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An end-to-end instance segmentation method based on improved ConvNeXt V2

International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking

arXiv

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arXiv 2023年

作者： Van Ma, Linh Hussain, Muhammad Ishfaq Park, JongHyun Kim, Jeongbae Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of Department of Mechanical-IT Convergence System Engineering Pusan National University Busan Korea Republic of

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack. © 2023, CC BY.

关键词： Object detection

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Trustworthy adaptation with few-shot learning for hand gesture recognition

Trustworthy adaptation with few-shot learning for hand gestu...

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2021 IEEE International Conference on Autonomous systems, ICAS 2021

作者： Rahimian, Elahe Zabihi, Soheil Asif, Amir Atashzar, S. Farokh Mohammadi, Arash Concordia University Concordia Institute for Information System Engineering MontrealQC Canada Concordia University Electrical and Computer Engineering MontrealQC Canada New York University Electrical and Computer Engineering Mechanical and Aerospace Engineering United States

ISBN: (纸本)9781728172897

This work is motivated by potentials of Deep Neural Networks (DNNs)-based solutions in improving myoelectric control for trustworthy Human-Machine Interfacing (HMI). In this context, we propose the Trustworthy Few Shot-Hand Gesture Recognition (TFS-HGR) framework as a novel DNN-based architecture for performing Hand Gesture Recognition (HGR) via multi-channel surface Electromyography (sEMG) signals. The main objective of the TFS-HGR framework is to employ Few-Shot Learning (FSL) formulation with a focus on transferring information and knowledge between source and target domains (despite their inherit differences) to address limited availability of training data. The NinaPro DB5 dataset is used for evaluation purposes. The proposed TFS-HGR achieves a performance of 83.17% for new repetitions with few-shot observations, i.e., 5-way 10-shot classification. Moreover, the TFS-HGR with the accuracy of 75.29% also generalize to new gestures with few-shot observations, i.e., 5-way 10-shot classification. © 2021 IEEE.

关键词： Deep neural networks

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Educational Training on Business Management Using Web-Based Applications with Cryptocurrency Integration

Educational Training on Business Management Using Web-Based ...

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Vocational Education and Electrical engineering, International Conference on

作者： Ervin Yohannes Yuni Yamasari Aditya Prapanca Aries Dwi Indriyanti I Kadek Dwi Nuryana Chih-Yang Lin Kahlil Muchtar Informatic Engineering State University of Surabaya Surabaya Indonesia Information System State University of Surabaya Surabaya Indonesia Mechanical Engineering National Central University Taoyuan Taiwan Electrical and Computer Engineering Syiah Kuala University Banda Aceh Indonesia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

The business model has developed significantly in various aspects and fields. One of the most popular models is the Business Model Canvas, which has greatly helped people build their businesses. With advancements in technology, this model has been combined with artificial intelligence (AI) into a web-based application. Moreover, we have integrated cryptocurrency into our application to provide players with a real-time business environment experience. Additionally, we have developed both administrator and player systems, which are user-friendly and adaptable to various languages. Based on the experimental results, anyone who has played this simulation/training has done so without any problems. The team with the highest Net Profit Value (NPV) is nominated as the winner in this business simulation/training. Our demo can be accessed at https://***/demo.

关键词： Electrical engineering Adaptation models Decision making Real-time systems Cryptocurrency Teamwork Artificial intelligence Management training Business

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Fashionable Four Wheeler Parking to Reduce the Human Intervention and to Enhance the Flexibility in Parking system Using IoT 3

Fashionable Four Wheeler Parking to Reduce the Human Interve...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Manoj, P. Varun, C.M. Keerthi Kumar, N. Padhye, Pradip Kumari, K. Aruna Das, Puja Sarkar, Swagata Vidyavardhaka College of Engineering Department of Civil Engineering Mysore India R.M.K. Engineering College Department of Computer Science and Business System Kavaraipettai India Bms Institute of Technology and Management Department of Mechanical Engineering Bengaluru India School of Management Dr. Vishwanath Karad Mit World Peace University Pune India Gitam University Department of Mathematics Visakhapatnam India Hiralal Mazumder Memorial College for Women Department of Computer Science Kolkata India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350336238

Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform. © 2023 IEEE.

