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Simulation of Cobalt-Rich Crust’s Crushing Process Based on ANSYS

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Journal of China University of mining and Technology 2006年第1期16卷 28-32页

作者： XIA yi-min MA Zhi-guo BU ying-yong ZHAO Hai-ming College of Mechanical and electronic Engineering Central South University Changsha Hunan 410083 China

It is a great concern in international oceanology to mine cobalt-rich crust. At present, spiral mining head is regarded as the best crushing scheme. Based on the dynamic model of spiral mining head, the of cobalt crust’s crushing process was simulated with the ANSYS software. The material model of Johnson and Cook Plasticity, maximal princi- pal stress failure criterion, and eroding contact arithmetic were selected. At the same time, the influence of cutting depth, feed speed, rotational speed of mining head on torque and force subjected by mining head was taken into account. At last a computer simulation model of crushing process of cobalt crust was established. By analyzing simulation results, the relationships among these controllable process parameters, torque and force can be obtained, which will provide a technical reference for practical mining system.

关键词： cobalt-rich crust mining head simulation finite element ANSYS

维普期刊数据库

A Hint Frequency Based Approach to Enhancing the I/O Performance of Multilevel Cache Storage Systems

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Journal of Computer Science & Technology 2017年第2期32卷 312-328页

作者： Xiao-Dong Meng Chen-Tao Wu min-yi guo Jie Li Xiao-Yao Liang Bin Yao Long Zheng Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China

With the enormous and increasing user demand, I/O performance is one of the primary considerations to build a data center. Several new technologies in data centers, such as tiered storage, prompt the widespread usage of multilevel cache techniques. In these storage systems, the upper level storage typically serves as a cache for the lower level, which forms a distributed multilevel cache system. However, although many excellent multilevel cache algorithms have been proposed to improve the I/O performance, they still have potential to be enhanced by investigating the history information of hints. To address this challenge, in this paper, we propose a novel hint frequency based approach （HFA）, to improve the overall multilevel cache performance of storage systems. The main idea of HFA is using hint frequencies （the total number of demotions/promotions by employing demote/promote hints） to efficiently explore the valuable history information of data blocks among multiple levels. HFA can be applied with several popular multilevel cache algorithms, such as Demote, Promote and Hint-K. Simulation results show that, compared with original multilevel cache algorithms such as Demote, Promote and Hint-K, HFA can improve the I/O performance by up to 20% under different I/O workloads.

关键词： storage system multilevel cache hint I/O performance

A Deep Learning-based Operation Optimization Strategy for BFG/coal Co-firing Boiler 36

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A Deep Learning-based Operation Optimization Strategy for BF...

Reverse Furthest Neighbors Query in Road Networks

第36届中国控制会议

作者： Jian-guo Wang Jin-Qiu min Li-Lan Liu Bang-Hua Yang Shi-Wei Ma min-Rui Fei yi-min guo Yuan Yao yi-Ping Wu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering National Tsing-Hua University Shanghai Baosight Software Co Ltd

ISBN: (纸本)9781538629185

How to operate a BFG/coal co-firing boiler in high efficiency is challenging for a gas/solid multi-fuel combustion *** operation data from a real boiler,this study proposes an operation optimization strategy of BFG/coal co-firing boiler based on deep ***,the thermal efficiency model is constructed based on deep learning with all the actual sampling data,which outperform the other modeling ***,the high efficiency data being selected as labeled data,a deep learning-based model of air flow is *** needs to be emphasized that,to take full advantage of all available information and improve the model accuracy,all the labeled and unlabeled data are utilized in auto-coder learning of the air flow ***,the constructed air flow model is used for the operation optimization and the thermal efficiency model is used to evaluate the level efficiency *** high accuracy of the proposed modeling approaches makes the implementation of the operation optimization strategy readily practicable.

