• 설비공학논문집
• /
• 제15권8호
• /
• pp.641-651
• /
• 2003

In order to assess the reliability of a building energy simulation program (TRNSYS) from the standpoint of user, a set of verification experiment and calculation of cooling load for a test space is carried out. This work is a complement of the previous study that dealt with heating load for the same space. The test space is kept airtight to eliminate the source of uncertainties in modeling. A window-mounted, on/off controlled air-conditioner is used for cooling, whose performance has been established a priori. The calculation encompasses two models for evaluating cooling load in TRNSYS: energy rate control and temperature level control. Comparison of the total cooling loads obtained from different sets of experimental data enables to validate the measurements. The experimental result shows that the latent load is fairly large even in the absence of apparent air change in the space, which needs to be clarified. Each of hourly and daily accumulated sensible loads is compared between the experiment and two calculation models. Despite an inconsistency associated with solar irradiation, both of the models agree favorably with the experiment within a tolerance, illustrating their capability of properly predicting space thermal loads.

• 정보보호학회논문지
• /
• 제13권1호
• /
• pp.115-129
• /
• 2003

본 논문에서는 암호프로토콜의 논리성 검증을 위한 방법 중 하나인 SVO 로직을 바탕으로 한 자동 검증 방법과 실험에 대해서 다룬다. 먼저 기존 SVO 로직 자동 검증의 문제점을 도출한 후 자동 검증을 고려한 ASVO 로직을 설계하고 검증하였으며, Isabelle/Isar 시스템을 이용하여 구현하였다 본 논문에서는 잘 알려진 NSSK 프로토콜 중심으로 추론 구성 사례를 소개하도록 한다. 결과적으로 이미 Denning-Sacco 공격에 취약한 것으로 알려진 NSSK 프로토콜의 문제점들을 ASVO 로직의 자동화 검증을 통해서 정확히 확인할 수 있었다. 그리고 최종적으로 ASVO 로직의 자동화 검증을 통해서 발견된 취약점들을 개선한 NSSK7 프로토콜을 설계하고 검증하였다.

• ETRI Journal
• /
• 제25권3호
• /
• pp.179-186
• /
• 2003

Using biometrics to verify a person's identity has several advantages over the present practices of personal identification numbers (PINs) and passwords. At the same time, improvements in VLSI technology have recently led to the introduction of smart cards with 32-bit RISC processors. To gain maximum security in verification systems using biometrics, verification as well as storage of the biometric pattern must be done in the smart card. However, because of the limited resources (processing power and memory space) of the smart card, integrating biometrics into it is still an open challenge. In this paper, we propose a fingerprint verification algorithm using a multi-resolution accumulator array that can be executed in restricted environments such as the smart card. We first evaluate both the number of instructions executed and the memory requirement for each step of a typical fingerprint verification algorithm. We then develop a memory-efficient algorithm for the most memory-consuming step (alignment) using a multi-resolution accumulator array. Our experimental results show that the proposed algorithm can reduce the required memory space by a factor of 40 and can be executed in real time in resource-constrained environments without significantly degrading accuracy.

• International Journal of Control, Automation, and Systems
• /
• 제6권6호
• /
• pp.800-808
• /
• 2008

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

• IEIE Transactions on Smart Processing and Computing
• /
• 제2권5호
• /
• pp.277-281
• /
• 2013

Modern speaker verification systems based on support vector machines (SVMs) use Gaussian mixture model (GMM) supervectors as their input feature vectors, and the maximum a posteriori (MAP) adaptation is a conventional method for generating speaker-dependent GMMs by adapting a universal background model (UBM). MAP adaptation requires the appropriate amount of input utterance due to the number of model parameters to be estimated. On the other hand, with limited utterances, unreliable MAP adaptation can be performed, which causes adaptation noise even though the Bayesian priors used in the MAP adaptation smooth the movements between the UBM and speaker dependent GMMs. This paper proposes a sparse MAP adaptation method, which is known to perform well in the automatic speech recognition area. By introducing sparse MAP adaptation to the GMM-SVM-based speaker verification system, the adaptation noise can be mitigated effectively. The proposed method utilizes the L0 norm as a regularizer to induce sparsity. The experimental results on the TIMIT database showed that the sparse MAP-based GMM-SVM speaker verification system yields a 42.6% relative reduction in the equal error rate with few additional computations.

