Pub Date : 2023-01-01DOI: 10.2478/ijssis-2023-0001
A. Yudhana, Andreyan Dwi Cahyo, L. Y. Sabila, Arsyad Cahya Subrata, I. Mufandi
Abstract This study aims to assist farmers in monitoring soil nutrients, especially phosphorus. To measure the phosphorus content of paddy soil, the TCS3200 converter, as an intelligent sensor, was applied. The geographical information system (GIS) was also involved in this research to map the phosphorus content. In addition, the Naïve Bayes method was applied to classify lowland soil phosphorus status. The result of this study indicated that the Naïve Bayes algorithm could classify lowland soil phosphorus status with a probability of 0.34 for moderate phosphorus conditions and 0.66 for high phosphorus conditions. The sample testing results showed that the error rate was 3% and the success rate was 97%. Testing with a phosphorus-measuring instrument can be carried out by mapping the soil phosphorus status with the ArcGIS software, whereby seven points of medium-phosphorus-status paddy soil and 13 locations of high-phosphorus-status soil samples were determined. This research thus successfully mapped the soil phosphorus.
{"title":"Spatial distribution of soil nutrient content for sustainable rice agriculture using geographic information system and Naïve Bayes classifier","authors":"A. Yudhana, Andreyan Dwi Cahyo, L. Y. Sabila, Arsyad Cahya Subrata, I. Mufandi","doi":"10.2478/ijssis-2023-0001","DOIUrl":"https://doi.org/10.2478/ijssis-2023-0001","url":null,"abstract":"Abstract This study aims to assist farmers in monitoring soil nutrients, especially phosphorus. To measure the phosphorus content of paddy soil, the TCS3200 converter, as an intelligent sensor, was applied. The geographical information system (GIS) was also involved in this research to map the phosphorus content. In addition, the Naïve Bayes method was applied to classify lowland soil phosphorus status. The result of this study indicated that the Naïve Bayes algorithm could classify lowland soil phosphorus status with a probability of 0.34 for moderate phosphorus conditions and 0.66 for high phosphorus conditions. The sample testing results showed that the error rate was 3% and the success rate was 97%. Testing with a phosphorus-measuring instrument can be carried out by mapping the soil phosphorus status with the ArcGIS software, whereby seven points of medium-phosphorus-status paddy soil and 13 locations of high-phosphorus-status soil samples were determined. This research thus successfully mapped the soil phosphorus.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48078929","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 : 2023-01-01DOI: 10.2478/ijssis-2023-0013
Abolfazl Abdollahi, Biswajeet Pradhan, Abdullah Alamri
Abstract Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.
{"title":"Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach","authors":"Abolfazl Abdollahi, Biswajeet Pradhan, Abdullah Alamri","doi":"10.2478/ijssis-2023-0013","DOIUrl":"https://doi.org/10.2478/ijssis-2023-0013","url":null,"abstract":"Abstract Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135104946","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 : 2022-05-20DOI: 10.21307/ijssis-2022-0005
Charlee Kaewrat, Yunyong Punsawad
Abstract This paper demonstrates the use of augmented reality (AR) to teach the fundamental aspects of the human brain and guide proper EEG electrode placement. The proposed application consists of two main parts: (1) the proposed marker-based AR system uses the Vuforia technique to determine the dimension of the head to create the virtual brain and virtual EEG electrodes; and (2) user interaction and implementation. We performed two experiments using a phantom head to verify the size and workspace area of the marker and validated the position of the virtual electrode with ground truth data. The results showed that the proposed method can be employed for electrode placement guidance in the recommended range. We aim to use the proposed system for beginners. We will further test the system with human heads to evaluate the usability and determine key areas for application improvement.
