• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.188-196
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• 2005

This paper presents a new test data compression and low power scan test method that can reduce test time and power consumption. A proposed method can reduce the scan-in power and test data volume using a modified scan cell reordering algorithm and hybrid adaptive encoding method. Hybrid test data compression method uses adaptively the Golomb codes and run-length codes according to length of runs in test data, which can reduce efficiently the test data volume compare to previous method. We apply a scan cell reordering technique to minimize the column hamming distance in scan vectors, which can reduce the scan-in power consumption and test data. Experimental results for ISCAS 89 benchmark circuits show that reduced test data and low power scan testing can be achieved in all cases. The proposed method showed an about a 17%-26% better compression ratio, 8%-22% better average power consumption and 13%-60% better peak power consumption than that of previous method.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.19-28
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• 2011

Adaptive Random Testing (ART) aims to enhance the performance of pure random testing by detecting failure region in a software. The ART algorithm generates effective test cases which requires less number of test cases than that of pure random testing. However, all ART algorithms currently proposed are designed for the tests of monolithic system or unit level. In case of integrated system tests, ART approaches do not achieve same performances as those of ARTs applied to the unit or monolithic system. In this paper, we propose an extended ART algorithm which can be applied to the integrated system testing environment without degradation of performance. The proposed approach investigates an input distribution of the unit under a test with limited number of seed input data and generates information to be used to resizing input domain partitions. The simulation results show that our approach in an integration environment could achieve similar level of performance as an ART is applied to a unit testing. Results also show resilient effectiveness for various failure rates.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.531-540
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• 2008

An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.583-585
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• 2001

하이퍼큐브의 정규적이며 계층적인 구조적 특성은 효율적인 진단 알고리즘 개발에 유리하게 적용될 수 있다. Feng et al.의 HADA/IHADA와 Choi와 Rhee의 적응적 큐브 분할 방법은 하이퍼큐브의 전체 노드를 하나의 링으로 임베딩하여 링의 진단 특성을 이용하기 위하여 분할 및 정복 방법을 이용하였다. 또한 Kranakis와 Pelc는 결함을 모두 포함하는 최소의 서브링을 하나의 노드로 하는 하이퍼큐브의 형태로 분할하는 HYP-DIAG 알고리즘을 제안하였다. 또한 최악의 경우에, 테스트 수만을 고려하여 2$^n$+3n/2의 테스트 수를 갖는 FAST-HYP-DIAG 알고리즘과 병렬 시간만을 고려하여 많아야 11테스트 라운드 이내에 진단을 수행하는 EXPRESS-HYF-DIAG 알고리즘을 제안하였다. 본 논문에서는 HYP-DIAG의 첫 번째 단계에서 얻어진 서브링들의 신드롬을 분석하여 테스트의 수와 테스트 라운드를 모두 고려하는 알고리즘을 제안한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2004.11a
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• pp.47-51
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• 2004

다양한 형태의 유무선 네트워크 환경에서의 비디오 서비스를 위해 컨텐츠의 비트율을 각 환경에 맞게 조절하는 트랜스코딩 기술이 필수적인 요소로 대두되고 있다. 본 논문에서는 MPEG 비트스트림의 효율적인 트랜스코딩을 위한 적응적 재양자화 기법을 제안한다. 압축된 비트스트림 상에서 비트율 변화를 위해서는 양자화 파라미터를 변화시켜야 하는데 이 과정에서 재양자화 에러가 발생하여 화질 및 비트율 조절에 큰 문제가 되고 있다 본 논문에서는 다양한 테스트 영상에 대해서 비트율 변화에 대한 왜곡 정도를 테스트한 결과 특정 양자화 파라미터 비율 구간에 대해서 왜곡 현상이 심해지는 현상에 따라 이를 효율적으로 모델링하는 기법을 제안한다. 또한 제안한 모델에 근거하여 영상에 적응적인 재양자화 알고리즘을 제안한다. 제안한 알고리즘은 적은 비트율을 가지면서 화질을 유지하고 간단한 조건 및 연산에 기반하여 실시간 구현이 가능하다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.12
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• pp.1045-1054
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• 2002

