PURPOSES : This study develops a model that can estimate travel speed of each movement flow using deep-learning-based probe vehicles at urban intersections. METHODS : Current technologies cannot determine average travel speeds for all vehicles passing through a specific real-world area under obseravation. A virtual simulation environment was established to collect information on all vehicles. A model estimate turning speeds was developed by deep learning using probe vehicles sampled during information processing time. The speed estimation model was divided into straight and left-turn models, developed as fully-offset, non-offset, and integrated models. RESULTS : For fully-offset models, speed estimation for both straight and left-turn models achieved MAPE within 10%. For non-offset models, straight models using data drawn from four or more probe vehicles achieved a MAPE of less than 15%. The MAPE for left turns was approximately 20%. CONCLUSIONS : Using probe-vehicle data(PVD), a deep learning model was developed to estimate speeds each movement flow. This, confirmed the viability of real-time signal control information processing using a small number of probe vehicles.

PURPOSES : In this study, an empirical approach was established to estimate the parameters of the resilient modulus based on various geotechnical properties of subgrade soils. METHODS : Multiple regression analyses were performed to analyze the relationship between resilient modulus (k1) and deformation. The most important factors are the #200 sieve passing ratio, moisture content, and dry unit weight of the soil. The applicability of this approach was verified using selected field data and the literature. RESULTS : The correlation between the results predicted using the prediction equation of the model constant (k1) and the actual k1-value was high. The applicability of the prediction equation was considered high owing to its high suitability with the existing data. The range of values obtained using the constant prediction equation of the proposed model was also judged to be reasonable. In the comparison of the CBR value of the subgrade material of the actual design section and the predicted elastic modulus (k1), almost no relationship was observed between the CBR and the model coefficient (k1). Thus, the estimation of the elastic modulus through CBR is likely to contain errors. CONCLUSIONS : Based on these results, the parameters of the universal model can be predicted using the stress-dependent modulus model proposed in this study.

of hazardous risk factors, risk estimation and determination steps by reflecting the trend of overseas risk assessment. METHODS : In deriving, estimating and determining risk factors, comparing the procedures presented by the ILO with the domestic guidline to find out the differences in procedural. and, According to the domestic manual, after setting the criteria for determining a deterministic perspective, analyze the risk assessment data of a specific domestic company and three overseas risk assessment research data to analyze the differences in methodology domestic and abroad. RESULTS : Within the country, there is a possibility that a deterministic view may be applied to all stages of procedure, and certain corporate data to the risk estimation and determination stage. In the case of overseas, the trend of applying deterministic perspectives to the risk determination stage was confirmed. CONCLUSIONS : Present the need for a standard model for improving deterministic methods in the other two stages, excluding risk determination in the domestic evaluation procedure.

PURPOSES : Construction cost estimates are important information for business feasibility analysis in the planning stage of road construction projects. The quality of current construction cost estimates are highly dependent on the expert's personal experience and skills to estimate the arithmetic average construction cost based on past cases, which makes construction cost estimates subjective and unreliable. An objective approach in construction cost estimation shall be developed with the use of machine learning. In this study, past cases of road projects were analyzed and a machine learning model was developed to produce a more accurate and time-efficient construction cost estimate in teh planning stage. METHODS : After conducting case analysis of 100 road construction, a database was constructed including the road construction's details, drawings, and completion reports. To improve the construction cost estimation, Mallow's Cp. BIC, Adjusted R methodology was applied to find the optimal variables. Consequently, a plannigs-stage road construction cost estimation model was developed by applying multiple regression analysis, regression tree, case-based inference model, and artificial neural network (ANN, DNN). RESULTS : The construction cost estimation model showed excellent prediction performance despite an insufficient amount of learning data. Ten cases were randomly selected from the data base and each developed machine learning model was applied to the selected cases to calculate for the error rate, which should be less than 30% to be considered as acceptable according to American Estimating Association. As a result of the analysis, the error rates of all developed machine learning models were found to be acceptable with values rangine from 17.3% to 26.0%. Among the developed models, the ANN model yielded the least error rate. CONCLUSIONS : The results of this study can help raise awareness of the importance of building a systematic database in the construction industry, which is disadvantageous in machine learning and artificial intelligence development. In addition, it is believed that it can provide basic data for research to determine the feasibility of construction projects that require a large budget, such as road projects.

