, thereby optimizing the time of both students and teachers. In conclusion, the ERNIE model emerges as one of the best, as it enables the generation of diverse and high-quality content efficiently. © 2024 IEEE.

关键词： Natural language processing systems

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Vehicle Detection and Speed Estimation Using Deep Learning 6

Vehicle Detection and Speed Estimation Using Deep Learning

引用

6th international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Mishra, Vishnu Kumar Mishra, Megha Tiwari, Rajesh Kumar, T. Ram Diwan, Tarun Dhar Patra, Raj Kumar Malla Reddy Engineering college for Women Dept. of Computer Science and Engineering Hyderabad India Csvtu Shri Shankaracharya Technical Campus Department of Computer Science and Engineering Bhilai India CMR Engineering College Department of Computer Science and Engineering Hyderabad India Atal Bihari Vajpayee University Dept. of Computer Science and CoE Chhattisgarh Bilaspur India

ISBN: (纸本)9798331505790

The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this study to solve the problems related to precise and timely monitoring of vehicle movement. By improving the effectiveness and dependability of current systems, the suggested approach hopes to promote traffic management for smart transportation. © 2024 IEEE.

关键词： Convolutional neural networks

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LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Model 1

LightFusionRec: Lightweight Transformers-Based Cross-Domain ...

引用

1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Kharidia, Vansh Paprunia, Dhruvi Kanikar, Prashasti Deemed-to-be University Mukesh Patel School of Technology Management & Engineering Department of Computer Engineering Mumbai400056 India

ISBN: (纸本)9798350352931

This paper presents LightFusionRec, a novel lightweight cross-domain recommendation system that integrates DistilBERT for textual feature extraction and FastText for genre embedding. Important issues in recommendation systems, such as data sparsity, computational efficiency, and cold start issues, are addressed in methodology. LightFusionRec uses a small amount of information to produce precise and contextually relevant recommendations for many media formats by fusing genre vector embedding with natural language processing algorithms. Tests conducted on extensive movie and book datasets show notable enhancements in suggestion quality when compared to conventional methods. Because of its lightweight design, the model can be used for a variety of purposes and allows for ondevice inference. LightFusionRec is a noteworthy development in cross-domain recommendation systems, providing accurate and scalable recommendations to improve user experience on digital content platforms. © 2024 IEEE.

关键词： Natural language processing systems

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Revolutionizing Connectivity and Data Processing in IoT Ecosystems through Edge Computing

Revolutionizing Connectivity and Data Processing in IoT Ecos...

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2024 IEEE international conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Kumari, V. Sheeja Pillai, N. Muthuvairavan Jayarin, P.Jesu David, D. Beulah Jenin, J.S. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computational Intelligence Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Information Technology Thandalam Tamil Nadu Chennai India Care College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350388602

This work, which explores the performance of the existing five IoT sensor classification algorithms and develops five more algorithms, intends to later compare the obtained classification accuracies. In this evaluation, the algorithms included are Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Naive Bayes, while the proposed ones are called EdgeNet, IoTBoost, EdgeSVM, DeepEdge, and EdgeFusionNet. The performances are evaluated using the key measures of precision and processing time. Results show that the algorithms under study are usually more accurate than well-known ones, with EdgeFusionNet reaching 94% accuracy which is the highest accuracy. In the selection of algorithms, existing ones show different performance but the Decision Tree and Naive Bayes demonstrate relatively faster processing times compared to the Random Forest and the Support Vector Machine. Yet, the proposed EdgeSVM algorithm shows almost the fastest processing time which exceeds Zokfl2 and other algorithms onference. On top of that, extra measures comprise memory usage, energy efficiency, scalability, model size, and robustness are evaluated for the suggested algorithms. Thus, the results may be inferred that the suggested algorithms provide the advanced solutions for the IoT sensor data classification, that have higher accuracy and comparable or better time complexity in comparison with the existing ones. These outcomes give useful information for researchers as well as technicians that using IoT techniques for the sake of various applications purposes. © 2024 IEEE.

关键词： Support vector machines

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