{"title":"Utilizing augmented reality technology for teaching fundamentals of the human brain and EEG electrode placement","authors":"Charlee Kaewrat, Yunyong Punsawad","doi":"10.21307/ijssis-2022-0005","DOIUrl":"https://doi.org/10.21307/ijssis-2022-0005","url":null,"abstract":"Abstract This paper demonstrates the use of augmented reality (AR) to teach the fundamental aspects of the human brain and guide proper EEG electrode placement. The proposed application consists of two main parts: (1) the proposed marker-based AR system uses the Vuforia technique to determine the dimension of the head to create the virtual brain and virtual EEG electrodes; and (2) user interaction and implementation. We performed two experiments using a phantom head to verify the size and workspace area of the marker and validated the position of the virtual electrode with ground truth data. The results showed that the proposed method can be employed for electrode placement guidance in the recommended range. We aim to use the proposed system for beginners. We will further test the system with human heads to evaluate the usability and determine key areas for application improvement.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":"15 1","pages":"1 - 12"},"PeriodicalIF":1.2,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48005906","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 : 2022-01-01DOI: 10.2478/ijssis-2022-0019
S. Juneidi
Abstract Many practices are available to apply to smart agricultural methods, defended as allowing monitoring and controlling the environment necessary for the growth of many crops. Due to the random conditions affecting crop growing and cultivation, the idea of greenhouse use is emerging, as a way of managing all variables affecting growth and production of crops. Photosynthesis is the most important process for plants, with three main inputs: water, light, and carbon dioxide. Atmospheric temperature and humidity, soil richness, and plant nutrition are also vital for crop production. This research is concerned with controlling the environment of plants and plant nutrition. The Internet of Things (IoT) is proposed in this research as a solution to control and to manage all of these variables. Most important was to find a method that provides perfect analysis and control of a suitable environment for crops in greenhouses. Smart management of variables to manage crops is becoming a necessity. With IoT we can keep an eye on and manage all variables to grow crops in the best conditions for crop production. This article will discuss the kinds of variables that play important roles in growing a good crop. The next issue is how to retrieve these variables as big data, using sensors; how to save them to the cloud for processing; and then how to create actions according to readings of variables and predefined interdependent relations, with the main aim of better production in smart greenhouses.
{"title":"Smart greenhouses using internet of things: case study on tomatoes","authors":"S. Juneidi","doi":"10.2478/ijssis-2022-0019","DOIUrl":"https://doi.org/10.2478/ijssis-2022-0019","url":null,"abstract":"Abstract Many practices are available to apply to smart agricultural methods, defended as allowing monitoring and controlling the environment necessary for the growth of many crops. Due to the random conditions affecting crop growing and cultivation, the idea of greenhouse use is emerging, as a way of managing all variables affecting growth and production of crops. Photosynthesis is the most important process for plants, with three main inputs: water, light, and carbon dioxide. Atmospheric temperature and humidity, soil richness, and plant nutrition are also vital for crop production. This research is concerned with controlling the environment of plants and plant nutrition. The Internet of Things (IoT) is proposed in this research as a solution to control and to manage all of these variables. Most important was to find a method that provides perfect analysis and control of a suitable environment for crops in greenhouses. Smart management of variables to manage crops is becoming a necessity. With IoT we can keep an eye on and manage all variables to grow crops in the best conditions for crop production. This article will discuss the kinds of variables that play important roles in growing a good crop. The next issue is how to retrieve these variables as big data, using sensors; how to save them to the cloud for processing; and then how to create actions according to readings of variables and predefined interdependent relations, with the main aim of better production in smart greenhouses.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42447018","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 : 2022-01-01DOI: 10.2478/ijssis-2022-0008
A. Roosta, Alireza Soltanimehr
Abstract In this paper, an optimal solution to the eigen-structure problem that dramatically reduces the control effort required for a multi-input multi-output (MIMO) multirate system is proposed. This technique not only minimize the amplitude of state transmission and control signal changes but also reduce the sensitivity to the effects of internal sampling in multirate MIMO systems results from interactions of the model order reduction. In this study, a constrained eigen-structure method with nonlinear constraints is used. The novelty of this study is that by using partial derivatives, a gain optimization process to optimize the nonlinearly constrained eigen-structure method is proposed. First, this method is explained, and then the desired method is applied for optimization operations, and finally, the results will be shown with a numerical example and a simulation.
