• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.15-21
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• 2014

FCAW(Flux Cored Arc Welding), SAW(Submerged Arc Welding), EGW(Electro Gas Welding), and three-pole SAW are applicable in manufacturing the offshore wind tower. In this paper, mechanical properties of these welded-joints for TMCP steels were evaluated in all above welding processes. The tensile strength of welded-joints for all the welding methods satisfied the standard guideline (KS D 3515). No cracking on weldment was found after the bending test. Changes of weldedments hardness with welding processes were observed. In a weld HAZ (heat-affected zone), a softened HAZ-zone was formed with high heat input welding processes (SAW and EGW). However, the welded-joint fractures were found in the base metal for all cases and small decrease in welded-joint strength was caused by a softened zone. The multi-pole SAW welds exhibited similar mechanical properties comparing to the one with one-pole SAW process.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.1
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• pp.41-46
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• 2014

In this paper, we propose a novel voice activity detection (VAD) algorithm using feature vectors based on TE (teager energy). Specifically, power spectral deviation (PSD), which is used as the feature for the VAD in the IS-127 noise suppression algorithm, is obtained after the input signal is transfomed by Teager energy operator. In addition, the TE-based likelihhod ratio are derived in each frame to modifiy the PSD for further VAD. The performance of our proposed VAD algorithm are evaluated by objective testing (total error rate, receiver operating characteristics, perceptual evaluation of speech quality) under various environments, and it is found that the proposed method yields better results than conventional VAD algorithms in the non-stationary noise environments under 5 dB SNR (total error rate = 2.6% decrease, PESQ score = 0.053 improvement).

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.1
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• pp.69-78
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• 2001

This paper presents the design and FPGA implementation of a FLEX PSP(Protocol Signal Processor) for the portable high speed paging system. In this approach, two algorithms are newly proposed for implementing the PSP which provides capabilities of the maximum 6,400bps at speed, high-channel throughput, real time error correction and an effective frame search function. One is an accurate symbol synchronization algorithm which is applied for synchronizing the interleaved 4-level bit symbols which are received at input stage of A/D converter, and the other is a modified fast decoding algorithm which is provided for realizing double error correction of (31,21)BCH signal. The PSP is composed of six functional modules, and each module is modelled in VHDL(VHSIC Hardware Description Language). Both functional simulation and logic synthesis have performed for the proposed PSP through the use of Synopsys$^{TM}$ tools on a Axil-320 Workstation, and where Altera 10K libraries are used for logic synthesis. From logic synthesis, we can see that the number of gates is about 2,631. For FPGA implementation, timing simulation is performed by using Altera MAX+ PLUS II, and its results will be also given. The PSP which is implemented in 6 FPGA devices on a PCB has been verified by means of Logic Analyzer.r.

• Transactions of the Korean hydrogen and new energy society
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• v.23 no.5
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• pp.530-537
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• 2012

This work experimentally investigates that Diesel-DME blended fuel influences combustion characteristics and emissions (NOx, CO, HC, smoke) in a single-cylinder DI diesel engine. Diesel is used as a main fuel and DME is blended for the use of its quick evaporating characteristics. Diesel and DME are blended by the method of weight ratio. Weight ratios for Diesel and DME are 95:5 and 90:10 respectively and the both ratios have been used altogether in blended fuel. The experiments are conducted in this study single cylinder engine is equipped with common rail and injection pressure is 700 bar at 1200 rpm. The amount of injected fuels is adjusted to obtain the fixed input calorie value as 972.2 J/cycle in order to compare with the fuel conditions. DME is compressed to 15 bar by using nitrogen gas thus it can be maintained the liquid phase. In this study, different system compared others paper is common rail system, also there is combustion and emission about compared DME and diesel fuel. It is expected to be utilized about blended fuel.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.1
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• pp.53-60
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• 2004

