• Information Systems Review
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• v.16 no.1
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• pp.89-106
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• 2014

The war simulation systems in Republic of Korea Army have been getting more and more important because soldiers can effectively and efficiently learn and share their war-related knowledge based on the interactions through the systems. But, up to now, the access to the war simulation systems has been limited to only soldiers. So, little research on them has been conducted. This study explores the effects of the antecedents to the flow experience and the characteristics of the systems on soldiers' intentions to use them. Based on the 118 samples collected from officers in Republic of Korea Army, this study empirically shows the logical reality of the war simulation systems and the flow experience positively influence soldiers' intentions to use the systems and the clarified goals, feedbacks, and the levels of the missions in the systems are significant antecedents to the flow experience. Useful implications are presented and discussed based on the new findings.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.4
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• pp.43-54
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• 2007

This paper presents design and verification of a face detection hardware for real time application. Face detection algorithm detects rough face position based on already acquired feature parameter data. The hardware is composed of five main modules: Integral Image Calculator, Feature Coordinate Calculator, Feature Difference Calculator, Cascade Calculator, and Window Detection. It also includes on-chip Integral Image memory and Feature Parameter memory. The face detection hardware was verified by using S3C2440A CPU of Samsung Electronics, Virtex4LX100 FPGA of Xilinx, and a CCD Camera module. Our design uses 3,251 LUTs of Xilinx FPGA and takes about 1.96${\sim}$0.13 sec for face detection depending on sliding-window step size, when synthesized for Virtex4LX100 FPGA. When synthesized on Magnachip 0.25um ASIC library, it uses about 410,000 gates (Combinational area about 345,000 gates, Noncombinational area about 65,000 gates) and takes less than 0.5 sec for face realtime detection. This size and performance shows that it is adequate to use for embedded system applications. It has been fabricated as a real chip as a part of XF1201 chip and proven to work.

• Journal of IKEEE
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• v.22 no.3
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• pp.829-835
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• 2018

Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.419-441
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• 2008

As the Web and digital libraries have become a commodity, they are used for a variety of purposes and tasks that may require a great deal of cognitive efforts. However, most search engines in the Web and digital libraries support users with only searching and browsing capabilities, leaving all the cognitive burdens of manipulating information objects to the users. We propose a two-level model for human-Web interactions, consisting of knowledge and information spaces, and a tool that provides knowledge space and inter-space operations in addition to searching and browsing at the information level. Knowledge space is an explication of user's conceptual view of the information objects being explored through interactions with the Web or a digital library. Topics are created and related with associations at the knowledge level and connected to information objects in information space. The tool implemented using the Topic Maps framework has been tested for efficacy as an aid to reducing cognitive burden under exploratory search task.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.7-14
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• 2010

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.157-163
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• 2016

BIM is currently applied in some domestic construction firms, but it is not being actively utilized due to changes in working environments and qualms about new studies. In order to utilize a BIM model in the design phase, process information is needed during construction, but the input system and utilization method of the process information's state are not complete. Therefore, we propose a BIM template for construction progress management that can show basic BIM information as the construction progresses in an easy and convenient way. This method will facilitate the adoption of BIM and enhance the productivity of construction companies. To this end, we designed a progress explorer for step-by-step progress and work schedules, and we generated three-dimensional views and a progress list by applying unit information (primary units, part units, and detail units) of the work breakdown structure (WBS) to the parameters. To use the BIM template, work progress information is input to the BIM modeling objects through Dynamo. We also used Dynamo for quick and easy calculation of the quantity of materials needed for construction work. To test the BIM template, we applied it to an actual project and evaluated its visibility and a progress list. The results showed that the proposed BIM template facilitates progress management of a project and can thus facilitate the adoption of BIM and improve the productivity of construction companies.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.219-230
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• 2014

An analysis of dependency relation is a job that determines the governor and the dependent between words in sentence. The dependency relation of predicate is established by patterns and selectional restriction of subcategorization of the predicate. This paper proposes a method of analysis of Korean dependency relation using homograph predicate disambiguated in morphology analysis phase. The disambiguated homograph predicates has each different pattern. Especially reusing a stage transition training dictionary used during tagging POS and homograph, we propose a method of fixing the dependency relation of {noun+postposition, predicate}, and we analyze the accuracy and an effect of homograph for analysis of dependency relation. We used the Sejong Phrase Structured Corpus for experiment. We transformed the phrase structured corpus to dependency relation structure and tagged homograph. From the experiment, the accuracy of dependency relation by disambiguating homograph is 80.38%, the accuracy is increased by 0.42% compared with one of undisambiguated homograph. The Z-values in statistical hypothesis testing with significance level 1% is ${\mid}Z{\mid}=4.63{\geq}z_{0.01}=2.33$. So we can conclude that the homograph affects on analysis of dependency relation, and the stage transition training dictionary used in tagging POS and homograph affects 7.14% on the accuracy of dependency relation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.447-452
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• 2020

The purpose of this research was to confirm that prefrontal neurofeedback training has an impact on adolescents. The objective of this study was to prove its scientific effect through experimentation. The effect of the training was measured by the difference in neuro?frequencies before and after the training. For this research, an experimental group and a control group, each with 22 students in J High School located in the city of S participated in this study. From May to July 2019, the training was conducted three times a week and for 30 minutes per session. The neuro?frequency data collected were analyzed through the methods of F.F.T. The resulting changes from the neurofeedback training for each group were analyzed by T-Tests. The result of the study is as follows; Neurofeedback training has had a positive effect on adolescent attention and sleep. In conclusion, the environmental and educational factors also play an important role. As the interaction of the latter two factors yield an individual's unique brain structure and functionality, the impact of the neurofeedback training is important on adolescents. The derivation of the above results by utilizing scientific and objective methods reemphasizes the importance of this study.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.43-54
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• 1997

Knowledge acquisition is one of the most difficult problems in designing fuzzy systems. As application domains of fuzzy systems become larger and more complex, it is more difficult to find the relations among the system's input- outpiit variables. Moreover, it takes a lot of efforts to formulate expert's knowledge about complex systems' control actions by linguistic variables. Another difficulty is to define and adjust membership functions properly. Soin conventional fuzzy systems, the membership functions should be adjusted to improve the system performance. This is time-consuming process. In this paper, we suggest a new approach to design a fuzzy system. We design a fuzzy system using two neural networks, Kohonen neural network and backpropagation neural network, which generate fuzzy rules automatically and construct inference network. Since fuzzy inference is performed based on fuzzy relation in this approach, we don't need the membership functions of each variable. Therefore it is unnecessary to define and adjust membership functions and we can get fuzzy rules automatically. The design process of fuzzy system becomes simple. The proposed approach is applied to a simulated automatic car speed control system. We can be sure that this approach not only makes the design process of fuzzy systems simple but also produces appropriate inference results.