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Performance of Feature Extraction Methods used for Facial Expression Recognition System 5

Performance of Feature Extraction Methods used for Facial Ex...

引用

5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Kashid, Shubhangi Patil Patil, Y.M. Kashid, Anandarao N. K. Orchid College of Engineering and Technology Solapur India KIT College of Engineering Maharashtra Kolhapur416234 India

ISBN: (纸本)9798331518097

Facial expression recognition is challenging task in computer vision. If machines will be able to understand facial expression then human machine interaction will be possible. Happy, angry, fear, surprise, disgust, sad and neutral are the expressions considered for classification. Texture and shape features are extracted using LBP and HOG. In proposed research work performance of LBP and HOG features is compared with reference to seven performance matrices. By combining LBP and HOG features it is possible to get advantage of both features. The features are classified using SVM. The performance is tested with two posed and two wild datasets. Combination of LBP and HOG features gives superior results than single feature method. It enhances the robustness and accuracy of FER system. 97.89% recognition accuracy is achieved by the proposed method. Normalized LBP and HOG features are robust to variation in lighting and contrast. The proposed work will be useful to design robust FER system for wild images. © 2024 ieee.

关键词： Feature extraction

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Analysis and Design of Direct Yaw-Moment control for Distributed Drive Electric Vehicles Considering Replay Attacks 8

Analysis and Design of Direct Yaw-Moment Control for Distrib...

引用

8th Asia-Pacific conference on intelligent Robot systems, ACIRS 2023

作者： Cao, Wanke Shen, Xingfu Ling, Heping Shenzhen Automotive Research Institute Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles Beijing100081 China BYD Automotive Engineering Research Institute BYD Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9798350302851

In this paper, the lateral motion control problem of distributed drive electric vehicles (DDEVs) under the coupling effects of large random attack-induced delays is studied. The external interface assembled in intelligent connected vehicles (ICVs) makes the in-vehicle controller area network (CAN) more vulnerable to cyber-Attack, which negatively impact real-Time control performance and stability. Based on the notion of multi-hop loop delay, the delay composition of direct yaw-moment control (DYC) under replay attack is analyzed in detail and its upper bound formula is derived based on reasonable analysis. Furthermore, the polytopic inclusion technique based on Tylor series expansion is used to describe network uncertainty, and a delay-Tolerant linear quadratic regulator (LQR) method based on is adopted for robust tracking control. Finally, simulation results based on hardware-in-loop (HIL) experiment indicate that proposed analysis and control scheme can deal with the negative effects of replay attack and ensure reliable vehicle dynamics performance. © 2023 ieee.

关键词： Electric vehicles

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AUV Local Path Planning Based on Fusion of Improved DWA and RRT Algorithms 20

AUV Local Path Planning Based on Fusion of Improved DWA and ...

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20th ieee international conference on Mechatronics and automation, ICMA 2023

作者： Li, Juan Zhang, Zihao Harbin Engineering University Science and Technology on Underwater Vehicle Technology China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China

ISBN: (纸本)9798350320831

For the local path planning problem of autonomous underwater vehicle (AUV) in unknown complex obstacle environment, the traditional Dynamic Window Approach (DWA) algorithm has problems such as easy to fall into local stagnation in complex obstacles. A path planning algorithm based on the fusion of Improved DWA and Rapid-exploration Random Tree (RRT) algorithms is proposed. The improved DWA algorithm velocity space is generated according to the period of the whole dynamic window and resets the evaluation function;the improved RRT algorithm plans guide points in the local known space to help DWA out of the local stagnation state. The two algorithms are fused to achieve local path planning for AUV in unknown complex underwater environment. Simulation shows that this fusion algorithm can plan a safe and feasible navigation path for AUV in unknown complex obstacle environment. © 2023 ieee.

关键词： Autonomous underwater vehicles

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Artificial Intelligence in cyber Defense: Unmasking Zero-Day Attacks on SCADA systems 4

Artificial Intelligence in Cyber Defense: Unmasking Zero-Day...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kumari, Ankita Gupta, Deepali Uppal, Mudita Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350364729

The possible catastrophic consequences of a security breach in SCADA (Supervisor control and Data Acquisition) systems within important infrastructure have raised questions about hackers aiming at these systems. Using holes that have not yet been discovered, zero-day attacks are risky as they might compromise operations, endanger security, and pilfers personal data. Attackers might target SCADA systems using a variety of strategies including code injection, malware infection, hardware and software defects taken advantage of, Behavioral study on humans and networked devices by artificial intelligence (AI) might be very essential for the protection against these hazards. AI-powered security analytics solutions can manage vast volumes of data from security systems, logs, and devices, therefore providing security professionals with insightful analysis. These systems may identify unusual or suspicious behavior - including attempts at unlawful login - as potential indicators of a zero-day attack including odd data flow patterns. This work proposes an artificial intelligence-based technique aimed to protect SCADA systems against zero-day assaults. The paper investigates a set of zero-day vulnerabilities and other SCADA system hazards in order to identify the optimal approach for including security concepts into SCADA architecture. Using innovative artificial intelligence techniques, the proposed strategy aims to enhance zero-day threat identification and prevention thereby guaranteeing the ongoing security and dependability of vital infrastructure. This work underlines the possibility of AI-driven solutions to provide robust defenses against zero-day assaults in SCADA systems and underscores the need of applying modern machine learning methods to properly handle emerging cyber threats. © 2024 ieee.

