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Research on Steel Production Scheduling optimization based on Deep learning 4

Research on Steel Production Scheduling Optimization based o...

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4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Zhao, Huayu Chen, Yixue Dang, Bo Jian, Xiangzhou Department of Electrical Automation Design Beijing Shougang International Engineering Technology Co. Ltd Beijing China Department of Mechanical and Aerospace Enginerring Case Western Reserve University Cleveland United States Department of Computer Science San Francisco Bay University San Francisco United States Department of Mechanical Engineering Columbia University New York United States

ISBN: (纸本)9798331541729

Steel production scheduling represents a pivotal aspect of the steel manufacturing process, encompassing the strategic allocation of resources and the optimization of production processes. this directly impacts the efficiency and cost-effectiveness of the production process. However, conventional scheduling optimization techniques are ill-equipped to address the intricate nuances of the steel production environment and the deluge of real-time data. In this work, a method of steel production scheduling optimization based on deep reinforcement learning was proposed and subsequently innovated and optimized. the Deep Q Network (DQN) was employed as the fundamental model, with the objective of enhancing the stability and convergence speed of the model. this was achieved through the design of the state space and action space of production scheduling, as well as the incorporation of experience playback and target networks. With regard to model optimization, an adaptive adjustment mechanism of the reward function is proposed, which enables the model to balance the optimization of multiple objectives, such as production efficiency and energy consumption, with greater accuracy. Furthermore, the network structure has been enhanced and a multi-head attention mechanism has been incorporated to augment the model's capacity for scheduling decisions in intricate production scenarios. the optimized model was subjected to experimental verification on an actual steel production dataset, and its performance in terms of scheduling efficiency and resource allocation accuracy was found to be excellent. © 2024 IEEE.

关键词： Deep reinforcement learning

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Application and Practice of intelligent Algorithms in Computer Software Design and Testing 4

Application and Practice of Intelligent Algorithms in Comput...

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4th International conference on Machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Zhang, Yun Wuhan Polytechnic Hubei Wuhan430074 China

With the rapid development of information technology, software has become a part of social life. However, for complex software systems, traditional design and testing methods face a series of problems such as low testing coverage and low efficiency. the main purpose of this article is to study the optimization of software design and testing processes using intelligent algorithms, in order to improve the quality and efficiency of software. this article established a real-time optimization and feedback adjustment mechanism during the software design and testing phase, enabling intelligent algorithms to respond promptly to the environment and requirements. Monitoring and control strategies were designed to ensure the algorithm runs stably for a long time. Test method 1 (traditional method) had 100 test cases, a total execution time of 1200 seconds, and an average execution time of 12 seconds per test case;test method 2 (intelligent Algorithm optimization) had 100 test cases, a total execution time of 800 seconds, and an average execution time of 8 seconds per test case. the research results of this article play a good promoting role in improving software testing coverage and software structure optimization, and also provide new ideas and methods for the promotion of intelligent algorithms in software engineering. © 2024 the Authors.

关键词： Software testing

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R2 Score Estimation with Various Time Series Models Incorporating 1000 Epochs for optimization of Accuracy of Renewable Energy Future Trends 4

R2 Score Estimation with Various Time Series Models Incorpor...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Chowdhury, Suman Saha, Apurba Kumar Das, Dilip Kumar International University of Business Agriculture and Technology Department of Electrical and Electronic Engineering Dhaka1230 Bangladesh Khulna University of Engineering and Technology Department of Electrical and Electronic Engineering Khulna Bangladesh International University of Business Agriculture and Technology Department of Mathematics Dhaka1230 Bangladesh

ISBN: (纸本)9798331529635

the 1°C rise in global temperature since the pre-industrial era is mainly attributed to the use of fossil fuels and human activities. To mitigate this temperature increase, it is crucial to reduce greenhouse gas emissions. In recent years, renewable energy has gained popularity due to its economic and environmental benefits. this study projects 450 days of renewable energy consumption based on a dataset spanning 5.5 years. three machine learning algorithms - Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Hist Gradient Boosting (HGBoost) - were utilized to predict renewable energy production and consumption. Based on the margin of error, it can be inferred that producing 2000MW to 5000MW of electricity is a viable option for Romania. the obtained r2 score for Random Forest Regressor is 0.95, whereas it is 0.83 for XGBoost and 0.84 for HGBoost Regressor as a performance parameter for the time series analysis. © 2024 IEEE.

关键词： Clean energy

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Spatiotemporal Graph Neural Networks for Traffic Forecasting: A Comparative Analysis 4th

Spatiotemporal Graph Neural Networks for Traffic Forecasting...

