• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.237-251
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• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.52-63
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• 2009

Recently in Korea, in the case of metropolis, the urban signalized intersections are controlled by traffic information center or ITS center. Cheon-an City also established traffic information center through the 1st.-$\sim$3rd. ITS public construction and has managed this center that includes bus information service, traffic information collection and providing service, parking information service, and traffic responsive control system. In the Cheon-an metropolitan traffic signal operation, traffic signal controllers were grouped by the each main traffic flow axes and performed with coordinated signal timing for the signalized arterials, and also cycle and split changed by realtime traffic demands. Cheon-an urban traffic responsive control system was evaluated by intersection delay and speed, then it was verified that the delay decreased and vehicle speed improved. However, the rural signal control system to connect adjacency town was evaluated to have lower status than urban area due to the unimproved TOD (Time of day) plan. Therefore actuated signal control was examined for substitutive control system in isolated signal intersection. The aim of this article is to compare actuated signal control with TOD mode in the rural intersection of Cheon-an and to fine superiority of these two control mode, with evaluation of vehicle delay by using HCM(2000) method and by micro-simulation CORSlM. The result of field test show that actuated signal control gave better performance in delay comparison than the existing TOD signal control. And simulation outcome verified that non-optimized TOD has higher delay than optimized TOD mode, non-optimal actuated mode, and optimal actuated signal control mode. Particularly, these three modes delays had not different values according to the paired sample t-test. This is because small traffic demands were loaded in each links. This suggested actuated signal control is expected to be more effective than TOD mode in some rural isolated intersections which frequently need to survey for traffic volume.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.543-557
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• 2021

Because aerosols have different spectral characteristics according to the size and composition of the particle and to the satellite sensors, a comparative analysis of aerosol products from various satellite sensors is required. In South Korea, however, a comprehensive study for the comparison of various official satellite AOD (Aerosol Optical Depth) products for a long period is not easily found. In this paper, we aimed to assess the performance of the AOD products from MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), Himawari-8, and Sentinel-3 by referring to the AERONET (Aerosol Robotic Network) sun photometer observations for the period between January 2015 and December 2019. Seasonal and geographical characteristics of the accuracy of satellite AOD were also analyzed. The MODIS products, which were accumulated for a long time and optimized by the new MAIAC (Multiangle Implementation of Atmospheric Correction) algorithm, showed the best accuracy (CC=0.836) and were followed by the products from VIIRS and Himawari-8. On the other hand, Sentinel-3 AOD did not appear to have a good quality because it was recently launched and not sufficiently optimized yet, according to ESA (European Space Agency). The AOD of MODIS, VIIRS, and Himawari-8 did not show a significant difference in accuracy according to season and to urban vs. non-urban regions, but the mixed pixel problem was partly found in a few coastal regions. Because AOD is an essential component for atmospheric correction, the result of this study can be a reference to the future work for the atmospheric correction for the Korean CAS (Compact Advanced Satellite) series.

• Journal of the Korean Geotechnical Society
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• v.15 no.4
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• pp.113-132
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• 1999

Recently in Korea, application of the soil nailing is gradually extended to the sites of excavations and slopes having various ground conditions and field characteristics. Design of the soil nailing is generally carried out in two steps, The First step is to examine the minimum safety factor against a sliding of the reinforced nailed-soil mass based on the limit equilibrium approach, and the second step is to check the maximum displacement expected to occur at facing using the numerical analysis technique. However, design parameters related to the soil nailing system are so various that a reliable design method considering interrelationships between these design parameters is continuously necessary. Additionally, taking into account the anisotropic characteristics of in-situ grounds, disturbances in collecting the soil samples and errors in measurements, a systematic analysis of the field measurement data as well as a rational technique of the optimum design is required to improve with respect to economical efficiency. As a part of these purposes, in the present study, a procedure for the optimum design of a soil nailing excavation wall system is proposed. Focusing on a minimization of the expenses in construction, the optimum design procedure is formulated based on the genetic algorithm. Neural network theory is further adopted in predicting the maximum horizontal displacement at a shotcrete facing. Using the proposed procedure, various effects of relevant design parameters are also analyzed. Finally, an optimized design section is compared with the existing design section at the excavation site being constructed, in order to verify a validity of the proposed procedure.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.2
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• pp.85-96
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• 2015

