• 융합정보논문지
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• 제9권1호
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• pp.116-122
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• 2019

최근 중소기업은 세계적인 경쟁력을 갖추기 위해서 공정/품질/에너지 데이터 집계가 자동 또는 실시간으로 처리할 수 있는 산업 구조로 급격하게 변화하고 있다. 특히, 중소기업 생산 공정에서 생산되는 실시간 정보 분석은 중소기업의 유의미한 성과들을 분석, 예측, 처방 및 이행하는 새로운 공정 프로세스 형태로 진화해 가고 있다. 본 논문에서는 중소기업에서 생상되는 데이터를 고도화할 수 있도록 중소기업의 자동화 생산 정보 시스템을 빅데이터화 할 수 있는 플랫폼 구축 모델을 제안한다. 제안 모델은 스마트한 중소기업의 데이터 수집을 위해 중소기업에서 생산되는 제품의 기본 정보에 대한 다양한 데이터를 활용해 중소기업의 운영 효율화(컨설팅 및 교육 등) 및 전략적 의사결정을 지원할 수 있는 기능이 있다. 또한, 제안 모델은 종소기업의 정보 공유 및 시스템 연계가 원활하게 서로 다른 지역적 특성 및 분야를 가지는 중소기업들간에 긴밀한 협조가 가능한 것이 특징이다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.155-160
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• 2022

In recent years, Unreal Engine has been increasingly included in the animation process produced in the studio. In this case, there will be more than one of main software, and it is very important to accurately transfer data between the software and Unreal Engine. In animation data, not only the animation data of the character but also the animation data of objects interacting with the character must be individually produced and transferred. Most of the objects that interact with the character have a condition of constraints with the part of character. In this paper, I tried to stipulate the production process for extracting animation data of constrained objects, and to analyze why users experience difficulties due to the complexity of the regulations in the process of executing them. And based on the flowchart prescribed for user convenience, I created a program using a Python script to prove the user's convenience. Finally, by comparing the results generated according to the manual flowchart with the results generated through the script command, it was found that the data were consistent.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.46-46
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• 2017

We developed a rice simulation model for remote-sensing (SIMRIW-RS, Homma et al., 2007) to evaluate rice production and management on a regional scale. Here, we reports its application trial to estimate rice production in farmers' fields in Sendai, Japan. The remote-sensing data for the application was periodically obtained by multispectral camera (RGB + NIR and RedEdge) attached with unmanned air vehicle (UAV). The airborne images was 8 cm in resolution which was attained by the flight at an altitude of 115 m. The remote-sensing data was relatively corresponded with leaf area index (LAI) of rice and its spatial and temporal variation, although the correspondences had some errors due to locational inaccuracy. Calibration of the simulation model depended on the first two remote-sensing data (obtained around one month after transplanting and panicle initiation) well predicted rice growth evaluated by the third remote-sensing data. The parameters obtained through the calibration may reflect soil fertility, and will be utilized for nutritional management. Although estimation accuracy has still needed to be improved, the rice yield was also well estimated. These results recommended further data accumulation and more accurate locational identification to improve the estimation accuracy.

• Journal of Forest and Environmental Science
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• 제29권2호
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• pp.131-137
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• 2013

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.382-395
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• 2021

In this study, character recognition using deep learning is performed among the various defects in the PCB, the purpose of which is to check whether the printed characters are printed correctly on top of components, or the incorrect parts are attached. Generally, character recognition may be perceived as not a difficult problem when considering MNIST, but the printed letters on the PCB component data are difficult to collect, and have very high redundancy. So if a deep learning model is trained with original data without any preprocessing, it can lead to over fitting problems. Therefore, this study aims to reduce the redundancy to the smallest dataset that can represent large amounts of data collected in limited production sites, and to create datasets through data enhancement to train a flexible deep learning model can be used in various production sites. Moreover, ResNet model verifies to determine which combination of datasets is the most effective. This study discusses how to reduce and augment data that is constantly occurring in real PCB production lines, and discusses how to select coresets to learn and apply deep learning models in real sites.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.78-81
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• 2021

