Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172029
Dickson Perdanakusuma, Warih Puspitasari, M. Saputra
XYZ Hospital is a government-owned hospital that has a strategic role to accelerate the improvement of public health in their areas, following the standards set in the regulation concerning minimum service standards in the health sector. To achieve these standards, XYZ Hospital has several applications that are used to support business processes, but there are still several main functions in the business processes of XYZ Hospitals that have not yet been digitized and integrated, one of these functions is the medical record. XYZ Hospital still uses paper media in recording patient medical records which often raises various problems, such as the difficulty of finding documents stored in warehouses and also the loss of patient medical record documents. Based on these problems the use of a system to manage medical record data is a solution that must be immediately applied. The use of ERP systems can be a solution to the problems that exist at XYZ Hospital. ERP systems offer the integration of processes and data using a single database. The ERP system design at XYZ hospitals uses Odoo Software which is an open-source ERP software. ERP system design at XYZ hospitals uses the QuickStart method which is one of the fastest and cheapest methods for implementing ERP. The implementation of the ERP system at XYZ Hospital aims to create a patient medical record management system.
{"title":"Utilizing Open ERP for Creating Medical Record Management System in Smart Hospital : A Case Study","authors":"Dickson Perdanakusuma, Warih Puspitasari, M. Saputra","doi":"10.1109/IAICT50021.2020.9172029","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172029","url":null,"abstract":"XYZ Hospital is a government-owned hospital that has a strategic role to accelerate the improvement of public health in their areas, following the standards set in the regulation concerning minimum service standards in the health sector. To achieve these standards, XYZ Hospital has several applications that are used to support business processes, but there are still several main functions in the business processes of XYZ Hospitals that have not yet been digitized and integrated, one of these functions is the medical record. XYZ Hospital still uses paper media in recording patient medical records which often raises various problems, such as the difficulty of finding documents stored in warehouses and also the loss of patient medical record documents. Based on these problems the use of a system to manage medical record data is a solution that must be immediately applied. The use of ERP systems can be a solution to the problems that exist at XYZ Hospital. ERP systems offer the integration of processes and data using a single database. The ERP system design at XYZ hospitals uses Odoo Software which is an open-source ERP software. ERP system design at XYZ hospitals uses the QuickStart method which is one of the fastest and cheapest methods for implementing ERP. The implementation of the ERP system at XYZ Hospital aims to create a patient medical record management system.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123276650","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}
Pub Date : 2020-07-01DOI: 10.1109/iaict50021.2020.9172025
{"title":"IAICT 2020 Cover Page","authors":"","doi":"10.1109/iaict50021.2020.9172025","DOIUrl":"https://doi.org/10.1109/iaict50021.2020.9172025","url":null,"abstract":"","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130210890","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}
Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172035
B. Mahatmanto, C. Apriono
Satellite communication widely uses antennas with reflectors to achieve high gain for the long-distance signal transmission. This configuration mainly consists of a feeding antenna and a parabolic reflector that should be optimized to obtain the most optimum performance. This paper investigates some factors affecting the antenna performances, especially gain, for a C-band satellite ground station, such as losses contributed by materials, efficiencies, and distance between feeder and reflector. CST Microwaves Studio is used to simulated and investigates the gain performance of the proposed antenna model. The feeder antenna is a 4x4 microstrip array antenna, which has gain and bandwidth of 13.7 dB at frequency 4.148 GHz and 3.794–4.528 GHz, respectively. The parabolic reflector diameter is 2.4 m. The analyzed parameters include gain and directivity generated by theoretical calculations and simulations. Theoretically, the maximum directivity is 39.85 dB. However, the simulated antenna gain is 31.1 dB. This reduced value is coming from the effect of efficiency, material losses, and unexpected radiation pattern from the feeding antenna. The proposed design has successfully increased the gain of 17.4 dB by combining a reflector antenna. This result still has a niche to be further improved by considering the affecting factors.
