• The Transactions of the Korea Information Processing Society
• /
• v.6 no.7
• /
• pp.1805-1816
• /
• 1999

This paper presents the Module Class Dependency Graph for expressing the dependency relations between classes effectively. The object-oriented language is developed independently at design time, and consists of relationship between classes. Therefore we need to consider these characteristics of independence, and to express effectively the relation of classes which is existed in class hierarchy. In the System Dependence Graph and Class Dependence Graph, the relationship of classes is not expressed. To express the class relationship, we propose the Module Class Dependence Graph, and we verify the effectiveness of this method applying to object constructor, inheritance relationship and dynamic binding. Also, we presents the expressing method of parameter to identify the member data of classes. Using this Module Class Dependency Graph, we can analyze the relationship of module class correctly at design time. This method can be applied to reverse engineering, testing, visualization and other various fields to analyze system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.11
• /
• pp.1538-1543
• /
• 2018

For n pairs of points (a,b) on a circle, the line segment to connect two points is called a chord. These chords define a new graph G. Each chord corresponds to a vertex of G, and if two chords intersect, the two vertices corresponding to them are connected by an edge. This makes a graph, called by a circle graph. In this paper, we deal with the problem to find the connected components of a circle graph. The connected component of a graph G is a maximal subgraph H such that any two vertices in H can be connected by a path. When the adjacent matrix of G is given, the problem to find them can be solved by either the depth-first search or the breadth-first search. But when only the information for the chords is given as an input, it takes ${\Omega}(n^2)$ time to obtain the adjacent matrix. In this paper, we do not make the adjacent matrix and develop an $O(n{\log}^2n)$ algorithm for the problem.

• Journal of applied mathematics & informatics
• /
• v.8 no.2
• /
• pp.387-401
• /
• 2001

If the distance between two vertices becomes longer after the removal of a vertex u, then u is called a hinge vertex. In this paper, a linear time sequential algorithm is presented to find all hinge vertices of an interval graph. Also, a parallel algorithm is presented which takes O(n/P + log n) time using P processors on an EREW PRAM.

• Journal of Advanced Marine Engineering and Technology
• /
• v.16 no.5
• /
• pp.77-84
• /
• 1992

In the most of acoustic system with low flow rate and low pressure level, one-dimensional, linear modeling techniques are used very well. At low frequency, the tube is modeled as inertia element and cavity as capacitance element, and to extend the range of frequency normal mode oscillators are represented. Bond graph modelling techniques are proposed to predict TL (Transmission Loss) and time response which is impossible by transfer matrix in muffler system. A simple acoustic filter which consists of several tubes and cawities is analyzed in both time and frequency domain.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2012.07a
• /
• pp.298-301
• /
• 2012

This paper presents a graph-based method of detecting and tracking moving objects in H.264/SVC bitstreams for video surveillance applications that makes use the information from spatial base and enhancement layers of the bitstreams. In the base layer, segmentation of real moving objects are first performed using a spatio-temporal graph by removing false detected objects via graph pruning and graph projection, followed by graph matching to precisely identify the real moving objects over time even under occlusion. For the accurate detection and reliable tracking of moving objects in the enhancement layer, as well as saving computational complexity, the identified block groups of the real moving objects in the base layer are then mapped to the enhancement layer to provide accurate and efficient object detection and tracking in the bitstreams of higher resolution. Experimental results show the proposed method can produce reliable results with low computational complexity in both spatial layers of H.264/SVC test bitstreams.

• Industrial Engineering and Management Systems
• /
• v.9 no.3
• /
• pp.227-241
• /
• 2010

A memory module industry's supply chain usually consists of multiple manufacturing sites and multiple distribution centers. In order to fulfill the variety of demands from downstream customers, production planners need not only to decide the order allocation among multiple manufacturing sites but also to consider memory module industrial characteristics and supply chain constraints, such as multiple material substitution relationships, capacity, and transportation lead time, fluctuation of component purchasing prices and available supply quantities of critical materials (e.g., DRAM, chip), based on human experience. In this research, a directed graph-based supply network planning (DGSNP) model is developed for memory module industry. In addition to multi-site order allocation, the DGSNP model explicitly considers production planning for each manufacturing site, and purchasing planning from each supplier. First, the research formulates the supply network's structure and constraints in a directed-graph form. Then, a proposed genetic algorithm (GA) solves the matrix form which is transformed from the directed-graph model. Finally, the final matrix, with a calculated maximum profit, can be transformed back to a directed-graph based supply network plan as a reference for planners. The results of the illustrative experiments show that the DGSNP model, compared to current memory module industry practices, determines a convincing supply network planning solution, as measured by total profit.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.267-271
• /
• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.33A no.2
• /
• pp.196-204
• /
• 1996

This paper presents a new scheduling algorithm, which is the most improtant subtask in the high level synthesis. The proposed algorithm performs scheduling in consideration of resource sharing concept based on characteristics of conditionsla bransches in the intermediate data structure. CDFG (control data flow graph) generated by a VHDL analyzer. This algorithm constructs a conditon graph based on time frame of each operation using both the ASAP and the ALAP scheduling algorithm. The conditon priority is obtained from the condition graph constructed from each conditional brance. The determined condition priority implies the sequential order of transforming the CDFG with conditonal branches into the CDFG without conditional branches. To minimize resource cost, the CDFG with conditional branches are transformed into the CDFG without conditonal brancehs according to the condition priority. Considering the data dependency, the hardware constraints, and the data execution time constraints, each operation in the transformed CDFG is assigned ot control steps. Such assigning of unscheduled operations into contorl steps implies the performance of the scheduling in the consecutive movement of operations. The effectiveness of this algorithm is hsown by the experiment for the benchmark circuits.

• International Journal of Contents
• /
• v.7 no.3
• /
• pp.19-28
• /
• 2011

This paper presents a fast and practical motion synthesis algorithm for massive number of quadruped animals. The algorithm constructs so called speed maps that contain a set of same style motions but different speed from a single cyclic motion by using IK(Inverse Kinematics) solver. Then, those speed maps are connected each other to form a motion graph. At run time, given a point trajectory that obtained from user specification or simulators, the algorithm retrieves proper speed motions from the graph, and modifies and stitches them together to create a long seamless motion in real time. Since our algorithm mainly targets on the massive quadruped animal motions, the motion graph create wide variety of different size of characters for each trajectory and automatically adjusted synthesized motions without causing artifact such as foot skating. The performance of algorithm is verified through several experiments

• Journal of applied mathematics & informatics
• /
• v.5 no.2
• /
• pp.329-336
• /
• 1998

The class of doubly chordal graphs is a subclass of chordal graphs and a superclass of strongly chordal graphs which arise in so many application areas. Many optimization problems like domination and Steiner tree are NP-complete on chordal graps but can be solved in polynomial time on doubly chordal graphs. The central to designing efficient algorithms for doulby chordal graphs is the concept of (canonical)doubly perfect elimination orderings. We present linear time algorithms to compute a (canonical) double perfect elimination ordering of a doubly chordal graph.