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作者：周欣张怡莎 The Research Center for Quality Control of Natural Medicine Guizhou Normal University 550001 Guiyang Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment 550001 GuiyangPeople's Republic of China The Research Center for Quality Control of Natural Medicine Guizhou Normal University 550001 GuiyangPeople's Republic of China

The aim of the paper was to establish an efficient and accurate fingerprint method for the identification and quality control of Poria cocos(Schw.) *** extracts of 43 samples of sclerotium and five samples of tegument were analysed by LC with photodiode array *** common peaks were found and eleven of them were identified by mass *** mutual pattern of 43 sclerotium samples was established and a Similarity Evaluation system and Principal Component Analysis were performed to analyze,differentiate and classify the *** method showed high precision,good repeatability and stability and all the peaks were well separated,so it can be used to assess the quality of *** and to identify possible counterfeiting.

关键词： Column liquid chromatography Mass spectrometry Chromatographic fingerprints Principal component analysis Poria cocos(Schw.) Wolf

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An Efficient Low Delay Task Scheduling Algorithm Based on Ant Colony system in Heterogeneous Environments

An Efficient Low Delay Task Scheduling Algorithm Based on An...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Pei Yang Jing Liu Cen Chen Yucheng Ding Chunhua Deng Zeng Zeng College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Institute for Infocomm Research Singapore Hunan *** Interactive Entertainment Media Co. Ltd. Changsha China

ISBN: (纸本)9781728176505

Low delay is an important issue in a variety of applications on heterogeneous computing platforms. A great number of algorithms including heuristics as well as metaheuristics have been designed to minimize the completion time (i.e., makespan) of applications and achieved good results. However, the demand of high performance never stops. In this paper, we propose a new and efficient Ant-Colony based Low Delay Scheduling (ACLDS) algorithm to minimize the makespan of applications which can form directed acyclic graphs and are executed on a heterogeneous system. Simulated results show that our proposed algorithm is highly efficient in reducing makespan in comparison with state-of-the-art scheduling algorithms. For instance, the reduction ratio can be up to 15.2174% for a Fast Fourier Transform (FFT) benchmark.

关键词： Directed acyclic graph Smoothing methods Scheduling algorithms Fast Fourier transforms High performance computing Heuristic algorithms Scheduling

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A Constructive Semi-global Differential Game Design Method for Multi-agent systems under Input Constraint and Collision Avoidance*

A Constructive Semi-global Differential Game Design Method f...

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Chinese Automation Congress (CAC)

作者： Xiongjun Wu Jialing Zhou Yuanlong Li Chunfang Chen Kai Li National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China School of Automation Nanjing University Science and Technology Nanjing The 802 Institute of Shanghai Academy of Space Flight Shanghai School of Automation Science and Electrical Engineering Beihang University Beijing

ISBN: (纸本)9781665426480

The constructive semi-global differential game concept is introduced into the multi-agent’s cooperative controller designing of typical autonomous unmanned systems subject to controller input constraint as well as the collision avoidance requirements simultaneously. The problem of driving each agent which is assume to subject to input constraint nonlinearity from the initial position to a desired one and at the same time avoiding collisions (referring to both static obstacles and other agents) is modeled and transformed into multi-agent’s collision avoidance problem. In view of the requirement that different agents working together might possibly have individual but conflicting purpose (such as in the multi-missile cooperative detection requiring slowly and continuing actions vs fast formation control scenario, and multi-agents’ fast arriving vs avoiding different kinds of obstacles). The differential game theory provided a mechanism or framework for investigating different kinds of problems in which several players seek to achieve individual purposes or goals, which might be competing rather than cooperation. In view of the input constraints, the problem is reduced as a semi-global differential game. Conditions are established to guarantee input constraints are satisfied, and mainly by using the Hamilton-Jacobi-Isaacs (HJI for short) PDIs and as well as ε-Nash equilibria. The semi-global property of the states are also guaranteed. It provides a description in terms of the invariant set for the overall differential game system under constraint controller input. Illustrative examples are carried out to verify the effectiveness of the method, and also the feasibility of the suggested control protocols.