{"title":"Gain Performance Analysis of A Parabolic Reflector Fed with A Rectangular Microstrip Array Antenna","authors":"B. Mahatmanto, C. Apriono","doi":"10.1109/IAICT50021.2020.9172035","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172035","url":null,"abstract":"Satellite communication widely uses antennas with reflectors to achieve high gain for the long-distance signal transmission. This configuration mainly consists of a feeding antenna and a parabolic reflector that should be optimized to obtain the most optimum performance. This paper investigates some factors affecting the antenna performances, especially gain, for a C-band satellite ground station, such as losses contributed by materials, efficiencies, and distance between feeder and reflector. CST Microwaves Studio is used to simulated and investigates the gain performance of the proposed antenna model. The feeder antenna is a 4x4 microstrip array antenna, which has gain and bandwidth of 13.7 dB at frequency 4.148 GHz and 3.794–4.528 GHz, respectively. The parabolic reflector diameter is 2.4 m. The analyzed parameters include gain and directivity generated by theoretical calculations and simulations. Theoretically, the maximum directivity is 39.85 dB. However, the simulated antenna gain is 31.1 dB. This reduced value is coming from the effect of efficiency, material losses, and unexpected radiation pattern from the feeding antenna. The proposed design has successfully increased the gain of 17.4 dB by combining a reflector antenna. This result still has a niche to be further improved by considering the affecting factors.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116086018","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}
Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172016
C. Kung, Wei-Sheng Yang, Ting-Ying Wei, Shu-Tsung Chao
Drone swarms are teams of autonomous unmanned aerial vehicles that act as a collective entity. We are interested in humanizing drone swarms, equip-ping them with the ability to emotionally affect human users through their nonverbal motions. We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. In this paper, we propose a fast flight trajectory verification algorithm and instant autonomous flight control alarm system, such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 20 drones. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.
{"title":"The fast flight trajectory verification algorithm for Drone Dance System","authors":"C. Kung, Wei-Sheng Yang, Ting-Ying Wei, Shu-Tsung Chao","doi":"10.1109/IAICT50021.2020.9172016","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172016","url":null,"abstract":"Drone swarms are teams of autonomous unmanned aerial vehicles that act as a collective entity. We are interested in humanizing drone swarms, equip-ping them with the ability to emotionally affect human users through their nonverbal motions. We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. In this paper, we propose a fast flight trajectory verification algorithm and instant autonomous flight control alarm system, such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 20 drones. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134579604","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}
Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172032
Theodore Bismo Waskito, S. Sumaryo, C. Setianingsih
Computer vision based on shape recognition has a lot of potential in human and computer interaction. Hand gestures can be used as symbols of human interaction with computers which are preferred in the use of various hand gestures in sign language. Various tasks can be used to set remote control functions, control robots, and so on. The process of processing images or hand drawings using computer vision is called image processing. In this paper, a wheeled robot control system can be moved according to the given hand gesture commands. There are 6 forms of hand gestures that are made as input, and each hand gesture gives one command for the movement of a wheeled robot. The method used to classify each hand gesture, namely Convolutional Neural Network (CNN). CNN is a branch of the Artificial Neural Network (ANN) that can perform extraction features and create desired categories. The results of the classification will be carried out and sent to a wireless robot to run a movement. The result of this system is the movement of the wheeled robot following the given hand gestures. Variables that affect this system are training parameters and environmental parameters which include the amount of light intensity, distance, and tilt angle. The accuracy of the entire system obtained is 91.33%.
