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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Shaina Suresh Kumar Rahul Dev Brajesh Kumar Singh Nishi Agarwal Sandeep Kumar Department of Electronics and Communication G L Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering G.L. Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Dept. of Electronics and Communication Engineering Galgotia College of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Accurate pill dispensing is crucial for ensuring effective medication management, particularly for elderly individuals and patients with chronic conditions. In this research, we propose an FPGA-based automatic pill dispenser utilizing a decision tree classifier. The decision tree algorithm classifies pills based on their shape and size features, facilitating precise dispensing at designated times. Our methodology involves training the decision tree classifier using a sample dataset of pill characteristics. Through entropy calculations and information gain analysis, we derive a decision tree tailored to the dataset. The FPGA implementation enables real-time pill dispensing, offering advantages such as low power consumption and parallel processing. Our findings demonstrate the efficacy of the proposed approach in accurately dispensing pills, addressing the critical need for reliable medication administration systems.

关键词： Training Accuracy Power demand Shape Parallel processing Real-time systems Decision trees Reliability Older adults Field programmable gate arrays

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FoodX, a System to Reduce Food Waste 3

FoodX, a System to Reduce Food Waste

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3rd International Conference on computer and Informatics engineering, IC2IE 2020

作者： Shinta Oktaviana, R. Febriani, Diana Ambarwati Yoshana, Intan Payanta, L.R. Politeknik Negeri Jakarta Dept. Computer and Informatics Engineering Indonesia Instituto Politecnico de Braganca Dept. Technology and Management Portugal

ISBN: (纸本)9781728182476

Food waste is a serious problem that occurs in various countries. Indonesia is a country that produces food waste, the second largest after Saudi Arabia. Currently, there are several communities who care about the issue of food waste and hunger in Indonesia. The Community collects excess food from eligible donor consumption to be distributed to people in need. They have the aim to reduce the problem of food waste and numbers starving in Indonesia. However, the process of channeling food to donors and the community is still practically a manual where the community contacts the donors one by one, so it is considered less effective. This research aims to create a system to connect the community with individuals or organizations that want to donate excess food. In order for users to give faster feedback, this system was made using the prototype methodology. At the final stage of the development, testing was carried out by involving several volunteers and 3 communities to see the completeness of the features system. FoodX system made already accommodate the needs of 2 types of food communities (with and without volunteer). © 2020 IEEE.

关键词： food waste hunger rate social entrepreneurship. sustainable development goals

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An optimization-based algorithm for simultaneous shaping of poles and zeros for non-collocated vibration suppression

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IFAC-PapersOnLine 2022年第16期55卷 394-399页

作者： Adrian Saldanha Haik Silm Wim Michiels Tomáš Vyhlídal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

This article presents a control design method for simultaneous shaping of the poles and zeros of linear time-invariant systems, motivated by the application of non-collocated vibration suppression to flexible multi-body systems. An entire suppression of vibrations at a target mass for a given excitation frequencies can be recast into the problem of assigning zeros of the transfer function from the excitation force to the target mass' position. The design requirement of achieving sufficient damping in the closed loop system combined with suppressing vibrations at the target, leads to the minimization of the spectral abscissa function of the closed loop system as a function of the controller parameters, subject to zero location constraints. These constraints exhibit polynomial dependence on the controller parameters. We present two approaches to solve the optimization problem which are both based on constraint elimination followed by application of an algorithm for non-smooth unconstrained optimization. The design approach is applicable to delay-free models as well as time-delay models of retarded and neutral type. Simulations results illustrate its applicability to a spring-mass-damper system.

关键词： vibration suppression zero placement time-delay systems spectral optimization

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A Hybrid Deep Learning Network for Long-Term Travel Time Prediction in Freeways 26

A Hybrid Deep Learning Network for Long-Term Travel Time Pre...

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26th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2021

作者： Ho, Ming-Chu Chen, Yu-Cing Hung, Chih-Chieh Wu, Hsien-Chu National Chung Hsing University Dept. of Management Information Systems Taiwan National Chung Hsing University Dept of Foreign Language and Literature Taiwan National Chin-Yi University of Technology Dept of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665408257

Travel time plays a vital role in people's daily lives. It can help them not merely avoid traffic congestion but save time as well. When people need to drive to different cities by taking highways, travel time become more and more important now that they can check it to arrange better routes. Moreover, because COVID-19 are epidemic across Taiwan, people prefer to drive rather than taking public transportation. Therefore, accurate predictions of travel time is of great significance. In order to obtain precise predictions and correspond to situations in real life, we divide data into long and short sequences and create three types of dataset, including the whole year, only national holidays, and non-holidays. Additionally, on account of the interactive influence of time in different segments of the freeway, we exploit data to predict next-hour travel time instead of next 5 minutes. We introduce a deep learning model which hybrids tendency from XGBoost and recency embeddings from a fully-connected neural network, respectively. It can capture crucial features of both long and short sequences and observe implicit correlations between XGBoost and a fully-connected neural network. Extensive experiments on the dataset illustrate that our model achieves eminent performances and outperforms other state-of-the-art models. © 2021 IEEE.

关键词： Travel time

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A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning

A Hybrid Edge-Cloud Computing Approach for Energy-Efficient ...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Jagendra Singh Partibha Dabas Sunita Bhati Santosh Kumar Kamal Upreti Nazeer Shaik School of Computer Science Engineering and Technology Bennett University Greater Noida India Dept. of Computer Science & Engineering Dr Akhilesh Das Gupta institute of Technology and Management Delhi India Department of Computer Applications JECRC University Jaipur India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science CHRIST University (Deemed to be University) Ghaziabad India Department of Computer Science Engineering SRIT-Autonomous Anantapur India

This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption.

