Impact of differential-itemfunctioning on the personal epistemological beliefs for senior high school students

The purpose of this study is to investigate whether the expression of items used to examine personal epistemological beliefs could affect the probabilities of response for compared groups and research outcomes. Differential-item-functioning (DIF) analysis on school types and location of school according to the Rasch model was performed in this study. Nonacademically inclined school students (n = 212) and academically inclined school students (n = 197) were selected to complete the questionnaire. The questionnaire consisted of three dimensions of beliefs about knowing and two dimensions of beliefs about learning. The results of the DIF analysis suggested that the items in the dimensions about knowing favor academically inclined students and students from schools located in the northern areas of Taiwan. The items in the learning dimensions favor nonacademically inclined students and students from schools located in the southern areas of Taiwan. The group comparisons were different between the scale that included the DIF items and the scale that excluded the DIF items. The discussion addresses the value of the precise detections for inappropriate items and the effect of the academic achievement on completing the survey

A current-controlled oscillator with temperature, voltage, and process compensation

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A computer vision assisted system for autonomous forklift vehicles in real factory environment

[[abstract]]Industry 4.0 is an important trend in factory automation nowadays. Among the Automated-Storage-and-Retrieval-System (ASRS) is one of the most important issues for industry. It is widely used in a variety of industries for a variety of storage applications in factories and warehouses. However, the cost of constructing an ASRS is so high that most small/medium enterprises cannot afford it. A forklift system is a cheaper alternative to a complicated ASRS. In this work, a new pallet detection method that uses an Adaptive Structure Feature (ASF) and Direction Weighted Overlapping (DWO) ratio to allow forklifts to pick up a pallet is proposed, using a monocular vision system on the forklift. Combining the ASF and DWO ratio for pallet detection, the proposed method removes most of the non-stationary (dynamic) background and significantly increases the processing efficiency. A Haar like-based Adaboost scheme uses an AS for pallets algorithm to detect pallets. It detects the pallet in a dark environment. Finally, by calculating the DWO ratio between the detected pallets and tracking records, it avoids erroneous candidates during object tracking. Therefore, this work improves the pallet detection to solve the problem with an effective design. As results show that the hybrid algorithms that are proposed in this work increase the average pallet detection rate by 95 %.[[notice]]補正完