• 한국동물번식학회:학술대회논문집
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• 한국동물번식학회 2004년도 춘계학술발표대회
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• pp.229-229
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• 2004

The identification of embryo-specific genes would provide insights into early embryonic development. However, the current methods employed to identify the genes that are expressed at a specific developmental stage are labor intensive and suffer from high rates of false positives. Here we employed a new and accurate reverse transcription-polymerase chain reaction (RT-PCR) technology that involves annealing control primers (ACPs) to identify the genes that are specifically or prominently expressed in bovine early blastocysts and hatched blastocysts produced in vitro. (omitted)

• Journal of Information Processing Systems
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• 제17권3호
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• 생물정신의학
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• 제18권4호
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• pp.217-224
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• 2011

Subsyndromal bipolar symptoms are common during maintenance treatment and appear to be associated with relapse into an episode of the same polarity. This implies subsyndromal symptoms are an important problem in recurrent bipolar disorder and require more additive and infallible therapeutic intervention. Undetected, untreated subsyndromal states lead patients to have poor prognosis and quality of life. The combination of a long undetected illness and significant psychosocial impairment renders early identification and intervention vital for the treatment of bipolar disorders. Methods for early identification includes finding prodromes, using screening tools such as the HCL-32 (Hypomania Checklist-32) and the BSDS (bipolar spectrum diagnostic scale). Various augmentation treatment methods would be needed to reduce subsyndromal symptoms, especially, psychosocial treatment has the potential to help patients address the multiple psychosocial problems associated with this chronic illness. To overcome difficulties of diagnosing subsyndromal disorder and to treat it appropriately, a staging system was suggested by some researchers. It assumes that earlier stages have better prognosis and require simpler therapeutic regimens. Staging may assist in treatment planning and prognosis of bipolar disorder, and emphasize the importance of early intervention. Further research is required in this exciting and novel area.

• 한국발생생물학회:학술대회논문집
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• 한국발생생물학회 1998년도 제3차 심포지움
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• pp.13-16
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• 1998

• 한국작물학회:학술대회논문집
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• 한국작물학회 2021년도 추계학술대회
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• pp.142-142
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• 2021

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.1049-1055
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• 2013

DC motors are widely used in industries like cement, paper manufacturing, etc., even today. Early fault identification in dc motors significantly improves its life time and reduces power consumption. Many conventional and soft computing techniques for fault identification in DC motors including a recent work using model based analysis with the help of fuzzy logic are available in literature. In this paper fuzzy logic and norm based wavelet analysis of startup transient current are proposed to identify and quantify the armature winding fault and bearing fault in DC motors, respectively. Results obtained by simulation using Matlab and Simulink are presented in this paper to validate the proposed work.

• 센서학회지
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• 제20권3호
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• pp.151-155
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• 2011

A variety of pattern recognition algorithms including neural networks may be applicable to the identification of odors. In this paper, an identification technique for an electronic odor sensing system applicable to wound state monitoring is presented. The performance of the radial basis function(RBF) network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those parameters are initialized by an ill-conditioned genetic fuzzy c-means algorithm, and the distribution of input patterns in the very first stage, the stochastic gradient(SG), is adapted. The adaptive RBF network with singular value decomposition(SVD), which provides additional adaptation capabilities to the RBF network, is used to process data from array-based gas sensors for early detection of wound infection in burn patients. The primary results indicate that infected patients can be distinguished from uninfected patients.

• Smart Structures and Systems
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• 제10권1호
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• pp.47-65
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• 2012

The presence of crack-like defects in mechanical and structural elements produces failures during their service life that in some cases can be catastrophic. So, the early detection of the fatigue cracks is particularly important because they grow rapidly, with a propagation velocity that increases exponentially, and may lead to long out-of-service periods, heavy damages of machines and severe economic consequences. In this work, a non-destructive method for the detection and identification of elliptical cracks in shafts based on stress wave propagation is proposed. The propagation of a stress wave in a cracked shaft has been numerically analyzed and numerical results have been used to detect and identify the crack through the genetic algorithm optimization method. The results obtained in this work allow the development of an on-line method for damage detection and identification for cracked shaft-like components using an easy and portable dynamic testing device.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1500-1507
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• 2021

Identification and analysis of the first and second heart sounds(S1, S2) is the easiest way for cardiovascular disease prevention and early diagnosis. However, accurate identification is difficult because the heart sound includes organ movement, blood vortex, user experience, and noise influenced by subjective judgment. Therefore, an algorithm to automatically identify the S1 and S2 heart sounds based on blood vessel transit time(VTT) is presented in this paper. According to the experimental results of comparing the algorithm developed for S1 and S2 heart sound analysis with the conventional Shannon energy algorithm in 10 volunteers, it has been proven that the proposed algorithm can automatically identify S1 and S2 heart sounds with higher accuracy than existing algorithms.

• Toxicological Research
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• 제32권1호
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• pp.47-56
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• 2016

The identification of biomarkers for the early detection of acute kidney injury (AKI) is clinically important. Acute kidney injury (AKI) in critically ill patients is closely associated with increased morbidity and mortality. Conventional biomarkers, such as serum creatinine (SCr) and blood urea nitrogen (BUN), are frequently used to diagnose AKI. However, these biomarkers increase only after significant structural damage has occurred. Recent efforts have focused on identification and validation of new noninvasive biomarkers for the early detection of AKI, prior to extensive structural damage. Furthermore, AKI biomarkers can provide valuable insight into the molecular mechanisms of this complex and heterogeneous disease. Our previous study suggested that pyruvate kinase M2 (PKM2), which is excreted in the urine, is a sensitive biomarker for nephrotoxicity. To appropriately and optimally utilize PKM2 as a biomarker for AKI requires its complete characterization. This review highlights the major studies that have addressed the diagnostic and prognostic predictive power of biomarkers for AKI and assesses the potential usage of PKM2 as an early biomarker for AKI. We summarize the current state of knowledge regarding the role of biomarkers and the molecular and cellular mechanisms of AKI. This review will elucidate the biological basis of specific biomarkers that will contribute to improving the early detection and diagnosis of AKI.