• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.129-134
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• 2003

In this paper, we efficiently implement the AMR speech coder by reducing the complexity of algebraic codebook search. To reduce the computational complexity of the algebraic codebook search, we propose a fast algebraic codebook search method that improves conventional depth first tree search method used in AMR speech coder algorithm. The proposed method reduces the search complexity by pruning the trees which are less possible to be selected as an optimum excitation. This method needs no additional computation for selecting the trees to be pruned and reduces the computational complexity considerably compared to the original depth first tree search method with slightly degradation or speech qualify. Applying our method to the implementation or AMR speech coder with 12.2 kbps mode by using the TeakLite DSP, we reduce the search complexity about 40% compared to the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.992-999
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• 2014

This paper presents an extensive experimental comparison of a variety of multi-label learning methods for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. We compared several methods from three categories of multi-label classification algorithms: algorithm adaptation, problem transformation, and meta learning. Experimental results are analyzed using 12 multi-label evaluation measures to assess the behavior of the methods from a variety of view-points. We also use a new summarization measure to find the best performing method. Experimental results show that the best performing methods are power-set method pruning a infrequently occurring subsets of labels and classifier chains modeling relevant labels with an additional feature. futhermore, ensembles of many classifiers of these methods enhance the performance further. The recommendation from this study is that the correlation of subcellular locations is an effective clue for classification, this is because the subcellular locations of proteins performing certain biological function are not independent but correlated.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.51-62
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• 2018

High utility sequential pattern (HUSP) mining has been considered as an important research topic in data mining. Although some algorithms have been proposed for this topic, they incur the problem of producing a large search space for HUSPs. The tighter utility upper bound of a sequence can prune more unpromising patterns early in the search space. In this paper, we propose a sequence expected utility (SEU) as a new utility upper bound of each sequence, which is the maximum expected utility of a sequence and all its descendant sequences. A sequence utility list for each pattern is used as a new data structure to maintain essential information for mining HUSPs. We devise an algorithm, high sequence utility list-span (HSUL-Span), to identify HUSPs by employing SEU. Experimental results on both synthetic and real datasets from different domains show that HSUL-Span generates considerably less candidate patterns and outperforms other algorithms in terms of execution time.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.409-419
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• 2007

Database community focuses on the similar music retrieval systems for music database when a humming query is given. One of the approaches is converting the midi data to time series, building their indices and performing the similarity search on them. Queries based on humming can be transformed to time series by using the known pitch detection algorithms. The recently suggested algorithm, scaled and warped matching, is based on dynamic time warping and uniform scaling. This paper proposes Humming BIRD(Humming Based sImilaR mini music retrieval system) using sliding window and center-aligned scaled and warped matching. Center-aligned scaled and warped matching is a mixed distance measure of center-aligned uniform scaling and time warping. The newly proposed measure gives tighter lower bound than previous ones which results in reduced search space. The empirical results show the superiority of this algorithm comparing the pruning power while it returns the same results.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.37-47
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• 2008

Exploitation of address generation units which are typically provided in DSPs plays an important role in DSP code generation since that perform fast address computation in parallel to the central data path. Offset assignment is optimization of memory layout for program variables by taking advantage of the capabilities of address generation units, consists of memory layout generation and address pointer assignment steps. In this paper, we propose an effective address pointer assignment method to minimize the number of address calculation instructions in DSP code generation. The proposed approach reduces the time complexity of a conventional address pointer assignment algorithm with fixed memory layouts by using minimum cost-nodes breaking. In order to contract memory size and processing time, we employ a powerful pruning technique. Moreover our proposed approach improves the initial solution iteratively by changing the memory layout for each iteration because the memory layout affects the result of the address pointer assignment algorithm. We applied the proposed approach to about 3,000 sequences of the OffsetStone benchmarks to demonstrate the effectiveness of the our approach. Experimental results with benchmarks show an average improvement of 25.9% in the address codes over previous works.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.183-186
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• 2000

