• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.12
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• pp.87-94
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• 2003

In this paper, we propose an IFFT/FFT design method to minimize quantization error in IEEE 802.11a WLAN. In the proposed algorithm, the twiddle coefficient of IFFT/FFT processor is manipulated by the statistics distribution of the input data at each stage. We applies this algorithm to radix-2/$^2$ SDF architecture. Both IFFT and FFT processor shares the circuit blocks cause to the symmetric architecture. The maximum quantization error with the 10 bits length of the input and output data is 0.0021 in IFFT and FFT that has a self-loop structure with the proposed method. As a result, the proposed architecture saves 3bits for the data to keep the same resolution compared with the conventional method.

• Journal of Korean Society on Water Environment
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• v.33 no.6
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• pp.769-779
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• 2017

Nutrient (i.e., nitrogen and phosphorus) budgets are required under a 'Livestock Excreta Survey'. A nutrient budget is one of the agri-environmental indicators that calculates the difference between the inputs and outputs of the amount of nutrients within a certain boundary and for a certain time period (e.g., 1 year). In this study, a nutrients budget model was developed to effectively determine the surplus of nutrients within a region in Korea. The C# program language was used in order to facilitate the deployment of a graphical user interface (GUI) and to enhance compatibility. Also, the model was developed on Windows OS, which is the commonly used operating system in Korea. The model was based on the OECD/Eurostat nutrient budget method, and it was modified to consider manure composting procedures as well. There are key features of the nutrient budget model, including directly use of the original data sets from various input and output sources, and a collectively exchange of the address in different formats. The model can quickly show the results of various spatial and temporal resolutions with the same data, as well as perform a sensitivity analysis with coefficients and easily compareresults using tables and graphs. Further, it would be necessary to study the extension of the scope of utilization, such as the application of various nutrient budget methods. It would also be helpful to investigate both pre and postprocessing information such as linking input data through online systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1274-1285
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• 1991

This paper presents a statistical convergence analysis of the normalized least mean square(NLMS)algorithm that employs a single-pole lowpass filter, In this algorithm the lowpass filter is used to adjust its output towards the estimated value of the input signal power recursively. The estimated input signal power so obtained at each time is then used to normalize the convergence parameter. Under the assumption that the primary and reference inputs to the adaptive filter are zero mean wide sense stationary, and Gaussian random processes, and further making use of the independence assumption. we derive expressions that characterize the mean and maen squared behavior of the filter coefficients as well as the mean squared estimation error. Conditions for the mean and mean squared convergence are explored. Comparisons are also made between the performance of the NLMS algorithm and that of the popular least mean square(LMS) algorithm Finally, experimental results that show very good agreement between the analytical and emprincal results are presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2349-2356
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• 2008

In this paper, we present the method to effectively extract and classify the EEG caused by only brain activity when a normal subject is in a state of mental activity. We measure the synchronous EEG on the auditory event when a subject who is in a normal state thinks of a specific task, and then shift the baseline and reduce the effect of biological artifacts on the measured EEG. Finally we extract only the mental task signal by averaging method, and then perform the recognition of the extracted mental task signal by computing the AR coefficients. In the experiment, the auditory stimulus is used as an event and the EEG was recorded from the three channel $C_3-A_1$, $C_4-A_2$ and $P_Z-A_1$. After averaging 16 times for each channel output, we extracted the features of specific mental tasks by modeling the output as 12th order AR coefficients. We used total 36th order coefficient as an input parameter of the neural network and measured the training data 50 times per each task. With data not used for training, the rate of task recognition is 34-92 percent on the two tasks, and 38-54 percent on the four tasks.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.87-108
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• 2020

The Korean logistics industry has grown with the development of domestic industries. The industry plays an important role in national and regional economic growth, and the government has continued policy efforts to foster the industry. This study analyzes the competitiveness of the regional logistics industry and its contribution to the regional economy. Location coefficients are used to analyze local specialization in each logistics industry. The value-added rate, GDP contribution, value-added induction coefficient, and net value-added income of regional logistics industries are analyzed using a regional input-output table. As a result, the logistics industry is found to have net value-added income and competitiveness in some regions, and there is no relationship between the location coefficient and the value-added contribution of the regional logistics industry. Seoul, Incheon, Gyeonggi, Busan, and Jeju have the competitiveness of each logistics industry. In addition, we identified the regions where the logistics infrastructure is well developed and those in which it needs to be supported. The regions where the logistics industry has developed require policies for making high value-added by logistics activity, and regions with insufficient growth need to support the development of the logistics industry by investing human resources and capital that can meet the local demand.

