• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.302-313
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• 2021

As the domestic consulting industry matured, consultants were required to have insight into customer's business and consulting business. Gaining these insights requires deep understanding of the business domains and high degree of competencies. This study empirically analyzed the data collected through the survey in order to apply the ICMCI competence model to domestic consultants. As a result of the study, it was found that business competency and technical competency had a positive effect on project performance, but values & behavior competency were not statistically significant. On the other hand, it was found that only technical competency, values & behavior competency had a positive effect on social-relational competency. Through this study, it was confirmed that a deep understanding and perception of the consulting business is necessary to grow into a professional consultant, but there is a limit to generalizing the research results because the characteristics of the population cannot be sufficiently reflected with a small sample.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.245-256
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• 2021

Poor logistical infrastructure and agri-food supply chain management leads to significant food waste in logistic system. The concept of the sustainable value added agri-food chains requires defined approach to the analysis of the existing situation, possible improving strategies and also assessment of these changes impact on further development. The purpose of research is to provide scientific substantiation of theoretical and methodological principles and develop practical recommendations for the improvement of the agri-food logistics distribution system. A case study methodology is used in this article. The research framework is based on 4 steps: Value Stream Mapping (VSM), Gap and Process Analysis, Validation and Improvement Areas Definition and Imitation Modelling. This paper presents the appropriateness of LEAN logistics tools using, in particular, Value Stream Mapping (VSM) for minimizing logistic losses and Simulation Modeling of possible logistics distribution system improvement results. The algorithm of VSM analysis of the agri-food supply chain, which involves its optimization by implementing the principles of sustainable development at each stage, is proposed. The methodical approach to the analysis of possible ways for optimizing the operation of the logistics system of the agri-food distribution is developed. It involves the application of Value Stream Mapping, i.e. designing of stream maps of the creation of the added value in the agri-food supply chain for the current and future state based on the minimization of logistic losses. Simulation modeling of the investment project on time optimization in the agri-food supply chain and economic effect of proposed improvements in logistics product distribution system functioning at the level of the investigated agricultural enterprise has been determined. Improvement of logistics planning and coordination of operations in the supply chain and the innovative pre-cooling system proposed to be introduced have a 3-year payback period and almost 75-80% probability. Based on the conducted VSM analysis of losses in the agri-food supply chain, there have been determined the main points, where it is advisable to conduct optimization changes for the achievement of positive results and the significant economic effect from the proposed measures has been confirmed. In further studies, it is recommended to focus on identifying the synergistic effect of the agri-food supply chain optimization on the basis of sustainable development.

• Cross-Cultural Studies
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• v.46
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• pp.297-325
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• 2017

In this paper, I would like to reconsider the evidential category (or the mediation category) in languages with language specific values, especially in Korean and French evidentials. We tried to analyze how the evidentials are represented in both languages including their linguistic markers (grammatical, lexical or discursive) and their semantic meanings. According to the precedent studies from the general linguistic point of view, we would like to reconsider the semantic meanings of both languages' grammatical markers, the so-called Korean retrospective marker '-te-' and French conditionals in the framework of the enunciative operation theory suggested by $Descl{\acute{e}}s$ & $Guentch{\acute{e}}va$ (2000), which proposed to classify the type of discourse by the language-independent description tools conceived after the enunciation theory suggested by Bally (1965), Benveniste (1956), Culioli (1973). Through this approach, we would like to contribute to establishing the linguistic basis not only for the general linguistic research to determine the invariant meaning of linguistic evidentials and their system, but also for the applied linguistics to the language engineering field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.43-50
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• 2019

Block-chain enjoys technical advantages such as "robust security," owing to the structural characteristic that forgery is impossible, decentralization through sharing the ledger between participants, and the hyper-connectivity connecting Internet of Things, robots, and Artificial Intelligence. As a result, public organizations have highly positive attitudes toward the adoption of technology using block-chain, and the design of university information services is no exception. Universities are also considering the application of block-chain technology to foundations that implement various information services within a university. Through case studies of block-chain applications across various industries, this study designs an empirical model of an integrated information service platform that integrates information systems in a university. A basic road map of university information services is constructed based on block-chain technology, from planning to the actual service design stage. Furthermore, an actual empirical model of an integrated information service in a university is designed based on block-chain by applying this framework.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.773-784
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• 2019

With the recent development of 4th industrial revolution technology, BIM information systems are spreading to civil engineering fields as a link to this technology. Accordingly, the Land, Infrastructure and Transport Ministry is announcing a technical policy to introduce the BIM information system into the construction sector from 2020. Usually, SOC-related facilities are complex, making it difficult to implement BIM without a standards framework. To overcome these problems, it is urgent to develop a BIM standard classification system. In this study, the BIM property classification system was developed to link the previously developed object classification system by analyzing domestic and foreign prior studies and working standards. This includes property information of businesses, facilities, parts of facilities and components that correspond to the level of object composition in the road and river sectors. It also suggested ways to apply expansion to various SOC areas and to organize spatial information by facility. The results of this study were applied to road facilities to verify the possibility of information building. The development of the BIM Standards Classification System through this R&D will contribute to the development of construction IT by providing conditions for convenient modeling and information system.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.483-488
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• 2018

Far field input utterance is one of the major causes of performance degradation of speaker verification systems. In this study, we used teacher student learning framework to compensate for the performance degradation caused by far field utterances. Teacher student learning refers to training the student deep neural network in possible performance degradation condition using the teacher deep neural network trained without such condition. In this study, we use the teacher network trained with near distance utterances to train the student network with far distance utterances. However, through experiments, it was found that performance of near distance utterances were deteriorated. To avoid such phenomenon, we proposed techniques that use trained teacher network as initialization of student network and training the student network using both near and far field utterances. Experiments were conducted using deep neural networks that input raw waveforms of 4-channel utterances recorded in both near and far distance. Results show the equal error rate of near and far-field utterances respectively, 2.55 % / 2.8 % without teacher student learning, 9.75 % / 1.8 % for conventional teacher student learning, and 2.5 % / 2.7 % with proposed techniques.

• Journal of Korea Technology Innovation Society
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• v.21 no.3
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• pp.938-968
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• 2018

Korea's basic research has been rapidly expended in both quantative and qualitative aspect since establishment of 'the Korea Science & Engineering Foundation' in 1977, proclaiming of 'the First Year of Basic Research Promotion' and enactment of 'the Basic Sciences Promotion Act' in 1989. Despite the continuous increment of government basic research funding, the problem of low perceptions of university researchers on the funding increment has been constantly raised. Based on an intrinsic review on the core issues are diagnosed based on Korean academics' basic research status analysis and future challenges are proposed based on the precedent diagnoses. The six key issues that need to take the next step in Korean academics' basic research are as follows: (1) basic research investment in universities, (2) appropriate research expenses for supporting individual researcher, (3) basic research funding allocation method, (4) maintaining the sustainable success rate of research projects, (5) systematic and strategic support for excellent researchers, (6) creating research-immersive environment. The five challenges to promote basic research in academics are as follows: (1) increasing in university research expenses, (2) diversification of basic research funding allocation method, (3) establishment of research field-specified support system and predictable principles, (4) stable and sufficient support for outstanding researchers, (5) reducing burden on research administration.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.