• The Korean Journal of Applied Statistics
• /
• v.19 no.2
• /
• pp.305-317
• /
• 2006

Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.1
• /
• pp.302-320
• /
• 2017

In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

• Korean Journal of Agricultural Science
• /
• v.42 no.1
• /
• pp.53-61
• /
• 2015

Development of highly efficient and energy-saving tractors has been one of the issues in agricultural machinery. For design of such tractors, measurement and analysis of load on major power transmission parts of the tractors are the most important pre-requisite tasks. Objective of this study was to perform pre-processing procedures before effective analysis of load data of agricultural tractors (30, 75, and 82 kW) during major field operations such as plow tillage, rotary tillage, baling, bale wrapping, and to select the suitable pre-processing method for the analysis. A load measurement systems, equipped in the tractors, were consisted of strain-gauge, encoder, hydraulic pressure, and radar speed sensors to measure torque and rotational speed levels of transmission input shaft, PTO shaft, and driving axle shafts, pressure of the hydraulic inlet line, and travel speed, respectively. The entire sensor data were collected at a 200-Hz rate. Plow tillage, rotary tillage, baling, wrapping, and loader operations were selected as major field operations of agricultural tractors. Same or different farm works and driving levels were set differently for each of the load measuring experiment. Before load data analysis, pre-processing procedures such as outlier removal, low-pass filtering, and data division were performed. Data beyond the scope of the measuring range of the sensors and the operating range of the power transmission parts were removed. Considering engine and PTO rotational speeds, frequency components greater than 90, 60, and 60 Hz cut off frequencies were low-pass filtered for plow tillage, rotary tillage, and baler operations, respectively. Measured load data were divided into five parts: driving, working, implement up, implement down, and turning. Results of the study would provide useful information for load characteristics of tractors on major field operations.