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IoT-Based Cultivation of Mitragyna Speciosa

IoT-Based Cultivation of Mitragyna Speciosa

引用

International electrical engineering Congress (iEECON)

作者： S. Meesomklin P. Ruaangchaiwuttikhun J. Duangprateep S. Saleekhao P. Akkaraekthalin Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper proposes a smart farm system, applying to cultivate mitragyna speciosa plants in Thailand. The greenhouse system with several sensors, microcontroller, and cultivation devices has been designed. The temperature, light intensity and moisture in the area of cultivation can be measured and the microcontroller has been used to read all data and then they are shown through the website. The cultivation data have been processed and then used for controlling temperature, light intensity and moisture inside the greenhouse. The proposed system is very effective, that the temperature values, light intensity, and humidity can be monthly, daily and hourly viewed and displayed on the computer and smartphone. When the proposed system has been applied for cultivating mitragyna speciose, the better growth results have been found comparing with the traditional cultivation.

关键词： Temperature measurement Smart agriculture Temperature sensors Temperature distribution Microcontrollers Plants (biology) Moisture Process control Sensor systems Temperature control

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Collaborative Learning in Bilateral Academic Training on Robotics Design and Development

Collaborative Learning in Bilateral Academic Training on Rob...

引用

STEM Education Conference (iSTEM-Ed), International

作者： Patinya Sang-Aroon Sethakarn Prongnuch Tadchanon Chuman Aphirak Thitinaruemit Kalaphath Kounlaxay Chanida Niyomtum Dept. of Industrial Design and Packaging Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Robotics Engineering Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Electrical Technology Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Computer Engineering Faculty of Engineering Souphanouvong University Luangprabang City Lao PDR Education Automation System Co. Ltd. Bangplee Samutprakran Thailand

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

This paper presents how collaborative learning can facilitate academic training in robotics design and development, with the objective of establishing a foundation in robotics structure design and microcontroller programming for controlling robots, particularly for bilateral undergraduate students. The students comprise 20 engineering students and 20 architecture students, who are divided into groups of two, consisting of an engineering students and an architecture students each. Group activities focusing on robot structure design and robot Development are provided for the students. The results demonstrate that collaborative learning can effectively facilitate the training activities.

关键词： Training Educational robots Federated learning Microcontrollers Focusing engineering students Programming profession

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Improving Distribution systems Maintenance Management, by Predicting Reliability Indices Using Fully Connected Neural Networks 12

Improving Distribution Systems Maintenance Management, by Pr...

引用

12th Smart Grid Conference, SGC 2022

作者： Tananaeifar, Mohammad-Hossein Alimadaday, Mahdis Ghadamgahi, SeyedMohammad-Mahdi Ghafourian-Nasiri, Mohammad Fereidunian, Alireza Amirkabir University of Technology Construction Engineering and Management Dep. Tehran Iran Tarbiat Modares University Industrial Engineering Dep. Tehran Iran Kharazmi University Computer Engineering Dep. Tehran Iran K. N. Toosi University of Technology Electrical Engineering Faculty Tehran Iran

ISBN: (纸本)9798350333961

Due to the increasing amount of industrial data worldwide, deep learning solutions have become extensively popular for preventive maintenance programs. Preventive maintenance reduces the costs of equipment failures, thus crafting detailed preventive maintenance programs are very effective. Neural networks are intelligent computing techniques inspired by biological neurons. Neural networks are one of the most common and practical machine learning algorithms that are utilized in many industrial applications. In this research, a fully connected neural network is developed to predict the systems reliability indices, i.e. the number of failures occurring in feeders and the amount of energy not served (ENS) in electricity distribution feeders. As a result, a data-driven improvement is provided for the preventive maintenance program. © 2022 IEEE.