{"title":"Wheeled Robot Control with Hand Gesture based on Image Processing","authors":"Theodore Bismo Waskito, S. Sumaryo, C. Setianingsih","doi":"10.1109/IAICT50021.2020.9172032","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172032","url":null,"abstract":"Computer vision based on shape recognition has a lot of potential in human and computer interaction. Hand gestures can be used as symbols of human interaction with computers which are preferred in the use of various hand gestures in sign language. Various tasks can be used to set remote control functions, control robots, and so on. The process of processing images or hand drawings using computer vision is called image processing. In this paper, a wheeled robot control system can be moved according to the given hand gesture commands. There are 6 forms of hand gestures that are made as input, and each hand gesture gives one command for the movement of a wheeled robot. The method used to classify each hand gesture, namely Convolutional Neural Network (CNN). CNN is a branch of the Artificial Neural Network (ANN) that can perform extraction features and create desired categories. The results of the classification will be carried out and sent to a wireless robot to run a movement. The result of this system is the movement of the wheeled robot following the given hand gestures. Variables that affect this system are training parameters and environmental parameters which include the amount of light intensity, distance, and tilt angle. The accuracy of the entire system obtained is 91.33%.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134414973","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}
Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172019
J. D. K. Disu, Clinton Elian Gandana, Hongzhi Xie, Lixu Gu
This research is focused on the performance of a Deep Reinforcement Learning method on an agent (mobile robot) in a simulated virtual environment (Operating Room) for medical applications. The purpose of this research is to compare suitable decisive actions taken by the agent to achieve its goal target. Executing this goal requires the implementation of a reward-penalty system for observation and analysis. The agent’s accumulated reward is based on the best-navigated decision to avoid collisions; solely generating an intelligent agent system. We reviewed previous works on the impact of Deep Reinforcement Learning algorithms on an agent in areas of navigation and exploration. Adopting a Deep Reinforcement Learning method and a physical simulator, we trained and tested the agent using existing environments and our modeled operating room, respectively. Measuring the positive reward output of the experiment with different parameters of the algorithm such as the learning rate, maximum Q-value and the average time to attain its goal position, we presented our work with plots of the experiment and compared it with a widely known traditional method. Our experimental results indicated that the agent achieved a high positive reward of 3800 in our operating room environment with a learning rate of 0.5. Our research aimed at training an agent to make intelligent decisions in achieving its goal destination without prior experience and input data. Reinforcement Learning provides a structure for robotics to function effectively; utilizing and engaging a robot to navigate and explore in any given environment.
{"title":"Short-range Robotic Navigation and Exploration Tasks via Deep Q-Networks for Biomedical Applications","authors":"J. D. K. Disu, Clinton Elian Gandana, Hongzhi Xie, Lixu Gu","doi":"10.1109/IAICT50021.2020.9172019","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172019","url":null,"abstract":"This research is focused on the performance of a Deep Reinforcement Learning method on an agent (mobile robot) in a simulated virtual environment (Operating Room) for medical applications. The purpose of this research is to compare suitable decisive actions taken by the agent to achieve its goal target. Executing this goal requires the implementation of a reward-penalty system for observation and analysis. The agent’s accumulated reward is based on the best-navigated decision to avoid collisions; solely generating an intelligent agent system. We reviewed previous works on the impact of Deep Reinforcement Learning algorithms on an agent in areas of navigation and exploration. Adopting a Deep Reinforcement Learning method and a physical simulator, we trained and tested the agent using existing environments and our modeled operating room, respectively. Measuring the positive reward output of the experiment with different parameters of the algorithm such as the learning rate, maximum Q-value and the average time to attain its goal position, we presented our work with plots of the experiment and compared it with a widely known traditional method. Our experimental results indicated that the agent achieved a high positive reward of 3800 in our operating room environment with a learning rate of 0.5. Our research aimed at training an agent to make intelligent decisions in achieving its goal destination without prior experience and input data. Reinforcement Learning provides a structure for robotics to function effectively; utilizing and engaging a robot to navigate and explore in any given environment.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128733338","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}
Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172014
Qusyairi Ridho Saeful Fitni, K. Ramli
In recent years, data security in organizational information systems has become a serious concern. Many attacks are becoming less detectable by firewall and antivirus software. To improve security, intrusion detection systems (IDSs) are used to detect anomalies in network traffic. Currently, IDS technology has performance issues regarding detection accuracy, detection times, false alarm notifications, and unknown attack detection. Several studies have applied machine-learning approaches as solutions. This study used an ensemble learning approach that integrates the benefits of each single detection algorithms. We made comparisons with seven single classifiers to identify the most appropriate basic classifiers for ensemble learning. The experiment shows logistics regression, decision trees, and gradient boosting are chosen for our ensemble model. The Communications Security Establishment and Canadian Institute for Cybersecurity 2018 (CSE-CIC-IDS2018) dataset was used to evaluate the proposed model. Spearman’s rank correlation coefficient facilitated the identification of the data features that might not be used. The experiment results showed that 23 of the 80 features were selected, and the model achieved the following scores: final accuracy, 98.8%; precision, 98.8%; recall, 97.1%; and F1, 97.9%.
