• Structural Engineering and Mechanics
• /
• v.33 no.5
• /
• pp.589-604
• /
• 2009

In the field of predicting structural safety and reliability the operating conditions play an essential role. Since the time and cost limitations are a significant factors in engineering it is important to predict the future operating conditions as close to the actual state as possible from small amount of available data. Because of the randomness of the environment the shape of measured load spectra can vary considerably and therefore simple distribution functions are frequently not sufficient for their modelling. Thus mixed distribution functions have to be used. In general their major weakness is the complicated calculation of unknown parameters. The scope of the paper is to investigate the load spectra growth for actual operating conditions and to investigate the modelling and extrapolation of load spectra with algorithm for mixed distribution estimation, REBMIX. The data obtained from the measurements of wheel forces and the braking moment on proving ground is used to generate load spectra.

• ETRI Journal
• /
• v.43 no.6
• /
• pp.1024-1037
• /
• 2021

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.6
• /
• pp.2761-2781
• /
• 2018

With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.5
• /
• pp.956-967
• /
• 2010

Rapid growth of internet applications has increased the importance of intrusion detection system (IDS) performance. String matching is the most computation-consuming task in IDS. In this paper, a new algorithm for multiple string matching is proposed. This proposed algorithm is based on the canonical Aho-Corasick algorithm and it utilizes a bidirectional and parallel processing structure to accelerate the matching speed. The proposed string matching algorithm was implemented and patched into Snort for experimental evaluation. Comparing with the canonical Aho-Corasick algorithm, the proposed algorithm has gained much improvement on the matching speed, especially in detecting multiple keywords within a long input text string.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.2
• /
• pp.13-20
• /
• 2021

Placement is a very important step in the VLSI physical design process. It is the problem of placing circuit modules to optimize the circuit performance and reliability of the circuit. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for circuit placement include the cluster growth, simulated annealing, integer linear programming and genetic algorithm. In this paper we propose a adapted genetic algorithm searching solution space for the placement problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of each implementation. As a result, it was found that the adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

• Food Science and Biotechnology
• /
• v.18 no.5
• /
• pp.1243-1252
• /
• 2009

User-friendly software was developed to determine the shelf-life of perishable Korean seasoned side dishes in real time based on growth models of spoilage and pathogenic microorganisms. In the program algorithm, the primary spoilage and fastest-growing pathogenic organisms are selected according to the product characteristics, and their growth is simulated based on the previously monitored or recorded temperature history. To predict the growth of spoilage organisms with confidence limits, kinetic models for aerobic bacteria or molds/yeasts from published works are used. Growth models of pathogenic bacteria were obtained from the literature or derived with regression of their growth rate data estimated from established software packages. These models are also used to check whether the risk of pathogenic bacterial growth exceeds that of food spoilage organisms. Many example simulations showed that the shelf-lives of the examined foods are predominantly limited by the growth of spoilage organism rather than by pathogenic bacterial growth.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.97-100
• /
• 2004

인터넷상에서 존재하는 많은 데이터베이스들 중 현실적으로 유용한 정보를 가지고 있는 것은 텍스트 데이타베이스이다. 텍스트 마이닝 기법에서 비구조적인 특징을 가진 텍스트 데이타로부터 유용한 정보를 분석하고 추출하여 연관된 패턴을 탐색하는 과정은 중요한 연구과제이다. 이에 본 논문은 인터넷에서 저장된 텍스트 데이터를 가지고 기존 텍스트 마이닝 기법 중 연관탐색 기법을 적용하여 사용자 중심의 연관된 패턴을 찾아서 의미있는 정보를 얻고자 한다. 탐색하기 위해 먼저 전처리 작업으로 용어의 객체를 추출하고. 추출된 각 객체들은 대용량 데이터에서 시간적, 공간적면에서 효율적인 연관탐색 기법인 NFP-Algorithm(N-most interesting k-itemsets Using FP-tree and FP-Growth)을 적용시켜서 의미있는 정보를 추출했다. 또한 Apriori계 Algorithm, FP-Algorithm, NFP-Algorithm을 비교하여 NFP-Algorithm이 시간적면에서 효율적임을 보여주었다.

• Proceedings of the KIEE Conference
• /
• 1998.11b
• /
• pp.648-650
• /
• 1998

This paper proposes a constructive algorithm for generating the Takagi-Sugeno type fuzzy model through the sequential learning from training data set. The proposed algorithm has a two-stage learning scheme that performs both structure and parameter learning simultaneously. The structure learning constructs fuzzy model using two growth criteria to assign new fuzzy rules for given observation data. The parameter learning adjusts the parameters of existing fuzzy rules using the LMS rule. To evaluate the performance of the proposed fuzzy modeling approach, well-known benchmark is used in simulation and compares it with other modeling approaches.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10a
• /
• pp.250-252
• /
• 2001

With the growth in computer and network technologies, some content-based music retrieval systems have been developed. However, their retrieval efficiency does not satisfy user's requirement yet. Of course users hope to have a more efficient and higher precision for music retrieval. In this paper so for these reasons, we Propose an efficient content-based music retrieval algorithm using melodies represented as music sequences. From the experimental result, it is shown that the proposed algorithm has higher exact rate than the related algorithms.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1883_1884
• /
• 2009

자동검침 데이터를 이용하여 고객의 가상 부하패턴을 생성하고 회선 및 구간의 부하를 분석하는 연구가 활발히 진행되고 있다. 본 논문에서는 기존에 연구된 산업분류 별 평균 부하패턴을 이용하는 방법과 고객의 부하 형태 인덱스를 이용한 방법의 문제점을 살펴보고, 이를 개선하기 위한 방법으로 고객의 속성정보를 이용하여 고객을 분류하는 방법을 제안하였다.