• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.100-100
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• 2020

Wheat is mainly composed of carbohydrate but it contains a moderate amount of protein, which gives a very useful characteristics to flour food such as the unique elasticity and stickiness of the dough. We developed a calibration equation for analyzing crude protein content using Near Infrared Spectroscopy to quick analyze the crude protein content of wheat germplasm stored in the National Agrobiodiversity Center, RDA, Korea. The 1,798 wheat germplasms were used to draw up the calibration formula. The crude protein's interval distribution of 1,798 wheat germplasms used for the calibration was 7.04-20.84%, the average content was 13.2%, and standard deviation was 2.6%. The germplasms distribution was composed of a suitable group for the preparation of the calibration formula because the content distribution was a normal, excluding the 13.0-15.5% content section. In order to verify the applicability of the NIRS prediction model, we measured the crude protein content of the 300 wheat germplasms that were not used for the calibration using both Kjeldahl analysis and NIR spectrum. The analysis value calculated using each method were statistically processed, and the test results and statistical indicators of the predictive model were compared. As a result, The R² value of the optimized NIRS prediction model was 0.997, and the Standard error of Calibration value(SEC) was 0.132, and slope value was 1.000. With prediction model selection, compared to Kjeldahl method, R² values were 0.994(Kjeldahl), 0.998(NIRS), and the SEC value were 0.191 and 0.132, respectively, comparing the statistical indices of the forecast model. And slope value were 1.013, 1.000, respectively. The analysis of crude protein content by the NIRS predictive model developed by each statistical index showing similar figures is judged to show a high degree of correlation with the Kjeldahl analysis. The proven calibration equation will be used to measure the crude protein content of wheat germplasms held by the National Agrobiodiversity Center, and by dividing the wheat germplasms by their use according to the crude protein content, it will provide useful information to relevant researchers.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.947-955
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• 2023

This study was conducted to investigate the awareness of spent nuclear fuel among residents in nuclear power plant areas and use it as basic data for establishing a disposal facility for high-level radioactive waste. 204 questionnaires collected online were analyzed using SPSS Window Ver 28.0. To verify differences between groups, t-test and one-way ANOVA were performed. And correlation analysis was conducted to confirm the relationship between variables. As a result, first, risk perception regarding nuclear-related accidents showed statistically significant differences depending on gender and educational level. The position on the construction of a permanent disposal facility for spent nuclear fuel showed a statistically significant difference depending on gender, education, and age, and the perception of the importance of each evaluation standard for establishing a spent nuclear fuel management plan showed a statistically significant difference depending on education and age. In terms of trust in information-providing institutions, trust in the National Assembly was found to be the lowest. Second, the results of the correlation analysis between variables showed that local residents are aware that an alternative to the current disposal of spent nuclear fuel is needed, and that financial support for the construction of a permanent disposal facility is needed. Therefore, in order to build a high-level radioactive waste disposal site, it is believed that it is necessary to increase trust in the government, collect opinions from local residents, and provide economic support.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.73-80
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• 2023

The recent steep increase in the minimum hourly wage has increased the burden of labor costs, and the share of unmanned stores is increasing in the aftermath of COVID-19. As a result, theft crimes targeting unmanned stores are also increasing, and the "Just Walk Out" system is introduced to prevent such thefts, and LiDAR sensors, weight sensors, etc. are used or manually checked through continuous CCTV monitoring. However, the more expensive sensors are used, the higher the initial cost of operating the store and the higher the cost in many ways, and CCTV verification is difficult for managers to monitor around the clock and is limited in use. In this paper, we would like to propose an AI image processing fusion algorithm that can solve these sensors or human-dependent parts and detect customers who perform abnormal behaviors such as theft at low costs that can be used in unmanned stores and provide cloud-based notifications. In addition, this paper verifies the accuracy of each algorithm based on behavior pattern data collected from unmanned stores through motion capture using mediapipe, object detection using YOLO, and fusion algorithm and proves the performance of the convergence algorithm through various scenario designs.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.81-89
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• 2023

In modern society, blind people face difficulties in navigating common environments such as sidewalks, elevators, and crosswalks. Research has been conducted to alleviate these inconveniences for the visually impaired through the use of visual and audio aids. However, such research often encounters limitations when it comes to practical implementation due to the high cost of wearable devices, high-performance CCTV systems, and voice sensors. In this paper, we propose an artificial intelligence fusion algorithm that utilizes low-cost video sensors integrated into smartphones to help blind people safely navigate their surroundings during walking. The proposed algorithm combines motion capture and object detection algorithms to detect moving people and various obstacles encountered during walking. We employed the MediaPipe library for motion capture to model and detect surrounding pedestrians during motion. Additionally, we used object detection algorithms to model and detect various obstacles that can occur during walking on sidewalks. Through experimentation, we validated the performance of the artificial intelligence fusion algorithm, achieving accuracy of 0.92, precision of 0.91, recall of 0.99, and an F1 score of 0.95. This research can assist blind people in navigating through obstacles such as bollards, shared scooters, and vehicles encountered during walking, thereby enhancing their mobility and safety.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.83-97
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• 2023

