• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.2071-2074
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• 2002

This paper introduces an encoder design method of Turbo TCM (Trellis Coded Modulation) with symbol puncturing. TTCM consists of two simple trellis codes in parallel and modulator. To obtain an good encoder, we calculate the free distance by the assumption that the punctured symbol is transmitted from the subset that consist of signals with the same systematic bit at random. We develop a search program to find the component encoder which maximize the free distance. Especially, for 8-PSK with code rate 2/3, we search for the component codes. We find a new encoder which has better BER performance than that of Robertson′s encoder. We verify the results through the simulation."

• Journal of Power Electronics
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• 제6권2호
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• pp.139-145
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• 2006

This paper describes the ultra precise position estimation of a servomotor using a sinusoidal encoder based on Arcsine Interpolation Method for the cost reduction of circuit design. The amplitude and offset errors of the sinusoidal encoder output signals, from the encoder itself and analog signal processing procedures, are effectively compensated and on-line tuned by utilizing a low cost programmable differential amplifier without any special expensive equipment. For a theoretical evaluation of the practical resolution of this system, the relationship between the amplitude of ADC(Analog to Digital Converter) input signal errors and the anticipated resolution is also addressed. The performance of the proposed method is verified by comparing it with speed control characteristics of the servomotor driving system using a digital incremental 50,000ppr encoder in the experiments.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• 한국기계가공학회지
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• 제16권5호
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• pp.69-77
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• 2017

This paper introduces an optical encoder using the reflection in the slit. The digital optical encoder is a sensor to generate a pulse according to the displacement. An optical encoder is composed of 3 parts: light source, slit plate and light-receiving element. In a conventional encoder, one slit produces one signal. The resolution of the digital optical encoder is determined by the number of slits in the encoder plate. The small slit size is most important among the factors that determine the resolution in a generic-type optical encoder. However, a small slit has low productivity and technical difficulties, so analog optical encoders have emerged as an alternative. Nonetheless, this alternative requires additional circuitry and equipment because of the noise and drafts in the analog signals. A new sensor is presented in this paper with a high resolution and a slit of the same size using the reflection in the slit. Then, the path of the light that passes through the slit ccording to the shape was analyzed, and some paths were expressed in the mathematical expressions. In addition, the optical paths were analyzed in the rectangular, octagonal, and circular encoders, and shown the obtained number of signals per slit by using them. Thus, we confirm that this method has the best performance in circle-shaped slits.

• 한국정밀공학회지
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• 제25권8호
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• pp.29-36
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• 2008

An optical rotary encoder is easy to implement for automation system applications. In particular, the output of the encoder has a digital form pulse, which is also easy to be connected to a popular digital controller. By using an incremental encoder and a counting device, it is easy to measure angular displacement, as the number of the output pulses is proportional to the rotational displacement. This method can only detect the angular placement once a pulse signal comes out of the encoder. The angular displacement detection period is strongly subject to the change of the angular displacement in case of ultimate low velocity range. They have ultimate long detection period or cannot even detect angular displacement at near zero velocity. This paper proposes an algorithm for detecting angular displacement by using a dual encoder system with two encoders of normal resolution. The angular displacement detecting algorithm is able to keep detection period moderately at near zero velocity and even detect constant angular displacement within nominal period. It is useful for motion control applications in case of changing rotational direction at which there occurs zero velocity. In this paper, various experimental results are shown for the angular displacement detection algorithm.

• 한국정보통신학회논문지
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• 제15권11호
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• pp.2285-2291
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• 2011

이 논문에서는, 화면내 예측기, CAVLC(구문기반 적응가변길이 부호화기), DDR2 메모리 제어모듈을 집적화한 H.264 하이프로파일 화면내 부호화기를 제안한다. 설계된 부호화기는 한 매크로블록당 440 cycle에 동작할 수 있으며, 부호화기의 기능을 검증하기 위하여, JM13.2으로부터 참조 C 코드를 개발하고, 참조 C코드로부터 생성된 테스트벡터를 이용하여 개발된 하드웨어를 검증하였다. 개발된 부호화기는 FPGA에서 검증하였으며, DMA 는 200MHz에서, 부호화기모듈은 50MHz에서, 영상입력모듈(VIM)은 25MHz에서 동작한다. 회로의 크기는 Virtex 5XC5VLX330을 사용시에 약 20%의 LUT(43099개)를 사용하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3823-3840
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• 2020

Recently, auto-encoder has emerged as the most popular method in convolutional neural network (CNN) based image compression and has achieved impressive performance. In the traditional auto-encoder based image compression model, the encoder simply sends the features of last layer to the decoder, which cannot allocate bits over different spatial regions in an efficient way. Besides, these methods do not fully exploit the contextual information under different receptive fields for better reconstruction performance. In this paper, to solve these issues, a novel auto-encoder model is designed for image compression, which can effectively transmit the hierarchical features of the encoder to the decoder. Specifically, we first propose an adaptive bit-allocation strategy, which can adaptively select an importance channel. Then, we conduct the multiply operation on the generated importance mask and the features of the last layer in our proposed encoder to achieve efficient bit allocation. Moreover, we present an additional novel perceptual loss function for more accurate image details. Extensive experiments demonstrated that the proposed model can achieve significant superiority compared with JPEG and JPEG2000 both in both subjective and objective quality. Besides, our model shows better performance than the state-of-the-art convolutional neural network (CNN)-based image compression methods in terms of PSNR.

• 한국통신학회논문지
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• 제30권3C호
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• pp.64-68
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• 2005

리드솔로몬 부호화기를 구현하기 위해서 제안된 구조는 널리 알려진 대로 일반적인 구조와 직렬승산기를 쓰는 구조가 있다. 일반적 구조의 부호화기는 구조가 복잡한 대신 처리속도가 빠르고, 반면에 직렬승산기를 쓰는 부호화기는 구조는 단순하지만 처리속도는 그다지 빠르지 않은 것으로 알려져 있다. 이 논문에서는, 이 널리 알려진 사실이 VHDL로 구현할 때는 사실이 아닐 수도 있다는 것을 보인다. 이는, 직렬승산기에 필요한 쌍대기저 변환테이블을 구현하는 데에는 많은 게이트가 필요한 경우가 있기 때문인 것으로 해석된다. 한편 두 가지 구조를 써서 VHDL로 구현한 부호화의 처리속도는 모두 같다.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1251-1258
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• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• 한국정보통신학회논문지
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• 제9권7호
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• pp.1485-1490
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• 2005

가변 길이 부호는 오늘날 이미지 및 비디오에 관한 많은 국제 표준의 통합된 요소이다. 문맥 기반의 가변 길이 코팅(CAVLC)는 오늘날 주목받고 있는 JVT에서 채용되었다. 본 논문에서는 coeff_token 인코더, level 인코더, total_zero 인코더 및 run_before 인코더를 포함하는 CALVC 인코더 아키텍처를 설계한다. 설계된 CAVLC 인코더는 매 사이클마다 하나의 신택스 요소를 부호화할 수 있다. 자일링스 버텍스 1000e를 사용하여 구현한 결과 68MHz로 동작하는 것을 확인하였다. 따라서 본 논문의 CAVLC 인코더는 고속의 쓰루풋을 요하는 비디오 응용에 아주 적합할 것으로 사료된다.