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Fault Tolerance Computer System Structures Functioning in Residue Classes 11

Fault Tolerance Computer System Structures Functioning in Re...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and Applications (IDAACS)

作者： Krasnobaev, Victor Kuznetsov, Alexandr Kiian, Anastasiia Kuznetsova, Kateryna Kharkov Natl Univ Svobody Sq 6 UA-61022 Kharkiv Ukraine JSC Inst Informat Technol Bakulin St 12 UA-61166 Kharkiv Ukraine

ISBN: (纸本)9781665426053

Currently, the main directions of development of computer systems and components (CSC) in a positional binary number system (PNS) are improving the speed of implementation of arithmetic operations (including integer arithmetic operations) and reliability of their functioning. the complexity, scale, and volume of various computational tasks and tasks of managing large and complex information systems which are solved by computer-controlled systems (CS) necessitate the expansion of the CS functions and capabilities. It leads to an increase in the number and complexity of computing equipment and systems equipment, and the complexity of their software. this, in turn, necessitates the adoption of additional measures to ensure the high reliability of the CS and, first of all, the need to take additional measures to ensure the high fault tolerance of the CSC. the purpose of the article is to design of structures of a fault-tolerance computer system that functions in residue classes in the case of applying the active fault tolerance method.

关键词： computer system structures non-positional number system fault tolerance system of residual classes

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Optimal Design of Heliostat Field Using Random Search Algorithm

Optimal Design of Heliostat Field Using Random Search Algori...

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IEEE international conference on Power, Intelligent computing and systems (ICPICS)

作者： Chenglin Mou Kaiqi Wu Pengcheng Du Kaibo Yang Department of Engineering Mechanics School of Civil Engineering and Architecture Henan University of Science and Technology Luoyang China Department of Computer Science and Technology School of Information Engineering Henan University of Science and Technology Luoyang China Department of Automation School of Information Engineering Henan University of Science and Technology Luoyang China Department of Civil Engineering School of International Education Henan University of Science and Technology Luoyang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. the heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency.

关键词： Integrated optics Green energy Shape Optimization models Computational modeling Poles and towers Linear programming Mirrors Power generation

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Selection and Deletion Model under Discovering Valid and Invalid Test Case

Selection and Deletion Model under Discovering Valid and Inv...

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international conference on Information Technology, Information systems and Electrical Engineering (ICITISEE)

作者： thacha Lawanna Jittima Wongwuttiwat Rujira Ounchaoen International College of Digital Innovation Chiang Mai University Chiang Mai Thailand Digital Business Management Martin De Tours School of Management and Economics Assumption University Samut Prakarn Thailand

Gathering the accurate test cases of any adopted program and eliminating unrelated ones follow the notion of retaining software to build a typical of a test suite by concerning with the judgment of test case formation, fixing bugs and methods of selection, deletion or minimization, while the lowest size and the ability of solving faults have been required for this work. therefore, several test case methods have been proposed to response the matters of keeping good cases or removing the bad groups that may drop the entire performance of a software. the proposed model comprises four main algorithms, which are clarifying, selecting, deleting, and collecting test cases. It gives lower percentage of size reduction than the regression test selection by 2%-4 % while proportion of solving fault is higher than random and regression approaches about 20%-70% and 0%-25% respectively.

关键词： Electrical engineering Software algorithms Computer bugs Minimization Software Information technology Information systems

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CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING WIth A CONTINUOUS MINER USING 3-D DEPth CAMERAS

CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING W...

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SME Annual conference and Expo 2022

作者： Miller, S. Sottile, J. Chiu, H.-P. Schafrik, S. Krasner, A. Sizintsev, M. Agioutantis, Z. Rajvanshi, A. Univ. of Kentucky LexingtonKY United States SRI International PrincetonNJ United States

ISBN: (纸本)9781713845089

In recent years, a great deal of effort has been put into automating mining equipment with the goal of improving worker health and safety and increasing mine productivity. Significant progress has been made in automating mining equipment such as load-haul-dumps and drills in underground environments where global positioning systems are unavailable. this paper addresses automating the task of positioning the shuttle-car (SC) under the continuous-miner (CM) coal-discharge conveyor during cutting and loading operations. A stereo depth camera is mounted on the SC. Machine learning based algorithms are applied on the camera’s output to identify the CM discharge conveyor and segment the scene into various regions such as roof, ribs, and personnel. this information is used to plan the shuttle car path to the CM discharge conveyor. the approach currently uses a 1/6th scale continuous miner and shuttle car in an appropriately scaled mock mine. Copyright © 2022 by SME.