关键词： Infrared detectors

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Subject-Independent Ankle Joint Power Estimation with Two IMUs During Flat and Inclined Walking

Subject-Independent Ankle Joint Power Estimation with Two IM...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Hong Wang Dongxuan Li Kairan Liang Peter Shull State Key Laboratory of Mechanical System and Vibration Shanghai Jiaotong University Shanghai China Department of Electrical and Computer Engineering Northeastern University Boston America

Assessing ankle joint power during real-life scenarios is crucial for analyzing human push-off and detecting abnormal gait patterns. However, traditional joint power monitoring methods require expensive and professional equipment, limiting their use to gait laboratories. To address this limitation, we propose a portable and robust two-stage approach that estimates ankle joint power using two inertial measurement units (IMU) sensors placed on the shank and foot, respectively. Our subject-independent CNN model accurately assessed ankle joint power during flat and inclined walking across 28 walking speeds and 6 ramp inclines. This solution facilitates ankle joint power assessment outside of gait laboratories and could serve as a foundation to enable gait abnormality evaluation in patients in hospitals, clinics, and home-based settings.

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Advancing Orchard Fruit Detection: An Innovative Agricultural Foundation Model Approach

Advancing Orchard Fruit Detection: An Innovative Agricultura...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University MI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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Optimizing the Performance of Sentiment Analysis on Social Media Data Through Feature Selection in Preprocessing Techniques

Optimizing the Performance of Sentiment Analysis on Social M...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Arif Ridho Lubis Yuyun Yusnida Lase Darwis A. R Deden Witarsyah Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia Department of mechanical engineering Politeknik Negeri Medan Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

The digital era directly builds user needs in communicating using social media, the data generated by social media has a large volume so it needs to be analyzed with a preprocessing process. The problems that exist in social media data contain a lot of repeated words with non-standard sentence structures and still contain lots of irregular punctuation making it difficult to determine which features are considered important in a sentence to carry out a sentiment analysis process in identifying positive, negative, and neutral sentences. So it is necessary to select features using preprocessing techniques using Recursive Feature Elimination (RFE). This study produces a comparison that shows that the selection of features and combinations in using features results in the accuracy of each algorithm increasing when compared to using one feature. The results showed that the CNN algorithm has an accuracy value of 0.95, the LSTM algorithm is 0.94 and the RNN algorithm is 0.93. when compared to the use of one feature, it increases by 0.09 for the CNN algorithm, increases by 0.07 for the LSTM algorithm, and increases by 0.05 for the RNN algorithm.

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Optimization of SVM Classification Accuracy with Bayesian Optimization Utilizing Data Augmentation

Optimization of SVM Classification Accuracy with Bayesian Op...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Arif Ridho Lubis Yuyun Yusnida Lase Darwis A. R Deden Witarsyah Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia Department of Mechanical Engineering Politeknik Negeri Medan Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

Optimizing the accuracy of SVM classification is one of the most important things in the world of machine learning. This study will use the SVM (Support Vector Machine) algorithm in classifying. This study aims to improve the classification accuracy of the Support Vector Machine (SVM) model by utilizing Bayesian Optimization techniques and data augmentation. The Bayesian Optimization technique will be used to find the most optimal values for the kernel and C parameters in the SVM model. While data augmentation is done to improve the quality of the training data by adding variations and data features. This research uses a synonym replacement technique in data augmentation. The data used was obtained from social media Twitter. The results of this study are in the form of a model with a combination of Bayesian Optimization and augmentation. The accuracy obtained with the SVM model using Bayesian Optimization and data augmentation is 89%. However, the conventional SVM model without Bayesian Optimization only produces 82.19% accuracy so the SVM model with Bayesian Optimization increases the accuracy by 7%.

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