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Conference on Lasers and Electro-Optics (CLEO)

作者： Baoluo Yan Yijun Sun Ziwen Zhou Hong Liu Hongya Wang Chen Huan Zhiyong Zhao Lipeng Feng Anxu Zhang Zhenhua Feng Hu Shi WDM System Department of Wireline Product R&D Institute ZTE Corp. Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Optics Valley Laboratory School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Beijing China

We propose a novel physical model-based QoT estimation with achieved OSNr estimation error ≤ 1.5 dB in primary paths and OSNr prediction error ≤ 1.76 dB in unestablished lightpaths for both super-C bands and super-C... 详细信息

ISBN: (纸本)9781957171258

关键词： Hole burning Optical amplifiers Optical networks Optical signal to noise ratio Stimulated scattering Variable optical attenuators

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A proactive defense mechanism for mobile communication user data

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Science China(Information Sciences) 2018年第10期61卷 266-268页

作者： Caixia LIU Xinsheng JI Jiangxing WU Xiao QIN The National Digital Switching System Engineering & Technological R&D Center Department of Computer Science and Software Engineering Samuel Ginn College of EngineeringAuburn University

dear editor,Several studies have recently reported on major vulnerabilities in the Signaling system No.7 (SS7)used in mobile networks [1, 2]. The reported vulnerabilities and security issues would lead to the illegal acquisition and tampering of mobile communication user data, known as cellphone user data. The cellphone user data contain important information such as identity identification, location identification, security parameter-set. due to

关键词： A proactive defense mechanism for mobile communication user data MSISdN

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rL SolVer Pro: reinforcement Learning for Solving Vehicle routing Problem

RL SolVeR Pro: Reinforcement Learning for Solving Vehicle Ro...

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International Conference on Artificial Intelligence and data Sciences (AidAS)

作者： Arun Kumar Kalakanti Shivani Verma Topon Paul Takufumi Yoshida R&D Division Toshiba Software India Pvt. Ltd. India System Engineering Laboratory Corporate R&D Center Toshiba Corporation Japan

ISBN: (纸本)9781728130422

Vehicle routing Problem (VrP) is a well-known NP-hard combinatorial optimization problem at the heart of the transportation and logistics research. VrP can be exactly solved only for small instances of the problem with conventional methods. Traditionally this problem has been solved using heuristic methods for large instances even though there is no guarantee of optimality. Efficient solution adopted to VrP may lead to significant savings per year in large transportation and logistics systems. Much of the recent works using reinforcement Learning are computationally intensive and face the three curse of dimensionality: explosions in state and action spaces and high stochasticity i.e., large number of possible next states for a given state action pair. Also, recent works on VrP don't consider the realistic simulation settings of customer environments, stochastic elements and scalability aspects as they use only standard Solomon benchmark instances of at most 100 customers. In this work, reinforcement Learning Solver for Vehicle routing Problem (rL SolVer Pro) is proposed wherein the optimal route learning problem is cast as a Markov decision Process (MdP). The curse of dimensionality of rL is also overcome by using two-phase solver with geometric clustering. Also, realistic simulation for VrP was used to validate the effectiveness and applicability of the proposed rL SolVer Pro under various conditions and constraints. Our simulation results suggest that our proposed method is able to obtain better or same level of results, compared to the two best-known heuristics: Clarke-Wright Savings and Sweep Heuristic. The proposed rL Solver can be applied to other variants of the VrP and has the potential to be applied more generally to other combinatorial optimization problems.

关键词： Vehicle routing problem (VrP) reinforcement learning (rL) Q-Learning Optimization rL solVer pro Vascular Endothelial Growth Factor C Solvers Learning Vehicle routing Curse of dimensionality learning (artificial intelligence) Proline Logistics systems

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Incremental Learning of SVM Using Backward Elimination and Forward Selection of Support Vectors

Incremental Learning of SVM Using Backward Elimination and F...

