• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.30 no.6
• /
• pp.501-506
• /
• 2017

An infrared seeking missile does not emit any signal by itself as it is guided by passive heat signature from an aircraft. Therefore, it is difficult for the target aircraft to notice the existence of incoming missile, making it a serious threat. The usage of MAW(missile approach warning) that can notify the approaching infrared seeking missile is currently limited due to its high cost. Furthermore, effectiveness of MAW against infrared seeking missile is not available in open literature. Therefore, effect of evasive maneuver by MAW on the survivability of the aircraft is simulated to evaluate the benefit of the MAW in this research. The lethal range is used as a measure of aircraft survivability. An aircraft flying at an altitude of 5km with Mach 0.9 being tracked by air-launched AIM-9 infrared seeking missile is considered in this research. As a variable for the evasive maneuver, the MAW recognition distance of 5~7km and the G-force of 3~7G that limits maximum directional change of the aircraft are considered. Simulation results showed that the recognition of incoming missile by MAW and following evasive maneuver can reduce the lethal range considerably. Maximum reduction in lethal range is found to be 29.4%. Also, the MAW recognition distance have a greater importance than the aircraft maneuverability that is limited by structural limit of the aircraft.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.2
• /
• pp.67-72
• /
• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.41-51
• /
• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.

• Smart Media Journal
• /
• v.12 no.10
• /
• pp.93-101
• /
• 2023

Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

• Korean Journal of Remote Sensing
• /
• v.40 no.1
• /
• pp.33-43
• /
• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2011.05a
• /
• pp.29-29
• /
• 2011

SWAT (Soil and Water Assessment Tool) 모형은 물리적 기반의 준분포형 강우-유출 모형으로서, 대규모의 복잡한 유역에서 장기간에 걸친 다양한 종류의 토양과 토지이용 및 토지관리 상태에 따른 유출과 유사 및 오염물질의 거동에 대한 토지관리 방법의 영향을 예측이 가능하여, 수자원 관리 계획 및 유역관리를 위한 의사결정 지원 등 그 적용 범위가 매우 광범위하다. 이러한 모형의 적용성 검증을 위해서는 매개변수 민감도 분석 및 검 보정, 예측 불확실성 분석을 필요로 한다. 최근 수문 모델의 불확실성을 분석하기 위한 다양한 기법들이 개발 되었는데, 본 연구는 충주댐 유역(6,581.1 m)을 대상으로 유역출구점의 실측 일 유출량 자료(1998~2003)를 바탕으로 SWAT 모형의 유출관련 매개변수(총 18개)에 대한 불확실성 분석을 실시하였다. 이때 사용된 분석 기법으로는 SUFI2 (Sequential Uncertainty FItting algorithm 2), GLUE (Generalized Likelihood Uncertainty Estimation), ParaSol (Parameter Solution)등을 적용 하였다. 이러한 기법은 모두 SWAT-CUP (SWAT-Calibration Uncertainty Program, Abbaspour, 2007) 모형에 탑재되어있으며, 모형의 결과로써 검 보정, 매개변수의 민감도 분석, 각종 목적 함수 및 불확실성의 범위 등이 자동으로 산출 되므로 모형의 사용자가 불확실성 평가 기법의 분석 및 비교를 손쉽게 할 수 있다. 그 결과 대표적인 목적 함수인 결정 계수( $^2$)와 NSE (Nash-Sutcliffe Model Efficiency)는 모두 0.65에서 0.92사이의 값을 나타내어 대체적으로 모의가 잘 이루어졌음을 알 수 있었다. 그러나 불확실성의 범위를 나타내는 지표인 p-factor 및 r-factor에서는 평가 기법 별로 그 차이가 확연하게 드러났다. 여기서 p-factor는 불확실성 범위에 실측치가 포함되는 비율이며, r-factor는 불확실성의 상대적인 범위로 각각 1과 0에 가까울수록 모의 기법의 성능이 우수함을 의미한다. 세 가지 알고리듬 중에서 SUFI2의 p-factor가 약 0.51로 가장 높게 나타났으며, ParaSol의 r-factor가 0.00으로 가장 작게 나타났다. 여기서 p-factor는 불확실성 범위에 실측치가 포함되는 비율이며, r-factor는 불확실성의 상대적인 범위를 의미한다. 본 연구의 결과는 SWAT 모형을 이용한 수문모델링에서 수문분석에 따른 예측결과의 불확실성을 정량적으로 평가함으로서, 모형의 적용성 평가 및 모의결과의 신뢰성 확보에 근거자료로 활용이 가능할 것으로 판단된다.