关键词： BFG/coal co-firing thermal efficiency auto-coder deep learning data-driven

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Journal of Computer Science & Technology 2017年第1期32卷 155-167页

作者： Xiao-Jun Xu Jin-Song Bao Bin Yao Jing-Yu Zhou Fei-Long Tang min-yi guo Jian-Qiu Xu School of Software Beijing Institute of Technology Beijing 100081 China First Research Institute of ministry of Public Security Beijing 100048 China College of Mechanical Engineering Donghua University Shanghai 200051 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

Given a road network G = （V, E）, where V（E） denotes the set of vertices（edges） in G, a set of points of interest P and a query point q residing in G, the reverse furthest neighbors （RFNR） query in road networks fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in P ∪ {q}. This is the monochromatic RFNR （MRFNR） query. Another interesting version of RFNR query is the bichromatic reverse furthest neighbor （BRFNR） query. Given two sets of points P and Q, and a query point q ∈ Q, a BRFNR query fetches a set of points p ∈ P that take q as their furthest neighbor compared with all points in Q. This paper presents efficient algorithms for both MRFNR and BRFNR queries, which utilize landmarks and partitioning-based techniques. Experiments on real datasets confirm the efficiency and scalability of proposed algorithms.

关键词： reverse furthest neighbor road network landmark hierarchical partition

A Nonnegative Garrote-based Method for Multivariate Fault Isolation 36

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A Nonnegative Garrote-based Method for Multivariate Fault Is...

SHA: QoS-Aware Software and Hardware Auto-Tuning for Database Systems

第36届中国控制会议

作者： yi-min guo Jian-guo Wang Bang-Hua Yang Shi-Wei Ma min-Rui Fei Li-Lan Liu Hui Xu Yuan Yao yi-Ping Wu School of Mechatronical Engineering and Automation Shanghai UniversityShanghai Key Lab of Power Station Automation Technology Department of Chemical Engineering National Tsing-Hua University Shanghai Baosight Software Co Ltd

ISBN: (纸本)9781538629185

Fault isolation is an essential step in multivariate statistical process monitoring,which discovers the cause of the detected *** common adopted techniques for fault isolation,such as contribution plots,reconstruction analysis,branch and bound,etc.,often suffer from the problems of the smearing effect,insufficient historical fault data,and heavy computational *** this paper,the nonnegative garrote（NNG） estimator is utilized for multivariate statistical fault isolation,which solves the problems of the existing *** proposed method is based on the finding that there is equivalence between fault isolation and variable selection in discriminant *** from the existing methods,the NNG-based fault isolation method does not suggest a single set of faulty ***,it provides a sequence of process variables according to their responsibilities for the detected *** results are informative for diagnosing the root cause of the fault.

关键词： Fault isolation Variable selection Nonnegative garrote Process monitoring

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Journal of Computer Science & Technology 2024年第2期39卷 369-383页

作者：李进陈全唐晓新过敏意 Department of Computer Science Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Technology Shanghai University of Finance and EconomicsShanghai 200433China

While databases are widely-used in commercial user-facing services that have stringent quality-of-service(QoS)requirement,it is crucial to ensure their good performance and minimize the hardware usage at the same *** investigation shows that the optimal DBMS(database management system)software configuration varies for different user request patterns(i.e.,workloads)and hardware *** is challenging to identify the optimal software and hardware configurations for a database workload,because DBMSs have hundreds of tunable knobs,the effect of tuning a knob depends on other knobs,and the dependency relationship changes under different hardware *** this paper,we propose SHA,a software and hardware auto-tuning system for *** is comprised of a scaling-based performance predictor,a reinforcement learning(RL)based software tuner,and a QoS-aware resource *** performance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance *** software tuner fine-tunes the DBMS software knobs to optimize the performance of the *** resource reallocator assigns the saved resources to other applications to improve resource utilization without incurring QoS violation of the database *** results show that SHA improves the performance of database workloads by 9.9%on average compared with a state-of-the-art solution when the hardware configuration is fixed,and improves 43.2%of resource utilization while ensuring the QoS.

