2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献

Photovoltaic-Thermoelectric Generator Monitoring System using Arduino Based Data Acquisition system Technique 基于Arduino数据采集系统技术的光电热电发电机监测系统

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573815

U. A. Saleh, S. A. Jumaat, M. A. Johar, W. Jamaludin

{"title":"Photovoltaic-Thermoelectric Generator Monitoring System using Arduino Based Data Acquisition system Technique","authors":"U. A. Saleh, S. A. Jumaat, M. A. Johar, W. Jamaludin","doi":"10.1109/IICAIET51634.2021.9573815","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573815","url":null,"abstract":"This paper presents the design and development of a data acquisition system (DAQ) for a hybrid photovoltaic-thermoelectric generator (PV-TEG) to monitor and store system parameters collected from the PV-TEG source in large memory storage. A DAQ is an electronic device that collects and records data through a time-based microcontroller (DS1307 Real-Time Clock (RTC) chip). It utilizes the Arduino Mega 328P board in conjunction with the ATmega 328P chip for monitoring the system parameters such as voltage, current and power every second of the logging process. The hybrid system comprises 200 W panels and a combination of 192 TEG connected in series and parallel for higher output power. The system converts the original data into digital input for data acquisition and stores it on a secure digital card (SD card). The hybrid system performance was examined. The results from the DAQ shows that data were stored on the SD Card at a 1-second update cycle. The DAQ recorded maximum system parameter values as 39 V, 4.9 A for the hybrid system, 33 V, 4.7 A for the PV subsystem, and 6 V, 0.2 A for the TEG subsystem. The maximum power of 191.1 W was computed across a resistive load of $8 Omega$, 300 W. This shows an increase of 15.38 % than the PV subsystem.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127122858","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}

引用次数: 1

Convolutional Neural Network - Long Short Term Memory based IOT Node for Violence Detection 卷积神经网络-基于长短期记忆的IOT节点暴力检测

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573691

Nouar Aldahoul, H. A. Karim, Rishav Datta, Shreyash Gupta, Kashish Agrawal, Ahmad Albunni

{"title":"Convolutional Neural Network - Long Short Term Memory based IOT Node for Violence Detection","authors":"Nouar Aldahoul, H. A. Karim, Rishav Datta, Shreyash Gupta, Kashish Agrawal, Ahmad Albunni","doi":"10.1109/IICAIET51634.2021.9573691","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573691","url":null,"abstract":"Violence detection has been investigated extensively in the literature. Recently, IOT based violence video surveillance is an intelligent component integrated in security system of smart buildings. Violence video detector is a specific kind of detection models that should be highly accurate to increase the model's sensitivity and reduce the false alarm rate. This paper proposes a novel architecture of end-to-end CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory) model that can run on low-cost Internet of Things (IOT) device such as raspberry pi board. The paper utilized CNN to learn spatial features from video's frames that were applied to LSTM for video classification into violence/non-violence classes. A complex dataset including two public datasets: RWF-2000 and RLVS-2000 was used for model training and evaluation. The challenging video content includes crowds and chaos, small object at far distance, low resolution, and transient action. Additionally, the videos were captured in various environments such as street, prison, and schools with several human actions such as eating, playing basketball, football, tennis, and swimming. The experimental results show good performance of the proposed violence detection model in terms of average metrics having an accuracy of 73.35 %, recall of 76.90 %, precision of 72.53 %, F1 score of 74.01 %, false negative rate of 23.10 %, false positive rate of 30.20 %, and AUC of 82.0 %. The proposed CNN-LSTM can balance good performance with low number of parameters and thus can be implemented on low-cost IOT node.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116366140","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}

引用次数: 6

IoT-Based Tracing and Communication Platform for Disease Control 基于物联网的疾病防控溯源与通信平台

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9574005

Yi Zhen Quak, Yi Xin Loke, Zhi Yuan Chan, Sze Qi Chew, P. Ooi

{"title":"IoT-Based Tracing and Communication Platform for Disease Control","authors":"Yi Zhen Quak, Yi Xin Loke, Zhi Yuan Chan, Sze Qi Chew, P. Ooi","doi":"10.1109/IICAIET51634.2021.9574005","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9574005","url":null,"abstract":"Coronavirus disease 2019 (COVID-19) is highly contagious and has swept the globe. Countries worldwide is in urgent need of efficient technological solutions to control the transmission of COVID-19 disease. The objective of this project is to develop an artificial intelligence-driven contact tracing platform and communication to come up with an integrated solution to block the transmission chain of the disease. Three elements are included in this platform, which are behavioral recognition system, mobile application and smart wristband. Mobile application developed through Android Studio SDK, has multiple functions, which are Quick Response (QR) code scanner for location tracking, close contact identification, COVID-19 cases update, district color alert system and exposure notification. Behavioral recognition system developed on Raspberry Pi v4 and Faster Region Based Convolutional Neural Network Version 2 (RCNN_v2) and Single Shot Multibox Detection MobileNet Version 2 (SSD MobileNet_v2) are adopted as machine learning algorithm can carry out close-proximity detection, people counting, and face mask detection. Smart wristband built with Arduino MKR GSM1400 microcontroller and various sensors are developed through Arduino Integrated Development Environment (IDE) to keep track on the location and vital signs of the quarantined people and is designed with an emergency button to allow the quarantined people to get help immediately if they are not feeling well. The data obtained from the three elements is uploaded to a centralized database, Firestore associating with accurate timestamp and location. This system integrated with various preventive measure and control measure can mitigate and manage COVID-19 pandemic effectively and efficiently.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128466560","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}

