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A Construction Method of Arbitrary-Length Polar Codes

China Communications 2023年第10期20卷 153-162页

作者： Xiaojun Zhang Chengguan Chen Rongcai Zhang Jianming Cui Qingtian Zeng Shandong University of Science and Technology Qingdao 266590China State Key Laboratory of High-End Server and Storage Technology Jinan 250101China

To remove the restriction on code length of polar codes,this paper proposes a construction scheme,called stepwise polar codes,which can gen-erate arbitrary-length polar *** stepwise polar codes are generated by sub-polar codes with different code *** improve coding performance,sub-polar codes are united by polarization effect priority algorithm,which can reduce the number of in-completely polarized ***,the construction method of the generator matrix of the stepwise po-lar code is ***,we prove that the proposed scheme has lower decoding complexity than punctured,multi-kernel polar *** results show that the proposed method can achieve similar decoding performance compared with the conventional punctured polar codes,rate-compatible punctured polar code,PC-short and asymmetric polar codes(APC)when code length N=48 and 72,respectively.

关键词： code construction polar codes arbitrary-length channel polarization union encoding

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Three-Branch BERT-Based Text Classification Network for Gastroscopy Diagnosis Text

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International Journal of Crowd Science 2024年第1期8卷 56-63页

作者： Wang, Zhichao Zheng, Xiangwei Zhang, Jinsong Zhang, Mingzhe School of Information Science and Engineering Shandong Normal University Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250013 China

During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and scheduling follow-up visits. However, given the free-text nature of GDTs, which lack a formal structure, physicians often find it challenging to extract meaningful insights from them. Furthermore, while deep learning has made significant strides in the medical domain, to our knowledge, there are not any readily available text-based pre-trained models tailored for GDT classification and analysis. To address this gap, we introduce a Bidirectional Encoder Representations from Transformers (BERT) based three-branch classification network tailored for GDTs. We leverage the robust representation capabilities of the BERT pre-trained model to deeply encode the texts. A unique three-branch decoder structure is employed to pinpoint lesion sites and determine cancer stages. Experimental outcomes validate the efficacy of our approach in GDT classification, with a precision of 0.993 and a recall of 0.784 in the early cancer category. In pinpointing cancer lesion sites, the weighted F1 score achieved was 0.849. © The author(s) 2024.

关键词： Diseases

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Graph Convolutional Neural Network Based Emotion Recognition with Brain Functional Connectivity Network

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International Journal of Crowd Science 2024年第4期8卷 195-204页

作者： Gao, Pengzhi Zheng, Xiangwei Wang, Tao Zhang, Yuang School of Information Science and Engineering Shandong Normal University Jinan250300 China State Key Laboratory of High-End Server & Storage Technology Jinan250300 China

Emotion recognition plays an important role in Human Computer Interaction (HCI) and the evaluation of human behavior based on emotional state is an important research topic. The purpose of emotion recognition is to automatically identify human’s emotional states by analyzing physiological or non-physiological signals. The conventional emotion classification methods cannot comprehensively leverage global and local features which are extracted from Electroencephalogram (EEG) signal generated after being stimulated. Therefore, we propose the graph convolutional neural network based emotion recognition with brain functional connectivity network (GERBN). Firstly, raw EEG data of the public DEAP and SEED datasets is preprocessed and adopted in this study. Secondly, emotion-related brain functional connection pattern is constructed using Phase-Locking Value (PLV) adjacency matrix to measure connectivity between the signals of different EEG channels according to phase synchronization. A novel graph structure is constructed where the EEG electrode channels are defined as the vertex, and the edge is strong connection of the binary brain network. Thirdly, the GERBN model that includes six layers is designed to classify and recognize emotional states on the two-dimensional emotional models of valence and arousal. Finally, extensive experiments are conducted on DEAP and SEED datasets. Experimental results demonstrate that the proposed method can improve classification accuracies, in which average accuracies of 80.43% and 88.47% on DEAP are attained on valence and arousal dimensions, respectively. On the SEED dataset, the accuracy reaches 92.37% higher than some of the other methods. © The author(s) 2024.

