• 한국수산과학회지
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• 제55권2호
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• pp.183-188
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• 2022

It is a difficult task to estimate parameters in even a simple stock assessment model such as a surplus production model, using only data about temporal catch-per-unit-effort (CPUE) (or survey index) and fishery yields. Such difficulty is exacerbated when time-varying parameters are treated as random effects (aka state variables). To overcome the difficulty, previous studies incorporated somewhat subjective assumptions (e.g., B₁=K) or informative priors of parameters. A key is how to build an objective joint prior of parameters, reducing subjectivity. Given the limited data on temporal CPUEs and fishery yields from 1999-2020 for common squid Todarodes pacificus, we built a joint prior of only two parameters, intrinsic growth rate (r) and carrying capacity (K), based on the resilience level of the population (Froese et al., 2017), and used a Bayesian state-space production assessment model. We used template model builder (TMB), a R package for implementing the assessment model, and estimating all parameters in the model. The predicted annual biomass was in the range of 0.76×10⁶ to 4.06×10⁶ MT, the estimated MSY was 0.13×10⁶ MT, the estimated r was 0.24, and the estimated K was 2.10×10⁶ MT.

• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.645-661
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• 2021

구성작가가 우리나라 방송 제작시스템에 등장하여 핵심적인 생산 주체가 된 지 40년이 넘었다. 이 연구에서는 비드라마인 교양·예능·뉴스·라디오 등 다양한 방송 장르에서 프로그램을 기획·구성하고 자료조사와 섭외, 대본 집필에 이르기까지 무수한 역할을 해온 구성작가의 업무 정체성과 노동 경험에 구체적으로 알아보았다. 현업에서 일하는 20명의 구성작가의 자기기술지를 바탕으로 구성작가의 업무 정체성과 노동 경험에 대해 알아본 결과, 구성작가들은 '없어서는 안 될' 프로그램 생산 주체이자 미디어 문화생산자로서의 정체성을 가지고 있었으며 동시에 명확하지 않은 업무 분담으로 PD가 해야 할 일들을 떠맡고 있다고 느끼고 있었다. 이러한 불평등의 원인은 제작시스템과 고용형태의 문제라고 느끼고 있었으나 개별적으로 해결할 수 없다는 것을 인식하고 자신의 능력을 키워나가거나 일을 얻기 위한 인맥을 쌓고, 무조건 최선을 다하는 태도와 자신의 영역을 확장시키는 방법으로 각자도생의 생존전략을 펼치는 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4439-4455
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• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• Journal of Genetic Medicine
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• 제19권1호
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• pp.7-13
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• 2022

Purpose: Preimplantation genetic testing for monogenic disorders (PGT-M) has been successfully used to prevent couples with monogenic disorders from passing them on to their child. Charcot-Marie-Tooth Disease (CMT) is a genetic disorder characterized by progressive extremity muscle degeneration and loss of sensory function. For the first time in Korea, we report our experience of applying single nucleotide polymorphism genotyping and karyomapping for PGT-M of CMT disease. Materials and Methods: Prior to clinical PGT-M, preclinical tests were performed using genotypes of affected families to identify informative single-nucleotide polymorphisms associated with mutant alleles. We performed five cycles of in vitro fertilization PGT-M in four couples with CMT1A, CMT2A, and CMT2S in CHA Fertility Center, Seoul Station. Results: From July 2020 through August 2021, five cycles of PGT-M with karyomapping in four cases with CMT1 and CMT2 were analyzed retrospectively. A total of 17 blastocysts were biopsied and 15 embryos were successfully diagnosed (88.2%). Ten out of 15 embryos were diagnosed as unaffected (66.7%). Five cycles of PGT-M resulted in four transfer cycles, in which four embryos were transferred. Three clinical pregnancies were achieved (75%) and the prenatal diagnosis by amniocentesis for all three women confirmed PGT-M of karyomapping. One woman delivered a healthy baby uneventfully and two pregnancies are currently ongoing. Conclusion: This is the first report in Korea on the application of karyomapping in PGT-M for CMT patients. This study shows that karyomapping is an efficient, reliable and accurate diagnostic method for PGT-M in various types of CMT diseases.

