Because cable-driven parallel robots (CDPRs) have lightweight moving parts, CDPRs have been used in various industrial applications requiring high speeds and accelerations. Especially, CDPRs with polymer cables can ac...
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Because cable-driven parallel robots (CDPRs) have lightweight moving parts, CDPRs have been used in various industrial applications requiring high speeds and accelerations. Especially, CDPRs with polymer cables can achieve higher accelerations and speeds compared to those with steel cables. However, they also have some disadvantages, such as a nonlinear creep, a hysteresis, and a short- and long-term recovery. Because these nonlinear characteristics, the accuracy of CDPRs gets worse and worse. In this study, we proposed a hybrid recurrent neural network (H-RNN) to predict nonlinear characteristics of the cable elongation and to solve the problems associated with CDPRs and apply the real-time control. In the algorithm, the long short-term memory (LSTM) algorithm was used to learn the characteristics of the low-frequency data, and the basic RNN learned the features of the high-frequency data. We also confirmed that the cut-off frequency was determined based on the non-operating frequency related to rest time. Also, it yielded more accurate results because the LSTM has a wider effective frequency range. Finally, the learning process was completed by combining these two algorithms. these results made it possible to predict position errors of CDPRs with high accuracy, in which error varies under both while operating and no operation conditions. the H-RNN had a lower root mean square error than boththe optimal RNN and the optimal LSTM, so it was effective for controlling systems that have errors across a range of frequencies.
Aiming at the problem of spatio-textual skyline query processing in cloud computing systems, we propose a Spark-based spatio-textual skyline query processing algorithm. In which, the spatial objects irrelevant to quer...
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Due to a high interest in microscopic cell migration analysis for biological research, numerous cell segmentation and tracking algorithms has emerged. the main tasks of cell tracking methods are to segment each cell a...
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the proceedings contain 26 papers. the special focus in this conference is on Business and IT Alignment. the topics include: Analysing and Predicting the Adoption of Anonymous Transactions in Cryptocurrencies;volatili...
ISBN:
(纸本)9783030611453
the proceedings contain 26 papers. the special focus in this conference is on Business and IT Alignment. the topics include: Analysing and Predicting the Adoption of Anonymous Transactions in Cryptocurrencies;volatility and Value at Risk of Crypto Versus Fiat Currencies;how Much Identity Management with Blockchain Would Have Saved Us? A Longitudinal Study of Identity theft;benefits of the Technology 4.0 Used in the Supply Chain - Bibliometric Analysis and Aspects Deferring Digitization;how Do Movie Preferences Correlate with e-Commerce Purchases? An Empirical Study on Amazon;financial Institutions and Use of Social Media: Analysis of the Largest Banks in the U.S. and Europe;Outsourcing of Social CRM Services in German SMEs;Customer-Focused Churn Prevention with Social CRM at Orange Polska SA (Research in Progress);Social CRM: A Literature Review Based on Keywords Network Analysis;digitalization of Small and Medium-Sized Enterprises: An Analysis of the State of Research;Social CRM Tools: A Systematic Mapping Study;Analyzing OpenStreetMap Contributions at Scale: Introducing OSM-Interactions Tilesets;enhancing the Interactive Visualisation of a Data Preparation Tool from in-Memory Fitting to Big Data Sets;materia: A Data Quality Control Embedded Domain Specific Language in Python;models for Arabic Document Quality Assessment;open Data Quality Dimensions and Metrics: State of the Art and Applied Use Cases;synthesizing Quality Open Data Assets from Private Health Research Studies;incremental Modeling Method of Supply Chain for Decision-Making Support;impact of New Mobility Services on Enterprise architectures: Case Study and Problem Definition;sharedWealth: Disincentivizing Mining Pools through Burning and Minting.
Object detection and tracking at real time is important and challenging tasks in many computer vision applications such as video robot navigation, surveillance, vehicle navigation, security applications, military appl...
