Detecting carbon emissions is the key to carbon peaking and carbon neutrality goals. Existing research has focused on utilizing data-driven method to study carbon emissions off a single object. This study proposes a regional carbon emissions prediction method. The area objects are divided into dynamic objects for vehicles and static objects for buildings. For the dynamic object, carbon emissions is modeled using the vehicle location provided by the BeiDou satellite navigation system (BDS). For the static object, the neural network R3det (rotation remote sensing target detection) is used to identify the buildings in remote sensing images, and then the trained ARIMA time series model is used to predict the carbon emissions. The model is tested in an industrial park in Tangshan, Hebei Province in China. The result of the regional three-dimensional emission map shows that the method provided a novel and feasible idea for carbon emissions prediction.
{"title":"Prediction of regional carbon emissions using deep learning and mathematical–statistical model","authors":"Yutao Mu, Kai Gao, Ronghua Du","doi":"10.3233/ais-220163","DOIUrl":"https://doi.org/10.3233/ais-220163","url":null,"abstract":"Detecting carbon emissions is the key to carbon peaking and carbon neutrality goals. Existing research has focused on utilizing data-driven method to study carbon emissions off a single object. This study proposes a regional carbon emissions prediction method. The area objects are divided into dynamic objects for vehicles and static objects for buildings. For the dynamic object, carbon emissions is modeled using the vehicle location provided by the BeiDou satellite navigation system (BDS). For the static object, the neural network R3det (rotation remote sensing target detection) is used to identify the buildings in remote sensing images, and then the trained ARIMA time series model is used to predict the carbon emissions. The model is tested in an industrial park in Tangshan, Hebei Province in China. The result of the regional three-dimensional emission map shows that the method provided a novel and feasible idea for carbon emissions prediction.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45709037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Path planning algorithms determine the performance of the ambient intelligence navigation schemes in autonomous mobile robots. Sampling-based path planning algorithms are widely employed in autonomous mobile robot applications. RRT*, or Optimal Rapidly Exploring Random Trees, is a very effective sampling-based path planning algorithm. However, the RRT* solution converges slowly. This study proposes a directional random sampling-based RRT* path planning algorithm known as DR-RRT* to address the slow convergence issue. The novelty of the proposed method is that it reduces the search space by combining directional non-uniform sampling with uniform sampling. It employs a random selection approach to combine the non-uniform directional sampling method with uniform sampling. The proposed path planning algorithm is validated in three different environments with a map size of 384*384, and its performance is compared to two existing algorithms: RRT* and Informed RRT*. Validation is carried out utilizing a TurtleBot3 robot with the Gazebo Simulator and the Robotics Operating System (ROS) Melodic. The proposed DR-RRT* path planning algorithm is better than both RRT* and Informed RRT* in four performance measures: the number of nodes visited, the length of the path, the amount of time it takes, and the rate at which the path converges. The proposed DR-RRT* global path planning algorithm achieves a success rate of 100% in all three environments, and it is suited for use in all kinds of environments.