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Efficient Feature Extraction for Classification of Alzheimer's Abnormalities Using Deep Learning

Efficient Feature Extraction for Classification of Alzheimer...

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Emerging Technology (INCET), International Conference for

作者： Minal A. Zope Rakesh K. Deshmukh Priya Pise Shrikant D. Bhopale Dept. of Computer Engineering AISSMS's Institute of Information Technology Kalinga University Raipur Pune India Kalinga University Raipur India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D. Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Recent machine learning AD predictions employed feature selection and extraction methods. The literature research revealed several attempts to identify AD by using different Deep learning layer and micro-simulation techniques. However, identifying important features that can detect AD extremely early remains a difficult task. Automation, poor classification accuracy, over fitting, etc. are the other challenges while using machine learning algorithms. The recent progression of using deep learning methods has received the significant attention for the automatic AD classification. CNN's strategy, while successful, has certain drawbacks: (1) The CNN model uses local receptive fields, shares weights, and subsamples, (2) deep neural networks need a lot of labeled sample training models to avoid over fitting, which may cause the loss of crucial information like representative features at various frequencies and orientations; and (3) the higher computational overhead is another challenge. Therefore, these challenges motivate us the need of the proposed study. The scope of the proposed study is limited to the classification of AD from the input medical images.

关键词： Deep learning Training Machine learning algorithms Automation Computational modeling Fitting Artificial neural networks

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A Visual Servoing Strategy for Coastline Tracking using an Unmanned Aerial Vehicle

A Visual Servoing Strategy for Coastline Tracking using an U...

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Mediterranean Conference on control and Automation (MED)

作者： Sotirios N. Aspragkathos George C. Karras Kostas J. Kyriakopoulos Control Systems Lab School of Mechanical Engineering National Technical University of Athens Athens Greece Dept. of Computer Science and Telecommunications University of Thessaly Lamia Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

In this paper, an Image-based Visual Servo (IBVS) control strategy for the autonomous surveillance of coastlines using an octocopter aerial vehicle is proposed. The implemented strategy is focused on the vision-based detection and tracking of dynamic coastlines and in the presence of waves while flying in low altitudes. For this purpose, a Deep Neural Network (DNN) for the detection of the coastline is employed. The DNN is ac-companied by an analytical formulation of an Extended Kalman Filter (EKF), which considers an approximate periodical wave motion model to provide an online estimate of the coastline motion directly in image space. The estimated feedback is provided to an appropriately formulated IBVS tracking controller for the autonomous guidance of the octocopter along the coastline, ensuring the latter is always kept inside the camera’s field of view. The efficacy of the proposed scheme is demonstrated via a set of comparative outdoor experiments using an octocopter flying along the coastline on various weather and beach settings.

关键词： Visualization Tracking Trajectory planning Surveillance Neural networks Estimation Visual servoing

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Using InterCloudFramework to migrate application and data between two public clouds 7

Using InterCloudFramework to migrate application and data be...

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7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021

作者： Koushik, S Patil, Annapurna P. Suma, C.S. M. S. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9781665428491

Cloud computing is an emerging computing platform for sharing resources over the internet, including infrastructure, applications, software, and business processes. Cloud computing is popular because of its public presentation, least cost, high availability. These days, many organizations use cloud services for their business activities to enhance their computing resource capabilities at lower prices. Cloud computing plays a vital role in research and academia to share resources and improve the teaching-learning process. One of the Cloud platforms' drawbacks is the data and application tightly coupled with the Cloud Service called Vendor Lock-In. Methods and procedures are available to migrate live data within the same cloud service providers. But to migrate the applications or data from one cloud service provider to another cloud service provider is complex. The paper proposes a novel methodology for migrating the application or data from one cloud service provider to another cloud service provider. The proposed approach presents CPU and RAM usage for the migration of applications or data. The paper also provides insights into the time taken for the Live Migration between heterogeneous clouds. © 2021 IEEE.

关键词： Cloud computing

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A Deep Reinforcement Learning Motion control Strategy of a Multi-rotor UAV for Payload Transportation with Minimum Swing

A Deep Reinforcement Learning Motion Control Strategy of a M...

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Mediterranean Conference on control and Automation (MED)

作者： Fotis Panetsos George C. Karras Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Lab National Technical University of Athens Athens Greece Dept. of Computer Science and Telecommunications University of Thessaly Lamia Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper addresses the problem of controlling a multirotor UAV with a cable-suspended load. In order to ensure the safe transportation of the load, the swinging motion, induced by the strongly coupled dynamics, has to be minimized. Specifically, using the Twin Delayed Deep Deterministic Policy Gradient (TD3) Reinforcement Learning algorithm, a policy Neural Network is trained in a model-free manner which navigates the vehicle to the desired waypoints while, simultaneously, compensating for the load oscillations. The learned policy network is incorporated into the cascaded control architecture of the autopilot by replacing the common PID position controller and, thus, communicating directly with the inner attitude one. The performance of the proposed policy is demonstrated through a comparative simulation and experimental study while using an octorotor UAV.

关键词： Navigation Attitude control Transportation Reinforcement learning Water pollution Autopilot Sensors

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