• 대한의용생체공학회:의공학회지
• /
• 제28권2호
• /
• pp.294-299
• /
• 2007

For efficient and accurate diagnosis of ultrasound images, appropriate time gain compensation(TGC) and dynamic range(DR) control of ultrasound echo signals are important. TGC is used for compensating the attenuation of ultrasound echo signals along the depth, and DR controls the image contrast. In recent ultrasound systems, these two factors are automatically set by a system and/or manually adjusted by an operator to obtain the desired image quality on the screen. In this paper, we propose an algorithm to find the optimized parameter values far TGC and DR automatically. In TGC optimization, we determine the degree of attenuation compensation along the depth by dividing an image into vertical strips and reliably estimating the attenuation characteristic of ultrasound signals. For DR optimization, we define a novel cost function by properly using the characteristics of ultrasound images. We obtain experimental results by applying the proposed algorithm to a real ultrasound(US) imaging system. The results verify that the proposed algorithm automatically sets values of TGC and DR in real-time such that the subjective quality of the enhanced ultrasound images may be sufficiently high for efficient and accurate diagnosis.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1985년도 춘계학술대회
• /
• pp.48-49
• /
• 1985

• 한국원자력학회:학술대회논문집
• /
• 한국원자력학회 2005년도 춘계학술발표회
• /
• pp.1259-1260
• /
• 2005

• 한국자동차공학회논문집
• /
• 제11권1호
• /
• pp.95-103
• /
• 2003

Driving safety of a vehicle is largely influenced by the damper and the tire. Developed in this research is a fault diagnosis algorithm for the two components so that the driver can be promptly informed when fault occurs in one or both of them. To this end, the damper and the tire were modeled using the neural network from their experimental data, and fault diagnosis was made using frequency responses of the damping force and the dynamic wheel force. The algorithm was tested via experiments, and it demonstrated successful diagnostic performance under various driving conditions.

• Journal of Interdisciplinary Genomics
• /
• 제2권1호
• /
• pp.5-9
• /
• 2020

von Willebrand disease (VWD) is a genetic bleeding disorders caused by a deficiency of von Willebrand factor (VWF). Diagnosis or exclusion of VWD is not an easy task for most clinicians. These difficulties in diagnosis or exclusion of VWD may be due to preanalytic, analytical and postanalytic laboratory issues. Analytical systems to diagnose VWD may produce misleading results because of limitations in their dynamic range of measurement and low sensitivity. However, preanalytical issues such as sample collection, processing, and transportation affect the diagnosis of VWD profoundly. We will review here the common preanlytical issues that may impact the laboratory diagnosis of VWD.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 B
• /
• pp.988-990
• /
• 1999

Using neural network approach, the diagnosis of faults in industrial process that requires observing multiple data simultaneously are studied in this paper. Two-stage diagnosis is proposed as the basic structure. The first stage detects the dynamic trend of each measurements and the second stage diagnosis the faults. This paper makes up for the disadvantage of neural about unknown faults. The potential of this approach is demonstrated in simulation using a model of tank reactor.

• 대한정형외과 초음파학회지
• /
• 제2권1호
• /
• pp.27-36
• /
• 2009

근골격계 분야에서의 초음파는 검사 비용이 비교적 낮고 동적인 검사가 가능하다는 장점에 힘입어 빠르게 발전하고 있다. 족부 및 족관절 분야는 초음파 검사가 용이하며 국소 증상이 있는 경우 그 감별 진단에 초음파가 중요한 역할을 할 수 있다. 본 종설에서는 흔하게 접할 수 있는 족부 및 족관절 질환을 중심으로 그 원인 및 초음파적 진단에 대해 설명하고자 하였다.

• Nuclear Engineering and Technology
• /
• 제36권1호
• /
• pp.73-82
• /
• 2004

The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the net positive suction head(NSPH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based fault detection and diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 & 4.

• Smart Structures and Systems
• /
• 제12권3_4호
• /
• pp.271-290
• /
• 2013

The acceleration information is significant for the structural health monitoring, which is the basic measurement to identify structural dynamic characteristics and structural vibration. The efficiency of the accelerometer is subsequently important for the structural health monitoring. In this paper, the distance measure matrix and the support level matrix are constructed firstly and the synthesized support level and the fusion method are given subsequently. Furthermore, the synthesized support level can be served as the determination for diagnosis on accelerometers, while the consensus data fusion method can be used to recover the acceleration information in frequency domain. The acceleration acquisition measurements from the accelerometers located on the real structure National Aquatics Center are used to be the basic simulation data here. By calculating two groups of accelerometers, the validation and stability of diagnosis and recovering on acceleration based on the data fusion are proofed in the paper.

• JSTS:Journal of Semiconductor Technology and Science
• /
• 제14권2호
• /
• pp.252-261
• /
• 2014

A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.