• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.1
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• pp.87-96
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• 2009

In conventional method, flight model is discribed to differential equation by linealization of nonlinear object motion equation. As state equation from differential equation of moving object, the controller is designed by transfer functions of each module under discrimination of stability criteria. But this conventional method is designed under limitation of nonlinearity from object's shape and speed. In other word, The greater part of guidance/navigation system was satisfied with the result of good performance for normal figure of flight object, not sudden changed flight condition, not high speed. But it is not able to give full play to its ability on flight object which has abnormal figure, sudden changeable motion, high speed. Therefore, in this paper was presented performance analysis of load control model for navigation/guidance system on flying object being uncertainty, non-linear like abnormal figure, sudden changeable motion, high speed and is presented method of trajectory control(controllability) ahead of controllability and stability to achieve flight mission. In other word, this paper shows the first step of Min-design method and flight control model.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.99-107
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• 2021

With the recent establishment of a ubiquitous-based medical and healthcare environment, the medical information system for obtaining situation information from various sensors is increasing. In the medical information system environment based on context-awareness, the patient situation can be determined as normal or emergency using situational information. In addition, medical staff can easily access patient information after simple user authentication using ID and Password through applications on smart devices. However, these services of authentication and patient information access are staff-oriented systems and do not fully consider the ubiquitous-based healthcare information system environment. In this paper, we present a authentication service model based context-awareness system for providing situational information-driven authentication services to users who access medical information, and implemented proposed system. The authentication service model based context-awareness system is a service that recognizes patient situations through sensors and the authentication and authorization of medical staff proceed differently according to patient situations. It was implemented using wearables, biometric data measurement modules, camera sensors, etc. to configure various situational information measurement environments. If the patient situation was emergency situation, the medical information server sent an emergency message to the smart device of the medical staff, and the medical staff that received the emergency message tried to authenticate using the application of the smart device to access the patient information. Once all authentication was completed, medical staff will be given access to high-level medical information and can even checked patient medical information that could not be seen under normal situation. The authentication service model based context-awareness system not only fully considered the ubiquitous medical information system environment, but also enhanced patient-centered systematic security and access transparency.

• Journal of the Korean Wood Science and Technology
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• v.49 no.3
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• pp.213-225
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• 2021

In this paper, we investigated the carbonization characteristics of lignin hydrochar prepared by hydrothermal carbonization and established a model for predicting the carbonization degree using near-infrared spectroscopy and partial least squares regression. The carbon content of the hydrothermally carbonized lignin at the temperature of 200 ℃ was higher by approximately 3 wt% than that of the untreated sample, and the carbon content tended to gradually increase as the heating time increased. Hydrothermal carbonization made lignin more carbon-intensive and more homogeneous by eliminating the microparticles. The discriminant and predictive models using near-infrared spectroscopy and partial least squares regression approppriately determined whether hydrothermal carbonization has been applied and predicted the carbon content of hydrothermal carbonized lignin with high accuracy. In this study, we confirmed that we can quickly and nondestructively predict the carbonization characteristics of lignin hydrochar manufactured by hydrothermal carbonization using a partial least squares regression model combined with near-infrared spectroscopy.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.83-98
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• 2021

This study identified the influence of employees' hybrid start-up intention (intention to start a business while maintaining a job) on the employees' self-determination motivation (intrinsic motivation, extrinsic motivation) and prior knowledge through the Model of Goal-directed Behavior (MGB). We used a PLS-SEM called SmartPLS 3.0 for 126 valid samples collected by judgement extraction for office workers throughout June 13, 2020 to July 3, 2020, and empirically evaluated the measurement model (internal consistency reliability, convergent and discriminant validity) and the structural model (multicollinearity, determination coefficient, effect size, predictive relevance, etc.). Only the intrinsic motivation for realizing the hybrid start-up goal of office workers had a significant impact on the hybrid start-up attitude and subjective norms, and the prior knowledge of hybrid start-up had a significant impact on the hybrid start-up desire and the hybrid start-up intention. In order to induce hybrid start-ups for workers with unstable employment, we need systems and programs that can inspire employees with intrinsic motivation and knowledge about hybrid start-up, so follow-up researches are necessary to analyze about government systems and consulting support that can promote hybrid start-up.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.139-159
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• 2022

As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.481-481
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• 2021

