Pub Date : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293721
Akito Nakadomari, Ryuto Shigenobu, T. Senjyu
This paper describes optimal voltage control and optimal placement of the three-phase individual step voltage regulator (3ϕSVR) considering voltage unbalance improvement. As a result of active efforts to promote renewable energy, there is a concern that voltage unbalance will increase due to an increase in distributed power sources. Therefore, this paper proposes the optimal control and placement method for 3ϕSVR for voltage unbalance improvement and loss minimization. Simulations verified that all the voltage unbalanced indices satisfied the constraint value and the objective function improved. These results confirmed that the effectiveness of the optimal control and placement method for 3ϕSVR.
{"title":"Optimal Control and Placement of Step Voltage Regulator for Voltage Unbalance Improvement and Loss Minimization in Distribution System","authors":"Akito Nakadomari, Ryuto Shigenobu, T. Senjyu","doi":"10.1109/TENCON50793.2020.9293721","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293721","url":null,"abstract":"This paper describes optimal voltage control and optimal placement of the three-phase individual step voltage regulator (3ϕSVR) considering voltage unbalance improvement. As a result of active efforts to promote renewable energy, there is a concern that voltage unbalance will increase due to an increase in distributed power sources. Therefore, this paper proposes the optimal control and placement method for 3ϕSVR for voltage unbalance improvement and loss minimization. Simulations verified that all the voltage unbalanced indices satisfied the constraint value and the objective function improved. These results confirmed that the effectiveness of the optimal control and placement method for 3ϕSVR.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121063345","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293903
J. Jayakody, E. Edirisinghe
This paper presents a non-invasive method to detect Anemia (a low level of Hemoglobin) easily. The Hemoglobin concentration in human blood is an important substance to health condition determination. With the results which are obtained from Hemoglobin test, a condition which is called as Anemia can be revealed. Traditionally the Hemoglobin test is done using blood samples which are taken using needles. The non-invasive Hemoglobin measurement system, discussed in this paper, describes a better idea about the hemoglobin concentration in the human blood. The images of the finger- tip of the different hemoglobin level patients which are taken using a camera is used to develop the neural network-based algorithm. The pre-mentioned algorithm is used in the developed noninvasive device to display the Hemoglobin level. Before doing the above procedure, an account is created in the mobile app and a questionnaire is given to answer by the patient. Finally, both the results which are obtained from the mobile app and the device are run through a machine learning algorithm to get the final output. According to the result patient would be able to detect anemia at an early stage.
{"title":"HemoSmart: A Non-invasive, Machine Learning Based Device and Mobile App for Anemia Detection","authors":"J. Jayakody, E. Edirisinghe","doi":"10.1109/TENCON50793.2020.9293903","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293903","url":null,"abstract":"This paper presents a non-invasive method to detect Anemia (a low level of Hemoglobin) easily. The Hemoglobin concentration in human blood is an important substance to health condition determination. With the results which are obtained from Hemoglobin test, a condition which is called as Anemia can be revealed. Traditionally the Hemoglobin test is done using blood samples which are taken using needles. The non-invasive Hemoglobin measurement system, discussed in this paper, describes a better idea about the hemoglobin concentration in the human blood. The images of the finger- tip of the different hemoglobin level patients which are taken using a camera is used to develop the neural network-based algorithm. The pre-mentioned algorithm is used in the developed noninvasive device to display the Hemoglobin level. Before doing the above procedure, an account is created in the mobile app and a questionnaire is given to answer by the patient. Finally, both the results which are obtained from the mobile app and the device are run through a machine learning algorithm to get the final output. According to the result patient would be able to detect anemia at an early stage.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115963967","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293765
Dhruv Verma, Sunaina Puri, S. Prabhu, Komal Smriti
Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either "Normal" or "Anomalous" based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.
