• 시스템엔지니어링워크숍
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• 통권4호
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• pp.90-94
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• 2004

System engineering(SE) management techniques applied for space launch system development are introduced to assess the current status and address the effwctiveness of these techniques. Management plans and guides are prepared for the work breakdown structure , data, comfiguration, interface control, Quality assurance, procurement, reliability, risk and verification/validation . Further improvement is required for the system engineering management plan(SEMP) to merge the international cooperation into current engineering managment system.

• 대한원격탐사학회지
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• 제39권5_1호
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• pp.577-587
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• 2023

크라우드소싱(crowdsourcing) 공간 데이터 활용 연구가 활발히 진행되고 있으나 데이터 품질의 불확실성으로 인한 문제점이 제기되고 있다. 특히 드론 영상 데이터셋에 품질이 낮은 데이터가 포함될 경우, 출력되는 공간 정보의 품질이 저하될 수 있다. 이를 위해 본 연구에서는 크라우드소싱된 영상의 기하학적 품질을 자동으로 검증하는 방법론을 제안하였다. 주요 품질 요소로는 영상의 공간해상도, 해상도 변화량, 매칭점 재투영 오차, 번들 조정 결과 등을 입력변수로 활용하였다. 공간 정보 생성에 적합한 영상을 분류하기 위해 학습 및 검증 데이터를 구축하고, radial basis function (RBF) 기반의 support vector machine (SVM) 모델로 학습을 진행하였다. 학습된 SVM 모델의 분류 정확도는 99.1%를 기록하였다. 품질 검증 모델 효과를 확인하기 위해 학습 및 검증에 사용하지 않은 드론 영상에 대하여 해당 모델을 적용하기 전후의 영상 데이터셋으로 각각 정사영상을 생성하고 비교하였다. 그 결과 모델 적용을 통하여 정사영상에 포함될 수 있는 다양한 왜곡을 줄이고 객체 식별력을 증대시키는 것을 확인하였다. 제안된 품질 검증 방법론은 다양한 품질의 크라우드소싱 데이터를 입력으로 받아 양질의 정보만을 자동 선별하게 함으로써 공간정보 생성에서의 활용 가능성을 증대시킬 것으로 기대한다.

• Genomics & Informatics
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• 제5권2호
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• pp.83-87
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• 2007

The analysis of DNA microarray data is a rapidly evolving area of bioinformatics, and various types of microarray are emerging as some of the most exciting technologies for use in biological and clinical research. In recent years, microarray technology has been utilized in various applications such as the profiling of mRNAs, assessment of DNA copy number, genotyping, and detection of methylated sequences. However, the analysis of these heterogeneous microarray platform experiments does not need to be performed separately. Rather, these platforms can be co-analyzed in combination, for cross-validation. There are a number of separate laboratory information management systems (LIMS) that individually address some of the needs for each platform. However, to our knowledge there are no unified LIMS systems capable of organizing all of the information regarding multi-platform microarray experiments, while additionally integrating this information with tools to perform the analysis. In order to address these requirements, we developed a web-based LIMS system that provides an integrated framework for storing and analyzing microarray information generated by the various platforms. This system enables an easy integration of modules that transform, analyze and/or visualize multi-platform microarray data.

• International Journal of Advanced Culture Technology
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• 제7권2호
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• pp.238-243
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• 2019

Recently, the importance of big data continues to be emphasized, and it is applied in various fields based on data mining techniques, which has a great influence on the health care industry. There are many healthcare industries, but only home health care is considered here. However, applying this to real problems does not always give perfect results, which is a problem. Therefore, data mining techniques are used to solve these problems, and the algorithms that affect performance are evaluated. This paper focuses on the effects of healthcare services on patient satisfaction and satisfaction. In order to use the CVParameterSelectin algorithm and the SMOreg algorithm of the classify method of data mining, it was evaluated based on the experiment and the verification of the results. In this paper, we analyzed the services of home health care institutions and the patient satisfaction analysis based on the name, address, service provided by the institution, mood of the patients, etc. In particular, we evaluated the results based on the results of cross validation using these two algorithms. However, the existence of variables that affect the outcome does not give a perfect result. We used the cluster analysis method of weka system to conduct the research of this paper.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• 한국환경과학회지
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• 제33권7호
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• pp.523-532
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• 2024

