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Solving General Ranking and Selection Problems with Risk-aversion

Solving General Ranking and Selection Problems with Risk-ave...

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IEEE international conference on Networking, Sensing and control

作者： Ming Liu Yecheng Zhao Feng Chu Mengchu Zhou Zhongzheng Liu School of Economics & Management Tongji University Shanghai China IBISC Univ Évry University of Paris-Saclay Évry France Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

In simulation optimization, a ranking and selection (R&S) problem aims to select the best from candidate solutions, subject to a limited budget of simulation runs. Existing R&S literature focuses on selecting the best solution, based on a ranking criterion defined by the mean performance. Ignoring performance variance in the ranking criterion definition, however, may lead to selecting a very risky solution, with low average performance but high variation. In this paper, we address a new risk-averse R&S problem, which is a generalization of the classic (risk-neutral) R&S problem, by ranking the solutions via the weighted sum of the mean and variance of the performance. For this novel problem, a new approach is developed based on Karush-Kuhn-Tucker conditions, which is a generalization of optimal computing budget allocation (OCBA). Numerical experiments are conducted to show its efficiency.

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Domain Adaptation for Classifying Spontaneous Smile Videos

Domain Adaptation for Classifying Spontaneous Smile Videos

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Amrijit Biswas Md Zakir Hossain Yan Yang Syed Mohammed Shamsul Islam Tom Gedeon Shafin Rahman Department of Electrical and Computer Engineering (ECE) North South University Bangladesh School of Computing Australian National University Canberra Australia School of Elec Eng Comp and Math Sci (EECMS) Curtin University Perth Australia School of Science Edith Cowan University Perth Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Distinguishing spontaneous and posed smiles has become an exciting topic due to its potential application in several sectors. However, it is a very challenging task, even for humans. Past researchers have proposed several semi and fully automatic approaches for smile classification. These approaches have explored both feature-based engineering and end-to-end deep neural network-based strategies. One major issue with past methods is the degradation of performance when deploying the model in a data domain different from the training domain, as smile patterns are different across diverse groups (e.g., young, adult, male, and female). In this paper, we present an end-to-end domain adaptation model to address these problems. We explore a new unsupervised domain adaptation application for smile veracity recognition. We propose an identity-invariant learning objective to align the training (source) data knowledge to the testing (target) data. Our approach penalizes identity information hidden in the feature space by enhancing sufficient distinctiveness among different smile phase features while maintaining inter-class cohesion. We have used UVA-NEMO, MMI, SPOS, and BBC datasets to validate the performance of our model and found that our domain adaptation approach outperforms the existing models by achieving state-of-the-art performance.

关键词： Training Degradation Adaptation models Computational modeling Digital images Manuals Interference Data models Testing Videos

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automatic Voting System Using Convolutional Neural Network

Automatic Voting System Using Convolutional Neural Network

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2021 international conference on computing, Communication, electrical and Biomedical Systems, ICCCEBS 2021

作者： Sruthi, M.S. Shanjai, K. Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore India

Electoral fraud or election fraud is an illegal intervention in the process of election, in which voters vote repeatedly for a favored party thereby increasing vote share. In Order to conduct elections ethically, this election fraud should be eliminated. Hence, the automatic voting system using Convolutional Neural Network (CNN) has been proposed. The Existing systems are time-consuming and difficult-to-maintain since they include manual processes. Presently different kinds of biometric processes available, among that face detection is the most effective. The proposed system develops an automatic voting system without any human intervence. This system includes a camera for capturing the images of electors, captured images will be stored in the database for analysis. data analysis will be performed on the database. In this method, all the label images are trained through the convolutional neural network to predict the output by classifying the images. This classification system produces an accuracy of approximately 90%. © Published under licence by IOP Publishing Ltd.

关键词： Convolutional neural networks

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A development of intelligent farm system for low carbon agricultural management 18

A development of intelligent farm system for low carbon agri...

