• Geomechanics and Engineering
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• v.18 no.1
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• pp.71-83
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• 2019

This investigation presented conventional triaxial and creep-permeability tests on sandstones considering thermally-induced damage (TID). The TID had no visible effects on rock surface color, effective porosity and permeability below $300^{\circ}C$ TID level. The permeability enlarged approximately two orders of magnitude as TID increased to $1000^{\circ}C$ level. TID of $700^{\circ}C$ level was a threshold where the influence of TID on the normalized mass and volume of the specimen can be divided into two linear phases. Moreover, no prominent variations in the deformation moduli and peak strength and strain appeared as TID< $500^{\circ}C$ level. It is interesting that the peak strength increased by 24.3% at $700^{\circ}C$ level but decreased by 11.5% at $1000^{\circ}C$ level. The time-related deformation and steady-state creep rate had positive correlations with creep loading and the TID level, whereas the instantaneous modulus showed the opposite. The strain rates under creep failure stresses raised 1-4 orders of magnitude than those at low-stress levels. The permeability was not only dependent on the TID level but also dependent on creep deformation. The TID resulted in large deformation and complexity of failure pattern for the sandstone.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.169-175
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• 2021

The lot sizing problem(LSP) is a hard problem that classified as non-deterministic(NP)-complete because of the polynomial-time optimal solution algorithm is unknown yet. The well-known W-W algorithm can be obtain the solution within polynomial-time, but this algorithm is a very complex, therefore the heuristic approximated S-M algorithm is suggested. This paper suggests O(n) linear-time complexity algorithm that can be find not the approximated but optimal solution. This algorithm determines the lot size Xt∗ in period t to the sum of the demands of interval [t,t+k], the period t+k is determined by the holding cost will not exceed setup cost of t+k period. As a result of various experimental data, this algorithm finds the optimal solution about whole data.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.227-236
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• 2021

This research proposes a modeling and controller design of a three-level boost (TLB) converter for the implementation of maximum power point tracking (MPPT) in the photovoltaic power conditioning system (PCS). Contrary to the output voltage control of the conventional controller, the Photovoltaic PCS requires an input voltage controller for MPPT operation. A TLB converter has the advantage of decreasing the inductor size and increasing efficiency compared with the existing booster converter. However, an optimal controller is difficult to design due to the complexity of the TLB operations, which have two operational modes on the duty ratio boundary of 0.5. Therefore, the unified linear model equations of the TLB converters, which can be applicable to both operational modes, are derived using linearized solar cell expressions. Furthermore, the transfer functions are obtained for the controller design. The MPPT voltage controller is designed using MATLAB SISOTOOL. In addition, a controller for capacitor voltage unbalancing is described and designed. The simulations and experimental verifications are conducted to verify the effectiveness of the small-signal analysis and control system design.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.7 no.3
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• pp.11-22
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• 1997

The number of output sequence was proposed as a characteristic of binary sequence generators for cryptographic application, but in general most of binary sequence generators have single number of output sequence. In this paper, we propose two types of binary sequence generators with a large number of output sequences. The first one is a Switched-Tap LFSR (STLFSR) and it applies to the generalized nonlinear function and the Geffe's generator as example. The other is a generalized memory sequence generator(GMEM-BSG) which is an improved version of the Golic's memory sequence generator (MEM-BSG) with a large number of output sequences, and its period, linear complexity, and the number of output sequence are derived.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.7 no.2
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• pp.93-106
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• 1997

Summation generator is a real adder generator with maximum period, near maximum linear complexity and maximum order of correlation immunity. But this generator has been analyzed by a correlation attack(a kind of known-plaintext attack), which confers carry bits from output sequences of consecutive 0's or 1's. As methods of immunizing carry-output correlation, an immunized summation generator which exclusively-ORed summation generator output with output of a stage of LFSR was proposed. But the immunized generator reuses the output of LFSR by k-bit later and does not garantees maximum period in special case. In this paper we proposed an improved summation generator with 2-bit memory and analyzed it.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.89-96
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• 2021

This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with a classifier that gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.151-156
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• 2023

This paper deals with the optimization problem that decides the number of advertising for any media among various medium to maximize the perception quality index of new product meets the given budget and over the minimum reached people constraints. For this problem, there is only in used the mathematical approach as linear programming (LP) software package and has been unknown the polynomial time algorithm. In this paper we suggest the heuristic algorithm with O(nlog n)time complexity to solve the optimal solution for this problem. This paper suggests the evaluation index to select the media most economically-efficient way and decides the media and the number of advertisement. While we utilize Excel, the proposed algorithm can be get the same optimal solution as LP for experimental data.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.1-7
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• 2022

Robots are widely used in industries and services. Traditional robots have been used to perform repetitive tasks in a fixed environment, and it is very difficult to solve a problem in which the physical interaction of the surrounding environment or other objects is complicated with the existing control method. Reinforcement learning has been actively studied as a method of machine learning to solve such problems, and provides answers to problems that robots have not solved in the conventional way. Studies on the learning of all physical robots are commonly affected by noise. Complex noises, such as control errors of robots, limitations in performance of measurement equipment, and complexity of physical interactions with surrounding environments and objects, can act as factors that degrade learning. A learning method that works well in a virtual environment may not very effective in a real robot. Therefore, this paper proposes a weighted sum method and a linear regression method as an effective and accurate learning method in a noisy environment. In addition, the bottle flipping was trained on a robot and compared with the existing learning method, the validity of the proposed method was verified.