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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： José Gómez-DelaHiz Aymen Fakhreddine Juan M. Murillo Jaime Galán-Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria Technology Innovation Institute Abu Dhabi UAE

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

The application of Unmanned Aerial Vehicle (UAV) networks to the coverage problem in rural and low-income areas is actively studied nowadays by the research community. A UAV-based network infrastructure is different from traditional cellular networks relying on Base Stations. By mounting small cells on top of UAVs, coverage can be enhanced in regions where network operators avoid to invest due to the low Return on Investment. In case there is a requirement for an enhanced throughput in a rural area (e.g., users requesting IoT applications with strict QoS requriements), a potential solution is to place several UAV s over the same area, thus maximizing the offered throughput. However, this situation would lead to an increase in the energy consumption. To tackle this problem, this paper proposes an optimal solution to the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications, while minimizing the energy consumption of the swarm of UAV s. A Mixed Integer Linear Programming (MILP)-based formulation is defined and evaluated over realistic scenarios. Experimental results demonstrate how the solution is able to perform UAV placement in a way to maximize the offered throughput in the highest number of areas, while minimizing the total number of deployed UAVs.

关键词： Energy consumption Costs Microservice architectures Quality of service Throughput Autonomous aerial vehicles Mixed integer linear programming

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Data-driven hierarchical Bayesian model for predicting wall deflections in deep excavations in clay

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computers and Geotechnics 2024年 168卷

作者： Tabarroki, Mohammad Ching, Jianye Yuan, Shih-Hsiang Phoon, Kok-Kwang Teng, Fuchen Dept. of Civil Engineering National Taiwan University Taipei Taiwan Information Systems Technology and Design/Architecture and Sustainable Design Singapore University of Technology and Design 8 Somapah Road #07-301 Building 3 Level 7 Singapore Dept. of Civil and Construction Engineering National Taiwan University of Science and Technology Taipei Taiwan

The current paper presents a data-driven hierarchical Bayesian model (HBM) for predicting maximum lateral wall deflections in deep excavations in clay. The presented approach can address a number of challenges, including handling missing input parameters, incorporating the observational method, and accounting for site uniqueness. A new database, EXCA-CLAY/11/901, comprising 302 excavation sites worldwide, is compiled to train the HBM. The trained HBM is applied to a real case study in downtown Shanghai. The HBM can be applied to predict the maximum lateral wall deflection at each excavation stage independent of measured deflections from past stages (non-observational approach) or the more typical observational approach that includes past deflection measurements. Extensive cross-validation analysis validates the performance of the HBM and compares it with three existing regression models. The results show that the regression models exhibit slightly better performance within their applicable range when compared to the non-observational HBM prediction, but are outperformed by the observational HBM. The HBM is shown to offer several advantages over the conventional regression models, including the ability to produce 95% confidence intervals, handle missing input parameters, accommodate a diverse database, and continuously refine predictions as new information becomes available. © 2024 Elsevier Ltd

关键词： Database systems

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Numerical Analysis of a Functional Equation for the Stochastic Optimal Control Problem 15

Numerical Analysis of a Functional Equation for the Stochast...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Chernov, Aleksey Zhukova, Aleksandra MIPT-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia Frc Csc Ras Dept. of Mathematical Modelling of Economic Systems Moscow Russia

This work presents the approach to solving the functional equation of non-local type that appears in the study of a stochastic optimal control problem. This experience demonstrates the challenges and possible solution... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Numerical methods

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Price Optimisation and Management 3

Price Optimisation and Management

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Shafkhan, M.T.M Jayasundara, P.R.S.S Kariyapperuma, K.A.D.R.L Lakruwan, H.P.S Rupasinghe, Lakmal Sri Lanka Institute of Information Technology Dept. of Computer and Software Eng Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

One of the most crucial decisions a company makes is its pricing strategy. When it comes to pricing, a company must consider the present, as well as the future and the past pricing. It enables a company to make sound judgments. In the process of marketing products, price is the only factor that creates income;everything else is a cost. Guessing at product pricing is a little like throwing darts blindfolded;some will hit something, but it probably will not be the dartboard. Large-scale enterprises throughout the world still depend on Excel sheets with numerous manpower or expensive pricing solutions. Expensive pricing systems are difficult to implement for Medium and Large Sized Enterprises in countries like Sri Lanka. Our goal in this research is to propose an affordable, efficient, easy-To-use and secure solution which can be implemented in Medium and Large Sized Enterprises in Sri Lanka. Manufacturing cost, shipping cost, competitor analysis, customer behaviour are taken as the root factors when deciding the price. The proposed solution includes Machine Learning components which is fed with historical data of these four factors to predict the manufacturing cost, shipping cost, competitor price and customer behavioural factors on a given date and as well as an optimisation component which enables the opportunities to minimise the cost and maximise the profit. The four Machine Learning components are implemented using LSTM, ARIMA, Facebook Prophet and a clustering model. The optimisation model is implemented using linear programming optimise these four components. A user-friendly web application is implemented using MEAN stack with micro service architecture to access this. © 2021 IEEE.

关键词： Cost benefit analysis

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Resilience Quantification Control of Uncertain Nonlinear Interconnected systems under Consecutive Replay Attacks

Resilience Quantification Control of Uncertain Nonlinear Int...

