• 한국해양학회지:바다
• /
• 제13권3호
• /
• pp.229-236
• /
• 2008

심해저 망간단괴를 채집하기 위한 집광기는 궤도차량 형태로 해저면을 주행하면서 망간단괴를 채집한다. 집광기의 운영은 수상선에서 실시간 원격으로 제어되며, 이를 위해서는 운영 소프트웨어가 중요한 역할을 차지하게 된다. 현재는 실제 심해저 망간단괴 집광기의 개발에 앞서 시험집광기를 개발하여 근해역 성능 실증시험을 준비중에 있다. 이러한 시험집광기는 기계부와 전기 전자부로 구성되는데, 이를 원격으로 제어, 계측하기 위해서 운영 소프트웨어가 필수적이다. 본 논문에서는 시험집광기의 제어와 계측을 위한 실시간 운영 소프트웨어의 설계와 개발에 대하여 소개하였다. 임베디드시스템으로 PXI 컨트롤러가 사용되고, 소프트웨어 개발툴로는 LabVIEW를 사용하였다. 시험집광기의 효과적인 성능 실증시험을 위하여 본 실시간 운영 소프트웨어가 개발되었다. 아울러 시험집광기의 모니터링 데이터를 관리하기 위한 데이터베이스 관리시스템(DBMS)이 MS SQL과 LabVIEW를 사용하여 개발되었다.

• 한국식품영양학회지
• /
• 제21권4호
• /
• pp.592-598
• /
• 2008

Deep sea water is found more than 200 m under the surface. As no sunlight reaches, no photosynthesis takes place, and it has very little organic matter or bacteria. In addition, deep sea water maintains a consistently low temperature throughout the year, and it does not mix with the water found closer to the surface, which means that its cleanliness is preserved. It is a long-term mature sea water resource that is rich in minerals. This paper examined the physical characteristics and the uses of deep sea water, a subject that has been attracting a great deal of public attention recently, together with the current status of domestic research into it and the direction of research in the USA and Japan, focusing on the existing literature. The aim of this paper was to provide are source to researchers in the field. Since the 1970s, scientists around the world have recognized the importance of deep sea water, and have been conducting research into it. In the USA, deep sea water has been researched with the view of its application to cooling, alternative energy, farming, and the development of new materials. In Japan, about 10 local self-governing bodies are currently promoting research and business relating to deep sea water, which has resulted in a number of products that have been released to the market. In Korea, the ministry of land transport and marine affairs has been studying deep sea water since 2000, and full-scale national R&D projects have been performed by 24 organizations, including KORDI, through industrial/academic cooperation. Large companies are participating in deep sea water research projects in several ways. A study of data foundusing NDSL relating to domestic studies of deep sea water found 50 theses, 177 domestic patents, 6 analyses, 2 reports, and 2 etc. in other areas.

• 방송공학회논문지
• /
• 제27권2호
• /
• pp.174-184
• /
• 2022

3차원 휴먼 자세 추정 모델은 다시점 모델과 단시점 모델로 분류될 수 있다. 일반적으로 다시점 모델은 단시점 모델에 비하여 뛰어난 자세 추정 성능을 보인다. 단시점 모델의 경우 3차원 자세 추정 성능의 향상은 많은 양의 학습 데이터를 필요로 한다. 하지만 3차원 자세에 대한 참값을 획득하는 것은 쉬운 일이 아니다. 이러한 문제를 다루기 위해, 우리는 다시점 모델로부터 다시점 휴먼 자세 데이터에 대한 의사 참값을 생성하고, 이를 단시점 모델의 학습에 활용하는 방법을 제안한다. 또한, 우리는 각각의 다시점 영상으로부터 추정된 자세의 일관성을 고려하는 다시점 일관성 손실함수를 제안하여, 이것이 단시점 모델의 효과적인 학습에 도움을 준다는 것을 보인다. Human3.6M과 MPI-INF-3DHP 데이터셋을 사용한 실험은 제안하는 방법이 3차원 휴먼 자세 추정을 위한 단시점 모델의 학습에 효과적임을 보여준다.

• Smart Structures and Systems
• /
• 제24권6호
• /
• pp.745-757
• /
• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• 한국수산과학회지
• /
• 제41권2호
• /
• pp.77-83
• /
• 2008

This study investigated the quality of tofu prepared by treating soybean milk with deep seawater as a coagulant. The quality and shelf-life of the tofu prepared using the deep seawater coagulant were determined and compared to those using $CaSO_4$, surface seawater, and intermediate seawater coagulant. The tofu made with the deep seawater coagulant was firmest. In addition, the deep seawater tofu product had more, smaller particles in the microscopic view, compared to the tofu made from surface and intermediate seawater coagulants. The deep seawater tofu product had the lowest viable cell counts and turbidity. In addition, the deep seawater tofu product had a longer extended shelf-life. From these results, deep seawater appears to improve the texture, taste, and shelf-life of tofu when used as a coagulant.

