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Optimal Linear Combination of Heuristic Strategies for 2D Rectangular Bin Packing Algorithms

Optimal Linear Combination of Heuristic Strategies for 2D Re...

引用

Image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Jipan Huang Xiangtan University Xiangtan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The 2D Bin Packing Problem (2DBPP) is an NP-hard combinatorial optimization problem, where common heuristic algorithms often exhibit characteristics of greedy strategy scoring during the selection of items and the determination of placement positions. However, the approach of using a linear combination of multiple greedy strategies as a scoring strategy is not common, due to the difficulty in determining the optimal combination coefficients. The method proposed in this paper effectively identifies the optimal coefficients for the strategy combination, and demonstrates that, compared to single strategies, the combined strategy offers advantages in noise resistance and robustness. This research uses the improvement process of two classic algorithms, Skyline and MaxRect, as examples. simulation experiments with five data sets show that our proposed optimal combination strategy method significantly improves the packing utilization and robustness of such algorithms. On five different test data sets, we introduced noise ratios varying from 1% to 50% and conducted 40 repeated experiments for each scenario to ensure the reliability of the results. By averaging these repeated run results according to different data sets and noise ratios, we obtained a comprehensive set of performance data. The results indicate that, under these test conditions, the improved algorithm outperforms the original algorithm in 93% of the cases on average, with a relative average improvement of 13.88%. In addition, this study provides a new perspective on heuristic algorithm research, integrating data distribution with algorithmic approaches.

关键词： Resistance Heuristic algorithms Two-dimensional displays Image processing Noise Evolutionary computation Multilayer perceptrons Robustness Noise measurement Optimization

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Optimization of Speed Tracking Performance of LADRC for PMSM

Optimization of Speed Tracking Performance of LADRC for PMSM

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Electronic Information Engineering and computer Technology (EIECT), international conference on

作者： Shaonan Wu Simeng Liang Mingming Zhou CSSC Haiwei Zhengzhou HighTech Co. Ltd Zhengzhou China

Based on the mathematical model of permanent magnet synchronous motor in synchronous rotating coordinate system, the first-order linear auto disturbance rejection controller of speed loop and current loop is designed. The introduction of the speed loop tracking differentiator solves the problem that the "quickness" and "overshoot" of the speed response of the conventional PID controller can not be taken into consideration at the same time, but it also leads to the decline of the tracking performance of the control system for the time-varying speed reference. Based on the classical linear tracking differentiator, the differential compensation of the transition process is introduced, and a new linear tracking differentiator is designed and applied to the speed loop auto disturbance rejection controller. The simulation proves that the improved auto disturbance rejection control strategy improves the tracking performance of the system for time-varying speed without affecting the anti-interference performance.

关键词： Tracking loops Time-frequency analysis simulation process control Permanent magnet motors Regulation Mathematical models

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thermal and Fluid Flow modelling of a Heating Bed for Application in Metal AM process 2nd

Thermal and Fluid Flow Modelling of a Heating Bed for Applic...

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2nd international conference on Recent Advances in Mechanical Engineering, RAME 2020

作者： Sharma, Gourav K. Pant, Piyush Jain, Prashant K. Kankar, Pavan Kumar Tandon, Puneet Mechanical Engineering Discipline PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India Mechanical Engineering Department Indian Institute of Technology Indore India

ISBN: (纸本)9789811596773

The process of deposition in metal additive manufacturing (AM) involves layer-by-layer stacking of material in a defined fashion. The temperature and temperature gradients involved during the deposition decides the final behaviour of the fabricated component. Hence, the temperature of the substrate over which the deposition is made is of equal importance as the temperature of the processing material during deposition. To consider this aspect, a design of heating bed on which the substrate is placed is explored through numerical simulation. In this direction, a thermal and fluid flow analysis for a heating bed is performed in the presented work by adopting the finite volume method (FVM). A 3D steady analysis is carried in ANSYS atmosphere. A heating bed with a specific configuration of cooling channels is simulated, and based on the uniformity of the thermal distribution, deposition region is identified. The presented work could find its probabilistic application in the additive manufacturing, wherein substrate heating is required for isothermal deposition of every layer, so as to ensure less residual stresses and reduced distortions in the final part. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Finite volume method Heat transfer Heating bed Turbulence modelling

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A Nonparametric Model for Forecasting Life Expectancy at Birth Using Gaussian process 2nd

A Nonparametric Model for Forecasting Life Expectancy at B...