{"title":"Implementation of Ensemble Learning and Feature Selection for Performance Improvements in Anomaly-Based Intrusion Detection Systems","authors":"Qusyairi Ridho Saeful Fitni, K. Ramli","doi":"10.1109/IAICT50021.2020.9172014","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172014","url":null,"abstract":"In recent years, data security in organizational information systems has become a serious concern. Many attacks are becoming less detectable by firewall and antivirus software. To improve security, intrusion detection systems (IDSs) are used to detect anomalies in network traffic. Currently, IDS technology has performance issues regarding detection accuracy, detection times, false alarm notifications, and unknown attack detection. Several studies have applied machine-learning approaches as solutions. This study used an ensemble learning approach that integrates the benefits of each single detection algorithms. We made comparisons with seven single classifiers to identify the most appropriate basic classifiers for ensemble learning. The experiment shows logistics regression, decision trees, and gradient boosting are chosen for our ensemble model. The Communications Security Establishment and Canadian Institute for Cybersecurity 2018 (CSE-CIC-IDS2018) dataset was used to evaluate the proposed model. Spearman’s rank correlation coefficient facilitated the identification of the data features that might not be used. The experiment results showed that 23 of the 80 features were selected, and the model achieved the following scores: final accuracy, 98.8%; precision, 98.8%; recall, 97.1%; and F1, 97.9%.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127207138","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}
Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172020
A. Rusli, J. Young, N. Iswari
Fake news detection has gained growing interest from both the industry and research community all around the world, including Indonesia. Based on recent surveys, people could receive fake news daily, if not more than once. The research community and practitioners, supported by the government, are trying to fight back the spreading of fake news. This paper aims to implement a supervised machine learning approach using the Multi-Layer Perceptron (MLP) for classifying news article in order to detect fake news articles and differentiate them from the valid ones, via a binary text classification approach. Furthermore, this paper uses TF-IDF in comparison with the Bag of Words model to extract features along with the use of the n-gram model. Based on the result, our final model could achieve a hoax precision and recall score of 0.84 and 0.73, respectively, and a macro-averaged F1-score of 0.82. Furthermore, our paper shows that some preprocessing methods such as stemming and stop-word removal could be very time-consuming while only barely affecting the performance of our classifier model using the dataset in this research for identifying fake news.
