• Journal of Industrial Technology
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• v.18
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• pp.187-193
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• 1998

In this study, about the fatigue life of welded structure material under fluctuation loading, the prediction life which is produced by using the Histogram Recorder System was compared with the experimental life which is produced by the RMC model which is imported by conception of equivalent stress. In this result, this is represented few difference by comparing prediction life which is produced by damage analysis depended on Miner's rule, by using the Histogram Recorder System, with experimental life which is produced by the RMC load model which is imported by conception of equivalent of stress, therefore fatigue life is easily predicted by using Histogram Recorder System, and result of prediction has equivalent accuracy with other method which is more complex than the Histogram Recorder System. Besides the damage which is produced by stress which is high thirty percentage rank in the stress range of damage inducing, is nearly equal to the damage which is induced the rest of seventy percentage, there fore we can see that damage accumulation which is induced few time overload which is effected welded structure material is great.

• Korean System Dynamics Review
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• v.11 no.4
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• pp.117-142
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• 2010

National territorial policies require a series of dynamic simulations, which would facilitate effectiveness measuring and forecasting works geared towards territorial policies under consideration or implementation. This paper aims at designing an integrated prototype for the proposed territorial policies. After the simulation exercises for the Ochang Industrial Complex(OIC) in Chungbuk Province, this study firstly finds meaningful mismatch phenomena between housing and population increases as the in-migration time lag seems inevitable even after the housing construction is in a mature state. Secondly, the OIC development exerts more significant impact on the number of employees than that of business units. Thirdly, in- and out-migration orders are different during the first and second stages of OIC development. That is, Chungbuk Province records the largest in terms of in-migration volume, followed by the Capital and Non-Capital Regions. Even though Chungbuk Province ranks the top position in the out-migration volume, the rank of the Capital and Non-Capital Regions is reversed: the our-migration volume towards the Non-Capital Region outruns that of the Capital Region.

• Journal of Korean Port Research
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• v.7 no.1
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• pp.13-24
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• 1993

The rapid progress of the intermodal freight transportation in recent years has induced fierce competition among the adjacent hub ports for container transport. This brings increased attention to the evaluation of the port competitive ability. But it is not easy to evaluate the port competitive ability because this belongs to ill-defined system which is composed of ambiguous interacting attributes. Paying attention to this point, this paper deals the competitive ability of container port in Far-East Asia by fuzzy integral evaluation which is adequate to interacting ambiguous attribute problem. For this, the proposed fuzzy evaluation algorithm is applied to the real problem, based on the factors such as cargo volumes, costs, services, infrastructure and geographical sites These are extracted from the precedent study of port competitive ability, etc. The results show that the port evaluation factors come in following order ; services, costs, infrastructure, geographical sites and cargo volumes. There are some interactions(interaction coefficient, ${\lambda}=-0.664$ between evaluation attributes. The port competitive ability comes in following order : Singapore, Hongkong, Kobe, Kaoshiung and Busan. According to the sensitivity analysis, the rank between Busan and Kaoshiung changes when ${\lambda}=0.7$. From the analysis of the results, we confirmed that the proposed fuzzy evaluation algorithm is very effective in the complex-fuzzy problem which is composed of hierarchical structure with interacting attributes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Journal of Korean Academy of Nursing
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• v.51 no.4
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• pp.454-464
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• 2021

Purpose: This study aimed to identify the level of complete knowledge about hand hygiene indications among nurses working at integrated nursing care service wards. Methods: A total of 127 nurses in eight integrated nursing care service wards completed structured sheets while observing a video based on six scenarios developed by the research team. Complete knowledge level was calculated as the percentage (%) of participants who responded correctly to all questions among participants. Complete knowledge levels according to the scenarios were calculated and compared according to general characteristics using the chi-squared test or Wilcoxon rank-sum test. Results: The complete knowledge level for each scenario ranged from 7.9% (scenario 6) to 42.5% (scenarios 4 and 5), and no one had complete knowledge of all scenarios. Only 3.1% of participants demonstrated complete knowledge in more than four scenarios, and 26.0% had complete knowledge of four or more hand hygiene moments. Complete knowledge level per scenario did not differ depending on work experience at hospitals and study wards, or prior hand hygiene training in the last year. Conclusion: As the complete knowledge level regarding hand hygiene moment is very low, it is suggested that regular hand hygiene training should be provided to nurses using video media that reflect real nursing tasks. Thus, they can acquire complete knowledge of when hand hygiene is needed or not during complex nursing work situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4456-4475
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• 2021

