2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)最新文献_第3页

Bio-Floc Monitoring and Automatic Controlling System using IoT 基于物联网的生物絮团监测与自动控制系统

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628543

Kushik Kumar Saha, Ashraf Islam, Sakib Shahriar Joy, Ishmam Writwik, Kawshik Shikder

引用次数: 1

System configuration of Human-in-the-loop Simulation for Level 3 Autonomous Vehicle using IPG CarMaker 基于IPG的3级自动驾驶汽车人在环仿真系统配置

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628587

Cheok Jun Hong, V. R. Aparow

{"title":"System configuration of Human-in-the-loop Simulation for Level 3 Autonomous Vehicle using IPG CarMaker","authors":"Cheok Jun Hong, V. R. Aparow","doi":"10.1109/IoTaIS53735.2021.9628587","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628587","url":null,"abstract":"The increasingly automated vehicles (AV) have increased the complexity of the testing methods and number of driven miles required to demonstrate the vehicle system’s reliability. Most modern autonomous driving systems also used deep neural networks which requires a large amount of data to develop. Physical driving alone to collect driving data and test system’s safety is no longer suitable for development of AV as this is costly, time consuming and could harm the road users if the safety system failed. This paper proposed human-in-the-loop simulation testing for evaluation of an autonomous vehicle using a 3D virtual vehicle driving platform that can be used for safety assessment of autonomous vehicle. The aim of this study is to establish human-computer interaction platform that can be used as safety testing for Level 3 autonomous vehicle whereby an emergency takeover is required during critical driving conditions. The proposed platform make use of IPG CarMaker to provide 3D virtual environment with accurate vehicle dynamics model, sensor model and environment model. We are able to interface the IPG CarMaker with Simulink and successfully developed a Simulink model that can interface a steering and pedal driving hardware with the virtual vehicle in the simulation. We can also collect driving data and simulation data from the IPG CarMaker as well as accessing the variable in the IPG CarMaker in real-time using Python. The recorded data can be used to train and fine-tune autonomous system based on machine learning.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121202723","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}

引用次数: 7

Ubiquitous System Integration as a Service in Smart Factories 智能工厂中的泛在系统集成服务

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628434

M. Soderi, Vignesh Kamath, J. Morgan, J. Breslin

{"title":"Ubiquitous System Integration as a Service in Smart Factories","authors":"M. Soderi, Vignesh Kamath, J. Morgan, J. Breslin","doi":"10.1109/IoTaIS53735.2021.9628434","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628434","url":null,"abstract":"The state-of-the-art in manufacturing technology identifies a data rich, horizontally and vertically integrated environment, with collaborative control and intelligence systems, to enable what is called a smart factory. Similar to digital factories, smart factories signify a greater digital convergence between manufacturing Operational Technology (OT), and wider enterprise Integrated Technology (IT). With these new capabilities comes new challenges in scale, complexity, and the skills needed to enable ubiquitous system integration. While in the past, engineers have utilized a variety of proprietary graphical configuration and programming tools to intuitively create simple to complex manufacturing systems, presently, the standard technology ecosystem which engineers design, control, and maintain, is observably expanding, and/or evolving to incorporate more open-source technologies, network services, and distributed devices. Therefore, this paper will examine the emerging next generation technologies and tools which will support engineers in designing and interacting with the new digital manufacturing ecosystem in smart factories. To achieve this: a state-of-the-art survey of commercial Industrial Internet of Things (IIoT) technology is presented; three key ubiquitous technology trends are examined; and a use-case is presented with a universal framework. Uniquely, this framework demonstrates the collective capabilities of the technologies to overcome ubiquitous system integration problems.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116251753","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}

引用次数: 6

Code Saga – A Mobile Serious Game For Learning Programming Code Saga -一个学习编程的手机严肃游戏

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628484

Heeya Tacouri, L. Nagowah

{"title":"Code Saga – A Mobile Serious Game For Learning Programming","authors":"Heeya Tacouri, L. Nagowah","doi":"10.1109/IoTaIS53735.2021.9628484","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628484","url":null,"abstract":"During this global pandemic of coronavirus, the amount of self-learning done by students has considerably increased. They cannot only rely on online classes to study as students easily lose interest in these online classes. A number of studies have shown that undergraduate students have problems grasping the introductory chapters of programming. If they cannot even understand the basics, then they might not be able to comprehend the more complex chapters. Tutors have to find a means to match their teaching methodology to the students’ interest so that the latter can be as engaged as possible. This brought about a need to devise a new method of learning for IT-related undergraduate courses. It is a well-known fact that many students show a keen interest in video games. Using video games for teaching can be an interesting way to encourage self-learning of students. The concept of integrating gameplay with educational contents is known as serious games. In this paper, a mobile serious game, Code Saga, is being proposed. The primary aim of Code Saga is to smooth out the learning process of students opting for IT courses at undergraduate level. The game has been design to cover key chapters on Programming based on the latest IEEE/ACM curriculum guidelines for undergraduate degree programs in Software Engineering. A combination of mixed gaming-approaches has been used to make the game as entertaining and educational as possible to the undergraduate students. The game includes fun assessment activities to assess the players’ understanding on various programming concepts. The game also consists of a scoring mechanism, which a tutor can use to track students’ strengths and weaknesses on specific topics. Code Saga has been tested by a group of students at the University of Mauritius and the results confirmed that most students prefer to learn programming using serious games compared to the traditional learning methods.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124729564","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}