关键词： Continuous miners

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Aggregating Statistically Correlated Metabolic Pathways Into Groups to Improve Prediction Performance

Aggregating Statistically Correlated Metabolic Pathways Into...

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13th international conference on Bioinformatics Models, Methods and algorithms (BIOINFORMATICS) held as part of 15th international Joint conference on Biomedical Engineering systems and Technologies (BIOSTEC)

作者： Basher, Abdur Rahman M. A. Hallam, Steven J. Univ British Columbia Grad Program Bioinformat Vancouver BC V5Z 4S6 Canada Univ British Columbia Dept Microbiol & Immunol Vancouver BC V6T 1Z3 Canada

ISBN: (纸本)9789897585524

Metabolic pathway prediction from genomic sequence information is an essential step in determining the capacity of living things to transform matter and energy at different levels of biological organization. A detailed and accurate pathway map enables researchers to interpret and engineer the flow of biological information from genotype to phenotype in both organismal and multi-organismal contexts. In this paper, we propose two novel hierarchical mixture models, SOAP (sparse correlated pathway group) and SPREAT (distributed sparse correlated pathway group), to improve pathway prediction outcomes. Both models leverage pathway abundance to represent an organismal genome as a mixed distribution of groups, and each group, in turn, is a mixture of pathways. Moreover, both models deal with missing potential pathways in the training set by provisioning supplementary pathways into the learning framework as part of noise reduction efforts. Because the introduction of supplementary pathways may lead to overestimation of some pathways, dual sparseness is applied. the resulting pathway group dataset is then used to train multi-label learning algorithms. Model effectiveness was evaluated on metabolic pathway prediction where correlated models, in particular, SOAP was able to equal or exceed the performance of previous pathway prediction algorithms on organismal genomes.

关键词： Pathway Group Relabeling Data Augmentation Correlated Models Metabolic Pathway Prediction MetaCyc

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Benchmarking Classifiers for Loan Default Prediction using Archetypal Analysis 23

Benchmarking Classifiers for Loan Default Prediction using A...

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proceedings of the 7th international conference on algorithms, computing and systems

作者： Anastasios Antoniadis Konstantinos Georgiou Konstantinos Charmanas Nikolaos Mittas Lefteris Angelis School of Informatics Aristotle University Greece Department of Chemistry International Hellenic University Greece

ISBN: (纸本)9798400709098

the prediction of loan default is a critical process for the successful development of financial institutions. To effectively manage credit risk, numerous machine learning models have been employed to distinguish creditworthy from high-risk applicants. However, determining an optimal model remains a challenge. To address this, in the current study, we explore an alternative approach for model benchmarking. the main concept involves the usage of a pipeline that constructs different classifiers for loan prediction and compares their performance across several evaluation metrics. To achieve this goal, we deploy an approach based on a multivariate statistical method, known as Archetypal Analysis (AA). the proposed methodology is applied to four datasets with diverse structural characteristics. the findings demonstrate that advanced classifiers like Random Forests (RF) and Artificial Neural Networks (ANN), with oversampling, simple parameter tuning, and feature selection consistently outperform traditional classifiers across most evaluation criteria. In conclusion, the results showcase the ability of AA to intuitively identify the best and worst models for each unique scenario.

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Performance Analysis of Machine Learning-Based Traditional and Ensemble Techniques for Smart Grid Stability Prediction

Performance Analysis of Machine Learning-Based Traditional a...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Md. Shakib Hassan MD. Adnan Siddique Nur Mohammad Arafat Ibne Ikram Institute of Energy Technology Chittagong University of Engineering & Technology Chattogram Bangladesh Department of Electrical and Electronic Engineering Chittagong University of Engineering & Technology Chattogram Bangladesh Department of Electrical and Electronic Engineering International Islamic University Chittagong Chattogram Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

A smart grid is integral to the digitalized transformation of the electricity sector, employing self-sufficient systems that integrate information, telecommunication, and advanced power technologies. Artificial Intelligence (AI), particularly Machine Learning (ML), plays a crucial role in overcoming the limitations of traditional modeling techniques. ML enables intelligent decision-making in response to dynamic factors like changing customer energy demands or disruptions in power supply within smart grids. this paper compares Machine Learning-Based Traditional and Ensemble techniques for predicting smart grid stability, utilizing an augmented dataset from Kaggle. For optimization, various classifiers and Ensemble Techniques, including Bagging, Boosting, Stacking, and Voting, were implemented with hyperparameter tuning. the experimental study highlights that ML-based Ensemble Techniques, with optimized parameters, outperform individual traditional techniques, showcasing higher accuracy and overall superior performance. A comprehensive comparative analysis based on evaluation metrics such as accuracy, precision, recall, F1-score, and ROC emphasizes the potential of these techniques to enhance the efficiency of predicting smart grid stability.

