• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.783-791
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• 2004

In this paper we review recent developments in nonlinear time series modeling on both conditional mean and conditional variance. Traditional linear model in conditional mean is referred to as ARMA(autoregressive moving average) process investigated by Box and Jenkins(1976). Nonlinear mean models such as threshold, exponential and random coefficient models are reviewed and their characteristics are explained. In terms of conditional variances, ARCH(autoregressive conditional heteroscedasticity) class is considered as typical linear models. As nonlinear variants of ARCH, diverse nonlinear models appearing in recent literature including threshold ARCH, beta-ARCH and Box-Cox ARCH models are remarked. Also, a class of unified nonlinear models are considered and parameter estimation for that class is briefly discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.875-880
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• 2019

A RC4 stream cipher is widely used for security applications such as IEEE 802.11 WEP, IEEE 802.11i TKIP and so on, because it can be simply implemented to dedicated circuits and achieve a high-speed encryption. RC4 is also used for systems with limited resources like IoT, but there are performance limitations. RC4 consists of two stages, KSA and PRGA. KSA performs initialization and randomization of S-box and K-box and PRGA produces cipher texts using the randomized S-box. In this paper, we initialize the S-box and K-box in the randomization of the KSA stage to reduce the initialization delay. In the randomization, we use clusters to process swap operation between elements of S-box in parallel and can generate two cipher texts per clock. The proposed RC4 cipher hardware can initialize S-box and K-box without any delay and achieves about 2 times to 6 times improvement in KSA randomization and key stream generation.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.337-347
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• 2020

In CRYPTO 2019, Gohr presents that Deep-learning can be used for cryptanalysis. In this paper, we verify whether Deep-learning can identify the structures of S-box. To this end, we conducted two experiments. First, we use DDT and LAT of S-boxes as the learning data, whose structure is one of mainly used S-box structures including Feistel, MISTY, SPN, and multiplicative inverse. Surprisingly, our Deep-learning algorithms can identify not only the structures but also the number of used rounds. The second application verifies the pseudo-randomness of and structures by increasing the nuber of rounds in each structure. Our Deep-learning algorithms outperform the theoretical distinguisher in terms of the number of rounds. In general, the design rationale of ciphers used for high level of confidentiality, such as for military purposes, tends to be concealed in order to interfere cryptanalysis. The methods presented in this paper show that Deep-learning can be utilized as a tool for analyzing such undisclosed design rationale.

• Journal of Korea Game Society
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• v.19 no.4
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• pp.49-64
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• 2019

In this paper, we examined the concept of three types of double loot box respectively and analyzed each characteristics of double loot box by game case study. Those types are divided into the following three cases; 1) Double-mileage gacha 2) Double-limited period gacha 3) Kompu gacha. As the result, Double loot boxs are characterized as below: 1) Double loot box has a tendency to combine various type of loot boxes. 2) Double loot box's reward is hard to gain in game, or not to sell in game store. With these features, double loot box could be a gambling sales strategy. To solve this problem, this study suggested that it is essential to revise game self-regulations, enhance the professionalism of monitoring groups, and propel the self-effort of game companies.

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.399-411
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• 2003

An approach determining a confidence interval of Nash's observed mean instantaneous unit hydrograph is developed. In the approach, both two parameters are treated as correlated gaussian random variables based on the theory of Box-Cox transformation and the regional similarity relation, so that linear statistical parameter estimation is possible. A parametric bootstrap method is adopted to give the confidence interval of the mean observed hydrograph. The proposed methodology is also applicable to estimate the parameters of Nash's model for un-gauged basins. An application to a watershed has shown that the proposed approach is adequate to assess the uncertainty of the Nash's hydrograph and to evaluate parameters for un-gauged basins.

