• Journal of electromagnetic engineering and science
• /
• v.6 no.3
• /
• pp.147-154
• /
• 2006

In this paper, we propose and present implementation results of a high-speed turbo decoding algorithm. The latency caused by (de) interleaving and iterative decoding in a conventional maximum a posteriori(MAP) turbo decoder can be dramatically reduced with the proposed design. The source of the latency reduction is come from the combination of the radix-4, dual-path processing, parallel decoding, and rearly-stop algorithms. This reduced latency enables the use of the turbo decoder as a forward error correction scheme in real-time wireless communication services. The proposed scheme results in a slight degradation in bit-error rate(BER) performance for large block sizes because the effective interleaver size in a radix-4 implementation is reduced to half, relative to the conventional method. Fixed on the parameters of N=212, iteration=3, 8-states, 3 iterations, and QPSK modulation scheme, we designed the adaptive high-speed turbo decoder using the Xilinx chip (VIRTEX2P (XC2VP30-5FG676)) with the speed of 17.78 Mb/s. From the results, we confirmed that the decoding speed of the proposed decoder is faster than conventional algorithms by 8 times.

• 한국연소학회:학술대회논문집
• /
• 1998.10a
• /
• pp.9-21
• /
• 1998

The optimization of frontal device including fuel nozzle and swirler is required to secure the mixing of fuel and air, and the combustion stability in the gas turbine combustor design for the reduction of pollutant emissions and the increase of combustion efficiency. The effects of injection nozzle and swirler on the flow field, spray characteristics and consequently the combustion stability, were experimentally investigated by measuring the velocity field, droplet sizes of fuel spray, lean combustion limit and the temperature field in the main combustion region. The effect of fuel injection nozzle was tested by adopting three different nozzles; a dual orifice fuel nozzle, a hollow cone nozzle and a solid cone nozzle. These tests were combined with the three different swirler geometries; a dual-stage swirler with 40$^{\circ}$ /-4 5$^{\circ}$ vanes and two single-stage swirlers with 40$^{\circ}$ vane angle having 12 and 16vanes, respectively. Flow fields and spray characteristics were measured with APV(Adaptive Phase Doppler Velocimetry) under atmospheric condition using kerosine fuel. Temperatures were measured by Pt-PtI3%Rh, R-type thermocouple which was 0.2mm thick. It was found that the dual swirler resulted in the biggest recirculation zone with the highest reverse flow velocity at the central region, which lead the most stable combustion. The various combustion characteristics were observed as a function of the combination between the injector and swirler, that gave a tip for the better design of gas turbine combustor.

• Proceedings of the KIEE Conference
• /
• 2005.04a
• /
• pp.152-154
• /
• 2005

This paper is proposed a fuzzy control and neural network based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability Also, this paper is proposed estimation and control of speed of Induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.1251-1253
• /
• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neuro-fuzzy control(NFC) and estimation of speed using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2006.05a
• /
• pp.321-326
• /
• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.394-399
• /
• 2002

In this paper, Multisource data classification methods based on Bayesian formula are considered. For this decision fusion scheme, the individual data sources are handled separately by statistical classification algorithms and then Bayesian fusion method is applied to integrate from the available data sources. This method includes the combination of each expert decisions where the weights of the individual experts represent the reliability of the sources. The reliability measure used in the statistical approach is common to all pixels in previous work. In this experiment, the weight factors have been assigned to have different value for all pixels in order to improve the integrated classification accuracies. Although most implementations of Bayesian classification approaches assume fixed a priori probabilities, we have used adaptive a priori probabilities by iteratively calculating the local a priori probabilities so as to maximize the posteriori probabilities. The effectiveness of the proposed method is at first demonstrated on simulations with artificial and evaluated in terms of real-world data sets. As a result, we have shown that Bayesian statistical fusion scheme performs well on multispectral data classification.

• Journal of Information Processing Systems
• /
• v.13 no.5
• /
• pp.1183-1202
• /
• 2017

Image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill-in of different structures and textures by processing in a wavelet domain. A combination of structure inpainting and patch-based texture synthesis is carried out, which is known as patch-based inpainting, for filling and updating the target region. The wavelet transform is used for its very good multiresolution capabilities. The proposed algorithm uses the wavelet domain subbands to resolve the structure and texture components in smooth approximation and high frequency structural details. The subbands are processed separately by the prioritized patch-based inpainting with isophote energy driven texture synthesis at the core. The algorithm automatically estimates the wavelet coefficients of the target regions of various subbands using optimized patches from the surrounding DWT coefficients. The suggested performance improvement drastically improves execution speed over the existing algorithm. The proposed patch optimization strategy improves the quality of the fill. The fill-in is done with higher priority to structures and isophotes arriving at target boundaries. The effectiveness of the algorithm is demonstrated with natural and textured images with varying textural complexions.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.1
• /
• pp.665-673
• /
• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.6
• /
• pp.115-126
• /
• 2022

The study's goal is to determine the amount of climate change's impact on ethnic minority (EM) households' livelihoods, as well as their adaptability to climate change and long-term viability. The research was conducted in Vietnam's Northwestern Sub-region, where ethnic minorities account for more than half of the overall population. The study uses a combination of qualitative and quantitative methods based on a survey of 480 households in 04 provinces severely affected by climate change in the Northwest sub-region of Vietnam. The results show that: climate change (extreme weather events) occurs with increasing frequency, mainly affecting the life expectancy, health, and capital of households; Vulnerable groups (women, ethnic minorities) have a poor adaptive capacity and mainly suffer the consequences of shocks, are afraid to change their livelihoods; Microfinance plays an important role in enhancing the sustainability of livelihoods through increasing capital and financial assets and reducing the vulnerability of ethnic minority households. Finally, research has some solutions for microfinance - special credit specifically for ethnic minority households in the Northwest Sub-region: support for microfinance advice, home credit with transition orientations to adapt to climate change response and relieves its impact on the social lives.

• Journal of Life Science
• /
• v.29 no.7
• /
• pp.824-835
• /
• 2019

In recent decades, oncolytic viruses (OVs) have extensively been investigated as a potential cancer drug. Oncolytic viruses have primarily the unique advantage in the fact that they can only infect and destroy cancer cells. Secondary, oncolytic viruses induce the activation of specific adaptive immunity which targets tumor-associated antigens that were hidden during the initial cancer progression. In 2015, one genetically modified oncolytic virus, talimogene laherparepvec (T-VEC), was approved by the American Food and Drug Administration (FDA) for the treatment of melanoma. Currently, various oncolytic viruses are being investigated in clinical trials as monotherapy or in combination with preexistent cancer therapies like immunotherapy, radiotherapy or chemotherapy. The efficacy of oncolytic virotherapy relies on the balance between the induced anti-tumor immunity and the anti-viral response. Despite the revolutionary outcome, the development of oncolytic viruses for the treatment of cancer faces a number of obstacles such as delivery method, neutralizing antibodies and induction of antiviral immunity due to the complexity, variability and reactivity of tumors. Intratumoral administration has been successful reducing considerably solid tumors with no notable side effects unfortunately some tumors are not accessible (brain) and require a systemic administration of the oncolytic viruses. In order to overcome these hurdles, various strategies to enhance the efficacy of oncolytic viruses have been developed which include the insertion of transgenes or combination with immune-modulatory substances.