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Multicriteria Decision Analysis Applied to the Sensing of Refrigeration systems 13th

Multicriteria Decision Analysis Applied to the Sensing of Re...

引用

13th Computer Science Online Conference

作者： Pinheiro, Placido Rogerio Cavalcante de Sousa, Torricelli Evangelista Furlan Duarte, Joao Batista Nunes, Luciano Comin Univ Fortaleza Fortaleza CE Brazil 7th September Univ Ctr Fortaleza Ceara Brazil

ISBN: (纸本)9783031702846;9783031702853

Vapour compression refrigeration systems are a growing need in hospitals, industries, shopping centres, and several other places where temperature control is essential to obtain thermal comfort or meet the production stages for goods and consumption. In information technology, the equipment responsible for data processing continuously uses vapour compression refrigeration systems. If there are failures in the power and cooling infrastructure, interruptions and data transmission will undoubtedly be lost. Even under optimal circumstances, refrigeration systems exhibit a voracious appetite for electricity, imposing a significant financial strain on organizations' operational expenses. In the absence of a sensing system to monitor the operation of machines, errors introduced and not identified in the installation and maintenance stages can lead to significant reductions in energy efficiency, the useful life of components, stability, and the operational availability of thermal systems. A sensing project can use dozens of sensors, and a method capable of guiding the implementation process becomes thoroughly recommended and valid. The present work aims to use the multicriteria decision analysis methodology to support vapour compression refrigeration systems' sensing and monitoring processes.

关键词： Multicriteria Decision Analysis Vapor Compression Refrigeration systems Energy Efficiency Sensing

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Solving Industrial Production Scheduling Challenges in the Era of Industry 4.0 and Green Manufacturing 7th

Solving Industrial Production Scheduling Challenges in the E...

引用

7th International Conference on Logistics Operations Management (GOL)

作者： Mimouni, Hafsa Jalid, Abdelilah Mohammed V Univ Rabat ENSAM Rabat Morocco

ISBN: (纸本)9783031686337;9783031686344

The evolution of production scheduling in the context of Industry 4.0 and green manufacturing has become a focal point in contemporary industrial research. Efficient scheduling, essential for optimal resource utilization and cost-effectiveness, faces new challenges due to the digital revolution and the green industry paradigm. This study conducts a comprehensive analysis of existing literature on scheduling problems, incorporating the dual perspectives of Industry 4.0 and green production. The research delves into diverse problem formulations and solution approaches, ranging from mathematical programming techniques to heuristic methods and machine learning concepts. The review reveals a scarcity of studies addressing the complexities arising from the integration of Industry 4.0 technologies and ecological considerations in production scheduling. Bridging this gap is crucial, urging further research at the intersection of Industry 4.0 and sustainable manufacturing practices. The study emphasizes the significance of addressing these challenges, considering the complexities of real-world scheduling scenarios, where practical problems involve operations ranging from thousands to hundreds of thousands. By exploring advanced solutions, encompassing machine learning, constraint programming, and metaheuristic methods, the research underscores the need for continued exploration and development of robust strategies applicable to large-scale, practical settings. This interdisciplinary approach is pivotal in shaping the future of industrial scheduling, ensuring efficiency, sustainability, and resilience in the face of evolving technological landscapes and environmental imperatives.

关键词： Production Scheduling Problems Industry 4.0 Green Manufacturing Constraint Programming Metaheuristic Methods

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Higher Education Students Performance and Feature Analysis Using Classification Models Based on Blended Learning Data

Higher Education Students Performance and Feature Analysis U...

引用

2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Bhurre, Shraddha Prajapat, Shaligram Raikwar, Sunny Choure, Purvi Kothari, Soham DAVV Int Inst Profess Studies Indore India Dr APJ Kalam Univ Indore India

ISBN: (纸本)9783031744426;9783031744433

Educational institutes and platforms generate an enormous amount of data. The digitization of the learning process also leads us to a variety of data, including interactive features, academic details, and log activity features. This data should be processed through appropriate methods to have useful insight into it. Classifying students according to features, and predicting their performance according to features is one of the major tasks that can be implemented through Machine Learning. This study aims to examine the features of a student's performance and analyze performance using a blended learning dataset through classification techniques. The dataset used in this study is Higher Education IT stream students of MTech(PG-1) and MCA(PG-2). This study also presents a performance evaluation of classification techniques(MLP(Multilayer Perceptron), LR(Logistic Regression), SVM(Support Vector Machine), and RF(Random Forest)) using listed features. The result shows that CGPA, Quiz participation, and forum posts/attentiveness have a high impact on students' performance and among these 4 classification models MLP has the highest accuracy in classifying students in appropriate classes.