{"title":"Anomaly detection in panoramic dental x-rays using a hybrid Deep Learning and Machine Learning approach","authors":"Dhruv Verma, Sunaina Puri, S. Prabhu, Komal Smriti","doi":"10.1109/TENCON50793.2020.9293765","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293765","url":null,"abstract":"Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either \"Normal\" or \"Anomalous\" based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123199748","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293723
Nazmul Amin, Mahbub Alam
In this article, the edge transport of Zigzag Graphene NanoRibbon (ZGNR) in the presence of an abrupt structure change due to missing atoms, which we define as ‘cut’ is studied through Non-Equilibrium Green’s Function formalism. Interesting results are found that are notably different for difference in the width of the ‘cut’. For ZGNR, depending on the width of the ‘cut’, the electrons can be fully transmitted (T = 1) or fully blocked (T = 0) in the device scattering region.
{"title":"Quantum Transport of Edge States in Zigzag Graphene NanoRibbon in the Presence of an Abrupt Structure Change due to Missing Atoms","authors":"Nazmul Amin, Mahbub Alam","doi":"10.1109/TENCON50793.2020.9293723","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293723","url":null,"abstract":"In this article, the edge transport of Zigzag Graphene NanoRibbon (ZGNR) in the presence of an abrupt structure change due to missing atoms, which we define as ‘cut’ is studied through Non-Equilibrium Green’s Function formalism. Interesting results are found that are notably different for difference in the width of the ‘cut’. For ZGNR, depending on the width of the ‘cut’, the electrons can be fully transmitted (T = 1) or fully blocked (T = 0) in the device scattering region.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":" 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120832291","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293841
Michael Pareja, A. Bandala
Irrigation is essential for growing crops and leads to gradual growth in the economy. This research proposal aims to resolve the issue of scarcity and proper water management in the tank system through the Fuzzy Irrigation System. Fuzzy logic improves the irrigation system that includes three input parameters, such as soil moisture, soil temperature, and the water level. The combinations of these parameters will produce the time duration to have an efficient flow of water to the crop fields. Likewise, the Rain Detection Model (RDM) and the Fertilizer Control Model (FCM) are other features that support, strengthen, and innovate the system. The pilot test conducted by the researcher through MATLAB simulations were performed to check the effectiveness of the proposed system before its actual implementation.
{"title":"Fuzzy Irrigation System with Rain Detection and Fertilizer Control","authors":"Michael Pareja, A. Bandala","doi":"10.1109/TENCON50793.2020.9293841","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293841","url":null,"abstract":"Irrigation is essential for growing crops and leads to gradual growth in the economy. This research proposal aims to resolve the issue of scarcity and proper water management in the tank system through the Fuzzy Irrigation System. Fuzzy logic improves the irrigation system that includes three input parameters, such as soil moisture, soil temperature, and the water level. The combinations of these parameters will produce the time duration to have an efficient flow of water to the crop fields. Likewise, the Rain Detection Model (RDM) and the Fertilizer Control Model (FCM) are other features that support, strengthen, and innovate the system. The pilot test conducted by the researcher through MATLAB simulations were performed to check the effectiveness of the proposed system before its actual implementation.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123229604","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293947
Sayan Sarkar, W. Ki
A class-D power amplifier (PA) powering a pair of resonant coils is studied. Time-domain analysis of the primary and the secondary sections inductor current with series-series (SS) and series-parallel (SP) resonance are derived, with either resistor or rectifier loads. Both the ripple and rectified output voltage of the secondary section are analyzed. Results are validated through extensive SPICE simulations. Analytical and simulated results are matched with better than 90% accuracy.
