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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

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Modeling and control of Testing Machine for Fire Sprinkler K - Factor Evaluation 13

Modeling and Control of Testing Machine for Fire Sprinkler K...

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13th International conference on control, Automation and Information Sciences, ICCAIS 2024

作者： Ho, Hai Dang Nguyen, Huu Nhan Truong, Cong Toai Duong, Van Tu Nguyen, Huy Hung 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam Saigon University Faculty of Electronics and Telecommunication Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331542047

During the period of demographic transition, when the demand for accommodation increases, the role of the fire protection system becomes more important than ever. Among these, the most popular fire protection component is the fire sprinkler. To ensure the safety requirements, this paper introduces the evaluation K-factor machine (EKFM) to test fire sprinklers according to the ISO 6182-1: 2004 standard. Overall, the EKFM comprises a water pump whose speed is adjusted by a variable frequency drive (VFD) to control pipeline flow and sensor system, including flow and pressure sensor to provide feedback on the value measure. Additionally, according to the ISO 6182-1: 2004 standard, the operation of the EKFM needs to create an initial pressure of 0.5 bar in the pipeline, increase by 1 bar per pressure set point until reaching 6. 5 bar, and then decrease back to 0.5 bar. Based on the properties of EKFM, the paper proposes model identification of the EKFM and designs the P controller to ensure the steady-state error is within 5 % of the setpoint for each pressure step. Specifically, the EKFM is modeled identification using a data-driven approach, comprising group one data for identification and another for validation. Besides, simulation scenarios are conducted following the ISO 6182-1: 2004 standard test procedure to find the suitable value for proportional (P) control and compare the simulation result with controller evaluation criteria. Furthermore, experimental data indicate that the EKFM can maintain an error of within 5 % error for pressures less than 3 bar and 2 % for pressures greater than 3 bar. As a result, the EKFM can evaluate the quality of the fire sprinkler before it is applied in the market. © 2024 ieee.

关键词： Sprinkler systems (fire fighting)

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PUROS: A Cpx-Ievered Framework for Ad Procurement in Autobidding Worlds

PUROS: A Cpx-Ievered Framework for Ad Procurement in Autobid...

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data driven control and learning systems (ddcls)

作者： Huixiang Luo Longyu Gao Pingchun Huang Tianning Li Alibaba Group Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Nowadays online advertisers acquire ad impressions from digital advertising channels through operating on their au-tobidding platforms: advertisers express high level ad performance constraints for ad procurement by specifying levers including budget and cpx (e.g. cpm: max cost-per-mille, cpc: max cost-per-click, cpa: target cost-per-action). Ad platforms procure impressions on behalf of advertisers soon afterwards, and feedback ad performances (e.g. cost) to advertisers. In practice, no procurement details but aggregated ad performences are access to advertisers, and they are usually unstable because of the non-stationary autobidding environment. As a result, it's challenging for advertisers to optimize levers by directly using 'Predict, then Optimize (PO)‘ Framework (i.e. use machine-learning model with history data to estimate ad performances, then optimize levers by solving the multi-constraint optimization problem), because poor estimation performance leads to unreliable lever so-lution and results in bad performances on ad procurement. thus, online-learning-based frameworks are recommended for lever optimization, with large amounts of history data neglected. To upgrade both the estimating & optimizing procedure in PO for better use of history data, 'Predict with Uncertainty estimation, then RObust Satisficing’(PUROS), a cpx-levered framework is presented to handle the chain reaction of poor estimation performance. In particular, an uncertainty-aware forecasting model is used for cpx-oriented ad performance estimation. then robust satisficing is applied in the optimizing procedure, so that cpx solutions with better Pareto efficiency and robustness are produced compared to those generated by PO. Experiment results on both open-source and real-world e-commerce datasets prove the effectiveness of PUROS over traditional PO ones. Further, PUROS performs better than the previous SOTA online-learning-based framework, and shows huge potential on performance improvement

关键词： Procurement learning systems Uncertainty Estimation Machine learning Predictive models Robustness

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Trustworthiness Evaluation of Internet of Vehicles Nodes Based on Real-Time Perception and Long-Term Reputation

Trustworthiness Evaluation of Internet of Vehicles Nodes Bas...

