• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.63-72
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• 2011

Binary translation is a kind of the emulation method which converts a binary code compiled on the particular instruction set architecture to the new binary code that can be run on another one. It has been mostly used for migrating legacy systems to new architecture. In recent, binary translation is used for instrumenting programs without modifying source code, because it enables inserting additional codes dynamically, For general application, there already exists some instrumentation software using binary translation, such as dynamic binary analyzers and virtual machine monitors. On the other hand, in order to be benefited from binary translation in kernel-level, a few issues, which include system performance, memory management, privileged instructions, and synchronization, should be treated. These matters are derived from the structure of the kernel, and the difference between the kernel and user-level application. In this paper, we present a scheme to apply binary translation and dynamic instrumentation on kernel. We implement it on Linux kernel and demonstrate that kernel-level binary translation adds an insignificant overhead to performance of the system.

• Journal of the Chungcheong Mathematical Society
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• v.31 no.1
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• pp.183-191
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• 2018

This paper deals with binary ideal topological space and discuss about generalized binary closed sets and generalized kernel in the same topological space. Further it will discuss various types of characterizations of generalized binary closed sets and generalized kernel.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.83-93
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• 2001

We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.567-572
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• 2019

In this paper, we propose an algorithm to perform convolution, pooling, and ReLU operations in CNN using binary image and binary kernel. It decomposes 256 gray-scale images into 8 bit planes and uses a binary kernel consisting of -1 and 1. The convolution operation of binary image and binary kernel is performed by addition and subtraction. Logically, it is a binary operation algorithm using the XNOR and comparator. ReLU and pooling operations are performed by using XNOR and OR logic operations, respectively. Through the experiments to verify the usefulness of the proposed algorithm, We confirm that the CNN operation can be performed by converting it to binary logic operation. It is an algorithm that can implement deep running even in a system with weak computing power. It can be applied to a variety of embedded systems such as smart phones, intelligent CCTV, IoT system, and autonomous car.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2078-2093
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• 2019

Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.325-327
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• 2006

현재 사용되고 있는 운영체제들은 그들의 기능을 확장하거나 교체하기 위해서 kernel extension을 사용해 왔다. 일반적으로 이러한 kernel extension들은 커널과 같은 주소공간에서 실행하기 때문에, 그것에서 발생하는 오류(fault)로 인해 전체 시스템이 망가지는 결과를 초래할 위험이 있다. 그래서 kernel extension의 안전한 실행에 관한 연구들은 kernel extension에서 발생한 오류를 전체 시스템으로부터 고립시키는 것이 주목적이었다. 하지만 이러한 방법들은 kernel extension의 어셈블리어로 된 코드를 분석하거나 사용하고 있는 커널의 소스 코드를 수정을 필요로 한다. 본 논문은 Sentry라는 kernel extension을 감시하기 위한 인터포지션 서비스를 제안한다. Sentry를 사용하기 위해서 별도의 커널 코드를 수정할 필요도 없으며, 이미 사용하고 있는 리눅스와 호환될 수 있는 특징을 지니고 있다. 그리고 kernel extension의 소스코드 및 어셈블리 코드에 대한 분석 없이 바이너리 파일을 직접 수정하여 kernel extension을 모니터링 할 수 있도록 한다. 게다가 Sentry는 재구성이 가능하기 때문에 얼마든지 kernel extension에 대한 보호정책을 동적으로 바꿀 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1219-1231
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• 2008

The main object of this paper is to develop a leave-one-out(LOO) bound of all pairwise comparison error correcting output codes (APC-ECOC). To avoid using classifiers whose corresponding target values are 0 in APC-ECOC and requiring pilot estimates we developed a bound based on mean misclassification probability(MMP). It can be used to tune kernel hyperparameters. Our empirical experiment using kernel mean squared estimate(KMSE) as the binary classifier indicates that the bound leads to good estimates of kernel hyperparameters.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.655-660
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• 2003

In general, Support Vector Machine has a good performance in binary classification, but it has the limitation on multi-pattern classification. So, we proposed an Import Vector Voting model for two or more labels classification. This model applied kernel bagging strategy to Import Vector Machine by Zhu. The proposed model used a voting strategy which averaged optimal kernel function from many kernel functions. In experiments, not only binary but multi-pattern classification problems, our proposed Import Vector Voting model showed good performance for given machine learning data.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.531-538
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• 2016

In this paper we deal with the small area estimation for the case that the response variables take binary values. The mixed effects models have been extensively studied for the small area estimation, which treats the spatial effects as random effects. However, when the spatial information of each area is given specifically as coordinates it is popular to use the geographically weighted logistic regression to incorporate the spatial information by assuming that the regression parameters vary spatially across areas. In this paper, relaxing the linearity assumption and propose a geographically weighted kernel logistic regression for estimating small area proportions by using basic principle of kernel machine. Numerical studies have been carried out to compare the performance of proposed method with other methods in estimating small area proportion.