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Stochastic Gradient-based Fast Distributed Multi-Energy Management for an Industrial Park with Temporally-Coupled Constraints

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arXiv

arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Ma, Chengbin Wang, Zhaojian Zhu, Shanying Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid-ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically. © 2022, CC BY-NC-ND.

关键词： Energy management

Ultrasensitive Ammonia Gas Sensor Based on Ti3c2tx/Ti3alc2 Planar Composite at Room Temperature

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SSRN 2022年

作者： Liu, Zhihua Han, Dan Liu, Lulu Li, Donghui Han, Xiaomei Chen, Yi Liu, Xiaoru Zhuo, Kai Cheng, Yongqiang Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Two-dimensional (2D) transition metal carbides (MXene) have attracted great attention for device applications due to their unique structures and excellent properties, such as high electrical conductivity and abundant functional groups on the surface. However, gas sensors based on Ti3C2Tx MXene have the problems of high detection limit and poor long-term stability. Herein, we report a simple method to prepare Ti3C2Tx/Ti3AlC2 planar composite gas sensing materials using high molar ratio of HCl and LiF with Ti3AlC2 as a framework. The Ti3C2Tx/Ti3AlC2 planar composite gas sensor has a high sensitivity, good selectivity and low detection limit for NH3, which can detect 50 ppb (1.2%) at room temperature (25°C). Furthermore, the gas sensor has good repeatability and long-term stability. After 120 days, the response of the gas sensor to 500 ppb of NH3 fluctuated little due to the Ti3AlC2 as a framework. The superior performances can be attributed to the special heterojunction between Ti3C2Tx and Ti3AlC2 and the high proportion of -O and -OH functional groups on the surface of Ti3C2Tx/Ti3AlC2 composite. This study provides an important reference for the realization of high performance NH3 sensing by Ti3C2Tx. © 2022, The Authors. All rights reserved.

关键词： Chemical detection

Bearing Fault Diagnosis Based on Dual-Data Input Convolutional Neural Network

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SSRN 2022年

作者： He, Chaoqian Hao, Runfang Yang, Kun Yuan, Zhongyun Shang, Shengbo Wang, Xiaorui Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Previous bearing fault diagnosis models show low accuracy and unstable performance in strong noise environment, which are not suitable for the actual complex industrial scenarios. In this paper, the dual-channel convolution model (DDCNN) based on dual-data inputs is established. The model used the reconstructed two-dimensional data as inputs. In one channel, convolution with small kernel is used to extract details of FE data, and large kernel obtain global information of DE data in the other. The output is classified through the full connection layer after the data features of both channels are fused. Besides, data are mixed from different loads to avoid cross-load effects as much as possible. The results demonstrate that the proposed model has good accuracy and anti-noise performance. The accuracy is as high as 92.65% when the SNR is -10. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

Ultrasensitive Ammonia Gas Sensor Based on Ti3c2tx/Ti3alc2 Planar Composite at Room Temperature

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SSRN 2022年

作者： Liu, Zhihua Han, Dan Liu, Lulu Li, Donghui Han, Xiaomei Chen, Yi Liu, Xiaoru Zhuo, Kai Cheng, Yongqiang Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System the Ministry of Education Taiyuan University of Technology Taiyuan030024 China

Two-dimensional (2D) transition metal carbides (MXene) have attracted great attention for device applications due to their unique structures and excellent properties, such as high electrical conductivity and abundant functional groups on the surface. However, gas sensors based on Ti3C2Tx MXene have the problems of high detection limit and poor long-term stability. Herein, we report a simple method to prepare Ti3C2Tx/Ti3AlC2 planar composite gas sensing materials using high molar ratio of HCl and LiF with Ti3AlC2 as a framework. The Ti3C2Tx/Ti3AlC2 planar composite gas sensor has a high sensitivity, good selectivity and low detection limit for NH3, which can detect 50 ppb (1.2%) at room temperature ((25 ± 2) °C). Furthermore, the gas sensor has good repeatability and long-term stability. After 120 days, the response of the gas sensor to 500 ppb of NH3 fluctuated little due to the Ti3AlC2 as a framework. The superior performances can be attributed to the special heterojunction between Ti3C2Tx and Ti3AlC2 and the high proportion of -O and -OH functional groups on the surface of Ti3C2Tx/Ti3AlC2 composite. This study provides an important reference for the realization of high performance NH3 sensing by Ti3C2Tx. © 2022, The Authors. All rights reserved.

关键词： Gases

SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation

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SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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Learning Hierarchical Color Guidance for Depth Map Super-Resolution

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arXiv

arXiv 2024年

作者： Cong, Runmin Sheng, Ronghui Wu, Hao Guo, Yulan Wei, Yunchao Zuo, Wangmeng Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong Jinan250061 China Artificial Intelligence Institute Beijing Normal University Beijing100875 China Changsha410073 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Lingnan University Hong Kong

Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively. The code and results can be found from the link of https://***/HCGNet_TIM2024. © 2024, CC BY-NC-SA.

关键词： Restoration

Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

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arXiv

arXiv 2023年

作者： Li, Shilei Shi, Dawei Lou, Yunjiang Zou, Wulin Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Xeno Dynamics Control Department Xeno Dynamics Co. Ltd Shenzhen518055 China

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters

Motion-Guided Spatial Perception for Enhanced Multi-Object Tracking: A Hierarchical Framework

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SSRN 2023年

作者： Zheng, Yujin Qi, Hang Li, Lei Li, Shan Huang, Yan He, Chu Wang, Dingwen School of Computer Science Wuhan University Hubei Wuhan430000 China The Beijing Electro-Mechanical Engineering Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Zmvision Technology Hubei Wuhan430000 China School of Electronic Information Wuhan University Hubei Wuhan430000 China

Multi-object tracking (MOT) aims to associate objects of interest across sequences, which has rapidly progressed with the development of object detection and optimal matching techniques. However, spatio-temporal modeling complex variations, such as camera motion and occlusion in crowded scenes, have been relatively underexplored in recent years. In this work, a temporal concern through motion-guided feature aggregation module is proposed to capture multi-frame variations for instances. Additionally,an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion through spatial perception. The above approach, namely MSPNet addresses the challenges posed by occlusion effectively, resulting in improved tracking performance. Evaluation on benchmark datasets shows that MSPNet achieves the state-of-the-art performance. Specifically, MSPNet achieves 75.4% MOTA, and 72.4% IDF1 on the test set of MOT16, 74.6% MOTA, and 71.9% IDF1 on MOT17, 75.9% MOTA, and 74.4% IDF1 on MOT20, respectively. © 2023, The Authors. All rights reserved.

关键词： Object detection

Spirometer and sEMG respiratory patterns for clinical decision support system

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Spirometer and sEMG respiratory patterns for clinical decisi...

IEEE Instrumentation and Measurement Technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

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