• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.4
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• pp.307-319
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• 2009

Carbon dioxide capture and storage (CCS) technology has been regarded as one of the most possible and practical option to reduce the emission of carbon dioxide ($CO_2$) and consequently to mitigate the climate change. Korean government also have started a 10-year R&D project on $CO_2$ storage in sea-bed geological structure including gas field and deep saline aquifer since 2005. Various relevant researches are carried out to cover the initial survey of suitable geological structure storage site, monitoring of the stored $CO_2$ behavior, basic design of $CO_2$ transport and storage process and the risk assessment and management related to $CO_2$ leakage from engineered and geological processes. Leakage of $CO_2$ to the marine environment can change the chemistry of seawater including the pH and carbonate composition and also influence adversely on the diverse living organisms in ecosystems. Recently, IMO (International Maritime Organization) have developed the risk assessment and management framework for the $CO_2$ sequestration in sub-seabed geological structures (CS-SSGS) and considered the sequestration as a waste management option to mitigate greenhouse gas emissions. This framework for CS-SSGS aims to provide generic guidance to the Contracting Parties to the London Convention and Protocol, in order to characterize the risks to the marine environment from CS-SSGS on a site-specific basis and also to collect the necessary information to develop a management strategy to address uncertainties and any residual risks. The environmental risk assessment (ERA) plan for $CO_2$ storage work should include site selection and characterization, exposure assessment with probable leak scenario, risk assessment from direct and in-direct impact to the living organisms and risk management strategy. Domestic trial of the $CO_2$ capture and sequestration in to the marine geologic formation also should be accomplished through risk management with specified ERA approaches based on the IMO framework. The risk assessment procedure for $CO_2$ marine storage should contain the following components; 1) prediction of leakage probabilities with the reliable leakage scenarios from both engineered and geological part, 2) understanding on physio-chemical fate of $CO_2$ in marine environment especially for the candidate sites, 3) exposure assessment methods for various receptors in marine environments, 4) database production on the toxic effect of $CO_2$ to the ecologically and economically important species, and finally 5) development of surveillance procedures on the environmental changes with adequate monitoring techniques.

• International Commerce and Information Review
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• v.15 no.3
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• pp.353-384
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• 2013

When it comes to the current inter-Korean relationship, the two Koreas are in the step of core practical negotiation on exchanges and cooperation away from deadlock so far. It is expected that they will have more and frequent meetings in the future. Therefore, now is the time the South Korea needed to come up with systematic countermeasures because there is nothing more important and giving more impact on our society than the matter of North Korea. As the purpose of social science lies with the explanation and prediction of the social phenomena in the society, it is considered to be meaningful to analyze the representative military negotiations such as the defense ministry-level talks, general-level talks, and working-level talks between the two Koreas where the participants from South Korea consisting of the military representatives discussed with their counterparts of North Korea since the signing of the armistice in Korea on July 27, 1953. This study analyzes and evaluates the behaviors of North Korea's military negotiations with the South Korea in the Kim Jong-il era on the overall basis. In particular, the research tries to prove that the behaviors of military negotiations under Kim Jeong-il regime were made in the frame of the negotiation model by analyzing many negotiations presented in 'With Century', Kim Il Sung's Memoirs under his anti-Japan-guerilla era and suggesting the analysis frame of anti-Japan-guerilla style negotiation model. Based on the results of this proof, the study looks at carefully the specific characteristics of anti-Japan guerrilla-type negotiation.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of dental hygiene science
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• v.7 no.4
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• pp.251-256
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• 2007