서울시는 '10년, '11년, '18년의 기록적인 호우로 인해 막대한 재산피해를 기록하였다. 이로 인해 서울시는 수재해 최소화 대책의 필요성을 인지하여 방재시설물 확충 등의 구조적 대책과 함께 침수지역 예측, 호우 영향 예보와 관련된 비구조적 대책 수립을 위해 노력하고 있다. 그 일환으로 2018~2019년 『서울시 강한 비구름 유입경로 및 침수위험도 예측 용역』 수행을 통해 레이더 실황강우 기반의 강한 비구름 이동경로 추정 기술, 강우시나리오 기반의 침수위험지역추정 기술이 적용된 서울시 도시침수 예측시스템을 개발하였다. 또한, 침수피해에 선제적으로 대응하기 위해 2019~2020년 『서울시 내수침수 위험지역 실시간 예측기술 개발』을 통하여 이류모델 기반의 예측강우정보 추정 기술, 예측강우정보 기반의 실시간 침수위험지역 추정기술을 적용하였다. 현재 서울시 도시침수 예측시스템은 서울시 전역의 강우 및 침수정보를 제공하며, 관로 113,286개(전체 385,768개), 맨홀 106,097개(전체 272,133개), 빗물펌프장 117개소(전체 121개소)가 반영되어 있다. 서울시 도시침수 예측시스템에서는 서울시 25개 자치구를 대상으로 실황 및 예측 강우정보, 강한 비구름에 대한 이동경로정보, 시나리오 및 실시간 침수정보를 제공하고 있다. 강우정보는 10분 및 1시간 단위 AWS 실황정보와 10분 단위 이류모델 기반 예측정보, 1시간 단위 LDAPS 기반 예측정보를 제공한다. 또한, 레이더 실황정보를 통해 판별된 강한 비구름에 대해 10분 단위 1시간 예측경로를 제공한다. 침수정보는 총강우량, 강우지속기간, 빗물받이효율 조건을 반영한 강우시나리오 기반의 6m 고해상도 격자단위 침수시나리오 정보와 자치구별 침수위험정보를 제공한다. 또한, 이류모델 기반의 레이더 예측정보를 이용하여 실시간 침수 예측정보를 제공한다. 향후 서울시 내 모든 수방시설물의 적용, 관로 유출구별 기점수위 반영, 관측자료를 이용한 도시유출 및 도시침수 모델 최적화 등 지속적으로 고도화를 수행하고자 하며, 서울시 도시침수 예측시스템을 통해 서울시 및 자치구 풍수해 담당자가 침수피해를 대비, 대응할 수 있을 것으로 기대된다.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Korea Technology Innovation Society
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• v.18 no.3
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• pp.416-443
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• 2015

The resource-based view (RBV) explains that the competitive advantage is mainly based on the resources which have strategic characteristics. Therefore, finding, developing and maintaining strategically valuable resources has been one of main research topics and a starting point in the corporate strategy structure in RBV. In this regards, attempts to recognize strategically valuable resources have been one of crucial issues in RBV researches. Especially, Barney's VRIO has been widely used as a practical tool for finding strategically valuable resources. However, empirical studies on VRIO framework's effectiveness have not been sufficiently implemented, and there has been no proven relation among the components of the VRIO so far. This is mainly because the concepts or definitions on core components of the VRIO - Value, Rareness, Inimitability, and Organization - are too comprehensively explained and measurements of each component cannot be easily quantified. Considering these, this paper presents empirical results of the relation between VRIO components and competitive advantage, and tests effectiveness of VRIO Framework with utilizing sufficient technology evaluation cases and financial statements of 2,252 technology based SMEs in Korea. As a result, the components of the VRIO have a positive influence on competitive advantage. The attributes of strategic resources - Value, Rareness, and Inimitability - have a statistically meaningful positive effect on organization, while organization has a positive effect on the competitive advantage serving as a parameter between the attributes of strategic resources and competitive advantage.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.383-394
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• 2008

We examine geoeffective directional parameters of coronal mass ejections (CMEs). We select 30 front-side halo CMEs from SOHO LASCO CMEs whose speed is larger than 1000km/s and longitude is less than ${\pm}30^{\circ}$. These are thought to be the most plausible candidate of geoeffective CMEs. We examine the relation between CMEs directional parameters (Earthward direction, eccentricity, ${\Delta}$ distance and central angle parameter) and the minimum value of the Dst index. We have found that the Earthward direction parameter has a good correlation with the Dst index, the eccentricity parameter has a much better correlation with the Dst index. The bo distance and central angle parameter has a poor correlation with the Dst index. It's, however, well correlated with the Dst index in very strong geomagnetic storms. Most of CMEs causing very strong storms (Dst ${\leq}$-200nT) are found to have large Earthward direction parameter $({\geq}0.6)$, small eccentricity, bo distance and central angle parameters $(E{\leq}0.4,\;{\Delta}X\;and\;sin\;{\theta}{\leq}0.2)$. These directional parameters are very important parameters that control the geoeffectiveness of very fast front-side halo CMEs.