{"title":"Eigen-structure problem optimization for multirate, multi-input multi-output systems applied to a roll rate autopilot","authors":"A. Roosta, Alireza Soltanimehr","doi":"10.2478/ijssis-2022-0008","DOIUrl":"https://doi.org/10.2478/ijssis-2022-0008","url":null,"abstract":"Abstract In this paper, an optimal solution to the eigen-structure problem that dramatically reduces the control effort required for a multi-input multi-output (MIMO) multirate system is proposed. This technique not only minimize the amplitude of state transmission and control signal changes but also reduce the sensitivity to the effects of internal sampling in multirate MIMO systems results from interactions of the model order reduction. In this study, a constrained eigen-structure method with nonlinear constraints is used. The novelty of this study is that by using partial derivatives, a gain optimization process to optimize the nonlinearly constrained eigen-structure method is proposed. First, this method is explained, and then the desired method is applied for optimization operations, and finally, the results will be shown with a numerical example and a simulation.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44086415","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 : 2022-01-01DOI: 10.2478/ijssis-2022-0003
Aqeela Hamad
Abstract Convolution is considered most significant layer in deep learning because it can extract best features of data through the network but it may result in huge volume of data. This problem can be solved by using pooling. In this paper, A novel pooling method is proposed by using discrete Fourier transform (DFT), this method is used DFT technique to transform the data from spatial domain into frequency domain to preserve the most important information from the details coefficients, where the details information of the image is less significant, therefore it can be discarded to down sample the size of dimensions. Its effect will be great with advantage of reducing the eliminated details information as compared with other standard methods. After applying DFT, the most significant coefficients, which represent most important features are cropped while less important details will be discarded then the data are reconstructed by applying inverse DF, therefore the high quality of features are extracted, which solve the problem of losing significant information during the pooling layer. Different methods are proposed based on the scenario of using DFT. The proposed methods are tested by extracting pooled image then the original images were retrieved using only the pooled images. Then the retrieved images are compared with original images by using different measures such as SNR, correlation and SSIM. Then the proposed layers used for image classification for two different datasets. The results proved that the proposed methods outperformed standard methods, thus it can be used for deep learning application.
{"title":"Fast fourier transform based new pooling layer for deep learning","authors":"Aqeela Hamad","doi":"10.2478/ijssis-2022-0003","DOIUrl":"https://doi.org/10.2478/ijssis-2022-0003","url":null,"abstract":"Abstract Convolution is considered most significant layer in deep learning because it can extract best features of data through the network but it may result in huge volume of data. This problem can be solved by using pooling. In this paper, A novel pooling method is proposed by using discrete Fourier transform (DFT), this method is used DFT technique to transform the data from spatial domain into frequency domain to preserve the most important information from the details coefficients, where the details information of the image is less significant, therefore it can be discarded to down sample the size of dimensions. Its effect will be great with advantage of reducing the eliminated details information as compared with other standard methods. After applying DFT, the most significant coefficients, which represent most important features are cropped while less important details will be discarded then the data are reconstructed by applying inverse DF, therefore the high quality of features are extracted, which solve the problem of losing significant information during the pooling layer. Different methods are proposed based on the scenario of using DFT. The proposed methods are tested by extracting pooled image then the original images were retrieved using only the pooled images. Then the retrieved images are compared with original images by using different measures such as SNR, correlation and SSIM. Then the proposed layers used for image classification for two different datasets. The results proved that the proposed methods outperformed standard methods, thus it can be used for deep learning application.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45653646","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 : 2022-01-01DOI: 10.2478/ijssis-2022-0011
Thobekile J. Ngcobo, Farzad Ghayoor
Abstract A crucial part of the smart grid communication network is the Advanced Metering Infrastructure (AMI), connecting a utility company to end-users to support telemetry and remote-control applications. Although different technologies and standards for smart metering systems exist, Power-Line Communication is the preferred technology for AMI networks in dense urban areas, and the G3-PLC, which uses DLMS/COSEM to support data exchange, is the most used standard in PLC-based AMI networks. This paper provides a holistic view of G3-PLC in smart metering systems, including its interaction with the DLMS/COSEM. It also covers the challenges and research opportunities in G3-PLC smart metering systems.
{"title":"An overview of DLMS/COSEM and g3-plc for smart metering applications","authors":"Thobekile J. Ngcobo, Farzad Ghayoor","doi":"10.2478/ijssis-2022-0011","DOIUrl":"https://doi.org/10.2478/ijssis-2022-0011","url":null,"abstract":"Abstract A crucial part of the smart grid communication network is the Advanced Metering Infrastructure (AMI), connecting a utility company to end-users to support telemetry and remote-control applications. Although different technologies and standards for smart metering systems exist, Power-Line Communication is the preferred technology for AMI networks in dense urban areas, and the G3-PLC, which uses DLMS/COSEM to support data exchange, is the most used standard in PLC-based AMI networks. This paper provides a holistic view of G3-PLC in smart metering systems, including its interaction with the DLMS/COSEM. It also covers the challenges and research opportunities in G3-PLC smart metering systems.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48443090","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 : 2022-01-01DOI: 10.2478/ijssis-2022-0009
A. S. Abdullah, Sadeq Adnan Hbeeb
Abstract The electro-optic effect is considered very important in optical communication systems. The small optical confinement factor is attributed to the weak overlap between the electric field and optical wave and hence the optical signal is not efficiently modulated. In this paper, the problem of the small optical confinement factor in the Mach–Zehnder modulator based on lithium niobate (LN) which is deeply studied. The data were analyzed through a proposed mathematical model to explain the relationship between the change in the ordinary negative refractive index and the confinement factor. The system is improved using a small length of the modulator arm as only 3 to 8 µm, low driving power of about 4 V/µm, a large change in the negative ordinary refractive index of about—0.2 × 10−7, and a compact optical modulator. This can reflect a strong optical confinement factor when the electric field is applied to the electrodes of the optical modulator.
