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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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Group-PTP: A Pedestrian Trajectory Prediction Method Based on Group Features

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Chuanyang Zhang, Guijuan Zheng, Zhuoran Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250014 China State Key Laboratory of High-End Server Storage Technology Organization Jinan250358 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Group features have significant effects on pedestrian movement and constitute a focal point in pedestrian trajectory prediction research. In reality, pedestrians within a group exhibit notable consistency features due to their compact spatial positions, close destinations, and factors such as coordination within the group. In contrast, owing to the dispersed destinations among groups and the lack of coordination, there are significant differences in velocity and direction between the groups, leading to strong conflicts. However, existing pedestrian trajectory prediction models based on group features lack sufficient quantification of both within-group and between-group features. To address this problem, we propose Group-PTP, a novel pedestrian trajectory prediction model based on group features. Specifically, we first propose a group graph attention network-based group features aggregation method (Group-GAT). By quantifying and aggregating the intra-consistency and inter-conflict features exhibited by the groups, our method can better capture the features and interactions both within and between groups. Second, we propose a group multi-feature information representation model that fuses captured group aggregate features, pedestrian coordinates, surrounding pedestrian features, and obstacle features through fusion concatenation. Finally, we propose a multi-feature temporal convolutional network (MF-TCN) that embeds the impact weights of multi-feature information into pedestrian coordinates to obtain feature outputs and conducts temporal operations on feature outputs to predict future trajectories. The experimental results demonstrate that our proposed Group-PTP achieves state-of-the-art performance on several different trajectory prediction benchmarks. © 1999-2012 IEEE.

关键词： Convolution

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DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

arXiv

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arXiv 2025年

作者： Liu, Guanlin Yu, Xiaomei Liu, Zihao Li, Xue Fan, Xingxu Zheng, Xiangwei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China State Key Laboratory of High-end Server and Storage Technology Jinan250300 China

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Toward Big-Data Sharing: A Unified Trusted Remote Attestation Scheme based on Blockchain

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IEEE Internet of Things Journal 2025年

作者： Wang, Ran Ma, Fuqiang Duan, Shihong Su, Zhiyuan Zhang, Xiaotong Xu, Cheng University of Science and Technology Beijing School of Computer and Communication Engineering Shunde Innovation School China Inspur Data Technology Co. Ltd. Shandong Jinan China State Key Laboratory of High-end Server & Storage Technology Beijing China Inspur Electronic Information Industry Co. Ltd Shandong Jinan China

The rapid expansion of the Internet of Things (IoT) has brought forth new challenges and opportunities in securely managing and sharing vast amounts of data generated by connected devices. Blockchain technology, with its decentralization, tamper-resistance, and traceability, offers a promising framework for IoT data sharing but struggles to safeguard smart contracts and sensitive data. Integrating Trusted Execution Environments (TEEs) with blockchain addresses these concerns, enabling secure execution and communication via remote attestation. However, existing remote attestation methods face challenges, including incompatibility across heterogeneous TEEs, inefficiency under frequent authentication, and vulnerability to DoS attacks. To tackle these, we propose a blockchain-based unified remote attestation scheme for IoT. Our three-tier blockchain architecture–comprising a Certificate Authority (CA) channel, an authoritative channel, and a business channel–separates authentication, attestation, and operations while ensuring auditability. An abstraction layer supports heterogeneous TEEs, and an authoritative blockchain stores authentication reports, enabling secure, frequent attestations. Additionally, a distributed CA system enhances resilience to DoS attacks. Experimental results validate our scheme’s efficiency and security, offering a robust solution for IoT data sharing. © 2014 IEEE.

关键词： blockchain confidential computing data security data sharing trusted execution environment

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A Construction Method of Arbitrary-Length Polar Codes

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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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CFS: The design and implementation of a cluster file system service on Inspur AS3000

CFS: The design and implementation of a cluster file system ...

