• KIISE Transactions on Computing Practices
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• v.21 no.5
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• pp.351-358
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• 2015

As smart phones with built-in sensors are developed, research on recognition using wearable devices is increasing. Existing papers are mostly limited on research to personal activity recognition. In this paper, we propose a method to recognize conceptual group activity. Before doing recognition, we generate new data based on the analysis of the conceptual group activity in a classroom. The study focuses on three activities in the classroom environment: Taking Lesson, Doing Presentation and Discussing. With the proposed algorithm, the recognition rate is over 96%. Using this method in real time will make it easy to automatically analyze the activity and the purpose of the classrooms. Moreover, it can increase the utilization of the classroom through the data analysis. Further research will focus on group activity recognition in other environments and the design of an group activity recognition system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3102-3119
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• 2021

In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.123-129
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• 2013

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.98-103
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• 2006

In this research, the single machine capacitated lot-sizing and scheduling problem with sequence- dependent setup costs and setup times (CLSPSD) is considered. This problem is the extension of capacitated lot-sizing and scheduling problem (CLSP) with an additional assumption on sequence-dependent setup costs and setup times. The objective of the problem is minimizing the sum of production costs, inventory holding costs and setup costs satisfying customers' demands. It is known that the CLSPSD is NP-hard. In this paper, the MIP formulation is presented. To handle the problem more efficiently, a conceptual model is suggested, and one of the well-known meta-heuristics, the simulated annealing approach is applied. To illustrate the performance of this approach, various instances are tested and the results of this algorithm are compared with those of the CLPEX. Computational results show that this approach generates optimal or nearly optimal solutions.

• Journal of the Korean Solar Energy Society
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• v.34 no.5
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• pp.33-41
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• 2014

Heliostat field in a tower type solar thermal power plant is the sun tracking mirror system which affects the overall efficiency of solar thermal power plant most significantly while consumes a large amount of energy to operate it. Thus optimal operation of it is very crucial for maximizing the energy collection and, at the same time, for minimizing the operating cost. Heliostat field operational algorithm is the logics to control the heliostat field efficiently so as to optimize the heliostat field optical efficiency and to protect the system from damage as well as to reduce the energy consumption required to operate the field. This work presents the heliostat field operational algorithm developed for the heliostat field of 200kW solar thermal power plant built in Daegu, Korea. We first review the structure of heliostat field control system proposed in the previous work to provide the conceptual framework of how the algorithm developed in this work could be implemented. Then the methodologies to operate the heliostat field properly and efficiently, by defining and explaining the various operation modes, are discussed. A simulation, showing the heat flux distribution collected by the heliostat field at the receiver, is used to show the usefulness of proposed heliostat field operational algorithm.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.

• Journal of The Korean Association of Information Education
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• v.16 no.1
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• pp.11-20
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• 2012

The core of programming learning is based on an algorithm learning and the promotion of problem solving abilities is the purpose of this learning. Then, we need to think about what kind of algorithms in what order we teach and we need to study the effect of this learning. The purpose of this study is development and implementation of algorithm instructional materials and examine the effect of an algorithm learning with conceptual algorithms in KOI(Korea Olympiad in Informatics) final test of elementary students.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.434-437
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• 1999

The primary aim of this paper is to introduce and illustrate a radial basis function (RBF) modeling approach fur software module characterization, as an alternative to current techniques. The RBF model has been known to provide a rich analytical framework fur a broad class of so-called pattern recognition problems. Especially, it features both nonlinearity and linearity which in general are treated separately by its learning algorithm, leading to offer conceptual and computational advantages. Furthermore, our new modeling methodology fer determining model parameters has a sound mathematical basis and showed very interesting results in terms of model consistency as well as performance.

• Korean Management Science Review
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• v.12 no.3
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• pp.135-161
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• 1995

This paper is concerned with a conceptual design of a knowledge-based modeler for network analysis. The "knowledge-based modeler" approach is suggested as a method for incorporating the user's qualitative knowledge and subjective decison in the course of the mathematical modeling and the subsequent solution procedure. The submodules of the proposed modeler such as database, model/algorithm base and functional knowledge bases are identified and the flows of information between the submodules are sequentially defined. A prototype system is implemented for experimental purpose by using the application software GURU.ware GURU.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.690-695
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• 2000

This article deals with the analytic results on the development and application of the Spherical CVT. The Spherical CVT is marked by its simple configuration, the infinite torque multiplication characteristic, and the smooth transitions between forward/neutral/reverse states of output speed. In this study, we describe the conceptual principles behind the Spherical CVT and some applications of it, which we developed recently. And, we propose the shifting algorithm based on the analytic consideration of CVT powertrain system. Contrary to conventional shifting algorithms using the OOL(optimal operating line) of the power source, the proposed shifting algorithm is represented as a $2^{nd}$ order equation in an explicit form, and it reveals the possibility of theoretic design of all optimal controller. As an example, we present numerical results that demonstrate the energy saving possible and the proposed shifting algorithm from the use of the Spherical CVT over standard reduction gear unit, using an ideal dc motor model.