• Geomechanics and Engineering
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• v.21 no.6
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• pp.583-598
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• 2020

This study is attempted to propose a new hybrid artificial intelligence model called integrative genetic algorithm with multivariate adaptive regression splines (GA-MARS) for settlement prediction of shallow foundations on sandy soils. In this hybrid model, the evolution algorithm - Genetic Algorithm (GA) was used to search and optimize the hyperparameters of multivariate adaptive regression splines (MARS). For this purpose, a total of 180 experimental data were collected and analyzed from available researches with five-input variables including the bread of foundation (B), length to width (L/B), embedment ratio (D_f/B), foundation net applied pressure (q_net), and average SPT blow count (N_SPT). In further analysis, a new explicit formulation was derived from MARS and its accuracy was compared with four available formulae. The attained results indicated that the proposed GA-MARS model exhibited a more robust and better performance than the available methods.

• Journal of electromagnetic engineering and science
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• v.6 no.1
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• pp.30-35
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• 2006

This paper describes fully integrated low phase noise MMIC voltage controlled oscillators(VCOs). The Asymmetrical Inductance Tank VCO(AIT-VCO), which optimize the shortcoming of the previous tank's inductance optimization approach, has lower phase noise performance due to achieving higher equivalent parallel resistance and Q value of the tank. This VCO features an output power signal in the range of - 11.53 dBm and a tuning range of 261 MHz or 15.2 % of its operating frequency. This VCO exhibits a phase noise of - 117.3 dBc/Hz at a frequency offset of 100 kHz from carrier. A phase noise reduction of 15 dB was achieved relative to only one spiral inductor. The AIT-VCO achieved low very low figure of merit of -184.6 dBc/Hz. The die area, including buffers and bond pads, is $0.9{\times}0.9mm^2$.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07a
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• pp.146-149
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• 2004

Porous silicon(PS) as an excellent light diffuser can be used as an antireflection layer without other antireflection coating(ARC) materials. PS layers were obtained by electrochemical etching(ECE) anodization of silicon wafers in hydrofluoric acid/ethanol/de-ionized(DI) water solution($HF/EtOH/H_2O$). This technique is based on the selective removal of Si atoms from the sample surface forming a layer of PS with adjustable optical, electrical, and mechanical properties. A PS layer with optimal ARC characteristics was obtained in charge density (Q) of 5.2 $C/cm^2$. The weighted reflectance is reduced from 33 % to 4 % in the wavelength between 400 and 1000 nm. The weighted reflectance with optimized PS layers is much less than that obtained with a commercial SiNx ARC on a potassium hydroxide(KOH) pre-textured multi-crystalline silicon(mc-Si) surface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3821-3841
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• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4385-4399
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• 2021

The Internet of Things (IoT) eHealth systems composed by Wireless Body Area Network (WBAN) has emerged recently. Sensor nodes are placed around or in the human body to collect physiological data. WBAN has many different applications, for instance health monitoring. Since the limitation of the size of the battery, besides speed, reliability, and accuracy; design of WBAN protocols should consider the energy efficiency and time delay. To solve these problems, this paper adopt the end-edge-cloud orchestrated network architecture and propose a transmission based on reinforcement algorithm. The priority of sensing data is classified according to certain application. System utility function is modeled according to the channel factors, the energy utility, and successful transmission conditions. The optimization problem is mapped to Q-learning model. Following this online power control protocol, the energy level of both the senor to coordinator, and coordinator to edge server can be modified according to the current channel condition. The network performance is evaluated by simulation. The results show that the proposed power control protocol has higher system energy efficiency, delivery ratio, and throughput.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.151-158
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• 2023

In this study, a new type of composite material combined with carbonyl iron, a relatively strong ferromagnetic material, was prepared to overcome the current application limitations of Prussian blue, which is effective in removing radioactive cesium. The surface of the prepared composite was analyzed using SEM and XRD, and it was confirmed that nano-sized Prussian Blue was synthesized on the particle surface. In order to evaluate the cesium removal ability, 0.2 g of the composite prepared for raw cesium aquatic solution at a concentration of 5 ㎍ was added and reacted, resulting in a cesium removal rate of 99.5 %. The complex follows Langmuir's adsorption model and has a maximum adsorption amount (q_e) of 79.3 mg/g. The Central Composite Design (CCD) of the Response Surface Method (RSM) was used to derive the optimal application conditions of the prepared composite. The optimal application conditions achieved using Response optimization appeared at a stirring speed of pH 7, 17.6 RPM. The composite manufactured through this research is a material that overcomes the Prussian Blue limit in powder form and is considered to be excellent economically and environmentally when applied to a cesium removal site.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Microbiology and Biotechnology Letters
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• v.26 no.5
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• pp.435-441
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• 1998

Optimal conditions for the production of natural color, betacyanin were investigated by varying light intensity, C/N ratio, concentrations of phosphate and kinds of elicitors. Batch cultivation was employed to characterize cell growth and betacyanin production of 32 days. The maximum specific growth rate, ${\mu}$$\sub$max/, was 0.3 (1/day) for batch cultivation. The maximum specific production rate, q$\^$max/$\sub$p/, was enhanced 0.11 (mg/g-cell/day) at 3 klux. A light intensity of 3 klux was shown to the best for both cell growth and betacyanin production. The maximum specific production rate was 0.125 (mg/g-cell/day) at 0.242 (1/day), the maximum specific growth rate. The dependence of specific growth rate on the light lintensity is fit to the photoinhibition model. The correlation between ${\mu}$ and q$\sub$p/ showed that the product formation parameters, ${\alpha}$ and ${\beta}$$\sub$p/ were 0.3756 (mg/cell) and 0.001 (mg/g-cell/day), respectively. The betacyanin production was partially cell growth related process, which is different from the production of a typical product in plant cell cultures. In C/N ratio experiment, high carbon concentration, 42.1 (w/w) improved cell growth rate while lower concentration, 31.6 (w/w) increased the betacyanin production rate. The ${\mu}$$\sub$max/ and q$\^$max/$\sub$p/ were 0.26 (1/day) and 0.075 (mg/g-cell/day), respectively. Beta vulgaris L. cells under 1.25 mM phosphate concentration produced 10.15 mg/L betacyanin with 13.46 (g-dry wt./L) of maximum cell density. The production of betacyanin was elongated by adding 0.1 ${\mu}$M of kinetin. This also increased the cell growth. Optimum culture conditions of light intensity, C/N, phosphate concentration were obtained as 5.5 klux, 27 (w/w), 1.25 mM, respectively by the response surface methodology. The maximum cell density, X$\sub$max/, and maximum production, P$\sub$max/, in optimized conditions were 16 (g-dry wt./L), 12.5 (mg/L) which were higher than 8 (g-dry wt./L), 4.48 (mg/L) in normal conditions. The ${\mu}$$\sub$max/ and q$\^$max/$\sub$p/ were 0.376 (1/day) and 0.134 (mg/g-cell/day) at the optimal condition. The overall results may be useful in scaling up hairy root cell culture system for commercial production of betacyanin.