• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.93-98
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• 2023

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.2
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• pp.138-144
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• 2023

In this study, the effect of electrochemical treatment in mitigating alkali leaching into an aquatic environment was investigated. To modify the surface of cement paste, 1000 mA/m² of the direct current was passed through anodic graphite to the external mesh for 4 weeks. Then, the cement paste specimen was exposed to still water in air-tight condition to prevent natural healing of alkali leaching in the water. For 100 days of monitoring in water, the pH value was marginally increased at the electrochemical treatment, while control specimen ranked to the even higher pH accounting for 13.2 in the pH. Moreover, after the pH monitoring, the pH profile for the paste specimen indicated that the electrochemical treatment was effective in securing the higher alkalinity of cement matrix. The water obtained from alkali leaching process, was used to ecological test for Daphnia magna. It was evident that the electrochemical treatment had minimal adverse effect on ecological impact, while control specimen mostly immobilized the standard Daphnia magna.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.444-444
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• 2021

하천의 오염부하량 관리 계획은 지속적인 모니터링을 통한 자료 구축과 모형을 이용한 예측결과를 기반으로 수립된다. 하천의 모니터링과 예측 분석은 많은 예산과 인력 등이 필요하나, 정부의 담당 공무원 수는 극히 부족한 상황이 일반적이다. 이에 정부는 전문가에게 관련 용역을 의뢰하지만, 한국과 같이 지형이 복잡한 지역에서의 오염부하량 배출 특성은 각각 다르게 나타나기 때문에 많은 예산 소모가 발생 된다. 이를 개선하고자, 본 연구는 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 BOD 및 총인의 부하량 예측 모형을 개발하였다. 합성곱 신경망의 입력자료는 일반적으로 RGB (red, green, bule) 사진을 이용하는데, 이를 그래도 오염부하량 예측에 활용하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이에, 본 연구에서는 오염부하량이 수문학적 조건과 토지이용 등의 변수에 의해 결정된다는 인과관계를 만족시키고자 수문학적 속성이 내재된 수문학적 이미지를 합성곱 신경망의 훈련자료로 사용하였다. 수문학적 이미지는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는데, 여기서 각 grid의 수문학적 속성은 SCS 토양보존국(soil conservation service, SCS)에서 발표한 수문학적 토양피복형수 (curve number, CN)를 이용하여 산출한다. 합성곱 신경망의 구조는 2개의 Convolution Layer와 1개의 Pulling Layer가 5회 반복하는 구조로 설정하고, 1개의 Flatten Layer, 3개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 마지막으로 1개의 Dense Layer가 연결되는 구조로 설계하였다. 이와 함께, 각 층의 활성화 함수는 정규화 선형함수 (ReLu)로, 마지막 Dense Layer의 활성화 함수는 연속변수가 도출될 수 있도록 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 연구의 대상지역은 경기도 가평군 조종천 유역으로 선정하였고, 연구기간은 2010년 1월 1일부터 2019년 12월 31일까지로, 2010년부터 2016년까지의 자료는 모형의 학습에, 2017년부터 2019년까지의 자료는 모형의 성능평가에 활용하였다. 모형의 예측 성능은 모형효율계수 (NSE), 평균제곱근오차(RMSE) 및 평균절대백분율오차(MAPE)를 이용하여 평가하였다. 그 결과, BOD 부하량에 대한 NSE는 0.9, RMSE는 1031.1 kg/day, MAPE는 11.5%로 나타났으며, 총인 부하량에 대한 NSE는 0.9, RMSE는 53.6 kg/day, MAPE는 17.9%로 나타나 본 연구의 모형은 우수(good)한 것으로 판단하였다. 이에, 본 연구의 모형은 일반 ANN 모형을 이용한 선행연구와는 달리 2차원 공간정보를 반영하여 오염부하량 모의가 가능했으며, 제한적인 입력자료를 이용하여 간편한 모델링이 가능하다는 장점을 나타냈다. 이를 통해 정부의 물관리 정책을 위한 의사결정 및 부족한 물관리 분야의 행정력에 도움이 될 것으로 생각된다.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.91-101
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• 2023

In this study, we estimated the concentration of chlorophyll-a (Chl-a) using hyperspectral water surface reflectance in an inland weir (Baekjae weir) and estuarine reservoir (Namyang Reservoir) for monitoring the occurrence of algae in freshwater in South Korea. The hyperspectral reflectance was measured by aircraft in Baekjae Weir (BJW) from 2016 to 2017, and a drone in Namyang Reservoir (NYR) from 2020 to 2021. The 30 reflectance bands (BJW: 400-530, 620-680, 710-730, 760-790 nm, NYR: 400-430, 655-680, 740-800 nm) that were highly related to Chl-a concentration were selected using permutation importance. Artificial neural network based Chl-a estimation model was developed using the selected reflectance in both water bodies. And the performance of the model was evaluated with the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The performance evaluation results of the Chl-a estimation model for each watershed was R²: 0.63, 0.82, RMSE: 9.67, 6.99, and MAE: 11.25, 8.48, respectively. The developed Chl-a model of this study may be used as foundation tool for the optimal management of freshwater algal blooms in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.673-683
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• 2008

