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IEEE Transactions on Intelligent Vehicles 2023年第9期8卷 4223-4236页

作者： Westny, Theodor Oskarsson, Joel Olofsson, Bjorn Frisk, Erik Linköping University Department of Computer and Information Science Linköping581 83 Sweden Lund University Department of Automatic Control Lund221 00 Sweden Linköping University Department of Electrical Engineering Linköping581 83 Sweden

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics. © 2016 IEEE.

关键词： Trajectories

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High-performance and Multi-functional control of Transformerless Single-phase Smart Inverter for Grid-connected PV System

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Journal of Modern Power Systems and Clean Energy 2021年第6期9卷 1386-1394页

作者： Kamran Zeb Saif Ul Islam Waqar Uddin Imran Khan Muhammad Ishfaq Tiago Davi Curi Busarello H.J.Kim Department of Electrical Engineering Pusan National University School of Electrical Engineering and Computer Science National University of Sciences and Technology Department of Control Automation and Computing EngineeringFederal University of Santa Catarina Blumenau

Highly reliable and flexible control is required for distributed generation(DG) to efficiently connect to the *** inverters play a key role in the control and integration of DG into the power grid and provide advanced functionalities. In this paper, an energy-based single-phase voltage-source smart inverter(SPV-SSI) of 5 k VA is designed and analyzed in detail. SPV-SSI is capable of supplying the power to local load and the utility load up to the rated capacity of the inverter, injecting the power into the grid, storing the energy in lead-acid battery bank, controlling the voltage at the point of common coupling(PCC) during voltage sags or faults, and making decisions on real-time pricing information obtained from the utility grid through advanced metering. The complete design of smart inverter in dq frame, bi-directional DC-DC buck-boost converter, IEEE standard 1547 based islanding and recloser, and static synchronous compensator(STATCOM) functionalities is presented in this paper. Moreover, adaptive controllers, i. e., fuzzy proportional-integral(F-PI) controller and fuzzy-sliding mode controller(F-SMC) are designed. The performances of F-PI controller and F-SMC are superior, stable, and robust compared with those of conventionally tuned PI controllers for voltage control loop(islanded mode) and current control loop(grid-connected mode).

关键词： Distributed generation(DG) inverter convert-er fuzzy-sliding mode controller(F-SMC) fuzzy proportionalintegral(F-PI)controller

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Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective 41

Rethinking Guidance Information to Utilize Unlabeled Samples...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yulong Yao, Yuan Chen, Shuhao Jin, Pengrong Zhang, Yu Jin, Jian Lu, Jiangang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples.A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their ***, EntMin emphasizes prediction discriminability while neglecting prediction *** alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled *** analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label *** by this finding, we propose a Label-Encoding Risk Minimization (LERM).It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label *** a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a ***, we analyze the relationships between LERM and ERM as well as ***, we verify the superiority of the LERM under several label insufficient *** codes are available at https://***/zhangyl660/LERM. Copyright 2024 by the author(s)

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A Novel LSTM-1DCNN-based Deep Network for Fault Diagnosis in Chemical Process

A Novel LSTM-1DCNN-based Deep Network for Fault Diagnosis in...

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2022 Chinese Automation Congress, CAC 2022

作者： Ding, Shan Zhu, Li Xu, Xirong Dalian University of Technology School of Control Science and Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9781665465335

Fault diagnosis in chemical process can provide equipment maintenance personnel with a basis for decision making and ensure the safe operation of production processes. Deep learning has shown unique strengths and promising for fault diagnosis in chemical process. In practical production, the occurrence of a fault can be uniquely determined by its temporal and spatial information. However, most deep learning models based on a single network are difficult to guarantee the accuracy meet the requirements due to the limited capabilities of feature extraction caused by the single network structure. To deal with these problems, a novel deep network model combining with long short-term memory network (LSTM) and one-dimensional convolutional neural network (1D-CNN) is proposed in this paper to improve fault diagnosis performance for chemical process, named DLCM in short, which uses LSTM layers to obtain the temporal features and 1D-CNN layers to extract the spatial features sequentially. Finally, the fault identification outputs are acquired by feature fusion through the fully connected layers. A benchmark chemical process is used to validate the effectiveness of the proposed DLCM. The results show that the DLCM achieved 99.23% classification accuracy, and the diagnosis results are better than other traditional methods based on the single network and combined network. © 2022 IEEE.