关键词： Power supplies Stacking Power system stability Prediction algorithms Stability analysis Real-time systems Communications technology

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Low-Latency Immersive 6D Televisualization with Spherical Rendering 20

Low-Latency Immersive 6D Televisualization with Spherical Re...

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20th IEEE-RAS international conference on Humanoid Robots (HUMANOIDS)

作者： Schwarz, Max Behnke, Sven Univ Bonn Autonomous Intelligent Syst Grp Bonn Germany

ISBN: (纸本)9781728193724

We present a method for real-time stereo scene capture and remote VR visualization that allows a human operator to freely move their head and thus intuitively control their perspective during teleoperation. the stereo camera is mounted on a 6D robotic arm, which follows the operator's head pose. Existing VR teleoperation systems either induce high latencies on head movements, leading to motion sickness, or use scene reconstruction methods to allow re-rendering of the scene from different perspectives, which cannot handle dynamic scenes effectively. Instead, we present a decoupled approach which renders captured camera images as spheres, assuming constant distance. this allows very fast re-rendering on head pose changes while keeping the resulting temporary distortions during head translations small. We present qualitative examples, quantitative results in the form of lab experiments and a small user study, showing that our method outperforms other visualization methods.

关键词： Visualization Head Robot vision systems Humanoid robots Reconstruction algorithms Cameras Rendering (computer graphics)

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2nd international Workshop on Industrial Recommendation systems (IRS) 21

2nd International Workshop on Industrial Recommendation Syst...

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27th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Xu, Jianpeng Wu, Lingfei Pang, Xiaolin Sharma, Mohit Yin, Dawei Karypis, George Basilico, Justin Yu, Philip S. WalmartLabs San Bruno CA 94066 USA JD Com Beijing Peoples R China Google Mountain View CA 94043 USA Baidu Beijing Peoples R China Univ Minnesota Twin Cities Minneapolis MN USA Netflix Los Gatos CA USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781450383325

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms and social networks, to provide suggestions that a user will most likely consume or connect;thus, improving the user experience [1]. this motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers [2, 3]. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. these key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. the gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions for industrial recommendation systems. this workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing the practical insights, approaches, and frameworks as well.

关键词： Industrial Recommendation systems

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Scenario-Based Robust Optimization of Tree Structures 39

Scenario-Based Robust Optimization of Tree Structures

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Angelopoulos, Spyros Dürr, Christoph Elenter, Alex Melidi, Georgii Sorbonne University LIP6 Paris France CNRS France International Laboratory on Learning Systems Montreal Canada

ISBN: (纸本)157735897X

We initiate the study of tree structures in the context of scenario-based robust optimization. Specifically, we study Binary Search Trees (BSTs) and Huffman coding, two fundamental techniques for efficiently managing and encoding data based on a known set of frequencies of keys. Given a number of distinct scenarios, each defined by a frequency distribution over the keys, our objective is to compute a single tree of best-possible performance, relative to any scenario. We consider, as performance metrics, the competitive ratio, which compares multiplicatively the cost of the solution to the tree of least cost among all scenarios, as well as the regret, which induces a similar, but additive comparison. For BSTs, we show that the problem is NP-hard across both metrics. We also obtain an optimal competitive ratio that is logarithmic in the number of scenarios. For Huffman Trees, we likewise prove NP-hardness, and we present an algorithm with logarithmic regret, which we prove to be near-optimal by showing a corresponding lower bound. Last, we give a polynomial-time algorithm for computing Pareto-optimal BSTs with respect to their regret, assuming scenarios defined by uniform distributions over the keys. this setting captures, in particular, the first study of fairness in the context of data structures. We provide an experimental evaluation of all algorithms. To this end, we also provide mixed integer linear program formulation for computing optimal trees. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Pareto principle

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