关键词： auto-tuning database configuration joint tuning utilization quality-of-service(QoS)

Reliability and Incentive of Performance Assessment for Decentralized Clouds

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Journal of Computer Science & Technology 2022年第5期37卷 1176-1199页

作者： Jiu-Chen Shi Xiao-Qing Cai Wen-Li Zheng Quan Chen De-Ze Zeng Tatsuhiro Tsuchiya min-yi guo Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science China University of GeosciencesWuhan 430074China Graduate School of Information Science and Technology Osaka UniversityOsaka 565-0871Japan

Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing *** any user or enterprise can be the cloud provider in the decentralized cloud,the performance assessment of the heterogeneous computing resources is of vital ***,with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric,the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized *** this paper,we present a robust performance assessment solution RODE to solve this *** mainly consists of a performance monitoring mechanism and an assessment of the claimed performance(AoCP)*** performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud *** on the performance monitoring results,the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as *** extensive experiments,we show RODE can accurately monitor the performance of cloud providers on the premise of reliability,and incentivize cloud providers to honestly present the performance information and maintain the performance stability.

关键词： decentralized cloud computing robust performance assessment trusted execution environment(TEE)

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 86-96页

作者： Qianyun Yun Wen-Biao Han yi-Yang guo He Wang minghui Du Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Shanghai Astronomical Observatory Chinese Academy of Sciences School of Physics and Astronomy Shanghai Jiao Tong University School of Astronomy and Space Science University of Chinese Academy of Sciences Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou) University of Chinese Academy of Sciences Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Province Key Laboratory of Quantum Theory and Applications of MoE Lanzhou University Institute of Theoretical Physics & Research Center of Gravitation Lanzhou University International Centre for Theoretical Physics Asia-Pacific (ICTP-AP) University of Chinese Academy of Sciences (UCAS) Center for Gravitational Wave Experiment National Microgravity Laboratory Institute of Mechanics Chinese Academy of Sciences

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a2-layer convolutional neural network(CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate(TPR) of 96.9% at a 1% false positive rate(FPR) for signal-to-noise ratio(SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole(SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter *** model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation.

关键词： gravitational wave EMRIs deep-learing

Improvement of chronic heart failure by dexamethasone is not associated with downregulation of leptin in rats

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Acta Pharmacologica Sinica 2007年第2期28卷 202-210页

作者： Qin-gui XIA~(2,4) Tao NA~3 yi-min guo~2 Yun-tian BI~2 Hai-yan ZHANG~2 De-zai DAI~(3,4)2 Department of Physiology,Wenzhou Medical College,Wenzhou 325003,China 3 Research Division of Pharmacology China Pharmaceutical University,Nanjing 210009,China Department of Physiology Wenzhou Medical College Wenzhou China Research Division of Pharmacology China Pharmaceutical University Nanjing China

Aim:To demonstrate the hypothesis that dexamethasone（Dex）could improvechronic heart failure（CHF）by inhibiting the downstream signaling transductionof leptin but had no influence on the upregulation of leptin and its receptor ***:CHF was induced by left coronary artery ligation for *** rats were treated with Dex 50 ***,histology,reactive oxygen species（ROS）-related parameters,and leptin concentrations inserum were *** mRNA expression of matrix metalloproteinases（MMP）2/9,tissue inhibitor of metalloproteinases（TIMP）1/2,tumor necrosis factor（TNF）-α,and OB-Rb were measured by ***:In the CHF rats,hemodynamicfunctions were deteriorated,which was accompanied with myocardium remodel-ing and histological *** rats showed hyperleptinemia and excessiveROS in the serum,and the upregulation of MMP-2/9,TNF-α,and leptin receptormRNA and downregulation of TIMP-1/2 mRNA in the myocardium compared withthe sham operation *** treatment significantly ameliorated CHF in asso-ciation with the reversion of the abnormalities of MMP-2/9,TIMP-1/2,TNF-α*** Dex had no influence on the hyperleptinemia and the upregulated leptinand its receptor in the myocardium during ***:Dex improves CHFby inhibiting TNF-α,MMP-2,MMP-9,and *** had no effects on upregulatedleptin and its receptor expression and hyperleptinemia induced by CHF.

关键词： leptin chronic heart failure matrix metalloproteinase 9 matrix metalloproteinase 2 tissue inhibitor of metalloproteinase 1 tissue inhibitor of metalloproteinase 2 tumor necrosis factor alpha dexamethasone