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18th international conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Srikun, Isaree Changul, Chulalak Oumaree, Kanittha Phranakhon Rajabhat University Department of Electrical Technology Bangkok Thailand Phranakhon Rajabhat University Department of Civil Engineering Technology Bangkok Thailand Phranakhon Rajabhat University Department of Food Science and Technology Bangkok Thailand

ISBN: (纸本)9780738111278

This research aims to develop at least 3 decision-making models for low carbon agricultural management, namely, the decision-making model for the use of water for agricultural purposes, the use of fertilizer, and the control of temperature and humidity. An automatic controlling system was designed and developed to link the humidity and temperature sensors with the nutrient sensors;and to analyze the system's environmental friendliness, in terms of the greenhouse gas emission. Fast growing plants such as green oak, red oak and green cos were selected to test the intelligent farm system's efficiency. The experimental farmland is located in the area of Chaibadan Piphat College, for convenience and ease of management. The experiment focuses on matching the plants' needs while reducing the over-usage of fertilizer and pesticide, in order to reduce the carbon dioxide emission. From the experiment, it can be summarized that using the intelligent farm system to grow vegetables can effectively help reduce the carbon dioxide emission, in comparison to using the traditional farming method. This is because the intelligent farm system grows plants in a closed system, in which the environmental factors are closely monitored and maintained. © 2021 IEEE.

关键词： Fertilizers

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E-CVP-MVSNet: Effective Hierarchical Multi-View 3D Reconstruction Based on Light-Weight Network

E-CVP-MVSNet: Effective Hierarchical Multi-View 3D Reconstru...

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Robotics, Intelligent control and Artificial Intelligence (RICAI), international conference on

作者： Xin Zhang Hongbo Huang Lu Lu Xiaoxu Yan Zhongman Bao Qisheng Zhao Meihui Gong Mechanical Electrical Engineering School Beijing Information Science and Technology University Beijing China Computer School Beijing Information Science and Technology University Beijing China

Multi-view stereo based on cost volume pyramid has achieved good results in depth inference using high-resolution images. However, most approaches require a lot of computing resources, in part because convolutional layers extract redundant features and pay too much attention to some unwanted feature information. In this work, we propose a pyramid network for feature extraction that is both effective and efficient. There are two main mechanisms in the proposed approach. An effective convolution module called SCConv (Spatial and Channel reconstruction Convolution) to reduce duplicate features and encourage representative feature learning. To encode spatial data and processes high-resolution photos more efficiently, Conv2Former is also introduced into the model to enhance feature extraction capabilities. Experiments show that our method can obviously decrease the parameters, complexity and computational costs, thus improve the performance and generative ability remarkably.

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Modelling Air Pollution and Traffic Congestion Problem Through Mobile Application 1st

Modelling Air Pollution and Traffic Congestion Problem Throu...

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1st international conference on Deep sciences for computing and Communications, IconDeepCom 2022

作者： Amudha, S. Shobana, J. Satheesh Kumar, M. Chitra, P. Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Information Technology K.L.N. College of Engineering Tamil Nadu Madurai India Department of Electrical and Electronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031276217

As we know, air pollution is one of the major problems in the metropolis. Due to this a lot of consequences have to be faced by all people. One of the main reasons for this are the vehicles. Most of the time people use their vehicles unnecessarily without any certain reasons. Due to the same reason another problem of traffic congestion arises. And this can be reduced if people become a bit more serious about it. They should prefer using public transport rather than personal vehicles for travelling large distances. By this the number of vehicles on the road will be turned down and it might solve the above two problems directly. So, the main approach is based on this theory itself to reduce the number of transports on the road. The solution is an application which runs on phone and keep track of the fuel used by the vehicle. A certain fuel limit shall be set based on vehicle type, its fuel consumption and other constraints. Once the limit got exceeded, fuel price shall differ and automated calculation would get you actual fuel amount. This aims at reducing the unnecessary usage of vehicles. There is a CO2 emission model in an application which can predict the amount of CO2 produced by the vehicle. The objective of the proposed work is to control the usage of vehicles based on the fuel used by a particular vehicle for a period of time. As the module has been tested with ML model for mileage prediction and CO2 emission with different algorithms and got the best accuracy of around 85%. There will be no hardware in this work, if any hardware implemented all over India then that prototype model will produce good impact in the vehicle fuel usage prediction and CO2 emissions. Hardware based projects using IOT devices, VTRS etc. is something which is very hard to implement in India as CCTV are still not installed and used properly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic congestion

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Neural foraminal stenosis classifications using multi-feature hierarchical clustering and delineation 5

Neural foraminal stenosis classifications using multi-featur...

