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IEEEE Southeast Conference (SoutheastCon)

作者： Vancurik, Stepan Callahan, Dale W. Univ Alabama Birmingham Elect & Comp Engn Dept Birmingham AL 35233 USA

ISBN: (纸本)9781665403795

In many sports, proper movement and precise footwork signify key components of high quality performance. Conditioning specialists and coaches regularly work with athletes on footwork improvements to increase their chances to reach desired performance levels. However, choking under pressure sensation can make it difficult for athletes to achieve such goals as they may underperform in pressure situations. Proper footwork performance has been deemed essential for success in many sports and choking under pressure can impede performance;an observation can be made that footwork performance and choking under pressure may be related. Additionally, specific heartrate patterns in pressure situations have been established as stress indicators, presenting a potential relationship between heartrate and choking. Therefore, this paper presents a model for choking detection in college tennis athletes based upon footwork performance information and heartrate measurements. A data collection network is designed to capture footwork performance and heartrate measurements using two wearable sensors. There were three distinct footwork performance parameters and heartrate measurements captured from the collected data. data were collected with five participants completing a total of 13 tennis sessions, eight matches and five practices. Choking occurrences were identified during the tennis matches by six observing tennis coaches. The observations served as measurements for the choking detection and recognition. The captured data reveal that choking moments can be associated with certain footwork performance and heartrate patterns. The data also reveal that choking patterns strongly vary individually. However, results reached with the collected data and the proposed model demonstrate that choking recognition cannot be successfully achieved solely based on the proposed footwork performance and heartrate variables. It is possible that the proposed recognition model and introduced variables may

关键词： data analysis data patterns wearable sensors footwork performance heartrate measurements choking under pressure data collection network college tennis

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An improved density-based single sliding clustering algorithm for large datasets in the cultural information system

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PERSONAL AND UBIQUITOUS COMPUTING 2020年第1期24卷 33-44页

作者： Tolba, Amr Al-Makhadmeh, Zafer King Saud Univ Dept Comp Sci Community Coll Riyadh 11437 Saudi Arabia Menoufia Univ Math & Comp Sci Dept Fac Sci Shibin Al Kawm 32511 Egypt

This research introduces a technique for using an unsupervised machine learning method to data mine cultural datasets on the cultural information system (CIS). This has been referred to as web data mining for cultural data analytics and it will help web users analyze large, historical cultural dataset objects within web clusters. However, current techniques are ineffective at classifying data points into specific CIS clusters. This issue was resolved by using a Hybridized Single Sliding Centroid with Density-based Clustering (HSSCDC) to optimize data analytic techniques. Density-based clustering improves the clustering precision of the cultural analysis system (CAS) and optimizes window size. Experimental validation shows that the proposed HSSCDC automatically identifies data patterns in CAS web clusters with greater precision, less computational complexity, and less error than previous methods. This will help web users predict historical dataset objects more accurately and minimize computational time.

关键词： data mining Single sliding centroid Web clusters Cultural data analysis data patterns

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Write Back Energy Optimization for STT-MRAM-based Last-level Cache with data Pattern Characterization

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ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS 2020年第3期16卷 29-29页

作者： Ni, Jiacheng Liu, Keren Wu, Bi Zhao, Weisheng Cheng, Yuanqing Zhang, Xiaolong Wang, Ying Beihang Univ 37 Xueyuan Rd Beijing 100191 Peoples R China Beijing ByteDance Technol Co Ltd 48 Zhichun Rd Beijing 100083 Peoples R China Chinese Acad Sci Inst Comp Technol 6 Kexueyuan South Rd Beijing 100190 Peoples R China

Traditional memory technologies face severe challenges in meeting the ever-increasing power and memory bandwidth requirements for high-performance computing and big-data analyses. Several emerging memory technologies are promising as the replacements of SRAM or DRAM. Among them, STT-MRAM can be used to replace SRAM as the last-level cache (LLC). However, it suffers from high write energy and latency. In this article, we investigate data patterns written from SRAM-based upper-level cache to STT-MRAM-based LLC to explore the write energy reduction potential. Depending on the data layoutwithin a cache line, redundant bits can be identified and eliminated from write back operations to save STT-MRAM write energy. We also propose a dynamic profiling method to accommodate different application characteristics. The extensive simulation results show that write energy can be saved by 37.05% similar to 38.89% for static profiling and 19.76% similar to 34.29% for dynamic profiling.

