• Education of Primary School Mathematics
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• v.22 no.3
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• pp.181-203
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• 2019

Even though the properties of operations in multiplication serve a fundamental basis of conceptual understanding the multiplication with whole numbers for elementary students, there has been lack of research in this field. Given this, the purpose of this study was to analyze instructional methods related to the properties of operations in multiplication (i.e., commutative property of multiplication, associative property of multiplication, distributive property of multiplication over addition) in a series of mathematics textbooks of Korea, Japan, and the US. The overall analysis was conducted in the following two aspects: (a) when and how to deal with the properties of multiplication in three instructional context (i.e., introduction, application, generalization), and (b) what models use to represent the properties of multiplication. The results of this showed that overall similarities in introducing the properties of multiplication .in (one digit) ${\times}$ (one digit) as well as emphasizing the divers representation. However, subtle but meaningful differences were analyzed in applying and generalizing the properties of multiplication. Based on these results, this paper closes with some implications on how to teach the properties of operations in multiplication properties in elementary mathematics.

• Journal of Korean Society of Transportation
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• v.33 no.4
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• pp.379-392
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• 2015

Freight flow data in Korea has been collected for a variety of purposes by various organizations. However, since the representation and format of the data varies, it has not been substantially used for freight analyses and furthermore for freight policies. In order to increase the applicability of those data sets, it is required to bring them in a table and compare for finding the differences. Then, it is shown that the raw data can be aggregated by a particular criterion such as mode, origin and destination, and type commodity. This study aims to examine the freight data issue in terms of three different points of view. First, we investigated various freight volume data sets which are released by several organizations. Second, we tried to develop formulations for freight volume data. Third, we discussed how to apply the formulations to network models in which particular OR (Operations Research) techniques are used. The results emphasized that some data might be useless for modeling once they are aggregated. As a result of examining the freight volume data, this study found that 14 organizations share their data sets at various aggregation levels. This study is not an ordinary research article, which normally includes data analysis, because it seems to be impossible to conduct extensive case studies. The reason is that the data dealt in this study are diverse. Nevertheless, this study might guide the research direction in the freight transport research society in terms of data issue. Especially, it can be concluded that this study is a timely research because the governmemt has emphasized the importance of sharing data to public throughout 'government 3.0' for research purpose.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.246-256
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• 2022

Recently, parents and teachers consider physical education as a minor subject for students in elementary and secondary schools. Physical education performance has become increasingly significant as parents and schools pay more attention to physical schooling. The sports mining with distribution analysis model considers different factors, including the games, comments, conversations, and connection made on numerous sports interests. Using different machine learning/deep learning approach, children's athletic and academic interests can be tracked over the course of their academic lives. There have been a number of studies that have focused on predicting the success of students in higher education. Sports interest prediction research at the secondary level is uncommon, but the secondary level is often used as a benchmark to describe students' educational development at higher levels. An Automated Student Interest Prediction on Sports Mining using DL Based Bi-directional Long Short-Term Memory model (BiLSTM) is presented in this article. Pre-processing of data, interest classification, and parameter tweaking are all the essential operations of the proposed model. Initially, data augmentation is used to expand the dataset's size. Secondly, a BiLSTM model is used to predict and classify user interests. Adagrad optimizer is employed for hyperparameter optimization. In order to test the model's performance, a dataset is used and the results are analysed using precision, recall, accuracy and F-measure. The proposed model achieved 95% accuracy on 400^th instances, where the existing techniques achieved 93.20% accuracy for the same. The proposed model achieved 95% of accuracy and precision for 60%-40% data, where the existing models achieved 93% for accuracy and precision.

• Industry Promotion Research
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• v.8 no.3
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• pp.201-210
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• 2023

This study is based on data ecology theory and takes Chinese local governments' open public data as the research object. Data asset value assessment methods are compared from a new perspective of data business operations. The results show that the assessment model constructed using the hierarchical analysis method (AHP) can more objectively reflect the commercial value of government open data assets than the traditional cost, revenue and market methods, has the advantage of a comprehensive assessment of data value index, and better reflects the findings of a comprehensive index of regional data value. The data show that the local government data value assessment index is positively proportional to the region's digital economy development index, highlighting the driving effect on the digital economy. The results of the study provide a good help for the identification of local government data value rights. The research and practice of promoting the construction of data innovation and data business operation models, improving social well-being and promoting the rapid development of the digital economy to achieve data realisation provides a good reference.