We present a new low power scan testing and test data compression mothod lot System-On-a-Chip (SOC). The don't cares in unspecified scan vectors are mapped to binary values for low Power and encoded by adaptive encoding method for higher compression. Also, the scan-in direction of scan vectors is determined for low power. Experimental results for full - scanned versions of ISCAS 89 benchmark circuits show that the proposed method has both low power and higher compression.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.393-398
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• 2016

Collective Adaptive System is an adaptive multi-agent system which accomplishes its goal by collaborating various agents. Because the collective property of the Collective Adaptive System is accomplished by the goal of the system being based on collaboration, testing the goal accomplishment and their interactions among heterogeneous agents is important. This paper presents a feasibility study of applying a model-based testing approach using task-based test model to a Collective Adaptive System. This paper describes additional information to be applied for Collective Adaptive System for future studies. To analyze our approach, we applied the proposed approach to a smart home system as a case study; our results indicated that we can systematically derive test cases to check whether the Collective Adaptive System successfully achieved its goals by modifying and extending the existing task model.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.180-191
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• 2008

An Adaptive Random Testing (ART) is one of test case generation algorithms that are designed to detect common failure patterns within input domain. The ART algorithm shows better performance than that of pure Random Testing (RT). Distance-bases ART (D-ART) and Restriction Random Testing (RRT) are well known examples of ART algorithms which are reported to have good performances. But significant drawbacks are observed as quadratic runtime and non-uniform distribution of test case. They are mainly caused by a huge amount of distance computations to generate test case which are distance based method. ART through Iterative Partitioning (IP-ART) significantly reduces the amount of computation of D-ART and RRT with iterative partitioning of input domain. However, non-uniform distribution of test case still exists, which play a role of obstacle to develop a scalable algerian. In this paper we propose a new ART method which mitigates the drawback of IP-ART while achieving improved fault-detection capability. Simulation results show that the proposed one has about 9 percent of improved F-measures with respect to other algorithms.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2
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• pp.84-89
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• 1998

본 논문에서는 임의의 단어를 인식하기 위하여 음성학적으로 최적화된 (phonetically-optimized word) 음성 데이터베이스를 사용하여 훈련된 가변어휘 고립단위 음 성인식기의 실제 인식기 사용 환경에서의 성능을 평가하였다. 이를 위하여, 훈련 데이터베이 스에서와 상이한 환경에서 수집된 음성학적으로 균형 잡힌(phonetically-balanced word) 고 립 단어 음성을 테스트 데이터로 사용하였다. 테스트 데이터는 일반적인 사무실에서 작동하 는 노트북 PC에서 내장 마이크를 사용하여 녹음되었다. 이렇게 녹음된 음성을 사용하여 고 립단어 인식기의 인식률을 측정하였다. 이 인식기는 최대 사후(maximum a posteriori) 추정 알고리듬을 사용하여 화자의 변화에 적응하였다. 컴퓨터 모의실험 결과에 의하면 화자 적응 을 하지 않은 기본 시스템은 깨끗한 음성에 대하여 81.3%에서 사무실 환경 음성에 대하여 69.8%로 인식률이 저하되었다. 사무실 환경 음성에 대하여, 비교사 점진(unsupervised incremental) 모드에서 최대 사후 추정 화자 적응 알고리듬을 적용하였을 경우에는 화자적 응을 하지 않은 경우에 비하여 9%의 에러를 감소시키며, 50단어의 적응 단어를 사용하여 교사 묶음(supervised batch) 모드에서 최대 사후 추정 화자 적응 알고리듬을 적용하였을 경우에는 16%의 에러를 감소시켰다.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.188-193
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• 2003

Regardless of the distribution of the adaptation data in the testing environment, model-based adaptation methods that have so far been reported in various literature incorporates the adaptation data undiscriminatingly in reducing the mismatch between the training and testing environments. When the amount of data is small and the parameter tying is extensive, adaptation based on outlier data can be detrimental to the performance of the recognizer. The distribution of the adaptation data plays a critical role on the adaptation performance. In order to maximally improve the recognition rate in the testing environment using only a small number of adaptation data, supervised weighted training is applied to the structural maximum a posterior (SMAP) algorithm. We evaluate the performance of the proposed weighted SMAP (WSMAP) and SMAP on TIDIGITS corpus. The proposed WSMAP has been found to perform better for a small amount of data. The general idea of incorporating the distribution of the adaptation data is applicable to other adaptation algorithms.