• Journal of KIISE:Databases
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• v.29 no.5
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• pp.381-392
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• 2002

This paper deals with the problem of shape-based retrieval in time-series databases. The shape-based retrieval is defined as the operation that searches for the (sub)sequences whose shapes are similar to that of a given query sequence regardless of their actual element values. In this paper, we propose an effective and efficient approach for shape-based retrieval of subsequences. We first introduce a new similarity model for shape-based retrieval that supports various combinations of transformations such as shifting, scaling, moving average, and time warping. For efficient processing of the shape-based retrieval based on the similarity model, we also propose the indexing and query processing methods. To verify the superiority of our approach, we perform extensive experiments with the real-world S&P 500 stock data. The results reveal that our approach successfully finds all the subsequences that have the shapes similar to that of the query sequence, and also achieves significant speedup up to around 66 times compared with the sequential scan method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1122-1127
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• 2007

We propose the complexity reduction algorithm of real root method that is mainly used in the Vocoder. The real root method is that if polynomial equations have the real roots, we are able to find those and transform them into LSP(Line Spectrum Pairs). However, this method takes much time to compute, because the root searching is processed sequentially in frequency region. The important characteristic of LSP is that most of coefficients are occurred in specific frequency region. So, the searching frequency region is ordered and adjusted by each coefficient's distribution in this paper. Transformation time can be reduced by proposed algorithm than the sequential searching method in frequency region. When we compare this proposed method with the conventional real root method, the experimental result is that the searching time was reduced about 48% in average.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.169-179
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• 2009

Communication log data consist of communication events such as sending and receiving e-mail or instance message and visiting web sites, etc. Many countries including USA and EU enforce the retention of these data on the communication service providers for the purpose of investigating or detecting criminals through the Internet. Because size of the retained data is very large, the efficient method for extracting valuable information from the data is needed for Law Enforcement Authorities to use the retained data. This paper defines the Interactive Communication Sequence Patterns(ICSPs) that is the important information when each communication event in communication log data consists of sender, receiver, and timestamp of this event. We also define a Mining(FDICSP) problem to discover such patterns and propose a method called Fast Discovering Interactive Communication Sequence Pattern(FDICSP) to solve this problem. FDICSP focuses on the characteristics of ICS to reduce the search space when it finds longer sequences by using shorter sequences. Thus, FDICSP can find Interactive Communication Sequence Patterns efficiently.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.274-286
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• 2004

In mobile client-server environments, mobile clients usually are disconnected with their server because of high cost of wireless communication and keep their own local copies to provide efficient updating the cached map. The update of the server database leads to invalidation of the cached map in the client side. To solve the issues of invalidation of the cached map, it is not efficient to resend part of the updated server database to clients whenever the updating of the server database occurs. This paper proposes a log-based update propagation method to propagate the server's update into its relevant clients by using only update logs. Too many logs increasingly accumulate as the sever database is updated several times. The sequential search of the relevant log data for a specific client is time-consuming. Sending of unnecessary logs should be avoided for reducing the overhead of communication.'re solve these problems, we first define unnecessary logs and then suggest log reduction methods to avoid or cancel creating unnecessary logs. The update log index is used for quickly retrieving relevant logs.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.817-824
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• 2009

In recent years, Weblog has become the most typical social media for citizens to share their opinions. And, many Weblogs reflect several social issues. There are many internet users who actively express their opinions for internet news or Weblog articles through the replying comments on online community. Hence, we can easily find internet blogs including more than 10 thousand replying comments. It is hard to search and explore useful messages on weblogs since most of weblog systems show articles and their comments to the form of sequential list. In this paper, we propose a visualizing and clustering system called TRIB (Telescope for Responding comments for Internet Blog) for a large set of responding comments for a Weblog article. TRIB clusters and visualizes the replying comments considering their contents using pre-defined user dictionary. Also, TRIB provides various personalized views considering the interests of users. To show the usefulness of TRIB, we conducted some experiments, concerning the clustering and visualizing capabilities of TRIB, with articles that have more than 1,000 comments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1318-1324
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• 2007

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.83-95
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• 2019

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of The Korean Association For Science Education
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• v.27 no.4
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• pp.309-317
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• 2007

The purpose of the study is to investigate the effects of problem solving models and middle school students' recognition inproblem solving activities and to get implications of problem solving activities in science education. We took the position of problem solving as consisting of four sequential stages: search of problems, performance of the plan, presentation of results, and evaluation of the presentation. Taking into account thechosen activity factors for each stage of problem solving, we developed detailed activity tools that are supposed to guide the stage. Recognition of problem solving activities in 7th grade middle school students were positive. Students felt that problem solvingactivities made them engage more and interested in science classes, and that they were helpful in solving problems in everyday life. Even though they found real problems in everyday life, they preferred problem solving activities to deal with real problems rather than simple minded ones.

• The Korean Journal of Pain
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• v.37 no.4
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• pp.367-378
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• 2024

Background: Fibromyalgia is characterized by the presence of chronic widespread pain that may impair patient's quality of life. Currently, the use of naltrexone as a therapeutic agent for fibromyalgia is not supported by enough evidence, especially from randomized controlled trials (RCTs). This study aims to analyze the efficacy and safety of low-dose naltrexone (LDN) for the management of fibromyalgia. Methods: A comprehensive search was conducted on the Scopus, Medline, ClinicalTrials.gov, and Cochrane Library databases up until May 20th, 2024. This review incorporates RCTs that examine the comparison between LDN and placebo in fibromyalgia patients. We employed random-effect models to analyze the odds ratio and mean difference (MD) for presentation of the outcomes. Results: A total of 4 RCTs with 222 fibromyalgia patients were incorporated. The results of our meta-analysis showed a significant reduction in pain scores (MD: -0.86, 95% confidence interval [CI]: -1.20, -0.51, P < 0.001, I² = 33%) and higher increment in pressure pain threshold (MD: 0.17, 95% CI: 0.08, 0.25, P < 0.001, I² = 0%) among fibromyalgia patients who received LDN than those who only received a placebo. The fibromyalgia impact questionnaire revised and pain catastrophizing scale did not differ significantly between the two groups. LDN was also associated with higher incidence of vivid dreams and nausea, but showed no significant difference with the placebo in terms of serious adverse events, headache, diarrhea, and dizziness. Conclusions: This study suggests the efficacy of LDN in mitigating pain symptoms for fibromyalgia patients with a relatively good safety profile.