关键词： Zero-day attack

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Enhancing Skin Cancer Diagnosis Through Optimized Convolutional Neural Network in a Smart Health Care System 5

Enhancing Skin Cancer Diagnosis Through Optimized Convolutio...

引用

5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Hussain, Mohd Mudassir Akhil, Lakkam Sai Prakash, Talasila Om Lakshmi Prasanna, B. Institute of Aeronautical Engineering Department of Computer Science and Information Technology Hyderabad India Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331518097

Skin cancer presents one of the most complex public health problems in many countries worldwide. Accurate and time-sensitive diagnosis may help in suitable treatment and enhanced patient benefits. The study is titled as 'Improving Skin Cancer Classification using Optimized Convolutional Neural Networks.' Here, it advances early diagnosis and categorization of this kind of skin cancer applying more sophisticated methodologies of artificial intelligence. Preprocessing and augmentation of the ISIC 2019 dataset improve the quality and diversity of the data set, solving problems of scarcity and imbalance. By using CNN architectures such as EfficientNetV2 and InceptionResNetV2, results have had high accuracy rates, 97.3% and 96.1%, respectively. The automated diagnostic reports with images can be downloaded as PDF files. It uses an AI-driven methodology in an enhanced way to improve diagnostic accuracy and early intervention towards better patient outcomes. © 2024 ieee.

关键词： Electronic health record

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BiLSTM based approach for Energy Demand Forecasting and Synchronization among Smart Grids 3

BiLSTM based approach for Energy Demand Forecasting and Sync...

引用

3rd ieee international conference on Power Electronics, intelligent control and Energy systems, ICPEICES 2024

作者： Sakib, Mohd Mustajab, Suhel Anwar, Khalid Alam, Mahfooz Aligarh Muslim University Department of Computer Science Aligarh India School of Camp. Sci. Engg. & Technology Bennett University Greater Noida India

ISBN: (纸本)9798350387193

Recent growth in industrialization and urbanization has increased pollution. Air pollution is one of the major concerns of this era. The main cause of air pollution is the use of fuel-based vehicles. Electric vehicles (EVs) are being used to reduce air pollution which has increased the demand for electric energy and created chaos. This study presents an innovative approach that enhances the synchronization among the various Stockholders in the smart grid (SG) system. The model is advantageous for several parties involved in the smart grid, such as power generation companies, owners of hybrid and electric vehicles (EVs), and electric vehicle charging stations. Charging stations serve as crucial intermediaries, ensuring a balance between demand and supply. A Bidirectional Long Short-Term Memory (BiLSTM) network is at the center of this approach, with the objective to correctly predict the consumption of energy. In order to improve smart grid synchronization, our research utilizes data from PJM Interconnection LLC, a regional transmission organization. The paper compares the performance of various forecasting models on different evaluation metrics like MAE, MSE, RMSE, and MAPE. We got an MAE score of 26.15 and an RMSE score of 35.15, which are comparatively less than other models. The results show that the BiLSTM model is more effective at making predictions than other methods. This study not only contributes to the field of energy forecasting but also paves the way for more efficient and sustainable energy management in smart grid systems. © 2024 ieee.

关键词： Charging stations

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Automated Assessment of Pizza Quality Using Computer Vision and Deep Learning Techniques 3

Automated Assessment of Pizza Quality Using Computer Vision ...

引用

3rd international conference on intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Arakeri, Megha Lakshmana Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India Seamovation Labs Pvt. Ltd Bangalore India

ISBN: (纸本)9798331523893

Quality control in the pizza-making process is essential to meet consumer expectations. In the age of online delivery, verifying the delivered pizza is necessary. The pizza usually consists of a base, sauce, cheese, vegetables, and meat. Low-quality pizza delivery may lead to consumer dissatisfaction and hinder pizza production business growth. Hence, this paper proposes a method to evaluate sauce spread, identify toppings, and assess their distribution on cooked and uncooked pizzas to address this issue. The proposed methodology uses a feature pyramid network for background and foreground separation in the pizza image. Then, the sauce spread thickness is classified into 3 categories using the U-Net model. Finally, a multi-label CNN and color segmentation were applied to the image to evaluate the spread of toppings on the pizza. Experiments were carried out on a standard image dataset, and it was observed that the proposed model achieved 95% accuracy in pizza quality evaluation. © 2025 ieee.

