Browsing by Author "Kang, Kyubyung"

Now showing 1 - 8 of 8
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    Building a Private LoRaWAN Platform
    (BEIESP, 2019) Lee, John J.; Souryal, Youssef; Tam, Darren; Kim, Dongsoo; Kang, Kyubyung; Koo, Dan D.; Electrical and Computer Engineering, School of Engineering and Technology
    LoRaWAN technology has been here for several years as one of LPWAN technologies. It consists of various components such as end nodes, a gateway, a network server, and an application server at the minimum. The servers have been exclusive products of commercial companies, and not many experimental or academic ones are available. Recently one such software has been developed. However, few fully functional academic ones have been reported. In this study, we implement a fully functional private independent LoRaWAN platform for the academic research of LPWAN Internet of Things (IoT) and demonstrate that our platform can support not only end-to-end LoRaWAN communication but also graphical user interface on an embedded and limited computing power system.
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    Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach
    (MDPI, 2020-11) Kang, Kyubyung; Chen, Donghui; Peng, Cheng; Koo, Dan; Kang, Taewook; Kim, Jonghoon; Computer and Information Science, School of Science
    Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.
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    Energy-Efficient Device Selection in Federated Edge Learning
    (IEEE, 2021-07) Peng, Cheng; Hu, Qin; Chen, Jianan; Kang, Kyubyung; Li, Feng; Zou, Xukai; Computer and Information Science, School of Science
    Due to the increasing demand from mobile devices for the real-time response of cloud computing services, federated edge learning (FEL) emerges as a new computing paradigm, which utilizes edge devices to achieve efficient machine learning while protecting their data privacy. Implementing efficient FEL suffers from the challenges of devices’ limited computing and communication resources, as well as unevenly distributed datasets, which inspires several existing research focusing on device selection to optimize time consumption and data diversity. However, these studies fail to consider the energy consumption of edge devices given their limited power supply, which can seriously affect the cost-efficiency of FEL with unexpected device dropouts. To fill this gap, we propose a device selection model capturing both energy consumption and data diversity optimization, under the constraints of time consumption and training data amount. Then we solve the optimization problem by reformulating the original model and designing a novel algorithm, named E2DS, to reduce the time complexity greatly. By comparing with two classical FEL schemes, we validate the superiority of our proposed device selection mechanism for FEL with extensive experimental results.
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    Fuzzy Controller Algorithm for Automated HVAC Control
    (IAARC, 2020-10) Chae, Myungjin; Kang, Kyubyung; Koo, Dan D.; Oh, Sukjoon; Chun, Jae Youl; Engineering Technology, School of Engineering and Technology
    This research presents the design framework of the artificial intelligent algorithm for an automated building management system. The AI system uses wireless sensor data or IoT (Internet of Things) and user's feedback together. The wireless sensors collect data such as temperature (indoor and outdoor), humidity, light, user occupancy of the facility, and Volatile Organic Compounds (VOC) which is known as the source of the Sick Building Syndrome (SBS) or New Building Syndrome because VOC are often found in new buildings or old buildings with new interior improvement and they can be controlled and reduced by appropriate ventilation efforts. The collected data using wireless sensors are post-processed to be used in the neural network, which is trained in accordance with the collected data pattern. When the users of the facility have the control of the building's ventilation system and the AI system is fully trained using the user input, it will mimic the user's pattern and control the building system automatically just as the user wants. In this research, data were collected from 4 different buildings: university library, university cafeteria, a local coffee shop, and a residential house. Fuzzy logic controller is also developed for better performance of the HVAC. Indoor air quality, temperature (indoor and outdoor), HVAC fan speed and heater power are used for fuzzified output. As a result, the framework and simulation model for the energy efficient AI controller has been developed using fuzzy logic controller and the neural network-based energy usage prediction model.
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    Intelligent Device Selection in Federated Edge Learning with Energy Efficiency
    (2021-12) Peng, Cheng; Hu, Qin; Kang, Kyubyung; Zou, Xukai
    Due to the increasing demand from mobile devices for the real-time response of cloud computing services, federated edge learning (FEL) emerges as a new computing paradigm, which utilizes edge devices to achieve efficient machine learning while protecting their data privacy. Implementing efficient FEL suffers from the challenges of devices' limited computing and communication resources, as well as unevenly distributed datasets, which inspires several existing research focusing on device selection to optimize time consumption and data diversity. However, these studies fail to consider the energy consumption of edge devices given their limited power supply, which can seriously affect the cost-efficiency of FEL with unexpected device dropouts. To fill this gap, we propose a device selection model capturing both energy consumption and data diversity optimization, under the constraints of time consumption and training data amount. Then we solve the optimization problem by reformulating the original model and designing a novel algorithm, named E2DS, to reduce the time complexity greatly. By comparing with two classical FEL schemes, we validate the superiority of our proposed device selection mechanism for FEL with extensive experimental results. Furthermore, for each device in a real FEL environment, it is the fact that multiple tasks will occupy the CPU at the same time, so the frequency of the CPU used for training fluctuates all the time, which may lead to large errors in computing energy consumption. To solve this problem, we deploy reinforcement learning to learn the frequency so as to approach real value. And compared to increasing data diversity, we consider a more direct way to improve the convergence speed using loss values. Then we formulate the optimization problem that minimizes the energy consumption and maximizes the loss values to select the appropriate set of devices. After reformulating the problem, we design a new algorithm FCE2DS as the solution to have better performance on convergence speed and accuracy. Finally, we compare the performance of this proposed scheme with the previous scheme and the traditional scheme to verify the improvement of the proposed scheme in multiple aspects.