引用次数: 0

Raspberry Pi based ANPR for Smart Access 基于树莓派的ANPR智能访问

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573652

C. Fernandez, R. R. Porle

{"title":"Raspberry Pi based ANPR for Smart Access","authors":"C. Fernandez, R. R. Porle","doi":"10.1109/IICAIET51634.2021.9573652","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573652","url":null,"abstract":"Automated Number Plate Recognition (ANPR) is a term that refers to a system that acquire image of a vehicle and recognises the characters on the number plate. The purpose of this paper is to investigate how a Raspberry Pi-based ANPR system for smart access can be used to replace the traditional access system for high-rise residents. Number plate recognition was chosen over other systems due to its high level of security. The process of recognising number plates is divided into four stages: image acquisition and preprocessing, extraction, segmentation, and character recognition. Preprocessing involves converting RGB to Grayscale, filtering out noise with a Gaussian Filter, and enhancing the image with Adaptive Thresholding. The number plate extraction step includes morphological operations, image binarization, and contour extraction. The techniques used in segmentation are Connected Component Analysis (CCA) and Boundary Box Analysis (BBA). Character recognition using the KNN method is the final stage. The primary hardware consists of a Raspberry Pi model 4, a Raspberry Pi camera, and servo motors. A total of 120 number plates from 24 different cars were used in the experiments. The number plates are divided into two categories: training and testing, with approximately 83 percent being used for training, which includes approximately 100 plates from four different cars. 17 percent, or approximately 20 number plates from four different cars, are used for testing purposes. The experiment establishes the optimal distance, angle, and height from which to capture the licence plate. At two metres, the system recognises the number plate. The system's design is 85 percent accurate.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130542227","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}

引用次数: 1

Drive-Awake: A YOLOv3 Machine Vision Inference Approach of Eyes Closure for Drowsy Driving Detection 驾驶觉醒:一种YOLOv3闭眼机器视觉推理方法用于疲劳驾驶检测

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573811

Jonel R. Macalisang, A. Alon, Moises F. Jardiniano, Deanne Cameren P. Evangelista, Julius C. Castro, Meriam L. Tria

{"title":"Drive-Awake: A YOLOv3 Machine Vision Inference Approach of Eyes Closure for Drowsy Driving Detection","authors":"Jonel R. Macalisang, A. Alon, Moises F. Jardiniano, Deanne Cameren P. Evangelista, Julius C. Castro, Meriam L. Tria","doi":"10.1109/IICAIET51634.2021.9573811","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573811","url":null,"abstract":"Nowadays, road accidents have become a major concern. The drowsiness of drivers owing to overfatigue or tiredness, driving while intoxicated, or driving too quickly is some of the primary causes of this. Drowsy driving contributes to or increases the number of traffic accidents each year. The study presented a technique for detecting driver drowsiness in response to this issue. The sleep states of the drivers in the driving environment were detected using a deep learning approach. To assess if the eyes of particular constant face images of drivers are closed, a convolutional neural network (CNN) model has been developed. The suggested model has a wide range of possible applications, including human-computer interface design, facial expression detection, and determining driver tiredness and drowsiness. The YOLOv3 algorithm, as well as additional tools like Pascal VOC and LabelImg, were used to build this approach, which collects and trains a driver dataset that feels drowsy. The study's total detection accuracy was 100%, with detection per frame accuracy ranging from 49% to 89%.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125631436","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}

引用次数: 12

Exploitation of Meta-Heuristic Search Methods with Bio-Inspired Algorithms for Optimal Feature Selection 基于生物启发算法的元启发式搜索方法用于最优特征选择

