• Asian-Australasian Journal of Animal Sciences
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• v.16 no.8
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• pp.1093-1101
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• 2003

Reproductive data, such as numbers of days to the first estrus and A.I. service postpartum, number of days to conception, number of A.I. services required for conception, interval between the first estrus and first A.I. service and the average interval of A.I. service in Thai native-Friesian crossbred and pure Friesian dairy cows, were compiled in the National Dairy Training and Applied Research Institute in Chiang Mai, Thailand. The data were analyzed statistically and the effect of milk production on these reproductive traits was investigated. The reproductive efficiency of purebred cows was obviously inferior when compared with crossbred animals, in spite of special care being given to the purebred only in order to alleviate the effect of a tropical climate and provide better feeding. However, the regression analysis between reproductive and lactational parameters revealed a definite antagonistic effect of lactation on reproduction, especially in the purebred cows, which had a larger amount of milk production and longer lactation period. If these effects of lactation were eliminated, there would be no evident difference in reproductive efficiency between purebred and crossbred cows in the conditions of this study. Among the reproductive parameters examined, the number of days to the first estrus and interval between the first estrus and first A.I. service were less affected by breed difference and the magnitude of lactation than other reasons.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.2
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• pp.177-185
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• 2001

This study shows the process design and forming analysis of permalloy shielding can that support the automobile multi-display parts to indicate the accurate information of car. This study is particularly important, since the strain and thickness of permalloy shielding can is known to affect the magnetic properties such as coercivity and permeability quite thickness of permalloy shielding can is known to affect the magnetic properties such as coercivity and permeability quite sensitively. The objective functions are strain and thickness deviation. The punch radius, die radius and blank holding force are considered as design parameters. Orthogonal array (OA) table and characteristics are applied to neural network (NN) as train data. After training, the optimal and robust condition of design parameters is selected. This study shows the correlation between the design methodology of NN and the statistical design of experiments (DOE) approach.

• Computers and Concrete
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• v.15 no.2
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• pp.167-181
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• 2015

"Mortar or concrete pneumatically projected at high velocity onto a surface" is called Shotcrete. Models that predict shotcrete design parameters (e.g. compressive strength, slump etc) from any mixing proportions of admixtures could save considerable experimentation time consumed during trial and error based procedures. Artificial Neural Network (ANN) has been widely used for similar purposes; however, such models have been rarely applied on shotcrete design. In this study 19 samples of shotcrete test panels with varying quantities of water, steel fibers and silica fume were used to determine their slump, cost and compressive strength at different ages. A number of 3-layer Back propagation Neural Network (BPNN) models of different network architectures were used to train the network using 15 samples, while 4 samples were randomly chosen to validate the model. The predicted compressive strength from linear regression lacked accuracy with $R^2$ value of 0.36. Whereas, outputs from 3-5-3 ANN architecture gave higher correlations of $R^2$ = 0.99, 0.95 and 0.98 for compressive strength, cost and slump parameters of the training data and corresponding $R^2$ values of 0.99, 0.99 and 0.90 for the validation dataset. Sensitivity analysis of output variables using ANN can unfold the nonlinear cause and effect relationship for otherwise obscure ANN model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.126-130
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion (FBFE) based on virus evolutionary genetic algorithm(VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter (EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FBFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by identifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.657-659
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• 1998

In order to optimize fuzzy modeling of nonlinear system, we proposed a optimal fuzzy model according to the characteristic of I/O relationship, HCM method, the genetic algorithm, and the objective function with weighting factor. A conventional fuzzy model has difficulty in definition of membership function. In order to solve its problem, the premise structure of the proposed fuzzy model is selected by both the partition of input space and the analysis of input-output relationship using the clustering algorithm. The premise parameters of the fuzzy model are optimized respectively by the genetic algorithm and the consequence parameters of the fuzzy model are identified by the standard least square method. Also, the objective function with weighting factor is proposed to achieve a balance between the performance results for the training and testing data.

