• Journal of Electrical Engineering and Technology
• /
• v.13 no.2
• /
• pp.550-558
• /
• 2018

This paper presents the current limit strategy of voltage controller of delta-connected H-bridge static synchronous compensator (STATCOM) under an unbalanced voltage fault event. When phase to ground fault happens, the feasibility to heighten the magnitude of sagging phase voltage is considered by using symmetric transformation method in delta-structure STATCOM. And the efficiency to cover the maximum physical current limit of switching device is considered by using vector analysis method that calculate the zero sequence current for balancing the cluster energy in delta connected H-bridge STATCOM. The result is simple and obvious. Only positive sequence current has to be used to support the unbalanced voltage sag. Although the relationship between combination of the negative sequence voltage with current and zero sequence current is nonlinear, the more negative sequence current is supplying, the larger zero sequence current is required. From the full-model STATCOM system simulation, zero sequence current demand is identified according to a ratio of positive and negative sequence compensating current. When only positive sequence current support voltage sag, the least zero sequence current is needed.

• Journal of Broadcast Engineering
• /
• v.6 no.1
• /
• pp.21-31
• /
• 2001

In the PN-sequence based audio watermarking system, the PN sequence is shaped by a filter derived from the psychoacoustic model to increase robustness and inaudibility The psychoacoustic model calculated in each audio segment, however, requires heavy computational loads. In this paper, we propose an efficient watermarking system adopting a fixed-shape perceptual filter that substitutes psychoacoustic model derived filter. The proposed filter can shape the PN-sequence to be inaudible and enable to embed the robust watermark in a simple manner. Moreover, we propose an anchitecture for the PN-sequence compensation fitter In the watermark detecter to increase correlation between the watermark and the PN-sequence. With the proposed architecture, the blind watermark detection performance has been enhanced.

• Annual Conference on Human and Language Technology
• /
• 2018.10a
• /
• pp.567-569
• /
• 2018

한국어의 경어체는 종결어미에 따라 구분하고, 서로 다른 경어체는 각각 고유한 경어 강도가 있다. 경어체 간의 어체 변환은 규칙기반으로 진행되어 왔다. 본 논문은 어체 변환을 위한 규칙 정의의 번거로움을 줄이고 어체 변환 데이터만을 사용한 심층 학습 기반의 어체 변환 방법을 제안한다. 본 연구는 '해요체-합쇼체' 쌍의 병렬 데이터를 이용하여 Attention-based Sequence-to-Sequence 모델을 바탕으로 한 어체 변환 모델을 학습하였다. 해당 모델을 학습하고 실험하였을 때, 정확도 91%의 우수한 성과를 얻을 수 있었다.

• Journal of Internet Computing and Services
• /
• v.13 no.1
• /
• pp.99-106
• /
• 2012

It is an important and a difficult task to analyze external inputs and interactions between objects for designing and modeling a reactive system consisting of multiple object. Also the reactive system is required huge efforts on confirm it can satisfy requirements under all possible circumstances. In this paper, we build from requirements to a scenario-based specification model using LSC(Live Sequence Chart) extending MSC(Message Sequence Chart) with richer syntax and semantic. Then the reactive system model satisfying all requirements for each object in this system can be automatically created through LTL Synthesis. Finally, we propose a method of reactive system development by iterative process transforming a reactive system model to codes.

• MALSORI
• /
• no.62
• /
• pp.133-147
• /
• 2007

In this paper, we introduce a phone vector discrete HMM(PVDHMM) that decodes a phone sequence string, and demonstrates the applicability to spoken document retrieval. The PVDHMM treats a phone recognizer or large vocabulary continuous speech recognizer (LVCSR) as a vector quantizer whose codebook size is equal to the size of its phone set. We apply the PVDHMM to decode the phone sequence strings and compare the outputs with those of a continuous speech recognizer(CSR). Also we carry out spoken document retrieval experiment through PVDHMM word spotter on the phone sequence strings which are generated by phone recognizer or LVCSR and compare its results with those of retrieval through the phone-based vector space model.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2000.11a
• /
• pp.28-31
• /
• 2000

We describe a hidden Markov model, HMMTIR, for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike the linear HMMs used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The model extends the I-sites library by describing the adjacencies of different sequence-structure motifs as observed in the database, and achieves a great reduction in parameters by representing overlapping motifs in a much more compact form. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model, predicts secondary structure with an accuracy of 74.6% and backbone torsion angles better than any previously reported method, and predicts the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction. HMMSTR has been incorporated into a public, fully-automated protein structure prediction server.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.14 no.3
• /
• pp.209-215
• /
• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Communications for Statistical Applications and Methods
• /
• v.12 no.1
• /
• pp.139-147
• /
• 2005

It is interesting to locate homogeneous segments within a DNA sequence. Suppose that the DNA sequence has segments within which the observations follow the same residue frequency distribution, and between which observations have different distributions. In this setting, change points correspond to the end points of these segments. This article explores the use of a binary segmentation procedure in detecting the change points in the DNA sequence. The change points are determined using a sequence of nested hypothesis tests of whether a change point exists. At each test, we compare no change-point model with a single change-point model by using the Bayesian information criterion. Thus, the method circumvents the computational complexity one would normally face in problems with an unknown number of change points. We illustrate the procedure by analyzing the genome of the bacteriophage lambda.

• The Journal of the Acoustical Society of Korea
• /
• v.41 no.3
• /
• pp.335-341
• /
• 2022

In this paper, we adopt a transformer that shows remarkable performance in natural language processing as an acoustic model of hybrid speech recognition. The transformer acoustic model uses attention structures to process sequential data and shows high performance with low computational cost. This paper proposes a method to improve the performance of transformer AM by applying each of the four algorithms of sequence discriminative training, a weighted finite-state transducer (wFST)-based learning used in the existing DNN-HMM model. In addition, compared to the Cross Entropy (CE) learning method, sequence discriminative method shows 5 % of the relative Word Error Rate (WER).

• Journal of the Korean Operations Research and Management Science Society
• /
• v.26 no.1
• /
• pp.71-85
• /
• 2001

This paper considers a cost model for a U-shaped manufacturing cell formation which incorporates a required number of machines and various material flows together under multi-part multi-cell environment. The model is required to satisfy both the specified operation sequence of each part and the total part demand volume, which are considered to derive material handling cost in U-shaped flow line cells. In the model several cost-incurring factors including set-up for batch change-over, processing time for operations of each part, and machine failures are also considered in association with processing load and capacity of each cell. Moreover, a heuristic for a good machine layout in each cell is newly proposed based on the material handling cost of each alternative sequence layout. These all are put together to present an efficient heuristic for the U-shaped independent-cell formation problem, numerical problems are solved to illustrate the algorithm.