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International Conference on Applied Machine Learning (ICAML)

作者： Venkata Pesala Arun Kumar Kalakanti Topon Paul Ken Ueno Ankit Kesarwani H.G.S. Praneeth Bugata R&D Division Toshiba Software (India) Pvt. Ltd. Bengaluru India System Engineering Laboratory Corporate R&D Center Toshiba Corporation Japan Toshiba Software (India) Pvt. Ltd. Toshiba Software (India) Pvt. Ltd. Bangalore India

ISBN: (纸本)9781728139098

Traditional pattern matching and classification algorithms in machine learning field build models by extracting patterns from whole data at once. It means once a model is learned about the data, the parameters of the model will never be updated and the model remains unchanged forever. However, in the industrial applications, a large volume of data is accumulated incrementally and the dynamics of the data may change over time. The traditional approach of learning the model using all the data at once may not be feasible because it may require a huge storage for data and take a very long time to build a model from a huge volume of data, and the one-time built model may not be able to learn the changed patterns automatically over time. To overcome this limitation of the traditional approach, we propose two novel algorithms of pattern matching and classification, based on Incremental Learning Support Vector Machine (ILSVM), which learn and update models as new data arrives. Our proposed algorithms work based on selection and elimination of support vectors while learning. By performing experiments on 13 publicly available data sets, we found that our proposed methods obtain better accuracy, are computationally faster and require lower memory than the traditional batch learning and ILSVM.

关键词： learning (artificial intelligence) pattern classification pattern matching support vector machines Pattern matching Support Vector Network learning (artificial intelligence) Pattern recognition forward selection large volume data

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A density-based Clustering Algorithm Using Adaptive Parameter K-reverse Nearest Neighbor

A Density-based Clustering Algorithm Using Adaptive Paramete...

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IEEE International Conference on Power, Intelligent Computing and systems

作者： Pengyu Pei dong Zhang Feng Guo Industrial Technology Research Institute Zhengzhou University System Software R&D Department Inspur Electronic Information Industry Co. Ltd

ISBN: (纸本)9781728137216

density-based clustering method dBSCAN is one of the most widely used clustering methods, this algorithm does not need to input the number of clustering, and can find clusters of any shape. However, dBSCAN algorithm requires two parameters, with different parameters, the clustering results are quite different. This paper proposes a density-based clustering algorithm using adaptive parameter k-reverse nearest neighbor: ArKNN-dBSCAN, which can effectively identify and separate clusters with different densities by analyzing the correlation through the reverse k-nearest neighbor of the observation in the dataset without input parameters. Our results show that this algorithm is more accurate than other density-based clustering algorithms using adaptive parameter.

关键词： Clustering algorithms K-reverse nearest neighbor dBSCAN Adaptive parameter Clustering algorithms Nearest neighbor algorithms Clustering methods Self tuning two parameter input parameter dataset

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OpenHI2 – Open source histopathological image platform

arXiv

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arXiv 2020年

作者： Puttapirat, Pargorn Zhang, Haichuan deng, Jingyi dong, Yuxin Shi, Jiangbo He, Hongyu Gao, Zeyu Wang, Chunbao Zhang, Xiangrong Li, Chen Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech R&D Xi'an Jiaotong University Xi'an Shaanxi710049 China School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China Department of Software Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Department of Power System and Automation School of Electrical Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi'an Shaanxi710061 China

— Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological diagnoses are sensitive to many external factors and is known to be subjective. Only systems that can meet strict requirements in pathology would be able to run along pathological routines and eventually digitized the study area, and the developed platform should comply with existing pathological routines and international standards. Currently, there are a number of available software tools which can perform histopathological tasks including virtual slide viewing, annotating, and basic image analysis, however, none of them can serve as a digital platform for pathology. Here we describe OpenHI2, an enhanced version Open Histopathological Image platform which is capable of supporting all basic pathological tasks and file formats;ready to be deployed in medical institutions on a standard server environment or cloud computing infrastructure. In this paper, we also describe the development decisions for the platform and propose solutions to overcome technical challenges so that OpenHI2 could be used as a platform for histopathological images. Further addition can be made to the platform since each component is modularized and fully documented. OpenHI2 is free, open-source, and available at https://***/BioAI/OpenHI. Copyright © 2020, The Authors. All rights reserved.

