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Design and Development of Smart Farming System for Monitoring and Bird Pest Control Based on Raspberry Pi 4 with Implementation of YOLOv5 Algorithm

Design and Development of Smart Farming System for Monitorin...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), International Conference on

作者： Muhammad Deagama Surya Antariksa Ario Yudo Husodo Raphael Bianco Huwae Ryan Adhitya Nugraha Informatics Engineering University of Mataram Mataram Indonesia School of Industrial and Systems Engineering Telkom University Indonesia

This research proposes a novel solution to address the issue of bird pests in agricultural fields by utilizing a Raspberry Pi 4 Model B, a webcam, and a connected speaker. Leveraging the capabilities of the Internet of Things and computer vision technologies, the webcam employs the YOLOv5 algorithm and OpenCV for real-time bird detection. The system incorporates a Telegram chatbot for instant notifications and allows for photo capture when birds cross predefined boundaries. Extensive testing showcases the webcam's ability to detect birds under various conditions. In open spaces, the detection range spans approximately 80-400 cm, while in environments resembling rice fields, the range is around 80-200 cm, although false positives may occur at a distance of 200 cm. The Raspberry Pi 4 Model B demonstrates a processing speed of 0.78-0.8 frames per second. During a 1-hour temperature test, the temperature of the Raspberry Pi 4 Model B ranged from 58.4 to 61.8 °C. Furthermore, the implementation of a speaker emitting loud noises effectively repels birds within the covered area, although some birds may still return to that area.

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A hybrid deep learning-based approach for rolling bearing fault prognostics

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IFAC-PapersOnLine 2023年第2期56卷 6588-6593页

作者： Domício Neto Lorena Petrella Jorge Henriques Paulo Gil Alberto Cardoso University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra (CISUC) Coimbra Portugal CTS-UNINOVA Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica Portugal

Predictive Maintenance (PdM) has the potential to revolutionize the industry by providing advanced techniques to assess the condition of an industrial system and yield key information that can help optimize maintenance planning and prevent unexpected faults and breakdowns. Nevertheless, PdM is far from being universally applied and it is still the subject of increasing research. Thus, developing new approaches has great relevance to help PdM become a practical reality for the industry. PdM can also bring benefits in terms of sustainability, by reducing human and material resources waste, which is one of the main objectives of Circular Manufacturing initiatives. In this context, rolling bearings are one of the most studied components, as most industrial systems with rotating mechanisms contain bearings, which are prone to a number of faults caused by natural and unnatural wear. In this work, an hybrid Deep Learning (DL) approach is proposed, combining a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network to predict Remaining Useful Life (RUL) using rolling bearing vibration data preprocessed with the Short-Time Fourier Transform (STFT). This model was trained and validated using the PRONOSTIA public dataset, which is a popular benchmark for rolling bearing prognostics. The obtained results are satisfactory, providing RUL estimates close to the true values in most test cases, proving the competitiveness of the approach and its potential.

关键词： Predictive Maintenance Circular Manufacturing Remaining Useful Life Rolling Bearings Deep Learning

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Guest Editorial Column: Special Issue of Distributed Generation & Alternative Energy Journal "Digital twin for Accelerating Sustainability in Energy Automation and Smart Grid"

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 V-VIII页

作者： Katina, Polinpapilinho F. Asirvatham, David Department of Informatics and Engineering systems University of South Carolina Upstate United States School of Computer Science Taylor's University Selangor Malaysia

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Litchi detection in the field using an improved YOLOv3 model

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International Journal of Agricultural and Biological engineering 2022年第2期15卷 211-220页

作者： Hongxing Peng Chao Xue Yuanyuan Shao Keyin Chen Huanai Liu Juntao Xiong Hu Chen Zongmei Gao Zhengang Yang College of Mathematics and Informatics South China Agricultural UniversityGuangzhou 510642China College of Mechanical and electronic engineering Shandong Agricultural UniversityTai’an 271018ShandongChina School of Electronic and Information Engineering Jiaying UniversityMeizhou 514015GuangdongChina School of Chemistry and Chemical Engineering South China Technology of UniversityGuangzhou 510641China Center for Precision and Automated Agricultural Systems Department of Biological Systems EngineeringWashington State UniversityProsserWA 99350USA

