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检索条件"主题词=Software Algorithms"

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Distribution Feeder Reconfiguration Algorithm for Electrical Outage Situations

Distribution Feeder Reconfiguration Algorithm for Electrical...

引用

International Conference for Convergence of Technology (I2CT)

作者： B.L.N.S.S Liyanarathne A.K.V Gunasekara D.V.A.V.Y Senarathna W.C.M Perera P.S.N de Silva H.M.A.I Herath General Sir John Kotelawala Defence University Rathmalana Sri Lanka Lanka Electricity Company Private Limited Colombo Sri Lanka

Feeder reconfiguration is a multi-objective process that modifies the topology of the network. This study aims to develop algorithms for distribution feeder reconfiguration during electrical outage situations to improve the reliability of the medium voltage distribution network. Matrix row/column operations are performed on the connection matrix of the graphical network model based on the incidence matrix for finding reconfiguration paths. Modern distribution networks consist of switching devices equipped with remote terminal units (RTU), that sense abnormal conditions and respond remotely. algorithms are designed to coordinate with RTU operations. The demand, which is forecasted using past current data taken from RTUs periodically, is compared with the calculated capacity of the adjacent feeders using an objective function, applying constraint conditions multiplied by weighting factors. Load shedding is performed when the total load exceeds the capacity. Radiality of the network and the safety of operation are ensured throughout the process. algorithms were tested using a prototype software and a demonstration network panel. 11kV overhead distribution network was taken as the case study. The speed of the process was compared with data collected from utility and effective results were observed.

关键词： software algorithms Prototypes Distribution networks Switches Medium voltage Linear programming Reliability engineering

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Stock Price Prediction based on Optimized Attribute and Cascaded Machine Learning Algorithm

Stock Price Prediction based on Optimized Attribute and Casc...

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Advances in Technology, Management & Education (ICATME), International Conference on

作者： Ritesh Kumar Yadav M. Sivakkumar SRK University Bhopal India

ISBN: (纸本)9781728185873

The wealth of currency and nation depends on the growth of the stock market. The prediction of stock price varies according to their parameters such as open price, close price and strike price. The variation of parameters creates an unstable and volatile situation for the stock market. The unstable nature of stock market diverts customers for the investments. In this paper proposed cascaded machine learning algorithm for the stock price prediction. The cascaded machine learning algorithm work with an optimized variance of stock parameters. The process of parameters optimization achieves by particle swarm optimization. The particle swarm optimization is a memory-based and iterative process. The proposed algorithm implemented in MATLAB software and tested with NSE data of different banks such as HDFC, IDBI and AXIS bank.

关键词： Machine learning algorithms software algorithms Education Prediction algorithms software Stock markets Particle swarm optimization

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Design of adaptive compliance control algorithm for upper limb exoskeleton rehabilitation robot

Design of adaptive compliance control algorithm for upper li...

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Chinese Automation Congress (CAC)

作者： Xiaoqing Yuan Tong Zhou Junjie Ji Yuxuan Liu Wendong Wang Fangfang Yue School of Mechanical Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781665426480

In order to improve the compliance and accuracy of upper limb exoskeleton rehabilitation robot system, this paper proposes an algorithm of adaptive compliance control combining admittance control and adaptive global fast terminal sliding mode control(AGFTSMC). Firstly, The exoskeleton platform and the control system consisting of the hardware system and software system are introduced. Secondly, The dynamic model and admittance control model of the robot system is established, and the adaptive global fast terminal sliding mode controller is designed. Thirdly, The stability of the system is proved by Lyapunov theory. Finally, the experiments of both passive mode and active mode are designed. The variation characteristics of trajectory tracking deviations, human-computer interaction forces and dynamic parameter estimation are compared and analyzed. The experimental results verify the effectiveness of the adaptive compliance control algorithm.

关键词： Adaptation models Trajectory tracking Heuristic algorithms Exoskeletons software algorithms Rehabilitation robotics software systems

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Hybrid Renewable Energy Resources Utilizing Hybrid PSO and Cuckoo Search Algorithm

Hybrid Renewable Energy Resources Utilizing Hybrid PSO and C...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Riah Ann Dr. Fermin-Cayanan Gorgonio C. Vallestero Ronan Cadmiel C. Castro Mark Adrian R. Lunaria School of Graduate Studies Mapua University City of Manila Philippines Bulacan State University City of Malolos Philippines

