• Asian-Australasian Journal of Animal Sciences
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• v.30 no.8
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• pp.1093-1098
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• 2017

Objective: The aim of the current study was to describe the relationship between milk yield and lactation number, stage, length and milking frequency in Korean Holstein dairy cows using an automatic milking system (AMS). Methods: The original data set consisted of observations from April to October 2016 of 780 Holstein cows, with a total of 10,751 milkings. Each time a cow was milked by an AMS during the 24 h, the AMS management system recorded identification numbers of the AMS unit, the cow being milking, date and time of the milking, and milk yield (kg) as measured by the milk meters installed on each AMS unit, date and time of the lactation, lactation stage, milking frequency (NoM). Lactation stage is defined as the number of days milking per cows per lactation. Milk yield was calculated per udder quarter in the AMS and was added to 1 record per cow and trait for each milking. Milking frequency was measured the number of milkings per cow per 24 hour. Results: From the study results, a significant relationship was found between the milk yield and lactation number (p<0.001), with the maximum milk yield occurring in the third lactation cows. We recorded the highest milk yield, in a greater lactation length period of early stage (55 to 90 days) at a $4{\times}$ milking frequency/d, and the lowest milk yield was observed in the later stage (>201 days) of cows. Also, milking frequency had a significant influence on milk yield (p<0.001) in Korean Holstein cows using AMS. Conclusion: Detailed knowledge of these factors such as lactation number, stage, length, and milking frequency associated with increasing milk yield using AMS will help guide future recommendations to producers for maximizing milk yield in Korean Dairy industries.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.207-216
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• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.800-808
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• 2008

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.37-47
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• 2013

Due to the evolution of war fields to the net-centric one, weapon systems have become very complex in terms of both mission capability and implementation scales. In particular, the net-centric war field is characterized by a set of interconnected and independently operable weapon systems. As such, the individual weapon systems are required to meet the interoperability and thus, assuring it has been becoming more crucial even in the early stage of development. Furthermore, the ever-growing complexity of the weapon systems has attracted a great deal of attention on the safety issues in the operation and development of weapon systems. The objective of the study is on how to assure the interoperability for safety-critical weapon systems while maintaining system complexity. To do so, the approach taken in the paper is to consider the interoperability from the early stage of the development. Specifically, the required functions to satisfy the interoperability are developed first. The functions are then analyzed in order to link the safety requirements to the reliability evaluation, which results in the study of quantifying the effects of the safety requirements on the system as a whole. As a result, we have developed a methodology and procedure on how to assure interoperability while applying the safety requirements in the weapon systems development.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.45-56
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• 2013

The precise identification of vehicle positions, known as the vehicle localization problem, is an important requirement for building intelligent vehicle ad-hoc networks (VANETs). To solve this problem, two categories of solutions are proposed: stand-alone and data fusion approaches. Compared to stand-alone approaches, which use single information including the global positioning system (GPS) and sensor-based navigation systems with differential corrections, data fusion approaches analyze the position information of several vehicles from GPS and sensor-based navigation systems, etc. Therefore, data fusion approaches show high accuracy. With the position information on a set of vehicles in the preprocessing stage, data fusion approaches is used to estimate the precise vehicular location in the local map building stage. This paper proposes an efficient local map building scheme, which increases the accuracy of the estimated vehicle positions via V2V communications. Even under the low ratio of vehicles with communication modules on the road, the proposed local map building scheme showed high accuracy when estimating the vehicle positions. From the experimental results based on the parameters of the practical vehicular environments, the accuracy of the proposed localization system approached the single lane-level.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4739-4743
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• 2012

Background: Predictive biomarkers for lung cancer recurrence after curative tumor resection remain unclear. This study set out to assess the role of FoxM1 in the recurrence of non-small cell lung cancer. Methods: Immunohistochemistry for FoxM1 expression was performed on paraffin-embedded tumor tissues from 165 NSCLC patients. Association of FoxM1 expression with clinicopathological parameters and disease free survival were evaluated. Results: Our results indicated FoxM1 expression to be significantly associated with poorer tissue differentiation (P =0.03), higher TNM stage (P <0.01), lymph node metastasis (P <0.01), advanced tumor stage (P <0.01), and poorer disease free survival (P <0.01). Multivariable analysis showed that FoxM1 expression increased the hazard of recurrence (hazard ratio= 1.96, 95% CI, 1.04-3.17, P <0.05), indicating that FoxM1 is an independent and significant predictor of lung cancer recurrence. Conclusion: Therefore, FoxM1 is an independent risk factor for recurrence of NSCLC. Elevated FoxM1 expression could be used as an indicator of poor disease free survival.

