• 지질공학
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• 제23권1호
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• pp.67-77
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• 2013

주암댐 보조여수로 건설공사 터널 구간 중 사전 조사가 미치지 못하는 구간의 정보취득과 파쇄대의 정확한 위치 및 크기를 예측하기 위하여 일부 구간에 대하여 터널 내 탄성파반사법(TSP)탐사를 실시하였다. 공사 착공 전에 실시한 사전조사 결과(지표 지질조사, 전기비저항 탐사, 시추조사 등), TSP탐사 결과, 그리고 실제 현장 굴진면에 대한 지질매핑 결과를 비교하였다. TSP탐사는 사전조사에서 감지하지 못한 주요 파쇄대를 잘 감지해내었으며 이에 따라 초기에 계획된 보강 계획을 변경하였다. TSP탐사 예측결과와 현장 막장면 비교 결과 파쇄대가 발달한 지점이 대부분 일치하는 것을 확인할 수 있었고, 이를 통해 터널 굴진면 예측에 비용 및 시간을 최소화 할 수 있고, 높은 정확성을 갖고 있는 TSP탐사의 현장 적용이 유용한 것으로 판단하였다. 다만 TSP탐사법이 갖는 몇 가지 단점으로 인하여 현장 적용에 일부 제약이 뒤따르나 이를 보완한다면 터널 굴착시 전방 예측을 위해 비교적 높은 효율성과 신뢰성을 갖고 TSP탐사를 활용할 수 있을 것이라 사료된다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2017년도 춘계공동학술대회
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• pp.31-31
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• 2017

On farm evaluation for the control of airborne diseases like FMD and flu virus has been done in past but control of disease in process of transportation of livestock and manures is still needed. The objective of this study was to predict a common pattern of livestock vehicles movement. The analysis were done on GPS data, collected from drivers of livestock vehicles in Jeju Island, South Korea in year 2012 and 2013. The GPS data include the coordinates of moving vehicles according to time and dates, livestock farms and manure keeping sites. 2012 year data was added to ArcGIS and different tools were used for predicting common vehicle moving pattern. The common pattern of year 2012 were determined and considered as predicted common pattern for year 2013. To compare with actual pattern of year 2013 the same analysis was done to find the difference in 2012 and 2013 pattern. When the manure keeping sites and livestock farms were same in both years, as a result common pattern of 2012 and 2013 were similar but difference were found in patterns when the manure keeping sites and livestock farms were changed. In future for more accurate results and to predict the accurate pattern of vehicles movement, more dependent and independent variables will be required to make a suitable model for prediction.

• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.649-654
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• 2009

기온과 더불어 지중온도는 기후 변동 및 농업 환경 예측의 중요한 기준이 되고 있다. 이 논문 에서는 대구지역의 1932년부터 1990년도까지의 토심 0.5m 지중온도의 연평균 변화와 1961년부터 2008년까지의 기온 자료를 이용하여 연평균 기온변화를 살펴보고, 지중온도와 기온의 관계 모델을 도출하였다. 이 모델을 통하여 현재 측정되지 않는 대구의 지중온도를 기온을 이용하여 복원하였으며 그 결과 지중온도는 매년 약 $0.028^{\circ}C$의 증가를 보이고 있다는 사실을 확인할 수 있었다. 복원을 통한 지중온도의 예측은 대구 지역 기후 변화 예측의 중요한 기준이 되며, 농업 환경의 변화에 대한 FTA 협약, 지구온난화 등에서 발생가능한 상황의 대비책을 마련할 수 있는 정보가 되겠다.

• 한국콘텐츠학회논문지
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• 제12권12호
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• pp.126-134
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• 2012

본 연구는 해양문화의 전반적인 가치가 본격화 되고 있는 이 시기에 해양디자인에 대한 디자인적 문제방향을 좀 더 인지과학적인 방향으로 접근 가능한가를 고찰 하였다. 이를 위해 해양디자인 초기 접근방법에 대한 고민과 문제해결과정을 살펴보고 인지적 접근에 의한 디자인 개발방향을 모형화하고자 한다. 연구방법으로 인지과학의 연구방법 가운데 사람의 사고과정을 추적 할 수 있는 프로토콜 분석을 위해 피험자를 선정하여 디자이너의 사고과정 속에서 나타나는 구두조서와 행동조서를 수집하였다. 수집된 자료를 바탕으로 해양디자인 프로세스에서 발생하는 전문적인 행동패턴을 실증적으로 분석하여 디자이너의 디자인행위에 대한 디자인행위그래프 패턴을 예제로 개발하여 객관적이고 체계적 방식으로 연구하고자 하였다. 이러한 행위그래프는 디자인의 초기 개발방향을 살펴볼 수 있으며 문제 해결을 위한 디자이너의 인지구조를 예측할 수 있었다. 향후 해양디자인을 계획하고 디자인할 때 디자이너의 인지적 방향을 예측하는데 기초자료로 활용할 수 있으리라 판단된다.

