• Transactions of Materials Processing
• /
• v.29 no.2
• /
• pp.76-88
• /
• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• Smart Media Journal
• /
• v.11 no.5
• /
• pp.9-16
• /
• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.

• Journal of the Korean Institute of Educational Facilities
• /
• v.4 no.4
• /
• pp.29-41
• /
• 1997

The objective of this paper is to construct a model to be used in deciding whether to repair or rebuild school buildings is depending on their ages and other factors. The theme of this paper is the age is the main variable but other factors such as floor, innerwall, ceiling, door, inner window of the class room, outer window of the class room, inner window of the corridor, outer window of the corridor, middle window between the classroom and the corridor, light, heater, speaker, fire protection sensor, TV monitor, and telephone status would influence the final decisions. This paper employs an experimental case study method. Using the stepwise, statistical, classification method commonly used in discriminant analysis, it evaluates 12,766 rooms of 87 different high schools in Seoul. The result of this study indicates that some critical variables influencing the final decisions are the status of TV monitor, middle window between the classroom and the corridor, light, inner window of the corridor, fire protection sensor, innerwall, speaker utensil, outer window of the class room, and door of the class room. This paper also suggests a linear discriminant function will be used for this kind of studies. Finally the paper recommends policies with respect to the variables and discriminant functions evaluated.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.135-155
• /
• 2022

This study analyzes patent trends by deriving sub-technical fields of Organic Light-Emitting Diodes (OLEDs) industry, and analyzing technology value, originality, and diversity for each sub-technical field. To collect patent data, a set of international patent classification(IPC) codes related to OLED technology was defined, and OLED-related patents applied from 2005 to 2017 were collected using a set of IPC codes. Then, a large number of collected patent documents were classified into 12 major technologies using the Latent Dirichlet Allocation(LDA) topic model and trends for each technology were investigated. Patents related to touch sensor, module, image processing, and circuit driving showed an increasing trend, but virtual reality and user interface recently decreased, and thin film transistor, fingerprint recognition, and optical film showed a continuous trend. To compare the technological value, the number of forward citations, originality, and diversity of patents included in each technology group were investigated. From the results, image processing, user interface(UI) and user experience(UX), module, and adhesive technology with high number of forward citations, originality and diversity showed relatively high technological value. The results provide useful information in the process of establishing a company's technology strategy.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.8
• /
• pp.23-32
• /
• 2024

NVS (Novel View Synthesis) is a field in computer vision that reconstructs new views of a scene from a set of input views. Real-time rendering and high performance are essential for NVS technology to be effectively utilized in various applications. Recently, 3D-GS (3D Gaussian Splatting) has gained popularity due to its faster training and inference times compared to those of NeRF (Neural Radiance Fields)-based methodologies. However, since 3D-GS reconstructs a 3D (Three-Dimensional) scene by splitting and cloning (Density Control) Gaussian points, the number of Gaussian points continuously increases, causing the model to become heavier as training progresses. To address this issue, we propose two methodologies: 1) Gaussian blending, an improved density control methodology that removes unnecessary Gaussian points, and 2) a performance enhancement methodology using a depth estimation model to minimize the loss in representation caused by the blending of Gaussian points. Experiments on the Tanks and Temples Dataset show that the proposed methodologies reduce the number of Gaussian points by up to 4% while maintaining performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.5
• /
• pp.137-147
• /
• 2023

LiDAR is used in autonomous driving and various industrial fields to measure the size and distance of an object. In addition, the sensor also provides intensity images based on the amount of reflected light. This has a positive effect on sensor data processing by providing information on the shape of the object. LiDAR guarantees higher performance as the resolution increases but at an increased cost. These conditions also apply to LiDAR intensity images. Expensive equipment is essential to acquire high-resolution LiDAR intensity images. This study developed artificial intelligence to improve low-resolution LiDAR intensity images into high-resolution ones. Therefore, this study performed parameter analysis for the optimal super-resolution neural network model. The super-resolution algorithm was trained and verified using 2,500 LiDAR intensity images. As a result, the resolution of the intensity images were improved. These results can be applied to the autonomous driving field and help improve driving environment recognition and obstacle detection performance

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.6
• /
• pp.530-536
• /
• 2016

The handling quality simulator including high fidelity flight mechanics model is indispensable component to design and verify the flight control system. Korea Aerospace Industries, LTD. (KAI) has been performing LCH (Light Civil Helicopter) core technology development program regarding automatic flight control system (AFCS) software development. And KAI has been developing flight mechanics model using FLIGHTLAB to design and evaluate the AFCS flight control law. This paper presents the handling quality assessment environment development results through the combining FLIGHTLAB with a legacy simulator. And this paper details the FLIGHTLAB model, application development process and FLIGHTLAB interface design. The developed handling quality assessment environment has been demonstrated with the ADS-33E hover and pirouette MTE (Mission Task Element) maneuver simulation.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.18 no.1
• /
• pp.170-181
• /
• 2015

A research on extracting croplands located nearby coastal areas using the spatial information data sets is the important task for managing the agricultural products in coastal areas. This research aims to extract the various croplands(croplands on mountains and croplands on plain areas) located nearby coastal areas using the KOMPSAT-2 imagery, the high-resolution satellite imagery, and the airborne topographic LiDAR(Light Detection And Ranging) data acquired in coastal areas of Uljin, Korea. Firstly, the NDVI(Normalized Difference Vegetation Index) imagery is generated from the KOMPSAT-2 imagery, and the vegetation areas are extracted from the NDVI imagery by using the appropriate threshold. Then, the DSM(Digital Surface Model) and DEM(Digital Elevation Model) are generated from the LiDAR data by using interpolation method, and the CHM(Canopy Height Model) is generated using the differences of the pixel values of the DSM and DEM. Then the plain areas are extracted from the CHM by using the appropriate threshold. The low slope areas are also extracted from the slope map generated using the pixel values of the DEM. Finally, the areas of intersection of the vegetation areas, the plain areas and the low slope areas are extracted with the areas higher than the threshold and they are defined as the croplands located nearby coastal areas. The statistical results show that 85% of the croplands on plain areas and 15% of the croplands on mountains located nearby coastal areas are extracted by using the proposed methodology.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.241-265
• /
• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.6
• /
• pp.506-511
• /
• 2017

LED lighting, which many companies are pursuing commercialization, is a representative green energy technology. However, the LED lighting for broadcasting image should have high output and easy portability compared with general LED lighting devices for street lamps, advertisement or transportation devices. Therefore, while shooting a broadcast image if you use LEDs as a substitute light source for halogen lamps and fluorescent lamps that are large in size and uncomfortable to handle it is expected that the lightening of the equipment will activate the broadcasting image lighting equipment industry. After considering the mass production of the LED module board and the SMT production size of the chip mounter, the board size was determined considering the overall size of the product by model. In this paper, four 20W LED boards are arranged vertically in order to produce an 80W board. In other words, by sharing LED module board size by model, high power LED lighting equipments of 120W and 200W can be selected as an increase in the number of boards.