关键词： Electroencephalography

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Research on the Effect of Fan Rotation Vibration on Hard Disk Drive Performance 4th

Research on the Effect of Fan Rotation Vibration on Hard Dis...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Gong, Baolong Zhang, Hailong Lu, Jilei Zong, Bin State Key Laboratory of High-End Server & Storage Technology Inspur Electronics Information Industry Co. LTD Jinan250101 China

ISBN: (纸本)9789819778867

In response to the problem of multiple composite vibration physical fields caused by high-speed rotation of fans on hard drive disk, this paper conducts in-depth research using testing and simulation methods, and optimizes vibration reduction design from the perspectives of chassis, airflow field, fans, etc. The main conclusions are as follows: 1. the optimal distance between the fan and the hard drive is 70 mm. 2. among the many optimization measures, adding honeycomb mesh and sound-absorbing foam have the most significant effect, which can improve the performance of hard drives by about 15%. This study plays an important guiding role in server architecture design, effectively guiding the RV vibration reduction design of mechanical hard disk configuration models, minimizing the vibration impact of high-speed rotating fans on the hard disk, and improving system vibration resistance and stability. © The Author(s) 2025.

关键词： Fans

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Simulation and Testing Analysis Involving Different Fan Parameters 24

Simulation and Testing Analysis Involving Different Fan Para...

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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Zong, Bin Zhou, Lizhi Lv, Zhibo Gong, Baolong Jia, Long State Key Laboratory of High-end Server & Storage Technology Inspur Electronics information Industry Co. LTD Jinan250101 China

ISBN: (纸本)9798400716942

In This article is based on the Star CCM+ solver, using simulation and testing methods to simulate and analyze different parameters of the fan, evaluate the optimization effect of different schemes. Summarize the optimization selection methods under different selection parameters: 1. Define the installation distance Z between the rear rotor and the static rotor, the maximum airflow of the fan is less affected by different Z-spacing of the fan, and the airflow slightly decreases with increasing spacing;2. Define the shape parameter K of the rotor bypass area structure. The shape of the fan's different air outlets has a significant impact on the fan's airflow;3. Define the static rotor structure parameter S. The different S of fans have little effect on the maximum airflow of the fan. Reducing the diameter of the protrusion can appropriately increase the airflow of the fan, especially when the back pressure is high. © 2024 ACM.

关键词： Fans

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C^(2)S:Class-aware client selection for effective aggregation in federated learning

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high-Confidence Computing 2022年第3期2卷 7-15页

作者： Mei Cao Yujie Zhang Zezhong Ma Mengying Zhao School of Computer Science and Technology Shandong UniversityJinanChina State Key Laboratory of High-End Server&Storage Technology JinanChina

Federated learning is proposed to train distributed data in a safe manner by avoiding to send data to *** server maintains a global model and sends it to clients in each communication round,and then aggregates the updated local models to derive a new global ***,the clients are randomly selected in each round and aggregation is based on weighted *** show that the performance on IID data is satisfactory while significant accuracy drop can be observed for Non-IID *** this paper,we explore the reasons and propose a novel aggregation approach for Non-IID data in federated ***,we propose to group the clients according to classes of data they have,and select one set in each communication *** models from the same set are averaged as usual and the updated global model is sent to next group of clients for further *** this way,the parameters are only averaged on similar clients and passed among different *** shows that the proposed scheme has advantages in terms of model accuracy and convergence speed with highly unbalanced data distribution and complex models.

关键词： Federated learning Non-IID data Aggregation Client selection

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Research and Implementation of DMA Based on SR-IOV

Research and Implementation of DMA Based on SR-IOV

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2023 International Conference on Mobile Internet, Cloud Computing and Information Security, MICCIS 2023

作者： Wang, Hongliang Dong, Peiqiang Mu, Qi Liu, Wei Lu, Shengcai Zhang, Runze Inspur Electronic Information Industry Co. Ltd State Key Laboratory of High-End Server and Storage Technology Jinan China

ISBN: (纸本)9798350300932

With the development of cloud computing, container cloud, and other technologies, CPU and memory virtualization becomes more mature. Since the performance of I/O virtualization affects the overall operational efficiency of the system, improving I/O performance and the utilization of transmission bandwidth of devices is the focus of virtualization technology research. Based on the theoretical research of Single Root I/O Virtualization (SR-IOV) technology, this paper designs and implements a Direct Memory Access(DMA) interaction architecture based on SR-IOV virtualization devices using Field Programmable Gate Array (FPGA), which solves the need for high-speed data transmission between cloud computing platforms and virtual devices. This architecture adopts the processing method of independent virtual device management descriptors and shared data movers, ensuring independent operation between virtual devices and reducing the resource utilization rate of the design. Firstly, the chain structure is employed flexibly and efficiently to achieve descriptor management. In the meantime, it also reduces the pressure of interrupts on the CPU. Secondly, the data mover implementation framework adopts asynchronous pipelining mode, with extremely high transmission performance. After testing and verification, the interactive architecture can achieve high-speed data communication, with read and write bandwidth utilization rates of up to 87% and 79%, respectively. At the same time, this architecture can balance the bandwidth allocation among virtual devices. © 2023 IEEE.

