• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.5023-5038
• /
• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• Proceedings of the IEEK Conference
• /
• 2001.09a
• /
• pp.85-88
• /
• 2001

In this paper we consider a problem of blind co-channel signal separation, the goal of which is to estimate multiple co-channel digitally modulated signals using an antenna array. We employ the joint maximum likelihood estimation and present a sequential algorithm, which is referred to as sequential joint maximum likelihood (SJML) algorithm. It separates multiple co-channel signal on-line and converges fast in overdetermined noisy communication environment. And the computational complexity of SJML for M-QAM (M=8, 16, 64,...) signals is less expensive compared to the SLSP. Useful behavior of this algorithm are confirmed by simulations.

• Industrial Engineering and Management Systems
• /
• v.14 no.3
• /
• pp.318-324
• /
• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.32 no.3
• /
• pp.47-61
• /
• 2007

This paper presents two elimination methods of subtours, which is obtained by applying the Out-of-Kilter algorithm to the sequential ordering problem (SOP) to produce a feasible solution for the SOP. Since the SOP is a kind of asymmetric traveling salesman problem (ATSP) with precedence constraints, we can apply the Out-of-Kilter algorithm to the SOP by relaxing the precedence constraints. Instead of patching subtours, both of two elimination methods construct a feasible solution of the SOP by using arcs constructing the subtours, and they improve solution by running 3-opt and 4-opt at each iteration. We also use a perturbation method. cost relaxation to explore a global solution. Six cases from two elimination methods are presented and their experimental results are compared to each other. The proposed algorithm found 32 best known solutions out of the 34 instances from the TSPLIB in a reasonable time.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.45
• /
• pp.101-112
• /
• 1998

Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.1
• /
• pp.109-114
• /
• 2012

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

• Journal of information and communication convergence engineering
• /
• v.8 no.4
• /
• pp.421-426
• /
• 2010

This paper presents a method of design the counter using sequential system based on synchronous techniques. For the design the counter, first of all, we derive switching algebras and their operations. Also, we obtain the next-state functions, flip-flop excitations and their input functions from the flip-flop. Then, we propose the algorithm which is a method of implementation of the synchronous sequential digital logic circuits. Finally, we apply proposed the sequential logic based on synchronous techniques to counter.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.1
• /
• pp.104-112
• /
• 1989

A design automation method for sequential circuits implementation by mans of PLA is discussed, and an optimal state-code assignment algorithm to minimize the PLA area is proposed. In order to design sequential circuit automatically, DASL (Design Automation Support Language) [8] which is easy to describe and powerful to synthesize, is proposed and used to describe sequential circuit, An optimal statecode assignment algorithm which considers next states and outputs simultaneously is proposed, and by adopting this algorithm to various examples, the area of PLA is reduced by 10% comparing privious methods. This system is constructed to design microinstruction, FSM, VLSI control part synthesis.

• ETRI Journal
• /
• v.35 no.6
• /
• pp.1084-1093
• /
• 2013

problem of pedestrian localization using mobile nodes containing impulse radio ultra wideband (IR-UWB) is considered. IEEE 802.15.4a-based IR-UWB can achieve accurate ranging. However, the coverage is as short as 30 m, owing to the restricted transmit power. This factor may cause a poor geometric relationship among the mobile nodes and anchor nodes in certain environments. To localize a group of pedestrians accurately, an enhanced cooperative localization method is proposed. We describe a sequential algorithm and define problems that may occur in the implementation of the algorithm. To solve these problems, a batch algorithm is proposed. The batch algorithm can be carried out after performing the sequential algorithm to linearize the nonlinear range equation. When a sequential algorithm cannot be performed due to a poor geometric relationship among nodes, a batch algorithm can be carried out directly. Herein, Monte Carlo simulations are presented to illustrate the proposed method and verify its performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.4
• /
• pp.403-410
• /
• 1999

In this study, a parallel algorithm has been developed that can quickly solve the optiaml control problem of large-scale dynamic systems. The algorithm adopts the sequential quadratic programming methods and achieves domain decomposition-type parallelism in computing sensitivities for search direction computation. A silicon wafer thermal process problem has been solved using the algorithm, and a parallel efficiency of 45% has been achieved with 16 processors. Practical methods have also been investigated in this study as a way to further speed up the computation time.