关键词： E-learning Classification Models Blended Learning MLP(Multilayer Perceptron) LR(Logistic Regression) DT(Decision Tree) and RF(Random Forest) Student Performance Analysis

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Fraud Detection Through Nature-Inspired Algorithms 20th

Fraud Detection Through Nature-Inspired Algorithms

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20th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Dugar, Meenal Asesh, Aishwarya Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9783031665936;9783031665943

The proliferation of credit card payments has led to increased convenience but also an uptick in transaction fraud. This necessitates data-mining approaches to detect and prevent fraudulent activities. Transaction fraud detection involves multiple steps, including data analysis, preprocessing, feature selection, and hyperparameter optimization. Given the prevalence of anonymized features in transaction datasets, we focused on enhancing model performance through feature selection using nature-inspired algorithms. Our experiments revealed that this approach, when combined with machine learning models, holds promise for fraud prevention. Among the tested nature-inspired algorithms, the Grey Wolf Optimizer stood out, improving the model's ROC AUC score by 1.4% while selecting only half of the features compared to recursive feature elimination.

关键词： Fraud Detection Nature-Inspired Algorithms Feature Selection Credit Card Transactions Machine Learning Data Mining

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UAV-Based Intelligent System for Australian Flood Reconnaissance and Surveillance

UAV-Based Intelligent System for Australian Flood Reconnaiss...

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Intelligent systems Conference (IntelliSys)

作者： Sheng, Ziheng Ge, Linlin Li, Chen Qi, Xuelei Bai, Ting Wu, Yunhao Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9783031477171;9783031477188

Due to climate change in the past two years, Australia has often suffered from natural disasters, such as floods. Since last year, some regions of Australia have experienced four major flood crises. Besides, unmanned aerial vehicles (UAVs) have merits in terms of economy, safety, and operability, and there is a strong demand for UAVs in many civil fields. Small UAVs can be widely used in disaster prevention and mitigation, search and rescue, and other fields. Therefore, in this paper, a UAV-based framework is proposed for the detection, localization, segmentation, and size evaluation of flood areas from aerial images taken with drones. First, an improved Green algorithm is designed to identify the boundaries of the flood area by integrating four sub-affected regions. Moreover, in the enclosed area, the Zigzag-Dubins trajectory planning algorithm is also designed to create a feasible detected flying trajectory so that the entire affected regions can be properly reconnoitered and surveilled. Finally, a real-time case study is implemented in Moama, Australia to demonstrate the effectiveness of the designed approaches and the flood disaster area.

关键词： Unmanned aerial vehicle Trajectory planning NSW flood reconnaissance

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Addressing Foot Geometry Trade-Offs to Achieve Amphibious Surf Zone Transitions with a Crab Robot 27th

Addressing Foot Geometry Trade-Offs to Achieve Amphibious Su...

引用

27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Graf, Nicole Grezmak, John Carmichael, Nathan Gong, Yifeng Daltorio, Kathryn Case Western Reserve Univ Dept Mech & Aerosp Engn Cleveland OH 44106 USA Case Western Reserve Univ Fac Dept Mech & Aerosp Engn Cleveland OH 44106 USA

ISBN: (纸本)9783031713002;9783031713019

Crabs have specialized curved dactyls that can be used during locomotion to anchor and control penetration in dry and wet substrates. To investigate the effect of crab leg's geometry, we added rings similar to ski pole baskets around the dactyl to show that distributing ground reaction forces over a larger surface area would permit our crab-like hexapod Sebastian to carry heavier loads and reduce cost of transport by controlling dactyl penetration depth. The rings help prevent slip, enabling the robot to walk farther and more efficiently in dry sand with payload. The results show a trade-off between locomotion efficiency with payload and anchoring force. We then developed a new dactyl which enabled Sebastian to successfully walk from being fully submerged through the transition zone on to dry land. This research aims to help other researchers to develop inexpensive robots for efficient shallow water locomotion in the future.

关键词： amphibious robots mobile robotics granular media terrain bio-inspired robotics

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A Step Towards a Finite Element Model for an Impact Situation in Human-Robot Interaction 1

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Hornung, Luisa Soti, Gergely Wurll, Christian Hsch Karlsruhe Univ Appl Sci Moltkestr 31 D-76133 Karlsruhe Germany

ISBN: (数字)9783031448515

ISBN: (纸本)9783031448508;9783031448515

To analyze the influence of different parameters on the contact force and pressure during a collision in human-robot interaction, an extensive experimental effort is required, which increases exponentially with the number of parameters. To reduce this effort, simulation models can be used that allow a large number of parameters to be included in the analysis. In this work, we have developed a finite element model of a biofidel measuring device representing the human hand in a collision. The robot is modeled as a point mass representing the effective mass. The model was validated using real-world experiments and for model optimization, a parameter study was conducted to determine the model parameters that have the greatest effect on the contact force. The model achieved good convergence for velocities in the interval of [40;115](mm/s). In follow-up work, drop tests are performed and, in addition, the model will be validated using contact pressure as a result.