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    Public Participation Consortium Blockchain for Smart City Governance
    (IEEE Xplore, 2022) Bai, Yuhao; Hu, Qin; Seo, Seung-Hyun; Kang, Kyubyung; Lee, John J.; Computer and Information Science, School of Science
    Smart cities have become a trend with improved efficiency, resilience, and sustainability, providing citizens with high quality of life. With the increasing demand for a more participatory and bottom–up governance approach, citizens play an active role in the process of policy making, revolutionizing the management of smart cities. In the example of urban infrastructure maintenance, the public participation demand is more remarkable as the infrastructure condition is closely related to their daily life. Although blockchain has been widely explored to benefit data collection and processing in smart city governance, public engagement remains a challenge. In this article, we propose a novel public participation consortium blockchain system for infrastructure maintenance that is expected to encourage citizens to actively participate in the decision-making process and enable them to witness all administrative procedures in a real-time manner. To that aim, we introduced a hybrid blockchain architecture to involve a verifier group, which is randomly and dynamically selected from the public citizens, to verify the transaction. In particular, we devised a private-prior peer-prediction-based truthful verification mechanism to tackle the collusion attacks from public verifiers. Then, we specified a Stackelberg-game-based incentive mechanism for encouraging public participation. Finally, we conducted extensive simulations to reveal the properties and performances of our proposed blockchain system, which indicates its superiority over other variations.
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    Rule-Based Scan-to-BIM Mapping Pipeline in the Plumbing System
    (MDPI, 2020-11) Kang, Taewook; Patil, Shashidhar; Kang, Kyubyung; Koo, Dan; Kim, Jonghoon; Engineering Technology, School of Engineering and Technology
    The number of scan-to-BIM projects that convert scanned data into Building Information Modeling (BIM) for facility management applications in the Mechanical, Electrical and Plumbing (MEP) fields has been increasing. This conversion features an application purpose-oriented process, so the Scan-to-BIM work parameters to be applied vary in each project. Inevitably, a modeler manually adjusts the BIM modeling parameters according to the application purpose, and repeats the Scan-to-BIM process until the desired result is achieved. This repetitive manual process has adverse consequences for project productivity and quality. If the Scan-to-BIM process can be formalized based on predefined rules, the repetitive process in various cases can be automated by re-adjusting only the parameters. In addition, the predefined rule-based Scan-to-BIM pipeline can be stored and reused as a library. This study proposes a rule-based Scan-to-BIM Mapping Pipeline to support application-oriented Scan-to-BIM process automation, variability and reusability. The application target of the proposed pipeline method is the plumbing system that occupies a large number of MEPs. The proposed method was implemented using an automatic generation algorithm, and its effectiveness was verified.
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    Temporary Traffic Control Device Detection for Road Construction Projects Using Deep Learning Application
    (ASCE, 2022-03-07) Seo, Sungchul; Chen, Donghui; Kim, Kwangcheol; Kang, Kyubyung; Koo, Dan; Chae, Myungjin; Park, Hyung Keun; Mechanical and Energy Engineering, School of Engineering and Technology
    Traffic control devices in road construction zones play important roles, which (1) provide critical traffic-related information for the drivers, (2) prevent potential crashes near work zones, and (3) protect work crews’ safety. Due to the number of devices in each site, transportation agencies have faced challenges in timely and frequently inspecting traffic control devices, including temporary devices. Deep learning applications can support these inspection processes. The first step of the inspection using deep learning is recognizing traffic control devices in the work zone. This study collected road images using vehicle-mounted cameras from various illuminance and weather conditions. Then, the study (1) labeled eight classes of temporary traffic control devices (TTCDs), (2) modified and trained a machine-learning model using the YOLOv3 algorithm, and (3) tested the detection outcomes of various TTCDs. The key finding shows that the proposed model recognized more than 98% of the temporary traffic signs correctly and approximately 81% of temporary traffic control devices correctly. The construction barricade had the lowest mean Average Precision (50%) out of eight classes. The outcomes can be used as the first step of autonomous safety inspections for road construction projects.