Pub Date : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466034
M. Marwan, Muhammad Dihyah Marwan, M. Anshar, J. Jamal, Aksan Aksan, A. Apollo
The goal of this research to optimize economic dispatch for power generation on the electrical system. In this research there are two kinds of power generation installed on the electrical power system: $2times 50 text{MW}$. The total load and cost for generators-1 and 2 are: 41.63 MW (23,489,069.25 IDR/h) and 41.73 MW (21,291,609.38 IDR/h), respectively. To define optimum cost for both generations, the Lagrange method was applied to compute total cost for every generator considering the electricity demand. Based on the results of research illustrated, the total load and cost for generation-1 and 2 can be optimized to be 43.41 MW (24,540,714 IDR/h) and 39.95 MW (20,221,493), respectively. This indicates that the Lagrange method is an effective way to reduce the total cost of generation. The total cost reduction can be achieved to be 31,205 IDR/h.
{"title":"Optimal economic dispatch for power generation under the lagrange method","authors":"M. Marwan, Muhammad Dihyah Marwan, M. Anshar, J. Jamal, Aksan Aksan, A. Apollo","doi":"10.1109/AIMS52415.2021.9466034","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466034","url":null,"abstract":"The goal of this research to optimize economic dispatch for power generation on the electrical system. In this research there are two kinds of power generation installed on the electrical power system: $2times 50 text{MW}$. The total load and cost for generators-1 and 2 are: 41.63 MW (23,489,069.25 IDR/h) and 41.73 MW (21,291,609.38 IDR/h), respectively. To define optimum cost for both generations, the Lagrange method was applied to compute total cost for every generator considering the electricity demand. Based on the results of research illustrated, the total load and cost for generation-1 and 2 can be optimized to be 43.41 MW (24,540,714 IDR/h) and 39.95 MW (20,221,493), respectively. This indicates that the Lagrange method is an effective way to reduce the total cost of generation. The total cost reduction can be achieved to be 31,205 IDR/h.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128940144","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466061
D. Novita, Muhamad Yerri Suyud Hasim, B. M. Wibawa, A. Turnip
Arm robot has a lack of control systems that depend on desired control for assistive medical. Our laboratory robotics & artificial intelligent at Padjadjaran University created skin cancer detection of arm robot with dark flow framework to identify skin cancer in real-time. The implementation of the arm robot was for increasing the accuracy, precision, and stability. The main purpose of this paper was to control an arm robot for skin cancer detection that is capable to scan the whole body skin to localize the skin cancers by driving the manipulator in circular or elliptical skimming. To initiate the communication with the arm robot which used Dynamixel as the actuators, we applied USB2Dynamixel as the communicator. SMPS2Dynamixel was used to supply the power into servo motors. 3D Control system software has designed, and it had some features such as; forward kinematic movement, inverse kinematic movement, and 3D simulation to help user visualize the position of the arm robot. Control software was built in MATLAB GUI environment and 3D simulation adapted Peter Corke Robotics Toolbox.
{"title":"3D Control System of Arm Robot Prototype for Skin Cancer Detection","authors":"D. Novita, Muhamad Yerri Suyud Hasim, B. M. Wibawa, A. Turnip","doi":"10.1109/AIMS52415.2021.9466061","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466061","url":null,"abstract":"Arm robot has a lack of control systems that depend on desired control for assistive medical. Our laboratory robotics & artificial intelligent at Padjadjaran University created skin cancer detection of arm robot with dark flow framework to identify skin cancer in real-time. The implementation of the arm robot was for increasing the accuracy, precision, and stability. The main purpose of this paper was to control an arm robot for skin cancer detection that is capable to scan the whole body skin to localize the skin cancers by driving the manipulator in circular or elliptical skimming. To initiate the communication with the arm robot which used Dynamixel as the actuators, we applied USB2Dynamixel as the communicator. SMPS2Dynamixel was used to supply the power into servo motors. 3D Control system software has designed, and it had some features such as; forward kinematic movement, inverse kinematic movement, and 3D simulation to help user visualize the position of the arm robot. Control software was built in MATLAB GUI environment and 3D simulation adapted Peter Corke Robotics Toolbox.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124443623","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466055
Saad Ur Rehman, Shaheryar Ahmad Khan, A. Arif, U. S. Khan
This paper proposes the design of an accident detection system for motorcycles that notifies the emergency contact of the injured motorcycle driver about their precise location so that necessary medical help can be provided timely. The proposed system is based on a tilt sensor that calculates the inclination of the motorcycle and then transmits notification to the concerned people through SMS and GPRS via an online server using a GSM module. The main contribution of this paper is that the developed system has extensively been tested in real time scenario and data has been collected from ten different bikes to determine an optimum tilt angle. Moreover, crash tests have also been performed. The system has a detection rate of 97.33%.