ISBN: (纸本)9781665401685

Renewable Energy Sources (RES) are the catalysts for climate change for these environmental challenges. From the Philippine Development Plan from 2017-2022, sustainable development supports required massive investment and fast track to improve power generation using renewable energy sources. The system’s reliability is still a challenge due to its intermittent supply of energy dependence on nature. Hybrid Renewable Energy System (HRES) is widely known today to ease effect and balance energy supply. HRES requires a wide selection of techniques to be used to satisfy the demand of a given grid. A recent study was conducted calculating the values of Levelized Cost of Energy (LCOE) using optimization technique; result yields with LCOE and LPSP higher than the current values. This study developed software for Hybrid Renewable Energy Systems in Pamarawan Island that will reduce the Levelized Cost of Energy using Hybrid PSO and Cuckoo Search Algorithm. The result appears that the HPSOCS Algorithm gives an optimized solution that leads to a value of LCOE that is within the acceptable range of the current actual value. For the twenty independent runs of the program, the average value of LCOE is 0. 1508 ${\$}$/kWh, and LPSP is 0.1096 or 10.96%. The researcher assessed the level of significance of the hypothesis using a right-tailed test for the values of LCOE. In all instances, all the observed values of LCOE using Hybrid PSOCS are lower than those of LCOE using HPSOGSA. This means that the Hybrid PSOCS yields LCOE significantly better than HPSOGSA, with a 39% relative difference at a 5% significance level. Thus, the algorithm used has an excellent performance in the optimization method of HRES. Also, the HRES software calculator can be used in sizing HRES in different locations in need of sustainable energy sources.

关键词： Renewable energy sources Costs Calculators software algorithms Optimization methods Search problems software

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An Automated Framework for Generating Synthetic Point Clouds from as-Built BIM with Semantic Annotation for Scan-to-BIM

An Automated Framework for Generating Synthetic Point Clouds...

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Simulation Winter Conference

作者： Jong Won Ma Bing Han Fernanda Leite The University of Texas at Austin Austin TX USA

ISBN: (纸本)9781665433129

Data scarcity is a major constraint which hinders Scan-to-BIM's generalizability in unseen environments. Manual data collection is not only time-consuming and laborious but especially achieving the 3D point clouds is in general very limited due to indoor environment characteristics. In addition, ground-truth information needs to be attached for the effective utilization of the achieved dataset which also requires considerable time and effort. To resolve these issues, this paper presents an automated framework which integrates the process of generating synthetic point clouds and semantic annotation from as-built BIMs. A procedure is demonstrated using commercially available software systems. The viability of the synthetic point clouds is investigated using a deep learning semantic segmentation algorithm by comparing its performance with real-world point clouds. Our proposed framework can potentially provide an opportunity to replace real-world data collection through the transformation of existing as-built BIMs into synthetic 3D point clouds.

关键词： Point cloud compression Deep learning Visualization Three-dimensional displays Annotations Semantics software algorithms

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Mining for Graph-Based Library Usage Patterns in COBOL Systems

Mining for Graph-Based Library Usage Patterns in COBOL Syste...

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Ruben Opdebeeck Johan Fabry Tim Molderez Jonas De Bleser Coen De Roover Vrije Universiteit Brussel Belgium Raincode Labs Belgium

When migrating legacy systems to more contemporary platforms, it is not always feasible to migrate their external library dependencies as well. A workaround can be to partially reimplement the library dependencies on the new platform, limiting oneself to the features that are used by the migrated system. For contemporary programming languages, several approaches to mining for library usage patterns have been proposed. For legacy programming languages, in contrast, such tools are lacking. This encumbers establishing what parts of a library need to be rewritten during a migration project, especially when considering large-scale *** this industry track paper, we present an approach to mining library usage patterns in COBOL code. We describe a library usage extractor for COBOL, which produces graphs that capture the control and data flow involved in library calls. The extractor supports legacy control flow features, such as GO TO. We use these graphs as input to two state-of-the-art frequent subgraph mining algorithms, and report on the scalability of their use for mining common library usage patterns in two industrial COBOL systems. The mined library usage patterns can help assess and subsequently steer the migration effort.

关键词： Industries Computer languages Limiting Scalability software algorithms Tools Feature extraction

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Continuous learning with random memory for object detection in robotic applications

Continuous learning with random memory for object detection ...

