• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.133-141
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• 2001

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However is we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set. eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case, set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Steel and Composite Structures
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• v.48 no.6
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• pp.611-623
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• 2023

This work considered an optimal control formulation in the sense of Caputo derivatives. The optimality of the fractional optimal control problem. The tumor immune interaction in fractional form provides an excellent tool for the description of memory and hereditary properties of inter and intra cells. So the interaction between effector-cells, tumor cells and are modeled by using the definition of Caputo fractional order derivative that provides the system with long-time memory and gives extra degree of freedom. In addiltion, existence and local stability of fixed points are investigated for discrete model. Moreover, in order to achieve more efficient computational results of fractional-order system, a discretization process is performed to obtain its discrete counterpart. Our technique likewise allows the advancement of results, such as return time to baseline that are unrealistic with current model solvers.

• Microbiology and Biotechnology Letters
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• v.2 no.1
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• pp.29-35
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• 1974

We have studied the applicability of the principles and inherent advantages of the two-stage dontinuous uclture technique to an enzyme process for the purpose of improving and optimizing the productivity of 3-ketosteroid-delta-1-dehydrogenase. By using a two-stage continuous culture system, the growth st ageand enzyme produdtion stage are separated. In each stage an optimal set of toperaing conditions was determined, and this was tested for feasibility for the period of 10 days. During this period, at least 70％ of the maximum enzyme productivity could be maintained. The important design parameters studied are: (1) optimal specific growth rate in the first stage which corresponds to the maximal cell productivity, (2) the optimal dilution rate in the second stage which in turn determines the size of second stage fermentor and the mean residence time of cells in the second stage, (3) cell concentration in both stages, add (4) the specific enzyme productivity and enzyme productivity of the second stage. In addition, by using two-stage continuous culture system we have been able to reduce or eliminate the effect of catabolite repression due to high medium concentration and the adverse effect of the solvent used to dissolve the inducer. We have found the balance between the opposing effects of induction and repression in the second stage judging from the observation that the enzyme productivity goes through a maximum.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.115-115
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• 2020

Water network partitioning (WNP) is an initiative technique to divide the original water distribution network (WDN) into several sub-networks with only sparse connections between them called, District Metered Areas (DMAs). Operating and managing (O&M) WDN through DMAs is bringing many advantages, such as quantification and detection of water leakage, uniform pressure management, isolation from chemical contamination. The research of WNP recently has been highlighted by applying different methods for dividing a network into a specified number of DMAs. However, it is an open question on how to determine the optimal number of DMAs for a given network. In this study, we present a method to divide an original WDN into DMAs (called Clustering) based on community structure algorithm for auto-creation of suitable DMAs. To that aim, many hydraulic properties are taken into consideration to form the appropriate DMAs, in which each DMA is controlled as uniform as possible in terms of pressure, elevation, and water demand. In a second phase, called Sectorization, the flow meters and control valves are optimally placed to divide the DMAs, while minimizing the pressure reduction. To comprehensively evaluate the WNP performance and determine optimal number of DMAs for given WDN, we apply the framework of multiple-criteria decision analysis. The proposed method is demonstrated using a real-life benchmark network and obtained permissible results. The approach is a decision-support scheme for water utilities to make optimal decisions when designing the DMAs of their WDNs.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.5
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• pp.379-387
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• 2010

In this study, a case study was introduced for the design of a twin tunnel along high speed national highway Route 12 from Damyang to Sungsan. It was related to determine the optimal tunnel support pattern and excavation method based on a risk analysis in order to incorporate the uncertainty of ground properties. To this end, three alternatives with different amounts of support and excavation method were selected and risk analysis was performed by applying Monte Carlo simulation technique, respectively. Stability of the tunnel was quantified by the factor of safety. To improve the result, the 729 cases of the combination of ground properties (deformation modulus, cohesion, and internal friction angle) satisfying a Gaussian distribution were generated and applied. Also, stability of the tunnel was confirmed by analyzing the distribution of both displacement and shotcrete bending stress.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.15-20
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• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• Structural Engineering and Mechanics
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• v.89 no.1
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• pp.61-75
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• 2024

Strengthening of hollow structural sections using through-bolts is a cost-effective and straightforward approach. It's a versatile method that can be applied during both design and service phases, serving as a non-disruptive and budget-friendly retrofitting solution. Existing research on axially loaded hollow sections T-joints has demonstrated that this technique can amplify the joint strength by 50%, where single bolt could enhance the strength of the joint by 35%. However, there's a gap in understanding their use for K-joints. As the behavior of K-joints is more complex, and they are widely existent in structures, this study aims to bridge that gap by conducting comprehensive parametric study using finite element analysis. Numerical investigation was conducted to evaluate the effect of through bolts on K-joints focusing on using single through bolt to achieve most of the strengthening effect. A full-scale parametric model was developed to investigate the effect of various geometric parameters of the joint. This study concluded the existence of optimal bolt location to achieve the highest strength gain for the joint. Moreover, a rigorous statistical analysis was conducted on the data to propose design equations to predict optimal bolt location and the corresponding strength gain implementing the verified by finite element models.

• Advances in materials Research
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• v.13 no.1
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.519-536
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• 2005

The purpose of this paper is to propose several approximating methods to obtain the American option prices under jump-diffusion processes. The first method is to extend an approximating method to the optimal exercise boundary by a multipiece exponential function suggested by Ju [17]. The second approach is to modify the analytical methods of MacMillan [20] and Zhang [25] in a discrete time space. The third approach is to apply the simulation technique of Ibanez and Zapareto [14] to the problem of American option pricing when the jumps are allowed. Finally, we compare the numerical performance of each suggesting method with those of the previous numerical approaches.