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检索条件"任意字段=Conference on Data Analysis and Modeling for Process Control II"

共 4770 条记录，以下是621-630 订阅

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Combining Computer Simulation Visualization and Multi-Objective Optimization Algorithm for Urban Light Pollution Research

Combining Computer Simulation Visualization and Multi-Object...

引用

Image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Yuhang Zhang Beijing Technology and Business University Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This study aims to solve the problem of urban light pollution by establishing a series of models to achieve the best balance between economy and efficiency. Firstly, we decompose the complex problem into a simple one and establish a light pollution risk level evaluation model, a light pollution threshold differential equation model, a lighting system optimisation strategy model and a Poisson process simulation model validation. In Model I, we use hierarchical analysis and Topsis algorithm, combined with multiple attribute data, to determine the evaluation indexes of light pollution risk level, and effectively classify the light pollution risk level of the area. In Model ii, we used the differential equation model to find out the light intensity threshold, and the results showed the diversity of different regions. In Model iiI, a multi-objective optimisation algorithm is used to achieve the balance of time, light intensity threshold and light range through the lighting system optimisation strategy. Model IV combines the data from Beijing and Shanghai and calculates the number of times residents exceed the permitted use of light using a Poisson distribution.

关键词： Analytical models Biological system modeling Computational modeling Diversity reception Differential equations Artificial light Mathematical models data models Optimization Light sources

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Rapid parameter identification of BUCK converter using Hilbert phase compensation

Rapid parameter identification of BUCK converter using Hilbe...

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International conference on Power Electronics and Motion control (IPEMC)

作者： Hongzhi Liu Hui Li Changhao Zhu Xiangtan University China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

This paper proposes a phase compensation method for rapid parameter identification aimed at addressing the hysteresis problem in the digital twin parameter identification of BUCK converters. Based on collected BUCK circuit data, this method performs Hilbert transformations on the data from both the physical model and the digital twin model. It calculates the phase difference between the two and mitigates the error caused by actual sampling data through phase compensation. This process reduces computational redundancy resulting from errors, enabling timely parameter updates in the digital twin model. Additionally, a BUCK converter digital twin platform is designed for experimental verification. The experimental results demonstrate that, while ensuring accuracy, this method effectively enhances the parameter identification speed of the digital twin model, facilitating subsequent field-circuit coupling analysis..

关键词： Parameter estimation Buck converters Computational modeling Redundancy Linear programming data models Power electronics

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Enhanced Air Quality Prediction Using Optimized Sarimax Model for Coimbatore Region

Enhanced Air Quality Prediction Using Optimized Sarimax Mode...

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Science, Technology, Engineering and Management (ICSTEM), International conference on

作者： Bande Revanth Manoranjitham R Division of Computer Science and Engineering Karunnya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

This research delves into examining the forecast of air quality in Coimbatore, India, according to data gleaned from the Central Pollution control Board (CPCB) specific to the area. The gathered information underwent a preprocessing step, using interpolation to ensure uniformity and completeness. Forecasting methods such as Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) and Long Short-Term Memory (LSTM) models were utilized to predict air quality levels. The SARIMAX model was fine-tuned through a grid search process, while the LSTM underwent hyperparameter adjustments to improve air quality predictions. Performance evaluation of these models involved metrics like R-squared, Mean absolute error, and Root mean square error. Based on the selected evaluation criteria, the outcome shows that SARIMAX outperformed LSTM in Coimbatore when it came to predicting the air quality. This analysis improves our understanding of air quality patterns in Coimbatore. Highlights SARIMAX's effectiveness as a modeling tool for atmospheric science applications.

关键词： Interpolation Solid modeling Atmospheric modeling Pollution control Predictive models Air quality Solids

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The Prediction Model of SAG Mill Load Based on Industrial Big-data Mining

The Prediction Model of SAG Mill Load Based on Industrial Bi...

