• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.25-30
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• 2024

Smart City, a key application area of the AIoT (Artificial Intelligence of Things), provides various services in safety, security, and healthcare sectors through location tracking and location-based services. an IPS (Indoor Positioning System) is required to implement location-based services, and wireless communication technologies such as WiFi, UWB (Ultra-wideband), and BLE (Bluetooth Low Energy) are being applied. BLE, which enables data transmission and reception with low power consumption, can be applied to various IoT devices such as sensors and beacons at a low cost, making it one of the most suitable wireless communication technologies for indoor positioning. BLE utilizes the RSSI (Received Signal Strength Indicator) to estimate the distance, but due to the influence of multipath fading, which causes variations in signal strength, it results in an error of several meters. In this paper, we conducted research on a path loss model that can be applied to BLE IPS for proximity services, and confirmed that optimizing the free space propagation loss coefficient can reduce the distance error between the Tx and Rx devices.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1289-1297
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• 2023

In this study, NF Complex of 0%, 0.5%, 2.5%, 12.5%, and 100% was prepared by complexing and extracting Indian gooseberry, Rosa multiflora Thunberg roots, and saw palmetto fruit in a ratio of 5:1:1. The manufactured NF Complex was applied to bleached sample hair and then compared and analyzed with damaged hair. To confirm the improvement effect, tensile strength, gloss, absorbance, and brightness were measured. As a result of the measurement, the tensile strength increased. The gloss content decreased by 100%, but the remaining content increased. The change in absorbance was minimal. There was also a change in brightness, but it was minimal. It was confirmed that there is a significant difference in the average value of NF Complex, and it is judged necessary to study various ratios in the future.

• Journal of KIBIM
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• v.14 no.2
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• pp.25-34
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• 2024

This study proposes a new methodology to increase the flexibility and efficiency of the design data evaluation system by combining Building Information Modeling (BIM) technology in the architectural industry, OpenAI's interactive artificial intelligence, and ChatGPT. BIM technology plays an important role in digitally modeling and managing architectural information. Since architectural information is included, research and development are underway to review and evaluate BIM data according to conditions through program development. However, in the process of reviewing BIM design data, if the review criteria or evaluation criteria according to design change occur frequently, it is necessary to update the program anew. In order for designers or reviewers to apply the changed criteria, requesting a program developer will delay time. This problem was studied by using ChatGPT to modify and update the design data evaluation program code in real time. In this study, it is aimed to improve the changing standards and accuracy by enabling programming non-professionals to change the design regulations and calculation standards of the BIM evaluation program system using ChatGPT. In this study, in the BIM-based design certification automation evaluation program, a program in which the automation evaluation method is being studied based on the design certification evaluation manual was first used. In the design certification automation evaluation program, the programming non-majors checked the automation evaluation code by linking ChatGPT, and the changed calculation criteria were created and modified interactively. As a result of the evaluation, the change in the calculation standard was explained to ChatGPT and the applied result was confirmed.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.201-209
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• 2024

This study investigates the operational facets of low-density urban air mobility (UAM) from an airline's perspective amid burgeoning concerns about urban congestion in megacities. UAM, employing electric vertical takeoff and landing (eVTOL) technology, emerges as a potential remedy to the challenges of traffic gridlock and environmental degradation. As the UAM market progresses from initial stages to maturity, tailored traffic control systems become paramount. Focused on the context of low-density environments during UAM's inception, this research scrutinizes operational frameworks, essential infrastructure, and likely scenarios. It aims to bolster the safety and efficiency of UAM operations by delving into the specifics of traffic control concepts designed for these unique settings. The study seeks to significantly contribute to optimizing UAM's initial phases, providing insights into crucial operational dynamics for a smoother integration of urban air mobility into contemporary urban landscapes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.237-244
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• 2024

Pavement management systems (PMS) provide procedures to quantify road serviceability based on pavement conditions such as cracks and plastic deformation and suggest proper maintenance methods. The deterioration of the road pavement is relevant to the time although the quantifications on road serviceability in PMS present road surface conditions at the evaluation. More systematic evaluation on road serviceability may need time-dependent factors of road environments and that can improve PMS. Rainfall, temperature and vehicle loads can be environmental factors for road serviceability evaluation. As no data are avablie that can link between road conditions and environmental road factors, we conducted experiments to suggest economical devices and methods to obtain relevant data. We used temperature sensors and accelerometers with Arduino to measure road surface temperature and dynamic loads and provide data to improve pavement serviceability evaluation.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.113-126
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• 2024

