• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.312-320
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• 2012

In this study, we introduce a new design methodology of a granular-oriented self-organizing polynomial neural networks (GoSOPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a methodological design strategy of GoSOPNNs as follows : (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context-based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets (so-called contexts) defined in the output space. (b) The proposed design procedure being applied at each layer of GoSOPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed GoSOPNN network, we describe a detailed characteristic of the proposed model using a well-known learning machine data(Automobile Miles Per Gallon Data, Boston Housing Data, Medical Image System Data).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3265-3276
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• 1996

The studies of document recongnition have been focused mainly on Korean documents. But most of documents composed of Korean and other characters. So, in this paper, we propose the document recognition system that can recognize the multi-size, multi font and mixed type characters. We have utilized a large scale network model, "CombNET" which consists of a 4 layered network with combstructure. And we propose recognition method that can recognize characters without discrimination of character type. The first layer constitutes a Kohonen's SOFM network which quantizes an input feature vector space into several sub-spaces and the following 2-4 layers constitutes BP network modules which classify input data in each sub-space into specified catagories. An experimental result demonstrated the usefulness of this approach with the recognition rates of 95.6% for the training data. For the mixed type character documents we obtained the recognition rates of 92.6% and recognition speed of 10.3 characters per second.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.28-36
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• 2010

Some context is characterized by a single event in computing environment, but many other contexts are determined by a lot of things which occur with a space and a time. The Realtime Sensor Network context-awareness service that interacts with the physical space can have property such as time. A methodology that is specified the relationship between the contexts and the service needs to be developed to Realtime context-awareness deal with spatio-temporal. In this paper, we propose an approach which should include spatio-temporal property in the context model, and verify its effectiveness using enhanced Petri-Net. The context-awareness service modeling of Realtime Sensor Network is discussed the properties of model such as basic Petri-Net, patterned Petri-Net, or Spatio-temporal Petri-Net. The proposed methodology demonstrated using an example that is SAEMANGUEM warming watching system. The use of Spatio-temporal Petri-Net will contribute not only to develop the application but also to model the spatio-temporal context awareness.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.21-28
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• 2007

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.35-43
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• 2001

In this paper, we propose a design method for GBSB (generalized brain-state-in-a-box) based associative memories. Based on the theoretical investigation about the properties of GBSB, we parameterize the solution space utilizing the limited number of parameters sufficient to represent the solution space and appropriate to be searched. Next we formulate the problem of finding a GBSB that can store the given pattern as stable states in the form of constrained optimization problems. Finally, we transform the constrained optimization problem into a SDP(semidefinite program), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2005.05a
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• pp.46-50
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• 2005

In the contemporary city, which is an information-oriented society, human is evolving for responding to the various situation. Because the city has changed into the multi-layered network system, which is different from the paths in the past, the paths in the city have become the usual space of life. The active programmed space which is corresponding to citizen's spontaneous activities should be reflected in the path of city However, the current subway station, which is the main path of the city, is not equipped with the elements responding to the various possibilities of citizens. So this study suggests the programmed station as the extended space of citizen's daily lives.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.1-8
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• 2023

Smart IoT over high speed network and high performance smart devices explodes the ubiquitous services and applications. Nearest Neighbor(NN) query is one of the important type of queries that have to be supported for ubiquitous information services. In order to process efficiently NN queries in the wireless broadcast environment, it is important that the clients determine quickly the search space and filter out NN from the candidates containing the search space. In this paper, we propose a hybrid index of Voronoi and grid partition to provide quick search space decision and rapid filtering out NN from the candidates. Grid partition plays the role of helping quick search space decision and Voronoi partition providing the rapid filtering. We show the effectiveness of the proposed index by comparing the existing indexing schemes in the access time and tuning time. The evaluation shows the proposed index scheme makes the two performance parameters improved than the existing schemes.

• ETRI Journal
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• v.45 no.2
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• pp.203-212
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• 2023

In this study, we developed a co-located and space-shared multiple-input multiple-output (MIMO) antenna module with a modular design and high integration level. The proposed antenna pair includes a half-wavelength loop antenna and a dipole-type antenna printed on the front and back sides of a compact modular board. Owing to their modal orthogonality, these two independent antenna elements are highly self-isolated and free of additional decoupling components, even though they are assembled at the same location and within the same space. Thus, the proposed antenna is attractive in 5G MIMO systems. Furthermore, the proposed co-located and space-shared MIMO antenna module was employed in a 5G smartphone to verify their radiation and diversity performances. A 12 × 12 MIMO antenna system was simulated and fabricated using the proposed module. Based on the results, the proposed module can be employed in large-scale MIMO antenna systems for current and future terminal devices owing to its high integration, compactness, simple implementation, and inherent isolation.

• Journal of the Korea Computer Industry Society
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• v.7 no.3
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• pp.145-154
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• 2006

This paper improves algorithms of Speculative Partial Redundancy Elimination(SPRE) proposed by Knoop et al. Improving SPRE algorithm performs the execution speed optimization based on the information of the execution frequency from profiling and the memory space optimization. The first purpose of presented algorithm is to reduce in space requirements and the second purpose is to de crease the execution time. Since too much weight on execution speed optimization may cause the explosion of the memory space, it is important to consider the size of memory. This fact can be a big advantage in the embedded system which concerns the required memory size more than the execution speed In this paper we implemented the min-cut algorithm, and this algorithm used the control flow graph is constructed with network and partitioned.