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第43届中国控制会议

作者： Guicheng Wang Changyu Wang Zhicheng Bai Changliang Guan Min Zhang Xiaobin Li School of Electrical and Electronics Engineering Shanghai Institute of Technology State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process and Beijing Key Laboratory of Process Automation in Mining & Metallurgy

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Regarding the modeling problem of grinding process in mining,taking the operating load of semi-automatic grinding mill as the research object,starting from industrial big data analysis,using data mining methods to establish decision tree models and random forest models,and evaluating and predicting the constructed *** testing,decision tree models with different effects can be obtained,indicating that the decision tree model has a certain degree of flexibility,and selecting parameters can lead to a model with good *** addition,under the same conditions,a random forest model was constructed,and simulation results showed that the combination model constructed using the random forest algorithm was superior to a single decision tree model,with better prediction performance.

关键词： SAG mill data mining big-data analysis decision tree random forest

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Grade prediction modeling for froth flotation based on NARX-NN and ensemble learning 40

Grade prediction modeling for froth flotation based on NARX-...

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40th Chinese control conference (CCC)

作者： Wang, Xiaoli Cao, Siyu Huang, Lei Yang, Yang Yang, Chunhua Xiong, Wei Cent South Univ Sch Automat Changsha 410083 Peoples R China Changsha Res Inst Min & Met Changsha 410012 Peoples R China

ISBN: (纸本)9789881563804

In mineral flotation, concentrate and tailing grade are indices directly relating to plant economics. Realizing online detection of grade is costly and difficult. In China, offline analysis is employed in most flotation plants, resulting in delayed detection and control. A grade prediction model based on nonlinear autoregressive networks with exogenous inputs (NARX) and ensemble learning is herein proposed. Concentrate and tailing grades are influenced by many factors in the complex flotation process. The prediction model structure is determined by mechanism analysis in that process. Based on the correlation analysis between process conditions and the grade, process condition input and feedback delays are determined. To improve the model accuracy and generalization ability, a NARX neural network was trained as the base learner and support vector regression was the second learner. A grade prediction model was established and shown to effectively track grade dynamic fluctuations with high prediction accuracy and stability.

关键词： froth flotation grade prediction data processing mechanism analysis NARX-NN ensemble learning

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Cloud and Edge Computing Architectures: A Survey

Cloud and Edge Computing Architectures: A Survey

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IEEE conference on Systems, process & control (ICSPC)

作者： Hasnae Nouhas Abdessamad Belangour Mahmoud Nassar Laboratory of Information Technology and Modeling LTIM Faculty of Sciences Ben M'sik Hassan II University Casablanca Morocco IMS Team ADMIR Laboratory ENSIAS Rabat IT Center Mohammed V University in Rabat Rabat Morocco

Cloud and Edge computing have transformed the digital landscape, each presenting distinct architectural features. As computing paradigms rapidly evolve, it has become crucial to comprehend their specific architectures. This paper conducts a survey focusing on the architectural dimensions and layers in both Cloud and Edge computing. It comprehensively explores key characteristics, main objectives, underlying concepts, advantages, and performance aspects to bridge the gap between these significant computing paradigms. Furthermore, the research affords insights into diverse architectural models within both the Cloud and Edge computing domains, leading to a comparative analysis. In conclusion, this paper highlights the key points and summarizes the study.

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Robust Hybrid Model for Social Media Sentiment analysis

Robust Hybrid Model for Social Media Sentiment Analysis

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International conference on Computing and Networking Technology (ICCNT)

作者： Dingari Jahnavi Sasank Sami Sandeep Pulata G Bharathi Mohan R Prasanna Kumar Department of Computer science And Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Social media platforms are home to massive amounts of content created by users, which can offer insightful information on attitudes, beliefs, and feelings. In order to extract valuable information from the abundance of data, sentiment analysis is essential. This work presents a novel hybrid sentiment analysis model using the Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa) models. The strategy tries to improve sentiment classification performance by utilizing the advantages of both models. The complexities of social media text are often difficult for traditional sentiment analysis models, like rule-based systems and machine learning algorithms like logistic regression and support vector machines, to capture because of issues with contextual understanding, linguistic complexity, inherent data variability, and noise. This hybrid sentiment analysis, on the other hand, excels at understanding natural language semantics and gathering contextual information. Labelled data and an innovative training approach are combined to train the model. Reporting accuracy metrics is part of the performance evaluation process when using a test dataset. Additionally, a thorough classification report and confusion matrix are produced. A test dataset performance evaluation yields an accuracy of 82%.

