• Journal of Integrative Natural Science
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• v.14 no.1
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• pp.11-20
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• 2021

On November 17, 2019, an infectious disease with symptoms of pneumonia, called the Wuhan virus at the time, occurred in Wuhan, China. Since then, the name has been changed to COVID-19, and the virus has spread all over the world, and the WHO has declared the highest warning level for infectious diseases, "Pandemic". The coronavirus has also caused great confusion in South Korea. This resulted in large infected people.The first confirmed cases occurred on January 20, 2020, and the number of infected patients is steadily increasing after experiencing several waves, and many corona confirmed cases are also occurring in 2021 after the year. As the whole world enters a pandemic, walls are created between people and people, companies and businesses, and countries and countries, and all growth stops or declines, including human relationships, domestic companies and industries, and foreign industries. As a result, society in general is experiencing a lot of stagnation. Among them, small and medium-sized enterprises (SMEs), which are the basis of all growth in Korea, and youth who are trying to contribute to the national development by entering society, are struggling to find jobs. Even before the coronavirus outbreak, the difficulty of job hunting and the prospect of small and medium-sized businesses were not very good. In this situation, as the country's overall economic situation is poor, the vitality of SMEs has decreased a lot, the prospects are not good, so jobs are reduced, and there are many difficulties due to reluctance to hire new employees. In this study, with 2019 before the corona era and 2020 after the corona era, we compare SMEs before and after the corona era and overall job search and job search activities through average difference analysis, and whether they are affecting through correlation analysis. Through this, it suggests a direction to increase job search through corporate and government policies after raising the prospects of SMEs first.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.4 no.2
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• pp.167-175
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• 1971

A pot experiment with an acid sulfate soil from Kimhae was carried out to find out the cause of toxicity in rice plant. The effect of liming on changes in pH, Eh, Al, and $Fe^{2+}$ in soil and leachate was examined at two-week interval during the growth of rice. Also, total $P_2O_5$, $SiO_2$, Fe and Al contents in plants at harvesting stage were determined. In the early stage, the rice plant in the check soil showed the same healthy growth as did in limed soil even at high Al in soil and leachate. Around panicle forming stage, reddish brown mottlings suddenly infested all over the plants when accompanied with strong reduction, and afterward growth was severely retarded, and finally caused the significant difference in yield. During the strong reduction, significant amount of sulfide was formed only in check soils, but no free $H_2S$ was detected. Appreciable Al was still present in soil and leachate, and $Fe^{2+}$ in check soil was lower than that in limed soil, but $Fe^{2+}$ in leachate was slightly higher. Limed soils were more reduced and produced more $Fe^{2+}$ due to increased microorganism activity in the neutral pH. In the leachate, the check showed slightly higher $Fe^{2+}$ concentration but considerably higher than limed one at later stage. Appreciable amount of Al was detected only in check soil and leachate from transplanting to panicle formation stage. Plant tissues at harvesting stage contained very low P regardless of liming. Uptake of Si was markedly increased by liming. Contents of Fe an Al was markedly higher in check than limed one, but difference in Fe content was more drastic possibly due to more Fe uptake in presence of markedly higher $Fe^{2+}$ in soil and leachate at later growing stage. In conclusion toxic symptom in this acid sulfate soil seems to be primarily due to Al when accompanied with low pH and strong reduction. But association with $Fe^{2+}$ toxicity is not completely excluded. In order to differentiate the effect of $Fe^{2+}$ from that of Al more detailed plant analysis at different stage is required.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.73-87
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• 2016

The purpose of this study is to investigate the empirical results of Analytic Hierarchy Process/Data Envelopment Analysis-Assurance Region(AHP/DEA-AR) by using multiple regression analysis during the period of 2009-2012 with 5 inputs (number of gantry cranes, number of berth, berth length, terminal yard, and mean depth) and 2 outputs (container TEU, and number of direct calling shipping companies). Assurance Region(AR) is the most important tool to measure the efficiency of seaports, because individual seaports are characterized in terms of inputs and outputs. Traditional AHP and multiple regression analysis techniques have been used for measuring the AR. However, few previous studies exist in the field of seaport efficiency measurement. The main empirical results of this study are as follows. First, the efficiency ranking comparison between the two models (AHP/DEA-AR and multiple regression) using the Wilcoxon signed-rank test and Mann-Whitney signed-rank sum test were matched with the average level of 84.5 % and 96.3% respectively. When data for four years are used, the ratios of the significant probability are decreased to 61.4% and 92.5%. The policy implication of this study is that the policy planners of Korean port should introduce AHP/DEA-AR and multiple regression analysis when they measure the seaport efficiency and consider the port investment for enhancing the efficiency of inputs and outputs. The next study will deal with the subjects introducing the Fuzzy method, non-radial DEA, and the mixed analysis between AHP/DEA-AR and multiple regression analysis.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Animal Environmental Science
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• v.16 no.1
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• pp.41-50
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• 2010

The goal of this study was to develop a suitable ventilation system for high-rise hog building (HRHB) for growing-fattening with combined slatted floor pen in second story and in situ manure management system in Korea. The HRHB was constructed as 29m long, 9m wide and 7.6m high for outer dimension with an indoor height of 3.1m and 2.4 for lower and upper floor, respectively. Ventilation systems for each treatment were installed in separated rooms of HRHB. The ventilation types installed in each room were following 3 types: ventilation type 1 (V1), where air was pulled through a circular duct inlet and exhausted by fans; ventilation type 2 (V2), where air was pulled through eave inlet (side ceiling inlet) and exhausted by fans; and ventilation type 3 (V3), where air was pulled through baffled ceiling inlet and exhausted by fans. For each ventilation system, investigated air velocity under minimum, medium and maximum ventilation ratio and air flow pattern inside. The results were as follows; For air flow pattern from top to bottom, V1 showed a homogeneous vertical type, V2 showed a bilateral symmetry type and V3 showed an vertical umbrella type. Under minimum ventilation ratio, air velocity in upper floor (80cm above the slated floor) was similar for V1, V2, and V3. Under maximum ventilation ratio, air velocity in upper floor was undeviating for V1 (0.10~0.26m/s) and varied for V2 (0.12~0.63m/s) while those for V3 was relatively slow and less varied (0.07~0.15m/s). In conclusion, Duct inlet type (V1) can be applied to the development of a new HRHB with additional evaluations such as field test hog feeding.