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IET International Radar Conference 2023, IRC 2023

作者： Jiang, Qi Zhang, Jichuan Ni, Na Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Real-time Information Processing Technology of Embedded Beijing100081 China

ISBN: (纸本)9781839539954

Effective observation of group targets (bird flocks, etc.) has significant importance in aviation safety and ecological research. Closely spaced group targets with swarm intelligence can form huge and complex linear formations. Tracking radar must feature a narrow beam and high bandwidth to discriminate different targets within the formation. However, there are two major problems in formation target tracking. First, the number of targets within the beam may fluctuate with the change of radar-target geometry;second, false measurements may be detected from the distributed Doppler spectra. This paper proposes a cardinalized probability hypothesis density (CPHD) filter for multitarget tracking using the formation priors form a scanning radar. The model of false measurements is optimized based on the experimental data of birds. The densities of new targets and the probability of target death are set according to the formation priors of group structure and radar-target geometry. Finally, the effectiveness of the proposed method is verified by experimental results. © The Institution of Engineering & technology 2023.

关键词： Target tracking

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Generating Molecules Conditional on 3D Protein Pockets with HGAF

Generating Molecules Conditional on 3D Protein Pockets with ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Qinglian Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350337488

Most current representations of protein pockets are the atom-pair graph, which ignore the global structural information of amino acids. Therefore, we propose a new molecular generation model, which uses the hypergraph to represent protein pocket structure, and combines the structural features obtained by atom-pair graph representation. These two levels of graphs are more capable of representing the complex structural information of protein pockets. Then, the graphs of the two levels of the protein pockets are input into the improved network model, which is named Hypergraph Graph Attention Fusion (HGAF), to obtain the embedding representation of the protein pockets, which is used as a condition to constrain the molecule generation. The molecules sampled by HGAF are subjected to quality assessment and docking targeting validation. Experimental results show that the molecules generated by proposed method can achieve better results in both of these assessment approaches. © 2023 IEEE.

关键词： 3D Protein Pockets Drug Design Graph Attention Networks

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SPACEBORNE SAR IMAGING SYSTEM WITH HIGH-PERFORMANCE POLYNOMIAL ENGINES

SPACEBORNE SAR IMAGING SYSTEM WITH HIGH-PERFORMANCE POLYNOMI...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Zhihan Xu, Yinsheng Li, Yongrui Zhang, Ao Xie, Yizhuang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai201109 China

ISBN: (纸本)9781839539954

In recent years, with the development of Synthetic Aperture Radar (SAR) technology, the required resolution and imaging bandwidth for spaceborne SAR have been continuously increasing, posing stringent requirements on real-time imaging systems. To address the issue of uneven computing power allocation in implementing the Chirp Scaling (CS) algorithm on an FPGA+DSP platform, a spaceborne SAR imaging system with high-performance polynomial engines was designed, which enables the mapping of the Taylor series-based Chirp Scaling algorithm onto a hardware platform. The system is implemented using Xilinx's XC7VX690T FPGA and TI's TMS320C6678 DSP. The high-performance polynomial engine is designed with a parallel pipeline structure, supporting computation of polynomial up to seventh order. It offers flexible configuration and high versatility. The spaceborne SAR imaging system with high-performance polynomial engines can achieve SAR imaging in 16K×16K resolution with a time of 15.64 seconds, meeting the requirements of real-time SAR imaging applications in large-scale data scenarios. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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Anti-3CLpro Molecular Design Based on the Model Constrained by Specific DTIs

Anti-3CLpro Molecular Design Based on the Model Constrained ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lin, Xiaoli Liu, Xuan Zhang, Xiaolong School of Computer Science and Technology Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350337488

Computer-aided drug design and artificial intelligence-driven drug design have accelerated drug discovery. However, how to design effective drugs that have strong interaction ability with target proteins to further improve the efficacy of drugs in treating diseases remains a key issue. This paper proposes a new target-specific drug generative model 3CLpro2mol to generate new drug molecules, which uses features of drug-target interactions (DTIs) to constrain the correlation between the drug and the target protein. To obtain as many drug-target interaction features as possible from a small amount of data, a small molecule extraction strategy is proposed to ensure the diversity of small molecules in the training samples. To improve the efficiency and accuracy of the generative model, a TOP K sampling strategy is used to generate tokens, which can improve the rationality and diversity of the generated molecules. The experimental results show that the proposed model has the potential to generate small molecules that interact better with the target protein. © 2023 IEEE.

关键词： COVID-19 DTIs generative model molecular design molecular docking

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Event-Triggered Cooperative Tracking Control of Multiagent Systems with a Dynamic Leader via Approximate Dynamic Programming

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2752-2765页

作者： Fu, Hao He, Haodong Chen, Yang Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System The School of Computer Science and Technology Wuhan430081 China Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

This article investigates the optimal cooperative tracking control problem for unknown nonlinear multiagent systems with a real dynamic leader and uncertainties. Its main difficulty lies in eliminating effect of the unmatched uncertainty and noncooperation in the event-triggered manner. To overcome this difficulty, a distributed robust event-triggered control algorithm is developed with the aid of a disturbance observer and robust approximate dynamic programming (ADP). Specifically, this problem is converted into a cooperative multiplayer game for the distributed auxiliary systems employing observer-based feedforward compensation, which contributes to reducing conservatism;via an adaptive event-triggered mechanism and a new weight tuning law, a distributed robust ADP approach is proposed with hope to obviate judging the existence of the saddle point beforehand. Moreover, the local neighbor tracking error and the weight estimation error are demonstrated to be uniformly ultimately bounded by means of Lyapunov stability theory. We also prove that Zeno behavior is excluded. A comparative simulation example for multiquadrotor systems is carried out to verify the effectiveness and superiority of our algorithm. © 2020 IEEE.