引用次数: 1

Electroencephalogram-Based User Authentication System for Bank Vault Security 基于脑电图的银行金库安全用户认证系统

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628779

Melchizedek I. Alipio

引用次数: 0

Optimizing Deep Convolutional Neural Network With Fine-Tuning and Data Augmentation For Covid-19 Prediction 基于微调和数据增强的深度卷积神经网络优化新冠肺炎预测

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628799

A. Syarif, Novi Azman, E. Sinaga

{"title":"Optimizing Deep Convolutional Neural Network With Fine-Tuning and Data Augmentation For Covid-19 Prediction","authors":"A. Syarif, Novi Azman, E. Sinaga","doi":"10.1109/IoTaIS53735.2021.9628799","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628799","url":null,"abstract":"Since Corona virus disease 2019 (Covid-19) has been infecting people worldwide, it is important to detect Covid-19 at an earlier phase to fight against the pandemic. Pathogenic and laboratory testing are needed to determine whether someone is infected or not by Covid-19. However, this laboratory test is relatively time consuming and could produce significant false negative rates. This paper presents a study on Covid-19 detection by using deep learning algorithms aiming to predict and detect Covid-19. A set of chest X-ray images are used as the input datasets to prepare and to train the proposed model. In this study, a deep learning architecture (DLA) and optimisation strategies have been proposed and investigated to maintain the automated Covid-19 detection. A platform and a model model based on convolutional neural network (CNN) is introduced to extract the feature of X-ray images for feature learning phase in order to make the model suitable for the problem. Two strategies are applied to improve the performance of proposed model, i.e. Data augmentation and fine-tuning with deep-feature-based. A classifier are employed in order to enhance the performance of model. The experimental investigation was performed between the proposed work with the pre-trained DLAs, such as VGG16 and ResNet50. The results of this study affirm that the proposed model and VGG16 obtain better classification accuracy of 98% and 95% of sensitivity respectively.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116682366","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}

引用次数: 0

Cost-Effective Mission Assurance Engineering Through Simulation 通过仿真的成本效益任务保证工程

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628592

K. Siil, A. Rubin, M. Elder, A. Dahbura, M. Green, Lanier A Watkins

{"title":"Cost-Effective Mission Assurance Engineering Through Simulation","authors":"K. Siil, A. Rubin, M. Elder, A. Dahbura, M. Green, Lanier A Watkins","doi":"10.1109/IoTaIS53735.2021.9628592","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628592","url":null,"abstract":"Cyber-physical systems have witnessed fantastic leaps in their capabilities, thanks to advances in artificial intelligence and machine learning. With these great capabilities, however, should come great assurance that they will behave as expected. For example, an autonomous vehicle (AV) must protect passengers, bystanders, property and itself. Safety alone is insufficient, however. The AV is built for a mission, and mission assurance must also be addressed, i.e., getting the AV’s job done despite foreseen and unforeseen circumstances. Mission assurance should begin as far left in the engineering lifecycle as possible, ideally before the first vehicle is assembled. If the many hours of operational experience that familiarize system builders and operators with the vehicle’s performance and potential risky behaviors could be accrued through simulation, rather than expensive prototypes, a better vehicle can be developed at significantly less cost. The purpose of this paper is to demonstrate the value of cost-effective open-source based simulation in exercising and analyzing AV algorithms. Our results with DESCRETE, a testbed we developed for engineering mission assurance in the maritime domain, show that sufficient fidelity can be realized practically in the lab for unforeseen, but realistic, situations to arise and be examined in a more controlled and less costly environment. Collision avoidance algorithms, for example, must consider complex interactions between multiple vehicles, trading off safety for mission efficiency. Our experimental results demonstrate the interplay between these two competing goals, and help inform what to deem appropriately safe by both eliminating the obviously unsafe situations and identifying what might be too safe, which necessitates either accepting some risks or changing the mission to avoid them.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114383105","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}

