• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.75-81
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• 2003

User authentication, including confidentiality, integrity over untrusted networks, is an important part of security for systems that allow remote access. Using human-memorable Password for remote user authentication is not easy due to the low entropy of the password, which constrained by the memory of the user. This paper presents a new password authentication and key agreement protocol suitable for authenticating users and exchanging keys over an insecure channel. The new protocol resists the dictionary attack and offers perfect forward secrecy, which means that revealing the password to an attacher does not help him obtain the session keys of past sessions against future compromises. Additionally user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the server. It does not have to resort to a PKI or trusted third party such as a key server or arbitrator So no keys and certificates stored on the users computer. Further desirable properties are to minimize setup time by keeping the number of flows and the computation time. This is very useful in application which secure password authentication is required such as home banking through web, SSL, SET, IPSEC, telnet, ftp, and user mobile situation.

• The Journal of Engineering Geology
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• v.18 no.4
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• pp.381-391
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• 2008

The eigenvalue technique is introduced to overcome the problem of truncation errors caused by temporal discretization of numerical analysis. The eigenvalue technique is different from simulation in that only the space is discretized. The spatially discretized equation is diagonized and the linear dynamic system is then decoupled. The time integration can be done independently and continuously for any nodal point at any time. The results of eigenvalue technique are compared with the exact solution and FEM numerical solution. The eigenvalue technique is more efficient than the FEM to the computation time and the computer storage in the same conditions. This technique is applied to the solute transport analysis in nonuniform flow fields around underground storage caverns. This method can be very useful for time consuming simulations. So, a sensitivity analysis is carried out by using this method to analyze the safety of caverns from nearly located contaminant sources. According to the simulations, the reaching time from source to the nearest cavern may takes 50 years with longitudinal dispersivity of 50 m and transversal dispersivity of 5 m, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.166-174
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. The important problem in recording ECG signal is a baseline wandering, which is occurred by rhythm of respiration and muscle contraction attaching to an electrode. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, baseline wander removing method based on morphological filter for efficient QRS detection method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. The signal distortion ratio of the proposed method is compared with other filtering method. Also, R wave detection is evaluated by using MIT-BIH arrhythmia database. Experiment result show that proposed method removes baseline wanders effectively without significant morphological distortion.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.479-488
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• 2001

As the SoC designs complexity constantly increases, the simulation that uses their software models simply takes too much time. To solve this problem, FPGA-based logic emulators have been developed and commonly used in the industry. However, FPGA-based logic emulators are facing with the problems of which not only very low FPGA resource usage rate due to the very limited number of pins in FPGAs, but also the emulation speed getting slow drastically as the complexity of designs increases. In this paper, we proposed a new innovative emulation architecture and its software that has high FPGA resource usage rate and makes the emulation extremely fast. The proposed emulation system has merits to overcome the FPGA pin limitation by pipelined ring which transfers multiple logic signal through a single physical pin, and it also makes possible to use a high speed system clock through the intelligent ring topology. In this topology, not only all signal transfer channels among EPGAs are totally separated from user logic so that a high speed system clock can be used, but also the depth of combinational paths is kept swallow as much as possible. Both of these are contributed to achieve high speed emulation. For pipelined singnals transfer among FPGAs we adopt a few heuristic scheduling having low computation complexity. Experimental result with a 12 bit microcontroller has shown that high speed emulation possible even with these simple heuristic scheduling algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.88-95
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• 2018

Maintainability indicates how easily a system can be restored to the normal state when a system failure occurs. Systems developed to have high maintainability can be competitive due to reduced maintenance time, workforce and resources. Quantification of the maintainability is possible in many ways, but only after prototype production or with historical data. As such, the graph theory and 3D model data have been used, but there are limitations in management efficiency and early use. To solve this problem, we studied the maintainability index of weapon systems using SysML-based modeling and simulation technique. A SysML structure diagram was generated to simultaneously model the system design and maintainability of system components by reflecting the maintainability attributes acquired from the system engineering tool. Then, a SysML parametric diagram was created to quantify the maintainability through simulation linked with MATLAB. As a result, an integrated model to account for system design and maintainability simultaneously has been presented. The model can be used from early design stages to identify components with low maintainability index. The design of such components can be changed to improve maintainability and thus to reduce the risks of cost overruns and time delays due to belated design changes.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.229-250
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• 2022

Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.29-41
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• 2022

Recently, with the development of deep learning technology, a variety of huge models with excellent performance have been devised by pre-training massive amounts of text data. However, in order for such a model to be applied to real-life services, the inference speed must be fast and the amount of computation must be low, so the technology for model compression is attracting attention. Knowledge distillation, a representative model compression, is attracting attention as it can be used in a variety of ways as a method of transferring the knowledge already learned by the teacher model to a relatively small-sized student model. However, knowledge distillation has a limitation in that it is difficult to solve problems with low similarity to previously learned data because only knowledge necessary for solving a given problem is learned in a teacher model and knowledge distillation to a student model is performed from the same point of view. Therefore, we propose a heterogeneous knowledge distillation method in which the teacher model learns a higher-level concept rather than the knowledge required for the task that the student model needs to solve, and the teacher model distills this knowledge to the student model. In addition, through classification experiments on about 18,000 documents, we confirmed that the heterogeneous knowledge distillation method showed superior performance in all aspects of learning efficiency and accuracy compared to the traditional knowledge distillation.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.