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Face Detection Based on Improved YOLOv5 Algorithm 3

Face Detection Based on Improved YOLOv5 Algorithm

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3rd international conference on Machine Learning and computer Application, ICMLCA 2022

作者： Jiang, Qiang Chen, Sihan School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9781510664814

Aiming at the problem that the traditional convolutional neural network has low accuracy face detection in complex scenes, an improved YOLOv5 lightweight network algorithm is proposed. By adding Swin Transformer Block backbone network, the window scheduling algorithm is implemented by using a moving Windows, which limits the self-attention calculation mechanism to non-overlapping local Windows. Meanwhile, the sensitivity field is increased under the condition of allowing cross-window connections to achieve better feature extraction. In order to verify the superiority of Swin-Transformer-YOLOv5, the model is compared with the traditional YOLOv5 algorithm. The model uses a WIDER face in complex scenarios to expose the dataset. The simulation results show that the algorithm can guarantee the original detection efficiency. Meanwhile, the face detection model based on Swin Transformer YOLOv5 has an average accuracy (mAP) of 82.5%, 4.5 percentage points higher than the traditional YOLOv5 algorithm. © 2023 SPIE.

关键词： Face recognition

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Power Quality Improvement In Three-Phase Distribution Line Using DSTATCOM 3

Power Quality Improvement In Three-Phase Distribution Line U...

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3rd IEEE international conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Waikhom, Orison Shimray, Benjamin A National Institute of Technology Manipur Department of Electrical Engineering Imphal India

ISBN: (纸本)9798350319972

In the modern power system, power quality is a significant issue that may have an impact on consumers and utilities. The integration of renewable energy sources, smart grid systems, and extensive use of power electronics equipment after the semiconductor revolution caused a drastic change in the load characteristics in a day. Since, the conventional methods of using inductors and capacitors for power quality improvement have been replaced by improved power conditioners using FACTS devices due to limitations like oversized, inefficiency, poor reliability and controllability. This paper presents a power quality improvement technique in the presence of non-linear load and harmonics using a Distribution Static Synchronous Compensator [DSTATCOM] with a capacitor as an energy storage element. DSTATCOM provides comparatively fast control of reactive and active powers for load compensation, harmonics elimination, mitigation of voltage flicker, and voltage regulation. The control strategy of the proposed DSTATCOM is provided based on Synchronous Reference Frame theory and Carrier-based Pulse Width Modulation [PWM] technique. Power quality problems like reactive power, low power factor, and harmonics due to load switching, active loads, inductive loads, and non-linear loads at the distribution side have been studied in this paper. Modelling of transmission lines, design and control of DSTATCOM, and monitoring of power quality are carried out in MATLAB/SIMULINK ode23t solver for different types of loads and the simulation results are compared with a typical power distribution system. © 2023 IEEE.

关键词： Power quality

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Multimodal Sentimental Analysis Using GloVe Convolutional Neural Network with Capsule Network 3

Multimodal Sentimental Analysis Using GloVe Convolutional Ne...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yamsani, Nagendar Abdul Hussein, Abbas Hameed Faith, S.Julia Al-Attabi, Kassem Arabi, Mohammed Yousif School of Computer Science and Artificial Intelligence SR University Telangana Warangal India College of Pharmacy Ahl Al Bayt University Karbala Iraq S.A.Engineering College Department of Information Technology Chennai India The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350317022

Leverage the power of deep learning-based Multimodal Sentiment Analysis to gain a deeper understanding of emotions and sentiments in various forms of data, including text, images, and video. This research is conducted on the CMU-MOSI and CMU-MOSIE datasets, which comprise text, audio, and video clips. In this paper, propose the utilization of GloVe for text feature extraction and sentiment classification (negative, positive, and neutral). For audio feature extraction, employ Mel-Frequency Cepstral Coefficients (MFCC) and utilize a Convolutional Neural Network (CNN) for audio sequence classification. Additionally, incorporate the Capsule Network for video clip classification, effectively addressing limitations seen in previous models. As a result, of the model achieves outstanding performance metrics on the CMU-MOSI dataset, with ACC2 at 86.95% and ACC7 at 56.95%, and on the CMU-MOSIE dataset, it attains ACC2 at 87.55% and ACC7 at 55.80%, surpassing the capabilities of existing models such as Hybrid Contrastive Learning of Tri-Modal Representation (Hycon) and (MMLATCH) Bottom-Up Top-Down Fusion. © 2023 IEEE.

关键词： Capsule Network Convolutional Neural Network Feature Extraction Glo Ve Modalities and Multimodal

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Optimal re-distribution of demand responsive loads in Microgrids 3

Optimal re-distribution of demand responsive loads in Microg...