关键词： Virtualization

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A Disk Failure Prediction Algorithm Based on Fusion Model 23

A Disk Failure Prediction Algorithm Based on Fusion Model

引用

15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Zhao, Liqiang Qi, Kaiyuan Su, Zhiyuan Pang, Liye Zhang, Lianfa Wu, Dong Cloud Computing R&D Department of Jinan Inspur Data Technology State Key Laboratory of High-end Server & Storage Technology China

ISBN: (纸本)9781450398411

Disk failure prediction is of great significance for ensuring the business continuity and data reliability of cloud data centers. Algorithm performance will directly affect operation and maintenance costs and user experience. Nowadays, many disk manufacturers use the threshold of S.M.A.R.T. data to check disk fault detection or make prediction, but the detection accuracy of this method is low. This paper proposes a disk fault prediction method based on deep learning, using deep residual network to extract high-dimensional hidden features;and using deep neural network combined with integrated tree model tool XGBoost to perform Failure prediction. Moreover, we use AutoML strategy based on improved GA to get the best model structure. Experiments based on Backblaze data show that this method has better prediction performance. Compared with pure XGboost methods, it can better reduce the false alarm rate about 50% and improve the accuracy rate, while maintaining a strong generalization ability. © 2023 ACM.

关键词： Fault detection

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A Low-complexity Max Unpooling Achitecture for CNNs 15

A Low-complexity Max Unpooling Achitecture for CNNs

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Zhang, Xiaojun Zhu, Chenshi Han, Qin Wang, Zhengrong Zhang, Dexue Shandong University of Science and Technology College of Electronic and Information Engineering China State Key Laboratory of High-end Server and Storage Technology Qingdao China

ISBN: (纸本)9798350312980

In the training of Convolutional Neural Networks (CNN), the conventional max unpooling architecture introduces significant sparsity for zero-filling during upsampling, leading to additional resource costs in adjacent deconvolution layers. In order to leverage the high sparsity of the recovery matrix, this paper proposes a low-complexity hardware architecture. This architecture solely stores the input matrix in the max unpooling layer, eliminating the need for the upsampling recovery process, while mapping the stored relative indices from the forward inference to deconvolution. Moreover, the deconvolution process exclusively involves non-zero elements, reducing computational and storage requirements. This paper derives the distribution of the maximum number of non-zero values corresponding to the deconvolution window for different layers, demonstrating its compatibility with networks of various sizes. Synthesizing the proposed architecture on Arria 10 AX115N2F45E1SG, for the VGG16, it can save 60.7% of ALMs and 68.8% of storage resources, Moreover, the proposed architecture enhances the frequency by 8.0%. © 2023 IEEE.

关键词： Convolution neural network FPGA Hardware acceleration Max unpooling

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Communication-Efficient Decision-Making of Digital Twin Assisted Internet of Vehicles: A Hierarchical Multi-Agent Reinforcement Learning Approach

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China Communications 2023年第3期20卷 55-68页

作者： Xiaoyuan Fu Quan Yuan Shifan Liu Baozhu Li Qi Qi Jingyu Wang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China Internet of Things&Smart City Innovation Platform Zhuhai Fudan Innovation InstituteZhuhai 519000China State Key Laboratory of High-End Server and Storage Technology Jinan 250101China

The connected autonomous vehicle is considered an effective way to improve transport safety and *** overcome the limited sensing and computing capabilities of individual vehicles,we design a digital twin assisted decision-making framework for Internet of Vehicles,by leveraging the integration of communication,sensing and *** this framework,the digital twin entities residing on edge can effectively communicate and cooperate with each other to plan sub-targets for their respective vehicles,while the vehicles only need to achieve the sub-targets by generating a sequence of atomic ***,we propose a hierarchical multiagent reinforcement learning approach to implement the framework,which can be trained in an end-to-end *** the proposed approach,the communication interval of digital twin entities could adapt to timevarying *** experiments on driving decision-making have been performed in traffic junction scenarios of different *** experimental results show that the proposed approach can largely improve collaboration efficiency while reducing communication overhead.

关键词： digital twin Internet of Vehicles hierar-chical reinforcement learning

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