• 한국컴퓨터정보학회논문지
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• 제27권4호
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• pp.27-36
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• 2022

본 논문에서는 이러한 어류 가공 현장의 문제점을 개선하기 위해서 AI 머신 비전을 이용한 어류의 목표 중량 절단 예측기법을 제안한다. 제안하는 방법은 먼저 입력된 물고기의 평면도와 정면도를 촬영하여 이미지기반의 전처리를 수행한다. 그런 다음 RANSAC(RANdom SAMmple Consensus)를 사용하여 어류의 윤곽선을 추출한 다음 3D 모델링을 사용하여 물고기의 3D 외부 정보를 추출한다. 이어서 추출된 3차원 특징 정보와 측정된 중량 정보를 머신러닝하여 목표 중량에 대한 절단 지점을 예측하기 위한 신경망 모델을 생성한다. 마지막으로 제안기법을 통해 예측된 절단 지점으로 직접 절단한 뒤 그 중량을 측정하였다. 그리고 측정된 무게를 목표 무게와 비교하여 MAE(Mean Absolute Error) 와 MRE(Mean Relative Error)와 같은 평가 방법을 사용해 성능을 평가하였다. 그 결과, 목표 중량과 비교해 3% 이내의 평균 오차율을 달성하였다. 제안된 기법은 향후 자동화 시스템과 연계되어 수산업 발전에 크게 기여할 것으로 전망한다.

• Applied Microscopy
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• 제46권2호
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• pp.93-99
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• 2016

The thickness of specimen is an important factor in microscopic researches. Thicker specimen contains more information, but it is difficult to obtain well focused image with precise details due to optical limit of conventional microscope. Recently, a microscope unit that combines improved illumination system, which allows real time three-dimensional (3D) image and automatic z-stack merging software. In this research, we evaluated the usefulness of this unit in observing thick samples; Golgi stained nervous tissue and ground prepared bone, tooth, and non-transparent small sample; zebra fish teeth. Well focused image in thick samples was obtained by processing z-stack images with Panfocal software. A clear feature of neuronal dendrite branching pattern could be taken. 3D features were clearly observed by oblique illumination. Furthermore, 3D array and shape of zebra fish teeth was clearly distinguished. A novel combination of two channel oblique illumination and z-stack imaging process increased depth of field and optimized contrast, which has a potential to be further applied in the field of neuroscience, hard tissue biology, and analysis of small organic structures such as ear ossicles and zebra fish teeth.

• 수산해양기술연구
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• 제53권4호
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• pp.404-412
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• 2017

A centrifugal cyclone dust collecting apparatus includes a hydro cyclone dust collecting apparatus for separating solid or liquid using liquid or suspension as a medium. In this study, the formation mechanism and improvement of air core and inner air layer were confirmed through Particle Image Velocimetry. These results showed that the modified experimental model was designed in the conventional method suitable for the separation of juvenile fish and eggs. The inlet speed of the multi-stage hydrocyclone dust collector, which can increase the inlet velocity and minimize floatage in the turbulence chamber, was increased from 0.15 to 0.30 m/s. As a result, the air core was stably formed, the inner air layer was increased with increasing speed. In addition, the dust collecting efficiency of egg and juvenile fish was 97.8% on average, It can infer that this system confirmed the ability to efficiently collect particles of $40{\mu}m$ or more.

• 한국정보전자통신기술학회논문지
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• 제16권5호
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• pp.347-353
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• 2023

양식장에서 물고기의 성장을 측정하는 작업은 아직도 사람의 손이 많이 가는 방식을 사용한다. 이 방식은 많은 노동력이 필요하고, 물고기가 스트레스를 받아 폐사율에 악영향을 준다. 이러한 문제를 해결하기 위해 물고기의 성장도를 자동화하기 위한 시스템 FGRS(Fish Growth Regression System)를 제안한다. FGRS는 두 개의 모듈로 구성된다. 첫째는 Yolo v8 기반의 물고기를 디텍팅하는 모듈이고, 둘째는 물고기 영상 데이터를 CNN 기반의 신경망 모델을 이용하여 물고기의 성장도를 예측하는 모듈로 구성된다. 시뮬레이션 결과 학습전에는 예측 오차가 평균 134.2일로 나왔지만 학습 이후 평균 오차가 39.8일 까지 감소했다. 본 논문에서 제안한 시스템을 이용해 생육일을 예측하여 물고기의 성장예측을 활용해 양식장에서의 자동화에 기여할 수 있고, 많은 노동력 감소와 비용 절감 효과를 가져 올 수 있을 것이라 기대한다.

• Clinical and Experimental Reproductive Medicine
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• 제24권3호
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• pp.335-340
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• 1997

Fluorescence in situ hybridization (FISH) techniques allow the enumeration of chromosome abnormalities and from a great potential for many clinical applications. In order to produce quantitative and reproducible results, expensive tools such as a cooled CCD camera and a computer software are required. We have developed a Chromosome Image Processing System (Chips) using FISH that allows the detection and mapping of the genetic aberrations. The aim of our study, therefore, is to evaluate the capabilities of our original system using a black-and-white video camera. As a model system, three repetitive DNA probes (D18Z1, DXZ1, and DYZ3) were hybridized to variety different clinical samples such as human metaphase spreads and interphase nuclei obtained from uncultured peripheral blood lymphocytes, uncultured amniocytes, and germ cells. The visualization of the FISH signals was performed using our system for image acquisition and pseudocoloring. FISH images were obtained by combining images from each of probes and DAPI counterstain captured separately. Using our original system, the aberrations of single or multiple chromosomes in a single hybridization experiment using chromosomes and interphase nuclei from a variety of cell types, including lymphocytes, amniocytes, sperm, and biopsied blastomeres, were enabled to evaluate. There were no differences in the image quality in accordance with FISH method, fluorochrome types, or different clinical samples. Always bright signals were detected using our system. Our system also yielded constant results. Our Chips would permit a level of performance of FISH analysis on metaphase chromosomes and interphase nuclei with unparalleled capabilities. Thus, it would be useful for clinical purposes.

