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检索条件"主题词=Software algorithms"

共 12291 条记录，以下是131-140 订阅

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Novel Algorithm to Solve the Constrained Path-Based Testing Problem

Novel Algorithm to Solve the Constrained Path-Based Testing ...

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IEEE International Conference on software Testing Verification and Validation Workshop, ICSTW

作者： Matej Klima Miroslav Bures Marek Miltner Chad Zanocco Gordon Fraser Sebastian Schweikl Patric Feldmeier Czech Technical University in Prague Czech Republic Stanford University California University of Passau Germany

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

Constrained Path-based Testing (CPT) is a technique that extends traditional path-based testing by adding constraints on the order or presence of specific sequences of actions in the tests of System Under Test (SUT) processes. Through such an extension, CPT enhances the ability of the model to capture more real-life situations. In CPT, we define four types of constraints that either enforce or prohibit the use of a pair of actions in the resulting test set. We propose a novel Constrained Path-based Testing Composition (CPC) algorithm to solve the Constrained Path-based Testing Problem. We compare the results returned by the CPC algorithm with two alternatives, (1) the Filter algorithm, which solves the CPT problem in a greedy manner, and (2) the Edge algorithm, which generates a set of test cases that satisfy edge coverage. We evaluated the algorithms on 200 problem instances, with the CPC algorithm returning test sets (T) that have, on average, 350 edges, which is 2.4% and 11.1% shorter than the average number of edges in T returned by the Filter algorithm and the Edge algorithm, respectively. Regarding the compliance of the generated T with the constraints, the CPC algorithm produced T that satisfied the constraints in 95% of the cases, the Filter algorithm in 45% cases, and the Edge algorithm returned T that satisfied the constraints only for 6% SUT instances. Regarding the coverage of edges, the CPC algorithm returned test sets that contained, on average, 91.5% of edges in the graphs, while for T returned by the Filter algorithm, it was 90.8% edges. When comparing the average results of the edge coverage criterion and the fulfillment of the constraint criterion by individual algorithms, we consider the incomplete edge coverage achieved by the CPC algorithm and, at the same time, 95% fulfillment of the graph constraints to be a reasonable compromise.

关键词： software testing Scalability Conferences software algorithms Semantics Metaheuristics Employment Directed graphs Filtering algorithms Testing

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Efficient Algorithm Analysis Of Kidney Images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

引用

IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： G.V. Thanuja Ramya S Dept. Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications.

关键词： Image edge detection Computed tomography software algorithms Signal processing algorithms Computer architecture Table lookup IP networks Logic Field programmable gate arrays Kidney

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PROCA: Programmable Probabilistic Processing Unit Architecture with Accept/Reject Prediction & Multicore Pipelining for Causal Inference

PROCA: Programmable Probabilistic Processing Unit Architectu...

引用

IEEE Symposium on High-Performance Computer Architecture

作者： Yihan Fu Anjunyi Fan Wenshuo Yue Hongxiao Zhao Daijing Shi Qiuping Wu Jiayi Li Xiangyu Zhang Yaoyu Tao Yuchao Yang Bonan Yan Institute for Artificial Intelligence Peking University Beijing China Beijing Advanced Innovation Center for Integrated Circuits School of Integrated Circuits Peking University Beijing China Independent Researcher School of Electronic and Computer Engineering Peking University Shenzhen China Chinese Institute for Brain Research Beijing China

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Causal inference is an important field in data science and cognitive artificial intelligence. It requires the construction of complex probabilistic models to describe the causal relationships between random variables. Probabilistic models rely on probabilistic programming as a flexible framework. However, the computing speed of probabilistic programming is often hindered by the extensive use of Markov chain Monte Carlo (MCMC) algorithms, even though they are powerful in Bayesian inference. To accelerate MCMC, this work presents PROCA, a programmable MCMC-based probabilistic processing unit architecture. PROCA exploits processing-in-memory function units to generate new samples of Markov chains. PROCA is programmable to execute the computation for arbitrary forms of posterior distribution formulas that software probabilistic programming frameworks support. We develop a novel accept/reject prediction methodology to accelerate the sequential MCMC computation, thereby introducing efficient multi-core pipelining methods. We implement and validate the PROCA architecture with commercial process development kits. The implementation is evaluated based on 9 representative benchmarks, covering PyMC official tutorial probabilistic problems, single-variable probabilistic problems, and real-world causal inference problems. Our comprehensive experiments demonstrate that PROCA achieves a speedup of 172~4871 $\times$ compared to Intel Xeon Gold CPU, $42 \sim 1058 \times$ compared to NVIDIA A100 GPU, and $1.765 \times$ over state-of-the-art MCMC accelerators, respectively. PROCA achieves comparable statistical robustness to the software probabilistic programming frameworks. Compared with state-of-the-art MCMC domain-specific accelerators, our design boosts the energy efficiency by $9.47 \times$.

