• Proceedings of the KWS Conference
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• 대한용접접합학회 2002년도 Proceedings of the International Welding/Joining Conference-Korea
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• pp.606-611
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• 2002

Welding using lasers can be mass-produced in high speed. In the laser welding, performing real-time monitoring system of the welding quality is very important in enhancing the efficiency of welding. In this study, the plasma and molten metal which are generated during laser welding were measured using the UV sensor and IR sensors. The results of laser welding were classified into five categories such as optimal heat input, little low heat input, low heat input, partial joining due to gap mismatch, and nozzle deviation. Also, a system was formulated which uses the measured signals with a fuzzy pattern recognition method which is used to perform real-time evaluation of the welding quality and the defects which can occur in laser welding.

• ETRI Journal
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• 제28권4호
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• pp.502-505
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• 2006

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.

• Journal of Institute of Control, Robotics and Systems
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• 제18권12호
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• Proceedings of the Korean Society for Language and Information Conference
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• 한국언어정보학회 2002년도 학술대회 발표논문집
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• pp.10-38
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• 2002

본고는 한국어 영형 대명사의 적절한 해석을 위해 개념적으로 전혀 새로운 이론을 제안한다. 일련의 다양한 제약들이 서로 연관되어 있음을 보인 후, 그러한 규칙의 다양성을 적절히 포착하기 위해 적절성 이론 (Optimality Theory)을 도입할 것을 제안하고, 그 토대 위에 다양한 제약들을 형식화한 후, 그 규칙들의 위계관계를 설정한다. 가장 우선순위를 갖는 제약으로 인접 요소간 어휘의미자질들이 일치해야 한다는 어휘의미제약(*Feature Mismatch)과 통사적 결속규칙을 의미론적으로 재해석한 결속원리 B(Principle B)를 선정한다. 그 다음 순위를 갖는 제약으로, 가능한 한 선행명사를 지칭하도록 요구하는 대용존중제약(DOAP: Don't Overlook Anaphoric Possibilities)과, 센터링 이론의 전이방식 개념을 도입하여 정의한 계속선호제약 (CONTINUE)을 제안한다

• MALSORI
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• 제60호
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• pp.145-164
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• 2006

This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.

• International Journal of Korean Welding Society
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• 제3권1호
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• pp.45-50
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• 2003

Welding using lasers can be mass-produced in high speed. In the laser welding, performing real-time monitoring system of the welding quality is very important in enhancing the efficiency of welding. In this study, the plasma and molten metal which are generated during laser welding were measured using the UV sensor and IR sensors. The results of laser welding were classified into five categories such as optimal heat input, little low heat input, low heat input, partial joining due to gap mismatch, and nozzle deviation. Also, a system was formulated which uses the measured signals with a fuzzy pattern recognition method which is used to perform real-time evaluation of the welding quality and the defects which can occur in laser welding.

• Genomics & Informatics
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• 제19권4호
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• pp.40.1-40.11
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• 2021

Mutation signatures represent unique sequence footprints of somatic mutations resulting from specific DNA mutagenic and repair processes. However, their causal associations and the potential utility for genome research remain largely unknown. In this study, we performed PanCancer-scale correlative analyses to identify the genomic features associated with tumor mutation burdens (TMB) and individual mutation signatures. We observed that TMB was correlated with tumor purity, ploidy, and the level of aneuploidy, as well as with the expression of cell proliferation-related genes representing genomic covariates in evaluating TMB. Correlative analyses of mutation signature levels with genes belonging to specific DNA damage-repair processes revealed that deficiencies of NHEJ1 and ALKBH3 may contribute to mutations in the settings of APOBEC cytidine deaminase activation and DNA mismatch repair deficiency, respectively. We further employed a strategy to identify feature-driven, de novo mutation signatures and demonstrated that mutation signatures can be reconstructed using known causal features. Using the strategy, we further identified tumor hypoxia-related mutation signatures similar to the APOBEC-related mutation signatures, suggesting that APOBEC activity mediates hypoxia-related mutational consequences in cancer genomes. Our study advances the mechanistic insights into the TMB and signature-based DNA mutagenic and repair processes in cancer genomes. We also propose that feature-driven mutation signature analysis can further extend the categories of cancer-relevant mutation signatures and their causal relationships.

• Journal of Korea Multimedia Society
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• 제11권10호
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• pp.1325-1337
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• 2008

The difference of environments between training and recognition is the major reason of degradation of speech recognition. To solve this mismatch of environments, various noise processing methods have been studied. Among them, ERN(log-Energy dynamic Range Normalization) and SEN(Silence Energy Normalization) for normalization of log energy features show better performance than others. However, these methods have a problem that they can hardly achieve normalization for the relatively higher values of log energy features and the environmental mismatch caused by this problem becomes bigger especially in low SNR environments. To solve these problems, we propose applying ARMA filter as post-processing for smoothing log energy features by calculating the moving average in auto-regression scheme. From the recognition results conducted on Aurora 2.0 DB, the proposed method shows improved recognition results comparing with conventional methods.

• Journal of Digital Contents Society
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• 제16권2호
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• pp.179-187
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• 2015

Spread of smartphone creates various contents. Among many contents, AR application using Location Based Service(LBS) is needed widely. In this paper, we propose error correction algorithm for location-based Augmented Reality(AR) system using computer vision technology in android environment. This method that detects the early features with SURF(Speeded Up Robust Features) algorithm to minimize the mismatch and to reduce the operations, and tracks the detected, and applies it in mobile environment. We use the GPS data to retrieve the location information, and use the gyro sensor and G-sensor to get the pose estimation and direction information. However, the cumulative errors of location information cause the mismatch that and an object is not fixed, and we can not accept it the complete AR technology. Because AR needs many operations, implementation in mobile environment has many difficulties. The proposed approach minimizes the performance degradation in mobile environments, and are relatively simple to implement, and a variety of existing systems can be useful in a mobile environment.

• Journal of Digital Convergence
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• 제13권10호
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• pp.313-318
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• 2015

A traditional speech enhancement methods distort the sound spectrum generated according to estimation of the remaining noise, or invalid noise is a problem of lowering the speech recognition performance. In this paper, we propose a speech detection method that convergence the sound energy distribution process and sound energy parameters. The proposed method was used to receive properties reduce the influence of noise to maximize voice energy. In addition, the smaller value from the feature parameters of the speech signal The log energy features of the interval having a more of the log energy value relative to the region having a large energy similar to the log energy feature of the size of the voice signal containing the noise which reducing the mismatch of the training and the recognition environment recognition experiments Results confirmed that the improved recognition performance are checked compared to the conventional method. Car noise environment of Pause Hit Rate is in the 0dB and 5dB lower SNR region showed an accuracy of 97.1% and 97.3% in the high SNR region 10dB and 15dB 98.3%, showed an accuracy of 98.6%.