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data driven control and learning systems (ddcls)

作者： Lina Sun Bo Mi Darong Huang Peng Lu Institute of Information Science and Engineering Chongqing Jiaotong University Chongqing P. R. China School of Artificial Intelligence Anhui University Anhui P. R. China Artificial Intelligence and Manufacturing Department Hechi University Guangxi P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In Internet of Vehicles, the way of collecting road environment data and sharing data through vehicle equipment has been widely used. However, due to the impact of vehicle equipment, environment and malicious attacks, different vehicles have different source qualities, and the perceived data from multiple vehicle sources may be different from the real road conditions. Truth discovery is to infer the truth closest to the truth from multiple different or even conflicting information sources by comprehensively considering the quality and reliability assessment of these information sources. Aiming at the quality and reliability assessment of information sources of vehicle nodes, this paper proposes a node credibility assessment method based on real-time perception data and long-term reputation of vehicle sources. Compared with the existing scheme, the reliability of vehicle source nodes is used as the initial weight of truth discovery in intelligent networked vehicles, and the prior knowledge is used to reduce the number of iterations required for truth discovery and improve its accuracy. the percentage of relevant data is calculated according to the factual perception data of vehicle sources. Finally, the reliability of vehicle source nodes is obtained by combining the long-term reputation accumulated by long-term tasks of vehicle sources. Experiments on GPS data sets show that the method of assigning initial weights based on vehicle source node confidence can reduce the number of iterations and have higher accuracy.

关键词： learning systems Accuracy Network topology Roads Real-time systems Topology Reliability

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Semi-Supervised Soft Sensor Modeling for Industrial Dynamic Information Mining Based on Two-Path Weight Comparison

Semi-Supervised Soft Sensor Modeling for Industrial Dynamic ...

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data driven control and learning systems (ddcls)

作者： Ruining Jia Xinyi Jin Xinru Ni Mengjiao Ruan Zeyu Yang Peiliang Wang Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Deep learning methods can extract reliable feature representations from massive process data to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and even limited in label samples due to untimely sampling and analysis. Aiming at the above problems, this paper proposes a semi-supervised soft sensor modeling method (SSACF-LSTM) for industrial dynamic information mining based on two-path weight comparison. An unsupervised long short-term memory network (LSTM) encoder-decoder is utilized to extract the hidden dynamic nonlinear information in unlabeled samples. this information is then spliced and masked with the initial input, which is subsequently fed into a deep neural network encoder-decoder for pretraining to learn a self-supervised task that mines correlation information between sequences as well as between samples that is useful for the model. In addition, the LSTM with attentional convolutional fusion serves as an auxiliary path that mines correlation information between variables directly from the initial input to complement the information extracted by self-supervision. Finally, the results of both are linearly weighted to obtain the final prediction. the method is validated in a debutanizer process. the results show that SSACF-LSTM can utilize both labeled and unlabeled data to extract quality-related features for soft sensor modeling, which is better than the semi-supervised LSTM network with historical feature fusion (HFF-ssLSTM), semi-supervised stacked auto-encoder (SSAE), and variational auto-encoder regression (SVAER).

关键词： Correlation Accuracy Soft sensors Production Feature extraction data models data mining

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Recognition of Scalp Psoriasis Dermoscopic Patterns Signs Based on Histogram Equalization and Convolutional Neural Networks

Recognition of Scalp Psoriasis Dermoscopic Patterns Signs Ba...

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data driven control and learning systems (ddcls)

作者： Honghai Ji Jiaqi Li Weiwei Jiang Li Wang Shida Liu Yang Chen School of Electronics & Control Engineering North China University of Technology Beijing China Department of Dermatology 72nd Group army hospital of PLA Zhejiang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Scalp psoriasis is a common chronic autoimmune inflammatory skin disease that affects a large number of people worldwide. Although scalp psoriasis is not physically a serious life-threatening condition for patients, the itchiness, inflammation, and social distress it causes have a significant negative impact on their quality of life. Currently, the urgent need for accurate diagnosis of scalp psoriasis has driven researchers to explore efficient diagnostic techniques utilizing deep learning techniques, among which dermoscopy is widely used as a simple and easy-to-use non-invasive testing device. In this paper, a convolutional neural network (HE-CNN) based on histogram equalization is proposed to solve the problem of automatic recognition of different dermoscopic patterns of scalp psoriasis. In this, histogram equalization is used to perform global brightness enhancement of dermoscopic images to ensure that important lesion features in the image are not obscured by hair shadows leading to neglect. CBAM is added to the CNN and the enhanced images are used to train a classification model for automated recognition of scalp psoriasis for clinical detection. We conducted experiments on a scalp psoriasis dataset provided by 72nd Group army hospital of PLA. In order to verify the effectiveness of the method, we use the unenhanced image as a control group and compare it with the image that has undergone enhancement, and the results after five-fold cross-validation show that the recognition effect of HE-CNN outperforms that of the control group in terms of accuracy, specificity, sensitivity and youden index. It is verified that HE-CNN has better effect in assisting doctors in the diagnosis of scalp psoriasis.

