• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.639-647
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• 2012

Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems. However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.51-59
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• 2021

The deep learning model can produce false prediction results due to inputs that deviate from training data through variation, which leads to fatal accidents in areas such as autonomous driving and security. To ensure reliability of the model, the model's coping ability for exceptional situations should be verified through various mutations. However, previous studies were carried out on limited scope of models and used several mutation types without separating them. Based on the CIFAR10 data set, widely used dataset for deep learning verification, this study carries out reliability verification for total of six models including various commercialized models and their additional versions. To this end, six types of input mutation algorithms that may occur in real life are applied individually with their various parameters to the dataset to compare the accuracy of the models for each of them to rigorously identify vulnerabilities of the models associated with a particular mutation type.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.1-9
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• 2015

As the use of software is more wider in the safety-critical nuclear fields, so study to improve safety and quality of the software has been actively carried out for more than the past decade. In the nuclear power plant, nuclear man-machine interface systems (MMIS) performs the function of the brain and neural networks of human and consists of fully digitalized equipments. Therefore, errors in the software for nuclear MMIS may occur an abnormal operation of nuclear power plant, can result in economic loss due to the consequential trip of the nuclear power plant. Verification and validation (V&V) is a software-engineering discipline that helps to build quality into software, and the nuclear industry has been defined by laws and regulations to implement and adhere to a through verification and validation activities along the software lifecycle. V&V is a collection of analysis and testing activities across the full lifecycle and complements the efforts of other quality-engineering functions. This study propose a methodology based on V&V activities and related tool-chain to improve quality for software in the nuclear power plant. The optimized methodology consists of a document evaluation, requirement traceability, source code review, and software testing. The proposed methodology has been applied and approved to the real MMIS project for Shin-Hanul units 1&2.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Geomechanics and Engineering
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• v.20 no.6
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• pp.475-484
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• 2020

The prediction of the ground conditions ahead of a tunnel face is very important, especially for tunnel boring machine (TBM) tunneling, because encountering unexpected anomalies during tunnel excavation can cause a considerable loss of time and money. Several prediction techniques, such as BEAM, TSP, and GPR, have been suggested. However, these methods have various shortcomings, such as low accuracy and low resolution. Most studies on electrical resistivity tomography surveys have been conducted using numerical simulation programs, but laboratory experiments were just a few. Furthermore, most studies of scaled model tests on electrical resistivity tomography were conducted only on the ground surface, which is a different environment as compared to that of mechanized tunneling. This study performed a laboratory experimental test to extend and verify a prediction method proposed by Lee et al., which used electrical resistivity tomography to predict the ground conditions ahead of a tunnel face in TBM tunneling environments. The results showed that the modified dipole-dipole array is better than the other arrays in terms of predicting the location and shape of the anomalies ahead of the tunnel face. Having longer upper and lower borehole lengths led to better accuracy of the survey. However, the number and length of boreholes should be properly controlled according to the field environments in practice. Finally, a modified and verified technique to predict the ground conditions ahead of a tunnel face during TBM tunneling is proposed.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.47-55
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• 2010

In this paper, we proposed a recognition system of smile facial expression for smile treatment training. The proposed system detects face candidate regions by using Haar-like features from camera images. After that, it verifies if the detected face candidate region is a face or non-face by using SVM(Support Vector Machine) classification. For the detected face image, it applies illumination normalization based on histogram matching in order to minimize the effect of illumination change. In the facial expression recognition step, it computes facial feature vector by using PCA(Principal Component Analysis) and recognizes smile expression by using a multilayer perceptron artificial network. The proposed system let the user train smile expression by recognizing the user's smile expression in real-time and displaying the amount of smile expression. Experimental result show that the proposed system improve the correct recognition rate by using face region verification based on SVM and using illumination normalization based on histogram matching.

• Journal of KIISE
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• v.42 no.8
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• pp.979-989
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• 2015

Machine to Machine (M2M) technology has gained attention due to the fast diffusion of Internet of Things (IoT) technologies and smart devices. Most wireless network experts believe that Bluetooth Low Energy (BLE) Communications technology in an iBeacon network has amazing advantages in terms of providing communication services at a low cost in smartphone applications. Specifically, BLE does not require any pairing process during its communication phases, so it is possible to send a message to any node without incurring additional transmissions costs if they are within the BLE communication range. However, BLE does not require any security verification during communication, so it has weak security. Therefore, a security authorization process would be necessary to obtain customer confidence. To provide security functions for iBeacon, we think that the iBeacon Message Encryption process and a Decryption (Authorization) process should be designed and implemented. We therefore propose the BLE message Authorization Mechanism based on a One Time Password Algorithm (BLE-OTP). The effectiveness of our mechanism is evaluated by conducting a performance test on an attendance system based on BLE-OTP.

• ETRI Journal
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• v.18 no.4
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• pp.265-286
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• 1997

This paper presents a new software development environment that supports an integrated methodology for covering all phases of software development and gives integrated methods with tools for ITUT (Telecommunication Standardization Section of the International Telecommunication Union) languages. The design of the environment to improve software productivity and quality is based on five main concepts: 1) formal specifications based on SDL (Specification and Description Language) and MSC (Message Sequence Charts) in the design phase, 2) verification and validation of those designs by tools, 3) automatic code generation and a safe separate compilation scheme based on CHILL (CCITT High-Level Language) to facilitate programming-in-the-many and programming-in-the-large. 4) debugging of distributed real-time concurrent CHILL programs, and 5) simulation of application software for integrated testing on the host machine based on CHILL. The application results of the environment compared with other approaches show that the productivity is increased by 19 % because of decreasing implementation and testing cost, and the quality is increased by 83 % because of the formal specifications with its static and dynamic checking facilities.