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arXiv 2024年

作者： Chen, Jiayi Deng, Chunhua School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

With the advancement of video analysis technology, the multi-object tracking (MOT) problem in complex scenes involving pedestrians is gaining increasing importance. This challenge primarily involves two key tasks: pedestrian detection and re-identification. While significant progress has been achieved in pedestrian detection tasks in recent years, enhancing the effectiveness of re-identification tasks remains a persistent challenge. This difficulty arises from the large total number of pedestrian samples in multi-object tracking datasets and the scarcity of individual instance samples. Motivated by recent rapid advancements in meta-learning techniques, we introduce MAML MOT, a meta-learning-based training approach for multi-object tracking. This approach leverages the rapid learning capability of meta-learning to tackle the issue of sample scarcity in pedestrian re-identification tasks, aiming to improve the model’s generalization performance and robustness. Experimental results demonstrate that the proposed method achieves high accuracy on mainstream datasets in the MOT Challenge. This offers new perspectives and solutions for research in the field of pedestrian multi-object tracking. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Model predictive control of event triggering in silicon single crystal growth processes

Model predictive control of event triggering in silicon sing...

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Chinese Automation Congress (CAC)

作者： Yankai Li Biao Ren Ding Liu Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

In this paper, the control issue of the semiconductor silicon monocrystalline growth process is studied through an event triggered model prediction approach. Firstly, a state-space model of silicon monocrystalline growth process is developed to describe the energy conversion and the dynamics and the geometry of the isodiametric stage silicon monocrystalline via the Czochralski method. Secondly, a small perturbation linearization is used to obtain a linear continuous model, which is translated into a linear discrete model under zero-order retainers. Finally, an event triggered model prediction controller is established to decrease the calculation of model prediction in the rolling optimization process. Based on experimental results, the event-triggered model prediction control algorithm is confirmed to be an effectively control scheme for the silicon monocrystalline growth process.

关键词： Crystal growth Semiconductor device modeling Computational modeling Photonic crystals Predictive models Prediction algorithms Silicon

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MBAPIS: Multi-Level Behavior Analysis Guided Program Interval Selection for Microarchitecture Studies 23

MBAPIS: Multi-Level Behavior Analysis Guided Program Interva...

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Proceedings of the 32nd International Conference on Parallel Architectures and Compilation Techniques

作者： Hongwei Cui Yujie Cui Honglan Zhan Shuhao Liang Xianhua Liu Chun Yang Xu Cheng Engineering Reserach Center of Microprocessor & System Ministry of Education School of Computer Science Peking University Beijing National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing

ISBN: (纸本)9798350342543

Understanding program behavior is crucial in computer architecture research, but the growing size of benchmarks makes analyzing and simulating entire programs increasingly challenging. In practice, researchers often select representative program intervals for analysis and testing. These intervals are different sections of continuous execution of a program. SimPoint is a well-known method for selecting representative intervals using hardware-independent information. However, when focusing on a specific microarchitecture study, it is desirable to select intervals that are more relevant to that study. For instance, intervals with more branch mispredictions are more appropriate for branch prediction studies. We refer to these intervals as "tailored intervals" for branch prediction *** paper presents a Multi-level Behavior Analysis guided Program Interval Selection (MBAPIS) for selecting tailored intervals. For a given microarchitecture study, the first level of MBAPIS uses hardware performance counters to prioritize selecting the intervals that exhibit clearer microarchitectural characteristics relevant to that study. The second level analyzes the processor performance bottlenecks to further select the intervals where the concerned microarchitecture design more strongly impacts performance. Finally, MBAPIS performs clustering analysis with the basic block information of each interval selected by the first two levels, and selects the representative intervals among them while preserving the diverse software behavior. Additionally, we present a general and extensible interval-replaying design to accurately re-execute selected *** SPEC CPU2006 and CPU2017 benchmarks are used for evaluation. The results demonstrate that MBAPIS can select representative and tailored intervals for two typical microarchitecture studies and deliver accurate estimates of the concerned hardware events for all tailored intervals in each benchmark, with an average error rate of le

关键词： Benchmark

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Determination of potassium sorbate and sorbic acid in agricultural products using THz time-domain spectroscopy

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Chinese Physics B 2020年第9期29卷 122-128页

