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Xception for Tomato Leaf Disease Detection: Hyperparameter Tuning and Fine-tuning Approaches

Xception for Tomato Leaf Disease Detection: Hyperparameter T...

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Maini, Yash Singh, Sanjay Kumar Saxena, Pranshu University School of Automation and Robotics Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar Delhi110092 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331517953

Tomato cultivation is essential to world agriculture, affecting food security and economic stability. Tomasto leaf diseases must be detected accurately to maintain crop health and maximize yield. This work improves disease classification by using deep learning models, notably CNN, Xception, DenseNet, and a hyper-tuned Xception model. We used transfer learning and data augmentation to improve model performance on the Kaggle dataset, which contains 10,000 training images and 1,000 validation images from the Plant Village Repository organized into ten groups. The hyper-tuned Xception model achieved the maximum performance by meticulously tweaking hyperparameters such as learning rate, batch size, and network architecture, with an accuracy of 97.23%, precision of 95.73%, recall of 94.40%, and F1-score of 95.06%. These results show a considerable improvement over the CNN, Xception, and DenseNet models. The results highlight the efficiency of sophisticated deep learning algorithms and hyperparameter tweaking in achieving high classification accuracy, making the proposed model a robust tool for practical tomato leaf disease diagnostics. © 2024 IEEE.

关键词： Fruits

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Remaining Useful Life Prediction of Lithium-Ion Batteries Using Lag-Llama Model with Auto-Correlation Analysis

Remaining Useful Life Prediction of Lithium-Ion Batteries Us...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Heng Zhu, Zeyu Chen, Xiaolong Fan, Yunsheng Yan, Lishen Liu, Weirong School of Electronic Information Central South University Changsha410083 China School of Computer Science and Engineering Central South University Changsha410083 China School of Automation Central South University Changsha410083 China

ISBN: (纸本)9781665410205

Predicting accurate capacity degradation and re-maining useful life (RUL) of lithium-lon battery is critical to health management and safe operation. However, variations in operating conditions and the variety of battery types present challenges to data-driven predictive models. Most data-driven methods rely on traditional machine learning models, which often have constrained predictive and generalization abilities. In this paper, a foundation model: Lag-Llama is used to predict capacity and RUL of battery with auto-correlation analysis. Firstly, the tokenization scheme of Lag-Llama is improved by auto-correlation analysis, which calculate the most probable periods in history capacity sequence. It is helpful for model to comprehend the capacity fluctuation pattern. Then, Lag-Llama is pre-trained to learn battery capacity degradation, and thus calculate the RUL. Additionally, the model is fine-tuned with a small amount of data to update the top-level module for application to the target cell. Finally, experimental results show that the proposed model exhibits accurate RUL prediction and strong transfer capability, within the average mean square error and absolute error less than 0.035 and 9 respectively. © 2024 IEEE.

关键词： Lithium-ion batteries

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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QSR: Q-Learning Based Algorithm of Support Recovery for Sparse Signals 100

QSR: Q-Learning Based Algorithm of Support Recovery for Spar...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Zhou, Tianzhi Wang, Yibo Wu, Zhangyan Liang, Hengwei Qi, Yuan Qian, Rongrong Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing University of Posts and Telecommunications School of Intelligent Engineering and Automation Beijing China

ISBN: (纸本)9798331517786

Within a number of compressed sensing (CS) problems, support recovery of sparse signals is the primary task that needs to be performed. In this paper, we propose a Q-learning based algorithm of support recovery (QSR) for sparse signals, which treats the atomic selection in traditional greedy CS algorithms as Markov Decision Process (MDP) in reinforcement learning (RL). We adopt parallel computing to address "the curse of dimensionality"in Q-learning, dividing the overall RL problem into multiple subproblems. The computational complexity can thus significantly decrease, which grows linearly with the increase of signal length. Experiments show that the proposed algorithm performs closely to other CS algorithms when dealing with high sparsity level signals and it has better performance when dealing with low sparsity level signals. © 2024 IEEE.

关键词： Markov processes

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PCA Lab's solution for RePSS 2024 by vision-based self-supervised remote heart rate sensing 3

PCA Lab's solution for RePSS 2024 by vision-based self-super...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Shao, Hang Luo, Lei Qian, Jianjun Zhuo, Wei Hu, Chuanfei Yang, Jian PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Automation Southeast University Nanjing211189 China Nanjing Center for Applied Mathematics Nanjing211135 China

Vision-based remote non-contact physiological measurements are crucial for detecting indicators (such as heart rate and blood pressure) that reflect important vital signs. This paper introduces the approach proposed by PCA Lab in the third challenge on Vision-based Remote Physiological Signal Sensing (RePSS) organized within IJCAI 2024. Specifically, we design an end-to-end self-supervised contrastive learning network for remote heart rate detection, which can generalize light reflection changes caused by cardiac activity in subcutaneous capillaries based on unlabeled facial videos. At the same time, we optimize the facial skin region of interest extraction method and remove most of the irrelevant content and redundant information. In addition, we construct the hybrid pipeline temporal attention module and spatiotemporal reconstruction pretraining paradigm to improve the network's ability to model long-distance sequence features. Our network is tested on 1000 samples from 200 participants provided by the Track 1 of this challenge, and it achieve the root mean squared error of 8.96941. The codes and model are publicly available at https://***/Sachiel0916/repss-track1-top3/. © 2024 Copyright for this paper by its authors.