This work describes a l0b 150 MSample/s CMOS pipelined A/D converter (ADC) based on advanced bootsuapping techniques for higher input bandwidth than a sampling rate. The proposed ADC adopts a typical multi-step pipelined architecture, employs the merged-capacitor switching technique which improves sampling rate and resolution reducing by $50\%$ the number of unit capacitors used in the multiplying digital-to-analog converter. On-chip current and voltage references for high-speed driving capability of R & C loads and on-chip decimator circuits for high-speed testability are implemented with on-chip decoupling capacitors. The proposed AU is fabricated in a 0.18 um 1P6M CMOS technology. The measured differential and integral nonlinearities are within $-0.56{\~}+0.69$ LSB and $-1.50{\~}+0.68$ LSB, respectively. The prototype ADC shows the signal-to-noise-and-distortion ratio (SNDR) of 52 dB at 150 MSample/s. The active chip area is 2.2 mm2 (= 1.4 mm ${\times}$ 1.6 mm) and the chip consumes 123 mW at 150 MSample/s.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Astronomy and Space Sciences
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• v.25 no.2
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• pp.139-148
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• 2008

It has been known that high-speed solar wind streams associated with coronal holes lead to quasi-periodic substorms that occur approximately every $2{\sim}4$ hours. In this paper we examined 222 repetitive substorms that occurred during high-speed stream periods in July through December in 2003 to quantitatively determine a range of energy input from the solar wind into the magnetosphere between two consecutive substorms. For this study, we have used the Akasofu ${\varepsilon}$-parameter to time-integrate it for the interval between two consecutive substorms, and have applied this method to the 222 substorms. We find that the average amount of solar wind input energy between two adjacent substorms is $1.28{\times}10^{14}J$ and about 85% out of the 222 substorms occur after an energy input of $2{\times}10^{13}{\sim}2.3{\times}10^{14}J$. Based on these results, we suggest that it is not practical to predict when a sub storm will occur after a previous one occurs purely based on the solar wind-magnetosphere energy coupling. We provide discussion on several possible factors that may affect determining substorm onset times during high-speed streams.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.1
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• pp.107-119
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• 2008

To determine the true digestible phosphorus (TDP) requirement of growing pigs, two experiments were designed with the experimental diets containing five true digestible P levels (0.16%, 0.20%, 0.23%, 0.26% and 0.39%) and the ratio of total calcium to true digestible P (TDP) kept at 2:1. In Experiment 1, five barrows (Duroc${\times}$Landrace${\times}$Yorkshire) with an average initial body weight of 27.9 kg were used in a $5{\times}5$ Latin-square design to evaluate the effect of different dietary P levels on the digestibility and output of P and nitrogen. In Experiment 2, sixty healthy growing pigs (Duroc${\times}$Landrace${\times}$Yorkshire) with an average body weight (BW) of 21.4 kg were assigned randomly to one of the five dietary treatments (12 pigs/diet), and were used to determine the true digestible phosphorus (TDP) requirement of growing pigs on the basis of growth performance and serum biochemical indices. The results indicated that the true digestibility of P increased (p<0.05) linearly with increasing dietary TDP level below 0.26%. The true P digestibility was highest (56.6%) when dietary TDP was 0.34%. Expressed as g/kg dry matter intake (DMI), fecal P output increased (p<0.05) linearly with increasing P input. On the basis of g/kg fecal dry matter (DM), fecal P output was lowest for Diet 4 and highest (p<0.05) for Diet 5. The apparent digestibility of crude protein (CP) did not differ (p>0.05) among the five diets, with the average nitrogen output of 12.14 g/d and nitrogen retention of 66% to 74% (p>0.05), which suggested that there was no interaction between dietary P and CP protein levels. During the 28-d experimental period of Experiment 2, the average daily gain (ADG) of pigs was affected by dietary TDP levels as described by Eq. (1): $y=-809,532x^4+788,079x^3-276,250x^2+42,114x-1,759$; ($R^2=0.99$; p<0.01; y = ADG, g/d; x = dietary TDP, %), F/G for pigs by Eq. (2): $y=3,651.1x^4-3,480.4x^3+1,183.8x^2-172.5x+10.9$ ($R^2=0.99$; p<0.01; y = F/G; x = dietary TDP, %), and Total P concentrations in serum by Eq. (3): $y=-3,311.7x^4+3,342.7x^3-1,224.6x^2+195.6x-8.7$ (R2 = 0.99; p<0.01; y = total serum P concentration and x = dietary TDP, %). The highest ADG (782 g/d), the lowest F/G (1.07) and the highest total serum P concentration (3.1 mmol/L) were obtained when dietary TDP level was 0.34%. Collectively, these results indicate that the optimal TDP requirement of growing pigs is 0.34% of the diet at a total Ca to TDP ratio of 2:1.