关键词： STT-MRAM write energy reductions data patterns cache hierarchy

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Forecasting hourly PM_2.5 based on deep temporal convolutional neural network and decomposition method

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107988-107988页

作者： Jiang, Fuxin Zhang, Chengyuan Sun, Shaolong Sun, Jingyun Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Xidian Univ Sch Econ & Management Xian 710126 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Lanzhou Univ Finance & Econ Sch Stat Lanzhou 730020 Peoples R China

For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM2.5 concentration, by modeling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables' data. Taking PM2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the statistics-based models, traditional machine learning models, the popular deep learning models and several existing hybrid models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations. (C) 2021 Elsevier B.V. All rights reserved.

关键词： PM2.5 concentration forecasting Complete ensemble empirical mode decomposition with adaptive noise Temporal convolutional data patterns Deep learning

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When quantum state tomography benefits from willful ignorance

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NEW JOURNAL OF PHYSICS 2021年第7期23卷

作者： Motka, Libor Paur, Martin Rehacek, Jaroslav Hradil, Zdenek Sanchez-Soto, Luis L. Palacky Univ Dept Opt 17 Listopadu 12 Olomouc 77146 Czech Republic Univ Complutense Fac Fis Dept Opt Madrid 28040 Spain Max Planck Inst Phys Lichts D-91058 Erlangen Germany

We show that quantum state tomography with perfect knowledge of the measurement apparatus proves to be, in some instances, inferior to strategies discarding all information about the measurement at hand, as in the case of data pattern tomography. In those scenarios, the larger uncertainty about the measurement is traded for the smaller uncertainty about the reconstructed signal. This effect is more pronounced for minimal or nearly minimal informationally complete measurement settings, which are of utmost practical importance.

关键词： quantum state tomography quantum detector tomography data patterns

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CACF: A Novel Circuit Architecture Co-optimization Framework for Improving Performance, Reliability and Energy of ReRAM-based Main Memory System

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2018年第2期15卷 1–26页

作者： Zhang, Yang Feng, Dan Tong, Wei Hua, Yu Liu, Jingning Tan, Zhipeng Wang, Chengning Wu, Bing Li, Zheng Xu, Gaoxiang Huazhong Univ Sci & Technol Minist Educ China Sch Comp Sci & Technol Wuhan Natl Lab OptoelectKey Lab Informat Storage Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Div Data Storage Syst Sch Comp Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Hubei Peoples R China

Emerging Resistive Random Access Memory (ReRAM) is a promising candidate as the replacement for DRAM due to its low standby power, high density, high scalability, and nonvolatility. By employing the unique crossbar structure, Re RAM can be constructed with extremely high density. However, the crossbar ReRAM faces some serious challenges in terms of performance, reliability, and energy consumption. First, ReRAM's crossbar structure causes an IR drop problem due to wire resistance and sneak currents, which results in nonuniform access latency in ReRAM banks and reduces its reliability. Second, without access transistors in the crossbar structure, write disturbance results in serious data reliability problem. Third, the access latency, reliability, and energy use of ReRAM arrays are significantly influenced by the data patterns involved in a write operation. To overcome the challenges of the crossbar ReRAM, we propose a novel circuit architecture co-optimization framework for improving the performance, reliability, and energy use of ReRAM-based main memory system, called CACF. The proposed CACF consists of three levels, including the circuit level, circuit architecture level, and architecture level. At the circuit level, to reduce the IR drops along bitlines, we propose a double-sided write driver design by applying write drivers along both sides of bitlines and selectively activating the write drivers. At the circuit architecture level, to address the write disturbance with low overheads, we propose a RESET disturbance detection scheme by adding disturbance reference cells and conditionally performing refresh operations. At the architecture level, a region partition with address remapping method is proposed to leverage the nonuniform access latency in ReRAM banks, and two flip schemes are proposed in different regions to optimize the data patterns involved in a write operation. The experimental results show that CACF improves system performance by 26.1%, decreases mem

关键词： ReRAM crossbar IR drop nonuniform access latency write disturbance data patterns

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A Novel ReRAM-based Main Memory Structure for Optimizing Access Latency and Reliability 17

A Novel ReRAM-based Main Memory Structure for Optimizing Acc...

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54th ACM/EDAC/IEEE Design Automation Conference (DAC)

作者： Zhang, Yang Feng, Dan Liu, Jingning Tong, Wei Wu, Bing Fang, Caihua Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Key Lab Informat Storage Syst Sch Comp Sci & TechnolMinist Educ China Wuhan Hubei Peoples R China

ISBN: (纸本)9781450349277

Emerging Resistive Memory (ReRAM) is a promising candidate as the replacement for DRAM because of its low power consumption, high density and high endurance. Due to the unique crossbar structure, ReRAM can be constructed with a very high density. However, ReRAM's crossbar structure causes an IR drop problem which results in non-uniform access latency in ReRAM banks and reduces its reliability. Besides, the access latency and reliability of ReRAM arrays are greatly influenced by the data patterns involved in a write operation. In this paper, we propose a performance and reliability efficient ReRAM-based main memory structure. At the circuit level, we propose a double-sided write driver design to reduce the IR drops along bitlines. At the architecture level, a region partition with address remapping method and two flip schemes are proposed to reduce the access latency and improve the reliability of ReRAM arrays. The experimental results show that the proposed design can improve the system performance by 30.3% on average and reduce the memory access latency by 25.9% on average over an aggressive baseline, meanwhile the design improves the reliability of ReRAM-based memory system.

