• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.11C
• /
• pp.694-701
• /
• 2011

This paper addresses a synchronization technique based on an adaptive combining of the sub-correlation functions obtained from multiband sine phased binary offset carrier (BOC) signals, allowing a BOC signal receiver to deal with multiband sine phased BOC signals. Specifically, we first obtain the sub-correlation functions composing the BOC autocorrelation function, and then, re-combine the sub-correlation functions generating a correlation function with no side-peak. Finally, by replacing the BOC autocorrelation with the correlation function with no side-peak in the delay lock loop, the proposed scheme performs unambiguous signal tracking. The proposed synchronization scheme is applicable to generic sine phased BOC signals. Numerical results demonstrate that the proposed scheme provides a performance improvement over the conventional unambiguous schemes in terms of the tracking error standard deviation.

• Radiation Oncology Journal
• /
• v.34 no.4
• /
• pp.239-249
• /
• 2016

Tumor hypoxia, a common feature occurring in nearly all human solid tumors is a major contributing factor for failures of anticancer therapies. Because ionizing radiation depends heavily on the presence of molecular oxygen to produce cytotoxic effect, the negative impact of tumor hypoxia had long been recognized. In this review, we will highlight some of the past attempts to overcome tumor hypoxia including hypoxic radiosensitizers and hypoxia-selective cytotoxin. Although they were (still are) a very clever idea, they lacked clinical efficacy largely because of 'reoxygenation' phenomenon occurring in the conventional low dose hyperfractionation radiotherapy prevented proper activation of these compounds. Recent meta-analysis and imaging studies do however indicate that there may be a significant clinical benefit in lowering the locoregional failures by using these compounds. Latest technological advancement in radiotherapy has allowed to deliver high doses of radiation conformally to the tumor volume. Although this technology has brought superb clinical responses for many types of cancer, recent modeling studies have predicted that tumor hypoxia is even more serious because 'reoxygenation' is low thereby leaving a large portion of hypoxic tumor cells behind. Wouldn't it be then reasonable to combine hypoxic radiosensitizers and/or hypoxia-selective cytotoxin with the latest radiotherapy? We will provide some preclinical and clinical evidence to support this idea hoping to revamp an enthusiasm for hypoxic radiosensitizers or hypoxia-selective cytotoxins as an adjunct therapy for radiotherapy.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.940-952
• /
• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.4
• /
• pp.359-365
• /
• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion(FBFE) based on virus evolutionary genetic algorithm (VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FDFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by idenLifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.6
• /
• pp.1-10
• /
• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• The KIPS Transactions:PartB
• /
• v.11B no.1
• /
• pp.21-26
• /
• 2004

Color image quantization is a process of selecting a set of colors to display an image with some representative colors without noticeable perceived difference. It is very important in many applications to display a true color image in a low cost color monitor or printer. The basic problem is how to display 256 colors or less colors, called color palette, In this paper, we propose improved binary tree vector quantization based on spatial sensitivity which is one of the human visual properties. We combine the weights based on the responsibility of human visual system according to changes of three Primary colors in blocks of images with the process of splitting nodes using eigenvector in binary tree vector quantization. The test results show that the proposed method generates the quantized images with fine color and performs better than the conventional method in terms of clustering the similar regions. Also the proposed method can get the better result in subjective quality test and WSNR.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.7
• /
• pp.1669-1679
• /
• 1996

In this paper, we address the problem of how to replace huffers in multimedia database systems with time-varying skewed data access. The access pattern in the multimedia database system to support audio-on-demand and video-on-demand services is generally skewed with a few popular objects. In addition the access pattem of the skewed objects has a time-varying property. In such situations, our analysis indicates that conventional LRU(least Recently Used) and LFU(Least Frequently Used) schemes for buffer replacement algorithm(ABRN:Adaptive Buffer Replacement using Neural suited. We propose a new buffer replacement algorithm(ABRN:Adaptive Buffer Replacement using Neural Networks)using a neural network for multimedia database systems with time-varying skewed data access. The major role of our neural network classifies multimedia objects into two classes:a hot set frequently accessed with great popularity and a cold set randomly accessed with low populsrity. For the classification, the inter-arrival time values of sample objects are employed to train the neural network.Our algorithm partitions buffers into two regions to combine the best roperties of LRU and LFU.One region, which contains the 핫셋 objects, is managed by LFU replacement and the other region , which contains the cold set objects , is managed by LRUreplacement.We performed simulation experiments in an actual environment with time-varying skewed data accsee to compare our algorithm to LRU, LFU, and LRU-k which is a variation of LRU. Simulation resuults indicate that our proposed algorthm provides better performance as compared to the other algorithms. Good performance of the neural network-based replacement scheme means that this new approach can be also suited as an alternative to the existing page replacement and prefetching algorithms in virtual memory systems.

• Journal of Advanced Navigation Technology
• /
• v.13 no.2
• /
• pp.194-200
• /
• 2009

This paper introduces a ring hybrid coupler using shunt capacitors, high impedance lines and CRLH-TLs (Composite Right/Left-Handed Transmission Lines) with size reduction and bandwidth enhancement. The reduced method of line length uses to combine a short length high impedance line and shunt capacitors. Also, there is combined CRLH meta-material so as to obtain wide bandwidth of transmission line using nonlinear phase characteristic of CRLH-TL that consists of series capacitors and shunt inductors. The implemented ring hybrid coupler shows a novel design with compact size that is smaller than 10% and bandwidth is larger than 60% of conventional ring hybrid coupler.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.6
• /
• pp.3879-3887
• /
• 2014

This paper presents a model to estimate the amount of $CO_2$ emitted by cars in cities. Based on the spatial configuration of street networks, building masses and usages, it first develops a deductive model to combine them in a way to account for $CO_2$ emission amount by cars. It then proceed to validate model behaviours through a series of simulations on some ideal urban settings and finally calibrate it following its real application to the five case study cities in Korea. In contrast to the conventional 'top-down' approaches, we expect our model to have high utilities, particularly in the field of urban planning and design, where we cannot but deal directly with the spatial configuration of urban components and microscopic human activities.

• Journal of Navigation and Port Research
• /
• v.36 no.10
• /
• pp.845-849
• /
• 2012

The next-generation wireless communication requires fast transmission speeds with various services and high reliability. In order to satisfy these needs we study MIMO system used layered space time coded system (LST) combining space time trellis codes (STTC) with turbo codes. In LST, two codes that are inner and outer codes are concatenated in the serial fashion. The inner codes are turbo Pi codes suggested in DVB-RCS NG system, and outer codes are STTC codes proposed by Blum. The interleaver technique is used to efficiently combine two codes. And we proposed and simulated that a full iteration method between turbo decoder and BCJR decoder to improve the performance instead of only processing inner-iteration turbo decoder. The simulation results of proposed effective layered method show improving BER performance about 1.3~1.5dB than conventional one.