• The KIPS Transactions:PartA
• /
• v.11A no.3
• /
• pp.149-156
• /
• 2004

As software is large and sophisticate, in order to increase the productivity and efficiency of programs in programming development environments, it is necessary to support the integrated system that offers user interface integrated editing, compiling, debugging, and running steps. The key tool in such environments is an incremental translation. In this paper, in order to increase the productivity and reusability of software, the goal is to construct the integrated incremental interpretation system that supports friendly user interface with editor, debugger, and incremental interpreter. We define the new object-oriented language, IMPLO(IMPerative Language with Object) using EBNF notation, and construct the integrated incremental interpretation system using incremental interpreter of the language. To do so, we extend attribute grammars for specifying static semantics and present new action equations to describe the dynamic semantics. We executed the incremental interpretation by using analyzing the dynamic semantics and then implemented integrated incremental interpretation system with editor and debugger in C, Lex and Yacc using X windows on SUN. We obtain about 50％ speedups in case of incremental execution time for example programs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.6
• /
• pp.645-650
• /
• 2018

Software stability is the possibility of operating without any malfunction in the operating environment over time. In a finite failure NHPP for software failure analysis, the failure occurrence rate may be constant, monotonically increasing, or monotonically decreasing. In this study, based on the NHPP model and based on the software failure time data, we compared and analyzed the attributes of the software development cost model using the exponential distribution Rayleigh distribution and inverse exponential distribution considering the shape parameter of the Weibull distribution as the life distribution. The results of this study show that the Rayleigh model is the fastest release time and has the economic cost compared to the inverse-exponential model and the Goel-Okumoto model. Using the results of this study, it can be expected that software developers and operators will be able to predict the optimal release time and economic development cost.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.2
• /
• pp.495-502
• /
• 2013

In the mobile Ad hoc Network(MANET), improving technique for management and control of topology is recognized as an important part of the next generation network. In this paper, we proposed an efficient node life time management of ATICC(Adaptive Time Interval Clustering Control) in Ad-hoc Networks. Ad-hoc Network is a self-configuration network or wireless multi-hop network based on inference topology. This is a method of path routing management node for increasing the network life time through the periodical route alternation. The proposed ATICC algorithm is time interval control technique depended on the use of the battery energy while node management considering the attribute of node and network routing. This can reduce the network traffic of nodes consume energy cost effectively. As a result, it could be improving the network life time by using timing control method in ad-hoc networks.

• Journal of KIISE:Databases
• /
• v.37 no.4
• /
• pp.177-184
• /
• 2010

In these day, many data including sensor, delivery, credit and stock data are generated continuously in massive quantity. It is difficult to learn from these data because they are large in volume and changing fast in their concepts. To handle these problems, learning methods based in sliding window methods over time have been used. But these approaches have a problem of rebuilding models every time new data arrive, which requires a lot of time and cost. Therefore we need very simple incremental learning methods. Bayesian method is an example of these methods but it has a disadvantage which it requries the prior knowledge(probabiltiy) of data. In this study, we propose a learning method based on attribute values. In the proposed method, even though we don't know the prior knowledge(probability) of data, we can apply our new method to data. The main concept of this method is that each attribute value is regarded as an expert learner, summing up the expert learners lead to better results. Experimental results show our learning method learns from data very fast and performs well when compared to current learning methods(decision tree and bayesian).

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.9
• /
• pp.1696-1702
• /
• 2008

A MANET(Mobile Ad-hoc Network) is a multi-hop routing protocol formed by a collection without the intervention of infrastructure. So the MANET also depended on the property as like variable energy, high degree of mobility, location environments of nodes etc. Generally the various clustering technique and routing algorithm would have proposed for improving the energy efficiency. One of the popular approach methods is a cluster-based routing algorithm using in MANET. In this paper, we propose an algorithm techniques which is TICC (Time Interval Clustering Control) based on energy value in property of each node for solving cluster problem. It provides improving cluster energy efficiency how can being node manage to order each node's energy level. TICC could be able to manage the clustering, re-configuration, maintenance and detection of Node in MANET. Furthermore, the results of modeling shown that Node's energy efficiency and lifetime are improved in MANET.

• The Journal of the Korea Contents Association
• /
• v.10 no.2
• /
• pp.99-110
• /
• 2010

An event means a flow which has a time attribute such as a symptom of patient. Stream data collected by sensors can be summarized as an interval event which has a time interval between the start-time point and the end-time point in multiple stream data environment. Most of temporal mining techniques have considered only the frequent events. However, these approaches may ignore the infrequent event even if it is important. In this paper, we propose a new temporal data mining that can find association rules for the significant temporal relation based on interval events in multidimensional stream data environment. Our method considers the weight of events and stream data on the sensing time point of abnormal events. And we can discover association rules on the significant temporal relation regardless of the occurrence frequency of events. The experimental analysis has shown that our method provide more useful knowledge than other conventional methods.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2010.04a
• /
• pp.121-123
• /
• 2010

Currently different local governments and research institutes have established and using DB for each purposes on the heterogeneous database. Normally physical unification method is used in order to unify this geospatial database, but this takes too much time and resources and require periodic renewal and synchronization. Therefore, this research would like to present a logical integrated unification method based on attribute information of heterogeneous database, and a system design and construction method, in order to subjugate inefficiencies from the physical unification method. Through this a system that could supply user customized geospatial database was established and verified the functionality using the road data of an actual region.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.201-208
• /
• 2015

With services such as SNS and facebook, Big Data popularize the use of small size such as micro blogs are increasing. However, the problem of accuracy and computational cost of the search result of big data of a small size is unresolved. In this paper, we propose a subnet selection techniques based probability to improve the browsing speed of the small size of the text information from big data environments, such as micro-blogs. The proposed method is to configure the subnets to give to the attribute information of the data increased the probability data search speed. In addition, the proposed method improves the accessibility of the data by processing a pair of the connection information between the probability of the data constituting the subnet to easily access the distributed data. Experimental results showed the proposed method is 6.8% higher detection rates than CELF algorithm, the average processing time was reduced by 8.2%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.4
• /
• pp.537-542
• /
• 2021

Smart TVs provide a variety of services and information compared to existing TVs based on the Internet. In order to provide more personalized services or information, it is necessary to analyze users' viewing patterns and provide customized services or information based on them. The proposed system receives the user's TV viewing pattern, analyzes it, and recommends a TV program or movie as customized information to the user. For this, the system was constructed with a preprocessor and a deep learning model. The preprocessor refines the name of the TV program watched by the user, the date the TV program was watched, and the watched time. Then, the multi-attribute LSTM model trains the refined data and performs prediction.The proposed system is a system that provides customized information to users, and is believed to be a leading technology in digital convergence that combines existing IoT technology and deep learning technology.

• Journal of Digital Contents Society
• /
• v.2 no.2
• /
• pp.103-115
• /
• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.