关键词： ReRAM crossbar IR drop non-uniform access latency data patterns

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Clustering Aluminum Smelting Potlines Using Fuzzy C-Means and K-Means Algorithms 1

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146th TMS Annual Meeting and Exhibition / Conference on Light Metals / 8th Symposium on Microstructural Progresses in Irradiated Materials (MPIM) / Symposium on Materials for High Temperature Applications - Next Generation Superalloys and Beyond

作者： de Lima, Flavia A. N. de Souza, Alan M. F. Soares, Fabio M. Cardoso, Diego Lisboa de Oliveira, Roberto C. L. Fed Univ Para Postgrad Program Elect Engn Belem Para Brazil Fed Univ Para Fac Elect & Comp Engn Belem Para Brazil

ISBN: (数字)9783319515410

ISBN: (纸本)9783319515410;9783319515403

Aluminum smelting potlines usually have a big number of cells, producing aluminum in a continuous and complex process. Analytical monitoring is essential to increase the industries' competitive advantage, however, during their operation, some cells share similar behaviors, therefore forming clusters of cells. These clusters rely on data patterns that are usually implicit or invisible to operation, but can be found by means data analysis. In this work we present two clustering techniques (Fuzzy C-Means and K-Means) to find and cluster the cells that present similar behaviors. The benefits of clustering are mainly in the simplification of potline analysis, since a large number of cells can be summarized in one single cluster, which can provide richer but compacted information for control and modelling.

关键词： Aluminium smelting potlines Clusters of cells data patterns Fuzzy C-Means K-Means

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Estimating the Number of Clusters Based on Sequential Clustering Algorithms 5

Estimating the Number of Clusters Based on Sequential Cluste...

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5th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Real, Eduardo Machado Univ Estadual Mato Grosso do Sul UEMS Nova Andradina MS Brazil

ISBN: (纸本)9781509035663

The main goal of clustering algorithms is to organize a given set of data patterns into groups (clusters) and their main strategy is to group patterns based on their similarity. However, some clustering algorithms also require as an input parameter, the number of clusters the induced clustering should have, or then, a threshold value used for limiting for the number of induced clusters. Both, the number of cluster as well a threshold value are often unknown;however it is well-known that results of clustering tasks can be very sensitive to them. This work presents a method for empirically estimating both values. The method is based on multiple runs of sequential clustering algorithms, by using increasing threshold values. Results from experiments conducted using several data domains from two repositories, the UCI and the Keel, as well as a few artificially created data, are presented and a comparative analysis is carried out, as evidence of the good estimates on both values given by the method.

关键词： clustering algorithms number of clusters data patterns sequential clustering

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Challenges and Perspectives in Big Eye-Movement data Visual Analytics

Challenges and Perspectives in Big Eye-Movement Data Visual ...

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2015 Big data Visual Analytics (BDVA)

作者： Blascheck, Tanja Burch, Michael Raschke, Michael Weiskopf, Daniel Univ Stuttgart Stuttgart Germany

ISBN: (纸本)9781467373432

Eye tracking has become an important technology to understand where and when people pay visual attention to a scene. Nowadays, eye tracking technology is moving from the laboratory to the real-world, producing more data at higher rates with extensive amounts of different data types. If this trend continues, eye tracking moves into the direction of big data. This requires developing new evaluation approaches beyond statistical analysis and visual inspection to find patterns in the data. As in big data analysis, visual analytics is one possible direction for eye movement data analysis. We look at current visual analytics methods and discuss how they can be applied to big eye-movement data. In this position paper we describe challenges for big eye-movement data visual analytics and discuss which techniques may be useful to address these challenges. Finally, we describe a number of potential scenarios for big eye-movement data.

关键词： Big data data visualisation gaze tracking pattern recognition statistical analysis big data big eye-movement data visual analytics data patterns eye tracking technology visual attention visual inspection Big data data visualization Gaze tracking Glass Tracking Visual analytics big data data visualisation Gaze tracking Visual analytics Visual Attention Statistical methods Visual inspection data analysis Statistical analysis Pattern recognition Glass

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