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Measures for Quantifying the Rate of Change in Firm Size in Econometrics Problems

Measures for Quantifying the Rate of Change in Firm Size in ...

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international conference on Soft Computing and measurements (SCM)

作者： Anastasia S. Semenova Konstantin K. Semenov National Research University ‘Higher School of Economics’ – St. Petersburg St. Petersburg Russia Higher School of Computer Tech. and Information Systems Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

This study discusses various measures for quantifying the firm size change rate in econometrics problems. The paper presents their comparative correlation analysis performed on a large dataset, discusses their indicative ability, identifies their features and outlines the boundaries of the possible application areas in econometric models. The study considers the robustness of the corresponding quantitative values of the considered measures to random errors in econometric data.

关键词： Analytical models Correlation Biological system modeling Computational modeling measurement uncertainty Size measurement Robustness

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Graded Error Compensation Method for Heavy-Load Manipulators Based on Laser Tracking measurement 16th

Graded Error Compensation Method for Heavy-Load Manipulators...

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16th international conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Chen, Zhangwei Zu, Hongfei Zhang, Xiang Chen, Xuwen Wang, Zhirong State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310058 China School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Premax Co. Ltd Ningbo315048 China Testing Standardization and Certification Changzhou Institute of Inspection Changzhou21 3000 China

ISBN: (纸本)9789819964888

In order to meet the high positioning accuracy requirements of industrial application scenarios for manipulators, the motion accuracy of industrial manipulators is generally improved through error compensation. However, due to the presence of non-geometric errors such as friction and joint backlash, error compensation is usually a complex nonlinear problem. Therefore, the existing methods of improving the absolute positioning accuracy of manipulators through model parameter calibration are difficult to obtain good performance, especially for heavy-load manipulators. To solve the above problems, this manuscript proposes a graded error compensation method for heavy-load manipulators based on laser tracking measurement, which realizes the compensation of kinematics error and joint stiffness, and improves the positioning accuracy of the manipulator in the entire workspace. Firstly, the geometric parameters were modeled and compensated by combining the distance model and the Denavit-Hartenberg (DH) model. Then, joint stiffness errors were studied and compensated. Finally, experiments were conducted on a heavy-load manipulator to verify the feasibility and effectiveness of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Error compensation

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international Scientific conference on Digital Transformation on Manufacturing, Infrastructure and Service, DTMIS 2022

International Scientific Conference on Digital Transformatio...

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international Scientific conference on Digital Transformation on Manufacturing, Infrastructure and Service, DTMIS 2022

ISBN: (纸本)9783031327186

The proceedings contain 79 papers. The special focus in this conference is on Digital Transformation on Manufacturing, Infrastructure and Service. The topics include: Forming a Methodology for Organizing Investment and Financial Reporting in the Activities of Subsidiaries in the Organization of Antitrust Compliance in Uzbekistan;agent-Based modeling of Tourist Flow Distribution Based on the Analysis of Tourist Preferences;being Smarter in the Pursuit of a Smart City;prospects for Improving the Benchmarking Activity of Automotive Enterprises in Uzbekistan;socioeconomic Mechanisms of Managing Intellectual Capital of the Industrial Ecosystem;assessing Global Trends in World Energy: Genesis of the New Energy Transition;developing the Informatization of the Technological Waste Management Process in the Lean Production System of an Enterprise;competencies for Digital Economy: Economic Engineer for Transport Industry;development of Tools for Decarbonization of Electricity Consumption in the Russian Federation;sino-Russian Industrial joint Investment—Taking Oil and Gas Resource Development and Energy Cooperation as an Example;use and Processing of Digital Data in the Era of Industry 4.0;specifics of Implementing Digital Technologies in Investment and Construction Projects in China;measurement and Evaluation of China’s Regional Innovation Efficiency: Analysis Based on Network Super SBM-Malmquist Model;the Influence of Announcement and Event Dates of M&A Deals on Return of Company’s Shares in Oil and Gas Industry;investments in the Fixed Capital of the Fuel and Energy Complex as a Factor in Growing Innovations and Increasing Digitalization;risk Assessment of Decarbonization Projects in the Context of Digital Transformation of the Oil and Gas Industry;preface.

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Useckit: An Open-Source Deep-Learning Toolkit Bundling State-Of-The-Art Algorithms for Evaluating Behavioral Biometrics

Useckit: An Open-Source Deep-Learning Toolkit Bundling State...