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3rd international conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Pothireddy, Krishna Mohan Reddy Vuddanti, Sandeep National Institute of Technology Andhra Pradesh Electrical Engineering Department Tadepalligudem India

ISBN: (纸本)9798350320749

The sudden surge in the fuel cost and increase in environmental impact causes a reduction in electricity generation through conventional energy sources. The power system is moving towards the incorporation of distributed energy resources (DERs) into the existing microgrid (MG). Distributed generation (DG), storage system and geographically scattered loads forms a MG. The stochasticity and unpredictable output associated with DGs may create imbalance in supply and demand. Further, the unexpected peak loads on the system mayjeopardize the system reliability. Hence, proper energy management system is obligatory for peak reduction so as to maintain the power balance. One of the emerging and efficient energy management techniques is demand response. While incorporating demand response, a part of curtailed loads i.e., shiftable loads at peak loads should be re-distributed to other off-peak hours where the load demand and electricity offer price is less. The re-distribution of loads at non-peak hours eventually increases the market clearing price (MCP). Increment in MCP mostly effects the inelastic loads as they cannot shift their schedule from one horizon to another since they are must run loads and of on/off in nature. In this context, the present work focuses on optimal rescheduling of the redistributed loads that doesn't impact much MCP and operational cost (OC) of the MG much and should able to maintain the dynamic balance between supply and demand. A cost-based method was employed in which grey wolf optimization (GWO) algorithm is used to optimize the OC of the MG. © 2023 IEEE.

关键词： Energy management

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Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis 3

Machine Learning Classification Algorithms for Accurate Brea...

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3rd international conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alzboon, Mowafaq Salem Qawasmeh, Sami Alqaraleh, Muhyeeddin Abuashour, Ahmad Bader, Ahmad Fuad Al-Batah, Mohammad Jadara University Faculty of Science and Information Technology Irbid Jordan Arab Open University Faculty of Computer Studies Farwanya Kuwait Jadara University Faculty of Engineering Irbid Jordan

ISBN: (纸本)9798350305333

The introduction of algorithms based on machine learning (ML) has revolutionized computer science by allowing machines to learn without explicit programming. These algorithms automate operations, like categorization and clustering, that formerly required human interaction. In the medical field, classification algorithms based on machine learning have been used extensively to diagnose various diseases, including breast cancer, which remains one of the leading causes of death among women. This study describes an experiment that analyzes the effectiveness of machine learning-based algorithms for breast cancer diagnosis. Among the techniques evaluated are Logistic Regression, Decision Tree, Neural Network, Naive Bayes, The kNN, support vector machines (SVM), AdaBoost, Stochastic gradient descent (SGD), CN2 rule inducer, Constant, and Random Forest. The investigation used the renowned Wisconsin Diagnosis Breast Cancer dataset (WDBC). The primary objective of this experiment is to evaluate the Precision and accuracy of these algorithms in discriminating between malignant and benign tumors. The SVM model has the best accuracy at 0.975%, suggesting that Random Forest is a viable breast cancer diagnostic tool. These findings indicate that algorithms based on machine learning can potentially increase the accuracy and speed of breast cancer diagnosis. This research can inform the development of more precise and influential breast cancer diagnostic tools, enabling physicians to administer quick and appropriate therapy to patients. © 2023 IEEE.

关键词： Support vector machines

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Research on Key Technologies of Intelligent Capture System for Personnel Position and Attitude 3

Research on Key Technologies of Intelligent Capture System f...

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3rd international conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2023

作者： Li, Hejia Yao, Zhao Li, Jiuchao Lu, Suo No. 1 Huayuan Road Changchun Jilin Changchun City China

ISBN: (纸本)9781643684581

This article addresses the issue of the lack of real-time monitoring of personnel's position and posture information technology in special environments. Relying on intelligent capture systems, key technologies such as Beidou positioning, inertial navigation, network communication, 3D modeling, virtual simulation, etc. are utilized to achieve real-time restoration of personnel information, human posture data, motion status, and trajectory on situational maps in various environments. The entire process is collected and stored for command and decision-making provide technical support for comprehensive analysis. © 2023 The Authors.

关键词： 3D modeling

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Real-time Optimal Charging Strategy for a Fleet of Electric Vehicles Minimizing Battery Degradation 3

Real-time Optimal Charging Strategy for a Fleet of Electric ...

引用

3rd IEEE international conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Rath, Subhajeet Medina, Róbinson Wilkins, Steven Powertrains Department Tno Netherlands Eindhoven University of Technology Electrical Engineering Department Netherlands

ISBN: (纸本)9798350319972

The electrification of the transportation industry has emerged as a promising solution to reduce automotive emissions. However, this transition presents various challenges, such as the limited availability of charging infrastructure, grid capacity, and the high total cost of ownership for Battery Electric Vehicles (BEVs), primarily due to the cost of the battery. This study proposes an algorithm that generates charging schedules for a fleet of BEVs during their operational period to address these challenges. The algorithm provides a time frame and charging power for all vehicles in the fleet, which can optimize the use of charging infrastructure, distribute the grid load throughout the day, and reduce BEV operational costs by extending the battery lifetime. The algorithm was tested in a realistic case study within the European project URBANIZED. Simulation results shows a saving of 13% on the total cost of charging. Furthermore, the algorithm's execution time was less than 160 seconds for a fleet of 200 vehicles, considered real-time for a fleet charging operation. In conclusion, this algorithm offers a viable solution to address challenges in the electrification of the transportation industry. Fleet managers can benefit from its cost saving benefits and efficient execution time, making it a promising tool for transitioning to BEVs. © 2023 IEEE.