In popular tourist destinations such as Jeju and Gangwon, electric rental cars are increasingly adopted. However, sudden battery drain due to weather conditions can pose safety issues. To address this, we developed a battery consumption analysis model that considers resistive energy factors such as acceleration, rolling resistance, and aerodynamic drag. Focusing on the effects of ambient temperature and wind speed, the model's performance was evaluated during an empirical validation period from November to December 2023. Comparing predicted and actual state of charge (SoC) across different routes identified ambient temperature, wind speed, and driving time as major sources of error. The mean absolute error (MAE) increased with lower temperatures due to reduced battery efficiency. Higher wind speeds on routes 1 and 6 resulted in larger errors, indicating the model's limitation in considering only tailwinds for aerodynamic drag calculations. Additionally, longer driving times led to higher actual SoC than predicted, suggesting the need to account for varying driver habits influenced by road conditions. Our model, providing more accurate SoC predictions to prevent battery depletion incidents, shows high potential for application in navigation apps for electric vehicle users in tourist areas. Future research should endeavor to the model by including wind direction, HVAC system usage, and braking frequency to improve prediction accuracy further.

• Smart Structures and Systems
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• 제33권5호
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

• Smart Structures and Systems
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• 제26권3호
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• pp.373-390
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• 2020

Hybrid simulation (HS) is a versatile tool for structural performance evaluation under dynamic loads. Although real structural responses are often multiple-directional owing to an eccentric mass/stiffness of the structure and/or excitations not along structural major axes, few HS in this field takes into account structural responses in multiple directions. Multi-directional loading is more challenging than uni-directional loading as there is a nonlinear transformation between actuator and specimen coordinate systems, increasing the difficulty of suppressing loading error. Moreover, redundant actuators may exist in multi-directional hybrid simulations of large-scale structures, which requires the loading strategy to contain ineffective loading of multiple actuators. To address these issues, lately a new strategy was conceived for accurate reproduction of desired displacements in bi-directional hybrid simulations (BHS), which is characterized in two features, i.e., iterative displacement command updating based on the Jacobian matrix considering nonlinear geometric relationships, and force-based control for compensating ineffective forces of redundant actuators. This paper performs performance validation and application of this new mixed loading strategy. In particular, virtual BHS considering linear and nonlinear specimen models, and the diversity of actuator properties were carried out. A validation test was implemented with a steel frame specimen. A real application of this strategy to BHS on a full-scale 2-story frame specimen was performed. Studies showed that this strategy exhibited excellent tracking performance for the measured displacements of the control point and remarkable compensation for ineffective forces of the redundant actuator. This strategy was demonstrated to be capable of accurately and effectively reproducing the desired displacements in large-scale BHS.

• 한국지리정보학회지
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• 제21권1호
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• pp.83-95
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• 2018

최근 조선 산업의 기술이 발전함에 따라 선박의 크기 및 성능이 향상되었다. 이에 따라 한 번의 선박 간 충돌로 인한 좌초 등의 사고가 대형 해난사고를 초래할 수 있게 되었다. 이러한 심각성을 고려하여 국제 사회에서는 해사안전 향상을 위해 지속적으로 전자해도 기준을 업데이트하고 있다. 국제수로기구(IHO)에서 관리하는 기존의 전자해도 관련 표준은 S-57로, S-57 안에는 기존 이진(Binary) 형태의 전자해도 데이터를 제작하기 위한 기준이 담겨있다. 그러나 S-57은 2000년 12월 3.1 버전이 발표된 이후 업데이트가 중단되어 지속적으로 성장하고 있는 해양공간정보의 기술 경향을 반영하지 못하고 있다. IHO에서는 이러한 흐름에 대처하기 위해 차세대 전자해도 제작기준 표준인 S-100을 제정하였으며, 기존 S-57과 다른 데이터의 교환 형식을 사용하였다. 기존 전자해도의 경우에는 이진 형태로 구성되었으나 차세대 전자해도 표준을 기반으로 한 S-10X 전자해도 데이터의 경우에는 피처 카탈로그(Feature Catalogue), 포트리얼 카탈로그(Portrayal Catalogue), GML로 구성되어 있다. 이러한 점을 고려할 때 전자해도의 유효성 검증 표준인 S-58의 업데이트 또는 새로운 유효성 검증 표준의 제정이 필요하다. 본 연구에서는 이에 변화된 데이터의 유효성 검증 시험을 위해 자체 소프트웨어를 개발하였고 테스트 결과에 따른 개선점을 도출하였다.