• The Mathematical Education
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• v.27 no.1
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• pp.15-23
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• 1988

We are at the onset of a major revolution in education, a revolution unparalleled since the invention of the printing press. The computer will be the instrument of this revolution. Computers and computer application are everywhere these days. Everyone can't avoid the influence of the computer in today's world. The computer is no longer a magical, unfamiliar tool that is used only by researchers or scholars or scientists. The computer helps us do our jobs and even routine tasks more effectively and efficiently. More importantly, it gives us power never before available to solve complex problems. Mathematics instruction in secondary schools is frequently perceived to be more a amendable to the use of computers than are other areas of the school curriculum. This is based on the perception of mathematics as a subject with clearly defined objectives and outcomes that can be reliably measured by devices readily at hand or easily constructed by teachers or researchers. Because of this reason, the first large-scale computerized curriculum projects were in mathematics, and the first educational computer games were mathematics games. And now, the entire mathematics curriculum appears to be the first of the traditional school curriculum areas to be undergoing substantial trasformation because of computers. Recently, many research-Institutes of our country are going to study on computers in orders to use it in mathematics education, but the study is still start ing-step. In order to keep abreast of this trend necessity, and to enhance mathematics teaching/learning which is instructed lecture-based teaching/learning at the present time, this study aims to develop/present practical method of computer-using. This is devided into three methods. 1. Programming teaching/learning method This part is presented the following five types which can teach/learn the mathematical concepts and principle through concise program. (Type 1) Complete a program. (Type 2) Know the given program's content and predict the output. (Type 3) Write a program of the given flow-chart and solve the problem. (Type 4) Make an inference from an error message, find errors and correct them. (Type 5) Investigate complex mathematical fact through program and annotate a program. 2. Problem-solving teaching/learning method solving This part is illustrated how a computer can be used as a tool to help students solve realistic mathematical problems while simultaneously reinforcing their understanding of problem-solving processes. Here, four different problems are presented. For each problem, a four-stage problem-solving model of polya is given: Problem statement, Problem analysis, Computer program, and Looking back/Looking ahead. 3. CAI program teaching/learning method This part is developed/presented courseware of sine theorem section (Mathematics I for high school) in order to avail individualized learning or interactive learning with teacher. (Appendix I, II)

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1235-1242
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• 2019

While several machine learning technique has been implemented for Android malware categorization, there is still difficulty in analyzing due to overfitting problem and including of un-executable code, etc. In this paper, we introduce our implemented tool to address these problems. Tool is consists of approximately 1,500 lines of Java code, and perform Flow analysis on set of APIs, or on control flow graph. Our tool groups all the API by its relationship and only perform analysis on actually executing code. Using our tool, we grouped 39032 APIs into 4972 groups, and 12123 groups with result of including class names. We collected 7,000 APKs from 7 families and evaluated our feature reduction technique, and we also reduced features again with selecting APIs that have frequency more than 20%. We finally reduced features to 263-numbers of feature for our collected APKs.

• Journal of Navigation and Port Research
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• v.35 no.10
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• pp.805-810
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• 2011

An experiment was carried out to figure out the instantaneous flow characteristics in the wake of the transom stern's 2-dimensional section by 2-frame grey level cross correlation PIV method at $Re=3.5{\times}103$, $Re=7.0{\times}103$. The stern angles of models were learning at $45^{\circ}$(Model "A"), $90^{\circ}$(Model "B") and $135^{\circ}$(Model "C") respectively based on the survey results of real ships. The depth of wetted surface is 40mm from free surface. As Reynolds number increases, vortices increase in volume and move their way to the downstream. Flow separation appeared at the end of model's bottom.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.117-129
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• 2020

The purpose of this paper is to explore ways to assess computational thinking from a formative perspective and to design a process for assessing programming learning using Python. Therefore, this study explored the computational thinking domain and analyzed research related to assessment design. Also, this study identified the areas of Python programming learning that beginners learn and the areas of computational thinking ability that can be obtained through Python learning. Through this, we designed an assessment method that provides feedback by analyzing syntax corresponding to computational thinking ability. Besides, self-assessment is possible through reflective thinking by using the flow-chart and pseudo-code to express ideas, and peer feedback is designed through code sharing and communication using community.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2480-2496
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• 2020

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset-TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.

• Journal of Elementary Mathematics Education in Korea
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• v.9 no.2
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• pp.181-200
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• 2005

The purpose of this thesis was to analyze communicational means of mathematical communication in perspective of languages and behaviors. Research questions were as follows; First, how are the characteristics of mathematical languages in communicating process of mathematical small group learning? Second, how are the characteristics of behaviors in communicating process of mathematical small group learning? The analyses of students' mathematical language were as follows; First, the ordinary language that students used was the demonstrative pronoun in general, mainly substituted for mathematical language. Second, students depended on verbal language rather than mathematical representation in case of mathematical communication. Third, quasi-mathematical language was mainly transformed in upper grade level than lower grade, and it was shown prominently in shape and measurement domain. Fourth, In mathematical communication, high level students used mathematical language more widely and initiatively than mid/low level students. Fifth, mathematical language use was very helpful and interactive regardless of the student's level. In addition, the analyses of students' behavior facts were as follows; First, students' behaviors for problem-solving were shown in the order of reading, understanding, planning, implementing, analyzing and verifying. While trials and errors, verifying is almost omitted. Second, in mathematical communication, while the flow of high/middle level students' behaviors was systematic and process-directed, that of low level students' behaviors was unconnected and product-directed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.19-27
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• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.795-805
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• 2019

Recently, there is a growing interest in network anomaly detection technology to tackle unknown attacks. For this purpose, diverse studies using data mining, machine learning, and deep learning have been applied to detect network anomalies. In this paper, we evaluate the decision tree to see its feasibility for network anomaly detection on NSL-KDD data set, which is one of the most popular data mining techniques for classification. In order to handle the over-fitting problem of decision tree, we select 13 features from the original 41 features of the data set using chi-square test, and then model the decision tree using TensorFlow and Scik-Learn, yielding 84% and 70% of binary classification accuracies on the KDDTest+ and KDDTest-21 of NSL-KDD test data set. This result shows 3% and 6% improvements compared to the previous 81% and 64% of binary classification accuracies by decision tree technologies, respectively.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.1
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• pp.83-94
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• 2018

UNESCO has continued to work in Africa, especially in the six southern sub-Saharan countries, and Asia, where international cooperation is needed. The CLC (Rwanda Community Learning Center) covered in this study aims to create a regional learning center in Rwanda and to recover local communities and provide learning environment. During the course of this study, we conducted field trips for actual planning and reviewed the current state of educational and cultural facilities that recently opened and are operated, and found implications. In consultation with the Rwandan Educational Commission, the site for CLC was decided, the building was designed, and the construction is about to start. The results of this study are as follows. First, in addition to the efforts of the activists in the village, which can be considered the smallest unit of a local community, the approach for establishing an architectural space and active education and community environment can be evaluated as a result of experimental efforts. Second, we can pay attention to the attempts to realize local communities. The bridge business is based on the multi-purposes such as early childhood education, technical education for adults, and community restoration of local residents and it reflects space and program plans for this purposes. It also reflects detailed plans such as differentiating the flow planning depending on users' time of use. Third, we can explain the characteristics of architectural planning considering local characteristics such as active use of local materials. Due to the characteristics of a developing country, there were significant considerations on maintenance, and to this end, the plan included plans for the environment and use of materials that are easily maintained. In addition, the participation of local residents in the process of establishment was suggested as a possibility to serve an educational role.