关键词： Neural networks

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On the Stability Analysis of Open Federated Learning systems

On the Stability Analysis of Open Federated Learning Systems

引用

American Control Conference (ACC)

作者： Youbang Sun Heshan Fernando Tianyi Chen Shahin Shahrampour the Department of Mechanical and Industrial Engineering Northeastern University Boston Massachusetts USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York USA

We consider the open federated learning (FL) systems, where clients may join and/or leave the system during the FL process. Given the variability of the number of present clients, convergence to a fixed model cannot be guaranteed in open systems. Instead, we resort to a new performance metric that we term the stability of open FL systems, which quantifies the magnitude of the learned model in open systems. Under the assumption that local clients’ functions are strongly convex and smooth, we theoretically quantify the radius of stability for two FL algorithms, namely local SGD and local Adam. We observe that this radius relies on several key parameters, including the function condition number as well as the variance of the stochastic gradient. Our theoretical results are further verified by numerical simulations on synthetic data.

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Sleep-Wake and Body Position Classification with Deep Learning using Pressure Sensor Mat Measurements

Sleep-Wake and Body Position Classification with Deep Learni...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Green, Carter Bouchard, Martin Goubran, Rafik Robillard, Rebecca Higginson, Caitlin Lee, Elliott Knoefel, Frank University of Ottawa School of Electrical Engineering and Computer Science OttawaON Canada Bruyere Research Institute University of Ottawa School of Electrical Engineering and Computer Science OttawaON Canada Carleton University Bruyere Research Institute Systems and Computer Engineering OttawaON Canada University of Ottawa Sleep Research Unit U. of Ottawa Institute for Mental Health Research at the Royal School of Psychology OttawaON Canada University of Ottawa Institute for Mental Health Research at the Royal Sleep Research Unit OttawaON Canada Royal Ottawa Mental Health Center Sleep Research Unit U. of Ottawa Institute for Mental Health Research at the Royal Sleep Disorders Clinic OttawaON Canada Carleton University Bruyere Research Institute Faculty of Medicine University of Ottawa Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665493840

Polysomnography, the gold standard of sleep measurement, is a time-intensive, costly, and rather invasive procedure. Using under-bed pressure sensor arrays data simultaneously recorded with standard polysomnography, this study demonstrates that deep learning can be used to classify body position and differentiate sleep from wake. All measurements were performed in people with suspected sleep disorders referred for clinical assessments at a sleep laboratory. To perform the classification tasks, we used supervised learning and temporal convolution networks. Performance was assessed with leave-one-out cross validation on 84 participants for body position classification and 70 participants for sleep-wake classification. Results demonstrate that a pressure sensor array placed under the mattress with less than 100 sensors can outperform previous sleep-wake detection methods and is competitive with previous methods for body position classification. Our pressure sensor arrays differ from the mats used in previous work as they use significantly less sensors and are located under the mattress, making them less obtrusive. This tool has great potential as a cost-efficient mean of assessing sleep while reducing patient burden and the workload of specialized staff. © 2023 IEEE.

关键词： Wakes

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A Review of EMF Radiation for 5G Mobile Communication systems

A Review of EMF Radiation for 5G Mobile Communication System...

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2021 IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2021

作者： Elbasheir, Mohammed S. Saeed, Rashid A. Ibrahim, Abeer A. Z. Edam, Salah Hashim, Fazirulhisyam Fadul, Saber M. E. Khartoum Sudan Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Malaysia Department of Electrical and Electronics Engineering Faculty of Engineering Universiti Putra Malaysia Serdang43400 Malaysia

ISBN: (纸本)9781665428279

5G is projected to be the dominating technology for mobile networks in the next years. The deployment of 5G is expected to substantially raise power density levels, which are necessary to cover a specific area of concern by a significant increase in base station numbers. Overall radiation levels pose a challenge for installing massive 5G base stations, especially in residential areas. This paper reviews current state of EMF radiation evaluation for 5G base stations (BS) on humans. This review covers the international standards exposure limits adopted by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Federal Communications Commission (FCC) USA, and also reviews some recent studies found related to Electromagnetic field (EMF) radiation evaluation and analysis from different perspectives, we show their work methodologies and results. The reviewed cases are classified into two main categories, assessment based on simulation model and assessment based on filed measurement. Both categories have the same objective to assess and evaluate the EMF radiation exposure from BSs. © 2021 IEEE.