스마트공장 구축사업을 통해 제조업의 생산설비에 센서가 설치되고 각종 공정데이터를 실시간으로 수집할 수 있게 되었다. 이를 통해 제조공정의 설비이상으로 인한 생산중단을 줄이기 위해 실시간 설비 이상 탐지에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 생산설비의 이상탐지를 위해 제조데이터를 딥러닝 모델인 Autoencoder(AE), VAE(Variational Autoencoder), AAE(Adversarial Autoencoder)에 적용하여 그 결과를 도출하였다. 제조데이터는 단순 이동 평균 기법과 전처리 과정을 거쳐 입력데이터로 사용하였으며, 단순이동평균 기법의 윈도우 크기와 AE 모델의 특징벡터 크기에 따른 성능분석을 실시하였다.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2009년도 춘계학술대회 논문집
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• pp.705-708
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• 2009

For the Gas hydrate Research and Development in Korea, the prospect area I & II was surveyed and drilled during the first phase. At the result, we succeeded to discovering gas hydrate real sample at BSR reflection and vent structure. This expedition processing contributes to developing the offshore seismic survey technologies and data processing of Korea. But Korean gas hydrate test production research, in spite of activating test production at other countries, is such a limitation about technician, GH production technologies and E&P processing. First of all, there is no exist in Korea to application site for the their production research results. In this paper, we have studied the gas hydrate reservoir selection technics of the DOE & BPXA for the ANS test production. And this result will helpful to preparation of gas hydrate test production in Korea.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.114-117
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• 2006

One of the most important tasks of ocean color observations is to determine the distribution of phytoplankton primary production. A variety of bio-optical algorithms have been developed estimate primary production from these parameters. In this communication, we investigated the possibility of using a novel universal approximator-support vector machines (SVMs)-as the nonlinear transfer function between oceanic primary production and the information that can be directly retrieved from satellite data. The VGPM (Vertically Generalized Production Model) dataset was used to evaluate the proposed approach. The PPARR2 (Primary Production Algorithm Round Robin 2) dataset was used to further compare the precision between the VGPM model and the SVM model. Using this SVM model to calculate the global ocean primary production, the result is 45.5 PgC $yr^{-1}$, which is a little higher than the VGPM result.

• 산업공학
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• 제16권1호
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• pp.111-116
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• 2003

Business integration has been considered as one of the most critical success factors that enable the firms to gain competitive edges. Despite this trend, it has also been found among not a few companies that the activities that should be functionally tied with are performed even independently. In this study, an integrated model of production planning and inventory has been developed. Computerization of the production planning activities is proposed and implemented. We also proposed the reasonable inventory levels of each item using historic data of the items, which are composed of safety stock from the given fill-rate, operating stock from the production patterns, and reserved stock from the production planning. This study has helped the firm to have clearer job definition of the related processes, to tightly control the inventory by setting and tracing the reasonable fill rates for every product, and to quickly respond to the market changes through the computerized production planning process.

• Asian-Australasian Journal of Animal Sciences
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• 제33권12호
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• pp.2039-2049
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• 2020

Objective: This study was conducted to provide models to accurately predict nitrogen (N) and phosphorus (P) excretion of dairy cows on smallholder farms in Indonesia based on readily available farm data. Methods: The generic model in this study is based on the principles of the Lucas equation, describing the relation between dry matter intake (DMI) and faecal N excretion to predict the quantity of faecal N (Q_FN). Excretion of urinary N and faecal P were calculated based on National Research Council recommendations for dairy cows. A farm survey was conducted to collect input parameters for the models. The data set was used to calibrate the model to predict Q_FN for the specific case. The model was validated by comparing the predicted quantity of faecal N with the actual quantity of faecal N (Q_FNACT) based on measurements, and the calibrated model was compared to the Lucas equation. The models were used to predict N and P excretion of all 144 dairy cows in the data set. Results: Our estimate of true N digestibility equalled the standard value of 92% in the original Lucas equation, whereas our estimate of metabolic faecal N was -0.60 g/100 g DMI, with the standard value being -0.61 g/100 g DMI. Results of the model validation showed that the R² was 0.63, the MAE was 15 g/animal/d (17% from Q_FNACT), and the RMSE was 20 g/animal/d (22% from Q_FNACT). We predicted that the total N excretion of dairy cows in Indonesia was on average 197 g/animal/d, whereas P excretion was on average 56 g/animal/d. Conclusion: The proposed models can be used with reasonable accuracy to predict N and P excretion of dairy cattle on smallholder farms in Indonesia, which can contribute to improving manure management and reduce environmental issues related to nutrient losses.