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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Multi-condition Quality Prediction of Production Process Based on Hybrid Transfer Learning

Multi-condition Quality Prediction of Production Process Bas...

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System Reliability and Safety engineering (SRSE), international conference on

作者： Dengji Liu Sheng Hu Xiaohui Zhao Qingan Qiu Yifeng Jiang Pengyao Fan School of Mechanical and Electrical Engineering Xi’an Polytechnic University Xi’an China School of Management Beijing Institute of Technology Beijing China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

the modern production process is gradually developing towards multi-variety and small-batch direction, and the production process presents the characteristic of multi-conditions. Under this characteristic, the data distribution differences of different working conditions are significant and the sample size is limited, resulting in low accuracy of the constructed quality prediction model. To address this problem, this paper proposes a method for multi-condition quality prediction of the production process based on hybrid transfer learning. Firstly, during the feature extraction stage, transfer component analysis (TCA) is used to extract the features which have little difference in distribution between the historical working conditions and the working conditions to be tested, so as to reduce the difference in quality data distribution between different working conditions. Secondly, during the instance transfer stage, the TrAdaBoost.R2 algorithm is used to transfer knowledge of historical working condition quality data to help build an effective quality prediction model for the working conditions to be tested. Finally, the effectiveness of the proposed method was validated through a case study. the results demonstrate that compared to traditional machine learning algorithms and instance transfer algorithm algorithms that do not reduce the difference in working condition data distributions, the prediction accuracy of the method presented in this article has been improved by an average of $49.2 \%$ and $46.4 \%$ respectively, indicating superior predictive performance.

关键词： Employee welfare Machine learning algorithms Accuracy Transfer learning Production predictive models Prediction algorithms Feature extraction Data models Safety

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Long short-term memory assisted FIR filter for tracking human upper limb motion

Long short-term memory assisted FIR filter for tracking huma...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Rui Gao QingAo Zhao Yuan Xu School of Electrical Engineering University of Jinan Jinan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

To better correct the spatial trajectory of human motion, this paper proposes a Long Short-Term Memory (LSTM)-assisted correction algorithm for the Finite Impulse Response (FIR) filter, aimed at improving the accuracy of human joint motion capture based on inertial sensors. In the proposed algorithm, the forward data is obtained from the backward-updated data smoothed by the Rauch-Tung-Striebel (RTS) smoother and compared with the forward-predicted data from the FIR. the resulting error is input into the pre-trained LSTM model to predict the error data, and the backward data at that moment is updated, making the positional information more accurate. this calculation shows significant effectiveness when estimating the elbow data. In the experiment, the data obtained using the LSTM-assisted FIR filter showed a 35.83% improvement compared to the data obtained with the LSTM-assisted Kalman Filter.

关键词： Limbs Accuracy Finite impulse response filters Inertial sensors predictive models Filtering algorithms Prediction algorithms Motion capture Data models Long short term memory

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House Price Prediction Model Based on Multi-Strategy Improved COOT Optimization Algorithm

House Price Prediction Model Based on Multi-Strategy Improve...

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Ziyang Han Xiangguo Zhu Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

the COOT optimization algorithm holds promise for optimizing machine learning algorithms and feature selection. However, its drawbacks, such as weak overall search capability and sometimes it goes to a local optimum, pose challenges when applied in practice. Aiming at the problems existing in the reference white crown chicken algorithm, a new algorithm named TLCOOT is proposed, which is based on tent Chaos Mapping and Levy Flight Strategy. this strategy changes and improves the strength of the original algorithm to jump out of partial optimal solutions. A variety of functions are used to evaluate the effect of the algorithm. the results show that TLCOOT is superior to the COOT optimization algorithm and several swarm intelligence algorithms in Best, Mean, Std and convergence curve. On this basis, TLCOOT optimized random forest house price prediction model (TLCOOT-RF) was used. the experimental results of the model verify the practicability of the proposed TLCOOT optimization algorithm.