• Smart Structures and Systems
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• 제31권4호
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• 대한이식학회지
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• 제31권4호
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• pp.170-176
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• 2017

We need to establish an informative guideline to increase inter-institutional and inter-observer reproducibility of renal transplant diagnosis, and to improve the diagnostic ability of pathologists in Korea. A first nation-wide survey for renal transplant pathology was conducted by Renal Pathology Study Group of the Korean Society of Pathologists in 2016, to provide the continued excellence in the transplantation pathology laboratory, and to improve the diagnostic ability for the best treatment of transplant patients. This survey revealed the significant variations in scale, work load and biopsy indications for the renal transplant pathology in various institutions in Korea. The Banff classification were used by all institutions for the diagnosis of renal transplant pathology, but different formats were used: most institutions (70%) used the "2013 Banff classification" while the others were using "2007 Banff classification" (20%) or even older formats. In daily diagnostic practice of the renal allografts, difficulties that pathologists encounter were quite diverse due to different environments they work in. Most respondents agreed that standardized diagnostic practice guidelines, regular education on renal transplant pathology and convenient ways of consultation are further needed. We are currently working toward the enhancement of the expertise of renal pathologists and to increase inter-institutional and inter-observer reproducibility by 1) development of a set of virtual slides of renal allograft biopsies for the training, 2) validation and gathering expert's consensus on the core variables of rejection diagnosis by using virtual slides, and 3) continued education by the developed virtual slide atlas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 융합정보논문지
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• 제12권2호
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• pp.198-205
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• 2022

본 연구의 목적은 20대 남녀의 뷰티 트렌드 정보를 바탕으로 성별에 따른 뷰티 상업광고의 영향력을 파악하고, 일종의 마케팅 수단으로 쓰이는 뷰티 상업광고의 효과를 확인하고자 한다. 이를 위해 외모에 대한 관심과 SNS 사용이 가장 높은 20대 남녀를 대상으로 설문조사를 진행하였다. 여성은 접근성, 신뢰성 정보성이 가장 높은 광고로 모두 유튜브 광고를 택했고 남성은 각각 인스타그램 광고, TV 광고, 유튜브 광고를 택했으며 그 결과 20대 여성에게 가장 영향력 있는 광고는 유튜브 광고, 남성에게 가장 영향력 있는 광고는 접근성이 높은 인스타그램 광고라는 점을 확인하였다. 이러한 결과는 앞으로의 뷰티 시장에서 서비스 및 마케팅 분야에 새로운 전략을 구축하는 데에 활용될 수 있다.

• 대한토목학회논문집
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• 제29권6D호
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• pp.703-709
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• 2009

본 연구는 교통약자의 이동시설에 대한 실태 및 의식조사를 바탕으로 향후 지하철역사에 추가 도입될 교통약자 시설 개선의 전략 수립시 기초자료를 제시하는 것이 목적이다. 본 연구를 위해 선행연구와 교통약자의 이동편의 증진법의 이동시설 관련 평가항목을 선정하고, 설문조사 및 변형된 중요도-만족도(M-IPA) 분석을 수행하였다. 본 연구는 장애인이 시급히 개선을 요구하는 사항에 대한 개선전략을 수립하는 것이 목적이므로 장애인을 중심으로 결과를 서술하였다. 첫째, 종합적인 측면의 M-IPA 분석결과 장애인은 안내시설에 대한 개선이 필요한 것으로 나타났다. 둘째, 세부적인 측정지표별 M-IPA 분석결과를 살펴보면 보행접근로, 안내방송 및 표지판, 경보 및 피난시설, 장애인화장실에 대한 시설개선이 필요한 것으로 나타나 일반인들에 비해 지하철 역사로의 접근과 정보제공에 대한 부분이 중점적으로 부각되는 것을 알 수 있었다. 따라서 장애인들을 위한 시설개선시 접근과 정보제공에 대한 시설을 시급히 개선하는 것이 지하철 역사의 접근과 이동성 제고를 위해 가장 우선순위 높은 개선사항이라 할 수 있을 것이다.

• 한국간호교육학회지
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• 제29권2호
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• pp.170-179
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• 2023

Purpose: This descriptive study aimed to identify the contribution of nurses' communication styles, nurse-mother partnerships, and mothers' anxiety levels to the coping of mothers of hospitalized children, with the goal of establishing effective intervention strategies based on these factors. Methods: Data were collected using a structured questionnaire from July 12 to October 29, 2018. The study's participants were 200 hospitalized children's mothers in the pediatric ward of a university hospital. Results: The coping of hospitalized children's mothers showed a significant relationship with nurses' informative communication style (r=.26, p<.001), affective communication style (r=.28, p<.001), nurse-mother partnership (r=.50, p<.001), authoritative communication style (r=-.28, p<.001), and mothers' anxiety (r=-.23, p=.001). A multiple regression analysis (adjusted R²=.32) indicated that the factors affecting the mothers' coping included nurse-mother partnership (𝛽=.47, p<.001), another caregiver (yes) (𝛽=.17, p=.006), and mothers' subjective health status (very healthy) (𝛽=.15, p=.047). Conclusion: Considering that the formation of cooperative partnerships between mothers and nurses found in this study had a positive effect on the mothers' coping skills, it appears necessary to develop and implement programs for improving nurses' communication skills and ability to form partnerships, beginning from undergraduate education.