关键词： contrastive learning remote heart rate sensing RePSS self-supervision framework spatiotemporal reconstruction

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Automatic messaging system by detecting the road accidents for vehicle applications

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Materials Today: Proceedings 2023年 80卷 3124-3128页

作者： Karthik, M. Sreevidya, L. Vinodha, K. Thangaraj, M. Hemalatha, G. Sena, T. Viswak Department of Electrical and Electronics Engineering Sri Ramakrishna Engineering College Tamil Nadu Coimbatore641 022 India Department of Electrical and Electronics Engineering VSB College of Engineering Tamil Nadu Coimbatore642 109 India Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bengaluru560 103 India Department of Electrical and Electronics Engineering Coimbatore Institute of Engineering & Technology 641 055 Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Tamil Nadu Coimbatore641 022 India

The emerging advancements in innovation has changed our lives drastically making it ease. Yet in this forefront world the rate of automobile accidents is still increasing resulting in increased fatality rate majorly due to untimely attention of emergency services. Our project will provide the best possible answer to this problem. When an automobile collides with another vehicle, the MEMS sensor senses this and sends the data, which is then processed by Arduino, according to this project. The Arduino sends an alarm message to a police control room or a rescue squad via the GSM Module, which includes the position. As a result, as soon as the police receive the information, they can use the GPS Module to track down the location. Then, once the location has been confirmed, the necessary steps will be taken. The project alerts the emergency services and the family members for immediate help by sending the location when an accident occurs. © 2021

关键词： Global system for mobile communications

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Auto-Adaptive Stator Ground Fault Protection for Synchronous Generators in Diesel-Electric Locomotives 14

Auto-Adaptive Stator Ground Fault Protection for Synchronous...

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14th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Mahtani, Kumar Guerrero, Jose M. Beites, Luis F. Platero, Carlos A. Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Dept. Madrid Spain Mondragon Unibertsitatea Electronics and Computer Science Dept. Arrasate-Mondragon Spain Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Department Madrid Spain

ISBN: (纸本)9798350320770

Diesel-electric locomotives are equipped with one or more synchronous generators driven by diesel engines, that provide the necessary electrical energy for their operation. These generators are usually provided with some protection relays, depending on their rated power and their type of service. Standard stator-ground fault protection has a setting for rated conditions. In case of variable speed and voltage operation, the protection does not cover all the operation range of the machine. In addition, unwanted tripping may occur due to incorrect filtering of the third harmonic. In this paper, an auto-adaptative stator ground protection function is presented, for the operation of synchronous machines at variable voltage and frequency. A specific case study is tackled, focused on the main generators of a diesel-electric locomotive. Numerous simulations are carried out to verify the proposed protection method. The obtained results are promising, and they can be used in other synchronous machines operating at variable speed. © 2023 IEEE.

关键词： Stators

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Task Allocation Optimization Strategy in UAV-enabled Mobile Edge Computing System

Task Allocation Optimization Strategy in UAV-enabled Mobile ...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Yang, Sanhuan Su, Zhou Xu, Qichao Xing, Rui Fang, Dongfeng Shanghai University School of Mechatronic Engineering and Automation Shanghai China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China California Polytechnic State University San Luis Obispo Department of Computer Science and Software Engineering United States

ISBN: (纸本)9798350333336

Unmanned Aerial Vehicles (UAVs) have been widely used as aerial computing devices in Internet of Things (IoT) to support time-sensitive and compute-intensive applications and services, e.g. automatic driving, data analysis, etc. In this paper, a UAV-enabled mobile edge computing (MEC) system is considered for computing offloading services, in which the offloaded tasks from ground devices are processed by UAVs. However, when multiple computing tasks exist, the task allocation for devices should be carried out at the lowest computing cost. To this end, we first propose a non-cooperative game model-based power allocation (NGPA) scheme, and the optimal power allocation strategy of devices is determined to minimize transmission energy consumption. Next, we model the problem of task allocation as a bilateral matching between devices and UAVs to minimize the overall computing cost. Then, a bilateral matching mechanism based on improved Hungarian algorithm is provided to obtain the optimal solution. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes on computing cost. © 2023 IEEE.

关键词： Mobile edge computing

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Enhanced EEG Classification via Riemannian Normalizing Flows and Deep Neural Networks

Enhanced EEG Classification via Riemannian Normalizing Flows...

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2025 International Symposium on Innovative Informatics of Biskra, ISNIB 2025

作者： Tibermacine, Imad Eddine Tibermacine, Ahmed Zouai, Meftah Russo, Samuele Bouchelaghem, Soufyane Napoli, Christian Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy University of Biskra Department of Computer Science Biskra Algeria Sapienza University of Rome Department of Psychology Rome Italy Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

ISBN: (纸本)9798331509576

Electroencephalography (EEG) signals present unique challenges for classification tasks due to their non-stationary and high-dimensional nature. In this paper, we propose a novel method that combines Riemannian geometry with deep learning to classify multi-class EEG data. Specifically, we compute covariance matrices of EEG signals and map them onto the tangent space of the Symmetric Positive Definite (SPD) manifold. A deep neural network architecture, termed NeuroSPDNet, is designed to effectively learn from these tangent space features. The method incorporates cross-validation for robust performance evaluation and utilizes Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves to assess classification effectiveness across four classes. Experimental results demonstrate that the proposed approach achieves an overall accuracy of 96.23% with high precision and recall, significantly outperforming traditional approaches in EEG signal classification. © 2025 IEEE.

关键词： component formatting insert style styling

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