• The Mathematical Education
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• v.57 no.4
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• pp.413-431
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• 2018

In this study, I developed an activity oriented lesson to support the understanding of probabilistic and quantitative estimating population ratios according to the standard statistical principles and discussed its implications in didactical respects. The developed activity lesson, as an efficient physical simulation activity by sampling with replacement, simulates unknown populations and real problem situations through completely closed 'Closed Box' in which we can not see nor take out the inside balls, and provides teaching and learning devices which highlight the representativeness of sample ratios and the sampling variability. I applied this activity lesson to the gifted students who did not learn estimating population ratios and collected the research data such as the activity sheets and recording and transcribing data of students' presenting, and analyzed them by Qualitative Content Analysis. As a result of an application, this activity lesson was effective in recognizing and reflecting on the representativeness of sample ratios and recognizing the random sampling variability. On the other hand, in order to show the sampling variability clearer, I discussed appropriately increasing the total number of the inside balls put in 'Closed Box' and the active involvement of the teachers to make students pay attention to controlling possible selection bias in sampling processes.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.471-478
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• 2013

This paper proposes a black-box based test case generation method for Simulink/Stateflow model utilizing the RRT algorithm which is a method to efficiently solve the path planning for complicated systems. The proposed method in the paper tries to solve the reachability problem with the RRT algorithm, which has to be solved for black-box based test case generations. A major problem of the RRT based test case generation algorithms is that the cost such as running time and required memory size is too much for complicated Stateflow model. The typical RRT algorithm expands rapidly-exploring random tree (RRT) in a single state space. But the proposed method expands it in dynamic state space based on the state of Simulink model, consequently reducing the cost. In the paper, a new definition of RRT state space, a distance measure and a test case generation algorithm are proposed. The performance of proposed method is verified through the experiment against Stateflow model.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.3
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• pp.223-228
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• 2023

Sustainable energy supplies without the recharging and replacement of the charge storage device have become increasingly important. Among various energy harvesters, the triboelectric nanogenerator (TENG) has attracted considerable attention due to its high instantaneous output power, broad selection of available materials, eco-friendly and inexpensive fabrication process, and various working modes customized for target applications. In this study, the amount of voltage and current generated was measured by applying the PSD profile random vibration test of the electronic vibration tester and ISTA 3A according to the time of Anodized Aluminum Oxide (AAO) pore widening of the manufactured TENG device Teflon and AAO. The discharge and charging tests of the integrated module during the random simulated transport environment and the recognition distance of RFID were measured while agricultural products (onion) were loaded into the returnable folding plastic box. As a result, it was found that AAO alumina etching processing time to maximize TENG performance was optimal at 31 min in terms of voltage and current generation, and the integrated module applied with the TENG module showed a charging effect even during the continuous use of RFID, so the voltage was kept constant without discharge. In addition, the RFID recognition distance of the integrated module was measured as a maximum of 1.4 m. Therefore, it was found that the surface condition of AAO, a TENG element, has a great influence on the power generation of the integrated module, and due to the characteristics of TENG, the power generation increases as the surface dries, so it is judged that the power generation can be increased if the surface drying treatment (ozone treatment, etc.) of AAO is applied in the future.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.45-50
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• 2008

Shock and vibration inputs are transmitted from the transporting vehicle through the packaging box to the fruit. The vibration causes sustained bouncing of fruits against each other and the container wall. The steady state vibration input may cause serous fruit injury, and the damage is particularly severe if the fruits are bounced at its resonance frequency. The determination of the resonance frequencies of the fruits and vegetables may help the packaging designer to design the proper packaging system providing adequate protection of the fruits from external impact or shock. In this study, to analyze the vibration properties of the apples for optimum packaging design during transportation, the random vibration tests were carried out. From the results of random vibration test, the resonance frequency and power spectral density (PSD) of the packaged freight of apples in the test were in the range of 82 to 97 Hz and 0.0013 to 0.0021 $G^2/Hz$ respectively and the resonance frequency and PSD of the packaged apples were in the range of 13 to 71 Hz and 0.0143 to 0.0923 $G^2/Hz$ respectively.