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arXiv 2024年

作者： Hoehner, Steven Roysdon, Michael Department of Mathematics & Computer Science Longwood University United States Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University United States

A new position is introduced and studied for the convolution of log-concave functions, which may be regarded as a functional analogue of the maximum intersection position of convex bodies introduced and studied by Artstein-Avidan and Katzin (2018) and Artstein-Avidan and Putterman (2022). Our main result is a John-type theorem for the maximal intersection position of a pair of log-concave functions, including the corresponding decomposition of the identity. The main result holds under very weak assumptions on the functions;in particular, the functions considered may both have unbounded *** Codes 46N10, 52A39, 52A40, 60E05 (Primary) 28A75 (Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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A New Tree Structure for Local Diagnosis

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Journal of Information science and Engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of Information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Coupled Generator Decomposition for Fusion of Electro- and Magnetoencephalography Data

Coupled Generator Decomposition for Fusion of Electro- and M...

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European Signal Processing Conference (EUSIPCO)

作者： Anders S. Olsen Jesper D. Nielsen Morten Mørup Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Data fusion modeling can identify common features across diverse data sources while accounting for source-specific variability. Here we introduce the concept of a coupled generator decomposition and demonstrate how it generalizes sparse principal component analysis (SPCA) for data fusion. Leveraging data from a multisubject, multimodal (electro- and magnetoencephalography (EEG and MEG)) neuroimaging experiment, we demonstrate the efficacy of the framework in identifying common features in response to face perception stimuli, while accommodating modality- and subject-specific variability. Through split-half cross-validation of EEG/MEG trials, we investigate the optimal model order and regularization strengths for models of varying complexity, comparing these to a group-level model assuming shared brain responses to stimuli. Our findings reveal altered ~ 170ms fusiform face area activation for scrambled faces, as opposed to real faces, particularly evident in the multimodal, multisubject model. Model parameters were inferred using stochastic optimization in PyTorch, demonstrating comparable performance to conventional quadratic programming inference for SPCA but with considerably faster execution. We provide an easily accessible toolbox for coupled generator decomposition that includes data fusion for SPCA, archetypal analysis and directional archetypal analysis. Overall, our approach offers a promising new avenue for data fusion.

关键词： Soft sensors Data integration Stochastic processes Brain modeling Generators Sparse matrices Quadratic programming Faces Optimization Principal component analysis

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Poultry raising light control system for laying hens and broilers 2

Poultry raising light control system for laying hens and bro...

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Pinthong, Thidarat Ganokratanaa, Thittaporn Phetchaburi Rajabhat University Department. of Business Information Management Phetchaburi Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand

ISBN: (纸本)9798350336573

Chicken is an economic animal. one kind At present, it has been raised as a business and has expanded extensively. This is because both domestic and foreign populations have a greater demand for products and processed products from laying hens. which the raising of laying hens must be more attention to the productivity and quality of eggs in order to meet the needs of consumers with high or low temperatures Too low has an effect on the physiological system of laying hens. Reduce egg production or may result in inefficient production. And with the aforementioned problems, technology has been used to help in notifying. Warn whether the temperature in the house is suitable to increase the production of high-quality eggs. © 2023 IEEE.

关键词： Internet of things

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Spline Approaches for Anomaly Detection in UAV-Based Aerial Surveillance

Spline Approaches for Anomaly Detection in UAV-Based Aerial ...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Anhelina Zhultynska Pylyp Prystavka Department of Applied Mathematics Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

This article presents a new approach to detecting anomalies in data obtained from unmanned aerial vehicles using spline models. The relevance of the study is driven by the need for fast and accurate identification of anomalies in real time, which is critical for military intelligence and civilian monitoring. The proposed model is based on the use of momentary characteristics, such as average illumination intensity and standard deviation, which allows for effective data parameterization and detection of anomalous observations. The advantage of the spline model is its ability to adapt to complex and heterogeneous image textures, which significantly reduces the risk of missing critical anomalies. The model also demonstrates high robustness to variations in the data, making it a reliable tool for analyzing images in different lighting and landscape conditions. Experiments using forest, road, and car textures have shown that the spline model is able to accurately identify anomalies, which increases the efficiency of analysis and allows for a quick response to detected changes.

关键词： Adaptation models Analytical models Accuracy Surveillance Lighting Autonomous aerial vehicles Data models Real-time systems Splines (mathematics) Standards

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Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set 10

Classification Models and Variable Selection for SME Credit ...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

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Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

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High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

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Comparing UAV camera data with satellite imagery for global localization

Comparing UAV camera data with satellite imagery for global ...

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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Oleksandr Kozachuk Pylyp Prystavka Department of Applied Mathematics Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

Global visual localization is critical for UAVs operating in environments where global navigation satellite systems (GNSS) are unreliable or unavailable. While many methods, such as visual odometry (VIO), rely on optical flow for localization, they often require prior knowledge of the current coordinates. In this paper, we propose an enhanced UAV localization method using the vector of locally aggregated descriptors (VLAD) combined with a cycle generative adversarial network (GAN) architecture. Unlike traditional approaches, our method does not require knowledge of the current coordinates; it only requires a pre-downloaded flight map, enabling location determination through image matching.

关键词： Location awareness Global navigation satellite system Satellites Image recognition Image matching Autonomous aerial vehicles Generative adversarial networks Cameras Satellite images Drones

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Fast algorithms for hypergraph pagerank with applications to semi-supervised learning 24

Fast algorithms for hypergraph pagerank with applications to...

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Proceedings of the 41st International Conference on Machine Learning

作者： Konstantinos Ameranis Adela DePavia Lorenzo Orecchia Erasmo Tani Department of Computer Science University of Chicago Chicago Computational and Applied Mathematics University of Chicago Chicago

A fundamental approach to semi-supervised learning is to leverage the structure of the sample space to diffuse label information from annotated examples to unlabeled points. Traditional methods model the input data points as a graph and rely on fast algorithms for solving Laplacian systems of equations, such as those defining PageRank. However, previous work has demonstrated that graph-based models fail to capture higher-order relations, such as group membership, which are better modeled by hypergraphs. Unfortunately, the scalable application of hypergraph models has been hampered by the non-linearity of the hypergraph Laplacian. In this paper, we present highly scalable algorithms for hypergraph primitives, such as hypergraph PageRank vectors and hypergraph Laplacian systems, over general families of hypergraphs. In addition to giving strong theoretical guarantees, we empirically showcase the speed of our algorithms on benchmark instances of semi-supervised learning on categorical data. We exploit their generality to improve semi-supervised manifold clustering via hypergraph models. By providing significant speed-ups on fundamental hypergraph tasks, our algorithms enable the deployment of hypergraph models on a massive scale.

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