2022 International Conference on Smart Systems and Power Management (IC2SPM)最新文献

{"title":"Logic-Based Neural Network for Pattern Correction","authors":"Ziad Doughan, Hadi Al Mubasher, Rola Kassem, Ahmad M. El-Hajj, A. Haidar, Layth Sliman","doi":"10.1109/IC2SPM56638.2022.9988994","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988994","url":null,"abstract":"As the 21st century continues, Deep Learning (DL) has become an important part in the digital world. The use of Artificial Neural Networks (ANNs), the core of DL, has led to the development of many novel applications, like speech recognition, character recognition, autonomous cars, etc. In this paper, we present a logic-based neural network processor for pattern correction, which can detect anomalies in a certain character, and can regenerate the character after removing the anomalies. The processor constitutes of sub-networks, namely the Generator network, the Inverter network, the Locator network, the Identifier network, and the Replacer network. A test for the proposed processor was done on the dataset of numeric characters.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128788784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Implementation of an EMG-based System for Monitoring and Help Request","authors":"Carla Zeine, Roy Abi Zeid Daou, Samer Telawi, Josef Boercsoek, A. Hayek, Gaby Abou Haidar","doi":"10.1109/IC2SPM56638.2022.9988920","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988920","url":null,"abstract":"Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-Computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"18 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129885232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simulation Model for Thermal Management of Endoscopic LED-based Light Source","authors":"Hussein Wehby, Mariam Khayreldeen, Mohamad Abou Ali, Houssein Hajj Hassan, M. Hajj-Hassan","doi":"10.1109/IC2SPM56638.2022.9988877","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988877","url":null,"abstract":"The advantages of endoscopic LED light sources over traditional endoscopic light sources, such as halogen and xenon light sources, have led to their current enormous use in the operating theaters in hospital surgical suites in and medical centers worldwide. The main challenge facing light sources is heat production. Here, we report the modeling of a 100-watt endoscopic LED light source with an operating voltage of 3.5 V and maximum current output 30 A using Simulink/Simscape. A buck converter has been used to convert 100-watt 12 V DC with maximum 8.3 A into 3.5 V DC with a maximum of 30 A. The operating voltage and current are controlled using PID controllers. The tuning of PID weights was made using auto tune feature. The simulation results showed that temperature can be controlled successfully to protect the entire system from high heat, and consequently avoid any damage to the LED source.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121477342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast Abnormalities' Classification Using Convolutional Neural Network","authors":"Hawraa Hoteit, F. Sbeity, Mohamad Abou Ali, Adnan Harb, L. Hamawy, Ali Hage-Diab, Mohamad Hajj-Hassan, A. Kassem","doi":"10.1109/IC2SPM56638.2022.9988854","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988854","url":null,"abstract":"Deep learning (DP) holds great promise in many areas, especially in the medical field. Abnormalities in the breast threaten patients' lives, so it is crucial to go through the correct diagnosis. Thus, the participation of a convolutional neural network (CNN) in image analysis and classification could support a proper diagnosis. A set of mammograms are collected in the CBIS-DDSM dataset to train different CNN architectures and various models to classify the labeled images into mass and calcification as a first step and benign or malignant as a second step. The first task is accomplished using two different models, the first one is a CNN and the second includes the VGG16, both achieved good results on the validation with accuracies of 88% and 90% respectively. Regarding the second task, it is performed using CNN only. The accuracy does not exceed 66% due to the limitation in the number of mammograms.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125375673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Marine Object Detection Based on Top-View Scenes Using Deep Learning on Edge Devices","authors":"J. Sharafaldeen, M. Rizk, D. Heller, A. Baghdadi, J-Ph. Diguet","doi":"10.1109/IC2SPM56638.2022.9988928","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988928","url":null,"abstract":"Marine object detection and tracking is an important application for several disciplines such as sea surface monitoring, marine area management, ship collision avoidance, search and rescue missions, etc. Top-view scenes based on aerial or satellite imaging offer capturing objects from new angles of view or for locations that are not seen by capturing nodes fixed at the port side or mounted on moving boats. Moreover, artificial intelligence techniques based on deep learning provide robust solutions for classification and detection. Convolutional neural network (CNN) architectures are being used to detect multiple objects in images and videos. The achieved performance proves the relevance of CNNs in circumventing existing computer vision challenges. In this paper, we investigate the state-of-the-art CNN-based technique, so called You only look once (YOLO), to detect marine objects in images showing sea ships and humans from top-view. YOLO available models are trained using our collected dataset. The evaluation of the trained models illustrates the effectiveness of YOLO in detecting targeted classes (humans and sea ships) with high precision (90 %). The deployment of the trained model on embedded edge devices achieves a high inference performance beyond 80 frames per second.