关键词： Base stations

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A Deep Learning Approach for Satellite and Debris Detection: YOLO in Action

A Deep Learning Approach for Satellite and Debris Detection:...

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International Conference on computer and Information Technology (ICCIT)

作者： Md Younus Ahamed Md Asif Bin Syed Paroma Chatterjee Al Zadid Sultan Bin Habib Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown USA Department of Industrial and Management Systems Engineering West Virginia University Morgantown USA

Satellite detection refers to the identification and recognition of satellites in satellite images. This task is crucial for various applications, including space surveillance, satellite tracking, and space debris monitoring. Several studies have been conducted to develop methods and techniques for satellite detection. The intricacy and scale of satellite images make satellite detection incredibly challenging. The process of manual identification and classification necessitates a significant amount of time. Recently, Deep Learning (DL) has emerged as a highly promising approach in computer Vision (CV) tasks. This study evaluates the effectiveness of three widely employed DL-based object detection algorithms, specifically YOLOv4, YOLOv5, and YOLOv8, within the field of satellite detection. The algorithms have undergone training utilizing a dataset of satellite photos comprising both satellites and debris. The evaluation of the algorithm’s performance involved the utilization of broadly accepted metrics, including Precision, Recall, Mean Average Precision (mAP), Box Loss, and Class Loss. The findings indicated that all three algorithms exhibited a high level of accuracy in detecting and precisely determining the location of objects inside satellite data. The performance of YOLOv5 and YOLOv8 surpassed that of YOLOv4 across various measures, with a notable advantage observed in detecting tiny objects. Overall, this study demonstrates the capabilities of DL algorithms in the context of satellite detection and classification techniques.

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Real-Time Floating Marine Litter Detection on USV

Real-Time Floating Marine Litter Detection on USV

引用

IEEE International Workshop on Metrology for the Sea;Learning to Measure Sea Health Parameters (MetroSea)

作者： Guido Lazzerini Alberto Topini Gherardo Liverani Lorenzo Cecchi Alessandro Ridolfi Fausto Ferreira Department of Industrial Engineering (DIEF) University of Florence Florence Italy Interuniversity Center of Integrated Systems for the Marine Environment (ISME) Italy Laboratory for Underwater Systems and Technologies (LABUST) Faculty of Electrical Engineering and Computing (FER) University of Zagreb Zagreb Croatia

ISBN: (数字)9798350379006

ISBN: (纸本)9798350379013

Marine litter is harmful to coastal and ocean environments for many reasons. Many devices have been developed in the last few years to recover as much litter as possible in ports and coastal areas. However, they usually employ a brute-force approach, leading to high energy and resource consumption. This is not negligible, considering their ecological goal. In this scenario, the deployment of Unmanned Surface Vehicles (USVs) to inspect the area of interest could complement the use of cleaning devices, adding relevant knowledge and intelligence to the context. Cleaning devices could be deployed or activated only where and when needed, leading to a more resource-aware approach. The contribution of this work is the development and deployment of an object detection neural network onto the H20mni-X USV, aiming to real-time detection of floating marine litter through an optical camera.

关键词： Sea surface Robot vision systems Sea measurements Optical computing Seaports Optical fiber networks Real-time systems Cleaning Robots Optical devices

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Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

引用

2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia Tallinn University of Technology Department of Computer Systems Akadeemia tee 15a Tallinn12618 Estonia Technion - Israel Institute of Technology Faculty of Electrical & Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9798350327748

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening 'knowledge-action gap'. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user's energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction. © 2023 IEEE.

关键词： Energy efficiency

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Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350396782

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort. © 2023 IEEE European Union.

关键词： Consumer comfort Demand side recommender system Distributed power systems Hybrid digital twins Industry 5.0

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