关键词： Computational modeling software algorithms Graphics processing units Computer architecture Tutorials Programming Probabilistic logic software Computational efficiency Pipeline processing

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Artificial Intelligence-Based Solar Power Generation Prediction and Analysis

Artificial Intelligence-Based Solar Power Generation Predict...

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Niraj H. Patel Prakruti Shah Siddharth Joshi Electrical and Electronics Engineering Department Navrachana University Vadodara India Electrical Engineering Department Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

Integration of renewable energy sources with the conventional grid is the key way to sustain the world with the limited conventional sources and also open the door to the green energy system. Solar, wind, hydro and many more are the renewable energy sources connected with the grid; however, its optimization is one of the crucial aspects as the input to these sources is variable and intermittent in nature. This paper deals with the prediction of solar power plant located at Ahmedabad. The simulation work has been carried out with System Advisor Model (SAM) software. Various parameters like temperature, solar irradiance and generated output power have been analysed through this study. Various Artificial Intelligence (AI) algorithms, Linear regression (LR) model, artificial neural network (ANN) model, support vector machines (SVM) model, random forest (RF) model, gradient boosting (GB) models has been simulated, analysed and compared in results w.r.t the root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and correlation coefficient (CC) and fond that for small amount of the power plant data provided here, ANN and SVM fits best as they provide 1 to 10 % error as compared to rest of all errors. CC gives result 1 except SVM algorithm and LR model provides linear relations.

关键词： Support vector machines Analytical models software algorithms Linear regression Artificial neural networks Predictive models Prediction algorithms Boosting Data models Root mean square

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RPC: A Modular Framework for Robot Planning, Control, and Deployment

RPC: A Modular Framework for Robot Planning, Control, and De...

引用

IEEE/SICE International Symposium on System Integration

作者： Seung Hyeon Bang Carlos Gonzalez Gabriel Moore Dong Ho Kang Mingyo Seo Ryan Gupta Luis Sentis Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin TX USA Department of Electrical and Computer Engineering The University of Texas at Austin TX USA Department of Mechanical Engineering The University of Texas at Austin TX USA

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

This paper presents an open-source, lightweight, yet comprehensive software framework, named RPC, which integrates physics-based simulators, planning and control libraries, debugging tools, and a user-friendly operator interface. RPC enables users to thoroughly evaluate and develop control algorithms for robotic systems. While existing software frameworks provide some of these capabilities, integrating them into a cohesive system can be challenging and cumbersome. To overcome this challenge, we have modularized each component in RPC to ensure easy and seamless integration or replacement with new modules. Additionally, our framework currently supports a variety of model-based planning and control algorithms for robotic manipulators and legged robots, alongside essential debugging tools, making it easier for users to design and execute complex robotics tasks. The code and usage instructions of RPC are available at https://***/shbang91/rpc.

关键词： Legged locomotion software architecture Scalability software algorithms Debugging System integration software Planning Robots Testing

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Exploration of Sustainable Excellent Engineer Cultivation Path Based on Intelligent Augmentation

Exploration of Sustainable Excellent Engineer Cultivation Pa...

引用

International Conference on Educational and Information Technology (ICEIT)

作者： Lingfei Xiao Zirui Han Ye Wei Zhifeng Ye College of Energy and Power Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

Regarding the problems in the training of engineers in Chinese universities, such as insufficient personalized training methods for different students, the effectiveness of student training needs to be pursued for excellence, sustainable personnel training path needs to be continuously improved, an original path for sustainable excellent engineer cultivation is proposed on the basis of intelligent augmentation (IA) in this paper. Firstly, in order to achieve friendly “human-machine coexistence”, a novel excellent engineer cultivation IA interaction framework is designed. Then, the modelling of IA-based engineers training is expressed by fitting with three methods based on Matlab software. Next, IA-based excellent engineer cultivation strategy is obtained by different optimization algorithms, such as sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), and human evolutionary optimization algorithm (HEOA), so to provide users with personalized training strategies based on their individual needs. After that, an IA-based sustainable excellent engineer cultivation measure is given. Finally, by the case analysis on students from Nanjing University of Aeronautics and Astronautics, the feasibility and effect of the proposed path for sustainable excellent engineer cultivation is verified effectively.

关键词： Training Human-machine systems software algorithms Fitting Optimization methods Extraterrestrial measurements Particle measurements software Personnel Particle swarm optimization

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Hybrid Feature Selection Technique for BigData Intrusion Detection Systems

Hybrid Feature Selection Technique for BigData Intrusion Det...