Korea is one of low-ranked countries in women's economic participation rate among OECD nations because well-educated females are not participating in economic activities. Regardless of current state of our society, opening a business is being considered as a effective method for job creation. Also, increasing the number of female business founders can lead to female job creation which promotes even growth of foundation and job creation and augments women's economic activity rate. Therefore, this study suggests the direction of foundation and inspires foundation factors and aims at increasing social re-participation through vitalization of business foundation by women in career discontinuity. For this study, I carried out a survey targeting career interrupted women who have attained entrepreneurial education using five- point scale by Likert and analyzed with SPSS Windows 18.0. The analysis set up 3 hypotheses with independent variables of psychological traits, entrepreneurial education and entrepreneurial environment and the dependent variable of entrepreneurial intention of the career interrupted women. Also, I looked if there is the modify effect when psychological traits and entrepreneurial education affect the entrepreneurial intention with entrepreneurial environment as a moderating variable. To summarize the positive analysis result, Firstly, all psychological traits, entrepreneurial education and entrepreneurial environment had similar positive affects on career interrupted women's entrepreneurial intention. Secondly, when psychological traits and entrepreneurial education affect the entrepreneurial intention, entrepreneurial environment had similar effects as a moderating effect. This study implies that psychological traits, entrepreneurial education and entrepreneurial environment are all important for the career interrupted women's entrepreneurial intention. There are so many women who are going through both professional experience and personal network's severance. Therefore, optimized entrepre neurship education must be provided to help those women return to economic activity considering their psychological traits. Additionally, we should put emphasis on producing the entrepreneurial environment that can positively convert others' perceptions and construct those women's personal network. There seems to be more productive information for the strategies which can induce those women's actual business foundation if the social problems of the women who have highly willing to open a business are treated in the future. Also, considering that psychological traits, entrepreneurial education and entrepreneurial environment all have effect on entrepreneurial intentions, there should be more related follow-up study on this.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.88-95
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• 2002

In recent years, the security of hardware and software systems is one of the most essential factor of our safe network community. As elliptic Curve Cryptosystems proposed by N. Koblitz and V. Miller independently in 1985, require fewer bits for the same security as the existing cryptosystems, for example RSA, there is a net reduction in cost size, and time. In this thesis, we propose an efficient hardware architecture of underlying field arithmetic processor for Elliptic Curve Cryptosystems, and a very useful method for implementing the architecture, especially multiplicative inverse operator over GF$GF (2^m)$ onto FPGA and futhermore VLSI, where the method is based on optimized unit operation components. We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed and inversion speed has been improved 150 times, 480 times respectively compared with the thesis presented by Sarwono Sutikno et al. [7]. The designed underlying arithmetic processor can be also applied for implementing other crypto-processor and various finite field applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2A
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• pp.152-161
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• 2007

IEEE 802.11 Wireless Local Area Network (WLAN) became a prevailing technology for the broadband wireless Internet access, and new applications such as Voice over WLAM (VoWLAN) are fast emerging today. For the battery-powered VoWLAN devices, the standby time extension is a key concern for the market acceptance while today's 802.11 is not optimized for such an operation. In this paper, we propose a novel Idle Mode operation, which comprises paging, idle handoff, and delayed handoff. Under the idle mode operation, a Mobile Host (MH) does not need to perform a handoff within a predefined Paging Area (PA). Only when the MH enters a new PA, an idle handoff is performed with a minimum level of signaling. Due to the absence of such an idle mode operation, both IP paging and Power Saving Mode (PSM) have been considered the alternatives so far even though they are not efficient approaches. We implement our proposed scheme in order to prove the feasibility. The implemented prototype demonstrates that the proposed scheme outperforms the legacy alternatives with respect to energy consumption, thus extending the standby time.