• 기술경영경제학회:학술대회논문집
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• 기술경영경제학회 1996년도 제10회 동계학술발표회 논문집
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• pp.77-98
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• 1996

This study is a critical assessment of research productivity through publication among scientists and engineers. This study scrutinizes previous findings on the correlates and determinant3 of publication productivity: Provides overview and organization of that knowledge ; indicates gape and shortcomings n the research; and identifies the questions and issues which are both answered and unanswered. through the analysis of the 223 mail questionnaires collected from professors of mechanical engineering, electrical engineering, chemistry and physics, this study obtains the particular determinants of publication productivity at the science and engineering schools in Korea. Especially, early research productivity and the number of doctoral students are very important to publish good research articles. Also the qualities of professors' Ph. D. institution and employing university are critical influencing factors to publication productivity. The data are analyzed using correlation, ANOVA, and multiple regression analysis and all the regression models are statistically significant. All the variables in this study are focused on the socialization of individual research scientists and any psychological or personal background variables are excluded, because the perspective of this article is not that of scientific sociologist but of science and technology Policy interest. This study proves that there exists an scriptive advantage according to the individual background such as his Ph. D. institution and employing university in Korea. This study also shows that all research resources and research performances are unequally distributed. This result proposes that supporting basic research at university must begins with relative assessment of researchers, departments, and institutions in consideration with their research environment and to evaluate researchers in compared with excellent research university like SNU, KAIST, POSTECH is unequal and inadequate.

• Structural Engineering and Mechanics
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• 제52권3호
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• pp.485-505
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• 2014

This study aimed to develop an approach to accurately predict the wind models and wind effects of large wind turbines. The wind-induced vibration characteristics of a 5 MW tower-blade coupled wind turbine system have been investigated in this paper. First, the blade-tower integration model was established, which included blades, nacelle, tower and the base of the wind turbine system. The harmonic superposition method and modified blade element momentum theory were then applied to simulate the fluctuating wind field for the rotor blades and tower. Finally, wind-induced responses and equivalent static wind loads (ESWL) of the system were studied based on the modified consistent coupling method, which took into account coupling effects of resonant modes, cross terms of resonant and background responses. Furthermore, useful suggestions were proposed to instruct the wind resistance design of large wind turbines. Based on obtained results, it is shown from the obtained results that wind-induced responses and ESWL were characterized with complicated modal responses, multi-mode coupling effects, and multiple equivalent objectives. Compared with the background component, the resonant component made more contribution to wind-induced responses and equivalent static wind loads at the middle-upper part of the tower and blades, and cross terms between background and resonant components affected the total fluctuation responses, while the background responses were similar with the resonant responses at the bottom of tower.

• Asian Pacific Journal of Cancer Prevention
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• 제17권5호
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• pp.2369-2374
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• 2016

Background: Differentiated thyroid cancer (DTC) is a cancer group that shares molecular and cellular origin but shows different clinical courses and prognoses. Several prognostic factors have been reported for predicting recurrence for individual patients. This literature review aimed to evaluate prognostic scores for predicting recurrence of DTC. Materials and Methods: A search of the MEDLINE database for articles published until December 2015 was carried out using the terms "thyroid neoplasms AND (recurrent OR persistent) AND (score OR model OR nomogram)". Studies were eligible for review if they indicated the development of prognostic scoring models, derived from a group of independent prognostic factors, in predicting disease recurrence in DTC patients. Results: Of the 308 articles obtained, five were eligible for evaluation. Two scoring models were developed for DTC including both papillary and follicular carcinoma, one for papillary carcinoma, and the other two for papillary microcarcinoma. The number of patients included in the score development cohort ranged from 59 to 1,669. The number of evaluated potential prognostic factors ranged from 4 to 25. Tumor-related factors were the most common factors included in the final scores, with cervical lymph node metastases being the most common. Only two studies showed internal validation of the derived score. Conclusions: There is a paucity of prognostic scores for predicting disease recurrence in patients with DTC, in particular for follicular thyroid carcinoma. Several limitations of the created scores were found. Performance of the scores has not been adequately studied. Comprehensive validation in multiple cohorts is recommended before widespread use.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• 지구물리
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• 제9권3호
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• pp.231-240
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• 2006

The crustal structure of the Korean Peninsula was investigated by analyzing phase velocity dispersion data of Rayleigh waves. Earthquakes recorded by three component broad-band velocity seismographs during 1999-2004 in South Korea were used in this study. The fundamental mode Rayleigh waves were extracted from vertical components of seismograms by multiple filter technique and phase match filter method. Phase velocity dispersion curves of the fundamental mode signal pairs for 14 surface wave propagation paths on the great circle in the range 10 to 80 sec were computed by two-station method. Treating the shear velocity of each layer as an independent parameter, phase velocity data of Rayleigh wave were inverted. All the result models can be explained by a rather homogeneous crust of shear-wave velocity increasing from 2.8 to 3.25 km/sec from top to about 33 km depth without any distinctive crustal discontinuities and an uppermost mantle of shear-wave velocity between 4.55 and 4.67 km/sec. Our results turn out to agree well with recent study of Cho et al. (2006 b) based on the analysis of seismic background noises to recover short-period (0.5-20 sec) Rayleigh- and Love-wave group velocity dispersion characteristics.

