Earticle

본 논문에서는 OFDM 시스템에서 임펄스 신호를 이용한 채널 추정방법을 제안한다. 채널 응답을 추정하기 위해 주파수 영역에서 새로운 프리엠블을 사용하여 시간영역에서 16샘플 간격으로 4개의 구간으로 나누고, 각 구간에서 임펄스 응답이 나오도록 한다. 이 임펄스 신호들은 한 OFDM 심볼동안에 전송한다. 수신단에서, 각 구간별 임펄스 신호를 더하여 평균을 취한다. 평균된 임펄스 응답에서 최대지연 이후 데이터는 잡음이기 때문에 zero padding을 하여 제거하고 FFT를 수행하여 채널 추정 응답을 찾아낸다. 제안된 채널 추정방법을 이용한 WLAN 시스템의 BER성능은 IEEE802.11a에서 사용하는 긴 훈련신호(long preamble)를 이용한 기존의 채널 추정 방법에 비해 약 3dB 성능 향상을 보인다.

In this paper, we propose a channel estimation method by impulse signal train in OFDM. In order to estimate the channel response, 4 impulse signals are generated and transmitted during one OFDM (Orthogonal Frequency Division Multiplexing) symbol. The intervals between the impulse signals are all equal in time domain. At the receiver, the impulse response signals are summed and averaged. And then, the averaged impulse response signal is zero padded and fast Fourier transformed to obtain the channel estimation. The BER performance of the proposed method is compared with those of conventional estimation method using the long training sequence in fast fading environments. The simulation results show that the proposed method improves by 3 dB in terms of Eb/No, compared with the conventional method.

Loan default evaluation and discrimination is a complicated issue because of its nonlinearity and uncertainty. Least square support vector machine (LS-SVM) has been successfully employed to solve regression and time series problem. This paper proposes a novel PSO-LS-SVM model based on the improved PSO algorithm to optimize parameters of LS-SVM, which is a new improved form by synthesizing the exiting model of PSO. Some evaluation indices, which are reduced without information loss by a genetic algorithm, are used to train PSO-LS-SVM and discriminate between healthy and default testing samples. A case study based on financial data acquired from listed companies has been carried out. Result has shown that the proposed model has a distinct improvement in the aspect of accuracy rate as compared to PSO-SVM, LS-SVM, SVM and BP neural network.

Least squares support vector machine(LS-SVM) is a well known and useful machine learning ways for statistical classification and regression analysis. LS-SVM can be a good substitute for traditional statistical method but computational difficulties are still remained to operate the inversion of matrix of huge data set. In modern information society, we can easily obtain a large data sets by on-line or batch mode. For the analysis of these kind of huge data sets, we suggest an on-line pruning regression method based on LS-SVM. With relatively small number of pruned support vectors, we can have almost same performance as regression with full data set.

In this paper the feature of background EEG is extracted by LS Prony Method for the analysis of background EEG in the time domain. From the experimential results the alpha band amplitude is the largest among bands and beta band amplitude is larger than that of the delta band and theta band. The sustained time for the alpha band, the beta band, the delta band and the theta band is 2.3461(sec), 1.8980(sec), 0.3120(sec), 0.2930(sec) respectively. Consequently the alpha band and the beta band are maintained in the whole, segment. The delta band, the theta band are existed intermittently in the segment.

This paper analyzes the method of cascading 74LS163 4-Bit Binary Counters. The 74LS163 4-Bit Binary Counter has synchronous LD. CLR functions and especially ENT, ENP, RCO to cascade some chips in order to count more 4bit binary number. The maximum operating frequency may vary according to the method of cascading. The Data sheet from Texas Instruments introduces two methods, The Ripple Carry Mode Circuit and The Carry Look Ahead Circuit, and shows that The Carry Look Ahead Circuit is more efficient than the other. However, there are only little information for user to understand and apply this to other circuits. Thus, we not only analyzed the two methods but also compared with each other in the point of performance.

This paper describes a method for identifying the dynamic characteristics of air foil bearings for high speed turbomachinerys with the LS/IV method. In fact identifying the characteristics of air foil bearings is very difficult work, and it is tried to identify it. Experiments were conducted to determine the structural dynamic and hydrodynamic characteristics of air foil bearings. Numerical predictions compare the static and dynamic force performances. The housing of the bearing on the journal was driven by the impact hammer which were used to simulate impact force acting on air foil bearings. The characteristics of air foil bearings were extracted from the frequency response function (FRF) by LS(Least Square) method and IV(Instrumental Variable) method. The experiment was tested at 0 rpm and $10000\sim16000rpm$. And the test results were introduced about the dynamic characteristics of air foil bearings, and also compared with theoritical results.

본 논문은 현재 고성능 가역압축 부호화의 표준이 되는 JPEG-LS의 예측오차에 대한 확률분포를 결정하는 컨텍스트 모델링(context modelling)에서 컨텍스트 정수의 결정 방법에 관해 기술한다. 먼저, 압축률 향상을 위해 낮은 대비(low contrast)를 적용하는 전처리 과정을 도입한다. 둘째로, 낮은 복잡도(low complexity)를 위해 주목화소와 주변화소 사이의 차분을 양자화하고, 그 값 Q$_1$,Q$_2$, Q$_3$로부터 컨텍스트 정수(Q)의 결정을 위해 우선 순위를 정한 후, 3차원 가상좌표 위로의 할당을 고려한다. 또한, 제안 방법과 JPEG-LS의 near-lossless부분을 비교하여, 다양한 영상에 대하여 적용시켜 본 결과, 제안한 방법의 압축률과 손실이득이 훨씬 좋음을 보인다.

Conentionally, LS factor for the USLE suggested by Wischmeier has been computed manually on topographic maps based on one dimensional approach. But outcomes of the equation could be severely affected by the convergence and divergence of surface runoff at complex terrains. Thus the objective of this research are to develop a method to automatically compute LS factor based on the multiple flow algorithm, and to test the accuracy of this method by comparing outcomes of this method to previous measurements or estimations of soil erosion. The program for the automatic calculation of LS factor was developed by utilizing Fox Pro 4.5, and outcomes of the program is designed to input to IDRISI. The accuracy test of LS factor was carried out by comparing the actual measurements of soil loss at two test sites in and around of Suwon. The calculated volume of soil erosion at Buju mountain, Mokpo, was also compared to the outcome of a previous research based on the LS factor calculated by the conventional onedimensional approach. The outcomes of this research are as follows. First, the computed L based on the multiple flow algorithm for concae slopes are greater than those of convex slopes,. Second, the estimated soil loss based on this method at the test site in Mokpo is much greater than the outcomes based on the conventional one-dimensional approach. It can e concluded that the application of this automatic calculation method of LS factor can improve the accuracy of USLE and facilitate soil erosion prevention methods.