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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Zhang, Hualu Deng, Boya Shi, Jie Northeast Electric Power University Jilin Key Laboratory of Modern Power System Simulation Control and Green Power New Technology Ministry of Education Jilin China Nanjing Nanrui Information Communication Technology Co. Ltd. Nari Group Corporation State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665414395

Accurate forecasting of day-ahead electrical load is a key to optimize the operation of the integrated energy system (IES) and a fundamental function of the cyber-physical system (CPS) of IES. Due to the characteristics of different features, data collected by CPS often have different sampling frequencies, resulting in mining data trends inadequately when making a load forecast. This paper proposes an GRU-based model to combine historical data with different sampling frequencies in forecasting day-ahead building-level load. First, day-level features are clustered by K-means. Hour-levels features are then partitioned according to clustering results and inputted separately into a gated recurrent unit (GRU) based neural network to capture relationships between external features and load fluctuations. The temporal dependencies of minute-level data are captured by the GRU network directly without any external features. Finally, the least square method is used to combine forecasting results at each hour of two models. The simulation results present that the proposed model performs better than a single model when applying to real-world data. © 2021 IEEE.

关键词： Forecasting

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Traffic Flow Estimation using Graph Neural Network with Aggregation of Traffic Features

Traffic Flow Estimation using Graph Neural Network with Aggr...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Adiyana Putri Ferio Brahmana Endra Joelianto Herman Y. Sutarto Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Artificial Intelligence Control and Automation Laboratory Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia

ISBN: (纸本)9781665476881

The increasing vehicle volume every year affects the prediction of the traffic system. The purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions due to connection disruptions or ages. In estimating traffic flow, obtaining and explaining the historical data from the nearest segment sensor is necessary. The graph model can describe the spatial relationship between segments by connecting nodes with edges. Graph Neural Network (GNN) has been used as a learning method to predict the traffic flow in the segment sensor. GNN gets input in the form of spatial data built through a graph model, and the features are traffic density, average vehicle speed, and delay time on the road network. This paper shows that GNN's capability performs better in extracting spatial features and predicting the traffic flow by aggregating the features through connectivity nodes and edges.

关键词： Learning systems Roads Estimation Feature extraction Robot sensing systems Graph neural networks Spatial databases

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Scenario-Based Thermal Management Parametrization Through Deep Reinforcement Learning

Scenario-Based Thermal Management Parametrization Through De...

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International Conference on Intelligent Transportation

作者： Thomas Rudolf Philip Muhl Sören Hohmann Lutz Eckstein department for Software Calibration and Diagnosis Porsche Engineering Group GmbH Weissach Germany department for Thermal Management Systems Dr. Ing. h.c. F. Porsche AG Weissach Germany Institute of Control System (IRS) Karlsruhe Institue of Technology (KIT) Karlsruhe Germany Institute for Automotive Engineering (ika) RWTH Aachen Aachen Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The thermal system of battery electric vehicles demands advanced control. Its thermal management needs to effectively control active components across varying operating conditions. While robust control function parametrization is required, current methodologies show significant drawbacks. They consume considerable time, human effort, and extensive real-world testing. Consequently, there is a need for innovative and intelligent solutions that are capable of autonomously parametrizing embedded controllers. Addressing this issue, our paper introduces a learning-based tuning approach. We propose a methodology that benefits from automated scenario generation for increased robustness across vehicle usage scenarios. Our deep reinforcement learning agent processes the tuning task context and incorporates an image-based interpretation of embedded parameter sets. We demonstrate its applicability to a valve controller parametrization task and verify it in real-world vehicle testing. The results highlight the competitive performance to baseline methods. This novel approach contributes to the shift towards virtual development of thermal management functions, with promising potential of large-scale parameter tuning in the automotive industry.

关键词： Industries Technological innovation system dynamics Thermal management Valves Deep reinforcement learning Robustness Scenario generation Tuning Testing

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Distributed temperature monitoring of a pumped-storage power station rockfill dam using optical fiber sensors 8

Distributed temperature monitoring of a pumped-storage power...