{"title":"IoT-based Accident Detection and Emergency Alert System for Motorbikes","authors":"Saad Ur Rehman, Shaheryar Ahmad Khan, A. Arif, U. S. Khan","doi":"10.1109/AIMS52415.2021.9466055","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466055","url":null,"abstract":"This paper proposes the design of an accident detection system for motorcycles that notifies the emergency contact of the injured motorcycle driver about their precise location so that necessary medical help can be provided timely. The proposed system is based on a tilt sensor that calculates the inclination of the motorcycle and then transmits notification to the concerned people through SMS and GPRS via an online server using a GSM module. The main contribution of this paper is that the developed system has extensively been tested in real time scenario and data has been collected from ten different bikes to determine an optimum tilt angle. Moreover, crash tests have also been performed. The system has a detection rate of 97.33%.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117254862","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466015
Sami Fauzan Ramadhan, M. Taufik, D. Novita, A. Turnip
Intelligent robot equipped with autonomous navigation system demand has been increased since Covid-19 pandemic to implement physical distancing among humans. To have autonomous navigation capability, the robot needs to be able to do mapping and use the map to navigate around the hospital. Mapping is performed in two unique places to simulate various hospital environments. Map results were then compared between the measurement on Rviz and the actual measured place. The system is proved accurate at 96% in the first place and 97% in the second place.
{"title":"Design of 2D LiDAR-based Indoor SLAM for Medical Robot Covid-19","authors":"Sami Fauzan Ramadhan, M. Taufik, D. Novita, A. Turnip","doi":"10.1109/AIMS52415.2021.9466015","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466015","url":null,"abstract":"Intelligent robot equipped with autonomous navigation system demand has been increased since Covid-19 pandemic to implement physical distancing among humans. To have autonomous navigation capability, the robot needs to be able to do mapping and use the map to navigate around the hospital. Mapping is performed in two unique places to simulate various hospital environments. Map results were then compared between the measurement on Rviz and the actual measured place. The system is proved accurate at 96% in the first place and 97% in the second place.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131676309","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466073
Irvan Aulia, D. Wijaya, W. Hidayat
Rice is the staple food consumed by most Indonesians. However, the quality of the rice can decline over time so that the rice becomes obsolete and cannot be consumed. For now, the traditional method to distinguish between expired rice and non-expired rice is still performed by perceiving the rice with the human's sense of smell. However, this method is considered less effective because the human sense of smell can change due to changes in body health. Therefore, we established a method for detecting the shelf life of rice by using the electronic nose dataset (e-nose). We propose a machine learning model that utilizes the e-nose to assess the quality of expired and non-expired rice. The dataset was obtained from the e-nose sensor by recording sensor information for 25 weeks and storing 1955 summaries of sensor information for seven days. Our study used the gradient tree boosting machine learning model for classification with an accuracy of 96% and an error of 4%.
