• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2148-2167
• /
• 2021

The proliferation of the Internet of Things (IoT) technologies and applications, especially the rapid rise in the use of mobile devices, from individuals to organizations, has led to the fundamental role of secure wireless networks in all aspects of services that presented with many opportunities and challenges. To ensure the CIA (confidentiality, integrity and accessibility) security model of the networks security and high efficiency of performance results in various resource-constrained applications and environments of the IoT platform, DDO-(data-driven operation) based constructions have been introduced as a primitive design that meet the demand of high speed encryption systems. Among of them, the TMN-family ciphers which were proposed by Tuan P.M., Do Thi B., etc., in 2016, are entirely suitable approaches for various communication applications of wireless mobile networks (WMNs) and advanced wireless sensor networks (WSNs) with high flexibility, applicability and mobility shown in two different algorithm selections, TMN64 and TMN128. The two ciphers provide strong security against known cryptanalysis, such as linear attacks and differential attacks. In this study, we demonstrate new probability results on the security of the two TMN construction versions - TMN64 and TMN128, by proposing efficient related-key recovery attacks. The high probability characteristics (DCs) are constructed under the related-key differential properties on a full number of function rounds of TMN64 and TMN128, as 10-rounds and 12-rounds, respectively. Hence, the amplified boomerang attacks can be applied to break these two ciphers with appropriate complexity of data and time consumptions. The work is expected to be extended and improved with the latest BCT technique for better cryptanalytic results in further research.

• Journal of Drive and Control
• /
• v.18 no.4
• /
• pp.19-27
• /
• 2021

A bunker bed is a type of furniture that efficiently utilizes a narrow indoor space by having a high bed and using the empty space below as a living and storage space. The demand for multi-purpose furniture is increasing due to the recent increase in single-person households and wide-spread shared accommodation. According to the consumer research, one of the major drawbacks of a bunker bed was to get on and off the bed through a ladder or stairs. In order to overcome these problems, it was confirmed that the height adjustment function that can easily adjust the minimum and maximum heights of the bed was necessary. In this study, a height adjustable bunker bed was designed by using a gas spring that generates a repulsive force by the compressed gas inside. The design process consisted of the following three steps: Firstly, the hysteresis characteristics due to a friction and spring constant of a commercial gas spring were confirmed by measuring the repulsive force vs. compressed displacement. Secondly, requirements of the vertical lifting force exerted on the bed against gravity force were derived. Finally, the height-adjustable bed using the four-bar link mechanism was designed with 4 parameters so that the bed weight of 60-70 kgf could be adjusted to 800 mm in height by an affordable initial operation force. The performance was verified through prototype production and the results of vertical displacement and force to move were nearly the same as designed. In addition, an electrically operated height-adjustable bed was also designed with linear actuators and the performance was proved with the prototype.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.6
• /
• pp.1229-1244
• /
• 2017

In recent years, as demand for data-based analytical methodologies increases in various fields, optimization methods have been developed to handle them. In particular, various constraints required for problems in statistics and machine learning can be solved by convex optimization. Alternating direction method of multipliers (ADMM) can effectively deal with linear constraints, and it can be effectively used as a parallel optimization algorithm. ADMM is an approximation algorithm that solves complex original problems by dividing and combining the partial problems that are easier to optimize than original problems. It is useful for optimizing non-smooth or composite objective functions. It is widely used in statistical and machine learning because it can systematically construct algorithms based on dual theory and proximal operator. In this paper, we will examine applications of ADMM algorithm in various fields related to statistics, and focus on two major points: (1) splitting strategy of objective function, and (2) role of the proximal operator in explaining the Lagrangian method and its dual problem. In this case, we introduce methodologies that utilize regularization. Simulation results are presented to demonstrate effectiveness of the lasso.

• Review of Culture and Economy
• /
• v.20 no.1
• /
• pp.3-20
• /
• 2017

This study proposes that the cultural corporation can be more profitable by adopting the 'Buy-One Give-One' Business Model (BOGO BM) under certain conditions. Specifically, the conditions are that 1) consumers' utility for donating is high enough and 2) the production cost is low enough due to economies of scale or process innovation from employee's intrinsic motivation. We adopt a mathematical model where the demand function is a simple linear function and two companies compete with each other by choosing their quantity of output (the Cournot model). Specifically, we investigate the profitability of the BOGO BM in four situations: 1) a monopolist enjoying reduced cost due to process innovation from employee's intrinsic motivation, 2) a monopolist enjoying reduced cost due to economies of scale, 3) the duopoly with BOGO BM and a general company, 4) the duopoly with two BOGO BM companies. In each situation, BOGO BM can be more profitable than other general companies can under certain conditions.