The system for re-identifying persons is used to find and verify the persons crossing through different spots using various cameras. Much research has been done to re-identify the person by utilising features with deep-learned or hand-crafted information. Deep learning techniques segregate and analyse the features of their layers in various forms, and the output is complex feature vectors. This paper proposes a distinctive framework called Integrated Level Feature Pattern (ILFP) framework, which integrates local and global features. A new deep learning architecture named modified XceptionNet (m-XceptionNet) is also proposed in this work, which extracts the global features effectively with lesser complexity. The proposed framework gives better performance in Rank1 metric for Market1501 (96.15%), CUHK03 (82.29%) and the newly created NEC01 (96.66%) datasets than the existing works. The mean Average Precision (mAP) calculated using the proposed framework gives 92%, 85% and 98%, respectively, for the same datasets.

• Earthquakes and Structures
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• v.27 no.2
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• pp.83-95
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• 2024

The prediction of seismic behavior of the existing building stock is one of the most impactful and complex problems faced by countries with frequent and intense seismic activities. Human lives can be threatened or lost, the economic life is disrupted and large amounts of monetary reparations can be potentially required. However, authorities at a regional or national level have limited resources at their disposal in order to allocate to preventative measures. Thus, in order to do so, it is essential for them to be able to rank a given population of structures according to their expected degree of damage in an earthquake. In this paper, the authors present a ranking approach, based on Machine Learning (ML) algorithms for pairwise comparisons, coupled with ad hoc ranking rules. The case study employed data from 404 reinforced concrete structures with various degrees of damage from the Athens 1999 earthquake. The two main components of our experiments pertain to the performance of the ML models and the success of the overall ranking process. The former was evaluated using the well-known respective metrics of Precision, Recall, F1-score, Accuracy and Area Under Curve (AUC). The performance of the overall ranking was evaluated using Kendall's tau distance and by viewing the problem as a classification into bins. The obtained results were promising, and were shown to outperform currently employed engineering practices. This demonstrated the capabilities and potential of these models in identifying the most vulnerable structures and, thus, mitigating the effects of earthquakes on society.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.10
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• pp.1038-1045
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• 2006

A series of experiments was conducted to test the compatibilities of two different techniques to determine elemental concentrations by INAA and ICP-MS based on both the NIST SRM 2783(air particulate on filter media) and the field samples for PM10. For NIST SRM the results of INAA were more accurate and precise for all target elements than those of ICP-MS. The comparative data set for PM10 samples collected in an industrial complex area showed that mean of concentration ratio, derived for the two different methods such as C(INNA/ICP-MS), were distinguished from each other: (1) Ba, Cu, K Mg, Na, and Sb: $0.9{\sim}1.1$; (2) Al, Co, Fe, and Mn: $0.8{\sim}1.2$; and (3) Se, Ti, and Zn: >1.3. When the results obtained from both methods were evaluated in terms of regression analysis, paired t-test, and Wilcoxon signed-rank test, the results of many elements determined from PM10 samples(such as Al, Ba, Co, Cu, Fe, K, Mn, Nd, and Sb) exhibited a fairly good agreement between the two methods, despite a wide range of variation.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.

• Korean Journal of Environmental Agriculture
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• v.38 no.4
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• pp.219-224
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• 2019

BACKGROUND: Soil bacteria play important roles in organic matter decomposition and nutrient cycling during the non-growing season. The purpose of this study was to investigate the effects of soil management and chemical properties on the utilization of carbon sources by soil bacteria in paddy fields. METHODS AND RESULTS: The Biolog EcoPlate was used for analyzing community-level carbon substrate utilization profiles of soil bacteria. Soils were collected from the following three types of areas: plain, interface and mountain areas, which were tested to investigate the topology effect. The results of canonical correspondence analysis and Kendall rank correlation analysis showed that soil C/N ratio and NH₄⁺ influenced utilization of carbon sources by bacteria. The utilization of carbohydrates and complex carbon sources were positively correlated with NH₄⁺ concentration. Cultivated paddy fields were compared with adjacent abandoned fields to investigate the impact of cultivation cessation. The level of utilization of putrescine was lower in abandoned fields than in cultivated fields. Monoculture fields were compared with double cropping fields cultivated with barley to investigate the impact of winter crop cultivation. Cropping system altered bacterial use of carbon sources, as reflected by the enhanced utilization of 2-hydroxy benzoic acid under monoculture conditions. CONCLUSION: These results show that soil use intensity and topological characteristics have a minimal impact on soil bacterial functioning in relation to carbon substrate utilization. Moreover, soil chemical properties were found to be important factors determining the physiological profile of the soil bacterial community in paddy fields.