• Animal Bioscience
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• v.35 no.11
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• pp.1752-1759
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• 2022

Objective: Effects of direct-fed Enterococcus faecium plus bacteriophages (EF-BP) were investigated as potential substitutes for pharmacological ZnO for weanling pigs. Methods: Dietary treatments were supplementations to a basal diet with none (NC), 3,000-ppm ZnO (PC), 1×10¹⁰ colony-forming units of E. faecium plus 1×10⁸ plaque-forming units (PFU) of anti-Salmonella typhimurium bacteriophages (ST) or 1×10⁶ PFU of each of anti-enterotoxigenic Escherichia coli K88 (F4)-, K99 (F5)-, and F18-type bacteriophages (EC) per kg diet. In Exp 1, twenty-eight 21-day-old crossbred weanling pigs were individually fed one of the experimental diets for 14 days and euthanized for histological examination on intestinal mucosal morphology. In Exp 2, 128 crossbred weanling pigs aged 24 days were group-fed the same experimental diets in 16 pens of 8 piglets on a farm with a high incidence of post-weaning diarrhea. Results: None of the diarrheal score or fecal consistency score (FCS), average daily gain (ADG), gain: feed ratio, structural variables of the intestinal villus, and goblet cell density, differed between the EF-BP (ST+EC) and NC groups, between EF-BP and PC, or between ST and EC, with the exception of greater gain: feed for EF-BP than for PC (p<0.05) during days 7 to 14 (Exp 1). In Exp 2, ADG was less for EF-BP vs PC during days 0 to 7 and greater for EF-BP vs NC during days 7 to 14. FCS peaked on day 7 and declined by day 14. Moreover, FCS was less for EF-BP vs NC, did not differ between EF-BP and PC, and tended to be greater for ST vs EC (p = 0.099). Collectively, EF-BP was comparable to or slightly less effective than PC in alleviating diarrhea and growth check of the weanling pigs, with ST almost as effective as PC, when they were group-fed. Conclusion: The E. faecium-bacteriophage recipe, especially E. faecium-anti-S. typhimurium, is promising as a potential substitute for pharmacological ZnO.

• Korean Circulation Journal
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• v.52 no.8
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• pp.623-631
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• 2022

Background and Objectives: Pulmonary valve replacement (PVR) is the most common operation in adults with congenital heart disease (CHD). There is controversy regarding the best bioprosthesis. We compare the performance of stented bioprosthetic valves (the Mosaic [Medtronic^TM] porcine pericardial against Carpentier Perimount Magna Ease [Edwards^TM] bovine) in pulmonary position in patients with CHD. Methods: Between January 1999 and December 2019, all the PVRs were identified from hospital databases in 2 congenital heart centres in Spain. Valve performance was evaluated using clinical and echocardiographic criteria. Propensity score matching was used to balance the 2 treatment groups. Results: Three hundred nineteen patients were retrospectively identified. After statistical adjustment, 79 propensity-matched pairs were available for comparison Freedom from reintervention for the porcine cohort was 98.3%, 96.1%, and 91.9% at 3, 5, and 10 years and 100%, 98%, and 90.8% for the bovine cohort (p=0.88). Freedom from structural valve degeneration (SVD) for the porcine cohort was 96.9%, 92.8% and 88.7% at 3, 5, and 10 years and 100%, 98%, and 79.1% for the bovine cohort (p=0.38). Bovine prosthesis was associated with a reintervention hazard ratio (HR), 1.12; 95% confidence intervals (CIs), 0.24-5.26; p=0.89 and SVD HR, 1.69 (0.52-5.58); p=0.38. In the first 5 years, there was no difference in outcomes. After 5 years, the recipients of the bovine bioprosthesis were at higher risk for SVD (reintervention HR, 2.08 [0.27-16.0]; p=0.49; SVD HR, 6.99 [1.23-39.8]; p=0.03). Conclusions: Both bioprosthesis have similar outcomes up to 5 years, afterwards, porcine bioprosthesis seem to have less SVD.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.1-11
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• 2024

Bridges crack and become damaged due to age and external factors such as earthquakes, lack of maintenance, and weather conditions. With the number of aging bridge on the rise, lack of maintenance can lead to a decrease in safety, resulting in structural defects and collapse. To prevent these problems and reduce maintenance costs, a system that can monitor the condition of bridge and respond quickly is needed. To this end, existing research has proposed artificial intelligence model that use sensor data to identify the location and extent of cracks. However, existing research does not use data from actual bridge to determine the performance of the model, but rather creates the shape of the bridge through simulation to acquire data and use it for training, which does not reflect the actual bridge environment. In this paper, we propose a bridge safety determination edge AI model that detects bridge abnormalities based on artificial intelligence by utilizing acceleration data from bridge occurring in the field. To this end, we newly defined filtering rules for extracting valid data from acceleration data and constructed a model to apply them. We also evaluated the performance of the proposed bridge safety determination edge AI model based on data collected in the field. The results showed that the F1-Score was up to 0.9565, confirming that it is possible to determine safety using data from real bridge, and that rules that generate similar data patterns to real impact data perform better.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Interdisciplinary Bio Central
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• v.3 no.4
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• pp.14.1-14.9
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• 2011

Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of 'Word' to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• Korean Journal of Biological Psychiatry
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• v.23 no.2
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• pp.63-68
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• 2016

Objectives Haenyeo are Korean professional women breath-hold divers in Jeju island. The aim of this study was to investigate the clinical characteristics of depressed Haenyeo group, compared to non-Haenyeo depressed group. Methods This study included 75 Haenyeo and 340 non-Haenyeo with depressive disorders recruited from the Dementia Early Detection Program in Jeju island. Structural diagnostic interviews were performed using the Korean version of Mini International Neuropsychiatric Interview. All patients completed the questionnaires, including the Subjective Memory Complaints Questionnaire (SMCQ), the Patient Health Questionnaire-15 (PHQ-15), and the Blessed dementia scale. Depression was evaluated by the Korean version of short form the Geriatric Depression Scale (K-SGDS) and cognition was assessed by the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment packet. Results Although the mean scores of the K-SGDS were similar between Haenyeo and non-Haenyeo depressed groups, the Haenyeo group showed a higher mean score on the PSQ-15 (p < 0.001, ANCOVA adjusting for age, the K-SGDS and education). The Haenyeo group showed poorer performance on the Korean Version of Frontal Assessment Batter (p < 0.001), the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (p < 0.018), the word fluency test (p < 0.001), and the word list memory test (p = 0.012) in ANCOVA adjusting for age and education. The mean SMCQ score was higher in the Haenyeo depressed group than in the non-Haenyeo depressed group. Conclusions The Haenyeo depressed group shows cognitive dysfunction, especially frontal lobe dysfunction, compared to the non-Haenyeo depressed group, indicating the Haenyeo depressed group may have more severe frontolimbic dysfunction due to chronic exposure to hypoxia. The Haenyeo depressed group suffers more somatic symptoms than the non-Haenyeo depressed group.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.417-428
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• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.