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573756

M. Basir, M. S. Hussin

{"title":"Exploitation of Meta-Heuristic Search Methods with Bio-Inspired Algorithms for Optimal Feature Selection","authors":"M. Basir, M. S. Hussin","doi":"10.1109/IICAIET51634.2021.9573756","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573756","url":null,"abstract":"It is very difficult and crucial to achieve the selection of optimal features, particularly for the classification task. Because the conventional method of identifying features that function independently has resulted in the selection of unrelated features, the consistency of the classification's accuracy has been degraded. The objective of this article is to optimize Meta-heuristic algorithms, particularly Tabu Search (TS) and Harmony Search (HS), using the capabilities of bioinspired search algorithms in conjunction with the wrapper. The essential stages are to idealize the TS and HS combination with appropriate bio-search methods, and to incorporate the creation of various feature subsets. The following step is to do a subset evaluation to confirm the optimum feature set. The evaluation criteria are based on the number of features utilized and the classification accuracy. To be tested, eight (8) comparison datasets of different sizes were carefully chosen. Extensive testing has indicated that the ideal combination of the chosen bio-search algorithm and meta-heuristics algorithms, especially TS and HS, promises to offer a better optimum solution (i.e. fewer features with greater classification accuracy) for the selected datasets. As a consequence of this research, the ability of bio-inspired algorithms with wrapper/filtered to select and identify characteristics would enhance the efficiency of TS and HS.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128920881","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}

引用次数: 0

1D Convolutional Neural Network with Long Short-Term Memory for Human Activity Recognition 具有长短期记忆的一维卷积神经网络用于人类活动识别

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573979

Jia Xin Goh, K. Lim, C. Lee

{"title":"1D Convolutional Neural Network with Long Short-Term Memory for Human Activity Recognition","authors":"Jia Xin Goh, K. Lim, C. Lee","doi":"10.1109/IICAIET51634.2021.9573979","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573979","url":null,"abstract":"Human activity recognition aims to determine the actions or behavior of a person based on the time series data. In recent year, more large human activity recognition datasets are available as it can be collected in easier and cheaper ways. In this work, a 1D Convolutional Neural Network with Long Short-Term Memory Network for human activity recognition is proposed. The 1D Convolutional Neural Network is employed to learn high-level representative features from the accelerometer and gyroscope signal data. The Long Short-Term Memory network is then used to encode the temporal dependencies of the features. The final classification is performed with a softmax classifier. The proposed 1D Convolutional Neural Network with Long Short-Term Memory Network is evaluated on MotionSense, UCI-HAR, and USC-HAD datasets. The class distributions of these datasets are imbalanced. In view of this, adjusted class weight is proposed to mitigate the imbalanced class issue. Furthermore, early stopping is utilized to reduce the overfitting in the training. The proposed method achieved promising performance on MotionSense, UCI-HAR, and USC-HAD datasets, with F1-score of 98.14%, 91.04%, and 76.42%, respectively.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131332927","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}

引用次数: 4

Facial Expression Based Automated Restaurant Food Review System using CNN 使用 CNN 的基于面部表情的餐厅食品评论自动系统

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573899

Niazi Mahrab, S. Salim, Abdullah Ibne Ali, Israt Jahan Mim, R. Khan

引用次数: 0

A Study on Region of Interest in Remote PPG and an Attempt to Eliminate False Positive Results Using SVM Classification 基于支持向量机分类的远程PPG感兴趣区域研究及误报排除

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573945

Hiroki Takeuchi, M. Ohsuga, Y. Kamakura

引用次数: 0

Enhanced Multi-Hop Mechanism in Vehicular Communication System using Swarm Algorithm 基于群算法的车载通信系统多跳增强机制

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573842

Kit Guan Lim, Ke Wen Teh, M. K. Tan, H. Lago, Soo Siang Yang, K. Teo

{"title":"Enhanced Multi-Hop Mechanism in Vehicular Communication System using Swarm Algorithm","authors":"Kit Guan Lim, Ke Wen Teh, M. K. Tan, H. Lago, Soo Siang Yang, K. Teo","doi":"10.1109/IICAIET51634.2021.9573842","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573842","url":null,"abstract":"As on-road vehicles are increasing every year, safety on the road has become one of the major concerns. Therefore, Vehicular Ad Hoc Network (VANET) becomes as an important role on the road. In VANET, communication occurs between vehicles and the infrastructures. During broadcasting, an emergency message is transferred to the surrounding vehicles to alert other vehicles in the area. However, blind flooding in wireless network might result in redundant rebroadcast, contentions and collision with the neighbouring nodes. This situation is named as broadcast storm. Broadcast storm might lead to the losses of the information or lead to the wrong information being transmitted to the neighbouring nodes. The paper aims to design a broadcast control system which is able to optimize the broadcast process in VANET. Vehicular network is modelled in Simulation of Urban Mobility (SUMO) and the algorithm is formulated in MATLAB. Data is extracted from SUMO through Traffic Control Interface for MATLAB (TraCI4Matlab). The broadcast protocol and Particle Swarm Optimization (PSO) algorithm are formulated in this paper. At the same time PSO is modified for the broadcast enhancement. Results showed that after parameters tuning the modified PSO is able to broadcast into a larger coverage area at a faster rate.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130455685","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}

引用次数: 0