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ISBN:
(纸本)9781728140421
Object detection and tracking at real time is important and challenging tasks in many computer vision applications such as video robot navigation, surveillance, vehicle navigation, security applications, military applications, patient monitoring system and traffic monitoring system. Object detection includes detecting the object in sequence of videos. In this paper we reviewed the different methods/algorithms for object detection and tracking at real time for high resolution video. Now day's high resolution imaging sensors/cameras is being used in different areas of applications such as security system, in military applications etc. For object detection and tracking in high resolution, greater frame rate requires more time to process a single frame, so it is an extreme challenges for researchers to detect and track target at real time. this sets a demand for fast computational algorithms for real time processing of high resolution videos. Moving object detection and tracking is one of the decisive active areas of research since last decade. In this paper we address and highlight a brief survey or review of various real time object detection and tracking algorithms for high resolution video available in the literature.
Searching frequent itemset in large size diverse database is one of the most important data mining problem and as existing algorithms are insufficient in mechanism that enables automatic parallelization, fault toleran...
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ISBN:
(纸本)9781728140421
Searching frequent itemset in large size diverse database is one of the most important data mining problem and as existing algorithms are insufficient in mechanism that enables automatic parallelization, fault tolerance and data distribution. Solution to this issue we design algorithm using MapReduce programming model. the overarching aim is to enhance the performance of parallel frequent itemset mining on Hadoop. Incorporating ultra-metric tress to improve more efficiency of mining frequent itemset and comparing Apriori algorithm and FP-Growth algorithm based on some parameters. We implement the algorithm with dataset of Market Basket Analytics
Object detection in remote sensing image is a challenging task in computer vision. Remote sensing images have different characteristics compared with conventional images. Especially object detection in remote sensing ...
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In this paper, we propose a distributed, unordered, label-correcting distance-1 Grundy (vertex) coloring algorithm, namely, Distributed Control (DC) coloring algorithm. Our algorithm eliminates the need for vertex-cen...
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ISBN:
(纸本)9781728136134
In this paper, we propose a distributed, unordered, label-correcting distance-1 Grundy (vertex) coloring algorithm, namely, Distributed Control (DC) coloring algorithm. Our algorithm eliminates the need for vertex-centric barriers and global synchronization for color refinement, relying only on atomic operations and local termination detection to update vertex color. DC proceeds optimistically, correcting the colors asynchronously as the algorithm progresses and depends on local ordering of tasks to minimize the execution of sub-optimal work. We implement our DC coloring algorithm and the well-known Jones-Plassmann algorithm and compare their performance with 4 different types of standard RMAT graphs and real-world graphs. We show that the elimination of waiting time of global and vertex-centric barriers and investing this time for local ordering leads to improved scaling for graphs with prominent power-law characteristics and densely interconnected local subgraphs.
Along withthe popularization of big data applications, real-time data analysis is playing an increasingly important role in data analytic applications. the distributed parallelprocessing framework is a good choice f...
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Utilizing the atomic primitives of a processor to access a memory location atomically is key to the correctness and feasibility of parallel software systems. the performance of atomics plays a significant role in the ...
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ISBN:
(纸本)9781450362955
Utilizing the atomic primitives of a processor to access a memory location atomically is key to the correctness and feasibility of parallel software systems. the performance of atomics plays a significant role in the scalability and overall performance of parallel software systems. In this work, we study the performance -in terms of latency, throughput, fairness, energy consumption-of atomic primitives in the context of the two common software execution settings that result in high and low contention access on shared memory. We perform and present an exhaustive study of the performance of atomics in these two application contexts and propose a performance model that captures their behavior. We consider two state-of-the-art architectures: Intel Xeon E5, Xeon Phi (KNL). We propose a model that is centered around the bouncing of cache lines between threads that execute atomic primitives on these shared cache lines. the model is very simple to be used in practice and captures the behavior of atomics accurately under these execution scenarios and facilitate algorithmic design decisions in multi-threaded programming.
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