Due to the illumination,complex background,and occlusion of the litchi fruits,the accurate detection of litchi in the field is extremely *** order to solve the problem of the low recognition rate of litchi-picking robots in field conditions,this study was inspired by the ideas of ResNet and dense convolution and proposed an improved feature-extraction network model named“YOLOv3_Litchi”,combining dense connections and residuals for the detection of ***,based on the traditional YOLOv3 deep convolution neural network and regression detection,the idea of residuals was to be put into the feature-extraction network to effectively avoid the problem of decreasing detection accuracy due to the excessive depths of the network ***,under the premise of a good receptive field and high detection accuracy,the large convolution kernel was replaced by a small convolution kernel in the shallow layer of the network,thereby effectively reducing the model ***,the idea of feature pyramid was used to design the network to identify the small target litchi to ensure that the shallow features were not lost and simultaneously reduced the model *** results show that the improved YOLOv3_Litchi model achieved better results than the classic YOLOv3_DarkNet-53 model and the YOLOv3_Tiny *** mean average precision(mAP)score was 97.07%,which was higher than the 95.18%mAP of the YOLOv3_DarkNet-53 model and the 94.48%mAP of the YOLOv3_Tiny *** frame frequency was 58 fps,which was higher than 29 fps of the YOLOv3_DarkNet-53 *** with the classic Faster R-CNN model with the feature-extraction network VGG16,the mAP was increased by 1%,and the FPS advantage was *** with the classic single shot multibox detector(SSD)model,both the accuracy and the running efficiency were *** results show that the improved YOLOv3_Litchi model had stronger robustness,higher detection accuracy,and less computationa

关键词： deep learning residual network dense connection feature pyramid network

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An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

An observer comparison study to evaluate a machine learning ...

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Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment

作者： Abdihamzehkolaei, Alireza Mirniaharikandehei, Seyedehnafiseh Choquehuanca, Angel Aedo, Marco Pacheco, Wilmer Estacio, Laura Cahui, Victor Huallpa, Luis Quiñonez, Kevin Calderón, Valeria Gutierrez, Ana Maria Vargas, Ana Gamero, Dery Castro-Gutierrez, Eveling Qiu, Yuchen Zheng, Bin Jo, Javier A. School of Electrical and Computer Engineering University of Oklahoma NormanOK United States School of Systems Engineering and Informatics Universidad Nacional de San Agustín de Arequipa Arequipa Peru Medicine Faculty Universidad Nacional de San Agustín de Arequipa Arequipa Peru

ISBN: (数字)9781510660403

ISBN: (纸本)9781510660397

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the abstract

关键词： COVID-19

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Learning Robotic Cutting from Demonstration: Non-Holonomic DMPs using the Udwadia-Kalaba Method

Learning Robotic Cutting from Demonstration: Non-Holonomic D...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Artūras Straižys Michael Burke Subramanian Ramamoorthy School of Informatics University of Edinburgh Department of Electrical and Computer Systems Engineering Monash University

Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manipulation skills from human demonstration. However, the reactive nature of DMPs restricts their applicability for tool use and object manipulation tasks involving non-holonomic constraints, such as scalpel cutting or catheter steering. In this work, we extend the Cartesian space DMP formulation by adding a coupling term that enforces a pre-defined set of non-holonomic constraints. We obtain the closed-form expression for the constraint forcing term using the Udwadia-Kalaba method. This approach offers a clean and practical solution for guaranteed constraint satisfaction at run-time. Further, the proposed analytical form of the constraint forcing term enables efficient trajectory optimization subject to constraints. We demonstrate the usefulness of this approach by showing how we can learn robotic cutting skills from human demonstration.

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ribeiro, Lucas Oliveira, Helder P. Hu, Xiao Pereira, Tania University of Porto Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9798350337488

PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events. © 2023 IEEE.