关键词： Analytical models Machine learning algorithms Computational modeling predictive models Prediction algorithms Stability analysis Forecasting Optimization Standards Convergence

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GABP-TroNet: Enhance Air-Ground Channel Modeling by Improving GZTDS Model for GNSS

GABP-TroNet: Enhance Air-Ground Channel Modeling by Improvin...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Bo Song Yiming Wang Tianyu Zhang Jinlong Sun School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Tropospheric delay is an error factor in high-precision positioning of Global Navigation Satellite Systems (GNSS).the simplified GNSS tropospheric delay model (GZTDS) predicts zenith tropospheric delay (ZTD) around the world and shows good global performance with a root mean square error (RMSE) of 3.46 cm. However, in some regions, the GZTDS model performs poorly. this paper intends to improve performance of the GZTDS model in the coastal regions of China by combining the GZTDS model with back propagation (BP) neural network based on genetic algorithm (GABP-TroNet). With the validation of data from the Vienna Mapping Function 1 (VMF1), the mean bias (MB) of the GABP-TroNet model is 0.08 cm and the RMSE is 3.11 cm, which improves the accuracy of predicting ZTD in the coastal regions of China compared to GZTDS.

关键词： Backpropagation Global navigation satellite system Analytical models Accuracy Atmospheric modeling Neural networks Sea measurements predictive models Delays Genetic algorithms

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A Novel Hybrid BAT-PSO Approach for Task Scheduling and Workload Forecasting for Cloud Environments

A Novel Hybrid BAT-PSO Approach for Task Scheduling and Work...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Abhishek Sharma Surendra Singh Chauhan CEA Department GLA University Mathura UP India

With increasing demands for data storage, processing and advanced computing, cloud computing has become almost indispensable in different domains of engineering and technology. However, cloud-based platforms are resource constrained and need to distribute the tasks in such a way that the workload is balanced. this becomes more streamlined if the estimate for cloud workload is available beforehand. Hence task scheduling and workload forecasting are non-trivial tasks which need to be executed meticulously. In this paper, a heuristic approach for task scheduling has been proposed along with a machine learning based approach for workload forecasting. the performance of the task scheduling approach has been evaluated in view of the response time and CPU utilization. the performance of the workload forecasting approach has been evaluated based on the regression, mean absolute percentage error, mean square error and number of iterations for training.

关键词： Training Cloud computing Processor scheduling Memory predictive models Prediction algorithms Scheduling

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predictive and Explainable Modelling in Economics on the Case Study of Remittance Prediction Using the NeuDen AI Computational Architecture 19th

Predictive and Explainable Modelling in Economics on the Cas...

引用

19th international conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： AbouHassan, Iman Kasabov, Nikola Trifonov, Roumen Popov, George Technical University of Sofia Sofia Bulgaria Central Bank of Lebanon Beirut Lebanon KEDRI SECMS Auckland University of Technology Auckland New Zealand IICT Bulgarian Academy of Sciences Sofia Bulgaria Dalian University Dalian China

ISBN: (纸本)9783031815416

the presence of complex time series and multimodal data in economics and finance necessitates the development of advanced analytical models capable of interpreting complex patterns and dynamics. Despite their fast development as LLM, the current AI systems cannot do that. this paper introduces how a novel computational architecture, NeuDen, which combines the strengths of evolving spiking neural networks (eSNNs) with the interpretability of evolving dynamic neuro-fuzzy systems (eDNFS), can be used to model high-dimensional temporal data in economics. the NeuDen model is applied to the analysis of remittance inflows, demonstrating its ability to not only model data but also uncover the underlying trends in intricate remittance data. this application highlights the model's potential as a tool for efficient modelling of economic data that also helps understand the dynamics of economic processes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neural network models

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Investigation of thermal Management and Control System of Lithium-Ion Battery Based on Multiphysics Model

Investigation of Thermal Management and Control System of Li...

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IEEE international conference on Knowledge Innovation and Invention (ICKII)

作者： Po-Yi Yu Jung-Chang Wang Department of Marine Engineering National Taiwan Ocean University Keelung Taiwan

Spontaneous thermal runaway has become a significant issue for thermal management and control systems during the operation of lithium-ion batteries recently. therefore, we discussed the heat distribution of lithium-ion batteries under different high discharge rates. Firstly, the lithium-ion batteries were tested to obtain the properties of electrochemical and thermal behavior at different discharge rates. One-dimensional electrochemical coupling three-dimensional heat transfer models was established by using numerical simulation commercial software. the simulation results were compared with the experimental results to verify the feasibility of the model. Finally, the electrochemical characteristics were incorporated into the three-dimensional model, and the heat distribution profile generated by the batteries connected in series and parallel was simulated to figure out the critical temperature when the thermal runaway was triggered under different C-rates.

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Application of Long-Term Time Series Forecasting in E-Learning

Application of Long-Term Time Series Forecasting in E-Learni...

