• 한국산림과학회지
• /
• 제99권1호
• /
• pp.85-95
• /
• 2010

비료주기는 묘목의 생산성과 품질을 향상시키는 방법으로 이용되어 왔다. 이 연구의 목적은 경제림 육성 수종인 물푸레나무, 들메나무, 잣나무, 전나무를 대상으로 포지에서 질소, 인, 칼륨 비료주기가 묘목의 생장과 양분변화에 미치는 영향을 정량적으로 측정하는 것이다. 비료주기에 대한 생장반응과 양분 동태를 각각 품질지수(Dickson's quality index, QI)와 양분벡터분석(Vector diagnosis)을 이용하여 분석하였다. 물푸레나무와 들메나무는 질소 비료 처리구에서 가장 높은 수고와 근원경 생장을 보였으며, 잣나무의 경우 질소 비료 처리구에서 수고 생장이 약간 감소했고, 전나무는 비료주기 효과가 없었다. 물푸레나무의 QI는 질소 비료 처리구에서 가장 높았고 칼륨 비료 처리구에서 가장 낮았다. 들메나무의 QI도 질소 비료 처리구에서 가장 높았고 대조구에서 가장 낮았다. 잣나무와 전나무의 QI는 비료주기 처리 간에 유의한 차이는 없었지만 인 비료 처리구에서 가장 낮은 값을 보였다. 질소 비료주기는 대조구에 비하여 물푸레나무, 들메나무, 잣나무, 전나무의 건중량을 각각 43, 41, 26, -9% 증가시켰고, 인 비료주기는 -2, 4, -11, -10% 증가시켰으며, 칼륨 비료주기는 -25, 23, 18, -11% 증가시켰다. 비료주기에 대한 양분벡터반응은 수종과 양분종류에 따라 상이한 경향이 나타났다. 예를 들면 물푸레나무의 경우 질소 비료주기에 대해 양분 N은 농도 변화 없이 양분함량이 증가하는 "양분최적" 상태를 보이고, 양분 P는 농도와 양분함량이 모두 감소하고, 양분 K는 농도 변화 없이 양분함량만 감소했다. 잣나무의 경우 질소 비료주기에 대해 양분 N, P, K 농도는 감소하고 양분함량은 증가하는 "양분희석" 현상이 나타났으며, 인 비료주기는 양분 N, P, K 농도 변화 없이 양분 함량이 감소하는 "과량집적" 현상이 나타났다. 묘목 생산 단계에서 우량한 것으로 판정된 묘목이 산지에서도 높은 적응성을 보이는지에 대한 연구가 향후 필요하지만, 물푸레나무와 들메나무의 경우 질소 비료주기 처리에서 높은 산지 적응성을 보일 것으로 예상된다.

• International Journal of Computer Science & Network Security
• /
• 제22권9호
• /
• pp.195-207
• /
• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• 생명과학회지
• /
• 제20권1호
• /
• pp.71-76
• /
• 2010

본 연구는 중년 남성들을 대상으로 3년간 1년에 1회씩 3차례에 걸쳐 운동부하 검사 시 심전도 반응을 측정하여 야외 및 트레드밀 걷기 운동 참여 여부에 따라 부하 심전도의 변화에 어떠한 양상이 나타나는지를 규명하고자, 안정 시 신체조성 분석, 운동부하 검사 시 심전도 반응을 측정하였다. 안정 시 신체조성 분석은 체지방율과 BMI를 측정하였고, 안정 시 및 운동부하 검사 시 심전도 반응은 ST/HR 경사 및 QRS 벡터를 측정하였는데, 그 결과는 다음과 같다. 1. ST/HR 경사의 변화에 있어서 운동 6분부터 통제 집단은 감소하기 시작하여 운동 9분과 peak시에 $2.4\;{\mu}V/bpm$ 이하로 감소하여 허혈 증상이 나타났다. 2. QRS 벡터의 축 각도에 있어서 안정 시 전면 축은 시간이 경과함에 따라 통제 집단이 우측으로 유의하게 편향되었고, 안정 시 수평면 축은 시간이 경과함에 따라 통제 집단이 등 쪽으로 유의하게 향하였다. 안정 시 수평면 파고의 길이에 있어서 통제 집단은 시간이 경과함에 따라 유의하게 감소하였고 걷기 운동 집단은 유의하게 증가하였다. 결론적으로 비 활동의 중년 남성들은 운동 중 심근 허혈 유발, QRS 벡터의 편향 등이 현저하게 나타난 반면, 규칙적인 야외 및 트레드밀 걷기 활동에 참여한 중년 남성들은 심혈관계 질환 위험 요소의 감소로 심장기능이 향상된 결과를 알 수 있었다.