作者： Yuying Jiang Guangming Li Ming Lv Hongyi Ge Yuan Zhang Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou 450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China

t The aim of this study was to investigate the feasibility of detecting potassium sorbate(PS)and sorbic acid(SA)in agricultural products using THz time-domain spectroscopy(THz-TDS).The absorption spectra of PS and SA were measured from 0.2 to 1.6 THz at room *** main characteristic absorption peaks of PS and SA in polyethylene and powdered agricultural products with different weight ratios were detected and *** partial least squares(iPLS)combined with a particle swarm optimization and support vector classification(PSO-SVC)algorithm was proposed in this *** was used for frequency optimization,and the PSO-SVC algorithm was used for spectrum analysis of the preservative content based on the optimal spectrum *** PSO-SVC models were obtained when the THz spectrum from the PS/SA mixture was divided into 11 or 12 *** optimal penalty parameter c and kernel parameter g were found to be 1.284 and 0.863 for PS(0.551-1.487 THz),1.374 and 0.906 for SA(0.454-1.216 THz),*** preliminary results indicate that THz-TDS can be an effective nondestructive analytical tool used for the quantitative detection of additives in agricultural products.

关键词： THz-TDS preservative content quantitative analysis iPLS PSO-SVC nondestructive detection

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Exact Noise-Robust Distributed Gradient-Tracking Algorithm for Constraint-Coupled Resource Allocation Problems

Exact Noise-Robust Distributed Gradient-Tracking Algorithm f...

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IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

The Industrial Internet of Things (1IoT) gradually becomes a new paradigm for information exchange in the industrial production environment. To ensure the high reliability of IloT services, an efficient resource allocation method with good robustness is urgently needed under complex industrial environments. This paper considers the distributed constraint- coupled resource allocation problem with noisy information exchange over an undirected network, where each agent holds a private cost function and obtains the solution via only local communications. Communication noise poses a challenge to gradient-tracking based algorithm as the impact of noise will accumulate and its variance tends to infinity when the noise is persistent. Adopting noise-tracing scheme, we propose an exact noise-robust distributed gradient-tracking algorithm to achieve cost-optimal distribution of resources, which can avoid noise-accumulation in the tracking step. Moreover, noise suppression parameters are introduced to further attenuate the impact of noise. With diminishing suppression parameters, it is theoretically proved that the proposed algorithm is able to achieve exact convergence to the optimal solution. Finally, a numerical example is provided for verification.

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Lightweight Monocular 3D Object Detection Based on Attention Mechanism

Lightweight Monocular 3D Object Detection Based on Attention...

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Image processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Feng Zhan Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

In the field of autonomous driving, 3D target detection is an important technology. In view of the shortcomings of existing monocular 3D detection algorithms in terms of accuracy and real-time performance, we propose a lightweight 3D target detection algorithm based on the attention mechanism. In order to ensure the real-time performance of the model, we use depthwise separable convolutions in the backbone network. The feature maps of each scale are then connected to an EMA module, which can fuse spatial attention and cross-dimensional Coordinate attention is used to enhance the feature extraction capability of the network, and BiFPN is subsequently used for multi-scale feature fusion. Finally, experiments were conducted on the KITTI dataset, and the results showed that our algorithm was superior to other methods in both accuracy and speed.

关键词： Three-dimensional displays Attention mechanisms Accuracy Head Fuses Object detection Feature extraction Real-time systems Data mining Visual perception

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Capture the Flag Based Assignment Algorithm for Tracking Task of Multi-UAVs 5

Capture the Flag Based Assignment Algorithm for Tracking Tas...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Dalei, Song Aoliang, Dai Xiaoqian, Wang Hongjun, Ma Donghui, Wei Shenyang University of Technology Shagnhai China School of Information Northeastern University Shagnhai China Beijing Research Institute of Mechanical Electrical Technology Key Laboratory of Complex System Control and Intelligent Collaboration Technology Beijing China