This study improved the existing method of using the longitudinal strain and concept of influence line to develop Bridge Weigh-in-Motion system without axle detector using the dynamic strain of the bridge girders and concrete slab. This paper first describes the considered algorithms of extracting passing vehicle information from the dynamic strain signal measured at the bridge slab, girders, and cross beams. Two different analysis methods of 1) influence line method, and 2) neural network method are considered, and parameter study of measurement locations is also performed. Then the procedures and the results of field tests are described. The field tests are performed to acquire training sets and test sets for neural networks, and also to verify and compare performances of the considered algorithms. Finally, comparison between the results of different algorithms and discussions are followed. For a PSC I-girder bridge, vehicle weight can be calculated within a reasonable error range using the dynamic strain gauge installed on the girders. The passing lane and passing speed of the vehicle can be accurately estimated using the strain signal from the concrete slab. The passing speed and peak duration were added to the input variables to reflect the influence of the dynamic interaction between the bridge and vehicles, and impact of the distance between axles, respectively; thus improving the accuracy of the weight calculation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.75-84
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• 2009

The present study assesses the chloride threshold level for corrosion of steel in concrete by examining the properties of four different binders used for blended concrete in terms of chloride binding, buffering of cement matrix to a pH fall and the corrosion behaviour. As binders, ordinary Portland cement (OPC), 30% pulverised fuel ash (PFA), 60% ground granulated blast furnace slag (GGBS) and 10% silica fume (SF) were used in a concrete mix. Testing for chloride binding was carried out using the water extraction method, the buffering of cement matrix was assessed by measuring the resistance to an artificial acidification of nitric acid, and the corrosion rate of steel in mortar with chlorides in cast was measured at 28 days using an anodic polarisation technique. Results show that the chloride binding capacity was much affected by $C_{3}A$ content and physical adsorption, and its order was 60% GGBS>30% PFA>OPC>10% SF. The buffering of cement matrix to a pH fall was varied with binder type and given values of the pH. From the result of corrosion test, it was found that the chloride threshold ranged 1.03, 0.65, 0.45 and 0.98% by weight of cement for OPC, 30% PFA, 60% GGBS and 10% SF respectively, assuming that corrosion starts at the corrosion rate of $0.1-0.2{\mu}A/cm^{2}$. The mole ratio of [$Cl^{-}$]:[$H^{+}$], as a new presentation of the chloride threshold, indicated the value of 0.008-0.009, irrespective of binder, which would be indicative of the inhibitive characteristic of binder.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.200-201
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• 2023

Digital twin technology is used in various fields as a method of creating a virtual world to minimize the cost of solving problems in the real world, and is also actively used in the maritime field, such as large-scale systems such as ships and offshore plants. In this paper, we tried to build a digital twin of coastal waters using a ship-handling simulator. The digital twin of the coastal waters developed in this way can be used to safely manage Korea's coastal waters, where maritime traffic is complicated, by providing a actual maritime traffic data. It can be usefully used to develop and advance technologies related to maritime autonomous surface ships and intelligent maritime traffic information services in coastal waters. In addition, it can be used as a 3D-based monitoring equipment for areas where physical monitoring is difficult but real-time maritime traffic monitoring is necessary, and can provide functions to safely manage maritime traffic situations such as aerial views of ports/control areas, bridge views/blind sector views of ships in operation.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.4
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• pp.183-205
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• 2023

This study was conducted to identify the operation status of Dobong-gu public libraries in order to establish a mid- to long-term development plan for Dobong-gu libraries, and to analyze local residents' perceptions of libraries by factors through user surveys to suggest specific development plans for Dobong-gu libraries. Overall, the satisfaction level of Dobong-gu residents with library services was found to be high, and all libraries were analyzed to be providing high-quality services. From this, the following operational strategies for the mid- to long-term development plan of Dobong-gu public libraries can be derived. First, libraries need to continue to collect and provide books that meet the quantitative and qualitative levels to satisfy the needs of the community and users. Electronic and online materials, including e-books and audiobooks, as well as subject-specific materials should be expanded to provide relevant programs. Second, although Dobong-gu is an aging city, a wide range of age live there, so there is a need to promote communication and understanding between generations and promote integration of the community through a generational empathy program. Third, it is necessary to remodel and improve the space of aging libraries by organizing library facilities and environments into open spaces and creating makerspaces and multicultural spaces for direct experience and practice, reflecting the latest trends in library space organization.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.