• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.254-260
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• 2019

Recently, a self-camera that generates and distributes a large amount of moving images has been rapidly increasing due to the appearance of SNS such as Facebook, Instagram, and Tweet using mobile. In particular, the amount of SNS connections using mobile phones is significantly increasing in terms of usage, number of connections, and usage time. However, the use of a self-recording system using a smartphone by itself is extremely limited not only in terms of usage but also in frequency of use. In addition, the conventional unattended recording system is a very expensive system that automatically records and tracks an object to be photographed using an infrared signal. Therefore, this paper developed a low cost unmanned recording system using mobile phone. The system consists of a commercial mobile camera, a servomotor for moving the camera from side to side, a microcontroller for controlling the motor, and a commercial wireless Bluetooth earset for video audio input. And it is an unmanned automation system using mobile, and anyone can record image by self image tracking.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.535-546
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• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.42
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• pp.22.1-22.6
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• 2020

Introduction: Facial fractures (FFs) occur after high- and low-energy trauma; differences in associated injuries and outcomes have not been well articulated. Objective: To compare the epidemiology, management, and outcomes of patients suffering FFs from high-energy and low-energy mechanisms. Methods: We conducted a 6-year retrospective local trauma registry analysis of adults aged 18-55 years old that suffered a FF treated at the Santa Barbara Cottage Hospital. Fracture patterns, concomitant injuries, procedures, and outcomes were compared between patients that suffered a high-energy mechanism (HEM: motor vehicle crash, bicycle crash, auto versus pedestrian, falls from height > 20 feet) and those that suffered a low-energy mechanism (LEM: assault, ground-level falls) of injury. Results: FFs occurred in 123 patients, 25 from an HEM and 98 from an LEM. Rates of Le Fort (HEM 12% vs. LEM 3%, P = 0.10), mandible (HEM 20% vs. LEM 38%, P = 0.11), midface (HEM 84% vs. LEM 67%, P = 0.14), and upper face (HEM 24% vs. LEM 13%, P = 0.217) fractures did not significantly differ between the HEM and LEM groups, nor did facial operative rates (HEM 28% vs. LEM 40%, P = 0.36). FFs after an HEM event were associated with increased Injury Severity Scores (HEM 16.8 vs. LEM 7.5, P <0.001), ICU admittance (HEM 60% vs. LEM 13.3%, P <0.001), intracranial hemorrhage (ICH) (HEM 52% vs. LEM 15%, P <0.001), cervical spine fractures (HEM 12% vs. LEM 0%, P = 0.008), truncal/lower extremity injuries (HEM 60% vs. LEM 6%, P <0.001), neurosurgical procedures for the management of ICH (HEM 54% vs. LEM 36%, P = 0.003), and decreased Glasgow Coma Score on arrival (HEM 11.7 vs. LEM 14.2, P <0.001). Conclusion: FFs after HEM events were associated with severe and multifocal injuries. FFs after LEM events were associated with ICH, concussions, and cervical spine fractures. Mechanism-based screening strategies will allow for the appropriate detection and management of injuries that occur concomitant to FFs. Type of study: Retrospective cohort study. Level of evidence: Level III.

• Journal of IKEEE
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• v.27 no.3
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• pp.273-279
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• 2023

The use of line memory is essential for image filtering in image processing hardware. After input data is stored in line memory, filtering is performed after synchronization to use the stored data. A sync generator is used for synchronization, and in the case of a conventional sync generator, the input sync signal is delayed by one row of the input image. If a signal delayed by two rows is required, it is necessary to connect two modules. This approach increases the size of the hardware and cannot be designed efficiently. In this paper, we propose a sync generator that generates multiple types of delayed signals by adding a finite state machine. The hardware design was coded in Verilog HDL, and performance is verified by applying it to image processing hardware using field programmable gate array board.