关键词： Damage detection

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Balancing Human-robot Collaborative Disassembly Line by Using Dingo Optimization Algorithm 4

Balancing Human-robot Collaborative Disassembly Line by Usin...

引用

4th ieee international conference on Human-Machine systems, ICHMS 2024

作者： Guo, Xiwang Zhou, Liangbo Zhou, Mengchu Wang, Weitian Wang, Jiacun Qi, Liang Liaoning Petrochemical University Information And Control Engineering Fushun China Liaoning Petrochemical University Information And Control Fushun China New Jersey Institute Of Technology Electrical And Computer Engineering NewarkNJ United States Montclair State University School Of Computing Montclair United States Monmouth University Computer Science And Software Eng. West Long BranchNJ United States Shandong University Of Sci. And Tech. Computer Intelligent Sci. And Tech. Qingdao China

ISBN: (纸本)9798350315790

Disassembling and recycling scrapped products play important roles in effectively reducing environmental pollution and improving resource sustainability. A multi-product human-robot collaborative disassembly-line-balancing problem (MHDP) arises from doing so and represents an important yet to be solved in the remanufacturing field. This work explores how to use optimization algorithms to schedule tasks between humans and robots to maximize remanufacturing profit in human-robot collaboration contexts. Specifically, we investigate MHDP and develop a mixed integer programming model considering various system constraints. We propose a dingo optimization algorithm that simulates wild dogs' social hierarchy and chasing processes to solve MHDP. We compare the proposed algorithm with an exact solution finder, i.e., IBM CPLEX and a well-known intelligent optimization method, i.e., Genetic Algorithm to show its competitive performance in terms of solution accuracy and efficiency. © 2024 ieee.

关键词： Genetic algorithms

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Research on Time Synchronization Collaboration Techniques in Converged Deployment of 5G and Industrial control Networks 4

Research on Time Synchronization Collaboration Techniques in...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Zhu, Jinyu Chen, Xi Duan, Shihui Li, He China Academy of Information and Communications Technology Beijing China Key Laboratory of Internet and Industrial Integration and Innovation Miit Beijing China Beijng University of Posts and Telecommunications School of Information and Communication Engineering Beijing China

ISBN: (纸本)9798331534622

With the continuous development of intelligent manufacturing and industrial Internet, the architecture of industrial systems requires industrial communication networks to provide greater bandwidth and higher connection density, and have higher cabling flexibility while ensuring low latency and high reliability. The integration deployment of 5G and industrial control network has become the focus of research. However, time synchronization is the basis of the converged deployment application, and the time synchronization mechanism of the 5G system is not fully compatible with the existing time synchronization method of industrial control system. In this paper, we will analyze the time synchronization requirements of 5G in carrying industrial applications, discuss the time synchronization challenges faced in the process of co-deployment of 5G and industrial control networks, and put forward the corresponding solutions, aiming at verifying the feasibility of the solutions through experiments, and providing a theoretical basis and technical support for the in-depth application of 5G technology in the industrial field. © 2024 ieee.

关键词： 5G mobile communication systems

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Deep Feature Compression Method for Collaborative intelligent Applications 2

Deep Feature Compression Method for Collaborative Intelligen...

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2nd ieee international conference on control, Electronics and Computer technology, ICCECT 2024

作者： Zhao, Jingping Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350380958

Collaborative Intelligence is a new paradigm for realizing distributed deployment of Deep Neural Network DNNs, which is widely applicable to visual scene sensing applications for IoT systems. In this paradigm, the DNN model is divided into two parts, the edge submodel and the cloud submodel, which are deployed on the mobile edge device and the cloud server respectively, and the two collaborate to accomplish the inference task. In the collaborative inference process, the deep features generated by the edge sub-model need to be compressed and transmitted to the cloud sub-model through the wireless channel to reduce the transmission delay and transmission energy consumption. In order to improve the compression rate of deep features, a deep feature compression method (LCFC) based on lightweight convolutional neural network is proposed. A simple and efficient deep feature compressor is designed in this method, which utilizes a lightweight convolutional neural network and a uniform quantizer for channel dimension compression, spatial dimension compression, and quantization of deep features, which can significantly improve the compression rate of deep features and reduce the number of bits required for transmitting deep features. The proposed method is fully experimented on the CIFAR-100 image classification task, and the experimental results show that it improves the deep feature compression rate by up to 31.5% compared to the baseline method, while ensuring that the degradation of inference accuracy is less than 1%. © 2024 ieee.

关键词： Deep neural networks

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