• International Commerce and Information Review
• /
• v.14 no.2
• /
• pp.249-269
• /
• 2012

The focus of our work is to identify and understand the drivers of alliance performance so that businesses can maximize their chances of a successful alliance-an area that has received little attention in empirical modeling. Although both conceptual and applied research on alliances has increased, an empirically tested comprehensive theoretical model that explains alliance performance has yet to be developed. Using five salient perspective, namely market power theory, transaction cost theory, the resource-based view, institutional theory, real option theory, this paper attempts to provide a theoretical rationale linking motives of global alliance networks on firm's capabilities, partner's capabilities, operating structures, and performances of Korean companies. The key contribution of this study is that it paints a picture of what matters in driving alliance performance. Our work shows the complex nature of driving performance and the interplay of firm's capabilities, partner's capabilities, and operating structures for understanding alliance performances. This study has given us a small but significant step forward towards understanding the intricacies of alliance performance. We are now better able to understand the respective roles played by various alliance factors and derive insights that lead to improved alliance performance.

• Journal of the Economic Geographical Society of Korea
• /
• v.17 no.1
• /
• pp.45-68
• /
• 2014

This research addressed improvement directions and problems of regional science and technology(S&T) policies in Daegu and Gyungbuk regions in terms of 'Creative Economy' which was a new paradigm of Park Gunhye Government. Creative Economy stressed the construction of an ecosystem in regional research and development activities, and thus it was deeply associated with building a regional innovation system(RIS). There were several problems to strengthen RIS with regional S&T policies of the regions as follows: limits in meeting regional needs due to excessive attraction of central government's projects into regions; the high ratio of programs for future basic research potentials; the lack of programs assessing and coordinating the policies; and the lack of experiences and expenditure of research institutes and firm supporting organizations. Due to these problems, the role of the policies in building RIS did not seem to be effective. Therefore, the policies need to be improved through the following measures: the expansion of regional own policies focusing regional needs; the enhancement of policy coordination by shifting to systematic approach; the expansion of soft supporting programs for constructing innovation systems; and the enhancement of stability and ability of research institutes and firm supporting organizations.

• The KIPS Transactions:PartB
• /
• v.18B no.3
• /
• pp.147-156
• /
• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Science of Emotion and Sensibility
• /
• v.20 no.4
• /
• pp.31-40
• /
• 2017

This research aims to investigate 'Hallyu' contents consumption tendency of consumers from Korea, Japan, and the United States by analyzing their emotional responses. With the development of social media, research on emotion analysis by reviewing text materials has grown. Whereas environmental variables affect consumer demand towards 'Hallyu' contents, little comparative analyses have been conducted on the emotional responses of consumers from different countries. In this research, the emotional prototype model proposed by Russell(1980) used to extract and distinguish emotional words to clarify how people in the three countries differently perceive the Korean drama "Goblin". First of all, the SNS reviews were collected during a two-month period (February 12 to April 12). Second, significant factors were identified in the collected data according to Russell's emotion model. Third, random forest was applied to organize the selected variables in the order of variable importance. Fourth, the correlations among the emotional words were compared. Lastly, the accuracy of the trained model was measured using the test dataset. The results show that "Happy" was found to be the greatest factor in Korea and in the United States and "Pleased" in Japan. Emotional words correlations showed that when watching the drama "Goblin", "passive unpleasure" was the main factor associated with individual's interest in Korea whereas "passive pleasure" was associated with individual's interest in Japan and in the United States. Based on the results, this research suggests the possibility of developing evaluation guidelines for emotional responses of different countries towards 'Hallyu' contents.