• 한국군사과학기술학회지
• /
• 제14권1호
• /
• pp.15-21
• /
• 2011

In this paper, the results of experimental analysis for the chemical composition and strengh verification of Bailey Panel Bridge have been presented. Some of main sections of bailey bridge colllected from military engineer troops were prepared for the chemical composition and strengh verification. The composition test and strength verification were conducted by using the optical microscope and Scanning Electron Microscope(SEM), Automatic Control Spark Emission Spectrometer(OBLF), X-ray Fluorescence Spectroscopy(XRF) and Instron measurement. The results showed that currently used sections of bailey bridge passed the strength verification and could be operated in drill of troops and battle fields.

• 한국ITS학회 논문지
• /
• 제9권3호
• /
• pp.67-72
• /
• 2010

본 논문에서는 음성과 얼굴 정보를 분석하여 자동차환경에서 운전자를 검증하는 기술을 소개한다. 음성정보를 이용한 화자검증을 위해서는 잘 알려진 Mel-scale Frequency Cepstral Coefficients(MFCCs)를 음성 특징으로 사용하였으며, 동영상을 이용한 얼굴검증에 대해서는 AdaBoost를 이용하여 검출된 얼굴 영역에 대해 주성분 분석을 수행하여 데이터의 크기가 현저히 줄어든 특징벡터를 추출하였다. 기존의 화자검증 방식에 비해 본 논문에서는 추출된 음성 및 얼굴 특징들을 Gaussian Mixture Models(GMM)-Supervector기반의 Support Vector Machine(SVM)커넬 방식에 적용하여 운전자의 음성과 얼굴을 효과적으로 검증하는 방식을 제안하였다. 실험결과 제안한 방법은 단순한 GMM 방식이나 SVM 방식보다 운전자 검증성능을 향상시킴을 알 수 있었다.

• Journal of Computing Science and Engineering
• /
• 제10권2호
• /
• pp.39-50
• /
• 2016

In this paper, we present a comparative study of spatial domain features for writer identification and verification with different ink width conditions. The existing methods give high error rates, when comparing two handwritten images with different pen types. To the best of our knowledge, we are the first to design the feature with different ink width conditions. To address this problem, contour based features were extracted using a chain code method. To improve accuracy at higher levels, we considered histograms of chain code and variance in bins of histogram of chain code as features to discriminate handwriting samples. The system was trained and tested for 1,000 writers with two samples using different writing instruments. The feature performance is tested on our newly created dataset of 4,000 samples. The experimental results show that the histogram of chain code feature is good compared to other methods with false acceptance rate of 11.67%, false rejection rate of 36.70%, average error rates of 24.18%, and average verification accuracy of 75.89% on our new dataset. We also studied the effect of amount of text and dataset size on verification accuracy.

• Journal of Information Processing Systems
• /
• 제12권3호
• /
• pp.468-488
• /
• 2016

The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.

• 제어로봇시스템학회논문지
• /
• 제19권12호
• /
• pp.1099-1104
• /
• 2013

This paper presents a learning-based visual inspection method that addresses the need for an improved adaptability of a visual inspection system for parts verification in panorama sunroof assembly lines. It is essential to ensure that the many parts required (bolts and nuts, etc.) are properly installed in the PLC sunroof manufacturing process. Instead of human inspectors, a visual inspection system can automatically perform parts verification tasks to assure that parts are properly installed while rejecting any that are improperly assembled. The proposed visual inspection method is able to adapt to changing inspection tasks and environmental conditions through an efficient learning process. The proposed system consists of two major modules: learning mode and test mode. The SVM (Support Vector Machine) learning algorithm is employed to implement part learning and verification. The proposed method is very robust for changing environmental conditions, and various experimental results show the effectiveness of the proposed method.