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Nonlinearity, Information and Robotics, (NIR) International Conference

作者： Ivan Nenakhov Ruslan Mazhitov Kirill Artemov SeyedHassan Zabihifar Aleksandr Semochkin Sergey Kolyubin ITMO University St. Petersburg Russia PJSC Sberbank Moscow Russia Innopolis University Innopolis Russia

ISBN: (纸本)9781665424080

If the robot is to interact with environment designed for humans it has to be able to cope with new objects in it’s surrounding, and not only to classify but also effectively localize objects in its reach based on visual sensing. We cannot predict all objects the robot will face. So it needs to learn them while operating, without an external update of software. One of the promising approaches to handle this task is using detectors based on convolution neural networks (CNN) with continuous learning setup. However, there is a well-known problem of catastrophic forgetting (CF) that negatively affects accuracy of neural networks, the main algorithms in object recognition nowadays. Moreover, we need to reach a trade off between training time (because it is unacceptable if the robot trains for hours) and good acccuracy of detection. In this paper we review some of the latest approaches in the field and adapt one of SOtA methods in classification task to detection task. Particularly, we adapt YOLOv5 to work in scenario with continuous learning by adding random memory mechanism from [1] and applying layer freezing in order to increase training speed. We benchmark the proposed method using iCubWorld Transformations dataset and report results with significantly improved metrics and inference speed.

关键词： Training Visualization Neural networks software algorithms Object detection Robot sensing systems software

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Research on intelligent manufacturing equipment visual inspection control based on machine vision

Research on intelligent manufacturing equipment visual inspe...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Chunyan Huo Tianshu Li Harbin Huade University Harbin Heilongjiang China

ISBN: (纸本)9781665428408

Enterprise mass production and zero waste control require on-line automatic detection in production and processing. Machine vision plays a key role in automatic detection. In recent years, with the development of economy and the progress of science and technology, the level of production and manufacturing technology has made remarkable achievements, and industrial robots are more and more widely used in the field of production and manufacturing. With the wide application of industrial robots, the technical level of industrial robots has also made significant progress. At present, industrial robots have higher functionality and stability, and industrial robots have also introduced visual monitoring technology, which makes the manufacturing field more intelligent. In order to meet the huge demand of the intelligent manufacturing equipment industry for machine vision technology, this paper combines the technical characteristics of equipment and special application environment to propose a general machine vision inspection and control technology system, which makes up for the current research deficiencies.

关键词： Manufacturing industries Visualization Service robots Machine vision software algorithms Production Manufacturing

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Development of Algorithm for Treatment of Extreme Outliers in Numerical Data, Conditional on Joint Distribution Relationship

Development of Algorithm for Treatment of Extreme Outliers i...

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Nikola Iliychev Iliev Marin Marinov Sergey Radukanov Raiffeisenbank Bulgaria Sofia Bulgaria D.A. Tsenov Academy of Economics Svishtov Bulgaria St. Cyril and St. Methodius University of Veliko Tarnovo Veliko Tarnovo Bulgaria

ISBN: (纸本)9781665406833

The paper describes the development of a model for treating extreme outliers in numerical data variables, using an alternative point of view - relationship conditional on data variables' joint distribution. The mentioned algorithm finds its roots in existing practices like Winsorization (or truncation) of extreme outliers independently for each variable. After establishing an overview of historical outlier treating, the authors turn their attention towards common erroneous assumptions, that, while often, being made, bring research analysis and conclusions towards big caveats. A common example is a joint distribution relationship shape's shift from that of a circular towards a rectangular shape, unexplainable or indefensible by statistics. Next the paper makes some propositions how to fix this prominent problem, by looking at existing joint distribution relationship's shape and how it could give insight on outlier treatment. Following that, the authors try to circumscribe a methodology for application of the proposed fix by going through the algorithm's mathematical specifics and assumptions. Lastly the paper studies the algorithm's application results, including possibilities for future studies, including interdisciplinary application within programing environment, for the possible purpose of creating an actual software solution.

关键词： Shape Statistical analysis software algorithms Data processing Data models software Mathematical models

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Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles

Pylot: A Modular Platform for Exploring Latency-Accuracy Tra...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ionel Gog Sukrit Kalra Peter Schafhalter Matthew A. Wright Joseph E. Gonzalez Ion Stoica UC Berkeley

We present Pylot, a platform for autonomous vehicle (AV) research and development, built with the goal to allow researchers to study the effects of the latency and accuracy of their models and algorithms on the end-to-end driving behavior of an AV. This is achieved through a modular structure enabled by our high-performance dataflow system that represents AV software pipeline components (object detectors, motion planners, etc.) as a dataflow graph of operators which communicate on data streams using timestamped messages. Pylot readily interfaces with popular AV simulators like CARLA, and is easily deployable to real-world vehicles with minimal code *** reduce the burden of developing an entire pipeline for evaluating a single component, Pylot provides several state-of-the-art reference implementations for the various components of an AV pipeline. Using these reference implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and attains a high score on the CARLA Autonomous Driving Challenge. We also present several case studies enabled by Pylot, including evidence of a need for context-dependent components, and per-component time allocation. Pylot is open source, with the code available at https://***/erdos-project/pylot.

关键词： Codes Runtime Pipelines software algorithms Reinforcement learning Detectors Resource management

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