• Journal of Drive and Control
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• v.20 no.2
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Food Science of Animal Resources
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• v.33 no.5
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• pp.640-645
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• 2013

The purpose of this study was to utilize the results as a basic data of benzoic acids in animal products that didn't mention in the quality standard of National Veterinary Research and Quarantine Service (NVRQS) to solve the conflict of international trade and administration. Set-Pak method listed in the quality standard of NVRQS, faster than auto distillation methods with same recovery selected as a pre treatment for the determination of benzoic acid. The regression curve of benzoic acid with Sep-Pak method was linear with the $R^2$ value of 0.999 and the limit of detection (LOD) and limit of quantitation (LOQ) was 0.058 mg/kg and 0.176 mg/kg, respectively. The benzoic acid in the fermented milk was detected after the fermentation stage by addition of starter culture with the level of 2.28~10.48 mg/kg and 0~16.5 mg/kg in the commercial fermented milk products without detection by the addition of syrup. In case of cheese products, the benzoic acids level was influenced by the curd formation (Camembert cheese) and the quality of natural cheese (processed cheese), by the way, the benzoic acid level of commercial natural cheese was 0~4.2 mg/kg, processed cheese was 0~20.8 mg/kg, respectively. Based on this result, it may be possible to utilize as a basic data for the systematic control the level of natural benzoic acids in raw material, processing and final products of animal origin.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6432-6439
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• 2014

The NAUT (Non-contact Air coupled Ultrasonic Testing) technique is one of the ultrasonic testing methods that enables non-contact ultrasonic testing by compensating for the energy loss caused by the difference in acoustic impedance of air with an ultrasonic pulser receiver, PRE-AMP and high-sensitivity transducer. As the NAUT is performed in a state of steady ultrasonic transmission and reception, testing can be performed on materials of high or low temperatures or specimens with a rough surface or narrow part, which could not have been tested using the conventional contact-type testing technique. For this study, the internal defects of spot weld, which are often applied to auto parts, and CFRP parts, were tested to determine if it is practical to make the NAUT technique commercial. As the spot welded part had a high ultrasonic transmissivity, the result was shown as red. On the other hand, the part with an internal defect had a layer of air and low transmissivity, which was shown as blue. In addition, depending on the PRF (Pulse Repetition Frequency), an important factor that determines the measurement speed, the color sharpness showed differences. With the images obtained from CFRP specimens or an imaging device, it was possible to identify the shape, size and position of the internal defect within a short period of time. In this paper, it was confirmed in the above-described experiment that both internal defect detection and image processing of the defect could be possible using the NAUT technique. Moreover, it was possible to apply NAUT to the detection of internal defects in the spot welded parts or in CFRP parts, and commercialize its practical application to various fields.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.718-724
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• 2008

The ion selective microelectrode (ISME) has been used for measuring the ion profile of DO, $NH_4{^+}-N$, $NO_2{^-}-N$ and $NO_3{^-}-N$ in biofilm. In this study we evaluated the detection limit and validity of ISME and applied ISME for the continuous measurement of $NH_4{^+}-N$ and $NO_3{^-}-N$ concentration in the modified Ludzack-Ettinger (MLE) process. Average detection limits of $NH_4{^+}-N$ and $NO_3{^-}-N$ ISME were $10^{-4.44}M$ and $10^{-4.62}M$, respectively. Since the ISME with $5{\sim}10{\mu}m$ of tip diameter showed a faster response time than that of $1{\sim}5{\mu}m$, the ISME with a tip diameter of $5{\sim}10{\mu}m$ was fabricated and used to make real-time ion detections. Direct monitoring of $NH_4{^+}-N$ and $NO_3{^-}-N$ concentrations in the aerobic (2) tank causes the instability of the electromotive force (EMF) for the initial 5~8 hours and also causes remarkable error values of $NH_4{^+}-N$ and $NO_3{^-}-N$ concentration. This phenomenon is caused by aeration and mixing in the reactor. Thus, the measuring chamber was newly designed for the aerobic (2) tank and then the EMF of the ISME were stabilized in less than 1 hour. Errors of $NH_4{^+}-N$ and $NO_3{^-}-N$ concentration were decreased after stabilization of the EMF. The ISME analysis were well corresponded to the results of auto analyzer and ion chromatography. Consequently, the concentration of $NH_4{^+}-N$ and $NO_3{^-}-N$ could be continuously measured for 178 hours by the ISME.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.