假新闻检测已经引起了包括印度尼西亚在内的世界各地产业界和研究界越来越大的兴趣。根据最近的调查，人们可能每天都会收到假新闻，如果不是不止一次的话。在政府的支持下，研究界和从业者正试图反击假新闻的传播。本文旨在实现一种监督机器学习方法，使用多层感知器(MLP)对新闻文章进行分类，以便通过二进制文本分类方法检测假新闻文章并将其与有效新闻区分开来。此外，本文使用TF-IDF与Bag of Words模型进行对比，并使用n-gram模型进行特征提取。基于实验结果，最终模型的恶作剧准确率和召回率分别为0.84和0.73，宏观平均f1得分为0.82。此外，我们的论文表明，一些预处理方法，如词干提取和停止词去除可能非常耗时，而使用本研究中的数据集识别假新闻的分类器模型的性能几乎没有受到影响。
{"title":"Identifying Fake News in Indonesian via Supervised Binary Text Classification","authors":"A. Rusli, J. Young, N. Iswari","doi":"10.1109/IAICT50021.2020.9172020","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172020","url":null,"abstract":"Fake news detection has gained growing interest from both the industry and research community all around the world, including Indonesia. Based on recent surveys, people could receive fake news daily, if not more than once. The research community and practitioners, supported by the government, are trying to fight back the spreading of fake news. This paper aims to implement a supervised machine learning approach using the Multi-Layer Perceptron (MLP) for classifying news article in order to detect fake news articles and differentiate them from the valid ones, via a binary text classification approach. Furthermore, this paper uses TF-IDF in comparison with the Bag of Words model to extract features along with the use of the n-gram model. Based on the result, our final model could achieve a hoax precision and recall score of 0.84 and 0.73, respectively, and a macro-averaged F1-score of 0.82. Furthermore, our paper shows that some preprocessing methods such as stemming and stop-word removal could be very time-consuming while only barely affecting the performance of our classifier model using the dataset in this research for identifying fake news.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126722539","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}
Pub Date : 2020-07-01DOI: 10.1109/IAICT50021.2020.9172031
Lyla B. Das, A. Lijiya, G. Jagadanand, A. Aadith, S. Gautham, V. Mohan, S. Reuben, Georgoulas George
An Unmanned Aerial Vehicle (UAV) is an airborne system or pilotless aircraft which is remotely controlled by a human operator on ground or by an onboard computer such that the vehicle moves autonomously. The range of applications in which UAVs are used is very large.This paper describes the application of developing an autonomous surveillance system using an UAV to identify a given target and/or objects of interest in the terrain over which it flies. Such a system can be used in rescue operations, especially in remote areas where physical access is difficult. It can also be used for military operations, farming or any field where surveillance of a given land area is required. The UAV developed in this work is capable of object detection. A mounted camera is used to give visual feedback, and an onboard processing unit runs image recognition software to identify the target in real time. Optimal algorithms are used to search and find the target from the given search area. After recognition of the target, the UAV can either be used to hold its position so as to have a video feed of the target, or return to its base station once the coordinates have been estimated using GPS modules or relay the GPS location to the base station.This paper describes the implementation of the hardware and software components that lead to the realization of the UAV and the application of object detection. The details of a new search algorithm and an example of object detection is presented . The work presented in this paper is the first part in the attempt to develop a cluster of UAVs meant to work in collaboration to be deployed for search and rescue operations.
{"title":"Human Target Search and Detection using Autonomous UAV and Deep learning","authors":"Lyla B. Das, A. Lijiya, G. Jagadanand, A. Aadith, S. Gautham, V. Mohan, S. Reuben, Georgoulas George","doi":"10.1109/IAICT50021.2020.9172031","DOIUrl":"https://doi.org/10.1109/IAICT50021.2020.9172031","url":null,"abstract":"An Unmanned Aerial Vehicle (UAV) is an airborne system or pilotless aircraft which is remotely controlled by a human operator on ground or by an onboard computer such that the vehicle moves autonomously. The range of applications in which UAVs are used is very large.This paper describes the application of developing an autonomous surveillance system using an UAV to identify a given target and/or objects of interest in the terrain over which it flies. Such a system can be used in rescue operations, especially in remote areas where physical access is difficult. It can also be used for military operations, farming or any field where surveillance of a given land area is required. The UAV developed in this work is capable of object detection. A mounted camera is used to give visual feedback, and an onboard processing unit runs image recognition software to identify the target in real time. Optimal algorithms are used to search and find the target from the given search area. After recognition of the target, the UAV can either be used to hold its position so as to have a video feed of the target, or return to its base station once the coordinates have been estimated using GPS modules or relay the GPS location to the base station.This paper describes the implementation of the hardware and software components that lead to the realization of the UAV and the application of object detection. The details of a new search algorithm and an example of object detection is presented . The work presented in this paper is the first part in the attempt to develop a cluster of UAVs meant to work in collaboration to be deployed for search and rescue operations.","PeriodicalId":433718,"journal":{"name":"2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"124 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113945768","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}
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