{"title":"Rice Quality Detection Using Gradient Tree Boosting Based On Electronic Nose Dataset","authors":"Irvan Aulia, D. Wijaya, W. Hidayat","doi":"10.1109/AIMS52415.2021.9466073","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466073","url":null,"abstract":"Rice is the staple food consumed by most Indonesians. However, the quality of the rice can decline over time so that the rice becomes obsolete and cannot be consumed. For now, the traditional method to distinguish between expired rice and non-expired rice is still performed by perceiving the rice with the human's sense of smell. However, this method is considered less effective because the human sense of smell can change due to changes in body health. Therefore, we established a method for detecting the shelf life of rice by using the electronic nose dataset (e-nose). We propose a machine learning model that utilizes the e-nose to assess the quality of expired and non-expired rice. The dataset was obtained from the e-nose sensor by recording sensor information for 25 weeks and storing 1955 summaries of sensor information for seven days. Our study used the gradient tree boosting machine learning model for classification with an accuracy of 96% and an error of 4%.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029393","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466074
Mehreen Naeem, S. Aslam, M. Suhaib, Seemab Gul, Zeeshan Murtaza, Muhammad Jawad Khan
This work presents an autonomous pick and pack system for industrial applications consisting of a 4-DOF (degree-of-freedom) robotic manipulator and the vision perception. The system is capable to detect and capture products/materials from a production line and place them in a packaging box one at a time. The final placement of the object in the box is carried out with the desired orientation. The adopted configuration of the manipulator is RRPR equipped with an electromagnetic end-effector. Inverse kinematics approach is used to determine the end-effector motion sequence in order to reach the desired locations within the workspace. The manipulator is equipped with Feetech SC servo motor with built-in PID controller at each joint to achieve precise motion of joint angles. An RGB camera is mounted right above the conveyer belt to capture the region of interest in the environment. Captured image is further preprocessed by image processing techniques. Region properties is used to extract the orientation of the object. Arduino Mega 2560 is used as a control board for motors and an interface with the PC. A graphical user interface (GUI) is developed in MATLAB to monitor and control the end-to-end pick and pack system featuring both manual and automatic modes.
这项工作提出了一个工业应用的自动拣选和包装系统，包括一个4自由度(自由度)机器人机械手和视觉感知。该系统能够从生产线上检测和捕获产品/材料，并一次将它们放入包装盒中。物体在方框中的最终放置是按照期望的方向进行的。机械手采用的构型为RRPR，配有电磁末端执行器。采用逆运动学方法确定末端执行器的运动序列，以达到工作空间内的期望位置。机械手在各关节处安装fetech SC伺服电机，内置PID控制器，实现关节角度的精确运动。RGB相机安装在传送带的正上方，以捕捉环境中感兴趣的区域。捕获的图像通过图像处理技术进行进一步预处理。区域属性用于提取对象的方向。Arduino Mega 2560被用作电机的控制板和与PC的接口。在MATLAB中开发了一个图形用户界面(GUI)来监控端到端采摘和包装系统，具有手动和自动模式。
{"title":"Design and Implementation of Pick and Place Manipulation System for Industrial Automation","authors":"Mehreen Naeem, S. Aslam, M. Suhaib, Seemab Gul, Zeeshan Murtaza, Muhammad Jawad Khan","doi":"10.1109/AIMS52415.2021.9466074","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466074","url":null,"abstract":"This work presents an autonomous pick and pack system for industrial applications consisting of a 4-DOF (degree-of-freedom) robotic manipulator and the vision perception. The system is capable to detect and capture products/materials from a production line and place them in a packaging box one at a time. The final placement of the object in the box is carried out with the desired orientation. The adopted configuration of the manipulator is RRPR equipped with an electromagnetic end-effector. Inverse kinematics approach is used to determine the end-effector motion sequence in order to reach the desired locations within the workspace. The manipulator is equipped with Feetech SC servo motor with built-in PID controller at each joint to achieve precise motion of joint angles. An RGB camera is mounted right above the conveyer belt to capture the region of interest in the environment. Captured image is further preprocessed by image processing techniques. Region properties is used to extract the orientation of the object. Arduino Mega 2560 is used as a control board for motors and an interface with the PC. A graphical user interface (GUI) is developed in MATLAB to monitor and control the end-to-end pick and pack system featuring both manual and automatic modes.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124072085","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466027
Ajie Kusuma Wardhana, R. Ferdiana, Indriana Hidayah
Chatbots are dialog engines for interactive user experience which help by providing stakeholders such as consumers, device owners, maintenance workers, and so on with real-time tools (answers to any questions, instructions to use the equipment, help for decisions, etc.). Nowadays, chatbot usage is not only for closed domain needs but has also become common across companies. Some businesses use chatbots for their customer support to provide details for the client and also to allow online transactions. It is crucial that businesses should not look at chatbots simply as a digital medium for advertisement. They should be focusing on the part of the Chatbot communication service. To improve the interaction of chatbot communication service, a blended skill chatbot was proven to have a great performance which also having an inference, personalization, empathy, and knowledge. In this paper, we conduct a literature review that giving an insight into the recent development and statistical inference for empathetic chatbots and having a result of 13% of a hybrid model, 27% of a retrieval model, and 60% of the generative model to be analyzed its trends.