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IEEE Conference on Decision and Control

作者： Ying Li Youqing Wang Thomas Parisini Dong Zhao College of Information Science and Technology Beijing University of Chemical Technology Beijing China Dept. of Electrical and Electronic Engineering Imperial College London London UK Dept. of Electronic Systems Aalborg University Denmark Dept. of Engineering and Architecture University of Trieste Trieste School of Cyber Science and Technology Beihang University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We address the resilience quantification in terms of control for a class of uncertain nonlinear interconnected systems subjected to consecutive replay attacks. The distributed controllers and parameter estimators in the framework of adaptive approximation are designed to stabilize the uncertain nonlinear interconnected systems. The explicit resilience condition for the resting time between two consecutive attacks is characterized by taking into account the stability requirements. Specifically, if the resting time between two consecutive replay attacks is larger than a suitable boundary constant, then all signals of the interconnected system controlled by the adaptive distributed controllers remain bounded. Simulation results are given to show the effectiveness of our theoretical analysis.

关键词： Adaptive systems Simulation Interconnected systems Control systems Stability analysis Resilience

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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Advancement in Renewable Energy and Intelligent systems (AREIS), International Conference on

作者： V. Raji S. Maheswari S. Sree Dharinya P. Chinniah Kavin K. S. Kiruba Thangam Raja Dept. of Information Technology S.K.P. Engineering College Tiruvannamalai India Dept. of CSE(IoT) Nandha Engineering College Erode Tamilnadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Dept. of ECE ST. Joseph College of Engineering Sriperumbudur Tamilnadu India Dept. of EEE Government College of Engineering Tirunelveli Tamil Nadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems(SCORE) VIT University Vellore India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Transfer learning computer architecture Brain modeling Data augmentation Data models Convolutional neural networks

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How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance

arXiv

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arXiv 2024年

作者： Li, Hongkang Zhang, Shuai Zhang, Yihua Wang, Meng Liu, Sijia Chen, Pin-Yu The Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States New Jersey Institute of Technology United States The Dept. of Computer Science and Engineering Michigan State University United States IBM Research United States

Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high average accuracy is accompanied by low accuracy in a minority group. Despite algorithmic efforts to improve the minority group accuracy, a theoretical generalization analysis of ERM on individual groups remains elusive. By formulating the group imbalance problem with the Gaussian Mixture Model, this paper quantifies the impact of individual groups on the sample complexity, the convergence rate, and the average and group-level testing performance. Although our theoretical framework is centered on binary classification using a one-hidden-layer neural network, to the best of our knowledge, we provide the first theoretical analysis of the group-level generalization of ERM in addition to the commonly studied average generalization performance. Sample insights of our theoretical results include that when all group-level co-variance is in the medium regime and all mean are close to zero, the learning performance is most desirable in the sense of a small sample complexity, a fast training rate, and a high average and group-level testing accuracy. Moreover, we show that increasing the fraction of the minority group in the training data does not necessarily improve the generalization performance of the minority group. Our theoretical results are validated on both synthetic and empirical datasets, such as CelebA and CIFAR-10 in image classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility

Reinforcement Learning for Unrelated Parallel Machine Schedu...

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Simulation Winter Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

关键词： Metalearning Adaptation models Heuristic algorithms Stochastic processes Reinforcement learning Parallel machines Dynamic scheduling Size measurement Dispatching Graph neural networks

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Design and Evaluation of an IoT-Based Real-Time Underground Water-Level Sensor System

Design and Evaluation of an IoT-Based Real-Time Underground ...

引用

Applied Sensing Conference (APSCON), IEEE

作者： Akash Reddy Kondapuram Albert Treytl Thomas Bigler Thilo Sauter Dept. for Integrated Sensor Systems University for Continueing Education Krems Wiener Neustadt Austria Institute for Computer Technology & TU Wien Vienna Austria

The efficient management of underground water resources is crucial for sustainable development and environmental preservation. Using Internet of Things (IoT) technology can automate data acquisition necessary for evidence-based decisions to react on volatile groundwater levels caused by extensive water use or climate change. This paper focuses on the design of a low-cost, easy-to-install sensor setup for automated underground water level monitoring. The innovation is a 3D printable design that allows cheap manufacturing and easy deployment. Together with a widespread off-the-shelf microcontroller with LoRaWAN connectivity, water levels in strategic or remote locations can be gathered accurately, at high frequencies, and over large areas. The system effectively addresses practical challenges including concerns regarding system connectivity, power efficiency, and data accuracy. In particular, for deep-well pipes the setup overcomes problems of kinks and misalignment. Further, an evaluation of operation in a field test is given demonstrating its effectiveness.

关键词：

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Computationally efficient predictive control based on ANN state-space models

arXiv

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arXiv 2023年

作者： Hoekstra, Jan H. Cseppento, Bence Beintema, Gerben I. Schoukens, Maarten Kollár, Zsolt Tóth, Roland Dept. of Measurement and Information Systems The Budapest University of Technology and Economics Hungary The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme exploits little of the structural properties or available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN state-space models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing ANN identification methods, such as the SUBNET method that uses a state encoder, can provide efficient implementation of MPCs. The performance of the proposed approach is demonstrated via a simulation study on an unbalanced disc system. © 2023, CC BY-SA.

关键词： Neural networks

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