• The KIPS Transactions:PartB
• /
• v.15B no.5
• /
• pp.457-464
• /
• 2008

In the real-world operational environment, most of text classification systems have the problems of insufficient training documents and no prior knowledge of feature space. In this regard, $Na{\ddot{i}ve$ Bayes is known to be an appropriate algorithm of operational text classification since the classification model can be evolved easily by incrementally updating its pre-learned classification model and feature space. This paper proposes the improving technique of $Na{\ddot{i}ve$ Bayes classifier through feature weighting strategy. The basic idea is that parameter estimation of $Na{\ddot{i}ve$ Bayes considers the degree of feature importance as well as feature distribution. We can develop a more accurate classification model by incorporating feature weights into Naive Bayes learning algorithm, not performing a learning process with a reduced feature set. In addition, we have extended a conventional feature update algorithm for incremental feature weighting in a dynamic operational environment. To evaluate the proposed method, we perform the experiments using the various document collections, and show that the traditional $Na{\ddot{i}ve$ Bayes classifier can be significantly improved by the proposed technique.

• The KIPS Transactions:PartB
• /
• v.9B no.4
• /
• pp.399-406
• /
• 2002

The goal of this research has been to develop an adaptive user agent for web surfing. To achieve this goal, the research has concentrated on three issues: collection of user data, construction and improvement of user profile, and adaptation by applying the user profile. The main outcome from the research is a prototype system that provides the functional definition and componential design scheme for an adaptive user agent for the web environment. Internally, the system achieves its operational goal from the cooperation of two independent agents. They are IIA (Interactive Interface Agent) and UPA (User Profiling Agent). As a tool for providing a user-friendly interface environment, the IIA employs the Keyword Index, which is a list of index terms of a webpage as well as a keyword menu for subsequent queries, and the Suggest Link, which is a hierarchical list of URLs showing the past browsing procedure of the user. The UPA reflects in the User Profile, both the static and the dynamic information obtained from the user's browsing behavior. In particular, a user's interests are represented in the form of Interest Vectors which, based on the similarity of the vectors, is subject to update and creation, thus dynamically profiling the user's ever-shifting interests.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.20 no.3
• /
• pp.10-17
• /
• 2016

This paper presents a laboratory validation of a new approach for Finite Element Model Updating(FEMU) on short-span bridges by combining ambient vibration measurements with static load input-deflection output measurements. The conventional FEMU approach based on modal parameters requires the assumption on the system mass matrix for the eigen-value analysis. The proposed approach doesn't require the assumption and even provides a way to update the mass matrix. The proposed approach consists of two steps: 1) updating the stiffness matrix using the static input-deflection output measurements, and 2) updating the mass matrix using a few lower natural frequencies. For a validation of the proposed approach, Young's modulus of the laboratory model was updated by the proposed approach and compared with the value obtained from strain-stress tests in a Universal Testing Machine. Result of the conventional FEMU was also compared with the result of the proposed approach. It was found that proposed approach successfully estimated the Young's modulus and the mass density reasonably while the conventional FEMU showed a large error when used with higher-modes. In addition, the FE modeling error was discussed.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.2
• /
• pp.85-97
• /
• 2011

Wireless sensor networks have emerged as one of the key enabling technologies for ubiquitous computing since wireless intelligent sensor nodes connected by short range communication media serve as a smart intermediary between physical objects and people in ubiquitous computing environment. We recognize the wireless sensor network as a massively distributed and deeply embedded system. Such systems require concurrent and asynchronous event handling as a distributed system and resource-consciousness as an embedded system. Since the operating environment and architecture of wireless sensor networks, with the seemingly conflicting requirements, poses unique design challenges and constraints to developers, we propose a very new operating system for sensor nodes based on finite state machine. In this paper, we clarify the design goals reflected from the characteristics of sensor networks, and then present the heart of the design and implementation of a compact and efficient state-driven operating system, SenOS. We describe how SenOS can operate in an extremely resource constrained sensor node while providing the required reactivity and dynamic reconfigurability with low update cost. We also compare our experimental results after executing some benchmark programs on SenOS with those on TinyOS.

• Journal of Korean Society for Geospatial Information Science
• /
• v.1 no.2 s.2
• /
• pp.195-206
• /
• 1993

As the first step in developing the urban information system it is very important to identify the location of the street, and the feature of objects on it Also it is necessary to understand the relationship between objects concerned. In order to manage these information efficiently, the road information should be well organized and standardized for digital data. Because the road is the base place under which most urban utilities are buried. However, the present real situation is that even if we have unique numbers authorized by law for some parts of the road it is too ambiguous to figure out the spatial location of the specific area because the assigned area is so large and incoherent. Therefore, the purpose of this study is to propose a road location identication scheme, to apply this scheme at Kangnam-ku Seoul, and finally to propose the guideline in developing the road management information system in Korea. The road identification scheme developed in this study are as follows: (1) The road is defined as a fixed factor, and was given the identification number which repressents the funtion, relationship, and direction of the road without the road section and absolute coordinates. (2) The parcel identification nutter was given to each route to understand it possible to understand the location of the road itself and surroundings. (3) To update the md information using the scheme developed in this study relative coordinate method(Dynamic Segmentation) based on the road centerline was applied.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.17 no.4
• /
• pp.115-129
• /
• 2007

The ubiquitous and autonomic computing environments is open and dynamic providing the universal wireless access through seamless integration of software and system architectures. The ubiquitous computing have to offer the user-centric pervasive services according to the wireless access. Therefore the roaming services with the predefined security associations among all of the mobile devices in various networks is especially complex and difficult. Furthermore, there has been little study of security coordination for realistic autonomic system capable of authenticating users with different kinds of user interfaces, efficient context modeling with user profiles on Smart Cards, and providing pervasive access service by setting roaming agreements with a variety of wireless network operators. This paper proposes a Roaming Coordinator-based security management framework that supports the capability of interoperator roaming with the pervasive security services among the push service based network domains. Compared to traditional mobile systems in which a Universal Subscriber Identity Module(USIM) is dedicated to one service domain only, our proposed system with Roaming Coordinator is more open, secure, and easy to update for security services throughout the different network domains such as public wireless local area networks(PWLANs), 3G cellular networks and wireless metropolitan area networks(WMANs).

• Journal of Korea Spatial Information System Society
• /
• v.6 no.1 s.11
• /
• pp.73-85
• /
• 2004

Recently, as the growth of the wireless Internet, PDA and HPC, the focus of research and development related with GIS(Geographic Information System) has been changed to the Real-Time Mobile GIS to service LBS. To offer LBS efficiently, there must be the Real-Time GIS platform that can deal with dynamic status of moving objects and a location index which can deal with the characteristics of location data. Location data can use the same data type(e.g., point) of GIS, but the management of location data is very different. Therefore, in this paper, we studied the Real-Time Mobile GIS using the HBR-tree to manage mass of location data efficiently. The Real-Time Mobile GIS which is developed in this paper consists of the HBR-tree and the Real-Time GIS Platform HBR-tree. we proposed in this paper, is a combined index type of the R-tree and the spatial hash Although location data are updated frequently, update operations are done within the same hash table in the HBR-tree, so it costs less than other tree-based indexes Since the HBR-tree uses the same search mechanism of the R-tree, it is possible to search location data quickly. The Real-Time GIS platform consists of a Real-Time GIS engine that is extended from a main memory database system. a middleware which can transfer spatial, aspatial data to clients and receive location data from clients, and a mobile client which operates on the mobile devices. Especially, this paper described the performance evaluation conducted with practical tests if the HBR-tree and the Real-Time GIS engine respectively.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.127-142
• /
• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.