• Korea Trade Review
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• v.48 no.4
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• pp.121-148
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• 2023

The rapid melting of Arctic sea ice has increased interest in the Northern Sea Route (NSR) as a viable alternative trade route between Europe and Asia. While extensive research has examined its competitiveness in terms of technical feasibility, safety, profitability, and environmental impact, the topic of the NSR ports remains relatively underrepresented in the literature. Hence, this study aims to contribute to the existing research by assessing the efficiency of 17 NSR ports to gain insights into their operations and identify areas for improvement using models of Data Envelopment Analysis(DEA). The obtained results show that efficient ports mainly belong to the western NSR region, with ports like Murmansk and Varandei consistently demonstrating high efficiency and constant returns to scale. Several ports, such as Onega, Arkhangelsk, Naryan-Mar, and Khatanga, showed inefficiencies in the utilization of berths and quay lengths. The findings not only contribute to academic knowledge but also offer practical implications for NSR port authorities, assisting them in making well-informed decisions regarding infrastructure development plans.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.4
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• pp.429-435
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• 2020

North Korea declared itself complete with nuclear force after its sixth nuclear test in 2017. Despite efforts at home and abroad to denuclearize the Korean Peninsula, the prospects for the denuclearization are not bright. Along with political and diplomatic efforts to deter NK's WMD threats, the government is required to strengthen its consequence management capabilities against 'catastrophic situations' expected in case of emergency. Accordingly, this study was conducted to present measures to strengthen follow-up management against CBRN threats. The research model was partially supplemented and utilized by the THIRA process adopted and utilized by the U.S. Department of Homeland Security among national-level disaster management plan development models. Korea's consequence management (CM) system encompasses risk and crisis management on disaster condition. The system has been carried out in the form of a civil, government and military integrated defense operations for the purpose of curbing the spread or use of CBRNs, responding to threats, and minimizing expected damages. The preventive stage call for the incorporation of CBRN concept and CM procedures into the national management system, supplementing the integrated alarm systems, preparation of evacuation facilities, and establishment of the integrated training systems. In the preparation phase, readjustment of relevant laws and manuals, maintenance of government organizations, developing performance procedures, establishing the on-site support systems, and regular training are essential. In the response phase, normal operations of the medical support system for first aid and relief, installation and operation of facilities for decontamination, and development of regional damage assessment and control guidelines are important. In the recovery phase, development of stabilization evaluation criteria and procedures, securing and operation of resources needed for damage recovery, and strengthening of regional damage recovery capabilities linked to local defense forces, reserve forces and civil defense committees are required.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.57-78
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• 2005

The vehicle routing problem (VRP) is to determine a set of feasible vehicle routes, one for each vehicle, such that each customer is visited exactly once and the total distance travelled by the vehicles is minimized. A feasible route is defined as a simple circuit including the depot such that the total demand of the customers in the route does not exceed the vehicle capacity. While there have been significant advances recently in exact solution methodology, the VRP is not a well solved problem. We find most approaches still relying on the branch and bound method. These approaches employ various methodologies to compute a lower bound on the optimal value. We introduce a new modelling approach, termed route-splitting, for the VRP that allows us to address problems whose size is beyond the current computational range of set-partitioning models. The route-splitting model splits each vehicle route into segments, and results in more tractable subproblems. Lifting much of the burden of solving combinatorially hard subproblems, the route-splitting approach puts more weight on the LP master problem, Recent breakthroughs in solving LP problems (Nemhauser, 1994) bode well for our approach. Lower bounds are computed on five symmetric VRPs with up to 199 customers, and eight asymmetric VRPs with up to 70 customers. while it is said that the exact methods developed for asymmetric instances have in general a poor performance when applied to symmetric ones (Toth and Vigo, 2002), the route splitting approach shows a competent performance of 93.5% on average in the symmetric VRPs. For the asymmetric ones, the approach comes up with lower bounds of 97.6% on average. The route-splitting model can deal with asymmetric cost matrices and non-identical vehicles. Given the ability of the route-splitting model to address a wider range of applications and its good performance on asymmetric instances, we find the model promising and valuable for further research.

• Journal of Advanced Navigation Technology
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• v.12 no.4
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• pp.295-303
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• 2008

Malfunction of satellite launch vehicles with high speed and long range can be a major concern for operations. Flight safety system that monitor the trajectory and identify any failure of the launch vehicles. Tracking filters for the flight safety systems are different from common tracking filters since filter reliability is more emphasized than accuracy. Reliable estimation of instantaneous impact points requires reliable velocity estimates as well as reliable position estimates. A fusion filter for a flight safety system was developed with the tracking sensor models for the Korea Satellite Launch Vehicle I. The fusion filter performances were evaluated by analyzing the trajectory and instantaneous impact point estimates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.