关键词： Training Performance evaluation Analytical models Sentiment analysis Accuracy Social networking (online) Computational modeling Bidirectional control Encoding Context modeling

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Three Dimensional Geological modeling for Mixed Reservoir of F Oilfield in Middle East 11th

Three Dimensional Geological Modeling for Mixed Reservoir of...

引用

11th International Field Exploration and Development conference, IFEDC 2021

作者： Wang, Long CNOOC International Ltd. Beijing100028 China

ISBN: (纸本)9789811921483

The F oilfield in the Middle East straddles Iran and Iraq, and the contract block only includes the Iraqi part, structural incompleteness is a difficult problem in oilfield research. Oilfield development and geological modeling also face several other challenges, including complex lithology, many factors affecting reservoir physical property, lack of testing data resulting in great uncertainty in physical property interpretation, and no effective constraints in attribute modeling. In view of these difficulties, a set of fine reservoir geological modeling technology series suitable for trans-boundary and mixed reservoirs are established, including set up virtual wells to complete the structure, multistage facies controlling geological modeling, mixed lithology classification and sedimentary microfacies study, evaluation of effective permeability by dynamic and static combination, conditional constraint saturation modeling. Referring to the structural form of Iranian part of F Oilfield in public data, the virtual wells are reasonably set up, and the incomplete anticline structure is complemented by combining the virtual wells with the structure in the contract area. A two-stage model of lithofacies and reservoir facies is established by using the idea of multistage facies control. The reservoir facies model is established under the constraint of sedimentary facies model to ensure the reasonable distribution of reservoir in space. By using the two-terminal element classification method, the classification standard of mixed reservoir suitable for F oilfield is established, and the research process of sedimentary microfacies in mixing background is formed, which solves the problems of complex rock classification and sedimentary microfacies research, and lays the foundation for the modeling of sedimentary facies and reservoir facies. Effective permeability of single well is evaluated by combining dynamic and static data, and the trend surface of dynamic/static permeabilit

关键词： Uncertainty analysis

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Anomaly Detection System for Semiconductor Deposition process using Transformer

Anomaly Detection System for Semiconductor Deposition Proces...

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International conference on control, Automation and Systems ( ICCAS)

作者： Min-Ho Ha Saba Arshad Min-Seon Chae Sung-Chul Yun Seung-Hwan Kim Tae-Hyoung Park Department of Intelligent Systems and Robotics Chungbuk National University Cheongju Korea Industrial AI Research Center Chungbuk National University Cheongju Korea GSF Solution Seongnam Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

As the artificial intelligence market develops, demand for semiconductors also increases. In order to increase the efficiency of the semiconductor production process, anomaly detection technology is needed based on sensor values attached to process machines. In this paper, we propose an anomaly detection system that determines whether there are abnormalities in the values obtained by attaching a sensor to the actual deposition process. The attached sensors include gas, temperature, and pressure sensors. A communication module is designed and made into a database, and the presence or absence of anomalies is determined using the characteristics of multivariate time series data through transformer-based anomaly detection network. An attention module-based transformer network is used in the AI-based anomaly detection network. Through evaluation, we confirmed that the transformer-based anomaly detection network achieves the best performance in terms of accuracy relative to other anomaly detection methods.

关键词： Semiconductor device modeling Training Temperature sensors Accuracy Temperature Time series analysis Semiconductor device manufacture Transformers data models Anomaly detection

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Advances in Type ii Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques

Advances in Type II Diabetes Prediction: A Comprehensive Rev...

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Intelligent control, Computing and Communications (IC3), International conference on

作者： Virendra Singh Kushwah Sivaneasan Bala Krishnan Kamal Upreti Manoj Kumar Pravin R. Kshirsagar Vinai K. Singh Singapore Institute of Technology SINGAPORE Department of Computer Science School of Sciences Christ University Ghaziabad INDIA Department of Mathematics and Statistics Gurukula Kangri University Haridwar INDIA Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur INDIA Department of Computer Science & Engineering GL Bajaj Group of Institutions Mathura India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Type ii diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type ii diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type ii diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions.

关键词： Deep learning Support vector machines Adaptation models Accuracy Biological system modeling Predictive models Prediction algorithms data models Diabetes Wearable devices

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