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8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Liang, Ruixin Mei, Ying Yang, Xiaojun Xu, Jinlong Zhang, Junjia Qin, Qianfeng Liaoning Qingyuan Pumped Storage Co. Ltd Qingyuan Manchu Autonomous County 22 Hunhe South Road Qingyuan Town Liaoning Province Fushun113300 China Beijing Institute of Aerospace Control Devices Beijing Engineering Research Center of Optical Fiber Sensing System No.1 Fengying East Road Haidian District Beijing100094 China Hongfeng Control Technology Co. Ltd. of Hubei Sanjiang Aerospace Group 9 Jinshan Road Hubei Province Wuhan430000 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

The pumped-storage power station is affected by external forces and environmental erosion during its long-term service, thus causing serious safety accidents and economic losses. In order to avoid casualties and reduce economic losses, long-term health monitoring of the power station is necessary. Optical fiber sensor is one potential technology for the pumped-storage power station structural health monitoring (SHM) by measuring distributed strain and temperature. In this paper, the dam construction site of the Liaoning Qingyuan Pumped Storage station is installed with the optical fiber sensors to monitor its temperature change during concrete cure process. The distributed temperature at four sides of the panel are recorded and compared. Temperature change due to curing process as well as energy interchange between the top and bottom of the panel is revealed by the optical fiber measurements. It is therefore verified that fiber optical sensor is complement for dam temperature monitoring and is well suited for the identification of energy interchange insides the dam panels. © 2022 SPIE

关键词： Temperature measurement

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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Stall phenomenon and research progress of centrifugal pumps

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Zhendong yu Chongji/Journal of Vibration and Shock 2025年第14期44卷 67-74页

作者： Fu, Li Li, Pengxing Wang, Xipu School of Energy and Power Engineering Lanzhou University of Technology Lanzhou730050 China LEO Group Zhejiang Pump Industry Co. Ltd. Hangzhou310000 China Key Laboratory of Advanced Pumps Valves and Fluid Control System of the Ministry of Education Lanzhou University of Technology Lanzhou730050 China

The centrifugal pump stall not only reduces the pump performance but also easily cause surges, seriously affecting the stability of centrifugal pumps operation. The researches on the centrifugal pump stall were reviewed, and three common types of stalls, namely, the rotational stall, fixed stall, and alternating stall occurring in centrifugal pumps were summarized. The characteristics, manifestations, and impact on the pump performance during each type of stall were elaborated. For the study of the centrifugal pump stall, a combination of experimental and computational fluid dynamics methods is often used. The experimental research mainly adopts particle image velocimetry technology, and the pressure measurement on the shroud in compressors can also be referenced for open centrifugal impellers. Moreover, the analysis methods including the wavelet analysis, dynamic mode decomposition, proper orthogonal decomposition, and cross-power spectrum can be employed to provide a more comprehensive understanding of stall in fluid machinery. The stall mechanism of centrifugal pumps can be attributed to two main factors: the geometric structure of centrifugal pumps and complex operating conditions. In accordance with the flow characteristics of stall, two main measures of compensation and suction methods can be taken to inhibit stall. © 2025 Chinese Vibration Engineering Society. All rights reserved.

关键词： Centrifugal compressors

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

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International Conference on Instrumentation control and Automation (ICA)

作者： Muhammad Farhan Fathurrahman Arne Catteeuw Sidharta Gautama Angel Lopez Aguirre Endra Joelianto Herman Y. Sutarto Department Of Industrial Systems Engineering and Product Design Ghent University Ghent Belgium Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia FlandersMake@UGent–Corelab ISyE Lommel Belgium Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant.

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Scheduling Dimension Reduction of LPV Models - A Deep Neural Network Approach

Scheduling Dimension Reduction of LPV Models - A Deep Neural...

