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IEEE International Conference on systems, Man and Cybernetics

作者： Qiang Jiao Wei Hu Fang Liu Yong Dong 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 China School of Computer Science Wuhan University Wuhan China Wuhan City College Wuhan China

ISBN: (纸本)9781665442084

Deep learning model Transformer has been widely used in natural language processing(NLP) filed, and its demand for computing resources is also growing. However, general-purpose processors(CPU and GPU) have invested excessive hardware resources in the design because they have to flexibly support a variety of tasks, they are not efficient for the implementation of Transformer. Consequently, various software optimizations that towards general-purpose processors have been proposed one after another. But, under the condition of ensuring sufficient accuracy, the degree of software optimization is limited. It is requisite to friendly-support Transformer at the hardware *** analysing the computational characteristics of the Transformer model, based on RISC-V, we designed a hardware friendly instruction set architecture for the Transformer model. In addition to the basic instruction, for the intensive and general computing part of the model, according to the expansion rules of RISC-V instruction, we design the matrix load/store instruction calculation instruction, softmax instruction, activation instruction and other user-defined instructions. They support any matrix scale, and deploy it on FPGA to realize a flexible and efficient custom processor RISC-VTF for Transformer. The design is integrated on the Xilinx toolkit zynq-7000 FPGA, and the resource consumption and performance are analyzed. Compared with the traditional common ISA(Instruction Set Architecture) such as x86, arm or MIPs, RISC-VTF provides higher code density and performance efficiency.

关键词： Analytical models Computational modeling Instruction sets Computer architecture Transformers Hardware Software

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A survey of facial expression recognition based on deep learning

A survey of facial expression recognition based on deep lear...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Heng Wei Zhi Zhang Collage 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 Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

Facial expression recognition is the key research direction in many fields such as machine vision, pattern recognition and artificial intelligence. lt has become a research hotspot of many scholars and experts. This paper gives a comprehensive overview of deep expression recognition from image preprocessing, common expression recognition networks, and common expression databases. Then we review the current design and training strategies of deep neural networks based on static images and dynamic images, and discuss their advantages and limitations. Finally, the future challenges and corresponding opportunities in the field are analyzed, as well as the future direction of deep learning expression recognition systems.

关键词： Deep learning Training Image recognition Databases Face recognition Neural networks Training data

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A Robust Mpc Approach with controller Tuning for Close Following Operation of Virtually Coupled Train Set

SSRN

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SSRN 2022年

作者： Luo, Xiaolin Tang, Tao Yin, Jiateng Liu, Hongjie School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing100044 China

Virtual coupling, as an emerging concept in railways, expects successive trains in a virtually coupled train set (VCTS) to maintain a short following distance. However, this close following operation is still difficult to be achieved given uncertain resistances and nonlinear safety constraints. To solve this problem, this paper proposes a robust model predictive control (RMPC) approach for the close following operation of VCTS while satisfying a nonlinear safety constraint with relative braking principle. First, we construct a robust positively invariant set that bounds the tracking errors caused by uncertain resistances. Further, a semi-definite program-based controller tuning algorithm is proposed to reduce the following distance in the premise of the tightened constraint for robustly satisfying the nonlinear safety constraint. Then, by mathematically examining the future trajectories of successive trains, we create a terminal constraint set to ensure the recursive feasibility of the proposed RMPC. This closed-loop property guarantees the satisfaction of the safety constraint in any situation, even in the case of sudden deceleration of VCTS. Finally, numerical experiments are conducted to evaluate the following distance with respect to heterogeneous trains and verify the effectiveness of our approach. Experimental results demonstrate that the expected close following operation can be achieved while robustly satisfying the nonlinear safety constraint with uncertain resistances. Moreover, our approach further reduces the following distance in a VCTS by over 5\%, compared with existing research. © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Robust Filtering for Networked systems with Random Transmission Delays and Packet Dropouts

Robust Filtering for Networked Systems with Random Transmiss...

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2011 American control Conference (ACC)

作者： Yunze Cai Hua O. Wang Kazuo Tanaka Xiaoming Xu Department of Mechanical Engineering Boston University Boston MA USA Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China Department of Mechanical Systems and Intelligent Systems University of Electro-Communications Tokyo Japan Department of Automation Shanghai Jiaotong University Shanghai China University of Shanghai for Science and Technology Shanghai China

This paper addresses a robust H_(infinity) filtering problem for networked systems that are subject to both random transmission delays and packet dropouts. To start with, a data transmission model is established by employing random series with Bernoulli distributions. A sufficient condition for robust stability with H_(infinity) constraints is derived for the filtering error system. The robust filter is designed in terms of the feasibility of a linear matrix inequality (LMI). The numerical examples are provided to show the effectiveness of the data transmission model and the proposed filtering method.

