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Ching-Hua Kuo

dblp:57/8712 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2013
0000-0001-5722-0360ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
metabolomics
0.112010
Chromaligner: a web server for chromatogram alignment · Bioinform. 2010
Bioinformatics and computational biology › metabolomics
retention time alignment
0.112010
Chromaligner: a web server for chromatogram alignment · Bioinform. 2010

Methods — techniques the papers use, named apart from their topics

correlation warping · 0.1constrained alignment · 0.1
YearPublicationVenuePosition
2013 An Efficient Approach to Solving Nonograms
abstract
A nonogram puzzle is played on a rectangular grid of pixels with clues given in the form of row and column constraints. The aim of solving a nonogram puzzle, an NP-complete problem, is to paint all the pixels of the grid in black and white while satisfying these constraints. This paper proposes an efficient approach to solving nonogram puzzles. We propose a fast dynamic programming (DP) method for line solving, whose time complexity in the worst case is O(kl) only, where the grid size is l×l and k is the average number of integers in one constraint, always smaller than l. In contrast, the time complexity for the best line-solving method in the past is O(kl2). We also propose some fully probing (FP) methods to solve more pixels before running backtracking. Our FP methods can solve more pixels than the method proposed by Batenburg and Kosters (before backtracking), while having a time complexity that is smaller than theirs by a factor of O(l). Most importantly, these FP methods provide useful guidance in choosing the next promising pixel to guess during backtracking. The proposed methods are incorporated into a fast nonogram solver, named LalaFrogKK. The program outperformed all the programs collected in webpbn.com, and also won both nonogram tournaments that were held at the 2011 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2011, Taiwan). We expect that the proposed FP methods can also be applied to solving other puzzles efficiently.
I-Chen Wu, Der-Johng Sun, Lung-Pin Chen, Kan-Yueh Chen, Ching-Hua Kuo, Hao-Hua Kang, Hung-Hsuan Lin
IEEE Trans. Comput. Intell. AI Games5
2010 Chromaligner: a web server for chromatogram alignment
abstract
UNLABELLED: Chromaligner is a tool for chromatogram alignment to align retention time for chromatographic methods coupled to spectrophotometers such as high performance liquid chromatography and capillary electrophoresis for metabolomics works. Chromaligner resolves peak shifts by a constrained chromatogram alignment. For a collection of chromatograms and a set of defined peaks, Chromaligner aligns the chromatograms on defined peaks using correlation warping (COW). Chromaligner is faster than the original COW algorithm by k(2) times, where k is the number of defined peaks in a chromatogram. It also provides alignments based on known component peaks to reach the best results for further chemometric analysis. AVAILABILITY: Chromaligner is freely accessible at http://cmdd.csie.ntu.edu.tw/~chromaligner.
San-Yuan Wang, Tsung-Jung Ho, Ching-Hua Kuo, Yufeng J. Tseng
Bioinform.3