{"title":"A novel directional sampling-based path planning algorithm for ambient intelligence navigation scheme in autonomous mobile robots","authors":"S. Ganesan, S. Natarajan","doi":"10.3233/ais-220292","DOIUrl":"https://doi.org/10.3233/ais-220292","url":null,"abstract":"Path planning algorithms determine the performance of the ambient intelligence navigation schemes in autonomous mobile robots. Sampling-based path planning algorithms are widely employed in autonomous mobile robot applications. RRT*, or Optimal Rapidly Exploring Random Trees, is a very effective sampling-based path planning algorithm. However, the RRT* solution converges slowly. This study proposes a directional random sampling-based RRT* path planning algorithm known as DR-RRT* to address the slow convergence issue. The novelty of the proposed method is that it reduces the search space by combining directional non-uniform sampling with uniform sampling. It employs a random selection approach to combine the non-uniform directional sampling method with uniform sampling. The proposed path planning algorithm is validated in three different environments with a map size of 384*384, and its performance is compared to two existing algorithms: RRT* and Informed RRT*. Validation is carried out utilizing a TurtleBot3 robot with the Gazebo Simulator and the Robotics Operating System (ROS) Melodic. The proposed DR-RRT* path planning algorithm is better than both RRT* and Informed RRT* in four performance measures: the number of nodes visited, the length of the path, the amount of time it takes, and the rate at which the path converges. The proposed DR-RRT* global path planning algorithm achieves a success rate of 100% in all three environments, and it is suited for use in all kinds of environments.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"15 1","pages":"269-284"},"PeriodicalIF":1.7,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69735507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Matsuhisa, K. Ide, Toru Nakamura, Yuki Kunugida, Takuya Yamamura, Makoto Komazawa, Koichi Masuda, Y. Kataoka
Sleep disorders are one of the causes that impair our quality of life, and adjustment of autonomic nervous activity can improve the sleep quality. The authors examined the effects on the sleep quality with adjustment of autonomic nervous activity by individually optimizing complex environment before sleep. Sixteen subjects underwent an environment optimization experiment during the day and subsequent sleep experiment (9 days/individual) and the ratio of low-frequency to high-frequency (LF/HF) components of heart rate variability was measured during the experiment. The LF/HF decreased under optimal conditions by 19% compared to the control conditions. Next, the effects of optimal conditions before sleep on the sleep quality were evaluated. Based on the index for the sleep quality (light sleep index), effect of the optimal environment conditions before sleep was not clearly observed for all subjects. Clustering analysis was evaluated to analyze the cause deeply. As a result, for the group of experiment subjects who did not feel nervous about the experiment, the light sleep index was decreased under optimal conditions by 29% compared to the control conditions. It was found that the effect on such stimuli could disappear in the subjects who were nervous about the experiment.
{"title":"Effects of environmental control before sleeping on autonomic nervous activity and sleep: A pilot study","authors":"Y. Matsuhisa, K. Ide, Toru Nakamura, Yuki Kunugida, Takuya Yamamura, Makoto Komazawa, Koichi Masuda, Y. Kataoka","doi":"10.3233/ais-210489","DOIUrl":"https://doi.org/10.3233/ais-210489","url":null,"abstract":"Sleep disorders are one of the causes that impair our quality of life, and adjustment of autonomic nervous activity can improve the sleep quality. The authors examined the effects on the sleep quality with adjustment of autonomic nervous activity by individually optimizing complex environment before sleep. Sixteen subjects underwent an environment optimization experiment during the day and subsequent sleep experiment (9 days/individual) and the ratio of low-frequency to high-frequency (LF/HF) components of heart rate variability was measured during the experiment. The LF/HF decreased under optimal conditions by 19% compared to the control conditions. Next, the effects of optimal conditions before sleep on the sleep quality were evaluated. Based on the index for the sleep quality (light sleep index), effect of the optimal environment conditions before sleep was not clearly observed for all subjects. Clustering analysis was evaluated to analyze the cause deeply. As a result, for the group of experiment subjects who did not feel nervous about the experiment, the light sleep index was decreased under optimal conditions by 29% compared to the control conditions. It was found that the effect on such stimuli could disappear in the subjects who were nervous about the experiment.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"276 1","pages":"165-178"},"PeriodicalIF":1.7,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80046012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Sivasankari, Ahilan Appathurai, A. Jeyam, A. Malar
Hyperbilirubinemia or jaundice occurs in 60% of healthy babies and 80% of preterm infants because of an increase in unconjugated bilirubin in red blood cells. It is subjective to determine the severity of jaundice by visual assessment of the skin color of a newborn, and clinical judgement is dependent on the doctor’s knowledge. The paper explains the development of a non-invasive bilirubin detection technique called CliNicS, to check the bilirubin level of premature babies and report premature births and deaths to the health organization via an IOT network. CliNicS provides a noninvasive, transcutaneous bilirubin monitoring system using LED having a wavelength of 410 nm to 460 nm, and it also provides the treatment automatically by using LCT (LED Controlled Therapy) method. The level of bilirubin will be detected by using the photo detector, and the bilirubin measurement will be displayed on the LCD display. The bilirubin levels will be transmitted to doctors and health organizations via the IOT network. The proposed method helps to detect neonatal jaundice earlier, which reduces the risk of hyperbilirubinemia in newborns and makes it easier to measure total serum bilirubin levels than ever before.