증산은 적정 관수 관리에 중요한 역할을 하므로 수분 스트레스에 취약한 토마토와 같은 작물의 관개 수요에 대한 지식이 필요하다. 관수량을 결정하는 한 가지 방법은 증산량을 측정하는 것인데, 이는 환경이나 생육 수준의 영향을 받는다. 본 연구는 분단위 데이터를 통해 수학적 모델과 딥러닝 모델을 활용하여 토마토의 증발량을 추정하 고 적합한 모델을 찾는 것을 목표로 한다. 라이시미터 데이터는 1분 간격으로 배지무게 변화를 측정함으로써 증산 량을 직접 측정했다. 피어슨 상관관계는 관찰된 환경 변수가 작물 증산과 유의미한 상관관계가 있음을 보여주었다. 온실온도와 태양복사는 증산량과 양의 상관관계를 보인 반면, 상대습도는 음의 상관관계를 보였다. 다중 선형 회귀 (MLR), 다항 회귀 모델, 인공 신경망(ANN), Long short-term memory(LSTM), Gated Recurrent Unit(GRU) 모델을 구 축하고 정확도를 비교했다. 모든 모델은 테스트 데이터 세트에서 0.770－0.948 범위의 R2 값과 0.495mm/min－ 1.038mm/min의 RMSE로 증산을 잠재적으로 추정하였다. 딥러닝 모델은 수학적 모델보다 성능이 뛰어났다. GRU 는 0.948의 R2 및 0.495mm/min의 RMSE로 테스트 데이터에서 최고의 성능을 보여주었다. LSTM과 ANN은 R2 값이 각각 0.946과 0.944, RMSE가 각각 0.504m/min과 0.511로 그 뒤를 이었다. GRU 모델은 단기 예측에서 우수한 성능 을 보였고 LSTM은 장기 예측에서 우수한 성능을 보였지만 대규모 데이터 셋을 사용한 추가 검증이 필요하다. FAO56 Penman-Monteith(PM) 방정식과 비교하여 PM은 MLR 및 다항식 모델 2차 및 3차보다 RMSE가 0.598mm/min으로 낮지만 분단위 증산의 변동성을 포착하는 데 있어 모든 모델 중에서 가장 성능이 낮다. 따라서 본 연구 결과는 온실 내 토마토 증산을 단기적으로 추정하기 위해 GRU 및 LSTM 모델을 권장한다.

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

The leopard plant has the characteristic of being used for ornamental purposes when there are yellow spots on the leaves, and is widely used as a bed plant for viewing flowers. To set several indicators to predict the growth of crops with ornamental value, and to quantitatively express the relationship between the indicators are necessary. In this study, we determine a model that estimates the leaf area and the number of flower of Farfugium japonicum Kitam. using leaf length and width, and conducting a regression analysis on some regression models. As an indicator for estimating the leaf area and the number of flower, the leaf length and width of F. japonicum were measured and applied to 8 regression models. As a result of regression analysis of 8 models that estimated leaf area and the number of flower, R2 values of the linear models were all higher than 0.84 and 0.80. As a result of validation, using the most reliable model among the models for estimating the leaf area and the number of flowering, R2 was 0.90 and 0.82, respectively. Using a model that estimates various indicators that can be used for quality evaluation from easy-to-measure morphological factors, the evaluation of ornamental plants will be facilitated.

Welding is one of representative manufacturing processes in the industrial field. Cryogenic storage containers are also manufactured through welding, and conversion to laser welding is issue in the field due to many advantages. Since welding causes thermal-elastic deformation, design considering distortion is required. Prediction of distortion through FEM is essential, but laser welding has difficulties in the field because there is no representative heat source model. The author presented the model that can cover various models using a multi-layer heat source model in previous studies. However the previous study has a limitation which is a welding heat source model must be derived after performing bead on plate welding. Thus this study was attempted to estimate the welding heat source parameters by comparing the shape of bead under various conditions. First, the difference between penetration shape and welding heat source parameters according to welding power was analyzed. The radius of the welding heat source increased according to the welding power, and the depth of the welding heat source also increased. The correlation between the penetration shape and the welding heat source parameter appears at a similar rate, however the follow-up research is necessary with more model data.

선박과 교각이 충돌하면 생명과 안전에 큰 위협이 될 수 있다. 따라서 선박-교각 충돌력 영향 인자를 식별하고 다양한 충돌 조 건에서의 충돌력에 대한 연구의 필요성이 있다. 본 논문에서는 선박-교각 충돌의 유한요소 모델을 설정하고, 수치 시뮬레이션을 통해 선 적상태, 운항속도, 충돌 각도의 세 가지 입력조건을 조합하여 50가지 케이스에서의 선박-교각 최대 충돌력을 계산하였다. 계산된 유한요 소해석 결과를 사용하여 신경망 추정 모델을 학습하고 최대 충돌력을 추정함으로써 빠른 시간에 최대 충돌력을 추정하는 프로세스를 제 안하였다. 신경망 예측 모델은 가장 기초적인 역전파 신경망과 시간정보를 고려할 수 있는 순환신경망인 Elman 신경망 2가지 모델을 사 용하였다. 10가지 케이스의 테스트 데이터로 시험한 결과 Elman 신경망을 사용했을 경우에 평균상대오차가 4.566%로 역전파 신경망보다 나은 최대 충돌력 추정이 가능함을 확인하였고 8가지 케이스에서 5%이하의 상대오차를 보여 주었다. 본 신경망을 이용한 최대 충돌력 추 정법은 유한요소해석을 수행하지 않아도 되므로 계산 시간이 짧아 선박 항해 중 충돌을 회피할 수 없는 경우 피해를 최소화하는 의사결 정의 기초 방법으로 사용할 수 있다.

