• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.4
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• pp.53-58
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• 2021

Based on surrogate model, a hydrocarbon mixture was analyzed by visualizing the impinging break up mechanism in subcritical and supercritical conditions. Decane and methylcyclohexane with different critical pressures and temperatures were selected as experimental fluids. The impinging injector was installed inside the chamber, and the spray was visualized through a speed camera in subcritical and supercritical conditions. The injection condition of the mixture and chamber was kept constant at Pr(P/P_c) = 1, and Tr(T/T_c) was increased from 0.48 to 1.02. As Tr increased, the spray angle increased, and the sheet length decreased as the properties of the mixture reached each critical point. In addition, when the mixture approached the near critical point, it was shown that the change in density gradient was largely observed out of the impinging break up mechanism.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.313-330
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• 2021

In an autonomous driving system equipped with a camera and lidar, depth completion techniques enable dense depth estimation. In particular, using self-supervised learning it is possible to train the depth completion network even without ground truth. In actual autonomous driving, such depth completion should have very short latency as it is the input of other algorithms. So, rather than complicate the network structure to increase the accuracy like previous studies, this paper focuses on network latency. We design a U-Net type network with RegNet encoders optimized for GPU computation. Instead, this paper presents several techniques that can increase accuracy during the process of self-supervised learning. The proposed techniques increase the robustness to unreliable lidar inputs. Also, they improve the depth quality for edge and sky regions based on the semantic information extracted in advance. Our experiments confirm that our model is very lightweight (2.42 ms at 1280x480) but resistant to noise and has qualities close to the latest studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• Journal of Korea Trade
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• v.23 no.8
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• pp.89-109
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• 2019

Purpose - We perform an empirical analysis of the effects of global consumer culture positioning (GCCP) in TV advertisements on consumer's brand evaluations (perceived quality, perceived price, and brand prestige) and attitude toward brand. Also, we analyze the moderating roles of consumer characteristics (ethnocentrism and level of product knowledge) in those effects. Design/methodology - This research is based on a survey of 210 randomly-selected university students in Seoul, Korea. The participants in the survey were shown a total of 8 TV advertisements of consumer goods of nondurable goods (fast food and carbonated drinks), and durable goods (sports shoes and digital camera), which included two advertisements for each product where one uses GCCP strategy while another uses LCCP strategy. We estimate the structural model using the AMOS 18.0 computer program. Findings - We find that GCCP has more positive effects on consumers' brand evaluations and attitude toward brand than LCCP in TV advertising. We also find that GCCP has stronger effects on brand evaluation and attitude toward brand in consumers with weak ethnocentrism and in those with a low level of product knowledge. Practical implications - Using GCCP in an advertisement is an effective way of improving consumer's evaluation of the brand and attitude toward the brand mainly when cosmopolitan consumers and consumers with low knowledge levels are segmented as targets. Originality/value - The study contributes to identify how and for what consumer groups' global brand positioning strategies in TV advertisements affect consumers' brand evaluations and their attitudes toward brands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4292-4307
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• 2021

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

• Composites Research
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• v.35 no.2
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• pp.61-68
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• 2022

Composite materials have been used in aerospace industry and many applications because of many advantages such as specific strength and stiffness and corrosion resistance etc. However, it is vulnerable to impacts, these impact lead to formation of cracks in composite laminate and failure of structures. In this paper, we analyzed Mode I fracture toughness of Carbon/Epoxy laminates using acoustic emission signal. DCB test was carried out to analyze Mode I failure characterization of Carbon/Epoxy laminates, and AE sensor was attached to measure AE signal induced by failure of specimen. Fracture toughness was calculated using cumulative AE energy and measured crack length using camera. The calculated fracture toughness was applied in FE model and the result of FE analysis compared with DCB test results. The results show good agreement with between FEM and DCB test results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1890-1897
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• 2021

People counting is an important technology to provide application services such as smart home, smart building, smart car, etc. Due to the social distancing of COVID-19, the people counting technology attracted public attention. People counting system can be implemented in various ways such as camera, sensor, wireless, etc. according to service requirements. People counting system using WiFi AP uses WiFi CSI data that reflects multipath information. This technology is an effective solution implementing indoor with low cost. The conventional WiFi CSI-based people counting technologies have low accuracy that obstructs the high quality service. This paper proposes a deep learning people counting system based on WiFi CSI data. Data preprocessing using auto-encoder, data augmentation that transform WiFi CSI data, and a proposed deep learning model improve the accuracy of people counting. In the experimental result, the proposed approach shows 89.29% accuracy in 6 subjects.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.1-13
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• 2022

Household refrigerator cabinets must undergo cyclic testing at -20 ℃ and 65 ℃ for quality control (QC) after their production is complete. These cabinets were assembled from different materials, including acrylonitrile butadiene styrene (ABS), polyurethane (PU) foam, and steel plates. However, different thermal expansion values could be observed owing to differences in the mechanical properties of the materials. In this study, a technique to predict delamination on a refrigerator wall caused by thermal deformation was developed. The mechanical properties of ABS and PU foams were tested, theload factors causing delamination were analyzed, delamination was observed using a high-speed camera, and comparison and verification in terms of stress and strain were performed using a finite element model (FEM). The results indicated that the delamination phenomenon of a refrigerator wall can be defined in two cases. A method for predicting and evaluating delamination was established and applied in an actual refrigerator. To determine the effect of temperature changes on the refrigerator, strain measurements were performed at the weak point and the stress was calculated. The results showed that the proposed FEM prediction technique can be used as a basis for virtual testing to replace future QC testing, thus saving time and cost.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.83-95
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• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.