{"title":"Empathetic Chatbot Enhancement and Development: A Literature Review","authors":"Ajie Kusuma Wardhana, R. Ferdiana, Indriana Hidayah","doi":"10.1109/AIMS52415.2021.9466027","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466027","url":null,"abstract":"Chatbots are dialog engines for interactive user experience which help by providing stakeholders such as consumers, device owners, maintenance workers, and so on with real-time tools (answers to any questions, instructions to use the equipment, help for decisions, etc.). Nowadays, chatbot usage is not only for closed domain needs but has also become common across companies. Some businesses use chatbots for their customer support to provide details for the client and also to allow online transactions. It is crucial that businesses should not look at chatbots simply as a digital medium for advertisement. They should be focusing on the part of the Chatbot communication service. To improve the interaction of chatbot communication service, a blended skill chatbot was proven to have a great performance which also having an inference, personalization, empathy, and knowledge. In this paper, we conduct a literature review that giving an insight into the recent development and statistical inference for empathetic chatbots and having a result of 13% of a hybrid model, 27% of a retrieval model, and 60% of the generative model to be analyzed its trends.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123043749","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}
Images have become a threat to the security of the systems and networks since JPEG headers are concealed with malicious payloads. Header in a JPEG has many segments which can be manipulated with executable codes to prepare for malware attack. Images are usually perceived as harmless and non-risky by the users so they have become the focus of attention for carrying the cyber-attacks. Security threats in systems and networks which are caused by malicious images, are needed to be minimized by introducing a detection technique, a technique which can involve features of headers. In our proposed method JPEG headers are transformed into grayscale images to employ classification. Convolutional Neural Network based model is proposed which aims the detection of malicious images. We have used a dataset of JPEGs which was collected from different honeypots installed by CRC of Bahria University. Dataset contains 1100 malicious and 1100 benign images to employ the detection method based on deep learning. We have achieved 96% accuracy. Our method of malicious image detection would help everyone to prevent the malware attacks which are carried through images.
{"title":"Malicious Image Detection Using Convolutional Neural Network","authors":"Ahsan Iqbal, Samabia Tehsin, Sumaira Kausar, Nayab Mishal","doi":"10.1109/AIMS52415.2021.9466042","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466042","url":null,"abstract":"Images have become a threat to the security of the systems and networks since JPEG headers are concealed with malicious payloads. Header in a JPEG has many segments which can be manipulated with executable codes to prepare for malware attack. Images are usually perceived as harmless and non-risky by the users so they have become the focus of attention for carrying the cyber-attacks. Security threats in systems and networks which are caused by malicious images, are needed to be minimized by introducing a detection technique, a technique which can involve features of headers. In our proposed method JPEG headers are transformed into grayscale images to employ classification. Convolutional Neural Network based model is proposed which aims the detection of malicious images. We have used a dataset of JPEGs which was collected from different honeypots installed by CRC of Bahria University. Dataset contains 1100 malicious and 1100 benign images to employ the detection method based on deep learning. We have achieved 96% accuracy. Our method of malicious image detection would help everyone to prevent the malware attacks which are carried through images.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122537932","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466085
Gholiyana Muntasha, N. Karna, S. Shin
With the rapid advancement of wireless communication, sensors, and battery technologies, Swarm Unmanned Aerial Vehicles (UAVs) have been widely used for traffic surveillance, and military application. Swarm UAVs, however, need to plan paths through the atmosphere, effectively preventing any collision that can occur when flying a multiple UAV simultaneously. This study proposes to design an anti-collision and a path planning system of swarm UAVs by using Artificial Bee Colony (ABC) algorithm. The ABC algorithm is an optimization method inspired by the foraging behavior of honeybees. The self-organization trait of honeybees enables them to coordinate themselves to create a global and local optimum. The proposed system, however, uses the ABC algorithm to optimize UAV's velocity, i.e., to reach its destination efficiently in the shortest path while avoiding collision among drones. The establishment of a constraint of a minimum acceptable distance among UAVs enables the algorithm to search for an alternative path in avoiding a collision. The simulation, however, reveals a successful convergence of a swarm UAVs towards a destination with no collision. During the trial with 12 and 20 drones, for instance, all UAVs successfully arrive at their goals with 0 potential collisions. However, during the test with 50 drones, there are 12 possible collisions. Once swarm drones reach their goal position at rest, the cluster will not overlap with other agents, as demonstrated in the visualization. Therefore, the ABC algorithm has satisfied the success criteria for this project and is suitable for swarm drone applications.