关键词： Convolutional Neural Network Heart Rate Estimation Long Short Term Memory Motion Artifacts Photoplethysmography

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An Efficient Learning Algorithm for Phase Shift Optimization in RIS-Aided MISO Wireless systems 15

An Efficient Learning Algorithm for Phase Shift Optimization...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Tian, Jiawen Guan, Wenwen Li, Xiao Tsiftsis, Theodoros A. Liu, Hongwu Jinan University School of Intelligent Systems Science & Engineering Zhuhai China Southeast University National Mobile Communications Research Laboratory Nanjing China University of Thessaly Dept. of Informatics & Telecommunications Lamia Greece Shandong Jiaotong University School of Information Science & Electrical Engineering Jinan China

ISBN: (纸本)9798350324662

Due to dynamic change of the wireless environment, devising the local phase shift matrix for reconfigurable intelligent surface (RIS) can be a thought-provoking task. Therefore, in this paper, we come up with a deep reinforcement learning (DRL)-based beamforming optimization algorithm for RIS-aided multiple-input single-output communication systems. To be precise, we exploit a DRL-based structure based on twin-delayed-deep-deterministic-policy-gradient (TD3) algorithm to automatically adjust the phase shift of each unit at RIS to maximize the downlink received signal-to-noise ratio (SNR). Simulation results show that the raised TD3 algorithm performs a higher received SNR than the conventional DRL algorithm with a reduced running time. © 2023 IEEE.

关键词： Deep reinforcement learning (DRL) passive phase shift design reconfigurable intelligent surface (RIS) TD3 algorithm

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Uncovering simultaneous breakthroughs with a robust measure of disruptiveness

arXiv

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arXiv 2025年

作者： Kim, Munjung Kojaku, Sadamori Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States School of Systems Science and Industrial Engineering Binghamton University State University of New York United States

Progress in science and technology is punctuated by disruptive innovation and breakthroughs. Researchers have characterized these disruptions to explore the factors that spark such innovations and to assess their long-term trends. However, although understanding disruptive breakthroughs and their drivers hinges upon accurately quantifying disruptiveness, the core metric used in previous studies—the disruption index—remains insufficiently understood and tested. Here, after demonstrating the critical shortcomings of the disruption index, including its conflicting evaluations for simultaneous discoveries, we propose a new, continuous measure of disruptiveness based on a neural embedding framework that addresses these limitations. Our measure not only better distinguishes disruptive works, such as Nobel Prize-winning papers, from others, but also reveals simultaneous disruptions by allowing us to identify the "twins" that have the most similar future context. By offering a more robust and precise lens for identifying disruptive innovations and simultaneous discoveries, our study provides a foundation for deepening insights into the mechanisms driving scientific breakthroughs while establishing a more equitable basis for evaluating transformative contributions. © 2025, CC BY.

关键词： Patents and inventions

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How To Achieve Learning Outcomes and Student Satisfaction In Open Distance Learning 4

How To Achieve Learning Outcomes and Student Satisfaction In...

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4th International Conference on Innovative Research in Applied Science, engineering and Technology, IRASET 2024

作者： Ikhsan, Ridho Bramulya Samhati, Siti Fernando, Erick Putranto, Agus Mariani, Vini Savor, Dara Sandrina Bina Nusantara University Binus Online Learning Management Department Jakarta Indonesia Universitas Lampung Fakultas Keguruan Dan Ilmu Pendidikan Lampung Indonesia Universitas Multimedia Nusantara Faculty Of Engineering And Informatics Department Of Information System Tangerang Indonesia School Of Information Systems Bina Nusantara University Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350309508

University support for open distance learning and the role of individual factors are important elements to optimize student performance and satisfaction. However, the main problem in open distance learning is to increase students' motivation to learn and achieve optimal learning performance, despite the university's efforts to provide support for student success. Therefore, this study examines the role of university and individual factors to achieve learning outcomes and student satisfaction. In addition, it explains how the achievement of learning outcomes can improve student satisfaction in open distance learning settings. A total of 357 students from five study programs were involved in this study. The results of this study have proven that university support and individual factors have a positive and significant influence on learning outcome achievement and student satisfaction. Furthermore, learning outcome achievement was found to increase student satisfaction. Therefore, the results of this study provide insights for universities to improve their support to students and how to increase students' motivation to achieve better learning performance. © 2024 IEEE.

关键词： Students

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