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4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Rao, Komati Venkateswara Selvakumar, R. K. Mohan Babu Univ Sch Comp Erstwhile Sree Vidyanikethan Engn Coll Tirupati Andhra Pradesh India CVR Coll Engn Dept Comp Sci & Engn Mangalpalle Telangana India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Forecasting traffic has been considered as the foundation for many applications such as traffic control, trip planning, and vehicle routing in intelligent transportation system. It is typically a prediction of traffic speed or flow from timeseries data. It can be modeled as predicting traffic in next n time steps given the previous k traffic observations. Different forecasting methods proposed in the literature are model-driven and data-driven such as deep learning models, machine learning models, and statistical methods. Timely precise traffic forecast is critical for traffic control and guidance. Traditional methods fail in precise prediction due to high complexity and nonlinearity of traffic data and negligence of temporal and spatial dependencies available in the data. Graph neural networks (GNNs) emerged recently as state-of-the-art methods to forecast traffic as they are better suitable for traffic forecasting systems with graph models. Spatiotemporal graph modeling is very important task for analysis of spatial relations and temporal dependencies to forecast traffic flow. the objectives of this paper are three fold to discuss;graph construction from spatiotemporal traffic data for GNNs, review of spatiotemporal GNN models to forecast traffic, comparative analysis of performance of the models in predicting traffic flow ahead of 15, 30, and 45 min on different datasets. Comparative analysis of various neural network-based models used in traffic prediction on different datasets highlighted the strengths and limitations of each model.

关键词： Traffic forecasting Graph neural networks Spatiotemporal graph construction Graph attention networks Deep learning intelligent transport system

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A flight slots allocation method for single airport based on Q-learning algorithm 4

A flight slots allocation method for single airport based on...

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2024 4th International conference on intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Fang, Xinyi Chen, Haiyan Qian, Yuhang Yang, Lei State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510686304

the air traffic demand surpasses the capacity of most of the busiest airports worldwide. the mismatch between airport capacity and air traffic demand leads to serious congestion and delay problems. Since airport capacity is difficult to expand in a short time, in order to reduce flight delays, we need to adjust and optimize flight slots for flight applications, and allocate flight time resources scientifically and efficiently under the premise of meeting airspace resource constraints such as airport capacity. In this paper, we proposed an airport flight slots allocation method based on the Q-learning algorithm in reinforcement learning. Firstly, we preprocess the original data set. Data cleaning processes data with missing attribute values and removes irrelevant attributes. through data transformation, the data of string attribute is transformed into discrete numbers, and the time attribute is processed. After that, we construct a mathematical model of single-airport flight time allocation, which is a combinatorial optimization problem and determines the optimization objectives and constraints. then we apply the reinforcement learning algorithm Q-learning to solve this optimization problem, and the algorithm updates the strategy through interaction with the environment to get a better solution. Finally, we take Guangzhou Baiyun International Airport as a study case to train and verify the proposed single-airport flight time allocation model. the experimental results show that, the optimized flight slots satisfy the airport capacity constraints while minimizing the adjustment of flight schedules as much as possible, and adjust the flights at some times to alleviate the congestion. © 2025 SPIE ·

关键词： Combinatorial optimization

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2024 IEEE 4th International conference on Electronic Technology, Communication and Information, ICETCI 2024

2024 IEEE 4th International Conference on Electronic Technol...

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4th IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2024

ISBN: (纸本)9798350361643

the proceedings contain 266 papers. the topics discussed include: scADGH: scRNA-seq clustering utilizing on attention-based DAE and hybrid similarity GAE;research on power equipment condition monitoring based on simulated annealing algorithm and convolutional neural network;efficient verification of cloud data security based on blockchain untrusted environment;research on robot cooperative intelligent manufacturing system based on machine vision;research on the application of computer deep learning technology in fault diagnosis model of smart photovoltaic power station;AUKF-based active collision avoidance of vehicles considering measurement noise uncertainty;research on high-performance framework of big data acquisition, storage and application for warfare simulation;and design of multi-intelligent body AGV path planning algorithm under cooperative stochastic game based on reinforcement learning improvement.

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Proceedings of the 2024 IEEE 4th International conference on Advanced learning Technologies for Education and Research, ICALTER 2024

Proceedings of the 2024 IEEE 4th International Conference on...

