• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.366-373
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• 2021

This study attempted to establish a field data-based write analysis standard by analyzing field observation data, which is non-linear data of southern garlic. Five regions, including Goheung, Namhae, Sinan, Changnyeong, and Haenam, were selected for analysis. Observation values for each observation station were extracted from the temperature data of farmland in the region through inverse distance weighted. Southern-type garlic production and temperature data were collected for 10 years, from 2010 to 2019. Local regression analysis (Kernel) of the obtained data was performed, and growth temperatures were analyzed, such as 0.8 (18.781℃), 0.9 (18.930℃), 1.0 (19.542℃), 1.1 (20.165℃), and 1.2 (21.042℃) depending on the bandwidth. The analyzed optimum temperature and the grown temperature (4℃/25℃) were applied to extract the growth temperature for each temperature by using the temperature response model analysis. Regression analysis and correlation analysis were performed between the analyzed growth temperature and production data. The coefficient of determination(R²) was analyzed as 0.325 to 0.438, and in the correlation analysis, the correlation coefficient of 0.57 to 0.66 was analyzed at the significance probability 0.001 level. Overall, as the bandwidth increased, the coefficient of determination was higher. However, in all analyses except bandwidth 1.0, it was analyzed that all variables were not used due to bias. The purpose of this study is to accommodate all data through non-linear data. It was analyzed that bandwidth 1.0 with a high coefficient of determination while accepting modeling as a whole is the most suitable.

• Structural Engineering and Mechanics
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• v.33 no.1
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• pp.79-93
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• 2009

This paper investigates the possibility of controlling the response of typical portal frame structures to blast loading using a combination of semi-active and passive control devices. A one storey reinforced concrete portal frame is modelled using non-linear finite elements with each column discretised into multiple elements to capture the higher frequency modes of column vibration response that are typical features of blast responses. The model structure is subjected to blast loads of varying duration, magnitude and shape, and the critical aspects of the response are investigated over a range of structural periods in the form of blast load response spectra. It is found that the shape or length of the blast load is not a factor in the response, as long as the period is less than 25% of the fundamental structural period. Thus, blast load response can be expressed strictly as a function of the momentum applied to the structure by a blast load. The optimal device arrangements are found to be those that reduce the first peak of the structural displacement and also reduce the subsequent free vibration of the structure. Semi-active devices that do not increase base shear demands on the foundations in combination with a passive yielding tendon are found to provide the most effective control, particularly if base shear demand is an important consideration, as with older structures. The overall results are summarised as response spectra for eventual potential use within standard structural design paradigms.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.337-348
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• 2016

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.747-754
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• 2017

Chaos-based secure communication systems are alternative of standard spread-spectrum systems that enable spreading the spectrum of the information signals and encrypting information signals with simple and inexpensive chaotic circuitry. In secure communication area, like Lorenz, Chua, Rossler, Duffing etc, classical systems are widely used. Malasoma chaotic system is topologically simple but their dynamical behaviors are non-linear synchronization and secure communication applications has not seen in paper. This paper aims for introducing a new chaotic system which is able to use as alternative to classical chaotic systems into secure communication fields. In addition, this new model simulates a synchronous communication system using P-C (Pecora-Carroll) method by verifying security with chaos signal through simulation. Modelling, synchronization and secure communication applications of Malasoma are realized respectively in MATLAB-Simulink environment. Retrieved results show that this novel chaotic system is able to use in secure communication fields.

• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.310-318
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• 2011

Due to the lack of a noise and vibration survey for military air bases, noise and vibration are not taken into consideration for new installations, which has resulted in a number of problems, including the separation of tiles. Furthermore, it is not possible to establish appropriate counter-vibration plans to minimize damage resulting from vibration on structures, since no standard is in place when location for residence areas, including military quarters, is determined. In this context, this research includes a noise and vibration survey, on which basis a quantitative evaluation model for vibration of structure by aircraft noise is generated, as well as through linear regression analysis, and suggests installation standards by purpose of structure, minimizing the damage due to noise and vibration, and establishing appropriate counter-vibration plans for future installation and repairs of on-base structures in the military bases.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1259-1259
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• 2001

Meat becomes brown and rancid during storage in the refrigerator and display in the case. Color changes, metmyoglobin formation and lipid oxidation are the important problems in the transportation / distribution of meat and retail display. The freshness of meat is determined by the sense of vision and smell. Since conventional method determining lipid oxidation is time consuming and destructive (it needs to homogenize meat with reagents, filtrate, time for reaction and read optical density using spectroscopy), more rapid and nondestructive technical tools are desired. The objective of this work was to evaluate near-infrared spectroscopy as an analytical tool for determining meat color, metmyoglobin formation and lipid oxidation. in beef, pork and chicken. Semitendinosus and longissimus thoracis muscles from six beef steers, biceps femoris and longissimus thoracis muscles from twelve LWD crossbred pigs, and superficial pectoral muscles from twenty-four broilers were used. About a 5-cm diameter and 1-cm thick sample (20.0g) was cut from the muscle and placed on plastic foam, over-wrapped with PVC film, and displayed under flourescent lights at 4 degrees C. during 10 days for beef and pork or 4 days for chicken. The spectra was measured by NIR systems Model 5500 Spectrophotometer using fiber optic scan at range of 400 - 1100 nm. Data were recorded at 2 nm intervals and 10 scans / 10 sec were averaged for every sample. Data obtained were saved as log 1/Re, where Re is the reflectance energy, and then mathematically transformed to second derivatives to reduce effects of differences in particle size. $L^{*}$, $a^{*}$ and $b^{*}$, and metmyoglobin formation were determined by conventional spectrophotometer using the integrating sphere unit. 2-Thiobarbituric acid reactive substances (TBARS) were measured for lipid oxidation. A multiple linear regression was used to find the equation which would best fit the data. The number of wavelengths used in the equation was selected based on the fewer number compared to the increasing multiple correlation and Decreasing standard error. (omitted)

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• Structural Engineering and Mechanics
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• v.40 no.3
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• pp.431-451
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• 2011

Time delay inevitably exists in active control systems, and it may cause the degradation of control efficiency or instability of the systems. So time delay needs to be compensated in control design in order to eliminate its negative effect on control efficiency. Today time delay in linear systems has been more studied and some treating methods had been worked out. However, there are few treating methods for time delay in nonlinear systems. In this paper, an active controller for a nonlinear and hysteretic building structure with time delay is studied. The nonlinear and hysteretic behavior of the system is illustrated by the Bouc-Wen model. By specific transformation and augmentation of state parameters, the motion equation of the system with explicit time delay is transformed into the standard state space representation without any explicit time delay. Then the fourth-order Runge-Kutta method and instantaneous optimal control method are applied to the controller design with time delay. Finally, numerical simulations and comparisons of an eight-story building using the proposed time-delay controller are carried out. Simulation results indicate that the control performance will deteriorate if time delay is not taken into account in the control design. The simulations also prove the proposed time delay controller in this paper can not only effectively compensate time delay to get better control effectiveness, but also work well with both small and large time delay problems.