关键词： Histograms Image recognition Accuracy Psoriasis Scalp Skin Pattern recognition

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Local-Global Dynamic Information Fusion Graph learning for Traffic Flow Prediction

Local-Global Dynamic Information Fusion Graph Learning for T...

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data driven control and learning systems (ddcls)

作者： Yuan Xu Fan Qin Wei Ke Yan-Lin He Ming-Qing Zhang Qun-Xiong Zhu Yang Zhang College of Information Science & Technology Beijing University of Chemical Technology Beijing China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the field of intelligent transportation systems, accurately predicting traffic flow is a challenging endeavor for enabling efficient services in urban road networks. While current research has made significant advancements in capturing nonlinear features and spatiotemporal dependencies at a fixed geographic node level, it often overlooks the dynamic interactions of traffic flow across different regions. In this work, we present a novel local-global dynamic information fusion graph learning model (LGDF-GL) for traffic flow prediction. Different from other graph learning model, we first design a dynamic adjacency matrix by region-adaptive parameter learning, and fuse it with a static adjacency matrix based on node geographic information by dynamic information fusion module, and the fused result serves as the diagonal elements of the local adjacency matrix. Subsequently, we input it into the graph convolutional network (GCN) and gated recurrent unit (GRU) aggregation model to extract local feature information containing dynamic spatiotemporal features from traffic flow data. the local feature information is pruned and concatenated to obtain global feature representations. then we achieve global feature representation prediction through multiple fully connected layers. Finally, we conducted experiments on two real-world traffic flow datasets to evaluate the performance of our proposed method. the results clearly demonstrate that the LGDF-GL method achieves superior prediction ability compared to several baseline models.

关键词： learning systems Graph convolutional networks Fuses Roads Predictive models Logic gates Feature extraction

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Improved Adaptive DBSCAN for data Cleaning in Molten Iron Weighing Process

Improved Adaptive DBSCAN for Data Cleaning in Molten Iron We...

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data driven control and learning systems (ddcls)

作者： Yu Feng Xiaoli Li Xiaowei Yu Kang Wang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the intricate process of steel manufacturing, the precise measurement of molten iron is a pivotal procedure, which directly impacts the quality of steel production. Rail weighbridges are commonly deployed in the steel industry for molten iron measurement. therefore, the accuracy of rail weighbridges weighing data is extremely important. However, due to process intricacies and equipment nuances, weighbridge weight data often exhibits outliers. this problem can potentially hinder data-centric modeling and predictive tasks. To solve this problem, this paper introduces a data cleaning methodology based on an Improved Adaptive Density-Based Spatial Clustering of Applications with Noise (IA-DBSCAN). the proposed data cleaning method is rooted in an improved adaptive DBSCAN approach, which is particularly effective in identifying clusters of arbitrary shapes and can differentiate noise from actual clusters. the application of the IA-DBSCAN algorithm facilitates the identification and cleaning of concentrated outliers. the algorithm dynamically seeks eps and MinPts parameters to achieve optimal clustering outcomes. Constraints have been incorporated into the improved algorithm, enabling it to achieve data cleaning more rapidly and accurately. After data cleaning, based on the characteristics of the selected dataset in this paper, data imputation using the linear regression method can align with the original data characteristics. Experimental results indicate that the proposed method can effectively identify and clean outliers data in the dataset, supplement missing values based on the original data characteristics, and construct a complete and anomaly-free dataset, establish a robust foundation for modeling and prediction.

关键词： Rails Heuristic algorithms Noise Linear regression Clustering algorithms Cleaning Imputation

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Design of a Full-Space Tracking and Measurement System Based on the Light Pen with Deep learning Methods

Design of a Full-Space Tracking and Measurement System Based...