关键词： Protocols Automation Design methodology Differential games Games control systems Collision avoidance

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Video-Based 3D Human Pose Estimation Research

Video-Based 3D Human Pose Estimation Research

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Siting Tao Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (纸本)9781665409858

At present, most of the video-based 3D human pose estimation methods process the video frame by frame, ignoring the timing information of the upper and lower frames of the video. When there is a single-frame estimation error caused by self-occlusion, the occlusion part will be Some strange movements appear due to information loss, and the estimated results make it difficult to produce accurate motion sequences. Therefore, considering the continuity of the upper and lower frames of the video, this paper adopts the HMR algorithm and introduces a bidirectional long-term memory network (BI-LSTM) in the encoding part to process the video sequence, and then outputs the parameters of the human body model through the SMPL parameters and inputs them into the discriminator for confrontation, train. In order for the discriminator to also take into account the semantic information of the temporal context, the discriminator in this paper also introduces a BI-LSTM network to improve the authenticity of the generated poses. Experimental results show that the accuracy of the model is improved when self-occlusion occurs.

关键词： Training Industrial electronics Solid modeling Estimation error Three-dimensional displays Biological system modeling Pose estimation

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Research on Transforming Effect and Calculation Method of MMC Converter on AC / DC Harmonics

Research on Transforming Effect and Calculation Method of MM...

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Sustainable Power and Energy Conference (iSPEC)

作者： Liu Cui Guangqiang Peng Jingsong He Jiyang Wu Chizu Mao Ning Jiao State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid GuangZhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System GuangZhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid GuangZhou China Maintenance & Test Center EHV Power Transmission Company Guangzhou CSGKEY LABORATORY FOR DC TRANSMISSIONEQUIPMENT STATE EVALUATION AND FAULT DIAGNOSIS GuangZhou China College of Electrical Engineering and Information Technology Sichuan University Chengdu China

ISBN: (纸本)9781665414401

Modular multilevel converter (MMC) is currently the most concerned voltage source converter topology in the field of flexible direct current transmission (VSC-HVDC). Due to its non-linear characteristics, harmonic components will inevitably be generated in AC and DC systems, which may affect the operation of equipment and system, cause protection equipment to malfunction, and cause interference to neighboring communications. The MMC converter is a non-linear device, which can be considered as an amplifying modulator with voltage and current conversion functions. The AC and DC networks are coupled to each other through the MMC converter to generate dynamic harmonic interaction. In recent years, harmonic instability problems have appeared in many VSC-HVDC projects. Compared with other converter topologies, the transfer relationship of harmonics on the AC and DC sides of MMC converters has not been studied in depth. In this paper, by studying the AC/DC harmonic conversion characteristics of MMC converters, it reveals the conversion law of MMC converters between AC/DC harmonics; through theoretical derivation, the quantitative calculation method of MMC harmonic transmission process is given. PSCAD simulation results prove the correctness of the above results.

关键词： Network topology Simulation Bridge circuits Voltage Power system harmonics Harmonic analysis Frequency conversion

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Under-determined Blind Speech Separation via the Convolutive Transfer Function and lp Regularization

Under-determined Blind Speech Separation via the Convolutive...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Liu Yang Junjie Yang Yi Guo School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT (GDUT) Guangzhou China Centre for Research in Mathematics School of Computing Engineering and Mathematics Western Sydney University Parramatta Australia

ISBN: (纸本)9781665406697

Convolutive blind speech separation (CBSS) attempts to recover speech sources from recorded mixtures transmitted by room impulse response filters (RIRs). The existing BSS methods are mainly based on a multiplicative narrowband approximation (MNA) model in the short-time Fourier transform (STFT) domain. However, the MNA model may result in serious system transformation error under the situation of a strongly reverberant environment due to its sensitivity to RIRs with large lengths. Moreover, the solution of source reconstruction is not unique when the system is underdetermined even if the RIRs are given in advance. To tackle these problems, a convolutive narrowband approximation (CNA) model is explored in the proposed approach to constrain the system transformation error. Then an $l p(0\lt p\leq 1)$ regularization is provided to reconstruct the sparse sources in which p can be dynamically selected to achieve the best performance. The experimental results demonstrate the robustness of the proposed method to room reverberation under various speech separation cases in comparison to conventional methods.