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2011 International Conference on Computational and Information Sciences, ICCIS 2011

作者： Wang, Dongfeng Hu, Zhen Zhang, Jinghai Zhang, Liqiang Wen, Zhongling State Key Laboratory of High-end Server and Storage Technology China

ISBN: (纸本)9780769545011

CFS is a massively parallel distributed file system used for large scale cluster computing and mass storage. It is a global file system sharing among all cluster nodes. In this paper, based on implementation experience and measurements, we will present an evaluation of design alternatives to a cluster file system CFS which is targeted for In spur AS3000 and the performance of the file system. © 2011 IEEE.

关键词： Cluster computing

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Integrated Route Planning and Resource Allocation for Connected Vehicles

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China Communications 2021年第3期18卷 226-239页

作者： Quan Yuan Bo Chen Guiyang Luo Jinglin Li Fangchun Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of High-end Server and Storage Technology Jinan 250101ShandongChina

Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning *** the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly,it becomes critical to efficiently orchestrate the communication and computation resources on edge *** methods usually perform resource allocation in a fairly effective but still reactive manner,which is subject to the capacity of nearby edge *** deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity,we proactively balance the edge computing demands across edge clouds by appropriate route *** this paper,route planning and resource allocation are jointly optimized to enhance intelligent *** propose a multi-scale decentralized optimization method to deal with the curse of *** large-scale optimization,backpressure algorithm is used to conduct route planning and load balancing across edge *** small-scale optimization,game-theoretic multi-agent learning is exploited to perform regional resource *** experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.

关键词： connected vehicles edge computing resource allocation route planning

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Characterizing and Modeling for Proactive Disk Failure Prediction to Improve Reliability of Data Centers 1

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AI Ops Competition on Large-Scale Disk Failure Prediction, AI Ops 2020, held at the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020

作者： Wang, Tuanjie Liang, Xinhui Xie, Quanquan Li, Qiang Li, Hui Zhang, Kai State Key Laboratory of High-end Server and Storage Technology Beijing China

ISBN: (数字)9789811577499

ISBN: (纸本)9789811577482

In modern datacenter, hard disk drive has the highest failure rate. Current storage system has data protection feature to avoid data loss caused by disk failure. However, data reconstruction process always slows down or even suspends system services. If disk failures can be predicted accurately, data protection mechanism can be performed before disk failures really happen. Disk failure prediction dramatically improve the reliability and availability of storage system. This paper analyzes disk SMART data features in detail. According the analysis results, we design an effective feature extraction and preprocessing method. And we have optimized the XGBoost’s hyperparameters. Finally, ensemble learning is applied to further improve the accuracy of prediction. The experimental results of Alibaba data set show that our system predict disk failures within 30 days. And the F-score achieves 39.98. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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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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FPGA Accelerates Deep Residual Learning for Image Recognition 2

FPGA Accelerates Deep Residual Learning for Image Recognitio...

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2017 IEEE 2nd Information technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Xuelei Li Liangkui Ding Li Wang Fang Cao State Key Laboratory of High-end Server & Storage Technology Inspur Group Co. Ltd

ISBN: (纸本)9781509064151;9781509064144

Deep learning is greatly promoting the fast development of artificial intelligence. In the image classification area, the recognition rate and inference performance become the main challenges in the real application. Recently, residual network(Res Net) has shown a competitive accuracy and nice convergence behaviors in deep neural networks. In this paper,we are devoted to accelerate this promising framework in the inference application on FPGA(Field Programmable Gate Array)using Open CL programming language. Firstly, a supplemented deep learning accelerator is constructed to realize the residual function in Res Net. Secondly, our construction reschedules three on-chip buffers in order to store the feature data and to stream it to processor elements alternately. In addition, we also implement data parallel and pipeline execution such that the filter parameters can be synchronously processed with the image data on FPGA. Moreover, we exploit a convertor to transform any Res Net in CAFFE framework into FPGA platform. It can generate the FPGA head files using its original prototxt files through the dictionary function of Python. The experiment result analysis shows that our acceleration has a competitive performance while maintaining the high accuracy rate. Finally,we provide a solution to accelerate any construction of Res Net using Open CL programming language on FPGA.

关键词： deep learning residual network FPGA OpenCL

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