• 한국지능시스템학회논문지
• /
• 제10권2호
• /
• pp.94-102
• /
• 2000

본 논문에서는 미지의 비선형 계통에 대한 동적 퍼지모델 기반 고장 검출 및 진단(FDI) 계통 설계 기법을 제시한다. 비선형 계통에 대한 일반적인 모델 기반 FDI 계통에서는 선형화된 모델을 이용하고 있다 이러한 방법은 계통에 대한 정확한 수학적 모델을 요구하게 되어 복잡한 비선형 계통에의 적용시 많은 어려움이 있다 제안되는 FDI계통에서는 미지의 비선형 계통을 다수의 선형 모델을 갖는 동적 퍼지모델 형태로 식별한다. 잔차벡터는 온라인 알고리즘에 의해 추정되는 파라미터의 변동치와 비선형 계통의 동작 영역을 나타내는 퍼지 규칙들의 소속값들로 구성된다. 계통의 고장 검출 및 진단은 잔차벡터와 고장종류간의 관계를 학습한 신경망 분류기에 의해 수행된다. 제안된 FDI 계통 설계법을 이용하여 2 탱크 계통에 대한 FDI 계통을 설계하고 시뮬레이션 연구를 통하여 그 유용성을 보였다.

• 한국생물정보학회:학술대회논문집
• /
• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
• /
• pp.57-62
• /
• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• 한국유체기계학회 논문집
• /
• 제7권2호
• /
• pp.41-49
• /
• 2004

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1997년도 춘계학술대회
• /
• pp.414-417
• /
• 1997

This paper presents vessel contour for extracting features and segmentating narrow blood vessels down to a diameter of two pixels in digital subtraction angiographic image. We present a new tracking algorithm for contour, mainly blood vessels in DSA image, and extracting properties such as their intensities, diameters, and center lines by exploiting spatial continuity. The proposed algorithm comes to detect blood vessel's boundary using difference edge detector one of homogeneity operator and find a next centerline position by direction vector of edge information. This algorithm enhanced variation of vessel's diameter compared to Sun's tracking algorithm and lessoned to compute as direction vector decide adaptively entire vessel's direction field. The processed images are intended to support radiologists in diagnosis, radiation therapy planning, and surgical planning. The algorithm should be useful for automating angiographic analyses of blood vessels.

• 농촌의학ㆍ지역보건
• /
• 제16권2호
• /
• pp.172-176
• /
• 1991

Lyme disease, or Lyme borreliosis. is an infection caused by spirohete Borrellia burgdorferi. This disease was recognized in Lyme, Conneticut U.S.A. in 1975. The onset of the disease is usually heralded by the appearance of a pathognomic skin lesion, known as erythema chronicum migrans, and accompanied by flue like or meningitis like symptoms. Unless treatment is initiated early, the disease usually disseminated, often resulting carduac, neurologic, or joint manifestations. All stages of the disease are usually curable by appropriate antibiotic therapy, and can prevent severe late cardiac, neurologic, and joint complications. Lyme disease is typically defined by clinical evidence supported by serologic test. The diagnosis require serologic confirmantion of erythema chronicum migrans, occurring in patient in nonendemic countries. Determination of antibody titer against B. burgdorferi by enzyme linked immunosorbent assay(ELISA) currently the most practical diagnostic test. Currently Lyme disease occurs in U.S.A. Europe, and Australia. It has recently recognized in China, Japan, and Soviet Union also. In United States, Lyme disease is most common vector borne infection. There is no reported case of patients with this disease in Korea. But the vector of this disease, -Ixodes ticks- had been identified in Korea. And Korea is geographically closely related to China and Japan where Lyme disease is already reported. We expect first case of Lyme disease could he reported in near future. We review the clinical manifestations and diagnostic method of Lyme disease.

• 제어로봇시스템학회논문지
• /
• 제20권6호
• /
• pp.635-640
• /
• 2014

Detection and diagnosis of incipient bearing fault in an induction motor is important for the prevention of serious motor failure. This paper presents an analysis of the effect of a faulty bearing on the stator current of an induction motor. A bearing fault leads to torque oscillations which result in phase modulation of the stator current. Since the torque oscillations cause specific frequency components at the stator current spectrum to rise sharply, the bearing fault can be detected by checking out the faultrelated frequency. In this paper, a mathematical model of the load torque oscillation caused by a bearing fault is presented. The proposed model can be used to analyze the physical phenomenon of a bearing fault in an induction motor. In order to represent the bearing fault effect, the proposed model is combined with an existing model of vector-controlled induction motors. A set of simulation results demonstrate the effectiveness of the proposed model and represent that bearing fault detection using a stator current is useful for vector-controlled induction motors.

• 대한의용생체공학회:의공학회지
• /
• 제27권5호
• /
• pp.291-299
• /
• 2006

A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.