{"title":"Care living instrument for neonatal infant connectivity solution (CliNicS) in smart environment","authors":"B. Sivasankari, Ahilan Appathurai, A. Jeyam, A. Malar","doi":"10.3233/ais-220103","DOIUrl":"https://doi.org/10.3233/ais-220103","url":null,"abstract":"Hyperbilirubinemia or jaundice occurs in 60% of healthy babies and 80% of preterm infants because of an increase in unconjugated bilirubin in red blood cells. It is subjective to determine the severity of jaundice by visual assessment of the skin color of a newborn, and clinical judgement is dependent on the doctor’s knowledge. The paper explains the development of a non-invasive bilirubin detection technique called CliNicS, to check the bilirubin level of premature babies and report premature births and deaths to the health organization via an IOT network. CliNicS provides a noninvasive, transcutaneous bilirubin monitoring system using LED having a wavelength of 410 nm to 460 nm, and it also provides the treatment automatically by using LCT (LED Controlled Therapy) method. The level of bilirubin will be detected by using the photo detector, and the bilirubin measurement will be displayed on the LCD display. The bilirubin levels will be transmitted to doctors and health organizations via the IOT network. The proposed method helps to detect neonatal jaundice earlier, which reduces the risk of hyperbilirubinemia in newborns and makes it easier to measure total serum bilirubin levels than ever before.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"41 1","pages":"425-438"},"PeriodicalIF":1.7,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81487625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to solve the problem of household garbage classification accurately and efficiently, convolutional neural network classifier is an effective method. In this study, a garbage classification device was designed, and the image dataset Wit-Garbage for garbage classification was constructed based on the device by collecting garbage images under different light intensity and weather environment. The performances of the five network models VGG16, ResNet50, DenseNet121, MobileNet V2, Inception V3 on this dataset were compared by transfer learning. Then, the lightweight convolutional neural network MobileNet V2 was optimized by fine-tuning the hyperparameters, such as the type of optimizer, learning rate, Dropout parameter and number of freezing layers, respectively, and the training accuracy and efficiency were discussed in detail. Finally, the optimized model MobileNet V2 was deployed to the self-made garbage classification device for verification. The results show that the MobileNet V2 network model is superior to other networks in terms of training accuracy and efficiency on the proposed dataset, when the image input size was 224 ∗ 224 pixels, the Adamax optimizer was adopted, the learning rate was 0.0001, the Dropout was less than 0.5, and the number of frozen layers is less than 30. The actual verification results show that the average accuracy of the optimized network model trained on the proposed dataset for MSW classification was up to 98.75%, and compared with the model before optimization, the average accuracy was improved by 2.83%, and the average detection time was reduced by 69%.
{"title":"Study on the CNN model optimization for household garbage classification based on machine learning","authors":"Wenzhuo Xie, Shiping Li, Wei Xu, Haotian Deng, Weihan Liao, Xianbao Duan, Xiang Wang","doi":"10.3233/ais-220017","DOIUrl":"https://doi.org/10.3233/ais-220017","url":null,"abstract":"In order to solve the problem of household garbage classification accurately and efficiently, convolutional neural network classifier is an effective method. In this study, a garbage classification device was designed, and the image dataset Wit-Garbage for garbage classification was constructed based on the device by collecting garbage images under different light intensity and weather environment. The performances of the five network models VGG16, ResNet50, DenseNet121, MobileNet V2, Inception V3 on this dataset were compared by transfer learning. Then, the lightweight convolutional neural network MobileNet V2 was optimized by fine-tuning the hyperparameters, such as the type of optimizer, learning rate, Dropout parameter and number of freezing layers, respectively, and the training accuracy and efficiency were discussed in detail. Finally, the optimized model MobileNet V2 was deployed to the self-made garbage classification device for verification. The results show that the MobileNet V2 network model is superior to other networks in terms of training accuracy and efficiency on the proposed dataset, when the image input size was 224 ∗ 224 pixels, the Adamax optimizer was adopted, the learning rate was 0.0001, the Dropout was less than 0.5, and the number of frozen layers is less than 30. The actual verification results show that the average accuracy of the optimized network model trained on the proposed dataset for MSW classification was up to 98.75%, and compared with the model before optimization, the average accuracy was improved by 2.83%, and the average detection time was reduced by 69%.