• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.59-64
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• 2020

As the number of elderly people rapidly increases, needs of patient safety monitoring system also increases in indoor and outdoor medical facilities. With developing technologies related to sensors and information and communication technology, various dementia patient monitoring systems have been proposed. However, previous studies that depend on wearable devices provides limited functionalities. In this paper, we designed an integrated system that includes smart devices to monitor patient's status, user friendly UI/UX, and interaction with hospital information system. Medical teams and carers can receive satus of each patient in real-time and trace the location of dementia patients outdoor as well as indoor by using the proposed system.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.475-480
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• 2020

Underwater operating platforms face difficulties regarding power supply and communications. To overcome these difficulties, this study proposes a hybrid surface and underwater vehicle (HSUV) and presents the development of the platform, control algorithms, and results of field tests. The HSUV is capable of supplying reliable power to the unmanned underwater vehicle (UUV) and obtaining data in real time by using a tether cable between the UUV and the unmanned surface vehicle (USV). The HSUV uses global positioning system (GPS) and ultra-short base line sensors to determine the relative location of the UUV. Way point (WP) and dynamic positioning (DP) algorithms were developed to enable the HSUV to perform unmanned exploration. After reaching the target point using the WP algorithm, the DP algorithm enables USV to maintain position while withstanding environmental disturbances. To ensure the navigation performance at sea, performance tests of GPS, attitude/heading reference system, and side scan sonar were conducted. Based on these results, manual operation, WP, and DP tests were conducted at sea. WP and DP test results and side scan sonar images during the sea trials are presented.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.48-53
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• 2021

The purpose of this study is to establish a crime prevention system through the Crime Prevention Through Environmental Design (CPTED). The research method went through the double diamond process and discovered the user's needs through the persona analysis. The most representative features are the functions that informs users of the safe and optimal route, checks the presence of streetlights or cctvs in real time to update them, and allows people with similar routs to return home together. It is a function to help safe return home with the help of an autonomous method, and a self-defense function to protect themselves. Therefore, the application presented in this study was intended to be of great help when actually returning home by adding these new functions. In particular, we help users to return home most safely by recommending the best safe route. Through the persona analysis, research method which we had chosen, the needs of users were discovered and implemented in a design that reflected those needs and requirements.

• Design & Manufacturing
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• v.15 no.4
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• pp.51-56
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• 2021

Piercing is the process of shearing a circular hole in sheet metal, whose high shear force makes it difficult to secure the durability of tools. In addition, uneven clearance between tools due to poor alignment of the piercing punch causes accelerated die wear and breakage of the tool. This study reviewed the feasibility of in-situ determining alignment failure during the piercing process by analyzing the signal deviation of a bolt-type piezo sensor installed inside the tool whose alignment level was controlled. Finite element analysis was performed to select the optimal sensor location on the piercing tool for sensitive detection of process signals. A well-aligned piercing process results in uniform deformation in the circumferential direction, and shearing is completed at a stroke similar to the sheet thickness. Afterward, a sharp decrease in shear load is observed. The misaligned piecing punch leads to a gradual decrease in the load after the maximum shear load. This gradual decrease is due to the progressive shear deformation that proceeds in the circumferential direction after the initial crack occurs at the narrow clearance site. Therefore, analyzing the stroke at which the maximum shear load occurs and the load reduction rate after that could detect the misalignment of the piercing punch in real-time.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.57 no.4
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• pp.390-400
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• 2021

One of the problems with abalone farms is that they need to be checked frequently after feeding them or visited once or twice a day and that the amount of food intake constantly fluctuates due to changes in water temperature around the farm and typhoons. In addition, the condition of abalone is not constant as it is divided into places that eat well and do not eat well according to its location. In order to solve this problem, there is a method of measuring the amount of food intake by using a load cell that can measure even the smallest weight in an abalone farm. Through this study, we implemented a system capable of measuring the amount of abalone feed required for systematic management of abalone farms and real-time monitoring using mobile and client PCs.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.258-267
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• 2022

Weak over-the-horizon perception and blind spot are the main problems in intelligent connected vehicles (ICVs). In this paper, a V2V image transmission-based road condition warning method is proposed to solve them. The encoded road emergency images which are collected by the ICV are transmitted to the on-board unit (OBU) through Ethernet. The OBU broadcasts the fragmented image information including location and clock of the vehicle to other OBUs. To satisfy the channel quality of the V2X communication in different times, the optimal fragment length is selected by the OBU to process the image information. Then, according to the position and clock information of the remote vehicles, OBU of the receiver selects valid messages to decode the image information which will help the receiver to extend the perceptual field. The experimental results show that our method has an average packet loss rate of 0.5%. The transmission delay is about 51.59 ms in low-speed driving scenarios, which can provide drivers with timely and reliable warnings of the road conditions.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2873-2878
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• 2023

The purpose of this study is to perform radiation monitoring by acquiring gamma images and real-time optical images for ^99mTc vial source using charge couple device (CCD) cameras equipped with the proposed compact gamma camera. The compact gamma camera measures 86×65×78.5 mm³ and weighs 934 g. It is equipped with a metal 3D printed diverging collimator manufactured in a 45 field of view (FOV) to detect the location of the source. The circuit's system uses system-on-chip (SoC) and field-programmable-gate-array (FPGA) to establish a good connection between hardware and software. In detection modules, the photodetector (multi-pixel photon counters) is tiled at 8×8 to expand the activation area and improve sensitivity. The gadolinium aluminium gallium garnet (GAGG) measuring 0.5×0.5×3.5 mm³ was arranged in 38×38 arrays. Intrinsic and extrinsic performance tests such as energy spectrum, uniformity, and system sensitivity for other radioisotopes, and sensitivity evaluation at edges within FOV were conducted. The compact gamma camera can be mounted on unmanned equipment such as drones and robots that require miniaturization and light weight, so a wide range of applications in various fields are possible.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1170-1178
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• 2022

Indoor POI data, an essential component of indoor spatial data, has attribute information of a specific place in the room and is the most critical information necessary for the user. Currently, indoor POI data is manually updated by direct investigation, which is expensive and time-consuming. Recently, research on updating POI using the attribute information of indoor photographs has been advanced to overcome these problems. However, the range of use, such as using only photographs with text information, is limited. Therefore, in this study, and to improvement this, I proposed a new method to update indoor POI data using a smartphone camera. In the proposed method, the POI name is obtained by classifying the indoor scene's photograph into artificial intelligence technology CNN and matching the location criteria to indoor spatial data through AR technology. As a result of creating and experimenting with a prototype application to evaluate the proposed method, it was possible to update POI data that reflects the real world with high accuracy. Therefore, the results of this study can be used as a complement or substitute for the existing methodologies that have been advanced mainly by direct research.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.