Shotcrete lining is an important material for the stability of tunnels in NATM tunnels. However, stressmeters for stress measurements of shotcrete lining are installed in the field without performance verification because of a lack of research on methods, procedures, regulations, and reliability of measurement equipment. To solve this problem, all shotcrete stressmeters currently used in Korea were investigated. For each stressmeter, external inspection and structural and functional inspection were performed to identify defects and problems in devices. For this purpose, a shotcrete stressmeter performance test device capable of load loading in stages was developed and obtained KOLAS certification. Using the device, stressmeter performance tests were conducted. Structural problems of integrated- and cell-type shotcrete stressmeters were identified through concrete mold tests, and improvement plans and performance verification procedures were suggested. The results of this study are expected to contribute to the preparation of regulations for the performance verification of shotcrete stressmeters and the selection of measuring instruments in the field in the future.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Korean Chemical Engineering Research
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• v.56 no.4
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• pp.479-495
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• 2018

This study was carried out to research the multimedia fate modeling, concentration distribution and impact assessment of polycyclic aromatic hydrocarbons (PAHs) emitted from automobiles, which are known as carcinogenic and mutation chemicals. The amount of emissions of PAHs was determined based on the census data of automobiles at a target S-city and emission factors of PAHs, where multimedia fugacity modeling was conducted by the restriction of PAHs transfer between air-soil at the impervious area. PAHs' Concentrations and their distributions at several environmental media were predicted by multimedia fugacity model (level III). The residual amounts and the distributions of PAHs through mass transfer of PAHs between environment media were used to assess the health risk of PAHs at unsteady state (level IV), where the sensitivity analyses of the model parameter of each variable were conducted based on Monte Carlo simulation. The experimental result at S-city showed that Fluoranthene among PAHs substances are the highest residual concentrations (60%, 53%, 32% and 34%) at all mediums (atmospheric, water, soil, sediment), respectively, where most of the PAHs were highly accumulated in the sediment media (more than 80%). A result of PAHs concentration changes in S-city over the past 34 years identified that PAHs emissions from all environmental media increased from 1983 to 2005 and decreased until 2016, where the emission of heavy-duty vehicle including truck revealed the largest contribution to the automotive emissions of PAHs at all environment media. The PAHs concentrations in soil and water for the last 34 years showed the less value than the legal standards of PAHs, but the PAHs in air exceeded the air quality standards from 1996 to 2016. The result of this study is expected to contribute the effective management and monitoring of toxic chemicals of PAHs at various environment media of Metropolitan city.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.325-345
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• 2020

Determination of Best available technology (BAT) was suggested to reduce volatile organic compounds (VOCs) in a petrochemical industrial complex, by conducting human health risk, environmental, and economic assessment based on multimedia fugacity model. Fate and distribution of benzene, toluene, ethylbenzene, and xylene (BTEX) was predicted by the multimedia fugacity model, which represent VOCs emitted from the industrial complex in U-city. Media-integrated human health risk assessment and sensitivity analysis were conducted to predict the human health risk of BTEX and identify the critical variable which has adverse effects on human health. Besides, the environmental and economic assessment was conducted to determine the BAT for VOCs reduction. It is concluded that BTEX highly remained in soil media (60%, 61%, 64% and 63%), and xylene has remained as the highest proportion of BTEX in each environment media. From the candidates of BAT, the absorption was excluded due to its high human health risk. Moreover, it is identified that the half-life and exposure coefficient of each exposure route are highly correlated with human health risk by sensitivity analysis. In last, considering environmental and economic assessment, the regenerative thermal oxidation, the regenerative catalytic oxidation, the bio-filtration, the UV oxidation, and the activated carbon adsorption were determined as BAT for reducing VOCs in the petrochemical industrial complex. The suggested BAT determination methodology based on the media-integrated approach can contribute to the application of BAT into the workplace to efficiently manage the discharge facilities and operate an integrated environmental management system.