The purpose of this study was to examine what induced dental hygienists to take up another employment and whether their job satisfaction had anything to do with it in an attempt to help curtail their turnover rate. The subjects in this study were approximately 200 dental hygienists who worked in dental institutions. A survey was conducted from July 24 through September 24, 2006, by using structured, self-administered questionnaires. For data analysis, SPSS 11.5 program was employed to see if their turnover experience was linked to their general characteristics, why they took up another employment, how long they wanted to do that and how their job satisfaction was related to that. The findings of the study were as follows: 1. In regard to turnover experience by age, marital status and career, those who had ever changed their employment accounted for 36.2 percent of the age group from 24 to 26, 83.0 percent of the unmarried ones and 50.0 percent of those whose career was less than one to three years (p < 0.001). By monthly mean income, 50.0 percent of the dental hygienists whose monthly mean income ranged from 1.0 to 1.29 million won had that experience(p < 0.05). The gap between these groups and the others was statistically significant. 2. As for the reason of turnover, working environments were cited most often(28.1%), followed by possibilities(18.0%), relationship with supervisors and colleagues(12.4%), and compensation(4.5%). 3. Concerning a preferred new workplace, 66.2 percent of the dental hygienists who worked in dentist's offices hoped to be newly hired by public dental clinics(p < 0.001). By education, 64.3 percent of the college-educated dental hygienists wanted to work at public dental clinics as well(p < 0.01). 4. The change of employment was under the greatest influence of the possibilities of workplace, followed by workload, pay and relationship with colleagues. All the factors had a negative impact on their turnover. Those who were less satisfied sought new employment more often, and job satisfaction made a statistically significant difference to that. The job satisfaction factors made a prediction of their turnover intention ($R^2=.254$).

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.143-159
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• 2017

Due to the rapid development of IT in recent years, not only personal information but also the key technologies and information leakage that companies have are becoming important issues. For the enterprise, the core technology that the company possesses is a very important part for the survival of the enterprise and for the continuous competitive advantage. Recently, there have been many cases of technical infringement. Technology leaks not only cause tremendous financial losses such as falling stock prices for companies, but they also have a negative impact on corporate reputation and delays in corporate development. In the case of SMEs, where core technology is an important part of the enterprise, compared to large corporations, the preparation for technological leakage can be seen as an indispensable factor in the existence of the enterprise. As the necessity and importance of Information Security Management (ISM) is emerging, it is necessary to check and prepare for the threat of technology infringement early in the enterprise. Nevertheless, previous studies have shown that the majority of policy alternatives are represented by about 90%. As a research method, literature analysis accounted for 76% and empirical and statistical analysis accounted for a relatively low rate of 16%. For this reason, it is necessary to study the management model and prediction model to prevent leakage of technology to meet the characteristics of SMEs. In this study, before analyzing the empirical analysis, we divided the technical characteristics from the technology value perspective and the organizational factor from the technology control point based on many previous researches related to the factors affecting the technology leakage. A total of 12 related variables were selected for the two factors, and the analysis was performed with these variables. In this study, we use three - year data of "Small and Medium Enterprise Technical Statistics Survey" conducted by the Small and Medium Business Administration. Analysis data includes 30 industries based on KSIC-based 2-digit classification, and the number of companies affected by technology leakage is 415 over 3 years. Through this data, we conducted a randomized sampling in the same industry based on the KSIC in the same year, and compared with the companies (n = 415) and the unaffected firms (n = 415) 1:1 Corresponding samples were prepared and analyzed. In this research, we will conduct an empirical analysis to search for factors influencing technology leakage, and propose an early warning system through data mining. Specifically, in this study, based on the questionnaire survey of SMEs conducted by the Small and Medium Business Administration (SME), we classified the factors that affect the technology leakage of SMEs into two factors(Technology Characteristics, Organization Characteristics). And we propose a model that informs the possibility of technical infringement by using Support Vector Machine(SVM) which is one of the various techniques of data mining based on the proven factors through statistical analysis. Unlike previous studies, this study focused on the cases of various industries in many years, and it can be pointed out that the artificial intelligence model was developed through this study. In addition, since the factors are derived empirically according to the actual leakage of SME technology leakage, it will be possible to suggest to policy makers which companies should be managed from the viewpoint of technology protection. Finally, it is expected that the early warning model on the possibility of technology leakage proposed in this study will provide an opportunity to prevent technology Leakage from the viewpoint of enterprise and government in advance.