关键词： Delay Filtering Robustness Data models Symmetric matrices Hidden Markov models Markov processes

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A new video retrieval approach based on clustering

A new video retrieval approach based on clustering

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhen Lei Liang-Da Wu Song-Yang Lao Gang Wang Chen Wang Multimedia Research and Development Center Key Laboratory of Information System Engineering National University of Defense Technology Changsha Hunan China Centre for Digital Video Processing University of Dublin Dublin Ireland College of Mechatronics Engineering and Automation National University of Defense Technology Changsha Hunan China

ISBN: (纸本)0780384032

The shot based clustering and retrieval is very important for video database organization and access. We can take advantage of semantic clustering to enhance the performance of traditional content-based video retrieval. In this paper, text feature and color feature are first extracted directly from shots. Subsequently, a two-level hierarchical clustering structure that provides indexing scheme for retrieval is constructed by incorporating text and color features. The top level is clustered by text features while the bottom level is clustered by color features. The constructed structure is then used for the cluster-based retrieval. The effectiveness and efficiency of our approach are demonstrated by the experimental results in retrieving and ranking similar video shots.

关键词： Content based retrieval Data mining Videoconference information retrieval Feature extraction information analysis Speech analysis Spatial databases Bridges Automatic speech recognition

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Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation

arXiv

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arXiv 2023年

作者： Li, Ziqiang Wang, Chaoyue Rui, Xue Xue, Chao Leng, Jiaxu Li, Bin Big Data and Decision Lab University of Science and Technology of China China The University of Sydney Australia JD Explore Academic China School of computer science Chongqing University of Posts and Telecommunications China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From Scratch (LFS) methods often generate images that closely resemble the training data while Transfer Learning (TL) methods try to improve performance by leveraging prior knowledge from GANs pre-trained on large-scale datasets. However, current TL methods may not allow for sufficient control over the degree of knowledge preservation from the source model, making them unsuitable for setups where the source and target domains are not closely related. To address this, we propose a novel pipeline called Peer is your Pillar (PIP), which combines a target few-shot dataset with a peer dataset to create a data-unbalanced conditional generation. Our approach includes a class embedding method that separates the class space from the latent space, and we use a direction loss based on pre-trained CLIP to improve image diversity. Experiments on various few-shot datasets demonstrate the advancement of the proposed PIP, especially reduces the training requirements of few-shot image generation. © 2023, CC BY.

关键词： Large dataset

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Robust Remote Sensing Scene Classification with Multi-View Voting and Entropy Ranking

arXiv

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arXiv 2023年

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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Application of Sensors in Flexible Process control of Intelligent Manufacturing

Application of Sensors in Flexible Process Control of Intell...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jianxiong Qiu Zhaoshui He Cheng Yan Hailong Zhou Guangdong University of Technology Guangzhou China School of Automation Guangdong University of Technology Key Laboratory of Information Processing and System Integration of IoT Ministry of Education Guangzhou China Huizhou Metal Cutting Processing Unit Intelligent Manufacturing Engineering Technology Research Center Huizhou China Tianjin Bonuo Intelligent Robot Technology Co. Ltd. Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In order to carry out the intelligent production of typical chemical products for the application of order-based flexible production system, based on intelligent measurement and control technology, a new generation of information technology such as industrial Internet, intelligent management and control, data visualization, and informatization is integrated. In this paper, we study the establishment of an order-based flexible production process with definable configuration according to the intelligent processing mode of process automation. After selecting the sensor reasonably, Configuration of the industrial intelligent detection system, system signal processing and digitalization, Industrial information network construction and debugging, Programming operation and debugging of intelligent measurement and control systems, Production tasks such as visualization and remote monitoring of the production process. The study found,The sensor plays a vital role in the control system control of the DCS -based controller, Liquid level sensors and flow/weight sensors and other key sensors can monitor various parameters in the production process in real time, provide accurate data support for the control system to ensure the accuracy and stability of the ingredient system. And realize the flexible ingredients controlled by program control, and then make the feeding at the same time to improve production efficiency. This research provides a strong technical guarantee for actual production, and provides a feasible implementation method for our intelligent manufacturing process industrial control.

关键词： Temperature measurement Temperature sensors Accuracy Data visualization Process control Production control systems

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Masked Transformer-based Multi-GAN for 5G Core Network KPI Anomaly Detection

Masked Transformer-based Multi-GAN for 5G Core Network KPI A...

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International Conference on Computer Supported Cooperative Work in Design

作者： Enze Zhao Peng Li Zhang Cheng Wenmao Liu Lei Nie Haizhou Bao Qin Liu Kai Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China NSFOCUS Technologies Group Co. Ltd. School of Cyber Science and Engineering Wuhan University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The fifth generation (5G) network is a crucial foundation for the industrial Internet. key performance indicator (KPI) anomaly detection in the 5G core network (5GC) plays a pivotal role in 5G applications. Some researchers have introduced Generative Adversarial Networks (GAN)-based techniques to detect anomalies. However, these methods remain limited, such as pattern collapse. In this paper, we propose MTMG, a Masked Transformer-based Multi-GAN model, to achieve highly accurate and robust anomaly detection. We use Transformer to learn the associations between data better. Specifically, MTMG employs multiple generators and a discriminator to deflect the pattern collapse dilemma. In addition, we introduce the mask mechanism to learn the normal distribution of data better and prevent the model degradation caused by anomalies in the training set. We also adopt a root cause strategy to locate the anomalies. Experimental results demonstrate that our model outperforms the baselines significantly in terms of detection performance.

关键词： 5G mobile communication Computational modeling key performance indicator Transformer cores Gaussian distribution Transformers Generative adversarial networks

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