• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.895-907
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• 2020

With the recent increasing influence of multimedia content other than the text-based content, services that help to process information in content brings us great convenience. These services' representative features are searching and masking the sensitive data. It is not difficult to find the solutions that provide searching and masking function for text information and image. However, even though we recognize the necessity of the technology for searching and masking a part of the audio data, it is not easy to find the solution because of the difficulty of the technology. In this study, we propose web application that provides searching and masking functions for audio data using audio partitioning method. While we are achieving the research goal, we evaluated several speech to text conversion APIs to choose a proper API for our purpose and developed regular expressions for searching sensitive information. Lastly we evaluated the accuracy of the developed searching and masking feature. The contribution of this work is in design and implementation of searching and masking a sensitive information from the audio data by the various functionality proving experiments.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.986-998
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• 2006

There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we constrict a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.29-38
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• 2011

In order to derive domain-independent heuristics from the specification of a planning problem, it is required to relax the given problem and then solve the relaxed one. In this paper, we present a new planning graph, Merged Planning Graph(MPG), and GD heuristics for solving contingent planning problems with both uncertainty about the initial state and non-deterministic action effects. The merged planning graph is an extended one to be applied to the contingent planning problems from the relaxed planning graph, which is a common means to get effective heuristics for solving the classical planning problems. In order to get heuristics for solving the contingent planning problems with sensing actions and non-deterministic actions, the new graph utilizes additionally the effect-merge relaxations of these actions as well as the traditional delete relaxations. Proceeding parallel to the forward expansion of the merged planning graph, the computation of GD heuristic excludes the unnecessary redundant cost from estimating the minimal reachability cost to achieve the overall set of goals by analyzing interdependencies among goals or subgoals. Therefore, GD heuristics have the advantage that they usually require less computation time than the overlap heuristics, but are more informative than the max and the additive heuristics. In this paper, we explain the experimental analysis to show the accuracy and the search efficiency of the GD heuristics.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.61-72
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• 2008

One of the most significant tasks of software development project is to know how much it will be the software development cost in the early stage of software development cycle. The software development environment and technology are changing very rapidly. For accuracy, we should apply those to the software cost estimation. And it is important that we select the suitable adjustment factor and the value of a suitable adjustment factor. For that, this paper have applied the method of AHP. And we have also analyzed the sensitivity of the adjustment factor which is influenced by decision metrics. In conclusion, the value of the application type adjustment factor is responded more sensitively to the data complexity and the control complexity than processing complexity. And the value of the language adjustment factor is responded more sensitively to the supplying manpower and the time of the coding than the time of the debugging. In the future, we will research the selection of an additional adjustment factor and a suitable value of the adjustment factor which are influenced by the environment and the technology of the domestic software development. And then, in the language adjustment factor, we will try to calculate the value about the individual programming language.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.317-326
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• 2009

Recently, there have been many research efforts proposed on trajectory information. Most of them mainly focus their attention on those objects moving in Euclidean space. Many real-world applications such as telematics, however, deal with objects that move only over road networks, which are highly restricted for movement. Thus, the existing methods targeting Euclidean space cannot be directly applied to the road network space. This paper proposes a new clustering scheme for a large volume of trajectory information of objects moving over road networks. To the end, we first define a trajectory on a road network as a sequence of road segments a moving object has passed by. Next, we propose a similarity measurement scheme that judges the degree of similarity by considering the total length of matched road segments. Based on such similarity measurement, we propose a new clustering algorithm for trajectories by modifying and adjusting the FastMap and hierarchical clustering schemes. To evaluate the performance of the proposed clustering scheme, we also develop a trajectory generator considering the observation that most objects tend to move from the starting point to the destination point along their shortest path, and perform a variety of experiments using the trajectories thus generated. The performance result shows that our scheme has the accuracy of over 95% in comparison with that judged by human beings.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.391-400
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• 2004

Within multimodal medical image registration techniques, which correlate different images and Provide integrated information, surface registration methods generally minimize the surface distance between two modalities. However, the features of two modalities acquired from one subject are similar. So, it can improve the accuracy of registration result to match two images based on optimization of both surface distance and shape feature. This research proposes a registration method which optimizes surface distance and surface curvature of two brain modalities. The registration process has two steps. First, surface information is extracted from the reference images and the test images. Next, the optimization process is performed. In the former step, the surface boundaries of regions of interest are extracted from the two modalities. And for the boundary of reference volume image, distance map and curvature map are generated. In the optimization step, a transformation minimizing both surface distance and surface curvature difference is determined by a cost function referring to the distance map and curvature map. The applying of the result transformation makes test volume be registered to reference volume. The suggested cost function makes possible a more robust and accurate registration result than that of the cost function using the surface distance only. Also, this research provides an efficient means for image analysis through volume visualization of the registration result.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.480-490
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• 2018

Recently, The MRV (monitoring, reporting and verification) regulation, which measures, reports and verifies the emission gas of vessel to head for member countries of Europe Union (EU), is being implemented. As part this reason, we develop a system that estimates simple fuel consumption and recommends optimized stop-over ports of vessel, to calculate amount of carbon emission. To do this, we analyze fuel, distance and time consumption between port and the other port based on stored voyage data for over 10 years of real-ship, and implement a simple fuel consumption estimation module using analyzed result. Also, we design and implement the optimized route recommendation algorithm, existing navigation route display function including comparison with the optimized routes and user custom route plan function. Therefore, we expect the developed system is helpful when makes a navigation route and so on by reference indexes and we anticipate the system to have a sense for future research which learns and predicts for accuracy result.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.273-280
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• 2009

Today, many services and products that used to be only provided on offline have been being provided on the web according to the improvement of computing environment and the activation of web usage. These web-based services and products tend to be provided to customer by customer's preferences. This paradigm that considers customer's opinions and features in selecting is called personalization. The related research field is a recommendation. And this recommendation is performed by recommender system. Generally the recommendation is made from the preferences and tastes of customers. And recommender system provides this recommendation to user. However, the recommendation techniques have a couple of problems; they do not provide suitable recommendation to new users and also are limited to computing space that they generate recommendations which is dependent on ratings of products by users. Those problems has gathered some continuous interest from the recommendation field. In the case of new users, so similar users can't be classified because in the case of new users there is no rating created by new users. The problem of the limitation of the recommendation space is not easy to access because it is related to moneywise that the cost will be increasing rapidly when there is an addition to the dimension of recommendation. Therefore, I propose the solution of the recommendation problem of new user and the usage of item quality as weight to improve the accuracy of recommendation in this paper.