• Structural Engineering and Mechanics
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• v.39 no.2
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• pp.267-285
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• 2011

There can be a significant amount of moment redistribution in composite frames consisting of steel columns and composite beams, due to cracking, creep and shrinkage of concrete. Considerable amount of computational effort is required for taking into account these effects for large composite frames. A methodology has been presented in this paper for taking into account these effects. In the methodology that has been demonstrated for moderately high frames, neural network models are developed for rapid prediction of the inelastic moments (typically for 20 years, considering instantaneous cracking, and time effects, i.e., creep and shrinkage, in concrete) at a joint in a frame from the elastic moments (neglecting instantaneous cracking and time effects). The proposed models predict the inelastic moment ratios (ratio of elastic moment to inelastic moment) using eleven input parameters for interior joints and seven input parameters for exterior joints. The training and testing data sets are generated using a hybrid procedure developed by the authors. The neural network models have been validated for frames of different number of spans and storeys. The models drastically reduce the computational effort and predict the inelastic moments, with reasonable accuracy for practical purposes, from the elastic moments, that can be obtained from any of the readily available software.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.729-734
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• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.3
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• pp.306-312
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• 2005

IMO $NO_x$ levels are generally possible to meet by means of primary on-engine measures. Further significant follow-on reductions are likely to require a secondary after-treatment technique. SCR(Selective Catalytic Reduction) technology is used almost exclusively for $NO_x$ removal in stationary combustion systems. In order to develop a practical SCR system for marine application on board ship, a primary SCR system using urea was made. The SCR system was set up on the ship, 'HANNARA' as a test vessel. employed a two-stroke cycle diesel engine as main propulsion, which is a training ship of Korea Maritime University. The purpose of this paper is to report the results about the basic effects of the below system parameters, The degree of $NO_x$ removal depends on some parameters, such as the amount of urea solution added, space velocity, reaction gas temperature and activity of catalyst.

• Annual Conference of KIPS
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• 2015.10a
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• pp.1388-1389
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• 2015

Electropulsegraphy is a medical device that was invented by an orient medical physician and a few engineers to help the physicians to diagnose patients in more systematic way by analyzing waveforms generated from the device. Data generated form the device has been collected for over several decades, and undergoes functional upgrades today. The device generates 33 waveforms that reflect the states of patients. As one of those upgrading efforts, we strive to develop an intelligent algorithm that makes the diagnostic process automatically, which was previously done manually for a long period of time. The logistic regression algorithm is used for our classification problems, which is one of those well-known algorithms for various classification problems such as character recognition systems. Out of the 33 waveforms, we only use 5 waveform data (Type1 toType5) as training data sets to estimate the parameters of the logistic regression. And the parameters are used to classify waveform inputs chosen at random.

• Clinics in Orthopedic Surgery
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• v.10 no.4
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• pp.491-499
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• 2018

Background: Advances in surgical techniques, implant technology, radiotherapy, and chemotherapy have increased the recovery chances of patients with bone sarcomas. Accordingly, patients' expectations on life quality have also increased, highlighting the importance of objective evaluation of the functional results of reconstruction. Methods: Thirteen patients with distal femoral endoprosthesis, who had been followed for an average of 2.9 years were evaluated. Postural stability, daily energy expenditure, muscle power, and range of motion were the four parameters analyzed in this study. The Musculoskeletal Tumor Society (MSTS) score and Toronto Extremity Salvage Score (TESS) were used to assess postoperative function and examine correlations with other parameters. Results: Patients had sedentary activities in 84% of their daily lives. They exhibited a slower speed in the walk across test and a higher sway velocity in the sit-to-stand test (p = 0.005). MSTS scores were significantly correlated with the daily energy expenditure and walking speed. Conclusions: Objective functional results acquired from various clinics will provide significant data to compare reconstruction techniques, rehabilitation protocols, and surgical techniques. In this way, it will be possible to satisfy the expectations of patients that increase in relation to enhanced recovery.