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Computer Science, engineering, and Education (CSEE), international conference on

作者： Fenglin Qi Kai Zhang Jiajie Shen Jun Gao Informatization office Fudan University Shanghai China Fudan University Shanghai China

ISBN: (数字)9798331505165

ISBN: (纸本)9798331505172

the E-Learning platform plays a crucial role in higher education teaching. Accurately predicting long-term future access patterns based on the platform's log data is essential for optimizing resource allocation in advance. However, many existing methods face challenges in both prediction period and accuracy. To this end, we propose a deep learning-based multi-period modeling network (MPMN) for analyzing log data analysis from the E-Learning platform. It learns the educational cyclical patterns to achieve longer-term and more accurate prediction. By conducting comparative analysis of common time series prediction methods from multiple dimensions, this paper highlights the superiority of the proposed method. the long-term time series forecasting analysis method could effectively assist E-Learning platform administrators in understanding the platform's access load over future periods, enabling them to plan resource allocation in advance, thereby improving teaching quality and enhancing the user experience for both teachers and students.

关键词： Electronic learning Accuracy Education Time series analysis predictive models User experience Resource management Forecasting Faces Load modeling

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Sustainable Data Analysis in engineering: 6- Year Review Emphasizing Open-Source Tools, Eco-Friendly Practices, and Emerging Trends for Smart Computing

Sustainable Data Analysis in Engineering: 6- Year Review Emp...

引用

international conference on Smart Computing & Communications (ICSCC)

作者： Radhakrishnan Subramaniam Prashobhan Palakkeel Manimuthu Arunmozhi Uthayakumar Manikandan Sridharan Practice lead of DevOps. Automation & AIOps NCS Singtel Group Singapore Amrita Vishwa Vidyapeetham India Dept. of Management Amrita Vishwa Vidyapeetham Coimbatore India Cybersecurity and Business Analytics Aston Business School UK Department of Mechanical Engineering Kalasalingam Academy of research and Education KrishnanKoil India E.G.S Pillay Engineering College Nagapattinam India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

the authors' examination of quantitative data analysis tools through Evidence-Based software engineering (EBSE) and PRISMA flow, within a 6-year review of published journal articles, is commendable for its potential impact on sustainable engineering practices. Identifying top tools like SPSS, R, Stata, Excel, SAS, SmartPLS, MATLAB, AMOS, LISREL, and MPlus, and emphasizing their usage trends provides valuable insights for researchers and educators. However, the paper could enhance its contribution to sustainability by urging the preference for open-source tools, aligning with principles of collaboration, and minimizing environmental impact. Including an evaluation of tools' ecological footprints, such as energy consumption and waste generation, would further align with sustainable engineering goals. Additionally, considering emerging trends in sustainable data analysis tools could help researchers stay informed about evolving technologies. In summary, while the paper offers crucial insights into quantitative tools, integrating sustainability considerations, promoting open-source options, and evaluating ecological footprints could significantly enhance its relevance to sustainable engineering practices concisely.

关键词： Data analysis Reviews Statistical analysis predictive models Market research Mathematical models Sustainable development Synthetic aperture sonar software engineering Matlab

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A Battery thermal Management Strategy Based on Model predictive Control with Online Markov Speed Predictor

A Battery Thermal Management Strategy Based on Model Predict...

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Energy, Power and Grid (ICEPG), international conference on

作者： Ningwei Jiang Ziyi Yang JunJun Deng Renjie Wang National Engineering Research Center of Electric Vehicles Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

the energy efficiency of battery electric vehicles has reached a high level, but there remains significant potential for optimizing the efficiency of battery thermal management systems (BTMS). Existing control strategies for BTMS often fail to adequately consider preview information of the trip, leading to suboptimal energy efficiency. In this paper, an online Markov Chain (MC)-based model predictive control (MPC) strategy is used to reduce the energy consumption of the battery thermal management system. the MC-based speed predictor gathers speed data during driving to continuously update the transition probability matrix(TPM), thereby enhancing prediction accuracy. Firstly, the predicted vehicle speed sequence is used as a disturbance of the model predictive control. then the battery degradation cost, electric power consumption cost, and deviation of battery temperature from target are selected as the objective functions, and finally the dynamic programming (DP) algorithm is employed to solve the local optimization problem within the prediction time domain. For simulation verification, the CLTC-P cycle was repeated 17 times as a test condition. Compared with the rule-based controller, the MPC algorithm reduced power consumption by 5.4% and battery degradation by 3% under test conditions. In addition, the BTMS with MPC tracks the battery reference temperature better.

关键词： Degradation Energy consumption Heuristic algorithms predictive models thermal management Prediction algorithms Batteries Optimization predictive control thermal degradation

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