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2nd International Conference on Machine Learning and Intelligent systems engineering, MLISE 2022

作者： Guan, Qinglan Ma, Yining Yang, Siqi The Ohio State University College Of Engineering Department Of Computer Science And Engineering ColumbusOH43210 United States Sichuan University College Of Mathematics Chengdu610065 China Zhongnan University Of Economic And Law Information Management And Information System Wuhan China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

Because new energy vehicles are not fully popularized, the data on the monthly sales of new energy vehicles are scattered, and nobody has taken the responsibility to organize it regularly until 2015, thus the recording period is short. To collect customer wishes, this study also carefully designed a questionnaire and spent a lot of time collecting answers for all ages. In this paper, this study mainly focusses on the field of new energy vehicle market forecast and analyze the key factors that affect people's purchase of new energy vehicles. To Figure out the most significant factor influencing the sale of the new vehicle market, this paper uses the least square regression, ridge regression, and the k nearest neighbor regression to build models and compare the coefficient of each independent variable. The accuracy of the normal Back Propagation (BP) model cannot meet the actual demand, this paper propose a working-well BP-LSTM model which is a combination of the BP model and long short-term memory (LSTM) model. First, this study decomposes the sales data into the non-random fluctuation series and the random fluctuation series. Then, this study builds models for each series by BP and LSTM network respectively. This paper also proposes a decision tree model based on the result of our questionaries to gain the importance score of different attributes. The experimental results show that the total number of charging piles plays the most important role in the sales of new energy vehicles and the BP-LSTM model performs well and the mean square error is 2.525. Reasons for buying the new energy car affect the willingness of people's purchase most and the distance influences which type of new energy vehicles would people buy. © 2022 IEEE.

关键词： Long short-term memory

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Advancing Healthcare IoT: Blockchain and Federated Learning Integration for Enhanced Security and Insights

Advancing Healthcare IoT: Blockchain and Federated Learning ...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Rida Malik Atta ur-Rehaman Hamza Razzaq Chandradeep Bhatt Keshav Kaushik Inam Ullah Khan Department of Information Security Riphah Institute of System Engineering Islamabad Pakistan Department of Data Science Riphah Institute of System Engineering Islamabad Pakistan Department of CSE Graphic Era Hill University Uttarakhand India Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Electronic Engineering SEAS Isra University Islamabad Campus Pakistan

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Fast-growing IoT devices in healthcare have ushered in a new era of data-driven patient care and treatment. These gadgets capture too much sensitive medical data, a major issue. We need to boost data analysis, security, and privacy. This research uses cutting-edge concepts like federated learning and blockchain technology to improve Healthcare IoT analytics and security. This study examines blockchain and federated learning's numerous integration aspects. It begins with data security, employing blockchain's immutability and decentralization to prevent manipulation. Federation learning was utilized to solve privacy concerns while adhering to tight healthcare data security regulations, providing strong data analysis without releasing raw, patient-specific information. This study examines how federated learning and blockchain technologies enhance healthcare IoT systems' analytics. This connection allows safe, collaborative analysis among healthcare professionals and devices, offering more insights for research, diagnostics, and tailored patient care. How to integrate these cutting-edge technologies into healthcare IoT networks is also explored.

关键词： Training Data privacy Technological innovation Federated learning Data security Collaboration Training data

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VENUS: A Geometrical Representation for Quantum State Visualization

arXiv

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arXiv 2023年

作者： Ruan, Shaolun Yuan, Ribo Guan, Qiang Lin, Yanna Mao, Ying Jiang, Weiwen Wang, Zhepeng Xu, Wei Wang, Yong School of Computing and Information System Singapore Management University Singapore Department of Computer and Information Sciences University of Delaware United States Department of Computer Science Kent State University United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Computer and Information Science Department Fordham University United States Electrical and Computer Engineering George Mason University United States Computational Science Initiative Brookhaven National Laboratory United States

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed system

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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Computing (ICOCO), IEEE International Conference on

作者： Muntadher Saadoon Siti Hafizah Ab. Hamid Hazrina Sofian Hamza Altarturi Nur Nasuha Zati Hakim Azizul Haqi Khalid Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer and Communication Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism.

