• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.767-787
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• 2018

Technology-driven innovation and job-creation has each been the subject of much scholarly attention, but have largely been considered separately rather than in conjunction with each other. While the previous literature on economics pinpointed the macro effects on industry-level, this study explores the micro-level comparisons on innovation sources over the employment and financial performances. The PSM (propensity-score matching) analysis presents that firms, involved in an inward technology, tend to have higher employees with dominant technology capabilities than in-house R&D firms. The in-house R&D firms, on the contrary, have superior financial performances, suggesting that external technology commercialized firms suffer from low transformative efficiency. The mediation test analysis corroborates that the external technology-driven innovation induces more human resources in internalizing the exogenous technology. The positive relationship between R&D innovation and employment allow verification of the government's intervention in the promotion of technology commercialization in public sector. On the other hand, it also signals that the policy needs to enhance the recipient firms' commercializing capacity rather than a 'one-hit' transaction.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.320-325
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• 2016

By developing the IoT(Internet of Things) technology, devices for smart home environment have rapidly increased. With respect to mobiles, these applications are used to control and manage the various smart devices effectively. However, the existing mechanisms only provide simple information, and hence a difficulty to search or control the smart devices persists, since there is no meaningful relationship between them. In this research, we suggest an interface which visualizes the device's data and controls them effectively, based on the user's device using pattern. As a solution for this problem, we classify the user pattern based on a timeline for the associated circumstance, and visualize the device's data to make a group or to control individually in an easier approach. Also, all meaningful information could be confirmed by summarizing all the data of smart devices.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.341-353
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• 2014

In order to deliver a centimeter-level kinematic positioning solution with GPS carrier-phase measurements, it is prerequisite to use correctly resolved integer ambiguities. Based on the mathematical modeling of GPS network with application of its geometrical constraints, this research has investigated an instantaneous ambiguity resolution procedure for the so-called 'integer constrained least-squares' technique which can be effectively implemented in real-time structure monitoring. In this process, algorithms of quality control for the float solutions and hypothesis tests using the constrained baseline for the ambiguity validation are included to enhance reliability of the solutions. The proposed procedure has been implemented by MATLAB, the language of technical computing, and processed field trial data obtained at a cable-stayed bridge to access its real-world applicability. The results are summarized in terms of ambiguity successful rates, impact of the stochastical models, and computation time to demonstrate performance of the instantaneous ambiguity resolution proposed.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.561-565
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• 2010

Many methods have been researched to prolong sensor network lifetime using mobile technologies. In the mobile sink research, there are the track based methods and the anchor points based methods as representative operation methods for mobile sinks. However, the existing methods decrease Quality of Service (QoS) and lead the routing hotspot in the vicinity of the mobile sink. The reason is that they use static mobile paths that are not concerned about the network environments such as the query position and the data priority. In this paper, we propose the novel mobile sink operation method that solves the problems of the existing methods. In our method, the probing priority of the mobile sink is determined with the data priorities for increasing the QoS and the mobile features are used for reducing the routing hotspot. The experimental results show that the proposed method reduces query response time and improves network lifetime over the existing methods.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.23-30
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• 2020

The use of increased computing power, machine learning, and deep learning techniques have dramatically increased in various sectors. In particular, image detection algorithms are broadly used in forestry and remote sensing areas to identify forest types and tree species. However, in South Korea, machine learning has rarely, if ever, been applied in forestry image detection, especially to classify tree species. This study integrates the application of machine learning and forest image detection; specifically, we compared the ability of two machine learning data collection methods, namely image data captured by forest experts (D1) and web-crawling (D2), to automate the classification of five trees species. In addition, two methods of characterization to train/test the system were investigated. The results indicated a significant difference in classification accuracy between D1 and D2: the classification accuracy of D1 was higher than that of D2. In order to increase the classification accuracy of D2, additional data filtering techniques were required to reduce the noise of uncensored image data.

• Journal of KIISE
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• v.44 no.11
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• pp.1149-1155
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• 2017

Recently, with the development of communication technology, it has become possible to collect various sensor data that indicate the environmental stimuli within a space. In this paper, we propose a group emotion prediction system using a modular Bayesian network that was designed considering the psychological impact of environmental stimuli. A Bayesian network can compensate for the uncertain and incomplete characteristics of the sensor data by the probabilistic consideration of the evidence for reasoning. Also, modularizing the Bayesian network has enabled flexible response and efficient reasoning of environmental stimulus fluctuations within the space. To verify the performance of the system, we predict public emotion based on the brightness, volume, temperature, humidity, color temperature, sound, smell, and group emotion data collected in a kindergarten. Experimental results show that the accuracy of the proposed method is 85% greater than that of other classification methods. Using quantitative and qualitative analyses, we explore the possibilities and limitations of probabilistic methodology for predicting group emotion.

• Journal of KIISE
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• v.42 no.12
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• pp.1584-1589
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• 2015

With the advancement of next-generation sequencing (NGS) technologies, various genome browsers have been introduced. Because existing browsers focus on comparison of the genomic data of extant species, however, there is a need for a genome browser for ancestral genomes and their evolution. In this paper, we introduce a genome browser, AGB (Ancestral Genome Browser), that displays ancestral genome data reconstructed from existing species. With AGB, it is possible to trace genomic variations that occurred during evolution in a simple and intuitive way. We explain the capability of AGB in terms of visualizing ancestral genomic information and evolutionary genomic variations. AGB is now available at http://bioinfo.konkuk.ac.kr/genomebrowser/.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.537-542
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• 2015

Database Shard is a technique that can be queried and stored by dividing one logical table into multiple databases horizontally. In order to analyze the shard data with aggregate or analysis functions, a process is required that integrates partial results on each shard database. In this paper, we introduce the design and implementation of a distributed processing system for aggregation and analysis on the CUBRID Shard distributed database, which is an open source database management system. The implemented system can accelerate the analysis onto multiple shards of partitioned tables; it shows efficient aggregation on shard distributed databases compared to stand-alone databases.