• 농촌지도와개발
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• 제18권1호
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• pp.153-197
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• 2011

The purpose of this study is to look at the development and status of Danish Agricultural Advisory Service (DAAS) and to find some implications on Korean agricultural and rural extension. Agriculture is main industry contributed to economic growth in Denmark. Main factors of this success would be strong farmers' organizations, commercial co-operatives, farmers' active participation in training and education, and independent advisory service owned and managed by farmers. DAAS has unique developmental history. First service was started by local farmer's organization in 1871. Farmers themselves wanted to start advisory service in order to improve the quality of butter. National center of DAAS was established in 1971 in order to disseminate knowledge to local centers, to develop new activities and computer programs, and to deliver in-service training of local advisors. In 2010, one national center with 550 employees and 32 local centers with 2,900 employes are serving for 48,000 farms. The service covers almost all farmers' needs such as production, finance, tax, buildings, crops, livestock, organic production, environment, legal matter. DAAS Academy tries to offer relevant, just-in-time training activities in order to develop the competences of advisors effectively.

• 한국병원경영학회지
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• 제10권3호
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• pp.1-24
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• 2005

This study purports to investigate the determinants of individual personnel rating of the employees at a university hospital in Seoul, Korea. The sample used in this study consisted of 63 nurses, 41 para-medical staff (Clinical Pathologist, and Radiologist), and 67 administrative staff. Independent variables of the study included the achievement level of the selection test (English, major subject, and interview), post-entrance development factors (education and training, career development, supervisory support, co-worker support, and organizational support), and demographic characteristics. Data for the achievement level of the entrance exam and years for the first promotion were collected from the administrative records of the study hospital, while data for the post-entrance development factors were collected from the survey with self-administered questionnaires using 5-point Likert Scale during June 10-25, 2003. Collected data were analyzed using hierarchical multiple regression. The results of the study showed that achievement level of the interview and English exam at the selection test, education and training, organizational support, and supervisory support while working at the hospital, and length of duration (below 8 years) and educational background (4-year college graduates) among demographic variables had significant positive effects on the personnel rating. The results of the study imply that hospital administrators should make an effort to improve the validity of the selection test, and to motivate the employees to receive more education and training.

• International Journal of Internet, Broadcasting and Communication
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• 제10권1호
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• pp.65-70
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• 2018

As technological development is progressing recently, various technologies are actively being studied in the course of the 4th industrial revolution. So, even in the educational field, virtual reality and augmented reality technology are used in educational environments, but specialized additional equipment is required and the price is very expensive. Also, since a plurality of equipment are required for a large number of people, it is urgent to study the technology that can be effectively applied to the industrial education field. So in this paper, we propose an industrial training system for HMD-AR, MPEG-DASH and SOAP based HTTP based Live Machinery Operation using Smartphone to solve the problems of existing system.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Advances in Computational Design
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• 제3권3호
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• pp.289-302
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• 2018

The use of optimum content of supplementary cementing materials (SCMs) such as limestone filler (LF) to blend with Portland cement has been resulted in many environmental and technical advantages, such as increase in physical properties, enhancement of sustainability in concrete industry and reducing $CO_2$ emission are well known. Artificial neural networks (ANNs) have been already applied in civil engineering to solve a wide variety of problems such as the prediction of concrete compressive strength. The feed forward back propagation (FFBP) algorithm and Tan-sigmoid transfer function were used for the ANNs training in this study. The training, testing and validation of data during the backpropagation training process yielded good correlations exceeding 97%. A parametric study was conducted to study the sensitivity of the developed model to certain essential parameters affecting the compressive strength of concrete. The effects and benefits of limestone filler on hardened properties of the concrete such as compressive strength were well established endorsing previous results in the literature. The results of this study revealed that the proposed ANNs model showed a high performance as a feasible and highly efficient tool for simulating the LF concrete compressive strength prediction.