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"41 1","pages":"439-454"},"PeriodicalIF":1.7,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75065266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elderly people requiring care the entire day usually depend on the availability of their family members to give assistance. However, the family members might not provide appropriate help especially in an emergent situation. The application of Internet of Things (IoT) technology with a variety of interconnected devices provides the solution. We propose an IoT-based smart healthcare system comprising wearable devices, which integrates a variety of contact sensors with location-based mesh networks (LBMN) such as Wi-Fi and Bluetooth Low Energy (BLE) connections to continuously sense various parameters of aging people. The BLE-connected devices such as wearable sensors, fixed sensors, seat cushions, pedal mats, magnetic reed switches, and mobile devices are all involved in collecting, processing, and transmitting physiological data and their locations to the cloud. Through the utilization of convenient interfaces such as software applications on smartphones and web pages on computers, it provides real time monitoring of the elderly in terms of localization, activity pattern, and health status. Thus the system enables early detection of health risks to the elderly. We used Platform as a service (PaaS) to receive and store the health data generated from the interconnected devices and to perform analysis. The essential feature of this LBMN is to generate a complete 6W(Who, What,When,Where,Why and How)big data for policy, feed it to the PaaS analysis to easily and quickly obtain more accurate data, and then develop possible health strategy or preventive measures. The proposed healthcare system detected that, out of the 20 participants recruited, 2 persons (10%) were often restless. It was also able to detect abnormal daily activity patterns with more tag positioning and the historical data from the devices. More importantly, it can help to prevent potential physical and neuropsychiatric disorders based on the real-time monitoring information and analyzed historical data for the aging people.
需要全天照顾的老年人通常取决于其家庭成员是否提供帮助。然而，家庭成员可能不会提供适当的帮助，特别是在紧急情况下。物联网(IoT)技术与各种互联设备的应用提供了解决方案。我们提出了一种基于物联网的智能医疗系统，包括可穿戴设备，该系统集成了各种接触传感器和基于位置的网状网络(LBMN)，如Wi-Fi和低功耗蓝牙(BLE)连接，以连续感知老年人的各种参数。ble连接的设备，如可穿戴传感器、固定传感器、座垫、脚垫、磁簧开关、移动设备等，都参与了生理数据的收集、处理，并将其位置传输到云端。通过智能手机上的软件应用和电脑上的网页等便捷的界面，对老年人的定位、活动模式和健康状况进行实时监测。因此，该系统能够及早发现老年人的健康风险。我们使用平台即服务(PaaS)来接收和存储从互联设备生成的健康数据并执行分析。该LBMN的本质特征是为政策生成完整的6W(Who, What,When,Where,Why and How)大数据，并将其提供给PaaS分析，从而轻松快速地获得更准确的数据，进而制定可能的健康策略或预防措施。拟议的医疗保健系统检测到，在招募的20名参与者中，有2人(10%)经常坐立不安。它还能够通过更多的标签定位和设备的历史数据来检测异常的日常活动模式。更重要的是，它可以根据老年人的实时监测信息和分析的历史数据，帮助预防潜在的身体和神经精神疾病。
{"title":"An IoT-based smart healthcare system using location-based mesh network and big data analytics","authors":"Hsinchuan Lin, Ming-jen Chen, Jung-Tang Huang","doi":"10.3233/ais-220162","DOIUrl":"https://doi.org/10.3233/ais-220162","url":null,"abstract":"Elderly people requiring care the entire day usually depend on the availability of their family members to give assistance. However, the family members might not provide appropriate help especially in an emergent situation. The application of Internet of Things (IoT) technology with a variety of interconnected devices provides the solution. We propose an IoT-based smart healthcare system comprising wearable devices, which integrates a variety of contact sensors with location-based mesh networks (LBMN) such as Wi-Fi and Bluetooth Low Energy (BLE) connections to continuously sense various parameters of aging people. The BLE-connected devices such as wearable sensors, fixed sensors, seat cushions, pedal mats, magnetic reed switches, and mobile devices are all involved in collecting, processing, and transmitting physiological data and their locations to the cloud. Through the utilization of convenient interfaces such as software applications on smartphones and web pages on computers, it provides real time monitoring of the elderly in terms of localization, activity pattern, and health status. Thus the system enables early detection of health risks to the elderly. We used Platform as a service (PaaS) to receive and store the health data generated from the interconnected devices and to perform analysis. The essential feature of this LBMN is to generate a complete 6W(Who, What,When,Where,Why and How)big data for policy, feed it to the PaaS analysis to easily and quickly obtain more accurate data, and then develop possible health strategy or preventive measures. The proposed healthcare system detected that, out of the 20 participants recruited, 2 persons (10%) were often restless. It was also able to detect abnormal daily activity patterns with more tag positioning and the historical data from the devices. More importantly, it can help to prevent potential physical and neuropsychiatric disorders based on the real-time monitoring information and analyzed historical data for the aging people.