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American control Conference

作者： P.J.W. Koelewijn R. Toth Control System Group Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction. The proposed DNN method and existing SDR methods are compared on a two-link robotic manipulator, both in terms of model accuracy and performance of controllers synthesized with the reduced models. The methods compared include SDR for state-space models using Principal Component Analysis (PCA), Kernel PCA (KPCA) and Auto-encoders (AE). On the robotic manipulator example, the DNN method achieves improved representation of the matrix variations of the original LPV model in terms of the Frobenius norm compared to the current methods. Moreover, when the resulting model is used to accommodate synthesis, improved closed-loop performance is obtained compared to the current methods.

关键词： Principal component analysis Kernel Transmission line matrix methods Trajectory Dimensionality reduction Neural networks Robots

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Reduced switching loss based cell capacitor voltage balancing strategy for MMC 9

Reduced switching loss based cell capacitor voltage balancin...

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9th IEEE International Conference on Power Electronics, Drives and Energy systems, PEDES 2020

作者： Choudhury, Abhijit Shimada, Takae Kanouda, Akihiko Mabuchi, Yuuichi Center for Technology Innovation-Controls Hitachi Ltd. Research and Development Group Drive System Control Research Department Hitachi-shi Ibaraki-ken319-1292 Japan

ISBN: (纸本)9781728156729

Modular multilevel converter (MMC) is considered to be an efficient solution for high voltage and high power applications, like for high voltage DC transmission (HVDC) system. This is due to their inherent modular structure, usage of low voltage power devices, inherent distributed energy storage capability for each cell unit and the absence of DC link capacitor. Due to their above-mentioned advantages they are considered to be a suitable candidate for medium voltage and medium power « 500 kW) application like for energy storage and DC microgrid applications as well. However, MMC has an inherent problem of cell capacitor voltage imbalance. To solve this issue a reduced switching loss-based cell capacitor voltage balancing strategy is proposed. The proposed strategy is based on the conventional sorting algorithm, which helps to reduce the switching losses considerably while maintaining the set capacitor voltage ripple. Detailed analytical analysis and simulation studies are carried out to show the efficacy of the proposed system for a 300 kVA system. Results show a considerable reduction in the total converter losses (40%) compared to the conventional scheme. © 2020 IEEE.

关键词： Cells

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A Service-Environment-Oriented Shape Reconstruction Method of Vehicle-borne Active Phased Array Antenna

A Service-Environment-Oriented Shape Reconstruction Method o...

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2021 CIE International Conference on Radar, Radar 2021

作者： Xu, Pengying Xu, Xiaoxian Wang, Yan Teng, Yucheng Wang, Congsi Ge, Dongming Kang, Mingkui Yuan, Shuai Wang, Xuezi Xidian University Key Laboratory of Electronic Equipment Structure Design Ministry of Education Xi'an China School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Beijing Institute of Spacecraft System Engineering Beijing China No.771 Institute of China Aerospace Science and Technology Corporation Xi'an China China Electronics Technology Group Corporation No.38 Research Institute Hefei China

ISBN: (纸本)9781665498142

Modern electronic equipment has been moving towards large scale and increased complexity. Affected by such factors as complex working environments, the performance of a large number of structures in engineering is degraded or even destroyed. Therefore, structure shape estimation is of great significance. The complexity of the structure, however, makes it impossible for a single deformation evaluation algorithm to evaluate the structure comprehensively and accurately. In this paper, a modal-approximate curve (M-AC) method, using a small amount of strain data, is proposed for block shape estimation of structures. Three typical wind load experiments are carried out on the finite element model (FEM) of the vehicle-borne active phased array antenna, and the effectiveness of the algorithm is verified. Under the wind speed of 28 (m/s), the deformation of antenna front is 104.70 mm and the estimation accuracy of the M-AC method is 87.43%, which is 16.47% higher than that of the modal approach. The results indicate that the estimated displacement is consistent well with the simulation results of the finite element method. © 2021 IEEE.

关键词： active phased array antenna Fiber Bragg grating mode shapes sensor shape estimation

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