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2nd international conference on Applied Intelligence and Informatics, AII 2022

作者： Biswas, Pranta Ireen, Fahmida Islam Nawar, Fairooz Ahsan Tabassum, Maisha Rahman, Muhammad Arifur Mahmud, Mufti Kaiser, M. Shamim Brown, David J. Department of ICT Bangladesh University of Professionals Dhaka Bangladesh Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom CIRC and MTIF Nottingham Trent University NottinghamNG11 8NS United Kingdom IIT Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9783031248009

Gaussian process Regression (GPR), a Bayesian nonparametric machine learning modelling technique, is gaining interest in recent times in many fields as a practical and powerful approach. To plan for economic services for any nation, projections of future Life Expectancy (LE) are required. In our research, we have proposed a model to forecast LE using GPR up to 2040. Initially, we sub-categorized countries into four sections based on income level. Then we treated LE at birth for different countries as a time series to create our model. Among the data of 165 countries we have, we used 27 countries’ 60 years of LE data (1960–2019) to optimize and visualize the performance of our model. In our model, we used to maximize log-marginal-likelihood (LML) for each prediction while optimizing the hyper-parameters of our models. We further verified our model using cross-validation, fitting the model into 40 years of data and validating the other 20 available. Our prediction model’s results demonstrated the subtle increase of LE over the years, which varied depending on the income groups. We have made the data processing and model development code publicly available via GitHub to carry forward this research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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COMSOL simulation of microwave plasma polishing on different surfaces 2

COMSOL simulation of microwave plasma polishing on different...

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2nd international conference on Aspects of Materials Science and Engineering (ICAMSE)

作者： Yadav, Hari Narayan Singh Kumar, Manjesh Kumar, Abhinav Das, Manas Indian Inst Technol Mech Engn Dept Gauhati 781039 Assam India

The characteristics of the surface and substrate have a significant role in the life and efficiency of the optical device. COMSOL (R) Multiphysics is a software program, which is applied to model the radio frequency (RF)-excited dielectric barrier for helium (processing gas) and oxygen (reactive gas). The software is flexible enough to accommodate multiple partial differential equations (PDEs) in a single domain model. This Multiphysics software is focused on finite element modelling program. The composition of the gas, the pressure, the configuration of the electrodes, and the excitation power of the RF are analyzed with regard to the formation of oxygen radicals and their consistency of allocation in the chamber. A computer program has been established to plan and optimize the microwave plasma physics utilized for the polishing of the surface of an optical specimen. The COMSOL (R) Multiphysics software is used for the simulation of microwave plasma coupled with an electromagnetic wave. COMSOL can easily simulate multiphysics problems. In the current study, a new method of producing Helium and Oxygen by microwave plasma is researched, evaluated, and simulated. The results show the distribution of density, temperature, and potential of electrons for two different specimens in the plasma chamber. (c) 2021 Elsevier Ltd. All rights reserved. Second international conference on Aspects of Materials Science and Engineering (ICAMSE 2021).

关键词： Microwave plasma Numerical simulation Plasma polishing COMSOL

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Topology Maintenance Optimization Algorithm Based on Deep Reinforcement Learning in High Dynamic Flying Ad-Hoc Networks 23

Topology Maintenance Optimization Algorithm Based on Deep Re...

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Proceedings of the 2nd international conference on Signal processing, computer Networks and Communications

作者： Liheng Li Xiulin Qiu Bo Song Yaqi Ke Yuwang Yang School of computer science and engineering Nanjing University of Science and Technology China School of Automation jiangsu University of Science and Technology China

ISBN: (纸本)9798400716430

In the realm of high-dynamic-flight self-organizing networks, traditional proactive and reactive routing protocols confront challenges arising from the delayed and inflexible determination of the validity of neighbour nodes. An untimely assessment of node validity may lead to an escalated overall network packet loss rate, consequently causing a proportional decrease in throughput. To mitigate this issue, this paper introduces a Reinforcement Learning based Topoloty Maintenance Algorithm (RLTM). This algorithm integrates the mobility characteristics and remaining energy of neighbour nodes into a stability metric for these nodes. By conceptualizing the validity of neighbour nodes in topology maintenance as a Markov process and incorporating the stability metric as a dimension in the state space, the model undergoes training via Proximal Policy Optimization (PPO) to generate decisions for establishing the effective time of neighbour nodes. simulation results demonstrate that, in comparison to other topology maintenance optimization algorithms, the proposed algorithm amplifies the average packet delivery rate and throughput of unmanned aerial vehicle (UAV) nodes in high-speed mobility scenarios.