• 말소리와 음성과학
• /
• 제7권4호
• /
• pp.9-16
• /
• 2015

In this paper, several experiments are performed according to deep neural network (DNN) based features for the performance comparison of speaker verification (SV) systems. To this end, input features for a DNN, such as mel-frequency cepstral coefficient (MFCC), linear-frequency cepstral coefficient (LFCC), and perceptual linear prediction (PLP), are first compared in a view of the SV performance. After that, the effect of a DNN training method and a structure of hidden layers of DNNs on the SV performance is investigated depending on the type of features. The performance of an SV system is then evaluated on the basis of I-vector or probabilistic linear discriminant analysis (PLDA) scoring method. It is shown from SV experiments that a tandem feature of DNN bottleneck feature and MFCC feature gives the best performance when DNNs are configured using a rectangular type of hidden layers and trained with a supervised training method.

• 동력기계공학회지
• /
• 제20권2호
• /
• pp.79-84
• /
• 2016

The thermodynamic performance of ocean thermal energy conversion with 1 kg/s geothermal water flow rate as a heat source was evaluated to obtain the basic data for the optimal design of cycle with respect to the classification of the working fluid. The basic thermodynamic model for cycle is rankine cycle and the geothermal water and deep seawater were adapted for the heat source of evaporator and condenser, respectively. R245fa, R134a are better to use as a working fluid than others in view of the use of geothermal water. It is important to select the proper working fluid to operate the ocean thermal energy conversion. So, this paper can be used as the basic data for the design of ocean thermal energy conversion with geothermal water and deep seawater.

• Current Optics and Photonics
• /
• 제6권3호
• /
• pp.260-269
• /
• 2022

Segmented planar imaging detector for electro-optical reconnaissance (SPIDER) is an emerging technology for optical imaging. However, this novel detection approach is faced with degraded imaging quality. In this study, a 6 × 6 planar waveguide is used after each lenslet to expand the field of view. The imaging principles of field-plane waveguide structures are described in detail. The local multiple-sampling simulation mode is adopted to process the simulation of the improved imaging system. A novel image-reconstruction algorithm based on deep learning is proposed, which can effectively address the defects in imaging quality that arise during image reconstruction. The proposed algorithm is compared to a conventional algorithm to verify its better reconstruction results. The comparison of different scenarios confirms the suitability of the algorithm to the system in this paper.

• Nuclear Engineering and Technology
• /
• 제54권5호
• /
• pp.1747-1753
• /
• 2022

Compton imaging is the main method for locating radioactive hot spots emitting high-energy gamma-ray photons. In particular, this imaging method is crucial when the photon energy is too high for coded-mask aperture imaging methods to be effective or when a large field of view is required. Reconstruction of the photon source requires advanced Compton event processing algorithms to determine the exact position of the source. In this study, we introduce a novel method based on a Deep Learning algorithm with a Convolutional Neural Network (CNN) to perform Compton imaging. This algorithm is trained on simulated data and tested on real data acquired with Caliste, a single planar CdTe pixelated detector. We show that performance in terms of source location accuracy is equivalent to state-of-the-art algorithms, while computation time is significantly reduced and sensitivity is improved by a factor of ~5 in the Caliste configuration.

• 전자통신동향분석
• /
• 제35권5호
• /
• pp.112-122
• /
• 2020

In 3D computer graphics, a depth map is an image that provides information related to the distance from the viewpoint to the subject's surface. Stereo sensors, depth cameras, and imaging systems using an active illumination system and a time-resolved detector can perform accurate depth measurements with their own light sources. The 3D image information obtained through the depth map is useful in 3D modeling, autonomous vehicle navigation, object recognition and remote gesture detection, resolution-enhanced medical images, aviation and defense technology, and robotics. In addition, the depth map information is important data used for extracting and restoring multi-view images, and extracting phase information required for digital hologram synthesis. This study is oriented toward a recent research trend in deep learning-based 3D data analysis methods and depth map information extraction technology using a convolutional neural network. Further, the study focuses on 3D image processing technology related to digital hologram and multi-view image extraction/reconstruction, which are becoming more popular as the computing power of hardware rapidly increases.