• Structural Engineering and Mechanics
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• v.75 no.3
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• pp.295-309
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• 2020

The construction of Reinforced Concrete (R/C) buildings with unreinforced masonry infills is part of the traditional building practice in many countries with regions of high seismicity throughout the world. When these buildings are subjected to seismic motions the presence of masonry infills and especially their configuration can highly influence the seismic damage state. The capability to avoid configurations of masonry infills prone to seismic damage at the stage of initial architectural concept would be significantly definitive in the context of Performance-Based Earthquake Engineering. Along these lines, the present paper investigates the potential of instant prediction of the damage response of R/C buildings with various configurations of masonry infills utilizing Artificial Neural Networks (ANNs). To this end, Multilayer Feedforward Perceptron networks are utilized and the problem is formulated as pattern recognition problem. The ANNs' training data-set is created by means of Nonlinear Time History Analyses of 5 R/C buildings with a large number of different masonry infills' distributions, which are subjected to 65 earthquakes. The structural damage is expressed in terms of the Maximum Interstorey Drift Ratio. The most significant conclusion which is extracted is that the ANNs can reliably estimate the influence of masonry infills' configurations on the seismic damage level of R/C buildings incorporating their optimum design.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1145-1167
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• 2016

This study presents a new method to computes analytical fragility curves of a structure subject to tsunami waves. The method uses dynamic analysis at each stage of the computation. First, the smooth particle hydrodynamics (SPH) model simulates the propagation of the tsunami waves from shallow water to their impact on the target structure. The advantage of SPH over mesh based methods is its capability to model wave surface interaction when large deformations are involved, such as the impact of water on a structure. Although SPH is computationally more expensive than mesh based method, nowadays the advent of parallel computing on general purpose graphic processing unit overcome this limitation. Then, the impact force is applied to a finite element model of the structure and its dynamic non-linear response is computed. When a data-set of tsunami waves is used analytical fragility curves can be computed. This study proves it is possible to obtain the response of a structure to a tsunami wave using state of the art dynamic models in every stage of the computation at an affordable cost.

• Advanced Composite Materials
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• v.18 no.4
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• pp.297-314
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• 2009

A design method is proposed to optimize the stacking sequence of laminated composite plates for desired vibration characteristics. The objective functions are the natural frequencies of the laminated plates, and three types of optimization problems are studied where the fundamental frequency and the difference of two adjacent frequencies are maximized, and the difference between the target and actual frequencies is minimized. The design variables are a set of discrete values of fiber orientation angles with prescribed increment in the layers of the plates. The four lamination parameters are used to describe the bending property of a symmetrically laminated plate, and are optimized by a gradient method in the first stage. A new technique is introduced in the second stage to convert from the optimum four lamination parameters into the stacking sequence that is composed of the optimum fiber orientation angles of all the layers. Plates are divided into sub-domains composed of the small number of layers and designed sequentially from outer domains. For each domain, the optimum angles are determined by minimizing the errors between the optimum lamination parameters obtained in the first step and the parameters for all possible discrete stacking sequence designs. It is shown in numerical examples that this design method can provide with accurate optimum solutions for the stacking sequence of vibrating composite plates with various boundary conditions.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.70-78
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• 1998

Spike detection in long-term EEG monitoring forepilepsy by wavelet transform(WT), artificial neural network(ANN) and the expert system is presented. First, a small set of wavelet coefficients is used to represent the characteristics of a singlechannel epileptic spikes and normal activities. In this stage, two parameters are also extracted from the relation between EEG activities before the spike event and EEG activities with the spike. then, three-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained from the first stage. Spikes are identified in individual EEG channels by 16 identical neural networks. Finally, 16-channel expert system based on the context information of adjacent channels is introducedto yield more reliable results and reject artifacts. In this study, epileptic spikes and normal activities are selected from 32 patient's EEG in consensus among experts. The result showed that the WT reduced data input size and the preprocessed ANN had more accuracy than that of ANN with the same input size of raw data. Ina clinical test, our expert rule system was capable of rejecting artifacts commonly found in EEG recodings.