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“A Novel Regularized Deep Learning Approach for Tuberculosis Risk Prediction”

“A Novel Regularized Deep Learning Approach for Tuberculosi...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pankaj Kumar Vishav Pratap Singh Ajay Pal Singh Department of Technical Education GPC Patiala Punjab Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Effective treatment of tuberculosis (TB) requires prompt and precise risk assessment, since it continues to be a major worldwide health concern. Labor-intensive and subjective approaches are frequently used in traditional methods. The neural network model for Tuberculosis risk categorization which includes the following categories: No_Risk, Low_Risk, Moderate_Risk, and High_Risk is found in this paper. Regularization, Batch normalization and the use of Reversed Linear unit (ReLU) activation functions is adopted by the model to enhance learning process. After successfully conducting the sophisticated and optimal simulation the proposed model attains 91% classification accuracy using a variety of physiological and clinical parameters. This potentially presents future deep learning techniques to automatically rate people for possible tuberculosis occurrence thus delivering a strong tool to medical practitioners to offer distinctive intervention techniques. With deep learning, the model is capable of detecting a very complex pattern and correlation between data sets inputs and its output, thus enhancing its predictive precision as well as offering recommendations that may assist in the early detection of actual clinical situations.

关键词： Correlation Tuberculosis Computational modeling Predictive models Data models Physiology Communications technology Risk management Batch normalization Computational intelligence

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Data-driven predictive control for the swing process of a cutter suction dredger 2

Data-driven predictive control for the swing process of a cu...

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2021 2nd international conference on Electrical Technology and Automatic Control, ICETAC 2021

作者： Yu, Chengxi Yu, Menghong Yuan, Wei School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China Ocean Equipment Research Institute Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China

The working process of cutter suction dredger is complex, so it is difficult to establish accurate model by traditional physical modeling method. The final yield of cutter suction dredger is directly related to the density of the slurry in the pipeline, while the density is controlled by the swing process. Therefore, by analyzing the measured data of the dredger, it establishes the swing process mathematical model based on subspace *** algorithm is based on input- output data, simulation results show the algorithm is effective for the control application. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Dredging

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Method for improving the accuracy of mobile robot odometry based on machine learning

Method for improving the accuracy of mobile robot odometry b...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), international conference on

作者： Bingbing Shao Tianjiang Zheng Qi Li Yikun Chen Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang China Key Laboratory of Robotics and Intelligent Equipment Technology of Zhejiang Province Ningbo Zhejiang China

Omnidirectional mobile robots have the characteristic of moving in any direction with three degrees of freedom in plane XYZ. The positioning accuracy of a mobile robot is not only affected by its manufacturing errors but also may be affected by vibration, load, and other factors, resulting in errors in the process of moving. To reduce the influence of the above factors, this paper presents an error compensation method based on Gaussian process regression (GPR) for omnidirectional mobile robots. The method requires fewer learning parameters and training points, which enables more computational efficiency in the learning process. To verify the effectiveness of the method, a simulation experiment is carried out. The simulation results show that the accuracy of the odometry in the three degrees of freedom of the plane is improved by 89.70%, 88.09%, and 96.32% respectively.

关键词： Training Vibrations simulation Machine learning Kinematics Predictive models Computational efficiency

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Evaluating the performance of various deep reinforcement learning algorithms for a conversational chatbot 2

Evaluating the performance of various deep reinforcement lea...

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2nd international conference for Emerging Technology, INCET 2021

作者： Rajamalli Keerthana, R. Fathima, G. Florence, Lilly Adhiyamaan College of Engineering Department of Computer Science and Engineering Hosur India

ISBN: (纸本)9781728170299

Conversational agents are the most popular AI technology in IT trends. Domain specific chatbots are now used by almost every industry in order to upgrade their customer service. The Proposed paper shows the modelling and performance of one such conversational agent created using deep learning. The proposed model utilizes NMT (Neural Machine Translation) from the TensorFlow software libraries. A BiRNN (Bidirectional Recurrent Neural Network) is used in order to process input sentences that contain large number of tokens (20-40 words). In order to understand the context of the input sentence attention model is used along with BiRNN. The conversational models usually have one drawback, that is, they sometimes provide irrelevant answer to the input. This happens quite often in conversational chatbots as the chatbot doesn't realize that it is answering without context. This drawback is solved in the proposed system using Deep Reinforcement Learning technique. Deep reinforcement Learning follows a reward system that enables the bot to differentiate between right and wrong answers. Deep Reinforcement Learning techniques allows the chatbot to understand the sentiment of the query and reply accordingly. The Deep Reinforcement Learning algorithms used in the proposed system is Q-Learning, Deep Q Neural Network (DQN) and Distributional Reinforcement Learning with Quantile Regression (QR-DQN). The performance of each algorithm is evaluated and compared in this paper in order to find the best DRL algorithm. The dataset used in the proposed system is Cornell Movie-dialogs corpus and CoQA (A Conversational Question Answering Challenge). CoQA is a large dataset that contains data collected from 8000+ conversations in the form of questions and answers. The main goal of the proposed work is to increase the relevancy of the chatbot responses and to increase the perplexity of the conversational chatbot. © 2021 IEEE.

关键词： Reinforcement learning

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