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5th international conference on Intelligent computing and control Systems, ICICCS 2021

作者： Leo, L. Megalan Yogalakshmi, S. Simla, A. Jerrin Prabhu, R. Thandiah Yokesh, V. School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Panimalar Institute of Technology Department of Computer Science and Engineering Jeppiaar Institute of Technology Department of Electronics and Communication Engineering

ISBN: (纸本)9781665412728

In recent years many people age above 25 are suffering from Neural Foraminal Stenosis (NFS) disease. Neural Formanial Stenosis is a problem of narrowing the spaces between the bones of the spine. It is curable at the earliest stage. A simple exercise can give relief to this problem. But Identification of this problem at the initial stage is a difficult task. It requires more screening techniques and knowledge about the problem. The motivation for this work is to identify the disease at the earliest stage. In clinically, it is identified by analyzing the CT and MRI images. Accurate boundary delineation is important in the clinical diagnosis and also for the proper treatment. Surveys say that boundary detection is subjective to the experience of the clinician. These variations make an impact on the detection of Boundary delineation. Image segmentation algorithms can be used to assist clinicians to detect the boundaries. The classifier can also be used to classify the level of the NFS. This paper utilizes the hierarchical clustering to detect the boundary of the affected region and it is found that, it gives a better assistance to the clinicians. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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international conference on electrical Power and Energy Systems (ICEPES)

作者： Mohd Khizar Syed Prakash Chandra Behera P Joel Josephson Shamim Ahmad Khan Timur Kholmukhamedov Ponni Valavan M Department of Physics Kohinoor college Maharashtra India Department of Science St. Claret College Bangalore India Department of ECE Malla Reddy Engineering College Medchal Malkajgiri Telangana India Glocal School of Science & Technology Glocal University Uttar Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems.

关键词： Renewable energy sources Costs Stability criteria Power system dynamics Optimization methods Power system stability Electric vehicle charging

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Quantum Key Distribution for ADS-B Data Transmission Security in Aircraft

Quantum Key Distribution for ADS-B Data Transmission Securit...

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international conference on computing and Networking Technology (ICCNT)

作者： Ahmad Zaher Judy Rabaa Lanke Ravi Kumar N Rajesh Pandian Department of Computer Science and Engineering JAIN (Deemed-t-be) University Bangalore India Department of Aerospace Engineering JAIN (Deemed-t-be) University Bangalore India

This research aims to investigate the security concerns of automatic Dependent Surveillance-Broadcast (ADS-B) communication in the aviation industry and propose the Quantum Key Distribution (BB84) algorithm to generate a symmetric key using the principles of quantum mechanics and use it as a secret key for all encryption/decryption algorithms used in the ADS-B system to increase the security level and avoid any security concerns. ADS-B is a critical technology for air traffic control, providing real-time position and velocity information to air traffic controllers. However, the security of ADS-B communication is a major concern, as it is vulnerable to various security threats, including eavesdropping, data manipulation and injection attacks that could lead to accidents and loss of life. To ensure safe and efficient air travel, A reliable and secure communication system for ADS-B is essential. In this study, we will provide a comprehensive literature review of ADS-B communication and existing encryption methods. We will evaluate the performance of the proposed BB84-Quantum algorithm through simulations and assess its effectiveness in securing ADS-B communication. Our research aims to provide insight into the potential application of the proposed algorithm in the aviation industry. The proposed algorithm's originality lies in its use of quantum mechanics principles to provide secure communication between aircraft and ground stations, ensuring a safe connection for sending and receiving messages in ADS-B packets.

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Event-Triggered Output Feedback Predictive control of Networked control Systems Against Denial-of-Service Attacks 7th

Event-Triggered Output Feedback Predictive Control of Networ...

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7th international conference on Life System Modeling and Simulation, LSMS 2021, and the 7th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Wang, Zhiwen Liang, Caihong Wang, Xiaoping Sun, Hongtao Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology Lanzhou China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China National Demonstration Centre for Experimental Electrical and Control Engineering Education Lanzhou University of Technology Lanzhou China College of Engineering QuFu Normal University Qufu China

ISBN: (纸本)9789811672125

This paper studies the event-triggered output feedback predictive control problem of networked control systems (NCSs) subject to Denial-of-Service (DoS) attacks. When the limited energy of attackers is taken into account, the impact of DoS attacks is reasonably assumed to be bounded consecutive packet dropouts. Then, a new-type predictive control sequence is designed by only employing successfully received triggering data at the last time. Furthermore, based on the switching Lyapunov functional approach and linear matrix inequality, the stability criterion and predictive control design are derived in detail. Compared with previous research, when only the latest successfully received output measurements are available, this event-based predictive control strategy can compensate arbitrary bounded packet dropouts under DoS attacks. In addition, a simulation example shows the effectiveness of the proposed control method. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Networked control systems

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