关键词： Failure analysis

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Uncertainty-Aware Path Planning with Multiple Constraints for Autonomous Vehicles Based on Nonlinear Reduced-Order Model Predictive control

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IEEE Transactions on Consumer Electronics 2024年

作者： Badiei, Mohammad Hossein Dashtaki, Saeed Mohammadi Vafamand, Navid Piran, Md. Jalil University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran Shiraz University Department of Power and Control Engineering Shiraz Iran Sejong University Department of Computer Science and Engineering Korea Republic of

In this paper, we present a Risk-Aware Nonlinear Reduced-Order Model Predictive control framework that utilizes real-time state estimation, reduced-order modeling, and optimization for reliable and efficient path planning with low energy consumption. Our method employs filtering techniques to estimate the system's state by correcting for process and measurement noise, providing an accurate representation of the vehicle's position and environment. A reduced-order model predicts future states based on the estimated input, while the optimizer adjusts control inputs to meet trajectory and safety constraints, such as avoiding obstacles. By incorporating adapted filtering techniques and optimized cost functions, our approach minimizes computational overhead while maintaining precise control over vehicle navigation. In the experiments, we introduce communication and process noises, allowing the robot to operate without knowing the precise location of obstacles and resulting in potential near-collisions. This involves redefining the constrained optimal control problem for a reduced-order model, addressing input constraints amidst uncertainties. Our method achieves over 94% success in collision-free navigation within the first 50 steps, while maintaining precise trajectory alignment and adapting to environmental changes. It also improves prediction speed up to 15% per cycle compared to NMPC and demonstrates 91% overall effectiveness in avoiding collisions and reaching the target. © 1975-2011 IEEE.

关键词： Cost functions

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Interference elimination in IRS-enabled indoor mmw-D2D communication 24

Interference elimination in IRS-enabled indoor mmw-D2D commu...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Han, Bingjie Chen, Xin Jiao, Libo Guo, Dongchao School of Control Engineering Beijing Information Science and Technology University Beijing China School of Computer Science Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350319934

In this paper, we study the interference elimination in intelligent reflecting surface (IRS) enabled indoor millimeter-wave device to device (mmw-D2D) communication. In order to improve spectrum utilization and transmission rate, we consider assisting IRS by the anti-phase signal reflection to reduce the co-channel interference in indoor mmw-D2D communication. We establish the system model of the IRS selection and enablement on interference elimatation (IRS-SEIE) problem based on integer programming theory. Then, we prove that IRS-SEIE problem is an NP-hard problem by transforming it into a knapsack problem. Therefore, we propose a heuristic algorithm based on genetic algorithm to obtain the optimal solution. Comparison experiments with particle swarm optimization (PSO), monte carlo (MC) algorithm and without IRS show that proposed algorithm's rate is 24.36%, 48.47% and 103.03% higher than them. And it can also reduce the blocking probability by 25.53%, 45.81% and 62.34%. © 2022 IEEE.

关键词： Millimeter waves

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Identification of coexistence of biological and non-biological aerosol particles with DAPI (4′,6-diamidino-2-phenylindole) stain

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Particuology 2023年第1期72卷 49-57页

作者： Ting Liu Jiaquan Zhang Junji Cao Han Zheng Changlin Zhan Hongxia Liu Lili Zhang Kai Xiao Shan Liu Dong Xiang Daizhou Zhang School of Environmental Science and Engineering Hubei Polytechnic UniversityHubei Key Laboratory of Mine Environmental Pollution Control and RemediationHuangshi 435003China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430063China Faculty of Environmental and Symbiotic Sciences Prefectural University of KumamotoKumamoto 862-8502Japan