{"title":"Time Domain Analysis of Class-D Amplifier Driving Series-Series and Series-Parallel Circuits","authors":"Sayan Sarkar, W. Ki","doi":"10.1109/TENCON50793.2020.9293947","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293947","url":null,"abstract":"A class-D power amplifier (PA) powering a pair of resonant coils is studied. Time-domain analysis of the primary and the secondary sections inductor current with series-series (SS) and series-parallel (SP) resonance are derived, with either resistor or rectifier loads. Both the ripple and rectified output voltage of the secondary section are analyzed. Results are validated through extensive SPICE simulations. Analytical and simulated results are matched with better than 90% accuracy.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128374640","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293804
Marife A. Rosales, Maria Gemel B. Palconit, A. Bandala, R. R. Vicerra, E. Dadios, Hilario A. Calinao
The study aims to design an intelligent total body water measuring device which will help to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis (BIA). The system utilized the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm. The system used the dataset splitting of 70%-15%15% for training, validation and testing. Different hidden neurons were used and compared during neural network training and found out that using 10 neurons will provide the lowest mean square error (MSE) with best value of Pearson’s correlation (R). Based on the results, using 10 neurons, Scaled Conjugate Gradient algorithm has better performance as compared to Levenberg-Marquardt algorithm with MSE equal to 0.180033, 0.118954, 0.529157 while the R value is equal to 0.997887, 0.997488, 0.99644 for training, validation and testing.
{"title":"Prediction of Total Body Water using Scaled Conjugate Gradient Artificial Neural Network","authors":"Marife A. Rosales, Maria Gemel B. Palconit, A. Bandala, R. R. Vicerra, E. Dadios, Hilario A. Calinao","doi":"10.1109/TENCON50793.2020.9293804","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293804","url":null,"abstract":"The study aims to design an intelligent total body water measuring device which will help to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis (BIA). The system utilized the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm. The system used the dataset splitting of 70%-15%15% for training, validation and testing. Different hidden neurons were used and compared during neural network training and found out that using 10 neurons will provide the lowest mean square error (MSE) with best value of Pearson’s correlation (R). Based on the results, using 10 neurons, Scaled Conjugate Gradient algorithm has better performance as compared to Levenberg-Marquardt algorithm with MSE equal to 0.180033, 0.118954, 0.529157 while the R value is equal to 0.997887, 0.997488, 0.99644 for training, validation and testing.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131084291","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293722
M. Rahul, Karnekanti Shiva Saketh, A. Sanjeet, Nenavath Srinivas Naik
Forest fires have become a serious threat to mankind. Besides providing shelter and protection to a large number of living beings, they have been a major source of food, wood, and a great supply of other products. Since ancient times forests have played an important role in social, economic, and religious activities and have enriched human life in a variety of ways both material and psychological. To protect our nature from these rapidly rising forest fires, we need to be cautious enough of every decision we take which could lead to a disastrous end, once and for all. So for the early detection of forest fires, we propose an image recognition method based on Convolutional Neural Networks (CNN). We have fine-tuned the Resnet50 architecture and added a few convolutional layers with ReLu as the activation functions, and a binary classification output layer which showed a huge impact on the training and test results when compared to the other SOTA methods like VGG16 AND DenseNet121. We achieved a training set accuracy of 92.27% and 89.57% test accuracy with a stochastic gradient descent optimizer and we have avoided the underfit/overfitting on the model with the help of the Stochastic Gradient Descent (SGD) algorithm.