• The Korean Journal of Applied Statistics
• /
• v.36 no.1
• /
• pp.49-62
• /
• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Korean Journal of Ecology and Environment
• /
• v.36 no.3 s.104
• /
• pp.336-343
• /
• 2003

Two types of statistical models, simple and multivariate log linear models, were studied for continuity simulation and trend analysis of water qualities in incoming flows to Lake Paldang. Water quality is a function of one independent variable (flow) in the simple log linear model, and of three different variables (flow, time, and seasonal cycle) in multivariate model. The independent variables act as surrogate variables of water quality in both models. The model coefficients were determined by the monthly data. The water qualities included 5-day Biochemical Oxygen Demand ($BOD_5$), Total Nitrogen (TN), and Total Phosphorus (TP) measured from 1995 to 2000 in the South and the North branches of Han River and the Kyoungan Stream. The results indicated that the multivariate model provided better agreements with field measurements than the simple one in a31 attempted cases. Flow dependency, seasonality, and temporal trends of water quality were tested on the determined coefficients of the multivariate model. The test of flow dependency indicated that BOD concentrations decreased as the water flow increased. In TN and TP concentrations, however, there were no discernible flow effects. From the temporal trend analyses, the following results were obtained: 1) no trends on BOD at all three upstreams, 2) increase on TN at the South Branch and the Kyoungan Stream, 3)decrease on TN at the North Branch,4) no trends on TP at the North and the South Branches and 5) increase on TP at the Kyoungan Stream by 3 to 8% per years. The seasonality test showed that there were significant seasonal variations in all three water qualities at three incoming flows.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.3 no.2
• /
• pp.119-126
• /
• 1983

The purposes of this study are to analyze/model the mode choice behavior of the regional traveler by express bus/express train and to offer useful source in deciding the public transportation policy. The data analyzed were trips of both modes from March, 1980 to November, 1981, between Seoul and other nineteen cities; the data were grouped as five groups according to the change of service variables. Service variables were travel time(unit: minute), cost(:won), average allocation time(:won), service hour(:hour), and dummy variables by mode. As model Logit Model with linear or log utility function were postulated. As the result of this study, some reseanable models were constructed at Model Type I(eq. 2. of this paper) based on the above data except the dummy. It was judged that the parameters calibrated by Group III and Group IV data in table 4, were optimal. Among the parameters, the parameter of travel cost was most reliable. There was a tendency preferring express bus to train in October and November. With the constructed model and Pivot-Point Method. the demand change of express train caused by the service variables' change could be forecasted over 99%.

• Environmental and Resource Economics Review
• /
• v.21 no.1
• /
• pp.93-127
• /
• 2012

We estimate how much KEPCO can save their loss and how much social welfare can be increased by applying the real-time pricing instead of current regulated retail price in the electricity market in order to analyze the problem of the regulated retail price which is fixed below the marginal cost. We estimate the demand functions of peak time and off-peak time in summer (June to August) and winter (December to February). We construct the supply function based on hourly step-wise linear marginal cost functions, too. We find that the increase of social welfare will be 67 billion won in summer if the fixed retail price is changed into the real-time pricing scheme. The total 705 billion won will be transferred from consumer surplus to producer surplus and the rest (67 billion won) will be saved from the reduction of deadweight loss among KEPCO's loss. In winter, the increase of social surplus will be 225 billion won and 1,174 billion won of KEPCO's loss will be transferred from consumer surplus. As a result, we conclude that the regulation of the retail price in the electricity market induces the social welfare loss and KEPCO suffers a huge loss.

• The Journal of the Korea Contents Association
• /
• v.22 no.7
• /
• pp.55-62
• /
• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.67 no.4
• /
• pp.211-221
• /
• 2022

Recently, the demand for the cultivation of upland soil has been increasing, and the rate of conversion of paddy soil into upland soil is also increasing. Theincrease in uneven precipitation due to climate change has resulted in dramatic effects of waterlogging stress on upland crops. Therefore, the present study was conducted to investigate the changes in growth characteristics and the expression patterns of proteins at the two-leaf stage of adzuki beans. The domestic cultivar, Arari (Miryang No. 8), was used to test waterlogging stress. At the two-leaf stage of adzuki beans, plant height slightly decreased androot fresh weight showed significant changes after 3 days of waterlogging treatment. Chlorophyll content was also significantly different after 3 days of waterlogging treatment compared to its content in control plants. Using two-dimensional gel electrophoresis, more than 400 protein spots were identified. Twenty-one differentially expressed proteins from the two-leaf stage were analyzed using linear trap quadrupole-Fourier transform-ion cyclotron resonance mass spectrometry. Of these 21 proteins, 9 were up-regulated and 12 were down-regulated under waterlogging treatment. Protein information resource (https://pir.georgetown.edu/) categories were assigned to all 49 proteins according to their molecular function, cellular component localization, and biological processes. Most of the proteins were found to be involved in the biological process, carbohydrate metabolism and were localized in chloroplasts.