• Proceedings of the Korea Environmental Mutagen Society Conference
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• 2003.10a
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• pp.34-63
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• 2003

Occupational and environmental exposure to manganese continue to represent a realistic public health problem in both developed and developing countries. Increased utility of MMT as a replacement for lead in gasoline creates a new source of environmental exposure to manganese. It is, therefore, imperative that further attention be directed at molecular neurotoxicology of manganese. A Need for a more complete understanding of manganese functions both in health and disease, and for a better defined role of manganese in iron metabolism is well substantiated. The in-depth studies in this area should provide novel information on the potential public health risk associated with manganese exposure. It will also explore novel mechanism(s) of manganese-induced neurotoxicity from the angle of Mn-Fe interaction at both systemic and cellular levels. More importantly, the result of these studies will offer clues to the etiology of IPD and its associated abnormal iron and energy metabolism. To achieve these goals, however, a number of outstanding questions remain to be resolved. First, one must understand what species of manganese in the biological matrices plays critical role in the induction of neurotoxicity, Mn(II) or Mn(III)? In our own studies with aconitase, Cpx-I, and Cpx-II, manganese was added to the buffers as the divalent salt, i.e., $MnCl_2$. While it is quite reasonable to suggest that the effect on aconitase and/or Cpx-I activites was associated with the divalent species of manganese, the experimental design does not preclude the possibility that a manganese species of higher oxidation state, such as Mn(III), is required for the induction of these effects. The ionic radius of Mn(III) is 65 ppm, which is similar to the ionic size to Fe(III) (65 ppm at the high spin state) in aconitase (Nieboer and Fletcher, 1996; Sneed et al., 1953). Thus it is plausible that the higher oxidation state of manganese optimally fits into the geometric space of aconitase, serving as the active species in this enzymatic reaction. In the current literature, most of the studies on manganese toxicity have used Mn(II) as $MnCl_2$ rather than Mn(III). The obvious advantage of Mn(II) is its good water solubility, which allows effortless preparation in either in vivo or in vitro investigation, whereas almost all of the Mn(III) salt products on the comparison between two valent manganese species nearly infeasible. Thus a more intimate collaboration with physiochemists to develop a better way to study Mn(III) species in biological matrices is pressingly needed. Second, In spite of the special affinity of manganese for mitochondria and its similar chemical properties to iron, there is a sound reason to postulate that manganese may act as an iron surrogate in certain iron-requiring enzymes. It is, therefore, imperative to design the physiochemical studies to determine whether manganese can indeed exchange with iron in proteins, and to understand how manganese interacts with tertiary structure of proteins. The studies on binding properties (such as affinity constant, dissociation parameter, etc.) of manganese and iron to key enzymes associated with iron and energy regulation would add additional information to our knowledge of Mn-Fe neurotoxicity. Third, manganese exposure, either in vivo or in vitro, promotes cellular overload of iron. It is still unclear, however, how exactly manganese interacts with cellular iron regulatory processes and what is the mechanism underlying this cellular iron overload. As discussed above, the binding of IRP-I to TfR mRNA leads to the expression of TfR, thereby increasing cellular iron uptake. The sequence encoding TfR mRNA, in particular IRE fragments, has been well-documented in literature. It is therefore possible to use molecular technique to elaborate whether manganese cytotoxicity influences the mRNA expression of iron regulatory proteins and how manganese exposure alters the binding activity of IPRs to TfR mRNA. Finally, the current manganese investigation has largely focused on the issues ranging from disposition/toxicity study to the characterization of clinical symptoms. Much less has been done regarding the risk assessment of environmenta/occupational exposure. One of the unsolved, pressing puzzles is the lack of reliable biomarker(s) for manganese-induced neurologic lesions in long-term, low-level exposure situation. Lack of such a diagnostic means renders it impossible to assess the human health risk and long-term social impact associated with potentially elevated manganese in environment. The biochemical interaction between manganese and iron, particularly the ensuing subtle changes of certain relevant proteins, provides the opportunity to identify and develop such a specific biomarker for manganese-induced neuronal damage. By learning the molecular mechanism of cytotoxicity, one will be able to find a better way for prediction and treatment of manganese-initiated neurodegenerative diseases.