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IEEE international joint conference on Biometrics (IJCB)

作者： Jonathan Liebers Tristan Kley Carina Liebers Uwe Gruenefeld Stefan Schneegass University of Duisburg-Essen Essen Germany

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

There is an endeavor in the Human-computer Interaction (HCI) community to create novel authentication schemes so that passwords finally become obsolete and practical security is enhanced. For this purpose, researchers combine behavioral biometrics with deep learning. However, because the process of creating neural networks is inherently complex and each model architecture has certain limitations, implementing research prototypes is a time-consuming and challenging task. Therefore, we present useckit, an open-source toolkit that provides deep learning algorithms to support the creation of scientific evaluations in authentication research. Useckit provides multiple paradigms for implementing user verification and identification, functions to calculate common metrics, and neural network architectures founded in literature. It is written in Python and supports researchers and practitioners in creating, implementing, and rigorously evaluating novel deep learning-based authentication schemes.

关键词： measurement Deep learning Object oriented modeling Biological system modeling Unified modeling language Neural networks Authentication Prototypes Passwords Security

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Prediction of Left Atrial Appendage Occluder Size Based on a 3D Printed Model Method Design 27

Prediction of Left Atrial Appendage Occluder Size Based on a...

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27th IEEE international conference on Intelligent Engineering systems, INES 2023

作者： Hecko, Jan Hudec, Miroslav Chovancik, Jan Jiravsky, Otakar Miklik, Roman Knybel, Lukas Sknouril, Libor Penhaker, Marek VSB-Technical University of Ostrava Faculty of Elektrical Engineering and Computer Science Czech Republic Hospital AGEL Třinec-Podlesi A.s. Cardiology Department Trinec Czech Republic University Faculty of Medicine Masaryk Brno625 00 Czech Republic Oncology Clinic Faculty Hospital Ostrava Ostrava Czech Republic

ISBN: (纸本)9798350328516

This paper describes the design of a method to simulate the implantation of an Amulet-type occluder using a 3D model of the left atrium appendage. A methodology was developed for the 3D printing of left atrial models followed by simulation of implantation. CT DICOM data for creating the 3D model were obtained from a retrospective cohort of patients with left atrial appendage occluder closure. These data were used to print 3D models, and a simulation of the Amulet occluder implantation was performed on these models. A demo set of all sizes was tested to select the most optimal size. The success of the methodology was verified in all 33 patients. The average left atrial modeling time was 11.3 min, and the average printing time was 4:40 hours. With a resin consumption of 21.2 mL, the average cost per model is 2. The final methodology has become a routine part of preoperative planning in patients prior to left atrial appendage closure, and prospective data will answer whether it contributes to a decrease in operative time, radiation dose during surgery, and number of repositions. Any improvements may contribute to reducing the cost of implantation. © 2023 IEEE.

关键词： Design

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Approximating Closed Queueing Networks in Semi-Markov Random Environments

Approximating Closed Queueing Networks in Semi-Markov Random...

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international Symposium on modeling, Analysis and Simulation of computer and Telecommunication systems (MASCOTS)

作者： Yaqi Zhou Matthew Sheldon Giuliano Casale Department of Computing Imperial College London UK

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

modeling queueing networks in random environments is a challenging problem that finds application in routing, reliability analysis, and evaluation of systems processing bursty workloads. In this paper, we introduce a method to evaluate closed queueing networks in random environments that can deal with non-Markovian transitions among environment stages and accurately approximate time-averaged performance metrics. Our technique supports both state-independent and state-dependent random environments, the latter allowing us to analyze challenging queueing network models with bursty service processes. Using simulation on representative case studies, we show that our technique provides low approximation errors. We further demonstrate the applicability of our result to a mobility case study demonstrating the applicability of the proposed approach.

关键词： measurement Analytical models Computational modeling Switched mode power supplies Approximation algorithms Routing Telecommunications Telecommunication network reliability Iterative methods Queueing analysis

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A theory of causal inference based on additive noise model and its application to radiological security projects

A theory of causal inference based on additive noise model a...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Huang Jiahan Wan Yaping Zhong Hua School of Computer University of South China Hengyang China

Security is particularly important in the process of radioactive material transportation. In order to solve the impact of the uncertain dynamic characteristics of the actual system and the complex uncertain noise containing in the target tracking and positioning of BDS(BeiDou Navigation Satellite System), an algorithm of causal inference is constructed to explain and deal with the state estimation method of the complex uncertain noise system. The measurement of noise variance is performed by using the covariance matching principle and real-time updated noise data with additive measurement noise of the system. an adaptive process detection model is proposed for the modeling problem of the Kalman filter. In comparing and testing the models, The adaptive process measurement model can give ideal estimation results regardless of the initial value of the measurement noise variance in the results. The paper demonstrates that the adaptive process measurement model has good robustness and is highly effective in dealing with state estimation problems for systems with noise which is complex and uncertain.