The fluorescent dye 4′,6-diamidino-2-phenylindole (DAPI) has been widely used to stain microorganisms in various environment media. We applied DAPI fluorescence enumeration to airborne microorganisms and found that non-biological particles, including organic compounds, minerals, and soot, were also visible upon exposure to UV excitation under fluorescence microscope. Using laboratory-prepared biological particles as the control, we investigated the feasibility of identifying both biological and non-biological particles in the same sample with DAPI staining. We prepared biological (bacterial, fungi, and plant detritus) and non-biological (biochar, soot, mineral, metal, fly ash, salt) particles in the laboratory and enumerated the particles and their mixture with DAPI. We found that mineral particles were transparent, and biochar, soot, metals and fly ash particles were black under a filter set at excitation 350/50 nm and emission 460/50 nm bandpass (DAPI-BP), while biological particles were blue, as expected. Particles of the water-soluble salts NaCl and (NH_(4))_(2)SO_(4) were yellow under a filter set at excitation 340–380 nm and emission 425 nm long pass (DAPI-LP). Case studies with samples of dustfall, atmospheric aerosols and surface soils could allow for the quantification of the relative number of different types of particles and particles with organic matter or salt coating as well. Fluorescence enumeration with DAPI stain is thus able to identify the co-existence of biological and non-biological particles in the air, at least to the extent of those examined in this study.

关键词： 4',6-diamidino-2-phenylindole(DAPI) Biological particles Nonbiological particles Atmospheric aerosols

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Research of Image De-noising Based on Deep Learning 6

Research of Image De-noising Based on Deep Learning

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Li, Kai Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Xi'an Technological University State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an710021 China

ISBN: (纸本)9798350305388

Image denoising is a technology to restore the image by changing the information before and after the image sequence. The restored image will become very clear, and it is used a lot now. Due to the imperfect equipment, when the image is transmitted, it will be affected by noise, thus losing the original details. In order to solve this problem and make the noise image clear and obvious, this paper proposes an image denoising method based on deep learning, and constructs the image denoising network structure based on convolutional neural network. At the same time, the directional propagation algorithm is optimized to speed up the training speed of the model and effectively improve the denoising effect. By comparing the experimental data of the classical algorithm experiment, it is concluded that this study has achieved a better denoising effect. Finally, the key issues of image denoising research are summarized, and the development trend of future research in this field is discussed. © 2023 IEEE.

关键词： Image denoising

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Design and Analysis of Weather Parameters Using MEMS Technology with Suitable Characteristics for Integration into a Single Chip for Environment Monitoring 3

Design and Analysis of Weather Parameters Using MEMS Technol...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Hassan, Abdurrashid S. Juliet, Vimala A. Pius Agbulu, G. Joselin Retna Kumar, G. Raghavendra Reddy, N.V. Gunasekar, S. SRM Institute of Science and Technology Department of Electronics and Control/Instrumentation Engineering Kancheepuram Chennai. Tamil Nadu Kattankulathur India Department of Mechatronic Engineering School of Engineering Sciences and Technology Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Kattankulathur Kancheepuram Tamil Nadu Chennai India RV Institute of Technology and Management Department of Mechanical Engineering Karnataka India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0. It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar. © 2024 IEEE.

关键词： Humidity sensors

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A UAV Object Detection Algorithm Based on Improved YOLOX 5

A UAV Object Detection Algorithm Based on Improved YOLOX

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5th International Conference on Intelligent Robotics and control engineering, IRCE 2022

作者： Wang, Shiyu Xu, Guowei Song, Qingzeng Yang, Zhenhao Xue, Yongjiang College of Control Science and Engineering Tiangong University Tianjin China College of Computer Science and Technology Tiangong University Tianjin China College of Material Science and Engineering Tiangong University Tianjin China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

In aerial photography, UAV will cause small objects and mutual occlusion between objects, bringing enormous challenges to the current one-stage object detection algorithm. In order to solve the above problems, this paper proposes a YOLOX-TBP one-stage object detection algorithm. Based on YOLOX-l, the algorithm adds a prediction head to detect objects of different scales to improve the detection effect of small objects. At the same time, the partial convolution of the backbone network is replaced by Transformer to improve the global information of the model, and a weighted bi-directional feature pyramid network (BiFPN) is introduced to improve the multi-scale feature fusion effect through the method of self-learning weights. The final experiments show that the MAP of the YOLOX-TBP model tested on the VisDrone-DET 2021 dataset reaches 40.27%, which is 0.84% higher than the previous SOTA method (DBNet). © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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