• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.13-20
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• 2005

This paper addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple recordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, withe fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.101-110
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• 2018

This study aims to objectively measure the efficiency of nanotechnology R&D programs by systematically evaluating the inputs and outputs of nanotechnology R&D activities and to find implications for improving the efficiency of nanotechnology R&D programs. Data on input factors such as R&D investment, R&D manpower, R&D period, and output factors such as paper, patent, and commercialization for R&D projects which started from 2008 or afterwards and ended by 2011 are gathered through National Science and Technology Knowledge Information Service, which are used for efficiency evaluation. In this study, we analyzed R&D efficiency in detailed technology units in depth. The process taken in this study is as follows. First, the basic statistics of input and output factors to compare and analyze R&D investment, R&D manpower, R&D period, paper, patent, and commercialization status by technology unit are analyzed. Next, DEA models are utilized to derive the overall efficiency, pure technology efficiency, and scale efficiency by conducting the efficiency evaluation for each technology unit, from which implications for strategic budget allocation are derived. In addition, partial efficiency evaluation is conducted to identify advantages and disadvantages of each technology unit. In turn, cluster analysis is performed to identify similar technology units, from which implications for efficiency improvement are derived.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.47-54
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• 2023

This article reviews recent advances and perspectives in neuroscience related to consciousness, cognition, and neural networks in the brain. The neural mechanisms underlying cognitive processes, such as perception, attention, memory, and decision-making, are explored. The article also examines how these processes give rise to our experience of consciousness. The implications of these findings for our understanding of the brain and its functions are presented, as well as potential applications of this knowledge in fields such as medicine, psychology, and artificial intelligence. Additionally, the article explores the concept of a quantum viewpoint concerning consciousness, cognition, and creativity and how incorporating DNA as a key element could reconcile classical and quantum perspectives on human behaviour, consciousness, and cognition, as explained by genomic psychological theory. Furthermore, the article explains how the human brain processes external stimuli through the sensory nervous system and how it can be simulated using an artificial neural network (ANN) consisting of one input layer, multiple hidden layers, and an output layer. The law of learning is also discussed, explaining how ANNs work and how the modification of weight values affects the output and input values. The article concludes with a discussion of future research directions in this field, highlighting the potential for further discoveries and advancements in our understanding of the brain and its functions.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.869-881
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• 2023

A genetic improvement program in Korea was implemented to improve the performance of Hanwoo cattle by generating livestock with genetically desirable economic characteristics. In particular, in response to external changes, such as the expansion of Free Trade Agreement (FTA), the livestock genetic improvement program has increased farm income by improving the productivity and quality of Hanwoo cattle. Using production cost data from Statistics Korea, the total input and output indices of Hanwoo feeding cattle from 2008 - 2021 were estimated and the growth and productivity changes were analyzed. The productivity change measures results were used to estimate the cumulative effects of the Hanwoo genetic improvement program on quality improvement, another purpose of the program, using a finite distributed lag model. The average annual increase in output (market weight) of Hanwoo was 0.9%. However, total input increased by 1.6%, resulting in a 0.6% decline in total factor productivity. In contrast, the Hanwoo genetic improvement program contributed significantly to the production of high quality beef, rather than contributing to improved productivity of the cattle. Hanwoo carcass weight, which is used as a performance indicator for the livestock genetic improvement program, has significantly improved and is projected to increase at a slower rate. The collective findings indicate the need for new performance indicators that can comprehensively indicate the performance of the genetic improvement of Hanwoo.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.163-171
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• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.23-35
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• 2020

The development of digital technology is changing the paradigm of the healthcare industry to preventive and consumer-oriented. The combination of the ICT industry and the bio-healthcare industry is emerging as a core industry in the era of the Fourth Industrial Revolution. The Korean government has also selected the bio-healthcare industry as one of the three key future development industries. In May, the government announced its bio-health industry innovation strategy and set a goal of 300,000 employees. Therefore, analyzing the effects of employment on the related industries of the digital bio-healthcare industry is very important for the establishment of future industrial and technology development policies. The research method restructures the integrated classification of 32 industries into 34, including the digital bio-healthcare industry, using the classification criteria of the government and professional institutions, and then reorganizes the digital bio-healthcare industry into eight industries classified as one industry group. The analysis data was taken from the Bank of Korea's 2019 data. Various trigger coefficients and ripple effects coefficients were rewritten using the analysis method of the Input-output Statistics. The analysis of the results compares the employment-induced effects of the digital bio-healthcare industry and the ripple effects of related industries in production, investment and value-added. In addition, in terms of investment effect, the effects of in-house and related industries were compared. It is hoped that the results of this study will be used to establish employment and industrial policies.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.41-51
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• 2014

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of Technology Innovation
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• v.5 no.2
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• pp.48-79
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• 1997

This study aims to evaluate and identify the patterns of the technological competitiveness of the Korean industry. Such statistics as R&D expenditure and R&D manpower as input indexes, US patent registrations and export sales as output indexse were used. It was turned out that such industrial types as specialized-suppliers industries, scale-intensive industries and science-intensive industries showed relatively strong technical competitiveness. However, resource-intensive industries and labor-intensive industries which had maintained a competitive advantage in the 1970s and the 1980s appeared to be gradually losing their technological competitiveness. These results are by and large in accordance with the trends of export performance. This study conducted the canonical discriminant analysis in order to test the correctness of the patterns displayed in the technological competitiveness of the Korean industry. The result of the analysis showed that the five patterns of technical strength of the Korean industries are significantly independent each other for four respective variables which are used to distinguish industries. This implies that the ex ante industrial classification into five types was correct in terms of the ex post statistics, and that the patterns of technological competitiveness discovered in this study are also statistically correct.