• Journal of KIISE
• /
• v.43 no.1
• /
• pp.87-95
• /
• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.2
• /
• pp.76-86
• /
• 2016

SQL Injection attacks are the most widely used and also they are considered one of the oldest traditional hacking techniques. SQL Injection attacks are getting quite complicated and they perform a high portion among web hacking. The big data environments in the future will be widely used resulting in many devices and sensors will be connected to the internet and the amount of data that flows among devices will be highly increased. The scale of damage caused by SQL Injection attacks would be even greater in the future. Besides, creating security solutions against SQL Injection attacks are high costs and time-consuming. In order to prevent SQL Injection attacks, we have to operate quickly and accurately according to this data analysis techniques. We utilized data analytics and machine learning techniques to defend against SQL Injection attacks and analyzed the execution plan of the SQL command input if there are abnormal patterns through checking the web log files. Herein, we propose a way to distinguish between normal and abnormal SQL commands. We have analyzed the value entered by the user in real time using the automated SQL Injection attacks tools. We have proved that it is possible to ensure an effective defense through analyzing the execution plan of the SQL command.

• 대한공업교육학회지
• /
• v.44 no.1
• /
• pp.190-208
• /
• 2019

This study was carried out to evaluate the job implementation of in-service training on career education & guidance teacher's career education in the National Education Training Institute. To accomplish this purpose, pre-survey & pre-survey, sucess case method, and return on expectation were investigated after 3 months completing in-service teacher training. The populations of this study were conducted for 136 career education & guidance teachers who completed in-service teacher training at the National Education Training Institute in July, 2018, and it was conducted by survey research and qualitative content analysis of job implementation. Among the 136 trainees who completed the training, 75 responded to the job implementation survey and 4 people participated in the successful case technique. As a result, the average value of job implementation was 4.17 out of 5 points, which was relatively high. The success case technique was analyzed by interviewing success cases and failure cases. Behavior change according to job implementation was the biggest role of Planner, followed by role of Instructor, role of Career educator, role of Learner. In addition, the case analysis provided the opinions of the interviewers in terms of motivation, competence, and environment for job implementation. In terms of the return on expectation, 85.3% of the respondents were positive, and the net promoter score was .85, indicating that the participants were satisfied with their willingness to participate in the training again. Based on the results of this study, we suggest that it will be required to study about new training methods and extra factor analysis.

• Journal of Internet Computing and Services
• /
• v.23 no.3
• /
• pp.13-20
• /
• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.4
• /
• pp.383-390
• /
• 2021

Recently, the drone industry is rapidly expanding as it is suggested that it will be used in various fields. As the size of the drone market grows, interest in drone-related certificates is also increasing. However, the current drone-related qualification system and education system are insufficient. Thus this study, analyzed the necessity of drone training, the features of functional games, and the effectiveness of educational training using AR through related technical studies to solve the practical difficulties of drone educational training. Later, drone educational training game contents using AR were divided into practice mode and test mode based on the drone national qualification course practical test, and the result screen was displayed at the end of the curriculum so that players could learn by level and evaluate the results on their own. In addition, constructed a hybrid processing system and network and AR operation system for response rate and response speed, implemented drone training game contents utilizing AR based on the design contents. It is expected that the use of game content using AR presented in this paper for drone training will further alleviate environmental difficulties and improve the sense of immersion in play, which will lead to a more effective drone educational training experience.

• Trans-
• /
• v.12
• /
• pp.141-171
• /
• 2022

This study was conducted to understand media and cultivate personality by using media as data for personality education. To achieve this purpose, the Personality Education Promotion Act and the Korea Educational Development Institute's personality virtues were selected as educational elements, and a personality education program using media was developed in combination with the middle school curriculum. For this study, first, in order to extract personality virtues, 13 personality virtues were finally selected as educational elements by comparing and synthesizing the personality virtues of the Personality Education Promotion Act and the Korea Education Development Institute. The final personality virtues selected are self-esteem, courage, sincerity, self-regulation, wisdom, consideration, communication, courtesy, social responsibility, cooperation, citizenship, justice, and respect for human rights. Second, in order to select media and set the direction of development of personality education programs, the process of collecting media data was confirmed, and the direction and goal of the program were set by analyzing the middle school curriculum. Third, in order to propose a method of applying a personality education program using media, the personality grafting unit was selected by referring to the commentary on all subjects of the 2015 revised curriculum.