关键词： Human-robot interaction Impact analysis Finite element model

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Adaptive Traffic Control Modeling Framework Sumo-atclib

Adaptive Traffic Control Modeling Framework Sumo-atclib

引用

Intelligent systems Conference

作者： Kazorin, Viktor Kholodov, Yaroslav Innopolis Univ Innopolis Russia

ISBN: (纸本)9783031663352;9783031663369

This article proposes the sumo-atclib framework, which provides a convenient uniform interface for testing adaptive control algorithms with different limitations, for example, restrictions on phase duration, phase sequences, restrictions on the minimum time between control actions, which uses the open source microscopic transport modeling environment SUMO. The framework shares the functionality of controllers (class TrafficController) and a monitoring and detection system (class StateObserver), which repeats the architecture of real traffic light objects and adaptive control systems and simplifies the testing of new algorithms, since combinations of different controllers and vehicle detection systems can be freely varied. At the same time, the algorithms themselves use the same interface and are abstracted from the specific parameters of the detectors, network topologies, that is, it is assumed that this solution will allow the transport engineer to test ready-made algorithms for a new scenario, without the need to adapt them to new conditions, which speeds up the development process of the control system, and reduces design overhead. At the moment, the package contains examples of Max-Pressure algorithms and the Q-learning reinforcement learning method, the database of examples is also being updated. The framework also includes a set of SUMO scripts for testing algorithms, which includes both synthetic maps and well-verified SUMO scripts such as Cologne and Ingolstadt.

关键词： Adaptive traffic control Reinforcement learning Sumo-atclib

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Optimization of Vacuum-Plasma Coating Application Technology by Strength and Plasticity Criteria 10th

Optimization of Vacuum-Plasma Coating Application Technology...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Solovykh, Evhen Shepelenko, Ihor Shumliakivsky, Volodymyr Katerynych, Stanislav Solovuch, Andrey Cent Ukrainian Natl Tech Univ Dept Exploitat & Repairing Machines 7 Univ Skyi Ave UA-25006 Kropyvnytskyi Ukraine Zhytomyr Polytech State Univ 103 Chudnivska St UA-10005 Zhytomyr Ukraine

ISBN: (纸本)9783031662676;9783031662683

The results of multi-criteria optimization of vacuum-plasma coating application modes on titanium alloys as applied to compressor blades of a gas turbine engine are presented. Based on operating conditions and part failures, the parameters of short-term static strength and plasticity were selected as optimization criteria for the technological process of vacuum-plasma coating application. When selecting the controlled parameters, the influence of design, technological and operational factors was taken into account in a complex way. Carrying out the experiment, its statistical analysis allowed to build a regression model of optimization criteria dependencies on design, technological and operational factors. The obtained dependencies adequately describe the process and they can be used for analysis and optimization of the technological process of vacuum-plasma coating application.

关键词： vacuum-plasma coatings technological process multi-criteria optimization strength plasticity mathematical model

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AI Anomaly Detection for Smart Manufacturing 1

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2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI)

作者： Bajic, Bojana Medojevic, Milovan Jovicic, Milos Rikalovic, Aleksandar Univ Novi Sad Fac Tech Sci Dept Ind Engn & Management Novi Sad Serbia Inst Artificial Intelligence Res & Dev S Novi Sad 21000 Serbia

ISBN: (数字)9783031608407

ISBN: (纸本)9783031608391;9783031608407

AI-based anomaly detection represents a significant research field with myriad application possibilities in smart manufacturing that involves using machine learning (ML) algorithms to identify abnormal patterns in the data collected from sensors and other sources. While substantial progress has been made in this field in recent years, some gaps in the literature still need to be addressed. One key challenge is the lack of labeled data for training ML algorithms, especially for rare anomalies. Moreover, unsupervised anomaly detection is also an obstacle to smart manufacturing implementation, as it can be prone to false positives or missing some anomalies. Additionally, the imbalanced dataset, where the collected data samples are predominantly from one specific class, results in no specific information about the anomalies that must be detected. Addressing these gaps, the present research provides a comparative analysis of three unsupervised ML techniques, namely: One-class Support Vector Machine - OCSVM, Isolation Forest - IF, and Local and Outlier Factor - LOF for product anomaly detection. The ML models were developed using an imbalanced dataset collected in the manufacturing company from the process industry. Finally, the results show the OCSVM technique provides the best performance regarding accuracy, precision, recall, and F1 score.

关键词： unsupervised learning artificial intelligence (AI) Industry 5.0 smart manufacturing big data analytics (BDA) machine learning (ML)

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