• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.359-364
• /
• 1999

Due to rapid growth of die and mould industries, it is urgently required to maximize the productivity and the efficiency of machining. In recent years, owing to the development of new kinds of material, die and mould materials are much harder and it is more difficult to cut. In this study, the workpiece SKD11(HRC45) is cut with TiAlN coated tungsten-carbide cutting tools. To find the general characteristics of difficult-to-cut materials, orthogonal turning test is performed. Orthogonal cutting theory can be expanded to oblique cutting model. The oblique cutting process in the small cutting edge element has been analyzed as orthogonal cutting process in the plane containing the cutting velocity vector and chip-flow vector. Hence, with the orthogonal cutting data obtained from orthogonal turning test, the cutting forces can be analyzed through oblique cutting model. The simulation results have shown a fairy good agreement with the test results.

• Journal of the Institute of Convergence Signal Processing
• /
• v.15 no.2
• /
• pp.37-41
• /
• 2014

The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

• Journal of Korea Multimedia Society
• /
• v.17 no.3
• /
• pp.277-286
• /
• 2014

In this paper, we propose a texture feature-based language identification by fusion of Gabor, MDLC (multi-lag directional local correlation), and co-occurrence features. In the proposed method, for a test image, Gabor magnitude images are first obtained by Gabor transform followed by magnitude operator. Moments for the Gabor magniude images are then computed and vectorized. MDLC images are then obtained by MDLC operator and their moments are computed and vectorized. GLCM (gray-level co-occurrence matrix) is next calculated from the test image and co-occurrence features are computed using the GLCM, and the features are also vectorized. The three vectors of the Gabor, MDLC, and co-occurrence features are fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. We evaluate the performance of our method by examining averaged identification rates for a test document image DB obtained by scanning of documents with 15 languages. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for the test DB.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.723-726
• /
• 1998

Fault dropping is a very important part of test generation process. It is used to reduce test generation time. Test generation systems use fault simulation for the purpose of fault dropping by identifying detectable faults with generated test patterns. Two kinds of delay fault model is used in practice, path delay fault model and gate delay fault model. In this paper we propose an efficient method for gate delay test generation which shares second test vector.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.11
• /
• pp.124-129
• /
• 2013

This paper proposes a high-performance vector inner product calculation circuit for real-time object recognition based on SVM algorithm. SVM algorithm shows a higher detection rate than other object recognition algorithms. However, it requires a huge amount of computational efforts. Since vector inner product calculation is one of the major operations of SVM algorithm, it is important to implement a high-performance vector inner product calculation circuit for real-time object recognition capability. The proposed circuit adopts the pipeline architecture with six stages to increase the operating speed and makes it possible to recognize objects in real time based on SVM. The proposed circuit was described in Verilog HDL at RTL. For silicon verification, an MPW chip was fabricated using TSMC 180nm standard cell library. The operation of the implemented MPW chip was verified on the test board with test application software developed for the chip verification.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.43 no.4 s.310
• /
• pp.39-49
• /
• 2006

This paper proposes an efficient verification environment of PCI Express controller that is the future I/O interface. This verification environment consists of a test vector generator, a test bench, and two abstract memories. We also define the assembler set to generate the verification scenarios. In this paper, we propose the random test environment which consists of a random vector generator, a .simulator part, and a compare engine. This verification methodology is useful to find the special errors which are not detected by the basic-behavioral test and hardware-design test.

• Ocean and Polar Research
• /
• v.40 no.1
• /
• pp.49-58
• /
• 2018

This study aimed to analyze the causality of hairtail prices among import and domestic distribution channels using a Vector Error Correction Model(VECM). The results are as follows. First, since the ADF unit-root test suggests that each of the price variables, apart from retail price, has a unit root, the price variables should be 1st-differenced to secure the stability of the prices. Next, through the Johansen co-integration test, it was discovered that there are long-term relationships among the price variables. On the basis of the co-integration test, VECM analysis shows that the producer price has a long-run balance with the import and wholesale prices. In particular, when the prices deviate from the balance, the producer price dynamically adjusts to return to the long-term relationship among prices. It also indicates that the producer price has an impact on the import, wholesale, and retail prices in the short-term, and the import price has an influence on the producer and wholesale prices. In addition, the impulse response analysis demonstrates that the impulse of import and producer prices has a lasting impact on each of the prices.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.1C
• /
• pp.37-47
• /
• 2009

When H.264 encoded video streams are transmitted over wireless network, packet loss is unavoidable. Responding on this environment, we propose methods to recover missed motion vector in the decoder: At first, A candidate vector set for missing macroblock is estimated from high correlation coefficient of neighboring motion vectors and missing block vectors the algorithm clusters candidate vectors through distances amongst motion vectors of neighboring blocks. Then the optimal candidate vector is determined by the median value of the clustered motion vector set. In next stage, from the candidate vector set, the final candidate vector of missing block is determined it has minimum distortion value considering directions of neighboring pixels' boundary. Test results showed that the proposed algorithm decreases the candidate motion vectors $23{\sim}61%$ and reduces $3{\sim}4sec$ on average processing(decoding) time comparing the existing H.264 codec. The PSNR, in terms of visual quality is similar to existing methods.

• The Journal of Industrial Distribution & Business
• /
• v.9 no.5
• /
• pp.63-72
• /
• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.708-713
• /
• 1994

A new flux observer based vector control system of an induction motor is constructed by using an observer in which the commanded stator currents are used to estimate the rotor flux. In this system, the flux observer is formulated by using a model of induction motor in a stationary coordinate system. By considering an observer of induction motor in a fixed co-ordinate system located on its secondary flux, a slip frequency controlled type of vector control system is also proposed. From these control schemes, the relation between the conventional slip frequency controlled type system and the observer based one is clarified. The steady-state error of the developed torque which is caused by the parameter change of induction motor is analyzed and discussed for the selection of observer gains. The poles of the observer error dynamics and those of the observer based vector control system are calculated analytically by neglecting the machine parameter change. In order to analyze the robust stability, a linear model of the observer based vector control system is proposed taking into account the machine parameter change. By using this model, the trajectories of the poles and zeros of the torque transfer function are computed and discussed. To test validity of the theoretical analysis, experiments are conducted by using a digital signal processor (TMS320C30) and a current controlled voltage source PWM inverter.