{"title":"The ordinary negative changing refractive index for estimation of optical confinement factor","authors":"A. S. Abdullah, Sadeq Adnan Hbeeb","doi":"10.2478/ijssis-2022-0009","DOIUrl":"https://doi.org/10.2478/ijssis-2022-0009","url":null,"abstract":"Abstract The electro-optic effect is considered very important in optical communication systems. The small optical confinement factor is attributed to the weak overlap between the electric field and optical wave and hence the optical signal is not efficiently modulated. In this paper, the problem of the small optical confinement factor in the Mach–Zehnder modulator based on lithium niobate (LN) which is deeply studied. The data were analyzed through a proposed mathematical model to explain the relationship between the change in the ordinary negative refractive index and the confinement factor. The system is improved using a small length of the modulator arm as only 3 to 8 µm, low driving power of about 4 V/µm, a large change in the negative ordinary refractive index of about—0.2 × 10−7, and a compact optical modulator. This can reflect a strong optical confinement factor when the electric field is applied to the electrodes of the optical modulator.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47243547","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 : 2022-01-01DOI: 10.21307/ijssis-2022-0001
A. Roosta, Alireza Soltanimehr
Abstract The main purpose of this study is to propose a method to achieve optimal response and performance of the multi rate systems in existence of different sampling rates and to avoid data losses in such systems. In this paper, a new approach based on the multi-rate digital system is proposed. Based on this method, we will be able to maintain optimal performance of these systems without losing data, in addition to establishing connections between subsystems with different sampling rates. By the proposed method, not only the effect of different sampling rates in multi-rate systems is ignored, but also, we can connect subsystems with different rate operations without compromising system performance and data loss which simplified system design and sensor selection by the digital system designers. This means the designer can choose any sensor without concerning about the sampling rate and the operation rate of the system and matching them together. Finally, a guided missile with different sampling rates between the seeker, autopilot, guidance law and the output feedback are simulated and its effective performance in pursuit, hitting and smooth motion toward the target in a motion scenario is shown.
{"title":"A novel optimal approach for control law of multi-rate systems with different rate operations","authors":"A. Roosta, Alireza Soltanimehr","doi":"10.21307/ijssis-2022-0001","DOIUrl":"https://doi.org/10.21307/ijssis-2022-0001","url":null,"abstract":"Abstract The main purpose of this study is to propose a method to achieve optimal response and performance of the multi rate systems in existence of different sampling rates and to avoid data losses in such systems. In this paper, a new approach based on the multi-rate digital system is proposed. Based on this method, we will be able to maintain optimal performance of these systems without losing data, in addition to establishing connections between subsystems with different sampling rates. By the proposed method, not only the effect of different sampling rates in multi-rate systems is ignored, but also, we can connect subsystems with different rate operations without compromising system performance and data loss which simplified system design and sensor selection by the digital system designers. This means the designer can choose any sensor without concerning about the sampling rate and the operation rate of the system and matching them together. Finally, a guided missile with different sampling rates between the seeker, autopilot, guidance law and the output feedback are simulated and its effective performance in pursuit, hitting and smooth motion toward the target in a motion scenario is shown.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":"15 1","pages":"1 - 10"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47387138","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 : 2022-01-01DOI: 10.2478/ijssis-2022-0017
Hoa T. Tran, Thanh C. Vo, Quan NA. Nguyen, Quang N. Pham, Duyen M. Ha, Thanh Q. Le, Thang K. Nguyen, Dong LT. Tran, Hai T. Do, M. T. Nguyen
Abstract The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.
{"title":"A novel design of a smart interactive guiding robot for busy airports","authors":"Hoa T. Tran, Thanh C. Vo, Quan NA. Nguyen, Quang N. Pham, Duyen M. Ha, Thanh Q. Le, Thang K. Nguyen, Dong LT. Tran, Hai T. Do, M. T. Nguyen","doi":"10.2478/ijssis-2022-0017","DOIUrl":"https://doi.org/10.2478/ijssis-2022-0017","url":null,"abstract":"Abstract The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42697740","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：