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Conference on Data Science and Machine Learning Applications (CDMA)

作者： Shafii M Abdulhamid Yousef Alsulaiti Faisal Albishri Sanad AL-Hadidy Nadim Rana Abdullah H. Al-Ghushami Department of Information Technology Community College of Qatar Lusail Qatar Department of Cybersecurity Science Federal University of Technology Minna Nigeria Department of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

The advancement of Big Data has posed significant challenges for intrusion detection systems (IDS) due to the high dimensionality and volume of data, which impairs traditional IDS methods' effectiveness. Addressing this, our study presents a novel hybrid feature selection technique that integrates hybrid feature selection, correlation-based feature selection (CFS), and dominance-based Initial set algorithms. The method aims to enhance classifier performance by reducing non-correlation and inconsistencies within Big Data, streamlining the decision analysis process. The method prioritizes computational efficiency and classification accuracy by applying three algorithms in sequence, beginning with data reduction and ending with feature extraction using Wicca software. The experiments and evaluations conducted on Korean datasets with different properties and sizes confirm the reliability of the proposed approach. The methodology significantly improves classification accuracy, outperforming traditional models with a notable 97% accuracy for both the Neural Network (NB) and Support Vector Machine (SVM) classifiers and a 65% accuracy for Logistic Regression (LR).

关键词： Support vector machines Accuracy software algorithms Neural networks Intrusion detection Big Data Feature extraction software Classification algorithms software reliability

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Testing of Asymmetric Cryptographic Authentication Protocols for Vehicle Communication

Testing of Asymmetric Cryptographic Authentication Protocols...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Alžbeta Kanáliková Emília Bubeníková Peter Peniak Department of Control and Information systems University of Zilina Zilina Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Many vehicles move in automobile traffic, and their increase is higher than the necessary traffic on roads. For this reason, situations arise when these roads in cities, and towns, but also outside them, are often crowded, which results in collisions or traffic accidents. For this reason, it is also appropriate to implement mutual communication in vehicles through appropriately selected network protocols. At the same time, vehicle communication is also related to their mutual authentication, which can be implemented through cryptographic algorithms, or complex cryptographic protocols (based on multiple algorithms). In this article, we discuss methods of cryptographic vehicle authentication using digital signatures. The main goal is to test selected cryptographic protocols suitable for authentication and suitable software libraries. The testing was carried out on various devices. The result is their mutual comparison and evaluation.

关键词： software libraries software algorithms Urban areas Authentication Road safety Cryptography Automobiles Digital signatures Testing Cryptographic protocols

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Group Method of Data Handling (GMDH) in Forecasting Electric Power Consumption

Group Method of Data Handling (GMDH) in Forecasting Electric...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Artem S. Babin Mikhail I. Baryshnikov Yuriy E. Gapanyuk Information Processing and Management Systems Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The group method of data handling is considered one of the most effective methods for solving the problem of supervised learning, while there are a few effective software libraries aimed at automating this method. The aim of the paper is to increase the efficiency of supervised learning problem solving by implementing a software library aimed at automating the group data handling method. The article analyses in detail the general approach, which is the basis of the group data handling method, as well as specific algorithms for implementing the method: COMBI, MULTI, MIA, RIA. The creation of an effective software library aimed at implementing the GMDH algorithms is considered. Experiments have been carried out for electric power consumption time series forecasting problem, showing the advantages of the developed library.

关键词： software libraries Data handling software algorithms Time series analysis Supervised learning C++ languages Prediction algorithms Power systems Forecasting Python

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Locating Faults on Overhead Power Transmission Lines of 110 kV and Above Using Emergency Mode Parameters

Locating Faults on Overhead Power Transmission Lines of 110 ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Dmitriy Sharygin Galina Filatova Andrey Yablokov Department of electric power systems automatic control Ivanovo State Power Engineering University (ISPU) Ivanovo Russia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article is devoted to the development of new fault location algorithms based on emergency mode parameters at instantaneous values on overhead power transmission lines of 110 kV and above. Increasing the accuracy of fault location algorithms is an important and urgent task. The methodological and simulation errors of fault location are directly related to the configuration of the power transmission line. The presence of parallel power transmission lines, mutual induction or branch substations affects the accuracy of fault location. In this work, the authors modernize their own previously developed fault location algorithms based on instantaneous values for complex power transmission line configurations. The developed fault location algorithms do not require additional corrections or means for processing primary signals and can be successfully applied when using data from digital in-strumental transformers. The studies of the algorithms were carried out on the simulation models of the overhead power lines in the Simulink/MATLAB software package. The remote fault location of the damage was carried out according to the parameters of the emergency mode. The assignment of parameters for the simulation modeling is carried out using the Monte Carlo method with subsequent statistical processing of the results and the construction of a justified inspection zone.

关键词： Instrument transformers Power transmission lines Accuracy Substations Voltage measurement software packages software algorithms Fault location Inspection Transmission line measurements

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