The growth of the live commerce market allows you to conveniently and simply start live commerce anytime, anywhere with a smartphone. The use of smartphone services provides continuous communication and is used while feeling psychological attachment, and it leads to psychological attachment, self-consistency with consumers themselves, and self-identity. This study focuses on the motives and perceptions of consumers using live commerce. In other words, we will examine the relationship with service attachment through the moderating effect of self-efficacy and control focus tendency as consumers' personal and psychological characteristics. In other words, the tendency of regulatory focus, which determines the direction of behavior of consumers according to their motives and goals, affects the service attachment of live commerce. We believe that self-efficacy, which is personal confidence and belief that you can plan and execute on your own for the desired outcome in a given situation or task, will control this relationship. As a result of this research, consumers who highly perceive prevention focus were more likely to avoid negative consequences and pursue safety and obligations. Their attachment to live commerce services was stronger, offsetting their confidence and self-efficacy. When using live commerce services, the more they perceive that information acquisition is beneficial, the higher their belief, and self-efficacy, so service attachment, which is an emotional experience as well as a cognitive experience, is strongly formed for consumers with a preventive focus to avoid safety-seeking and negative consequences. Through the present research results, we believe that it will be helpful in operating strategies and management for companies and small business owners who want to understand the psychological behavior of consumers in using live commerce services.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.

• Journal of Life Science
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• v.33 no.10
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• pp.767-775
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• 2023

Sulfasalazine is a disease-modifying antirheumatic abiotic agent. It is a derivative of aminosalicylic acid and has been used for the treatment of various inflammatory diseases, such as rheumatoid arthritis, ulcerative colitis, and Crohn's disease, since it was first synthesized in 1941 and approved as a medicine in the United States in 1950. However, its mechanism of action has not yet been clearly identified. In this study, the effects of sulfasalazine on cell survival, apoptosis, and cell cycle progression in macrophages, which are major immune cells that regulate inflammatory responses, were investigated using mouse macrophage RAW 264.7 cells. Sulfasalazine inhibited the viability of RAW 264.7 cells in a dose-dependent manner, starting at a concentration of 0.25 mM. Annexin-V staining was used to confirm that the decrease in cell viability was due to apoptosis, and the number of Annexin-V-positive cells increased significantly at a concentration of 0.25 mM or higher. The effect of sulfasalazine on the expression of key proteins that regulate the G0/G1 phase of the cell cycle was also investigated. Sulfasalazine treatment significantly increased the expression of the cyclin-dependent kinase inhibitors p21 and p27 in RAW 264.7 cells. Although sulfasalazine is frequently used as a control drug in studies on inflammatory diseases, such as inflammatory colitis and rheumatoid arthritis, studies on its effect on macrophages are very limited. Therefore, the results of this study are expected to provide vital information on the use of sulfasalazine as a disease treatment.

• Clean Technology
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• v.29 no.4
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• pp.244-248
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• 2023

Recently, displays play an important role in quickly delivering a lot of information. Research is underway to reproduce various colors close to natural colors. In particular, research is being conducted on the light emitting structure of displays as a method of expressing accurate and rich colors. Due to the advancement of technology and the miniaturization of devices, the need for small but high visibility displays with high efficiency in energy consumption continues to increase. Efforts are being made in various ways to improve OLED efficiency, such as improving carrier injection, structuring devices that can efficiently recombine electrons and holes in a numerical balance, and developing materials with high luminous efficiency. In this study, the electrical and optical properties of the seven-layer stacked structure rear-light emitting blue OLED device were analyzed. 4,4'-Bis(carazol-9-yl)biphenyl:Ir(difppy)2(pic), a blue light emitting material that is easy to manufacture and can be highly efficient and brightened, was used. OLED device manufacturing was performed via the in-situ method in a high vacuum state of 5×10-8 Torr or less using a Sunicel Plus 200 system. The experiment was conducted with a seven-layer structure in which an electron or hole blocking layer (EBL or HBL) was added to a five-layer structure in which an electron or hole injection layer (EIL or HIL) or an electron or hole transport layer (ETL or HTL) was added. Analysis of the electrical and optical properties showed that the device that prevented color diffusion by inserting an EBL layer and a HBL layer showed excellent color purity. The results of this study are expected to greatly contribute to the R&D foundation and practical use of blue OLED display devices.