As the spread of new and renewable power generation facilities, the fixed investment cost CAPEX(Capital Expenditure) of solar power generation facilities decreases due to continuous technological development, and the impact of O&M costs that determine investment success has increased. For this reason, the importance of technologies such as accuracy of O&M cost calculation through ICT, failure prediction, and predictive maintenance have emerged. In the above paper, based on the cost-breakdown structure design and failure rate model design of the solar power generation facility using engineering estimation method, the maintenance cost of the solar power generation facility, which is a renewable power generation facility, is predicted and the maintenance cost used was compared and confirmed. In addition, the cost-breakdown structure and failure rate model of solar power generation facilities were designed and developed by incorporating them into a new program of economic evaluation of new and renewable power generation facilities.

This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

Identifying the water circulation status is one of the indispensable processes for watershed management in an urban area. Recently, various water circulation models have been developed to simulate the water circulation, but it takes a lot of time and cost to make a water circulation model that could adapt the characteristics of the watershed. This paper aims to develop a water circulation state estimation model that could easily calculate the status of water circulation in an urban watershed by using multiple linear regression analysis. The study watershed is a watershed in Seoul that applied the impermeable area ratio in 1962 and 2000. And, It was divided into 73 watersheds in order to consider changes in water circulation status according to the urban characteristic factors. The input data of the SHER(Similar Hydrologic Element Response) model, a water circulation model, were used as data for the urban characteristic factors of each watershed. A total of seven factors were considered as urban characteristic factors. Those factors included annual precipitation, watershed area, average land-surface slope, impervious surface ratio, coefficient of saturated permeability, hydraulic gradient of groundwater surface, and length of contact line with downstream block. With significance probabilities (or p-values) of 0.05 and below, all five models showed significant results in estimating the water circulation status such as the surface runoff rate and the evapotranspiration rate. The model that was applied all seven urban characteristics factors, can calculate the most similar results such as the existing water circulation model. The water circulation estimation model developed in this study is not only useful to simply estimate the water circulation status of ungauged watersheds but can also provide data for parameter calibration and validation.

The slip-weakening model developed by Ohnaka and Yamashita is extended over the breakdown zone by equating the scaling relationships for the breakdown zone and the whole rupture area. For the extension, the study uses the relationship between rupture velocity and radiation efficiency, which was derived in the theory of linear elastic fracture mechanics, and the definition of fmax given in the specific barrier model proposed by Papageorgiou and Aki. The results clearly show that the extended scaling relationship is governed by the ratio of rupture velocity to S wave velocity, and the velocity ratio can be determined by the ratio of characteristic frequencies of a Fourier amplitude spectrum, which are corner frequency, fc, and source-controlled cut-off frequency, fmax, or vice versa. The derived relationship is tested by using the characteristic frequencies extracted from previous studies of more than 130 shallow crustal events (focal depth less than 25 km, MW 3.0~7.5) that occurred in Japan. Under the assumption of a dynamic similarity, the rupture velocity estimated from fmax/fc and the modified integral timescale give quite similar scale-dependence of the rupture area to that given by Kanamori and Anderson. Also, the results for large earthquakes show good agreement to the values from a kinematic inversion in previous studies. The test results also indicate the unavailability of the spectral self-similarity proposed by Aki because of the scale-dependent rupture velocity and the rupture velocity-dependent fmax/fc; however, the results do support the local similarity asserted by Ohnaka. It is also remarkable that the relationship between the rupture velocity and fmax/fc is quite similar to Kolmogorov’s hypothesis on a similarity in the theory of isotropic turbulence.

In this paper, we studied the applicability of data driven model, AAKR(Auto-Associative Kernel Regression) for generator power loss estimation. Correlation analysis performed on 39 turbine system variables for dataset construction and then 13 variables were selected as highly correlated with generator power output. For a memory vector, 95~100% thermal power section data were used and data at normal power condition for 3 month were extracted for query vector. Analysis result shows that 9 variables show good prediction between measured and estimated data, 2 variables show good correlation but with small bias and 2 variables show increasing difference and low correlation with the passage of time, which assumed to be cause of electric output loss.