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4th IEEE International conference on Advanced learning Technologies for Education and Research, ICALTER 2024

ISBN: (纸本)9798331510244

the proceedings contain 45 papers. the topics discussed include: application of regression techniques to measure the impact of social programs in Peru;mobile application to learn Peruvian sign language for people with hearing disabilities using motion tracking;mobile application for the intelligent management of small shops using the GPT-4 model;impact of social networking use in youth and the relationship of mood states;evaluating the impact of innovation on national competitiveness: application of a regression technique in a global context;satisfaction of university students despite adverse context;quantum-based multi-model machine learning for security data analysis;and Vik's adventures: enhancing mathematical learning in secondary school students through a serious game integrated with a deep learning model.

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Computational Complexity of Neural Network Linear Layer Inference optimizations 4

Computational Complexity of Neural Network Linear Layer Infe...

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4th IEEE Interdisciplinary conference on Electrics and Computer (INTCEC)

作者： Pendl, Klaus Rudic, Branislav Linz Ctr Mechatron GmbH Linz Austria

ISBN: (纸本)9798350349467;9798350349450

the deployment of machine learning models on devices with limited memory and processing capabilities demands interdisciplinary expertise in the areas of data science, algorithms and computer architecture. We present a thorough analysis for inference optimization in linear layers, which are fundamental in many models and can induce computational bottlenecks. the considerations are detached from the executing hardware, which allows for a detailed understanding of the computations involved on an arithmetic level. We conclude with appropriate metrics and expressions, which are derived based on concepts of computational complexity, and provide quantitative or implicit measures of the linear layer's performance and resource usage. the framework is systematically applied to various optimization methods, each offering a different approach to reduce the computational complexity of linear layers and thus the overall model, and can therefore be crucial for deployment on resource limited devices.

关键词： neural network linear layer computational complexity optimization methods evaluation inference

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optimization of Machine learning Models Applied to Robot Localization in the RobotAtFactory 4.0 Competition 1

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4th International conference on optimization learning Algorithms and Applications

作者： Klein, Luan C. Mendes, Joao Braun, Joao Martins, Felipe N. Fabro, Joao Alberto Costa, Paulo Pereira, Ana I. Lima, Jose Univ Tecnol Fed Parana UTFPR PR Campus Curitiba Toledo Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Associated Lab Sustainabil & Technol Mt Reg SusTE Braganca Portugal Univ Porto Fac Engn Porto Portugal INESC Technol & Sci Porto Portugal Hanze Univ Appl Sci Sensors & Smart Syst Grp Inst Engn Groningen Netherlands

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

Several approaches have been developed over time aiming to improve the localization aspects, especially in mobile robotics. Besides the more traditional techniques, mainly based on analytical models, artificial intelligence has emerged as an interesting alternative. the current study proposes to explore the machine learning model structure optimization for pose estimation, using the RobotAtFactory 4.0 competition as the main context. Using a Bayesian optimization-based framework, the parameters of a Multi-Layer Perceptron (MLP) model, trained to estimate the components of the 2D pose (x, y, and theta) of the robot were optimized in four different scenarios of the same context. the results obtained showed a quality improvement of up to 60% on the estimation when compared with the modes without any optimization. Another aspect observed was the different optimizations found for each model, even in the same scenario. An additional interesting result was the possibility of the reuse of optimization between scenarios, presenting an interesting approach to reduce time and computational resources.

关键词： Localization Machine learning optimization Robotics

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A Reinforcement learning Driven Iterated Greedy Algorithm for Energy-Efficiency Flexible Job-Shop Scheduling Problem 4

A Reinforcement Learning Driven Iterated Greedy Algorithm fo...

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4th International conference on Computer, Control and Robotics (ICCCR)

作者： Bao, Haizhu Wang, Chuang Pan, Quanke Wang, Bingtao Rong, Miao Yang, Aolei Wang, Xiaohua Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

ISBN: (纸本)9798350373141;9798350373158

the escalating environmental challenges have sparked significant interest in energy-efficient scheduling as a potent strategy for realizing sustainable development and fostering green manufacturing. this approach is characterized by its dual focus on economic efficiency and energy conservation. this paper tackles the energy-efficient scheduling of the flexible job-shop scheduling problem (EFJSP) with the dual objective of minimizing both makespan and total energy consumption. the mixed-integer linear programming (MILP) model of EFJSP is devised. A reinforcement learning driven iterated greedy algorithm (RLIGA) is proposed to solve the EFJSP. Furthermore, upon a thorough analysis of the problem's characteristics, two optimization strategies-a energy-saving strategy and an acceleration strategy-are developed to enhance the solution further. Extensive benchmark tests have substantiated the superior efficiency and significance of the RLIGA over state-of-the-art algorithms in addressing the EFJSP.

关键词： flexible job-shop energy-efficient scheduling iterated greedy algorithm reinforcement learning multi-objective optimization

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