Earticle

This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot’ posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot’ posture and payload change. That’ why the moments ,  and  working on every joint of a robot vary with robot’ posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot’ posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot’ posture and payload through neural network learning.

Lee weight is more appropriate for some practical situations than Hamming weight as it takes into account magnitude of each digit of the word. In this paper, we obtain a sufficient condition over the number of parity check digits for codes correcting random errors and simultaneously detecting burst errors with Lee weight consideration.

In [1], Sharma and Goel obtained a bound for random error correcting codes with Lee weight considerations. The purpose of this paper is to first point out a discrepancy in this result and then give a correct version of the same, improving upon the bound tremendously.

When a control chart signals that a special cause is present, process engineers must initiate a search for and an identification of the special cause. Knowing the time of the process change could lead to identify the special cause more quickly, and to take the appropriate actions immediately to improve quality. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the parameters of a process in which the observations can be modeled as a first-order autoregressive(AR(1)) process plus an additional random error.

When heteroscedasticity occurs in random effects linear model, the error variance may depend on the values of one or more of the explanatory variables or on other relevant quantities such as time or spatial ordering. In this paper we derive a score test as a diagnostic tool for detecting non-constant error variance in random effefts linear model based on the model expansion on error variance. This score test is compared to loglikelihood ratio test.

Statistics are proposed to assist in choosing the number of trees in random forests for classification, one of which is very useful in monitoring of the out-of-bag (OOB) error rate. These statistics are used sequentially as the forest is constructed and are computed from OOB votes or test set votes. They provide a measure of the expected disagreement between the current forest and the limiting forest with infinitely many trees. Examples and simulation studies of the statistic for monitoring of the OOB error rate are provided to illustrate the performance of these statistics.

In this paper, a detailed investigation of the random vibration test is presented for strapdown inertial navigation systems (INS). The effect of the random vibration test has been studied from the point of view of navigation performance. The role of distribution functions and RMS value is represented to determine a feasible method to reject or reduce vibration related error in position and velocity estimation in inertial navigation. According to a survey conducted by the authors, this is the first time that the effect of the distribution function in vibration related error has been investigated in random vibration testing of INS. Recorded data of navigation grade INS is used in offline static navigation to examine the effect of different characteristics of random vibration tests on navigation error.

This paper presents a robust optimal design method for a periodic structure type of MEMS resonator that is vulnerable to mode localization. The robust configuration of such a MEMS resonator to fabrication error is implemented by changing the regularity of periodic structure. For the mathematical convenience, the MEMS resonator is first modeled as a multi pendulum system. The index representing the measure of mode variation is then introduced using the perturbation method and the concept of modal assurance criterion. Finally, the optimal intentional mistuning, minimizing the expectation of the irregularity measure for each substructure, is determined for the normal distributed fabrication error and its robustness in the design of MEMS resonator to the fabrication error is demonstrated with numerical examples.

안드로이드 앱의 인텐트 취약점을 테스트하는 인텐트 퍼저에서 에러 확인 방법을 효율적으로 개선한 새로운 아이디어를 제안한다. 인텐트 퍼저는 랜덤 인텐트를 생성하여 앱을 실행한 다음 앱이 비정상 종료되는지 확인하는 테스트 도구이다. 이 논문에서 동일한 에러로 인해 발생한 다수의 비정상 종료 로그들을 하나의 그룹으로 만드는 자동 분류 방법을 제안한다. 테스터는 각 그룹의 대표 로그만 확인하면 된다. 최장 공통 부분 수열을 구하는 알고리즘을 응용하여 이 방법을 설계하였고, 이 방법을 상용 안드로이드 앱 10개에 적용해 실험하였다. 모든 로그를 분석하는 대신 대표 로그를 분석하는 것으로 대체할 수 있음을 확인하였다. 그 결과 분석 대상 로그의 수가 크게 줄었다.