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第三十九届中国控制会议

作者： Hao Yu Zhe Guan Tongwen Chen Toru Yamamoto Department of Electrical and Computer Engineering University of Alberta Data-Driven Smart Systems Collaborative Research Laboratory KOBELCO Construction Machinery Dream-Driven Co-Creation Research Center Hiroshima University Graduate School of Advanced Science and Engineering Hiroshima University

This paper studies adaptive event-triggered PID controllers for 2nd-order linear discrete-time plants for set-point tracking. A new adaptive law is designed by introducing a decay parameter to improve the robustness of the closed-loop *** utilizing the extra robustness, an absolute type of event-triggering conditions is proposed, where the threshold parameter is co-designed with the adaptive laws. Then, the closed-loop system is formulated into a nonlinear interconnected system among the tracking errors, the differences of outputs, and the parameter estimation errors. A rigorous proof of stability is provided to ensure that the tracking error converges to a vicinity of zero when the initial state belongs to some compact sets. Finally,numerical simulations are given to illustrate the efficiency and feasibility of the proposed results.

关键词： Adaptive control event-triggered control linear systems PID controllers

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application of expert system for oil palm smallholder-owned plantations

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aiP Conference Proceedings 2023年第1期2594卷

作者： A. Umami H. G. Mawandha D. D. Puruhito Purwadi T. Suparyanto D. Sudigyo B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Most of the oil palm smallholders operate independently. As a result of this lack of support and assistance, oil palm owned by independent smallholders has the lowest productivity compared to large private (corporation) plantations and large state plantations. In addition, smallholder-owned plantations often operate without paying attention to sustainability aspects. There is a need for an effective, efficient, and user-friendly mentoring tool for oil palm farmers that are accessed independently. The Android-based platform was developed by applying an expert system to support increased production of oil palm cultivation for smallholders. Several stages of the expert system implementation, including the identification of planters and land profiles, were carried out using the direct interview method. The expert system includes land preparation management, planting material selection, seeding, weeds control, pests and diseases, fertilization, harvesting and transport, plantation administration, chat platforms, and data scrapping from data providers. The type of expert system is a data-driven Decision Support System (DSS).

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POSTER: Will Sentiment of Forex News Effect Forecast of the RMB Exchange Rate? 16

POSTER: Will Sentiment of Forex News Effect Forecast of the ...

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16th ACM International Conference on Computing Frontiers, CF 2019

作者： Cheng, Zhou Wang, Jixiang Qi, Tianmei Zhao, Junfeng Wang, Zhihong Guo, Yi Zhou, Yu Co. Ltd Shanghai China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China

ISBN: (纸本)9781450366854

The forecast and analysis of the trend of the RMB exchange rate have been deeply explored by many researchers in the financial field, but the combination of public opinion sentiment data and historical market data to forecast the RMB exchange rate in the short term has been studied little. With the rapid development of the Internet, the influence of public opinion sentiment on the economy and society is increasing. Online public opinion sentiment data not only have an impact on stock prices [1] and commodity prices, but also have a significant impact on foreign exchange (Forex) rates. However, the public opinion data are not applied for the RMB exchange rate forecast, because the impact of public events on the exchange rate is ignored. Besides, the lack of exact temporal sliding window of public opinion ignores its timeliness and sensibility. © 2019 Association for Computing Machinery.

关键词： Forecasting

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Partial Task Shuffle First Strategy for Spark

Partial Task Shuffle First Strategy for Spark

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2019 4th International Conference on Automatic Control and Mechatronic engineering(ACME 2019)

作者： Tianlei Zhou Yuyang Wang School of computer science and technology Chongqing University of Posts and Telecommunications Chongqing Engineering Research Center of Mobile Internet Data Application

Apache Spark is an in-memory distributed computing framework,which is more suitable for iterative jobs than ***,the shuffle process needs to synchronize tasks between nodes,which may lead to waste the computing resources of the cluster and ultimately reduce the computing performance of the *** is an important reason to limit the performance of *** this paper,we proposes a Partial Task Shuffle First（PTSF） Strategy to dynamically generate Shuffle Write tasks and perform Shuffle operations on partial completed *** strategy increases the parallel degrees of data calculation and transmission,lowering the peak of the Shuffle stage,allowing the cluster to be more balanced in the course of the ***,experiments show that the proposed strategy can improve Shuffle execution efficiency.

关键词： Big data spark shuffle task

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Binary neural networks: A survey

arXiv

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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Read/write Performance Optimization based on Ceph Heterogeneous Storage

Read/write Performance Optimization based on Ceph Heterogene...

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作者： Pengcheng Yao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Engineering Research Center of Mobile Internet Data Application

The arrival of the era of big data has brought great challenges to the storage and management of massive data. Distributed storage is an extremely important solution to storage capacity pressure and cost in the era of big data. As an emerging open source distributed system, Ceph can provide three storage functions: object storage, block storage and file storage. The pseudo-random data mapping function CRUSH is used to complete the mapping of data to storage nodes, eliminating the traditional centralized metadata nodes, while system scalability has no theoretical upper limit. However, Ceph adopts a strong consistency strategy and only reads the primary node when reading objects, which results in low read-write efficiency. At the same time, Ceph does not perceive heterogeneous storage and does not make full use of SSD storage characteristics. Therefore, a combined storage strategy based on weak consistency model and multi-node read optimization is proposed to optimize Ceph storage. The system can effectively reduce the read-write delay of Ceph and improve its storage efficiency. Experiments show that compared with the original mechanism of Ceph, the proposed optimization mechanism can improve the writing efficiency by more than ten times and the reading efficiency by more than six times.