关键词： Dynamic programming

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Code Generation Based on Quality-Aware Reordering 9

Code Generation Based on Quality-Aware Reordering

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9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Song, Yuan Yu, Luo College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan430065 China

ISBN: (纸本)9798350376548

Translation efforts from natural language to code generation have achieved great success with large pre-trained language models. However, selecting the correct code output for the problem posed by the generated set of candidate codes is still challenging. In this paper, we address this problem by proposing an outcome-based quality-aware reordering method for code selection. This method scores each code by whether the set of candidate codes passes the test cases or not as a reference to select the top k best code outputs. In addition, this method depends on the quality and number of test cases. However, it is impractical to manually create a large number of test cases for each problem, so this paper adopts an automated approach that utilizes the current model to generate test cases for each problem. The experimental results show that the approach proposed in this paper obtains a significant improvement in the pass@k benchmark for each model, and the correctness and quality of test cases generated automatically using the model are discussed experimentally. Summarizing the experimental results and discussions, the result-based quality-aware reordering method proposed in this paper achieves a significant improvement in solving the problem of selecting the correct output from natural language to code generation, effectively improves the accuracy of evaluation by automating the generation of test cases, and provides an effective solution to improving the correctness of code generation. © 2024 IEEE.

关键词： code generation natural language processing (NLP) pre-trained large models quality estimation reranking test case generation

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Attention information Fusion Emotion Analysis Model Based on BiLSTM-BERT 4

Attention Information Fusion Emotion Analysis Model Based on...

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4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Cheng, Zhi-Wei Li, Shun-Xin Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Wuhan China

ISBN: (纸本)9798350384062

Aiming at the problem that most sentiment analysis methods do not focus on the weight fusion of local context and global context information and the important information in fusion features is not prominent, AIFM-BB (Attention information fusion emotion analysis model based on BiLSTM-BERT) is proposed. The model uses a pre-trained BERT model to process text vectors and a collaborative speech analysis algorithm to process audio vectors, and introduces BiLSTM and Attention mechanisms. The weight of the feature vector is dynamically adjusted by combining the global information and local information of text and audio and the attention information of text and audio modes. The emotion analysis results are obtained through the Softmax layer after the fusion features are self-focused. The experimental results show that the accuracy of the proposed AIFM-BB model on the CMU-MOSI dataset is 85.3%, which is 2.8 percentage points higher than the baseline. In the multi-category emotion classification task, the accuracy of the model is 44.7%, an improvement of 1.1 percentage points compared with baseline. The accuracy is 83.6% on the CMU-MOSEI dataset, an improvement of 1.4 percentage points compared to baseline. © 2024 IEEE.

关键词： Sentiment analysis

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Common Targets Polarimetric Calibration Technique for Long Blind Range Radar

Common Targets Polarimetric Calibration Technique for Long B...

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IET International Radar Conference 2023, IRC 2023

作者： Tan, Lijia Li, Weidong Wang, Rui Wang, Jiangtao Li, Muyang Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Many research indicates the importance of polarimetric information in radar systems for purposes such as detection, tracking, and parameter estimation. The calibration process is essential for obtaining precise polarimetric information. However, conducting calibration experiments for long blind range radars can be challenging in fields where ground clutter is prominent. For multi-pulse width switching radar, this paper introduces a polarimetric calibration algorithm by measuring common targets of short- and long-range waveforms. Firstly, we switch between various waveforms to measure the strong echo common targets in the overlapping detection range. The scattering matrices of common targets can be obtained by the short-range waveform calibrated by the conventional method. Then, the polarization error model is built based on the measured scattering matrices of long-range waveform, and the true values of the scattering matrices of common targets are used to estimate the polarization errors of long-range waveform. The effectiveness of the algorithm is validated through simulation results. © The Institution of Engineering & technology 2023.

关键词： Polarimeters

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The Optimal Partition Number Estimation Algorithm Based on Variational Inference

The Optimal Partition Number Estimation Algorithm Based on V...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Jichuan Jiang, Qi Xu, Liang Wang, Rui Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China The Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

The partition of individual targets into proper clusters is crucial in tracking group targets with complex spatial structures. However, most of the current clustering methods suffer from heavy computation burdens and unfitting a priori knowledge, resulting in the degradation of robustness. This paper proposes an optimal estimate of partition number based on the variational inference for group targets. By maximizing the variational evidence lower bound, which serves as the model-selected criterion, the optimal partition number can be estimated. The K-means++ algorithm is used to initialize the variation iteration. The proposed algorithm does not require a priori information and has low computation complexity, which benefits from the deterministic approximation character of variational inference. The effectiveness of the proposed method is verified by simulation results. © The Institution of Engineering & technology 2023.

关键词： Consensus algorithm

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MAML MOT: Multiple Object Tracking Based on Meta-Learning

MAML MOT: Multiple Object Tracking Based on Meta-Learning

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

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

ISBN: (纸本)9781665410205

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. © 2024 IEEE.

关键词： Contrastive Learning

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