• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.119-124
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• 2020

In this paper, in order to encourage the user's exercise, we presented an exercise goal that considers the user's weight and exercise state, and dealt with a study on an app that gives a goal using GPS information. Unlike the vague numbers and times suggested in the existing app, it is presented specifically with the surrounding buildings or structures using GPS information. In addition, to use competitive psychology to exercise encouragement, it shows the movement information of people connected to the app and allows users to use the competitive psychology to get the effect of exercising many people. The app creates coordinates of major buildings and sets markings using the Naver Map SDK location information to present specific targets. It is easy for users to get bored if they give a goal every time, and the boredom that the user feels decreases the interest in the exercise. In order to not to lose interest in athletic interest. the app switches to game mode and give a light goal that doesn't matter user's weight or exercise status, and rewards user for achieving the suggested goals. Game mode is added to app that connects a person's will to practice. It adds fun elements to create interest, and uses competitiveness to help you live a healthy life with a steady workout. Technically, to improve the accuracy of smart-phone map display using GPS and the tilt processing was to be able to display the exact location.

• Journal of Gifted/Talented Education
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• v.26 no.1
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• pp.211-230
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• 2016

The study aimed to investigate the characteristics of algebraic thinking of the mathematically gifted students and search for how to teach algebraic thinking. Research subjects in this study included 93 students who applied for a science gifted education center affiliated with a university in 2015 and previously experienced gifted education. Students' responses on an algebraic item of a creative thinking test in mathematics, which was given as screening process for admission were collected as data. A framework of algebraic thinking factors were extracted from literature review and utilized for data analysis. It was found that students showed difficulty in quantitative reasoning between two quantities and tendency to find solutions regarding equations as problem solving tools. In this process, students tended to concentrate variables on unknown place holders and to had difficulty understanding various meanings of variables. Some of students generated errors about algebraic concepts. In conclusions, it is recommended that functional thinking including such as generalizing and reasoning the relation among changing quantities is extended, procedural as well as structural aspects of algebraic expressions are emphasized, various situations to learn variables are given, and activities constructing variables on their own are strengthened for improving gifted students' learning and teaching algebra.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.92-98
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• 2017

Generally, image contains geometrical and radiometric errors. Census transform can solve the stereo mismatching problem caused by the radiometric distortion. Since the general census transform compares center of window pixel value with neighbor pixel value, it is hard to obtain an accurate matching result when the difference of pixel value is not large. To solve that problem, we propose a census transform method that applies different 4-step weight for each pixel value difference by applying an assistance window inside the window kernel. If the current pixel value is larger than the average of assistance window pixel value, a high weight value is given. Otherwise, a low weight value is assigned to perform a differential census transform. After generating an initial disparity map using a weighted census transform and input images, the gradient information is additionally used to model a cost function for generating a final disparity map. In order to find an optimal cost value, we use guided filtering. Since the filtering is performed using the input image and the disparity image, the object boundary region can be preserved. From the experimental results, we confirm that the performance of the proposed stereo matching method is improved compare to the conventional method.

• Communications of Mathematical Education
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• v.32 no.4
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• pp.495-517
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• 2018

This paper is based on the effects of Zone of Proximal Development and scaffolding theory of social constructivist, Russian psychologist Vygotsky. He insisted that a social interaction play a fundamental role in the development of cognition. This study is to examine the efficient of the scaffolding types in Math class. The ZPD is the distance between a student's ability to perform a task under adult guidances or with peer collaboration and the student's ability solving the problem independently. To conduct the research was grouped into an experimental first grader five students in H high school in Y county. After class, students were questioned through Semi-structured interviews. The results of this study are below. First, Students were satisfied with the class mixed micro-scaffolding types and Macro-scaffolding types and improved their math thinking ability and the ways of solving problems. Second, The results of the class showed that students' ability to perform a task was transferred to the higher level through the help of a teacher or peers. Students could have more time to listen to peers' opinions and to say their own thoughts freely than they were under the lecture method instruction. Third, Students were interested in math through the experimental class. That's because the appropriate help of the scaffolding type, a cooperate study, relative with real life, using an engineering tools. They made a change of perception.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.221-232
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• 2023