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data driven control and learning systems (ddcls)

作者： Yu Cui Xu Li Jing Na Guanbin Gao Yashan Xing Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Intelligent Control and Application Kunming China Luzhou Laojiao Co.Ltd Luzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Visual measurement, a critical component in industrial production, often faces accuracy challenges due to image distortion at the edges of the field of view, particularly in large spaces. this paper capitalizes on the robust feature-learning capabilities of deep-learning algorithms tailored for industrial robots in factory settings, and introduces a comprehensive tracking and measurement system, grounded in deep learning, specifically designed for light pen industrial robots. the approach begins with an analysis of the industrial robot's workspace, leading to the installation of a binocular vision sensor on a rotating platform. this setup forms the basis for a novel full-space tracking and measurement device for industrial robots, utilizing a light pen. the system integrates a target detection algorithm with a rotation platform control algorithm, ensuring the light pen remains centered in the camera's field of view in real-time. this innovation addresses the limitations of traditional light pens, particularly their restricted measurement range and reduced accuracy. Furthermore, the transformation matrix between the pixel and world coordinate systems is established by analyzing the relationship between the binocular vision sensor and the precision rotating platform. In the process of pinpointing the sphere center coordinates of the light pen probe, the system establishes a mapping relationship between the marker point and the probe's sphere center. It then employs the cross-ratio invariance principle and the least squares method for precise calculations. Finally, the system's accuracy is validated using the actual dimensions of standard gauge blocks and balls as references. Experimental results demonstrate that the geometric measurement error in the proposed tracking and measurement system on these standard objects is less than 0.1mm. these results offer an alternative for full-space tracking and measurement in complex industrial environments.

关键词： Deep learning Visualization Accuracy Coordinate measuring machines Service robots Robot kinematics Vision sensors

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Research on Short Term Load Forecasting of User Side of Combined Cooling Heating and Power System

Research on Short Term Load Forecasting of User Side of Comb...

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data driven control and learning systems (ddcls)

作者： Yun-Peng Guo Jian-Guo Wang Xiang-Zhi Li Si-Shuo Fan Jia-Lin Kang He-Lin Chen Shanghai Key Lab of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai University Shanghai China Department of Chemical and Materials Engineering National Yunlin University of Science and Technology Yunlin Taiwan ROC Ironmaking Plant Baoshan Iron & Steel Co. Ltd. Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Combined Cooling, Heating and Power (CCHP) system is being compared to traditional centralized energy supply systems, which are increasingly being considered as the key energy supply mode of the future due to their advantages such as adaptability, high energy efficiency and low environmental impact. the exact load prediction is the key factor to ensure the stable and efficient operation of the combined cooling heating and power system. the load prediction on different time scales is helpful to effectively plan the operation strategy, which has important practical significance. this paper mainly studies the short-term load forecasting of the user side of the combined cooling heating and power system. the causal analysis method is used to verify the causal relationship between different variables and load, and the corresponding variables are selected as the characteristic input of the prediction model. Simplifying Recurrent Units (SRU) are used for short-term load forecasting. Aiming at the low accuracy of SRU algorithm compared with the traditional prediction algorithm, the periodic factor is used as the characteristic variable to introduce the periodicity of load data. Experiments show that SRU short-term load forecasting model has the best comprehensive evaluation in terms of running time and forecasting accuracy compared with other forecasting algorithms.

关键词： Training Analytical models Accuracy Load forecasting Predictive models Prediction algorithms data models

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Anti-Windup Active Power control for Wind Turbines Considering Loads Reduction

Anti-Windup Active Power Control for Wind Turbines Consideri...

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data driven control and learning systems (ddcls)

作者： Guozhong Wang Hao Luo Xuguo Jiao Zhibo Yang Xinxin Zhao School of Information and Control Engineering Qingdao University of Technology Qingdao P. R. China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the proportion of wind power in electricity contribution is increasing, and wind power generation technology has developed rapidly. How to enhance the stability of wind turbine systems and extend their service life are still challenging problems due to uncertain and intermittent characteristics of natural wind. In order to ensure that variable speed wind turbines track the active power instructions released by the power system, an improved anti-windup rotor speed control strategy is designed to switch different control strategies according to the wind turbine rotor speed value, with the aim of optimizing the mechanical loads of pitch systems. Firstly, in the low wind speed region, the maximum wind energy capture is achieved by optimal torque control. When the rotor speed increases to a critic value, torque control determined by the turbine's rated output power and rotor speed is adopted. In the high wind speed region, the pitch controller is used to adjust the wind energy capture coefficient to maintain the rotor speed at its rated value. Moreover, in order to solve the problem of integral saturation in traditional rotor speed control methods, this paper proposes a bounded constraints assisted Proportional-Integral (PI) pitch angle control scheme to optimize the operation of pitch system. this control method can effectively eliminates integral saturation, mitigate fatigue loads of wind turbines, reduces overshoot of rotor speed and ensures safe and stable operation of wind turbines. Finally, the proposed method is simulated on OpenFAST platform to verify its advancement.

关键词： Wind energy Wind speed Heuristic algorithms Velocity control Torque control Rotors Wind power generation

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