• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1960-1963
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• 2021

In this paper, we propose a multiplay Thompson sampling algorithm in multipath communication system. Multipath communication system has advantages on communication capacity, robustness, survivability, and so on. It is important to select appropriate network path according to the status of individual path. However, it is hard to obtain the information of path quality simultaneously. To solve this issue, we propose Thompson sampling which is popular in machine learning area. We find some issues when the algorithm is applied directly in the proposal system and suggested some modifications. Through simulation, we verified the proposed algorithm can utilize the entire network paths. In summary, our proposed algorithm can be applied as a path allocation in multipath-based communications system.

• 한국음향학회지
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• 제14권1호
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• pp.40-47
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• 1995

본 논문은 redundancy를 제거함으로 해서 데이타의 축약을 할 수 있는 새로운 방법론 즉, 병렬 컴퓨터인 MasPar 머쉰에 적합한 새로운 데이타 구조를 제시하고자 하는데 그 주된 목적이 있다. 이것을 실제로 구현한 결과, 본 논문에 제시된 방법인 PDOCM (Parallel Dynamic Octal Compact Mapping)은 기존의 방법중 가장 효율이 좋은 것으로 나타난 Huffman 코드와 비교할때는 평균적으로 $30\%$정도, bit-mapping방법과 비교할때는 평균적으로 $40\%$ 정도의 우수성을 보였다. 그리고 10 백만개의 영문자를 이용해서 MasPar 기계에서 64개의 프로세서를 이용하여 구현시킨 결과 54.188의 가속화율을 얻으므로서 우수한 방법임을 알 수가 있었다.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• 대한수학회논문집
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• 제13권2호
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• pp.393-403
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• 1998

We consider $M_1,M_2/M/1$ retrial queues with two classes of customers in which the service rates depend on the total number or the customers served since the beginning of the current busy period. In the case that arriving customers are bloced due to the channel being busy, the class 1 customers are queued in the priority group and are served as soon as the channel is free, whereas the class 2 customers enter the retrical group in order to try service again after a random amount of time. For the first $N(N \geq 1)$ exceptional services model which is a special case of our model, we derive the joint generating function of the numbers of customers in the two groups. When N = 1 i.e., the first exceptional service model, we obtain the joint generating function explicitly and if the arrival rate of class 2 customers is 0, we show that the results for our model coincide with known results for the M/M/1 queues with smart machine.

• 한국통신학회논문지
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• 제36권3B호
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• pp.287-296
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• 2011

시각장애인들이 주로 사용하고 있는 백색 지팡이(White Stick)는 지상의 부유장애물에 대한 감지가 어려울 뿐만 아니라, 보행중인 시각장애인에게 현재 위치정보와 응급상황 발생시 효과적인 보호 조치를 제공하는데 문제점이 있다. 본 논문에서는 이와 같은 문제점을 해결하여 시각장애인들이 보다 안전하고 편리한 보행을 할수 있는 사물지능통신(M2M : Machine-to-Machine) 기반의 지능형 보행보조시스템을 제안한다. 제안하는 시스템은 시각장애인이 사용하는 보행보조지팡이와 이동통신망을 통하여 다수의 보행보조지팡이 사용자를 원격 지원하는 서버로 구성된다. 보행보조지팡이는 초음파센서와 문자-음성 변환기술 등을 이용한 장애물 감지 및 인지 가능뿐만 아니라, 서버에서 제공하는 지리정보와 GPS(Global Positioning System) 수신 장치 등을 이용한 현재위치 알림 및 확인, 위급상황 대처 기능 등을 제공한다. 서버는 지리정보 제공뿐만 아니라, 보호자나 구호기관에 위급상황 발생위치 알림, 보행 동안의 주요 정보(장소 시간 및 횟수 등)를 제공한다. 제안 시스템의 시제품을 통한 기능 및 성능 시험 결과 만족스런 결과를 얻을수 있었다.

• 디지털융복합연구
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• 제11권2호
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• pp.309-316
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• 2013

사물지능통신(Machine to Machine) 은 사람의 도움없이 언제, 어디서나 독립적으로 기기간 통신을 가능하게 한다. M2M통신은 보통 무선구간의 통신을 포함하므로 도청, 가로채기, 변조, 프라버시 침해 등의 보안문제가 많이 발생할 수 있다. 따라서 무엇보다 기기들간의 안전한 통신을 이루는 것이 가장 중요한 문제 중 하나이다. 본 논문에서는 M2M 아키텍쳐에서 M2M 도메인과 네트워크 도메인간에 인증을 통해 데이터 노출을 피하고 안전한 통신을 제공하기위해 동적 ID기반의 원격 인증 기법을 제안한다. 제안된 기법은 로직기반의 정형검증을 통해서 우수한 보안성과 안전성이 증명되었다.

• Journal of Advanced Marine Engineering and Technology
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• 제37권7호
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• pp.760-770
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• 2013

Detecting grammatical errors from a text is a long-history application. In this paper, we compare the performance of two grammatical error detection techniques, which are implemented as a sub-module of an automated English scoring system. One is to use a full syntactic parser, which has not only grammatical rules but also extra-grammatical rules in order to detect syntactic errors while paring. The other one is to use a finite state machine which can identify an error covering a small range of an input. In order to compare the two approaches, grammatical errors are divided into three parts; the first one is grammatical error that can be handled by both approaches, and the second one is errors that can be handled by only a full parser, and the last one is errors that can be done only in a finite state machine. By doing this, we can figure out the strength and the weakness of each approach. The evaluation results show that a full parsing approach can detect more errors than a finite state machine can, while the accuracy of the former is lower than that of the latter. We can conclude that a full parser is suitable for detecting grammatical errors with a long distance dependency, whereas a finite state machine works well on sentences with multiple grammatical errors.