• Journal of Energy Engineering
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• v.25 no.3
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• pp.1-8
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• 2016

North Korea is currently undergoing an economic crisis of industrial productivity reduction, which resulted from decreased energy production and economic sanctions due to conflicts with the international society. This paper examined the technological status of North Korea's natural gas and coal industries which are essential sectors for recovery of the economy and North-South cooperation on energy industry. This paper also analyzed investment strategies in North Korean energy industries and calculated the size of economic ripple effect of the investment on North and South Korea. In order to analyze the effect of the investment on North Korean economy, we constructed an inter-industry relation table of North Korea for year 2014 and used an input-output model. The ripple effect of the investment in natural gas and coal industries turned out to be 1.012 billion dollars and 2.742 billion dollars respectively. In order to analyze the ripple effect of the investment on South Korean economy, we constructed an inter-industry relation table of South Korea for year 2013 and used a demand-driven model for inter-industry analysis. As a result, production, added-value and employment inducement coefficients of the investment were calculated as 2.02073, 0.62697 and 8.99409 for the natural gas industry and 2.02130, 0.62701 and 9.00413 for the coal industry respectively.

• Journal of Korea Port Economic Association
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• v.27 no.3
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• pp.311-329
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• 2011

This study examined economic ripple effect of marine transport and port industry using Input-Output Tables. The results of the study are summarized as follows: first, in 2005 production inducement coefficients of harbour facilities was the highest(1.958), followed by coast and inland water transportation(1.857), load and unload(1.842), other transportation services(1.768), storage and warehouse(1.676), water transportation assistant services(1.422), and outport transportation (1.283). Second, value added inducement coefficient of water transportation assistant services was the highest(0.924), followed by load and unload, storage and warehouse(0.902), other transportation services(0.885), harbor facilities(0.832), coast and inland water transportation (0.752), and outport transportation(0.258). Third, import inducement coefficient of outport transportation was the highest(0.742), followed by coast and inland water transportation, harbor facilities, other transportation services, load and unload, storage and warehouse, and water transportation assistance services. Fourth, indexes of the sensitivity of dispersion of other transportation services and load and unload were 1.125 and 0.882 respectively while those of harbor facilities and outport transportation were 0.514. Indexes of power of dispersion of harbor facilities, coast and inland water transportation, load and unload, and other transportation services were the highest, respectively 1.006, 0.954, 0.946, and 0.908 while that of outport transportation was low, 0.659.

• Journal of Digital Convergence
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• v.9 no.6
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• pp.137-151
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• 2011

This dissertation aims to empirically analyze the effect of cultivation of software industry on the local economy through Inter-regional Software Input-Output Analysis. The temporal range of analysis of effect of software industry on the local economy shall be for the year 2005 since analysis is made on the basis of the Regional Industrial Input-Output Table published by the Bank of Korea in 2005, and spatial domain shall be limited to the 16 metropolitan cities and provinces, which are the standards for each administrative zone. Results of analysis of this dissertation are as follows. Firstly, average inverse matrix coefficient of software industry for each region was computed to be 1.6248, which is lower than the average inverse matrix coefficient of 1.7979 for the entire industries. Secondly, among these, inverse matrix coefficient of software industry for each region on other industry within the same region was 0.1794, which is higher than that of entire industries at 0.1382. However, average inverse matrix coefficients of software industry for each region on self-industry within the same region and entire industries in other regions were found to be 1.0119 and 0.4335, respectively, which is lower than those of entire industries at 1.0982 and 0.5616, respectively. Thirdly, domestic produces induced by final demand items of software industry for each region was the highest for Seoul with 17.3309 trillion Korean won, accounting for 81.0% of the total, followed by Gyeonggi with 2.3370 trillion Korean won, 10.9% of the total. Fourthly, distribution ratios of domestic produces induced by final demand items of software industry for each region were found to be 19.1%, 72.1% and 8.8% with respect to the weight of consumption, investment and export, respectively, thereby illustrating very high level of distribution ratios of domestic produces being induced by investment in comparison to the distribution ratios of domestic produces being induced for the entire industries at 47.3%, 19.8% and 32.9%, respectively.