• Journal of Technology Innovation
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• v.25 no.4
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• pp.263-284
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• 2017

The purpose of this research is to analyze the performance of national R&D projects and to find out measures to improve the performance indicators, focusing on energy HRD projects. The main analysis target is 86 energy manpower projects supported since 2010. The performance indicators of the energy HRD projects are related to the research capacity, the number of emission workers, industry-university linkage, job creation and so on, and analyzed by using the 11 indicators of human resource performance index called KPI index. As a result of analyzing the attainment level of the proposed target by task, the index with the highest achievement level is the corporation linkage rate, and the index with the lowest achievement level is the participating company employment. As a result of examining the effects of job creation in company - linked activities, it was found that the greater the number of participating companies in the business, the greater the employment creation effect of the number of internships. As a result of the above analysis, the following policy alternatives are proposed. First, it is necessary to consider adding indicators that can express the quality performance of the business and performance indicators that can express actual business linkages. Second, it is necessary to strengthen the management of differentiated performance indicators according to policy performance targets and major target groups. Third, it is necessary to improve information input and accumulation system along with improvement of performance index.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.2
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• pp.87-94
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• 2017

The utilization of the achievements derived from the national R & D project is a key task of the science and technology industry policy that should lead the national economic growth by enhancing the investment efficiency of the national R&D. Although Korea has implemented various programs supporting technology transfer, commercialization, Performance is not sufficient. One of several causes may include inflexibility of a small or medium-sized company Priority System. This study is exploratory research on the directions for improving the current a small or medium-sized company Priority System. Results: First, Because the current SMEs Priority System contributes positively to enhancing SMEs R&D capability, We have to keep the system in principle. However, it is necessary to improve the direction of giving the strategic flexibility of the system so that the system is not operated formally. First, it is appropriate to make an exceptional contract with a person other than a small or medium-sized company, if a small and medium-sized company is not suitable for a technology execution contract due to the nature of technology. Second, it is desirable to consider the fulfillment of the obligations of this system when "sufficient efforts" are made to find a technical user.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.178-187
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• 2009

Six Sigma emphasizes KPI and establishes the present level as well as the goal level through statistical calculation and tries to achieve management innovation through process improvement. But in the area of new product development or service, sufficient data for statistical measurement may not be secured. On the other side, looking for contradictions through problem analysis, TRIZ is a methodology that stresses the process of solving these problems. TRIZ also has its own problems: it is hard to define its initial task, to objectify the measurement of effect, and to optimize the drawn out idea. The purpose of this report is to give a comprehensive understanding about each methodology (Sigma Six and TRIZ) through its analysis, to confirm the need of linking both methodologies, and to suggest a model for this linking process. Also, they will be verified through examples, and the synergy effect will be discussed.

• Design & Manufacturing
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• v.16 no.4
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• pp.60-66
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• 2022

Recently, with the development of industry and the improvement of living standards, various wastes are generated along with the production of various products. Most of these wastes are used as containers for products, and plastic or aluminum is used. Various attempts are being made to automate the classification of these wastes due to the high labor cost, but most of them are solved by manpower due to the geometrical shape change due to the nature of the waste. In this study, in order to automate the waste sorting task, Deep Learning technology is applied to a robot system for waste sorting and a vision system for waste sorting to effectively perform sorting tasks according to the shape of waste. As a result of the experiment, a Deep Learning parameter suitable for waste sorting was selected. In addition, through various experiments, it was confirmed that 99% of wastes could be selected in individual & group image learning. It is expected that this will enable automation of the waste sorting operation.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of Korea Technology Innovation Society
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• v.12 no.2
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• pp.430-456
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• 2009

The industrial Technology Development Program enables the recipient to set up technology infrastructure and to facilitate technology diffusion. In return, government charges royalties to the program recipient. However, the current royalty system is not effective form. This study analyzes the various aspects of royalty collection methods applied to the Industrial Technology Development Program and recommends the following: a) to establish a royalty collection system with appropriate charges for both fixed royalties and running royalties, dependent upon type of technology development b) to seek a method to setup different collection periods for industrial categories in consideration of product life cycle c) to review on ownership of intangible property. In addition, in order to manage the entire royalty process effectively, prompt establishment is needed in order for a responsible evaluation institute to create task forces to evaluate technology value, to transfer technology, to support technology commercialization, to collect and manage royalty and expand and report result.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.35-46
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• 2023

In this study, we propose a Dual Approach methodology to enhance the accuracy of document classifiers by utilizing both contextual and keyword information. Firstly, contextual information is extracted using Google's BERT, a pre-trained language model known for its outstanding performance in various natural language understanding tasks. Specifically, we employ KoBERT, a pre-trained model on the Korean corpus, to extract contextual information in the form of the CLS token. Secondly, keyword information is generated for each document by encoding the set of keywords into a single vector using an Autoencoder. We applied the proposed approach to 40,130 documents related to healthcare and medicine from the National R&D Projects database of the National Science and Technology Information Service (NTIS). The experimental results demonstrate that the proposed methodology outperforms existing methods that rely solely on document or word information in terms of accuracy for document classification.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1036-1042
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• 2013

In this paper, an elderly assistance system is developed based on Xenomai, a real-time development framework cooperating with the Linux kernel. A Kinect sensor is used to recognize the behavior of the elderly and A-star search algorithm is implemented to find the shortest path to the person. The mobile robot also generates a trajectory using a digital convolution operator which is based on a Bezier curve for smooth driving. In order to follow the generated trajectory within the control period, we developed real-time tasks and compared the performance of the tracking trajectory with that of non real-time tasks. The real-time task has a better result on following the trajectory within the physical constraints which means that it is more appropriate to apply to an elderly assistant system.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.46 no.6
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• pp.78-86
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• 2014

Since cellulose nanofibrils (CNF) has large specific surface area and high water holding capacity, it is very difficult task to remove water from the CNF suspension. However, dewatering of CNF suspension is a prerequisite of following processes such as mat forming and drying for the application of CNF. In this study, we evaluated the drainage of cellulose fibers suspension under vacuum and pressure conditions depending on the number of grinding passes. Also, the effect of the addition of cationic polyelectrolyte on dewatering ability of CNF suspension was investigated. Regardless of dewatering condition, the total drained water amount as well as the drainage rate were decreased with an increase in the number of grinding passes. Pressure dewatering equipment enables us to prepare wet CNF mat with relatively higher grammage. The cationic polyelectrolytes improved the dewatering ability of CNF suspension by controlling the zeta potential of CNF. The fast drainage was obtained when CNF suspension had around neutral zeta potential.

• Journal of IKEEE
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• v.25 no.4
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• pp.766-769
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• 2021

In order to perform tasks such as design, control, optimization, and prediction of flight vehicle trajectories based on machine learning techniques including deep learning, a certain amount of flight vehicle trajectory data is required. However, there are cases in which it is difficult to secure more than a certain amount of flight vehicle trajectory data for various reasons. In such cases, synthetic data generation could be one way to make machine learning possible. In this paper, to explore this possibility, we generated and evaluated synthetic flight vehicle trajectory data using time-series generative adversarial neural network. In addition, various ablation studies (comparative experiments) were performed to explore the possibility of using synthetic data in the aircraft trajectory prediction task. The experimental results presented in this paper are expected to be of practical help to researchers who want to conduct research on the possibility of using synthetic data in the generation of synthetic flight vehicle trajectory data and the work related to flight vehicle trajectories.