关键词： Adaptation models Heuristic algorithms Computational modeling Real-time systems Robustness Data models Noise measurement

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Mean Field Analysis of Serverless Edge Computing for Smart Vehicle Applications

Mean Field Analysis of Serverless Edge Computing for Smart V...

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Electronic Information Engineering and computer Communication (EIECC), international conference on

作者： Yuan Yao Rong He School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Smart cities and vehicles generate numerous time sensitive tasks, making edge computing a key solution for reducing latency. However, using serverless models at the edge faces challenges like dynamic city traffic and resource constraints. This study analyzes serverless edge computing for smart vehicles through modeling and experiments. we develop a mean field model captures steady-state system behavior under stochastic job arrivals, enabling efficient analysis of metrics like mean sojourn time and service degradation probability. Through experiments and simulations, we demonstrate the feasibility of serverless edge servers for low-latency offloading, providing insights for optimizing these systems in urban environments.

关键词： measurement Degradation Analytical models Smart cities Computational modeling Stochastic processes Real-time systems Steady-state Vehicle dynamics Edge computing

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Detecting Semantic Similarity of Diseases based Machine Learning

Detecting Semantic Similarity of Diseases based Machine Lear...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qumsiyeh, Emma Yousef, Malik Salah, Zaidoun Jayousi, Rashid Al-Quds University Department of Computer Science and Information Technology Jerusalem Palestine Zefat Academic College Galilee Digital Health Research Center Department of Information Systems Zefat Israel Arab American University Molecular Genetics and Genetic Toxicology Ramallah Palestine

ISBN: (纸本)9798350337488

Identifying disease-disease associations (DDAs) is critical in medicine and systems biology, providing valuable insights into the complex relationships among different diseases. Recent advances in systems biology and the increasing availability of diverse biological data have provided opportunities for such research. However, the heterogeneity of the data makes it difficult to identify disease associations. To develop effective methods for predicting DDAs, it is necessary to leverage data at the molecular level. In this study, utilizing the Grouping-Scoring-modeling approach, we have developed a statistical technique to compute a semantic similarity metric between the disease under study and other diseases. We have also used Jaccard similarity to compute similarity among disease groups. We have considered GediNETPro, using machine learning techniques, to record, over the Monte Carlo cross-validation, all the ranks based on the scores assigned by the S component to each disease group. The K-means clustering algorithm is applied to these recorded ranks for each disease group to detect patterns of similar diseases by placing them into coherent clusters. A cluster score (named as cluster-score) is assigned for each cluster by averaging the ranks of its members. Notably, the lowest cluster score is the more significant one. The semantic approach is applied to the top-ranked clusters to detect a semantic relationship between the disease under investigation and other diseases (groups). In addition, we have applied diversity metrics based on the Jaccard measurement to each disease cluster to support the semantic findings. A high value of diversity indicates a greater likelihood of detecting distinct groups with semantic relationships. This study enhances our understanding of disease associations and increases our ability to improve treatment strategies. This is especially important in a context where disease associations go beyond the scope of genetic factors. © 2023 IEEE.

关键词： disease-disease associations diversity metrics GediNETPro Grouping-Scoring-modeling approach Jaccard index Jaccard similarity K-means clustering algorithm machine learning Monte Carlo cross-validation semantic similarity

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Enhancing Sidewalk Maintenance through Accurate joint Deflection measurement

Enhancing Sidewalk Maintenance through Accurate Joint Deflec...

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IEEE international conference on Consumer Electronics (ICCE)

作者： Jisoo Park Seonghak Lee Paul Hyunbin Cho Zongkai Xia Loucif Hebbache Dept. of Artificial Intelligence Chung-Ang University Seoul South Korea Dept. of Computer Science Chung-Ang University Seoul South Korea IRIS RaD Group Inc. Toronto Canada

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In this work, we propose a novel real-time method for precise measurement of sidewalk joint deflections utilizing stereo vision and deep learning. This method is designed to address key challenges in infrastructure management within the context of smart cities. Our approach leverages stereo vision to establish a highly accurate Pixel-to-Meter ratio, enabling the precise conversion of image data into real-world distances. This allows for precise measurement of joint deflections by capturing depth information between adjacent sidewalk slabs. We then integrate instance segmentation, which enables the precise identification of individual sidewalk slabs, facilitating the accurate detection of joint deflections between these sections. Together, these techniques provide a robust method for detecting and measuring sidewalk joint deflections with high precision. Our results demonstrate the potential of our method for large-scale deployment in smart cities, with future work focused on integrating the system into predictive maintenance frameworks and expanding its application across diverse urban settings.

关键词： Instance segmentation Accuracy Pedestrians Smart cities Scalability Real-time systems Stereo vision Maintenance Slabs Predictive maintenance

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