• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.549-555
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• 2006

An edge detection is an very important area in image processing and computer vision, General edge detection methods (Robert mask, Sobel mask, Kirsh mask etc) are a good performance to detect step edge in a image but are no good performance to detect step edge in a noses image. We suggested a step edge detection method based on image information measure and neutral network. Using these essential properties of step edges, which are directional and structural and whose gray level distribution in neighborhood, as a input vector to the BP neutral network we get the good result of proposed algorithm. And also we get the satisfactory experimental result using rose image and cell images an experimental and analysing image.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.790-803
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• 2019

The effects of different heat treatments on the sensory characteristics of abalones are studied in this study. In this paper, the sensory evaluation of abalone samples under different heat treatment conditions is carried out, and the evaluation results are analyzed. The three-dimensional (3D) scanning and reverse engineering are used in tooth modeling of the sensory evaluation of abalone samples under different heat treatment conditions. Besides, the chewing movement models are simplified into three modes, including the cutting mode, compressing mode and grinding mode, which are simulated using finite element simulation. The elastic modulus of the abalone samples is obtained through the compression testing using a texture analyzer to distinguish their material properties under different heat treatments and to obtain simulated mechanical parameters. Finally, taking the mechanical parameters of the finite element simulation of abalone chewing as input and sensory evaluation parameters as the output, BP neural network is established in which the sensory texture evaluation model of abalone samples is obtained. Through verification, the neural network prediction model can meet the requirements of food texture evaluation, with an average error of 9.12%.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1289-1298
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• 2016

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.62-66
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• 2002

An attempt of using SOCMAC neural network for the prediction of a nonlinear sequence, which is generated by Mackey-Glass equation, is reported. The ,report shows the SOCMAC can handle a system with multi-dimensional continuous inputs, which has been considered very difficult, if not impossible, task to be implemented by a CMAC neural network because of a huge amount of memory required. Also, an improved training method based on the variable receptive fields is proposed. The Performance ranged somewhere around those of TDNN and BP neural networks.

• IMMUNE NETWORK
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• v.24 no.1
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• pp.1.1-1.6
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• 2024

IL-18 binding protein (IL-18BP) was originally discovered in 1999 while attempting to identify an IL-18 receptor ligand binding chain (also known as IL-18Rα) by subjecting concentrated human urine to an IL-18 ligand affinity column. The IL-18 ligand chromatography purified molecule was analyzed by protein microsequencing. The result revealed a novel 40 amino acid polypeptide. To isolate the complete open reading frame (ORF), various human and mouse cDNA libraries were screened using cDNA probe derived from the novel IL-18 affinity column bound molecule. The identified entire ORF gene was thought to be an IL-18Rα gene. However, IL-18BP has been proven to be a unique soluble antagonist that shares homology with a variety of viral proteins that are distinct from the IL-18Rα and IL-18Rβ chains. The IL-18BP cDNA was used to generate recombinant IL-18BP (rIL-18BP), which was indispensable for characterizing the role of IL-18BP in vitro and in vivo. Mammalian cell lines were used to produce rIL-18BP due to its glycosylation-dependent activity of IL-18BP (approximately 20 kDa). Various forms of rIL-18BP, intact, C-terminal his-tag, and Fc fusion proteins were produced for in vitro and in vivo experiments. Data showed potent neutralization of IL-18 activity, which seems promising for clinical application in immune diseases involving IL-18. However, it was a long journey from discovery to clinical use although there have been various clinical trials since IL-18BP was discovered in 1999. This review primarily covers the discovery of IL-18BP along with how basic research influences the clinical development of IL-18BP.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.1-10
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• 2020

In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.349-351
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• 2005

현재 다양한 인터넷 콘텐츠들에 의해 많은 정보가 공유되고 있으며, 유익한 정보들과 더불어 성인물과 같은 유해한 정보들이 있다. 이로 인하여 여러 문제점들이 야기되고 있으며, 이를 해결하기 위해 다양한 방법들이 제안되고 있다. 그 중에서 성인 영상 차단을 위한 연구도 많이 행해지고 있으며 주로 색상을 이용한 방법을 사용하고 있다. 그러나 살색과 유사한 영상이나 노출이 심한 영상에는 성인 영상 검출의 신뢰성이 떨어지는 단점을 갖는다. 본 논문에서는 이런 문제점을 해결하기 위해 새로운 성인 영상 차단 방법을 제안한다. 기존의 제안된 살색 검출을 이용한 방법을 기반으로 성인 영상물로 판정될 수 있는 신체 부위를 검출함으로써 강인한 성인 영상 차단을 한다. 신체 부위에 대한 판별을 위해 여러 기저 영상에서 특징 벡터를 추출하고. 이 벡터를 Back Propagation(BP) 신경망의 데이터로 이용하여 학습한다. 제안한 성인 영상 차단 방법의 성능을 여러 장의 살색과 유사한 색상의 물체 영상과 노출이 심한 영상, 성인 영상을 이용한 종합적인 실험 결과인 성인 영상 검출률을 통해 증명한다.

• Steel and Composite Structures
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• v.52 no.2
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• pp.135-143
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• 2024

Once the foundation displacement of the transmission tower occurs, additional stress will be generated on the tower members, which will affect the seismic response of transmission tower-line systems (TTLSs). Furthermore, existing research has shown that the reciprocating slippage of joints needs to be considered in the seismic analysis. The hysteretic behavior of joints is obtained by model tests or numerical simulations, which leads to the low modeling efficiency of TTLSs. Therefore, this paper first utilized numerical simulation and model tests to construct a BP neural network for predicting the skeleton curve of joints, and then a numerical model for a TTLS considering the bolt slippage was established. Then, the seismic response of the TTLS under foundation displacement was studied, and the member stress changes and the failed member distribution of the tower were analyzed. The influence of foundation displacement on the seismic performance were discussed. The results showed that the trained BP neural network could accurately predict the hysteresis performance of joints. The slippage could offset part of the additional stress caused by foundation settlement and reduce the stress of some members when the TTLS with foundation settlement was under earthquakes. The failure members were mainly distributed at the diagonal members of the tower leg adjacent to the foundation settlement and that of the tower body. To accurately analyze the seismic performance of TTLSs, the influence of foundation displacement and the joint effect should be considered, and the BP neural network can be used to improve modeling efficiency.