• 복식문화연구
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• 제18권6호
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• pp.1274-1290
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• 2010

This study analyzed the design elements and fashion images of women's knitwear in collections of Paris, Milan, London and New York between 2003 and 2008, and examined knitwear trends in an effort to verify whether knitwear trends are repeated in certain cycles, whether they show complicated patterns in cycles and yet occur in quasi cycles, or whether they occur non-periodically in complicated forms of chaotic cycles. Trend cycle analysis results are deemed to identify the time series attribute of knit fashions. It also sought to categorize the attribute of various factors influencing knitwear trends with a view to determining relevancy between design elements, and to present the direction of predicting knitwear fashion trends and the progression of short-term knitwear trends. This study reached the following conclusion. According to design elements or fashion images, knitwear fashion trends occur in cycles, quasi cycles, non-periodical cycles. These cyclic characteristics can be used as scientific data for planning knitwear products. The study confirmed close relevancy between fashion images and fashion elements. It identified close relevancy between designs with similar fashion elements and images through coordinates by year and season, and it is possible to make short-term prediction of trend direction through the flow of coordinates. Time series data were insufficient, thereby making it difficult to perfectly verify chaos indices and giving limitations to this study. A study with more time series data will produce a more effective method of predicting and using knitwear fashion trends.

• 한국전문물리치료학회지
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• 제20권4호
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• pp.55-64
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• 2013

The first purpose was to identify the plantar pressure distributions (peak pressure, pressure integral time, and contact area) during level walking, and stair ascent and descent in asymptomatic flexible flatfoot (AFF). The second purpose was to investigate whether peak pressure data during level walking could be used to predict peak pressure during stair walking by identifying correlations between the peak pressures of level walking and stair walking. Twenty young adult subjects (8 males and 12 females, age $21.0{\pm}1.7$ years) with AFF were recruited. A distance greater than 10 mm in a navicular drop test was defined as flexible flatfoot. Each subject performed at least 10 steps during level walking, and stair ascent and descent. The plantar pressure distribution was measured in nine foot regions using a pressure measurement system. A two-way repeated analysis of variance was conducted to examine the differences in the three dependent variables with two within-subject factors (activity type and foot region). Linear regression analysis was conducted to predict peak pressure during stair walking using the peak pressure in the metatarsal regions during level walking. Significant interaction effects were observed between activity type and foot region for peak pressure (F=9.508, p<.001), pressure time integral (F=5.912, p=.003), and contact area (F=15.510, p<.001). The regression equations predicting peak pressure during stair walking accounted for variance in the range of 25.7% and 65.8%. The findings indicate that plantar pressures in AFF were influenced by both activity type and foot region. Furthermore the findings suggest that peak pressure data during level walking could be used to predict the peak pressure data during stair walking. These data collected for AFF can be useful for evaluating gait patterns and for predicting pressure data of flexible flatfoot subjects who have difficulty performing activities such as stair walking. Further studies should investigate plantar pressure distribution during various functional activities in symptomatic flexible flatfoot, and consider other predictors for regression analysis.

• 대한산업공학회지
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• 제39권4호
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• pp.271-277
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• 2013

In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.

• Geomechanics and Engineering
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• 제37권1호
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Ocean Systems Engineering
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• 제14권1호
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• pp.85-99
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• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• 한국컴퓨터정보학회논문지
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• 제29권3호
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• pp.1-10
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• 2024

해상 운송의 대부분을 차지하고 있는 선박의 경로를 예측하는 연구는 해상의 위험을 사전에 탐지하여 사고를 예방할 수 있다. 도로와 달리 해상에는 신호체계가 따로 존재하지 않고, 교통 관리가 어렵기에 해상 안정성을 위해 선박 경로 예측은 필수적이다. 그러나 선박의 경로 데이터셋의 시간 간격은 통신 장애로 인해 불규칙하다. 본 연구는 이 문제를 해결하기 위해 선박 경로 예측에 적합한 보간법을 사용하여 데이터의 시간 간격을 조정하는 방법을 제시한다. 또한, 선박의 경로를 예측하기 위한 선박 경로 예측 딥러닝 모델을 개발하였다. 본 연구의 모델은 선박의 실시간 경로 정보를 담고 있는 AIS 데이터를 통해 선박의 이동패턴을 파악하여 이후에 위치할 선박의 GPS 좌표를 예측하는 LSTM 모델이다. 본 논문은 선형 보간법을 사용한 데이터 전처리 방법과 선박 경로 예측에 적합한 딥러닝 모델을 제시하고, 실험을 통해 MSE 0.0131, Accuracy 0.9467로 본 논문에서 제시하는 방법의 예측 성능이 우수함을 나타낸다.