• 동력기계공학회지
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• 제19권6호
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• pp.68-74
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• 2015

NOx emission has been controlled because it is a major cause of the acid rain and effects considerably on formation and destruction of ozone. A SCR system on diesel engine is necessary to clear TierIII, because IMO(International Maritime Organization) plans on tightening regulations to TierIII at $1^{st}$ January 2016. In this study, flow analysis was accomplished with ANSYS Fluent program so that the SCR system would be retrofitted in training ship KAYA and the temperature distributions of exhaust gas in SCR sytem were investigated after it was installed. As a result, it was confirmed that pressure and velocity distributions in SCR system were depended on pipe line shapes, then it was designed as the pressure was lower. The temperature differential between 1 and 3 point was $15^{\circ}C$ because of evaporative latent heat of urea and the temperature of 4 point after catalyst was increased by $5^{\circ}C$ than 3 point because of exothermic reaction.

• 한국해양공학회지
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• 제18권6호
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• pp.44-50
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• 2004

A small, highly-trained crew working on the ship's automation has contributed to the improvement of operations and the labor environment on board ship. However, at the same time, having a small crew adds more responsibility to the ship's officers to safely operate and manage the ship. With the use of information and computer technology, efforts are being made towards the development of a system that will concentrate important information from the various pieces of navigational equipment. The purpose of this study is to set up and implement an integrated marine data networking and communication system on the training-research ship. Information relating to navigation, engine and office automation are investigated and analyzed, and implementation methods associated with navigation, engine and management information system were designed and presented. In addition, the networking system of the navigational signal interface unit for the integrated communication system, and the data communication method between the ship and land are also discussed.

• 한국인터넷방송통신학회논문지
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• 제21권6호
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• pp.111-116
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• 2021

초분광 영상은 적외선 영역의 전자기파 대역을 수백 개의 파장으로 나누어 영상화한 데이터로 다양한 분야에서 물체를 찾거나 분류하는 것에 활용된다. 최근에는 딥러닝을 사용하여 분류하는 방법이 주목받고 있지만 초분광 영상 데이터의 특성으로 인해 초분광 영상을 학습 데이터로 사용하기 위해서는 기존의 가시광 영상과는 다른 처리 기법이 필요하다. 이를 위해 초분광 큐브에서 특정 파장의 영상을 선택하여 Ground Truth 작업을 수행하고 환경정보를 포함하여 데이터를 관리하는 소프트웨어를 개발하였다. 본 논문에서는 해당 소프트웨어의 구성과 기능에 대하여 설명한다.

• Steel and Composite Structures
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• 제49권2호
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• pp.231-244
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• 2023

Many of the recent investigations in the field of geotechnical engineering focused on the bearing capacity theories of multilayered soil. A number of factors affect the bearing capacity of the soil, such as soil properties, applied overburden stress, soil layer thickness beneath the footing, and type of design analysis. An extensive number of finite element model (FEM) simulation was performed on a prototype slope with various abovementioned terms. Furthermore, several non-linear artificial intelligence (AI) models are developed, and the best possible neural network system is presented. The data set is from 3443 measured full-scale finite element modeling (FEM) results of a circular shallow footing analysis placed on layered cohesionless soil. The result is used for both training (75% selected randomly) and testing (25% selected randomly) the models. The results from the predicted models are evaluated and compared using different statistical indices (R² and RMSE) and the most accurate model BBO (R²=0.9481, RMSE=4.71878 for training and R²=0.94355, RMSE=5.1338 for testing) and TLBO (R²=0.948, RMSE=4.70822 for training and R²=0.94341, RMSE=5.13991 for testing) are presented as a simple, applicable formula.

• 방송공학회논문지
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• 제25권3호
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• pp.428-438
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• 2020

영상 내 흔들림은 비디오의 가시성을 떨어뜨리고 영상처리나 영상압축의 효율을 저하시킨다. 최근 디지털 영상처리 분야에 딥러닝이 본격 적용되고 있으나, 비디오 안정화 분야에 딥러닝 적용은 아직 초기 단계이다. 본 논문에서는 Wobbling 왜곡 경감을 위한 triplet 형태의 CNN 기반 비디오 안정화기 구조를 제안하고, 비디오 안정화기 학습을 위한 학습데이터 합성 방법을 제안한다. 제안한 CNN 기반 비디오 안정화기는 기존 딥러닝 기반 비디오 안정화기와 비교되었으며, Wobbling 왜곡은 감소하고 더 안정적인 학습이 이루어지는 결과를 얻었다.