关键词： Sensitivity Fourier transforms Transfer functions Reconstruction algorithms Robustness Sensors Reverberation

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Combined Representation Learning for Uncertain Knowledge Graphs

Combined Representation Learning for Uncertain Knowledge Gra...

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Mechatronics, IoT and Industrial Informatics (ICMIII), International Conference on

作者： Rui Qiao Xiaolei Zhong Yang Fu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Dianchi College Kunming China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

Uncertain knowledge graphs store the relations between entities in the real world in the form of weighted triples, where the weights indicate the confidence scores of the triples to be true. However, there is limited research on the inference of uncertain knowledge graphs, especially in the integration of symbolic and numerical methods. Rules are explicit knowledge. Encoding the rules directly can provide more knowledge graph semantic information and can make full use of the interpretability and accuracy of logical rules. Unfortunately, none of the existing methods directly encode rules for uncertain knowledge graphs, but only use rules to calculate the confidence of unseen triples in training data. In this paper, we propose a new reasoning model CRLUKG for uncertain knowledge graphs, that combines the advantages of symbolic and numerical reasoning. We directly encode rules mined from the knowledge graph as one of the inputs of the CRLUKG model. Compositional representation learning with triple and rule embedding is implemented for optimizing objective function for triples and relations pairs. Experimental results demonstrate that our proposed method enhances the accuracy of reasoning, and furthermore, CRLUKG exhibits superior performance in comparison to baseline models when handling multi-task scenarios.

关键词： Representation learning Accuracy Large language models Semantics Training data Knowledge graphs Tail

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Energy Consumption Prediction for Manufacturing in Industrial IoT Based on Heterogeneous GNN

Energy Consumption Prediction for Manufacturing in Industria...

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IEEE International Conference on Communications in China (ICCC)

作者： Xuan Su Mingyan Li Qihao Li Chao Chen Songtao Guo Xu Wang College of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China College of Computer Science Chongqing University Chongqing P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China College of Communication Engineering Jilin University Changchun P. R. China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Energy consumption prediction plays a crucial role in green manufacturing for industry, providing a theoretical foundation for energy-saving planning, workshop scheduling, and process optimization. However, owing to the processing complexity of mechanical products and the multiplicity of production information, previous methods can only achieve prediction for individual devices, which are quite restrictive to collaborative manufacturing planning. To deal with product-level energy management, this paper first proposes a heterogeneous manufacturing association graph (HetMG) which contains various manufacturing elements including product projects, decomposed orders and processing devices. Then, with HetMG, an energy consumption prediction method, HetEP, is introduced based on heterogeneous graph neural network (GNN), which incorporates Relational Graph Convolutional Networks (R-GCNs), and Long Short-Term Memory (LSTM) to mine the spatial-temporal relation of heterogeneous elements during the manufacturing process. Moreover, Graph Attention Network (GAT) is utilized in HetEP to adaptively learn the graph-structured data so that informative representations of each element can be obtained. The experimental results based on a manufacturing dataset from a generator factory demonstrate that the proposed HetEP yields superior prediction performance compared to the device-oriented prediction methods in terms of prediction accuracy.

关键词： Representation learning Performance evaluation Energy consumption Green manufacturing Job shop scheduling Prediction methods Production facilities

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Cancan Wu 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 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall.

关键词： Training Runtime Computational modeling Superresolution Memory management Feature extraction Diffusion models Convolutional neural networks Image reconstruction Standards

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What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs

arXiv

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arXiv 2019年

作者： Huang, Zhongling Pan, Zongxu Lei, Bin School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Institute of Electronics Chinese Academy of Sciences Beijing100190 China

Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated dataset in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in SAR image interpretation. Transfer learning provides an effective way to solve this problem by borrowing the knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pre-trained with a large-scale natural image dataset, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR application because of the prominent discrepancy between SAR and optical images. In this paper, we attempt to discuss three issues that are seldom studied before in detail: (1) what network and source tasks are better to transfer to SAR targets, (2) in which layer are transferred features more generic to SAR targets and (3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multi-source data with domain adaptation is proposed in this paper to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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