• 스마트미디어저널
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• 제11권4호
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• pp.62-68
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• 2022

수산 양식장 질병 감염의 확산을 사전에 차단을 위해서는 양식장의 수질 환경 및 생육 어류의 상태를 실시간 모니터링하면서 어류의 질병을 예측하는 시스템이 필요하다. 어류 질병 예측의 기존 연구는 이미지 처리 기법이 대부분이었으나 최근에는 딥러닝 기법을 통한 질병 예측방법의 연구가 활발히 진행되고 있다. 본 논문에서는 수산 양식장에서 발생할 수 있는 넙치의 질병을 딥러닝 기술로 예측하는 방법에 대한 연구결과를 소개하고자 한다. 이 방법은 양식장에서 수집된 카메라 영상에 데이터 증강과 전처리 포함하여 질병 인식률의 성능을 높인다. 이것을 통해 질병 어류를 조기 발견으로 양식 어업에서 어류 집단 폐사 등 어업 재해를 예방하고 지역 수산 양식장으로 어류의 질병 확산 피해를 줄여 매출액 감소 차단될 것으로 기대한다.

• 제어로봇시스템학회논문지
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• 제20권8호
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• pp.868-874
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• 2014

This paper proposes a novel mapping algorithm in Omni-directional Vision SLAM based on an obstacle's feature extraction using Lucas-Kanade Optical Flow motion detection and images obtained through fish-eye lenses mounted on robots. Omni-directional image sensors have distortion problems because they use a fish-eye lens or mirror, but it is possible in real time image processing for mobile robots because it measured all information around the robot at one time. In previous Omni-Directional Vision SLAM research, feature points in corrected fisheye images were used but the proposed algorithm corrected only the feature point of the obstacle. We obtained faster processing than previous systems through this process. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we remove the feature points of the floor surface using a histogram filter, and label the candidates of the obstacle extracted. Third, we estimate the location of obstacles based on motion vectors using LKOF. Finally, it estimates the robot position using an Extended Kalman Filter based on the obstacle position obtained by LKOF and creates a map. We will confirm the reliability of the mapping algorithm using motion estimation based on fisheye images through the comparison between maps obtained using the proposed algorithm and real maps.

• 한국수산과학회지
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• 제49권2호
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• pp.224-233
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• 2016

The objective of this study was to develop an artificial neural network (ANN) model for the acoustic identification of commercially important fish species in Korea. A broadband echo acquisition and processing system operating over the frequency range of 85-225 kHz was used to collect and process species-specific, time-frequency feature images from six fish species: black rockfish Sebastes schlegeli, black scraper Thamnaconus modesutus [K], chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, konoshiro gizzard shad Konosirus punctatus and large yellow croaker Larimichthys crocea. An ANN classifier was developed to identify fish species acoustically on the basis of only 100 dimension time-frequency features extracted by the principal components analysis (PCA). The overall mean identification rate for the six fish species was 88.5%, with individual identification rates of 76.6% for black rockfish, 82.8% for black scraper, 93.8% for chub mackerel, 90.6% for goldeye rockfish, 96.9% for konoshiro gizzard shad and 90.6% for large yellow croaker, respectively. These results demonstrate that individual live fish in well-controlled environments can be identified accurately by the proposed ANN model.

• 한국수산과학회지
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• 제48권2호
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• pp.221-232
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• 2015

Broadband echoes measured in live chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, and fat greenling Hexagrammos otakii with different morphologies and internal characteristics were analyzed in time and frequency domains to understand the species-specific echo feature characteristics for classifying fish species. The mean echo image for each time-frequency representation dataset obtained as a function of orientation angle was extracted to mitigate the effect of fish orientation on acoustic scattering. The joint time-frequency content of the broadband echo signals was obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The SPWVDs were analyzed for each echo signature of the three fish species. The results show that the time-frequency analysis provided species-specific echo structure patterns and metrics of the broadband acoustic signals to facilitate fish species classification.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.554-562
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• 2023

This research offers a sports video action recognition approach based on the fish swarm algorithm in light of the low accuracy of existing sports video action recognition methods. A modified fish swarm algorithm is proposed to construct invariant features and decrease the dimension of features. Based on this algorithm, local features and global features can be classified. The experimental findings on the typical sports action data set demonstrate that the key details of sports action can be successfully retained by the dimensionality-reduced fusion invariant characteristics. According to this research, the average recognition time of the proposed method for walking, running, squatting, sitting, and bending is less than 326 seconds, and the average recognition rate is higher than 94%. This proves that this method can significantly improve the performance and efficiency of online sports video motion recognition.