ISBN: (纸本)9781665487085

In order to solve the problem of real-time target allocation in the multi-UAVs task for cooperative tracking the moving targets on ground, based on the behavior model of capture the flag, this paper proposes a dynamic assignment algorithm, which has fast convergence performance, for cluster UAVs. Based on satisfaction and total flight distance, the proposed algorithm constructs the assignment target function, and the optimal value of the target function is achieved quickly and the uniform saturation tracking is realized for the target group through the behavior model of grabbing flags. Finally, compared with traditional genetic algorithm, the simulation results show that the algorithm has a significant improvement in convergence speed and single assignment imply operation speed with the increase of the number of assigned targets and cluster UAVs © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Self-organized Criticality in C. elegans Neural Network

Self-organized Criticality in C. elegans Neural Network

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第42届中国控制会议

作者： Xiao Gu Jian Liu Ye Yuan Peng Zhao Kuankuan Xin Tao Fang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Engineering Research Centre of Intelligent Control and Management Institute of Machine Intelligence University of Shanghai for Science and Technology Queensland Brain Institute The University of Queensland

The self-organized criticality is a fundamental property for organisms to perform normal functional *** large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments.

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Prediction of Drug-Target Interactions Using Molecular Graph and GDNet-DTI Model

Prediction of Drug-Target Interactions Using Molecular Graph...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xu, Shuai Lin, Xiaoli Yu, Haiping Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Inf Ormation Processing and Realtime Industrial System School of Computer Science Huanggang Normal University Wuhan China

ISBN: (纸本)9781665401265

The prediction of drug-target interactions (DTIs) is of great significance to the fields of drug design and drug development. However, traditional biological experiments are time-consuming and cost-effective, which has prompted more people to turn their attention to the use of computers to assist in predicting DTIs. This paper proposes an improved prediction model based on multiple graph representation methods, which is GDNet-DTI that combined GCN and DeepWalk. First, a molecular map with atoms as nodes and chemical bonds as edges is generated using the SMILE sequence of drugs, and then GIN is used to extract the features of molecular map for better obtaining the complex interactions between atoms. For target proteins, the protein sequence is first represented by a word vector, and then the one-dimensional convolution is used to extract features for extracting the different levels of features. Then, based on obtained drug features and target features, a DTI-graph is generated, in which drugs and targets are represented as nodes and interactions are represented as edges. Finally, GDNet-DTI are used to obtain node neighborhood information and graph topology information of the DTI-graph. Compared with other advanced models, the results show that GDNet-DTI combined with multiple graph features can predict DTIs more accurately and effectively with DrugBank and four benchmark datasets. In addition, a case study with COVID-19 data is presented, which shows that the proposed method has the potential to predict the actual DTIs and can contribute to the development of drug discovery. © 2021 IEEE.

关键词： Forecasting

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Direction-Constrained control for Efficient Physical Human-Robot Interaction under Hierarchical Tasks

arXiv

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arXiv 2024年

作者： Xu, Mengxin Wan, Weiwei Wang, Hesheng Harada, Kensuke Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of System Innovation Graduate School of Engineering Science Osaka University Toyonaka Osaka560-0043 Japan Department of Automation the Key Laboratory of System Control and Information Processing of Ministry of Education the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

This paper proposes a control method to address the physical Human-Robot Interaction (pHRI) challenge in the context of hierarchical tasks. A common approach to managing hierarchical tasks is Hierarchical Quadratic Programming (HQP), which, however, cannot be directly applied to human interaction due to its allowance of arbitrary velocity direction adjustments. To resolve this limitation, we introduce the concept of directional constraints and develop a direction-constrained optimization algorithm to handle the nonlinearities induced by these constraints. The algorithm solves two sub-problems, minimizing the error and minimizing the deviation angle, in parallel, and combines the results of the two sub-problems to produce a final optimal outcome. The mutual influence between these two sub-problems is analyzed to determine the best parameter for combination. Additionally, the velocity objective in our control framework is computed using a variable admittance controller. Traditional admittance control does not account for constraints. To address this issue, we propose a variable admittance control method to adjust control objectives dynamically. The method helps reduce the deviation between robot velocity and human intention at the constraint boundaries, thereby enhancing interaction efficiency. We evaluate the proposed method in scenarios where a human operator physically interacts with a 7-degree-of-freedom robotic arm. The results highlight the importance of incorporating directional constraints in pHRI for hierarchical tasks. Compared to existing methods, our approach generates smoother robotic trajectories during interaction while avoiding interaction delays at the constraint boundaries. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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