본 논문에서는 인식이 진행되는 동안 탐색 공간을 효과적으로 줄임으로써 음성인식의 고속화를 달성할 수 있는 새로운 프레임 단위 적응 프루닝 알고리즘을 제안하고 실험을 통하여 그 유효성을 확인하였다. 이것은 앞 프레임과 뒤 프레임 사이의 최대확률은 높은 상관성을 가지므로 프루닝 문턱치를 앞 프레임의 최대 확률로부터 효과적으로 구할 수 있다는 사실에 근거를 두고있다. 이 방법에서는 앞 프레임의 최대 우도 확률과 후보 확률들의 조합으로 현재 프레임의 프루닝 문턱치를 갱신함으로써 현재 프레임의 문턱치를 인식 과정 중에 얻을 수 있기 때문에, 인식 태스크가 바뀌어도 문턱치를 구하기 위한 사전 실험을 수행할 필요가 없게 된다. 또한, 프레임 단위로 적응적으로 얻어진 문턱치는 다른 환경 하에서도 인식 속도의 향상을 가져올 수 있게 된다. 제안된 알고리즘의 유효성을 확인하여 위하여 한국어 주소 인식 시스템에 적용하였다. 본 시스템은 48개의 유사음소단위(PLUs)를 인식의 기본단위로 하고, 적응알고리즘으로는 최대사후확률추정법((MAP: Maximum A Posteriori Probability Estimation)을, 인식 알고리즘으로는 OPDP(One Pass Dynamic Programming)법을 이용하였다 남성화자 3인이 25개의 연결 주소명을 대상으로 인식 실험을 수행한 결과, 제안된 프레임단위 적응프루닝 문턱치를 적용한 경우를 기존의 고정 프루닝 문턱치와 가변 프루닝 문턱치를 적용한 경우와 비교하였을 때 인식률의 변화 없이 탐색공간이 상대적으로 각각 $14.4\%와 $9.14\%가 감소되어 제안된 프레임 단위 적응 프루닝 알고리즘의 유효성을 확인할 수 있었다. 시,공간적 분포 특성이 구체적으로 규명되면 보다 정확한 음장변화 추정이 이뤄져야 할 것으로 보인다. 또한 내부파와 음파의 상대적인 진행 방향에 따라 음장변화가 크게 다를 것이 예상되므로 이를 규명하기 위해서는 궁극적으로 3차원적인 음장분포 연구가 필요하다. 음향센서를 해저면에 매설할 경우 수충의 수온변화와 센서 주변의 수온변화 사이에는 어느 정도의 시간지연이 존재하게 되므로 이에 대한 영향을 규명하는 것도 센서의 성능예측을 위해서 필요하리라 사료된다.가지는 심부 가스의 개발 성공률을 증가시키기 위하여 심부 가스가 존재하는 지역의 지질학적 부존 환경 및 조성상의 특성과 생산시 소요되는 생산비용을 심도에 따라 분석하고 생산에 수반되는 기술적 문제점들을 정리하였으며 마지막으로 향후 요구되는 연구 분야들을 제시하였다. 또한 참고로 현재 심부 가스의 경우 미국이 연구 개발 측면에서 가장 활발한 활동을 전개하고 있으며 그 결과 다수의 신뢰성 있는 자료들을 확보하고 있으므로 본 논문은 USGS와 Gas Research Institute(GRI)에서 제시한 자료에 근거하였다.ऀĀ耀Ā삱?⨀؀Ā Ā?⨀ጀĀ耀Ā?돀ꢘ?⨀硩?⨀ႎ?⨀?⨀넆돐쁖잖⨀쁖잖⨀／ࠐ?⨀焆덐瀆倆Āⶇ퍟ⶇ퍟ĀĀĀĀ磀鲕좗?⨀肤?⨀⁅Ⴅ?⨀쀃잖⨀䣙熸ጁ↏?⨀

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.79-89
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• 2019

While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).