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122021566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Tuning of Power System Stabilizers Using Metaheuristic Optimization Algorithms: A Comparative Study","authors":"Anas Abu-Fedda, E. Feilat","doi":"10.1109/IC2SPM56638.2022.9988908","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988908","url":null,"abstract":"Power system stabilizers are very effective in reducing the low frequency oscillations following small disturbances. The stabilizer works on increasing the damping torque by providing additional feedback signal to the excitation system of the synchronous generator. For optimal performance and enhancement of the generator's stability the parameters of the stabilizer have to be tuned optimally. In this paper, an improved grey wolf optimizer is proposed to enhance the small signal stability of a single-machine infinite bus system. The optimal performance of the proposed algorithm is compared with that of other competing metaheuristic algorithms. The performance of the optimization algorithms is assessed in terms of four performance indices. The simulations results showed that the improved grey wolf optimizer presents superior and robust performance in comparison with the other investigated optimization algorithms.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129564939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Angle Calculation for Heliostat Tracking System","authors":"Youssef Farachi, Khodor Hamzeh, Fadi EL Assal, N. Karami","doi":"10.1109/IC2SPM56638.2022.9988950","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988950","url":null,"abstract":"A solar tower power plants, also known as ‘central tower’ power plant or ‘heliostat’ power plant is based on hundred of heliostat implemented to concentrate the sun ray onto a collecting tower. In this paper, a modeling approach is proposed for calculating the angle of a heliostat mirror based on its location, the tower location, the time and the date in which the sun position is calculated. The modeled equation are encapsulated in a GUI Matlab interface in which orientation of the heliostat are calculated for different parameters.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126930479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thalassemia Diagnosis Through Medical Imaging: A New Artificial Intelligence-Based Framework","authors":"A. Zaylaa, Mohammad Makki, Rola Kassem","doi":"10.1109/IC2SPM56638.2022.9988891","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988891","url":null,"abstract":"Thalassemia is the most common single-gene disorder throughout the world and represents a major public health problem resulting in abnormal ratios of hemoglobin subunits. It is widely spread throughout the Mediterranean region, Middle East, Southeast Asia, and some parts of Africa. There are different types of Thalassemia characterized by abnormal hemoglobin production. Most of the current hemoglobinopathy screening methods include High Performance Liquid Chromatography (HPLC), hemoglobin electrophoresis, screening of Polymerase Chain Reaction (PCR) mutations, and Deoxyribonucleic Acid (DNA) tests. However, all these methods are costly and require specialized instrumentation and trained technicians. Despite some studies used Artificial Intelligence (AI) they focused merely on Machine Learning (ML) and results were not optimal. This study aims to design a new AI-based framework for Thalassemia diagnosis using Deep Learning (DL) and a new combination of metrics for evaluation. This was achieved through the development and evaluation of a supervised semantic image segmentation model, and the implementation of different data engineering methods such as data annotation, augmentation, pre-processing, and preparation. Transfer learning was utilized and the Prediction Time Augmentation (PTA) was employed to get smoother and more accurate predictions. Quantitative results showed that, the mean Intersection Over Union (IoU) score of prediction of Thalassemia was 88% with PTA and 82% without PTA. Results also showed that as the combined metric of loss scores decreases the prediction of Thalassemia increases. Qualitative results showed that the final prediction of Thalassemia focuses on the Codocytes and labels the other unidentified cells as the background. Also, the resulting image was smoother and less bulky than the original annotated ground truth, and thus could be feasibly diagnosed. As a future prospect we aim to implement more algorithms and extend the diagnosis to include other diseases.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114992207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Enhanced MPPT with Incremental Conductance and Owl Search Algorithm under Partial Shading","authors":"Sara N. Altamimi, Hani O. Jamleh, E. Feilat, Dia Abualnadi","doi":"10.1109/IC2SPM56638.2022.9988850","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988850","url":null,"abstract":"Using a maximum power point tracking approach, the highest feasible power is extracted from photovoltaic modules. Nonetheless, the complexity of this approach is increased by some environmental situations that photovoltaic arrays may encounter, such as partial shading, in which the efficiency will decrease and causes less power generation. In this research, we present a novel methodology for a medium-sized 100 kWp photovoltaic system based on the Incremental Conductance Algorithm and the Owl Search Algorithm which is utilized primarily to improve the convergence under partial shadowing situations. The proposed method outperforms the conventional method, and the results are compared.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123451991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Objects Classification based on Hand Grasping in Virtual Reality Environment","authors":"A. Ibrahim, Mohamad Hajj-Hassan, Hoda Fares, Maurizio Valle","doi":"10.1109/IC2SPM56638.2022.9988797","DOIUrl":"https://doi.org/10.1109/IC2SPM56638.2022.9988797","url":null,"abstract":"Recent advancements in Artificial Intelligence and machine learning methods have been the focus of much research in different application domains due to their possibility to enable intelligent tasks. Integrating intelligence for human-machine interaction is an interesting topic that may improve the quality of life when used for robotics, prosthetics, and rehabilitation domains, to name a few. This work presents the analysis of the touch interaction between hands and objects at the moment of grasp. A virtual reality environment has been employed to collect the dataset in order to apply machine learning methods. Three different algorithms have been adopted to recognize the touched object achieving a classification accuracy of 94.7% for the KNN and SVM, and 98.2% for the LSTM network.","PeriodicalId":179072,"journal":{"name":"2022 International Conference on Smart Systems and Power Management (IC2SPM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134485652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}