• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2008.11a
• /
• pp.551-552
• /
• 2008

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.1
• /
• pp.54-60
• /
• 1996

In most pattern recognition and image understanding applications, images are degraded by noise and other distortions. Therefore, it is more relevant to decide how similar two objects are rather than to decide whether the two are exactly the same. In this paper, we propose a method for recognizing degraded symbols using a distance measure between two graphs representing the symbols. a symbol is represented as a graph consisting of nodes and edges based on the run graph concept. The graph is then transformed into a reference model graph with production rule containing the embedding transform. The symbols are recognized by using the distance measure which is estimated by using the number of production rules used and the structural homomorphism between a transformed graph and a model graph. the proposed approach is applies to the recognition of non-note musical symbols and the result are given.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.10a
• /
• pp.984-987
• /
• 2018

In an Internet of Thing (IoT) environment, entities with different attributes and capacities are going to be connected in a highly connected fashion. Specifically, not only the mechanical and electronic devices but also other entities such as people, locations and applications are connected to each other. Understanding and managing these connections play an important role for businesses, which identify opportunities for new IoT services. Traditional approach for storing and querying IoT data is used of a relational database management system (RDMS) such as MySQL or MSSQL. However, using RDMS is not flexible and sufficient for handling heterogeneous IoT data because these data have deeply complex relationships which require nested queries and complex joins on multiple tables. In this paper, we propose a graph model for constructing a graph database of heterogeneous IoT data. Graph databases are purposely-built to store highly connected data with nodes representing entities and edges representing the relationships between these entities. Our model fuses social graph, spatial graph, and things graph, and incorporates the relationships among them. We then present a case study which applies our model for representing data from a Smart Campus using Neo4J platform. Through the results of querying to answer real questions in Smart Campus management, we show the viability of our model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.4008-4023
• /
• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.75-84
• /
• 2018

Social engineering attack means to get information of Social engineering attack means to get information of opponent without technical attack or to induce opponent to provide information directly. In particular, social engineering does not approach opponents through technical attacks, so it is difficult to prevent all attacks with high-tech security equipment. Each company plans employee education and social training as a countermeasure to prevent social engineering. However, it is difficult for a security officer to obtain a practical education(training) effect, and it is also difficult to measure it visually. Therefore, to measure the social engineering threat, we use the results of social engineering training result to calculate the risk by system asset and propose a attack graph based probability. The security officer uses the results of social engineering training to analyze the security threats by asset and suggests a framework for quick security response. Through the framework presented in this paper, we measure the qualitative social engineering threats, collect system asset information, and calculate the asset risk to generate probability based attack graphs. As a result, the security officer can graphically monitor the degree of vulnerability of the asset's authority system, asset information and preferences along with social engineering training results. It aims to make it practical for companies to utilize as a key indicator for establishing a systematic security strategy in the enterprise.

• Journal of Advanced Marine Engineering and Technology
• /
• v.39 no.2
• /
• pp.159-165
• /
• 2015

Motion control schemes are generally classified into three categories (point stabilization, trajectory tracking, and path following). This paper deals with the problem which is associated with the initial deployment of a group of Unmanned Surface Vehicle (USVs) and corresponding point stabilization. To keep the formation of a group of USVs, it is necessary to set the relationship between each vehicle. A forcing functions such as potential fields are designed to keep the formation and a graph Laplacian is used to represent the connectivity between vehicle. In case of fixed topology of the graph representing the communication between the vehicles, the graph Laplacian is assumed constant. However the graph topologies are allowed to change as the vehicles move, and the system dynamics become discontinuous in nature because the graph Laplacian changes as time passes. To check the stability in the stage of deployment, the system is modeled with Kronecker algebra notation. Filippov's calculus of differential equations with discontinuous right hand sides is then used to formally characterize the behavior of USVs. The stability of the system is analyzed with Lyapunov's stability theory and LaSalle's invariance principle, and the validity is shown by checking the variation of state norm.

• Smart Structures and Systems
• /
• v.3 no.1
• /
• pp.1-22
• /
• 2007

The energy efficiency of a self-powered wireless sensing system for pressure monitoring in injection molding is analyzed using Bond graph models. The sensing system, located within the mold cavity, consists of an energy converter, an energy modulator, and a ultrasonic signal transmitter. Pressure variation in the mold cavity is extracted by the energy converter and transmitted through the mold steel to a signal receiver located outside of the mold, in the form of ultrasound pulse trains. Through Bond graph models, the energy efficiency of the sensing system is characterized as a function of the configuration of a piezoceramic stack within the energy converter, the pulsing cycle of the energy modulator, and the thicknesses of the various layers that make up the ultrasonic signal transmitter. The obtained energy models are subsequently utilized to identify the minimum level of signal intensity required to ensure successful detection of the ultrasound pulse trains by the signal receiver. The Bond graph models established have shown to be useful in optimizing the design of the various constituent components within the sensing system to achieve high energy conversion efficiency under a compact size, which are critical to successful embedment within the mold structure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.819-822
• /
• 2016

This paper proposes an algorithm recognizing finger using a distance graph of a detected finger's contour in a depth image. The distance graph shows angles and Euclidean distances between the center of palm and the hand contour as x and y axis respectively. We can obtain hand gestures from the graph using the fact that the graph has local maximum at the positions of finger tips. After we find the center of mass of the wrist using the fingers is thinner than the palm, we make its angle the orienting angle $0^{\circ}$. The simulation results show that the proposed algorithm can detect hand gestures well regardless of the hand direction.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3761-3779
• /
• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.11
• /
• pp.2103-2108
• /
• 2017

For n points $p_i$ on the m-dimensional plane $R^m$ and a fixed range r, consider a set $T_i$ containing points the distances from $p_i$ of which are less than or equal to r. In case m=1, $T_i$ is an interval on a line, it is a circle on a plane when m=2. For the vertices corresponding to the sets $T_i$, there is an edge between the vertices if the two sets intersect. Then this graph is called an intersection graph G. For m=1 G is called a proper interval graph and for m=2, it is called an unit disk graph. In this paper, we are concerned in the intersection graph G(r) when r changes. In particular, we consider the problem to find the minimum r such that G(r)is connected. For this problem, we propose an O(n) algorithm for the proper interval graph and an $O(n^2{\log}\;n)$ algorithm for the unit disk graph. For the dynamic environment in which the points on a line are added or deleted, we give an O(log n) algorithm for the problem.