• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Journal of Sensor Science and Technology
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• v.14 no.3
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• pp.191-197
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• 2005

When fabricating a vision chip, we should consider the noise problem, such as the fixed pattern noise(FPN) due to the process variation. In this paper, we propose an edge-detection circuit based on biological retina using the offset-free column readout circuit to reduce the FPN occurring in the photo-detector. The offset-free column readout circuit consists of one source follower, one capacitor and five transmission gates. As a result, it is simpler and smaller than a general correlated double sampling(CDS) circuit. A vision chip for edge detection has been designed and fabricated using $0.35\;{\mu}m$ 2-poly 4-metal CMOS technology, and its output characteristics have been investigated.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.2
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• pp.89-92
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• 2010

A bolometer sensor used in an infrared thermal imaging system has many advantages on the process because it does not need a separate cooling system and its manufacturing is easy. However the sensitivity of the bolometer is low and the fixed pattern noise(FPN) is large, because the bolometer sensor is made by micro electro mechanical systems (MEMS). These problems can be fixed-by using the high performance readout integrated circuit(ROIC) with noise reduction techniques. In this paper, we propose differential delta sampling circuit to remove the mismatch noise of ROIC itself, the FPN of the bolometer. And for reduction of FPN noise, the reference signal compensation circuit which compensate the reference signal by using on-resistance of MOS transistor was proposed.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.6
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• pp.721-728
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• 2008

Hepcidin is a peptide hormone produced by the liver, of which secretion is closely related to iron status in the body. However, little is known about the molecular mechanism(s) by which this peptide regulates body iron homeostasis. The purpose of this study was to determine the effects of hepcidin treatment within the physiological concentration range on the expressions of two different iron transporter proteins-ferroportin (FPN) and divalent metal transporter 1 (DMT1). Differentiated Caco-2 intestinal cells and macrophage J774 cells were treated with either synthetic hepcidin or hepcidin-rich fraction separated from human urine at the concentration of 10 nM and 100 nM for 24 hours. Results show that hepcidin treatment in differentiated Caco-2 cells or in J774 cells did not change the level of either FPN mRNA or DMT1 mRNA. On the other hand, hepcidin treatment at the dose of 100 nM significantly decreased the FPN protein levels and DMT1 protein levels in differentiated Caco-2 cells. Similarly, urinary hepcidin treatment (10 nM & 100 nM) also significantly decreased the levels of FPN and DMT1 proteins in J774 macrophage cells. These results showed that hepcidin might play an important role in the regulation of iron homeostasis by lowering the protein levels of iron transporter FPN and DMT1 both in enterocytes and in macrophage cells.

• Journal of Ginseng Research
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• v.41 no.4
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• pp.487-495
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• 2017

Background: Although flowers of Panax ginseng Meyer (FPG), Panax quinquefolius L. (FPQ), and Panax notoginseng Burk. (FPN) have been historically used as both medicine and food, each is used differently in practice. Methods: To investigate the connection between components and enhancing immunity activity of FPG, FPQ, and FPN, a method based on a rapid LC coupled with quadrupole time-of-flight MS and immunomodulatory activity study evaluated by a carbon clearance test were combined. Results: According to quantitative results, the ratio of the total content of protopanaxatiol-type ginsenosides to protopanaxadiol-type ginsenosides in FPN was 0, but ranged from 1.10 to 1.32 and from 0.23 to 0.35 in FPG and FPQ, respectively. The ratio of the total content of neutral ginsenosides to the corresponding malonyl-ginsenosides in FPN ($5.52{\pm}1.33%$) was higher than FPG ($3.2{\pm}0.64%$) and FPQ ($2.39{\pm}0.57%$). The colorimetric analysis showed the content of total ginsenosides in FPQ, FPG, and FPN to be $13.75{\pm}0.60%$, $17.45{\pm}0.42%$, and $12.45{\pm}1.77%$, respectively. The carbon clearance assay indicated that the phagocytic activity of FPG and FPQ was higher than that of FPN. A clear discrimination among FPG, FPQ, and FPN was observed in the principal component analysis score plots. Seven compounds were confirmed to contribute strongly by loading plots, which may be the cause of differences in efficacy. Conclusion: This study provides basic information about the chemical and bioactive comparison of FPG, FPQ, and FPN, indicating that protopanaxtriol-type ginsenosides and malonyl-ginsenosides may play a key role in their enhancing immunity properties.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.247-256
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• 2022