• 전자통신동향분석
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• 제35권3호
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• pp.20-33
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• 2020

Temporal action detection (TAD) in untrimmed videos is an important but a challenging problem in the field of computer vision and has gathered increasing interest recently. Although most studies on action in videos have addressed action recognition in trimmed videos, TAD methods are required to understand real-world untrimmed videos, including mostly background and some meaningful action instances belonging to multiple action classes. TAD is mainly composed of temporal action localization that generates temporal action proposals, such as single action and action recognition, which classifies action proposals into action classes. However, the task of generating temporal action proposals with accurate temporal boundaries is challenging in TAD. In this paper, we discuss TAD technologies that are considered high performance in terms of representative TAD studies based on deep learning. Further, we investigate evaluation methodologies for TAD, such as benchmark datasets and performance measures, and subsequently compare the performance of the discussed TAD models.

• Journal of Chest Surgery
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• 제47권6호
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• pp.523-528
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• 2014

Background: Numerous statistical models have been developed to accurately predict outcomes in multiple trauma patients. However, such trauma scoring systems reflect the patient's physiological condition, which can only be determined to a limited extent, and are difficult to use when performing a rapid initial assessment. We studied the predictive ability of the systemic inflammatory response syndrome (SIRS) score compared to other scoring systems. Methods: We retrospectively reviewed 229 patients with multiple trauma combined with chest injury from January 2006 to June 2011. A SIRS score was calculated for patients based on their presentation to the emergency room. The patients were divided into two groups: those with an SIRS score of two points or above and those with an SIRS score of one or zero. Then, the outcomes between the two groups were compared. Furthermore, the ability of the SIRS score and other injury severity scoring systems to predict mortality was compared. Results: Hospital death occurred in 12 patients (5.2%). There were no significant differences in the general characteristics of patients, but the trauma severity scores were significantly different between the two groups. The SIRS scores, number of complications, and mortality rate were significantly higher in those with a SIRS score of two or above (p<0.001). In the multivariant analysis, the SIRS score was the only independent factor related to mortality. Conclusion: The SIRS score is easily calculated on admission and may accurately predict mortality in patients with multiple traumas.

• Journal of Ecology and Environment
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• 제48권2호
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• pp.196-206
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• 2024

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R² and adjusted [adj]-R²), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R² ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH₁₀), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R² varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH₁₀ as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGB_Stem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD²H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH₃₀)} AGB_Branch = exp {-131.6 + 15.0013 Ln (ρD²H) + 13.176 Ln (CW) + 21.8506 Ln (DSH₃₀)} AGB_Foliage = exp {-0.9496 + 0.5282 Ln (DSH₃₀) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGB_Total = exp {-1.8245 + 1.4358 Ln (DSH₃₀) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.

• Journal of Veterinary Science
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• 제23권1호
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• pp.17.1-17.10
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• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

• Journal of Yeungnam Medical Science
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• 제40권1호
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• pp.78-85
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• 2023

Background: Poor sleep quality is associated with psychoactive substance abuse/addiction/withdrawal. Auricular acupuncture (AA) is a nonpharmacological method used for the treatment of sleep disturbances. This study aimed to examine the quality of sleep before and after AA in participants with mental and behavioral disorders due to prior multiple drug use in the therapeutic community. Methods: This was a consecutive case series of 27 participants (25 male [92.6%]). The median age was 35.0 years (interquartile range [IQR], 29.0-37.2 years), methadone/buprenorphine were not used, and the participants were treated with AA (median number of treatments, 15.0 [IQR, 12.0-18.0]) during a median period of 51.0 days (IQR, 49.0-51.0 days) according to the National Acupuncture Detoxification Association (NADA)-Acudetox protocol. Sleep quality was determined using the Pittsburgh Sleep Quality Index (PSQI), a self-rated questionnaire that assesses sleep quality and disturbances over a 1-month interval. Results: The global PSQI score dropped (indicating better sleep quality) by a median of 3.0 points (IQR, 0.0-8.0 points) after treatment. In the multivariate logistic regression analysis, with an increase in global PSQI score during AA by 1 point, there was a 0.73-fold reduction in the risk of poor sleep quality post-AA (adjusted odds ratio, 0.73; 95% confidence interval, 0.52-1.01; p<0.055; Nagelkerke's R² =0.66). Conclusion: The results revealed a positive effect of AA (by the NADA-Acudetox protocol) on sleep quality (as measured by PSQI) among participants in a treatment center with mental and behavioral disorders due to multiple drug use.