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Automatic Mail Reception system for Patients to Identify Health Conditions 4

Automatic Mail Reception System for Patients to Identify Hea...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Prema, S. Harshith, N. Kousar, Heena Malini, K.V. Meenatchi, S. Kumar, M Jogendra Mahendra Engineering College Department of Information Technology Tamil Nadu Namakkal637503 India Jss Academy of Higher Education & Research Department of Health System Management Studies Karnataka Mysuru570004 India East Point College of Engineering and Technology Department of Computer Science and Engineering Karnataka Bengaluru560049 India Sri Sairam College of Engineering Department of Electrical and Electronics Engineering Bengaluru562106 India School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Predicting and detecting human health conditions in the early stages is a crucial thing to be considered in the medical field. Lung tumour has been one of the most often common malignancies in the past few decades, as well as the high incidence of melanoma-related fatality globally. As a cost-effective alternative, computer-aided diagnostic systems can combine complicated features and identify a patient's probability of getting a lung tumour, reducing the need for unneeded and costly medical treatments. The computing profession is completely digitizing everything, and the healthcare profession is following suit with image identification and data analytics. As a modern solution to predict the presence of tumours in scan images aConvolutional Neural Network (CNN),Deep Learning (DL) model is proposed in this study. The dataset required for this study is collected from the Kaggle website and pre-processed using various pre-processing methods to make it compatible with the DL model. After pre-processing the data, the dataset is divided into a training set, testing set and validation set. The CNN model is trained using the training set, validated using the validation set and tested for efficiency using the testing set. After training and testing the CNN model a user-friendly webpage is created to make this CNN model easily accessible to the individuals. The webpage is designed in such a way that when the patients upload their lung CT scan image and enter the necessary contact details requested by the webpage, the scan is analysed and evaluated by the trained CNN model and the result is sent as an email to the user. © 2022 IEEE.

关键词： Tumors

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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It Is Hard to Distinguish Between Dominance Resistant Solutions and Extremely Convex Pareto Optimal Solutions 11th

It Is Hard to Distinguish Between Dominance Resistant Soluti...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Yang, Qite Wang, Zhenkun Ishibuchi, Hisao School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9783030720612

It has been acknowledged that dominance resistant solutions (DRSs) often exist in the feasible region of multi-objective optimization problems. DRSs can severely degrade the performance of many multi-objective evolutionary algorithms (MOEAs). In previous work, some coping strategies (e.g., the ϵ -dominance and the modified objective calculation) have been demonstrated to be effective in eliminating DRSs. However, these strategies may in turn cause algorithm inefficiency in other aspects. We argue that these coping strategies prevent the algorithm from obtaining extremely convex Pareto optimal solutions (ECPOSs), which are located around the boundary of the convex Pareto front (PF). That is, there is a dilemma between eliminating DRSs and preserving ECPOSs. To illustrate such a dilemma, we propose a new multi-objective optimization test problem with the extremely convex PF as well as the hardly dominated boundaries. Using this test problem, we investigate the performance of six representative MOEAs in terms of ECPOS preservation and DRS elimination. The results indicate that it is indeed challenging to distinguish between ECPOSs and DRSs. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping 2

METER-ML: A Multi-Sensor Earth Observation Benchmark for Aut...

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2nd Workshop on Complex Data Challenges in Earth Observation, CDCEO 2022

作者： Zhu, Bryan Lui, Nicholas Irvin, Jeremy Le, Jimmy Tadwalkar, Sahil Wang, Chenghao Ouyang, Zutao Liu, Frankie Y. Ng, Andrew Y. Jackson, Robert B. Department of Computer Science Stanford University United States Department of Statistics Stanford University United States Department of Civil and Environmental Engineering Stanford University United States Department of Earth System Science Stanford University United States Woods Institute for the Environment Precourt Institute for Energy Stanford University United States

Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is necessary. Recent advancements with deep learning on remotely sensed imagery have the potential to identify the locations and characteristics of methane sources, but there is a substantial lack of publicly available data to enable machine learning researchers and practitioners to build automated mapping approaches. To help fill this gap, we construct a multi-sensor dataset called METER-ML containing 86, 599 georeferenced NAIP, Sentinel-1, and Sentinel-2 images in the U.S. labeled for the presence or absence of methane source facilities including concentrated animal feeding operations, coal mines, landfills, natural gas processing plants, oil refineries and petroleum terminals, and wastewater treatment plants. We experiment with a variety of models that leverage different spatial resolutions, spatial footprints, image products, and spectral bands. We find that our best model achieves an area under the precision recall curve of 0.915 for identifying concentrated animal feeding operations and 0.821 for oil refineries and petroleum terminals on an expert-labeled test set, suggesting the potential for large-scale mapping. We make METER-ML freely available at this link to support future work on automated methane source mapping. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Methane

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Convex Optimization-Based Policy Adaptation to Compensate for Distributional Shifts

Convex Optimization-Based Policy Adaptation to Compensate fo...

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IEEE Conference on Decision and Control

作者： Navid Hashemi Justin Ruths Jyotirmoy V. Deshmukh Thomas Lord Department of Computer Science University of Southern California Los Angeles USA Department of Mechanical and System Engineering University of Texas at Dallas Dallas USA

In many real-world cyber-physical systems, control designers often model the dynamics of the physical components using stochastic dynamical equations, and the design optimal control policies for the model. At any given time, a stochastic difference equation essentially models the distribution on next states conditioned on the state and controller action at that time. Due to shifts in this distribution, modeling assumptions on the stochastic dynamics made during initial control design may no longer be valid when the system is deployed in the real-world. In safety-critical systems, this can be particularly problematic; even if the system follows the designed control trajectory that was deemed safe and optimal, it may reach unsafe states due to the distribution shift. In this paper, we address the following problem: suppose we obtain an optimal control trajectory in the training environment, how do we ensure that in the real system this optimal trajectory is tracked with minimal error? In other words, we wish to adapt an optimal trained policy to distribution shifts in the environment. We show that this problem can be cast as a nonlinear optimization problem solvable using heuristic optimization methods. However, a convex relaxation of this problem allows us to learn policies that track the optimal trajectory with much better error performance and faster computation times. We demonstrate the efficacy of our approach on two different case studies: optimal path tracking using a Dubin's car model, and collision avoidance using both a linear and nonlinear model for adaptive cruise control

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Geometric Constellation Shaping for Wireless Optical Intensity Channels: An Information-Theoretic Approach

arXiv

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arXiv 2024年

作者： Zhou, Suhua Li, Tianqi Fang, Zhaoxi Zhou, Jing Zhang, Wenyi School of Automation Beijing Information Science and Technology University Beijing100192 China National Computer System Engineering Research Institute of China Beijing100083 China Department of Computer Science and Engineering Shaoxing University Shaoxing312000 China Henan Key Laboratory of Visible Light Communications Zhengzhou450002 China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

A simple geometric shaping method is proposed for optical wireless communication systems based on intensity modulation and direct detection (IM/DD) from an information-theoretic perspective. Constellations consisting of equiprobable levels with exponential-like distribution are obtained, which possesses asymptotic optimality in the sense that the high-SNR capacity of average-intensity constrained optical intensity channel can be approached by such constellations with increasing size. All 2b levels (b ∈ N) of the obtained constellation can be represented by a basic level and b + 2 bits, thereby reducing the required resolution of the digital-to-analog converter (DAC) without affecting the asymptotic optimality. Achievable information rate evaluations verify the asymptotic optimality. As an example, error performance results of a simple 16-level LDPC coded modulation scheme show that a shaping gain of 0.65 dB can be obtained by applying the proposed constellation design. This method can also be applied to more specific IM/DD channel models, since it only requires a near-optimal continuous input distribution. © 2024, CC BY-NC-ND.

关键词： Digital to analog conversion

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