关键词： Secondary batteries

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Modified Controller for Smooth Transition of Grid-Connected and Standalone Modes in AC Microgrid 3

Modified Controller for Smooth Transition of Grid-Connected ...

引用

3rd IEEE international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Govind, D. Suryawanshi, H.M. Nachankar, P.P. Borghate, V.B. Visvesvaraya National Institute of Technology Department of Electrical Engineering Nagpur440010 India

ISBN: (纸本)9798350304732

Microgrids have significant potential in delivering high power quality and an uninterrupted power supply, enhancing reliability and stability for all consumers. Intelligent control techniques are essential for the reliable and stable operation of microgrids. These systems can function in two modes: utility grid-connected and standalone. In the utility grid-connected mode, distributed generators (DGs) typically operate under grid-following control, while in standalone mode, at least one DG must use grid-forming strategies. Microgrids may encounter voltage and current fluctuations during unintentional standalone events. This paper introduces a novel approach to seamlessly synchronize utility grid-connected and standalone modes while effectively mitigating disturbances. The strategy involves implementing a Modified Voltage Controller (MVC) with capacitor current feedback (CCF) and load current feed-forward (LCFF) to enhance system dynamics, broaden the controller's bandwidth, and suppress voltage, current, and frequency fluctuations. This ensures a smooth transition between utility grid-connected and standalone modes in AC microgrids, validated through simulation results in the paper. © 2023 IEEE.

关键词： AC microgrid current control mathematical model voltage control

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Design of underground multi-mode remote transmission sub system 3

Design of underground multi-mode remote transmission sub sys...

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3rd international conference on Digital Signal and computer Communications, DSCC 2023

作者： Jin, Wenhao Yan, Zhengguo Zhang, Ying Li, Jinjiang Key Laboratory of Shaanxi Province for Measurement and Control Technology of Oil and Gas Wells Xi'an Shiyou University Shaanxi Xi'an710065 China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

Downhole TV plays an important role in oil exploitation, among which it is a key part of downhole TV. At present, most of the downhole TV industry uses armored cable for data transmission, but with the development of technology, it has begun to be equipped with optical fiber in the field working environment, and continuous tubing also began to be equipped with optical fibers. Based on this situation, this paper introduces a system design of photoelectric multi-mode remote *** paper first introduces the principle of data coding and demodulation. After analyzing the characteristics of data transmission, TC-PAM coding modulation mode is adopted, and during the cable transmission, the cable has adaptive technology to improve the decoding accuracy of the decoding terminal during high-speed data transmission in the deep well test channel ***, the working principle and system composition of teletransmission section are introduced, and the design and system design requirements of photoelectric composite cable, the selection of core modules and the design method of temperature and pressure resistance of the system are also given. Finally, the field environment test verifies the stable operation of the system and the feasibility of the design method. © 2023 SPIE.

关键词： Data transfer

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Selective Unlearning in Face Recognition: Forgetting Faces without Compromising Accuracy 3

Selective Unlearning in Face Recognition: Forgetting Faces w...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Nair, Adithyan M Kumar, Akshit Sudheer Kumar, Devakrishna Sanil Anjali, T. Amrita School of Engineering Department of Computer Science Amritapuri India

ISBN: (纸本)9798350343632

Facial recognition technology is increasingly being used in current applications ranging from smartphone unlocking to security and surveillance systems. This extensive adoption, however, has generated severe concerns about individual privacy and data policies. In response to these concerns, this study introduces a novel methodology that gives people more control over their data in facial recognition systems. This study's main contribution is the proposal of a strategy for selectively unlearning certain faces from trained facial recognition algorithms. An iterative model adaption approach is used to achieve this selective unlearning process. It becomes feasible to empower individuals to control the presence of their facial data in these systems by iteratively fine-tuning the model. This not only improves individual privacy but also conforms with ethical technology development standards. Furthermore, the methodology presented in this study is an important step towards improving privacy in the age of ubiquitous facial recognition. It tackles increasing concerns about surveillance, tracking, and unauthorized use of personal data by adapting and personalizing facial recognition algorithms to accommodate individual tastes. This study provides an innovative answer to the ethical and privacy concerns raised by facial recognition technologies. It empowers users to exercise greater control over their privacy and data in the face of increasingly prevalent facial recognition technologies by allowing them to selectively unlearn their data from training algorithms. © 2023 IEEE.

关键词： and Recognition Accuracy Face Recognition Model Adaptation Privacy Selective Unlearning

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