Visual tracking of selected target objects is fundamental challenging problems in computer vision. Object tracking localize the region of target object with bounding box in the video. We propose a Siam-FPN based custom fully CNN to solve visual tracking problems by regressing the target area in an end-to-end manner. A method of preserving the feature information flow using a feature map connection structure was applied. In this way, information is preserved and emphasized across the network. To regress object region and to classify object, the region proposal network was connected with the Siamese network. The performance of the tracking algorithm was evaluated using the OTB-100 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.621 in Success Plot and 0.838 in Precision Plot were achieved.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.143-145
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• 2022

MPEG-VCM(Video Coding for Machine)은 입력된 비디오 특징(feature)를 압축하는 Track1 과 입력 영상을 직접 압축하는 Track2 로 나뉘어 표준화가 진행중이다. 본 논문은 VCM Track 1 에 해당하는 Detectron2 FPN(Feature Pyramid Network)에서 추출한 다중 스케일 특징맵을 VVC 로 압축하는 MSFC(Multi-Scale Feature Compression)을 구조를 제안한다. 본 논문의 MSFC 에서는 다중 스케일 특징을 결합하여 부호화/복호화하는 기존의 구조에서 특징맵의 해상도를 줄여 압축하는 개선된 MSFC 를 제시한다. 제안 방법은 VCM 의 Track2 의 영상 앵커(image anchor) 보다 우수한 BPP-mAP 성능을 보이고 최대 -84.98%의 BD-rate 성능향상을 보인다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.35-44
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• 2012

Most recent infrared (IR) sensors have a focal-plane array (FPA) structure. Spatial non-uniformity of a FPA structure, however, introduces unwanted fixed pattern noise (FPN) to images. This non-uniformity correction (NUC) of a FPA can be categorized into target-based and scene-based approaches. In a target-based approach, FPN can be separated by using a uniform target such as a black body. Since the detector response randomly drifts along the time axis, however, several scene-based algorithms on the basis of a video sequence have been proposed. Among those algorithms, the state-of-the-art one based on Kalman filter uses one-directional warping for motion compensation and only compensates for offset non-uniformity of IR camera detectors. The system model using one-directional warping cannot correct the boundary region where a new scene is being introduced in the next video frame. Furthermore, offset-only correction approaches may not completely remove the FPN in images if it is considerably affected by gain non-uniformity. Therefore, for FPN reduction in IR videos, we propose a joint correction algorithm of gain and offset based on bi-directional warping. Experiment results using simulated and real IR videos show that the proposed scheme can provide better performance compared with the state-of-the art in FPN reduction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.523-529
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• 2021

In the era of the Fourth Industrial Revolution, technological changes are being applied in various fields. Automation digitization and data management are also in the field of bills. There are more than tens of thousands of forms of bills circulating in society and bill recognition is essential for automation, digitization and data management. Currently in order to manage various bills, OCR technology is used for character recognition. In this time, we can increase the accuracy, when firstly recognize the form of the bill and secondly recognize bills. In this paper, a logo that can be used as an index to classify the form of the bill was recognized as an object. At this time, since the size of the logo is smaller than that of the entire bill, FPN was used for Small Object Detection among deep learning technologies. As a result, it was possible to reduce resource waste and increase the accuracy of OCR recognition through the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2762-2764
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• 2001

In this study, we introduce and investigate a class of dynamic perceptron architectures, discuss a comprehensive design methodology and carry out a series of numeric experiments. The proposed dynamic perceptron architectures are called as Polynomial Neural Networks(PNN). PNN is a flexible neural architecture whose topology is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated on the fly. In this sense, PNN is a self-organizing network. PNN has two kinds of networks, Polynomial Neuron(FPN)-based and Fuzzy Polynomial Neuron(FPN)-based networks, according to a polynomial structure. The essence of the design procedure of PN-based Self-organizing Polynomial Neural Networks(SOPNN) dwells on the Group Method of Data Handling (GMDH) [1]. Each node of the SOPNN exhibits a high level of flexibility and realizes a polynomial type of mapping (linear, quadratic, and cubic) between input and output variables. FPN-based SOPNN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulations involve a series of synthetic as well as experimental data used across various neurofuzzy systems. A detailed comparative analysis is included as well.