{"title":"Performance Analysis on Artificial Bee Colony Algorithm for Path Planning and Collision Avoidance in Swarm Unmanned Aerial Vehicle","authors":"Gholiyana Muntasha, N. Karna, S. Shin","doi":"10.1109/AIMS52415.2021.9466085","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466085","url":null,"abstract":"With the rapid advancement of wireless communication, sensors, and battery technologies, Swarm Unmanned Aerial Vehicles (UAVs) have been widely used for traffic surveillance, and military application. Swarm UAVs, however, need to plan paths through the atmosphere, effectively preventing any collision that can occur when flying a multiple UAV simultaneously. This study proposes to design an anti-collision and a path planning system of swarm UAVs by using Artificial Bee Colony (ABC) algorithm. The ABC algorithm is an optimization method inspired by the foraging behavior of honeybees. The self-organization trait of honeybees enables them to coordinate themselves to create a global and local optimum. The proposed system, however, uses the ABC algorithm to optimize UAV's velocity, i.e., to reach its destination efficiently in the shortest path while avoiding collision among drones. The establishment of a constraint of a minimum acceptable distance among UAVs enables the algorithm to search for an alternative path in avoiding a collision. The simulation, however, reveals a successful convergence of a swarm UAVs towards a destination with no collision. During the trial with 12 and 20 drones, for instance, all UAVs successfully arrive at their goals with 0 potential collisions. However, during the test with 50 drones, there are 12 possible collisions. Once swarm drones reach their goal position at rest, the cluster will not overlap with other agents, as demonstrated in the visualization. Therefore, the ABC algorithm has satisfied the success criteria for this project and is suitable for swarm drone applications.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116356630","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 : 2021-04-28DOI: 10.1109/AIMS52415.2021.9466091
Dendi Hazik Fuadi, D. Novita, Mohammad Taufik
The technology of robots has developed to help humans in many ways. It is not only limited to assist but also to interact socially with humans. Robots can interact socially with humans by utilizing the concept of artificial intelligence based on machine learning. In this paper, the prototype of Socially Assistive Robot (SAR) implemented artificial intelligence by using object detection and face recognition based on convolutional neural network and communication reports to Telegram Apps. To interact verbally, Google Assistant was required. The prototype was a successfully created and social interaction robot with an average face recognition accuracy is 85.41%.
{"title":"Socially Assistive Robot Interaction by Objects Detection and Face Recognition on Convolutional Neural Network for Parental Monitoring","authors":"Dendi Hazik Fuadi, D. Novita, Mohammad Taufik","doi":"10.1109/AIMS52415.2021.9466091","DOIUrl":"https://doi.org/10.1109/AIMS52415.2021.9466091","url":null,"abstract":"The technology of robots has developed to help humans in many ways. It is not only limited to assist but also to interact socially with humans. Robots can interact socially with humans by utilizing the concept of artificial intelligence based on machine learning. In this paper, the prototype of Socially Assistive Robot (SAR) implemented artificial intelligence by using object detection and face recognition based on convolutional neural network and communication reports to Telegram Apps. To interact verbally, Google Assistant was required. The prototype was a successfully created and social interaction robot with an average face recognition accuracy is 85.41%.","PeriodicalId":299121,"journal":{"name":"2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130642195","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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