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"20 1","pages":"483-509"},"PeriodicalIF":1.7,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78249112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smart devices, such as smart phones, voice assistants and social robots, provide users with a range of input modalities, e.g., speech, touch, gestures, and vision. In recent years, advancements in processing of these input channels enable more natural interaction (e.g., automated speech, face, and gesture recognition, dialog generation, emotion expression etc.) experiences for users. However, there are several important challenges that need to be addressed to create these user experiences. One challenge is that most smart devices do not have sufficient computing resources to execute the Artificial Intelligence (AI) techniques locally. Another challenge is that users expect responses in near real-time when they interact with these devices. Moreover, users also want to be able to seamlessly switch between devices and services any time and from anywhere and expect personalized and privacy-aware services. To address these challenges, we design and develop a cloud-based middleware (CMI) which helps to develop multi-modal interaction applications and easily integrate applications to AI services. In this middleware, services developed by different producers with different protocols and smart devices with different capabilities and protocols can be integrated easily. In CMI, applications stream data from devices to cloud services for processing and consume the results. It supports data streaming from multiple devices to multiple services (and vice versa). CMI provides an integration framework for decoupling the services and devices and enabling application developers to concentrate on “interaction” instead of AI techniques. We provide simple examples to illustrate the conceptual ideas incorporated in CMI.
{"title":"A cloud-based middleware for multi-modal interaction services and applications","authors":"Bilgin Avenoglu, V. J. Koeman, K. Hindriks","doi":"10.3233/ais-220161","DOIUrl":"https://doi.org/10.3233/ais-220161","url":null,"abstract":"Smart devices, such as smart phones, voice assistants and social robots, provide users with a range of input modalities, e.g., speech, touch, gestures, and vision. In recent years, advancements in processing of these input channels enable more natural interaction (e.g., automated speech, face, and gesture recognition, dialog generation, emotion expression etc.) experiences for users. However, there are several important challenges that need to be addressed to create these user experiences. One challenge is that most smart devices do not have sufficient computing resources to execute the Artificial Intelligence (AI) techniques locally. Another challenge is that users expect responses in near real-time when they interact with these devices. Moreover, users also want to be able to seamlessly switch between devices and services any time and from anywhere and expect personalized and privacy-aware services. To address these challenges, we design and develop a cloud-based middleware (CMI) which helps to develop multi-modal interaction applications and easily integrate applications to AI services. In this middleware, services developed by different producers with different protocols and smart devices with different capabilities and protocols can be integrated easily. In CMI, applications stream data from devices to cloud services for processing and consume the results. It supports data streaming from multiple devices to multiple services (and vice versa). CMI provides an integration framework for decoupling the services and devices and enabling application developers to concentrate on “interaction” instead of AI techniques. We provide simple examples to illustrate the conceptual ideas incorporated in CMI.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"20 1","pages":"455-481"},"PeriodicalIF":1.7,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89468371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.
{"title":"A low-cost air quality monitoring system based on Internet of Things for smart homes","authors":"Mehmet Taştan","doi":"10.3233/ais-210458","DOIUrl":"https://doi.org/10.3233/ais-210458","url":null,"abstract":"Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"137 1","pages":"351-374"},"PeriodicalIF":1.7,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86492226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
People around the world have experienced fundamental transformations during mass events. The Industrial Revolution, World War II, and the collapse of the Berlin Wall are some of the cases that have caused radical societal changes. COVID-19 has also been a process of mass experiences regarding society. Determining the mass impact the pandemic has had on society shows that the pandemic is facilitating the transition to the so-called new normal. Istanbul is a multi-identity city where 16 million people have intensely experienced the pandemic’s impact. While determining the identities of cities in the world, one can see that different city structures provide different data sets. This study models a machine learning algorithm suitable for the data set we’ve determined for the 39 different districts of Istanbul and 82 different features of Istanbul. The aim of the study is to indicate the changing societal trends during the COVID-19 pandemic using machine learning techniques. Thus, this work contributes to the literature and real life in terms of redesigning cities for the post-COVID19 period. Another contribution of this study is that the proposed methodology provides clues on what people in cities consider important during a pandemic.