• Journal of Korea Water Resources Association
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• v.41 no.11
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• pp.1079-1094
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• 2008

This study is to evaluate the impact of varying spatial resolutions on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, non-point source (NPS) pollution loads transport in a small agricultural watershed (1.21 $km^2$) for three cases of model input; Case A is the combination of 2 m DEM, QuickBird land use, Case B is the combination of 10 m DEM, 1/25,000 land use, and Case C is the combination of 30 m DEM, Landsat land use, soil data is used 1/25,000 for three cases respectively. The model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality records, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and RMSE were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The model was run for a small agricultural watershed with three cases of spatial input data. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow. On the other hand, The NPS loadings from the model prediction showed that the sediment, T-N and T-P of QuickBird land use (Case A) showed 23.7 %, 43.3 % and 48.4 % higher value than 1/25,000 land use (Case B) and 50.6 %, 50.8 % and 56.9 % higher value than Landsat land use (Case C) respectively.

• Journal of radiological science and technology
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• v.31 no.2
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• pp.161-169
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• 2008

Since measuring the size of kidney with sonography becomes an important index for diagnosis, treatment, and prognostic prediction in kidney disease, the accurate measurement and evaluation on this are clinically very important. Accordingly, the purpose of this study was to increase reproducibility and objectivity in measuring the size of kidney by enumerating factors that have an impact for measurement. It targeted 44 adults in Korea at the age of 21-27. It measured in order for both kidneys to be seen most largely while changing a subject-examiner's position in a state of fasting for 8 hours and a transducer's approaching direction. It compared a size of kidney by measuring, respectively, with the same method in 30 minutes and in 1 hour after drinking water in 700-1,000cc. In case of the lateral approach scan in decubitus position, the average length of the kidney both to the right and the left and the deviation of measurement to be the largest. In NPO(None Per Oral) state, the average length in the right kidney was 10.19cm, and the average length in the left kidney was 10.33cm. In 60 minutes after taking moisture, the average length in the right kidney was 10.94cm, and the average length in the left kidney was 11.13cm. In comparing the average length of the kidney in NPO state and its average length in 60 minutes after taking moisture, the size swelled by 7.3% for the length in the right kidney and by 7.7% in the left, thereby having been indicated to be statistically significant(P<0.003). The measurement in a size of kidney by using ultrasound may be measured differently depending on a patient's state of taking moisture and a transducer's approaching direction. It is thought that when the measurement in a size of kidney is especially important clinically, the intake and intake time in moisture need to be considered and that measuring with the posterior approach in prone position is a good method aiming to increase reproducibility in measuring length of the kidney.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.763-774
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• 2020

Wind data forecasted from the numerical weather prediction (NWP) model is generally used as the first-guess of the target tracking process to obtain the atmospheric motion vectors(AMVs) because it increases tracking accuracy and reduce computational time. However, there is a contradiction that the NWP model used as the first-guess is used again as the reference in the AMVs verification process. To overcome this problem, model-independent first guesses are required. In this study, we propose the AMVs derivation from Lucas and Kanade optical flow method and then using it as the first guess. To retrieve AMVs, Himawari-8/AHI geostationary satellite level-1B data were used at 00, 06, 12, and 18 UTC from August 19 to September 5, 2015. To evaluate the impact of applying the optical flow method on the AMV derivation, cross-validation has been conducted in three ways as follows. (1) Without the first-guess, (2) NWP (KMA/UM) forecasted wind as the first-guess, and (3) Optical flow method based wind as the first-guess. As the results of verification using ECMWF ERA-Interim reanalysis data, the highest precision (RMSVD: 5.296-5.804 ms^-1) was obtained using optical flow based winds as the first-guess. In addition, the computation speed for AMVs derivation was the slowest without the first-guess test, but the other two had similar performance. Thus, applying the optical flow method in the target tracking process of AMVs algorithm, this study showed that the optical flow method is very effective as a first guess for model-independent AMVs derivation.