• Journal of Korea Water Resources Association
• /
• v.34 no.4
• /
• pp.303-316
• /
• 2001

In this study, Radial Basis Function(RBF) Neural Networks Model, a kind of Hybrid Neural Networks was applied to hydrological forecasting in a small watershed. RBF Neural Networks Model has four kinds of parameters in it and consists of unsupervised and supervised training patterns. And Gaussian Kernel Function(GKF) was used among many kinds of Radial Basis Functions(RBFs). K-Means clustering algorithm was applied to optimize centers and widths which ate the parameters of GKF. The parameters of RBF Neural Networks Model such as centers, widths weights and biases were determined by the training procedures of RBF Neural Networks Model. And, with these parameters the validation procedures of RBF Neural Networks Model were carried out. RBF Neural Networks Model was applied to Wi-Stream basin which is one of the IHP Representative basins in South Korea. 10 rainfall events were selected for training and validation of RBF Neural Networks Model. The results of RBF Neural Networks Model were compared with those of Elman Neural Networks(ENN) Model. ENN Model is composed of One Step Secant BackPropagation(OSSBP) and Resilient BackPropagation(RBP) algorithms. RBF Neural Networks shows better results than ENN Model. RBF Neural Networks Model spent less time for the training of model and can be easily used by the hydrologists with little background knowledge of RBF Neural Networks Model.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.6
• /
• pp.187-193
• /
• 2015

The ICT healing platform sets a couple of goals including preventing chronic diseases and sending out early disease warnings based on personal information such as bio-signals and life habits. The 2-step open system(TOS) had a relay designed between the healing platform and the storage of personal health data. It also took into account a publish/subscribe(pub/sub) service based on large-scale connections to transmit(monitor) the data processing process in real time. In the early design of TOS pub/sub, however, the same buffers were allocated regardless of connection idling and type of message in order to encode connection messages into a deflate algorithm. Proposed in this study, the dynamic buffer allocation was performed as follows: the message transmission type of each connection was first put to queuing; each queue was extracted for its feature, computed, and converted into vector through tf-idf, then being entered into a k-means cluster and forming a cluster; connections categorized under a certain cluster would re-allocate the resources according to the resource table of the cluster; the centroid of each cluster would select a queuing pattern to represent the cluster in advance and present it as a resource reference table(encoding efficiency by the buffer sizes); and the proposed design would perform trade-off between the calculation resources and the network bandwidth for cluster and feature calculations to efficiently allocate the encoding buffer resources of TOS to the network connections, thus contributing to the increased tps(number of real-time data processing and monitoring connections per unit hour) of TOS.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.533-538
• /
• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.1
• /
• pp.1-6
• /
• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.8
• /
• pp.2013-2026
• /
• 1998

In optical passive star interconnections, all packets are transmitted between nodes ina broadcast and-select manner. It is assumed that each node has a innable transmitter and a fixed-savelength receiver, ad that all packet lengths are equal so that each transmission can be done in a unit time. The tuning delay, denoted by $\delta$, means the amount of time for transmitter to change its wavelength to another one. The problec is , given ay value of the mumber of nodes N and the number of wavelengths $\kappa$ according to WDM implementations, to find transmission schedules with minimum cycle length for all-to all brondcaxt where no one sends any packet to itself. In this paper, we prove that the cycle length of optimal transcission schedules should be at least $max[[{\frac{N}{k}](N-1)}]$,$k\delta$$+N-1$. A novel algorithm for optimal transmission schedules is then presented when N-1 is divisible by $\kappa$. This algorithm also can be used for any values of N and $\kappa$ if the tuning delay $\delta$ does not affect strictly the cycle length of transmission schedules, i,e, $[\frac{N}{k}](N-1)$ > $\kappa\delta$+N-1.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.1
• /
• pp.195-201
• /
• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• The KIPS Transactions:PartD
• /
• v.12D no.3 s.99
• /
• pp.461-470
• /
• 2005

This paper suggests a new policy for consolidating a company's profits by segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM, which is mainly applied to marketing. In this case, CRM refers to the strategy of consolidating a company's profits by efficiently managing the clients, providing them with a more effective, personalized service, and managing the resources more effectively. For the realization of a new service policy, this paper analyzes the level of contribution $vis-\acute{a}-vis$ the clients' service pattern (total number of visits to the homepage, service type, service usage period, total payment, average service period, service charge per homepage visit) and profits through the cluster analysis of clients' data applying the K-Means Method. Clients were grouped into 4 clusters according to the contribution level in terms of profits. Likewise, the CRFA (Client Request Filtering algorithm) was suggested per cluster to allocate media server resources. CRFA issues approval within the resource limit of the cluster where the client belongs. In addition, to evaluate the efficiency of CRFA within the Client/Server environment the acceptance rate per class was determined, and an evaluation experiment on network traffic was conducted before and after applying CRFA. The results of the experiments showed that the application of CRFA led to the decrease in network expenses and growth of the acceptance rate of clients belonging to the cluster as well as the significant increase in the profits of the company.

• The KIPS Transactions:PartB
• /
• v.14B no.7
• /
• pp.513-522
• /
• 2007

Clustering is to divide given data and automatically find out the hidden meanings in the data. It analyzes data, which are difficult for people to check in detail, and then, makes several clusters consisting of data with similar characteristics. On-Line Document Clustering System, which makes a group of similar documents by use of results of the search engine, is aimed to increase the convenience of information retrieval area. Document clustering is automatically done without human interference, and the number of clusters, which affect the result of clustering, should be decided automatically too. Also, the one of the characteristics of an on-line system is guarantying fast response time. This paper proposed a method of determining the number of clusters automatically by geometrical information. The proposed method composed of two stages. In the first stage, centers of clusters are projected on the low-dimensional plane, and in the second stage, clusters are combined by use of distance of centers of clusters in the low-dimensional plane. As a result of experimenting this method with real data, it was found that clustering performance became better and the response time is suitable to on-line circumstance.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.21-37
• /
• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.20 no.1
• /
• pp.46-52
• /
• 2012

A PAEM(Pneumatic Active Engine Mount) system has been developed to improve NVH performance of a SUV in idle state. Control objective to attenuate the vibration of a vehicle should be determined prior to the design of control algorithm. This study presents the correlation analysis of output variables of PAEM system by means of TPA(Transfer Path Analysis) using experimental data obtained by vehicle test. The analysis results show that the vibration of vertical direction is more serious than those of longitudinal and lateral direction of the vehicle, and that the correlation between the vibration of front seat rail and that of steer wheel is highest. In conclusion, the vibrations of front seat rail and steer wheel in vertical direction should be considered as the control objectives of the PAEM.