• Journal of the Korean Geotechnical Society
• /
• v.32 no.7
• /
• pp.25-34
• /
• 2016

Recently, in Korea, problems related with unstability of slope or sinkhole in urban area due to erosion of compacted granite soil which was used as a backfill or embankment material have been treated as important issues. Small hole might develop inside of backfill area due to erosion of not only weathered granite soil but also clay, silt, fine sand size particles when underground water flows. Once erosion starts in a soil mass, erosion rate increases gradually to cause rapid destruction. In this study, a rotating cylinder test (RCT) was performed to evaluate the hydraulic resistance characteristics of compacted weathered granite soil under various relative densities and preconsolidation pressures. Meanwhile, an image analysis method was introduced to analyze radius of irregularly eroded sample. It was found that image analysis is an effective means of minimizing the error in calculating a critical shear stress and threshold shear stress on the irregularly eroded sample. Furthermore, in general, hydraulic resistance capacity increases with the increase of relative density and preconsolidation pressure.

• Korean Journal of Cognitive Science
• /
• v.26 no.4
• /
• pp.435-452
• /
• 2015

Recent researches on Entity Linking(EL) have attempted to disambiguate entities by using a knowledge base to handle the semantic relatedness and up-to-date information. However, EL for tweets using a knowledge base is still unsatisfactory, mainly because the tweet data are mostly composed of short and noisy contexts and real-time issues. The EL system the present work builds up links ambiguous entities to the corresponding entries in a given knowledge base via exploring the news articles and the user history. Using news articles, the system can overcome the problem of Wikipedia coverage (i.e., not handling real-time issues). In addition, given that users usually post tweets related to their particular interests, the current system referring to the user history robustly and effectively works with a small size of tweet data. In this paper, we propose an approach to building an EL system that links ambiguous entities to the corresponding entries in a given knowledge base through the news articles and the user history. We created a dataset of Korean tweets including ambiguous entities randomly selected from the extracted tweets over a seven-day period and evaluated the system using this dataset. We use accuracy index(number of correct answer given by system/number of data set) The experimental results show that our system achieves a accuracy of 67.7% and outperforms the EL methods that exclusively use a knowledge base.

• Journal of Internet Computing and Services
• /
• v.18 no.1
• /
• pp.57-64
• /
• 2017

Recent automobile manufacturing technology has improved not only the function and performance of cars, but also the audio systems in cars so as to increase their marketability. Automobile manufacturers always have the option of simply installing an expensive acoustic system to help customers enjoy a high-level sound quality car audio system. However, this also tends to increase the MSRP (Manufacturer's Suggested Retail Price) of the car. Therefore, it is desirable, where possible, to enhance the sound quality of plainer, less expensive audio devices to help customers feel as if they have a high-quality and expensive audio device in their car. In order to make this happen, the manufacturer must develop an optimal interior environment and audio system at a relatively lower cost. To this end, features of the car audio system can be enhanced by analyzing audio frequency response and using performance metrics to figure out the characteristics of the human auditory system. This study analyzed the sound field of Korean Recreation Vehicles (RVs) using the Internet of Things (IoT) sensor for the measurement of car audio system. As a result, high energy of sensitive bandwidth, one of the human auditory characteristics often makes annoying sound. This study also found that increasing the frequency response flatness is required by taking human auditory field into account when designing the car audio system for the future.

• Journal of Broadcast Engineering
• /
• v.21 no.2
• /
• pp.169-179
• /
• 2016

Recently, MPEG-H has been standardizing for a multimedia coder in UHDTV (Ultra-High-Definition TV). Thus, the demand for not only channel-based audio contents but also object-based audio contents is more increasing, which results in developing a new technique of converting channel-based audio contents to object-based ones. In this paper, a non-uniform linear microphone array based source separation method is proposed for realizing such conversion. The proposed method first analyzes the arrival time differences of input audio sources to each of the microphones, and the spectral magnitudes of each sound source are estimated at the horizontal directions based on the analyzed time differences. In order to demonstrate the effectiveness of the proposed method, objective performance measures of the proposed method are compared with those of conventional methods such as an MVDR (Minimum Variance Distortionless Response) beamformer and an ICA (Independent Component Analysis) method. As a result, it is shown that the proposed separation method has better separation performance than the conventional separation methods.