This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Information Systems Review
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• v.25 no.1
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• pp.93-110
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• 2023

Recently, attempts have been made to convert unstructured text into vectors and to analyze vast amounts of natural language for various purposes. In particular, the demand for analyzing texts in specialized domains is rapidly increasing. Therefore, studies are being conducted to analyze specialized and general-purpose documents simultaneously. To analyze specific terms with general terms, it is necessary to align the embedding space of the specific terms with the embedding space of the general terms. So far, attempts have been made to align the embedding of specific terms into the embedding space of general terms through a transformation matrix or mapping function. However, the linear transformation based on the transformation matrix showed a limitation in that it only works well in a local range. To overcome this limitation, various types of nonlinear vector alignment methods have been recently proposed. We propose a vector alignment model that matches the embedding space of specific terms to the embedding space of general terms through end-to-end learning that simultaneously learns the autoencoder and regression model. As a result of experiments with R&D documents in the "Healthcare" field, we confirmed the proposed methodology showed superior performance in terms of accuracy compared to the traditional model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.4
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• pp.77-85
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• 1990

The application of remote sensing in small scale mapping has recently been widened to various fields such as information analysis of landuse, environmental conservation and natural resources. SPOT imagery, in particular, offers data which can be processed for 3-dimensional point determination. This is made possible by its high resolution, appropriate swatch width/altitude ratio and stereo imaging capabilities. This study aims to develop a suitable polymonial and an algorithm in the determination of exterior orientation which is essential in the 3-dimensional point determination of SPOT imgery. An algorithm is presented in this study to determine the exterior orientation of a preprocessed level lB film of the satellite image. It was found that a polynominal of 15 parameters is the best fit polynominal for exterior orientation determination, where 1st order line function is used for positon ($X_o$, $Y_o$, $Z_o$) and 2nd order line function is used for orientation (${\kappa}_o$, ${\phi}_o$, ${\omega}_o$).

• Journal of the Economic Geographical Society of Korea
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• v.15 no.3
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• pp.343-357
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• 2012

The Seoul metropolitan subway system can be represented by a graph which consists of nodes and edges. In this paper, we study classification of subway stations and trip behaviour of subway passengers through partitioning the graph of the subway system into roughly equal groups. A weight of each edge of the graph is set to the number of passengers who pass the edge, where the number of passengers is extracted from the transportation card transaction database. Since the graph partitioning problem is NP-complete, we propose a heuristic algorithm to partition the subway graph. The heuristic algorithm uses one of two alternative objective functions, one of which is to minimize the sum of weights of edges connecting nodes in different groups and the other is to maximize the ratio of passengers who get on the subway train at one subway station and get off at another subway station in the same group to the total subway passengers. In the experimental results, we illustrate the subway stations and edges in each group by color on a map and analyze the trip behaviour of subway passengers by the group origin-destination matrix.

• Explosives and Blasting
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• v.33 no.3
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• pp.29-50
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• 2015

Safe blast criteria based on the concept of scaled distances can be obtained from the statistical analysis on measured peak particle velocity data of blast vibrations. Two types of scaled distance widely used in Korea are the square root scaled distance (SRSD) and cube root scaled distance (CRSD). In contrast to SRSD scheme, however, the function of maximum charge per delay for CRSD increases without bound after the intersection point of these two functions despite of the similar goodness of fits. To prevent structural damage that may be caused by the excessive charge in the case of CRSD, it is suggested that CRSD be used within a specified distance slightly beyond the intersection point. On the other hand, there are several attempts that predict vibration level(VL) from the peak particle velocity(PPV) or estimate VL based on the scaled distances without considering their frequency spectra. It appears that these attempts are conducted in blasting contracts only for the purpose of satisfying the environment-related law, which mainly deals with the annoyance aspects of noises and vibrations in human life. But, in principle there could no correlation between peaks of velocity and acceleration over entire frequency spectrum. Therefore, such correlations or estimations should be conducted only between the waves with the same or very similar frequency spectra. Finally, it is a known fact that structural damage due to ground vibration is related to PPV level, the safety level criteria for structures should be defined by allowable PPV levels together with their zero crossing frequencies (ZCF).