{"title":"Early Detection of Forest Fire using Deep Learning","authors":"M. Rahul, Karnekanti Shiva Saketh, A. Sanjeet, Nenavath Srinivas Naik","doi":"10.1109/TENCON50793.2020.9293722","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293722","url":null,"abstract":"Forest fires have become a serious threat to mankind. Besides providing shelter and protection to a large number of living beings, they have been a major source of food, wood, and a great supply of other products. Since ancient times forests have played an important role in social, economic, and religious activities and have enriched human life in a variety of ways both material and psychological. To protect our nature from these rapidly rising forest fires, we need to be cautious enough of every decision we take which could lead to a disastrous end, once and for all. So for the early detection of forest fires, we propose an image recognition method based on Convolutional Neural Networks (CNN). We have fine-tuned the Resnet50 architecture and added a few convolutional layers with ReLu as the activation functions, and a binary classification output layer which showed a huge impact on the training and test results when compared to the other SOTA methods like VGG16 AND DenseNet121. We achieved a training set accuracy of 92.27% and 89.57% test accuracy with a stochastic gradient descent optimizer and we have avoided the underfit/overfitting on the model with the help of the Stochastic Gradient Descent (SGD) algorithm.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129557390","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293849
Vincent Jan D. Almero, Ronnie S. Concepcion, Jonnel D. Alejandrino, A. Bandala, Jason L. Española, R. Bedruz, R. R. Vicerra, E. Dadios
Dehazing through Dark Channel Prior (DCP), originally developed for land-based images, has translated its potential for improving the quality of underwater images. However, the DCP default parameters, which are just adapted from land-based applications, may not be applicable for underwater images. Such constraint limits the capability of this restoration algorithm to improve the quality of an underwater image; the values of these parameters must be searched for each underwater image. A proposed approach on the parameter values assignment problem is to conduct an optimized search based on Genetic Algorithm. The presentation of this proposed approach focuses on the Genetic Algorithm processes: chromosome encoding, fitness function development, and selection, mutation, and crossover, to perform an effective search of the best solution out of a pool of possible solutions. Qualitative and quantitative evaluations show that utilization of optimized combination of DCP parameters, achieves images of higher quality in comparison to the utilization of established default DCP parameters.
{"title":"Genetic Algorithm-based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing","authors":"Vincent Jan D. Almero, Ronnie S. Concepcion, Jonnel D. Alejandrino, A. Bandala, Jason L. Española, R. Bedruz, R. R. Vicerra, E. Dadios","doi":"10.1109/TENCON50793.2020.9293849","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293849","url":null,"abstract":"Dehazing through Dark Channel Prior (DCP), originally developed for land-based images, has translated its potential for improving the quality of underwater images. However, the DCP default parameters, which are just adapted from land-based applications, may not be applicable for underwater images. Such constraint limits the capability of this restoration algorithm to improve the quality of an underwater image; the values of these parameters must be searched for each underwater image. A proposed approach on the parameter values assignment problem is to conduct an optimized search based on Genetic Algorithm. The presentation of this proposed approach focuses on the Genetic Algorithm processes: chromosome encoding, fitness function development, and selection, mutation, and crossover, to perform an effective search of the best solution out of a pool of possible solutions. Qualitative and quantitative evaluations show that utilization of optimized combination of DCP parameters, achieves images of higher quality in comparison to the utilization of established default DCP parameters.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829534","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 : 2020-11-16DOI: 10.1109/TENCON50793.2020.9293694
Natthaphon Bunathuek, Pudit Laksanacharoen
This work presents simulation of a Reconfigurable Spherical Robot IV for confined environment. The robot is a spherical shape with three legs kept inside spherical shell. Each leg has four degrees of freedom. All three legs can be extended for two types of locomotion such as legged locomotion and rolling sphere. A number of simulation has been done in steering in a wide and small radius of turning, rolling forward motion, and walking breaststroke concept. The simulation results show a promising concept of this new robot.
{"title":"Simulation of A Reconfigurable Spherical Robot IV for Confined Environment","authors":"Natthaphon Bunathuek, Pudit Laksanacharoen","doi":"10.1109/TENCON50793.2020.9293694","DOIUrl":"https://doi.org/10.1109/TENCON50793.2020.9293694","url":null,"abstract":"This work presents simulation of a Reconfigurable Spherical Robot IV for confined environment. The robot is a spherical shape with three legs kept inside spherical shell. Each leg has four degrees of freedom. All three legs can be extended for two types of locomotion such as legged locomotion and rolling sphere. A number of simulation has been done in steering in a wide and small radius of turning, rolling forward motion, and walking breaststroke concept. The simulation results show a promising concept of this new robot.","PeriodicalId":283131,"journal":{"name":"2020 IEEE REGION 10 CONFERENCE (TENCON)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126776437","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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