{"title":"Feature selection by machine learning models to identify the public's changing priorities during the COVID-19 pandemic","authors":"Kenan Mengüç, Nezir Aydin","doi":"10.3233/ais-220200","DOIUrl":"https://doi.org/10.3233/ais-220200","url":null,"abstract":"People around the world have experienced fundamental transformations during mass events. The Industrial Revolution, World War II, and the collapse of the Berlin Wall are some of the cases that have caused radical societal changes. COVID-19 has also been a process of mass experiences regarding society. Determining the mass impact the pandemic has had on society shows that the pandemic is facilitating the transition to the so-called new normal. Istanbul is a multi-identity city where 16 million people have intensely experienced the pandemic’s impact. While determining the identities of cities in the world, one can see that different city structures provide different data sets. This study models a machine learning algorithm suitable for the data set we’ve determined for the 39 different districts of Istanbul and 82 different features of Istanbul. The aim of the study is to indicate the changing societal trends during the COVID-19 pandemic using machine learning techniques. Thus, this work contributes to the literature and real life in terms of redesigning cities for the post-COVID19 period. Another contribution of this study is that the proposed methodology provides clues on what people in cities consider important during a pandemic.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"10 1","pages":"385-403"},"PeriodicalIF":1.7,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81757698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the social evolution, economic development, and continuously improved living standards, the dramatically increasing garbage produced by human beings has seriously affected our living environment. There are 3 main ways to dispose of garbage: sanitary landfill, incineration, or recycling. At present, a huge amount of labor resources is required for pre-sorting before garbage disposal, which greatly reduces efficiency, increases costs, and even leads to direct incineration without sorting. Hence, this study proposes a solution scenario of how to use object detection technology for garbage sorting. With the development of the deep learning theory, object detection technology has been widely used in all fields, thus, how to find target objects accurately and rapidly is one of the key technologies. This paper proposes a highly efficient garbage pick-up embedded system, where detection is optimized based on the Single Shot MultiBox Detector (SSD) neural network architecture and reduced model parameters. The experimental verification scenario was conducted in a dynamic environment integrating a robotic arm with a conveyor belt simulated by an electronic rotating turntable. The experimental results show that the modified model can accurately identify garbage types, with a significant speed of 27.8 FPS (Frames Per Second) on NVidia Jetson TX2, and an accuracy rate of approximately 87%.
{"title":"A highly efficient garbage pick-up embedded system based on improved SSD neural network using robotic arms","authors":"Shih-Hsiung Lee, Chien-Hui Yeh","doi":"10.3233/ais-210129","DOIUrl":"https://doi.org/10.3233/ais-210129","url":null,"abstract":"With the social evolution, economic development, and continuously improved living standards, the dramatically increasing garbage produced by human beings has seriously affected our living environment. There are 3 main ways to dispose of garbage: sanitary landfill, incineration, or recycling. At present, a huge amount of labor resources is required for pre-sorting before garbage disposal, which greatly reduces efficiency, increases costs, and even leads to direct incineration without sorting. Hence, this study proposes a solution scenario of how to use object detection technology for garbage sorting. With the development of the deep learning theory, object detection technology has been widely used in all fields, thus, how to find target objects accurately and rapidly is one of the key technologies. This paper proposes a highly efficient garbage pick-up embedded system, where detection is optimized based on the Single Shot MultiBox Detector (SSD) neural network architecture and reduced model parameters. The experimental verification scenario was conducted in a dynamic environment integrating a robotic arm with a conveyor belt simulated by an electronic rotating turntable. The experimental results show that the modified model can accurately identify garbage types, with a significant speed of 27.8 FPS (Frames Per Second) on NVidia Jetson TX2, and an accuracy rate of approximately 87%.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"46 1","pages":"405-421"},"PeriodicalIF":1.7,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77886985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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