关键词： Open source software

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Workload-Aware Scheduling for data Analytics upon Heterogeneous Storage

Workload-Aware Scheduling for Data Analytics upon Heterogene...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Zhuzhong Qian Yuan Gao Mingtao Ji Hui Peng Peng Chen Yibo Jin Sanglu Lu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China NARI Research Institute NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing System R&D Center NARI Technology Co. Ltd. Nanjing State Key Laboratory of Smart Grid Protection and Control NARI Group Corporation Nanjing

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

A trend in nowadays data centers is that equipped with SSd, Hdd, etc., heterogeneous storage devices are widely deployed to meet diverse demands of various big data workloads. Since the reading performance of various storage devices are quite different, traditional concurrent data fetching easily incurs unbalanced use among devices. As a result, the straggler in terms of the data fetching, derived from the unbalanced use, directly increases the overall latency of data analytics. To avoid such unbalanced use on fetching large volume of data concurrently from storage devices, we formulate Workload-Aware Scheduling problem for Heterogeneous storage devices (WASH), the goal of which is to minimize the maximum data fetching time for parallel data analytical tasks. We design a randomized algorithm (rWASH) to select a proper source device for each task based on delicate calculated probabilities, which can be proved concentrated on its optimum with high probability. Extensive experiments show that rWASH reduces the average data fetching time for tasks by up to 55% over the state-of-the-art algorithms.

关键词： Task analysis Performance evaluation data analysis Scheduling data centers Probability Hardware

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Predicting failures in hard drivers based on isolation forest algorithm using sliding window

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Journal of Physics: Conference Series 2019年第4期1187卷

作者： Tinglei Zhang Endong Wang dong Zhang School of the Zhengzhou's Smarter City Zhengzhou University Henan 450000 China System Software R&D Department Inspur Electronic Information Industry Co. Ltd China

With the surge in the development of cloud computing and big data, the scale of the data center is constantly expanding. The data missing caused by the damage of large data center disk has become a problem that cannot be ignored. Predicting the failures of the hard drivers can effectively improve the reliability and reduce the maintenance cost of the data center. The SMArT data is volume and multi-dimensional, as well as the high dynamic characteristics of data center hard disk accessing, so we propose an algorithm based on isolation forest using sliding window. The experiments show that this algorithm can effectively save the time of modeling and ensure high prediction accuracy.

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Method for Higher Availability of the SSd Storage system

Method for Higher Availability of the SSD Storage System

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International Conference on Computational Intelligence and Networks (CINE)

作者： Taranisen Mohanta ranjith reddy Basireddy B. Govindaraja Nayaka Narendra Chirumamilla Storage System Architect Storage R&D Lab HP India Software Operations Bangalore India Storage System Specialist Storage R&D Lab HP India Software Operations Bangalore India

In the recent era of the high speed network, the enterprise storage systems expect storage devices to provide very high IOPS at minimal cost and faster access (low latency). The advents of the NANd flash solid state devices (SSd) have seen an unprecedented growth which mitigates the shortcoming of the disks to address the performance aspect of the storage system. Enterprise ready SSds have started to fulfill that promise as they segment into SATA and PCIe based storage products. One of the major challenges among many of managing SSd drives in enterprise storage systems is the endurance (total number of years to fail). SSd devices have limited write / erase endurance cycles depending on the type of SSd device being used (SLC, TLC, and MLC etc.). The life or availability of the storage systems using SSd drives is in turn dependent on the durability and reliability of SSd drives. The proposed solution is a method to increase the availability of SSd based storage systems using the pool of SSds optimally. In other words, the proposed solution is to optimize the life cycle of the SSd storage array considering the different endurance and occupancy value of the SSd drives.

关键词： drives Availability Heuristic algorithms Ash Performance evaluation Media redundancy

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A hardware-in-the-loop simulator for demand response energy management in industrial facilities

A hardware-in-the-loop simulator for demand response energy ...

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Workshop Modeling and Simulation of Cyber-Physical Energy systems (MSCPES)

作者： Zhe Luo Musharraf Alam Seung Ho Hong Yuemin ding Aidong Xu daehyun Kwon Dept. of Electronics and System Engineering Hanyang University Ansan Korea Industrial Control and Systems Laboratory Shenyang Institute of Automation Shenyang China Integrated Network & Software Research Team LSIS Automation R&D Center Anyang Korea

demand response (dr) used in smart grid (SG) can enhance the reliability of the power system as well as reduce the energy costs for customers. One of the major consumers of electrical energy is industry. In this study, we develop a hardware-in-the-loop (HIL) simulator to demonstrate how to practically implement dr in industrial facilities. The HIL simulator includes an energy management system (EMS), a monitoring and control system (MCS), an industrial Ethernet backbone network based on rAPIEnet protocol, and a wireless field network based on ISA100.11a protocol. The results show that the electricity demand of industrial facilities can be shifted from peak to off-peak demand periods to improve the reliability of the electrical grid.

关键词： Energy management Industrial plants Wireless communication Monitoring Energy consumption Logic gates

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