引用次数: 0

Development of Smart Indoor Workplace System Using Decision Tree Algorithm 基于决策树算法的室内智能办公系统开发

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628852

Melchizedek I. Alipio

{"title":"Development of Smart Indoor Workplace System Using Decision Tree Algorithm","authors":"Melchizedek I. Alipio","doi":"10.1109/IoTaIS53735.2021.9628852","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628852","url":null,"abstract":"Commonly controlled factors in smart buildings are heating, ventilation, air conditioning and lighting mainly due to optimization requirements. Controlling the physical environment is a dominant factor not only in smart buildings, but also in worker’s productivity. This work aims to develop a system that correlates the occupant work efficiency and the physical environment using machine learning. The system consists of a hardware system for data gathering and a data processing unit which help create and employ a prediction model that correlates the work efficiency of an occupant and the air conditioning and luminance levels in an indoor workplace. The input of the system monitors the ambient lighting, temperature conditions, along with the sitting behavior of the occupants in the workplace and typing job. From the gathered dataset, a machine learning model is produced using decision tree algorithm. All sensor data and predicted outputs are sent to a cloud server and can be accessed remotely through a web interface. Results shows that the prediction model achieved an area under the receiver operating characteristic curve of 0.89 for air conditioning setting and 0.50 for the light setting which shows good and random prediction performance, respectively.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129977718","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}

引用次数: 0

A Systematic Comparison of Traditional and Hybrid AI Models for NHANES Big Data Analytics NHANES大数据分析中传统与混合AI模型的系统比较

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628435

Komal Barge, Nidhi Patel, M. Fouda, Z. Fadlullah

{"title":"A Systematic Comparison of Traditional and Hybrid AI Models for NHANES Big Data Analytics","authors":"Komal Barge, Nidhi Patel, M. Fouda, Z. Fadlullah","doi":"10.1109/IoTaIS53735.2021.9628435","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628435","url":null,"abstract":"The National Health and Nutrition Examination Survey (NHANES) is a big health dataset, which has recently raised much research interest to use data mining and analytics techniques to examine the prevalence and risks of chronic diseases related to sedentary lifestyle, inadequate dietary, nutritional and behavioral habits, household environment, whole body measurement, bone measurements, and so forth. In this paper, we carry out a systematic investigation of comparative analytics based on several traditional machine learning techniques and hybrid AI models to estimate the association among all the features of the 2017-2018 NHANES dataset and classify hypertension diseased participants as a use-case. Based on the use-case, our research work utilizes a highly imbalanced dataset of 8,366 people with 81.20% participants with no hypertension and 18.80% participants with hypertension. Empirical results demonstrate that our proposed AI model, with an accuracy of 94%, significantly outperforms the other machine learning techniques and two hybrid model variants in identifying patients with a risk of having hypertension by considering all the health conditions.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129842971","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}

引用次数: 0

Behavioral Disorder Test to Identify Attention-Deficit / Hyperactivity Disorder (ADHD) in Children Using Fuzzy Algorithm 使用模糊算法识别儿童注意缺陷/多动障碍(ADHD)的行为障碍测试

2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2021-11-23 DOI: 10.1109/IoTaIS53735.2021.9628642

Nola Ristiyanti, B. Dirgantoro, C. Setianingsih

{"title":"Behavioral Disorder Test to Identify Attention-Deficit / Hyperactivity Disorder (ADHD) in Children Using Fuzzy Algorithm","authors":"Nola Ristiyanti, B. Dirgantoro, C. Setianingsih","doi":"10.1109/IoTaIS53735.2021.9628642","DOIUrl":"https://doi.org/10.1109/IoTaIS53735.2021.9628642","url":null,"abstract":"Attention-Deficit / Hyperactivity Disorder (ADHD) is the most common behavioral disorder in children. The main symptoms of ADHD in children are characterized by difficulty paying attention, and impulsive and hyperactive behavior that interferes with function or development. So that children will have difficulty when in various settings such as school, home, or with their peers. In this study, a behavioral disorder test system was built to identify the disorder. In other words, this behavioral disorder test can be used as a screening tool to help parents in early identification of ADHD in their children. The identification of ADHD is carried out by utilizing a fuzzy algorithm that can make decisions based on symptoms. More precisely, decision making using the fuzzy inference system will provide the most dominant type of ADHD suffered by children. In addition, its own decision-making system which is similar to the human reasoning system can handle uncertainty accuracy. Based on the test results of the ADHD identification system that was built, the accuracy value was 100%. Based on the results of this study, the authors provide suggestions for the development of further research by using other fuzzy inference systems such as Tsukamoto and Sugeno or using other algorithms to compare the accuracy of the ADHD identification system in children.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124110039","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}

引用次数: 2