关键词： Ceph CRUSH Heterogeneous storage Weak consistency Multi-node read

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Learning management system for oil palm smallholder-owned plantations

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aiP Conference Proceedings 2023年第1期2594卷

作者： E. Firmansyah D. Nurjannah S. I. Dinarti D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The production gap between oil palm smallholder-owned plantations and corporations shows a real problem in the national palm oil industry. To reduce the gap, user-friendly assistance for smallholder-owned plantations is needed for the cultivation of sustainable palm oil production. Android-based software can be a platform for bridging the transfer of knowledge and technology, transfer of problems, and transfer of solutions between planters and experts. The platform development was formulated in five stages which began with Learning Management System (LMS) application development, identification of Good Agricultural Practices (GAP) and Best Management Practices (BMP) materials, development of digital contents, uploading the digital content to the LMS server, and dissemination. The curriculum in the application platform was prepared based on the needs of the growers according to the identification results of the farmers' profiles. The curriculum consists of GAP and BMP material blocks. Each block was divided into discussion topics. The digital contents related to GAP and BMP were set in the LMS, which was built using Moodle platform.

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Semi-supervised Learning to Rank with Uncertain data 16th

Semi-supervised Learning to Rank with Uncertain Data

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16th Web Information Systems and applications Conference, WISA 2019

作者： Zhang, Xin Zhao, ZhongQi Liu, ChengHou Zhang, Chen Cheng, Zhi Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Technology Department of Computer Science and Technology Hefei University Hefei China Suzhou Vocational University Suzhou China Changan Automobile R&D Center Hefei China

ISBN: (纸本)9783030309510

Although, semi-supervised learning with a small amount of labeled data can be utilized to improve the effectiveness of learning to rank in information retrieval, the pseudo labels created by semi-supervised learning may not reliable. The uncertain data nearby the boundaries of relevant and irrelevant documents for a given query has a significant impact on the effectiveness of learning to rank. Therefore, how to utilize the uncertain data to bring benefit for semi-supervised learning to rank is an excellent challenge. In this paper, we propose a semi-supervised learning to rank algorithm, that builds a query-quality predictor by utilizing uncertain data. Specially, this approach selects the training queries following the empirical observation that the relevant documents of high quality training queries are highly coherent. This approach learns from the uncertain data to predict the retrieval performance gain of a given training query by making use of query features. Then the pseudo labels for learning to rank are aggregated iteratively by semi-supervised learning with the selected queries. Experimental results on the standard LETOR dataset show that our proposed approaches outperform the strong baselines. © 2019, Springer Nature Switzerland AG.

关键词： Iterative methods

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A Lowest Cost RDD Caching Strategy for Spark

A Lowest Cost RDD Caching Strategy for Spark

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2019 4th International Conference on Automatic Control and Mechatronic engineering(ACME 2019)

作者： Yuyang Wang Tianlei Zhou School of computer science and technology Chongqing University of Posts and Telecommunications Chongqing Engineering Research Center of Mobile Internet Data Application

Spark abstracts intermediate results into RDD in memory and manages them with LRU strategy to improve ***,RDD will be reloaded in many cases because RDD for different computing tasks have different lifecycle,which incurs additional system *** this paper we proposed a lowest cost replacement strategy as Spark's cache replacement strategy to eliminate this *** strategy preemptively evicts RDD with small weight values from memory based on the weight *** then,in this process,we select the solution with the lowest cost to replace the RDD in memory to improve the efficiency of ***,experiment results show that strategy we proposed can speed up the efficiency of the whole cluster.

关键词： RDD Spark memory management Memory computing Cache strategy

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A Distributed Block Storage Optimization Mechanism Based on Ceph

A Distributed Block Storage Optimization Mechanism Based on ...

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2019 4th International Conference on Automatic Control and Mechatronic engineering(ACME 2019)

作者： Yang Pi Zhanglong Wang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Engineering Research Center of Mobile Internet Data Application

RADOS block device（RBD） is the storage service component in Ceph,which provides an important block storage service for OpenStack and other cloud computing *** response to the challenges faced by enterprises in terms of higher resource utilization and read/write rates on block storage services,Ceph provides Cache Tiering to improve cluster performance in heterogeneous storage ***,the least recently used（LRU） algorithm in Cache Tiering would evict more valuable data due to cache pollution which causes higher latency for some requests;Meanwhile,when the data is allocated on the storage node,the Controlled Replication Under Scalable Hashing（CRUSH） algorithm only considers the storage node capacity,which makes Ceph unable to dynamically balance the node’s I/O *** solve these problems,a storage selection strategy based on the prediction model is proposed to increase the hit ratio of object access in the cache pool and improve the overall I/O performance of the cluster;Besides,the cache pool I/O load balancing strategy is also *** with the native mechanism,the proposed block storage optimization mechanism can achieve higher I/O throughput and more balanced I/O load.

关键词： Ceph Block storage Cache Tiering Storage selection Load balancing

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