Effects of organizational and professional identification on the relationship between administrators’ social influence and professional employees’ adoption of new work behavior.

Administrative social influence is a principal tool for motivating employee behavior. The authors argue that the compliance of professional employees (e.g., doctors) with administrative social influence will depend on the degree to which these employees identify with their profession and organization. Professional employees were found to be most receptive to administrator social influence to adopt new work behavior when they strongly identified with the organization and weakly identified with the profession. In contrast, administrator social influence was counterproductive when professional employees strongly identified with the profession and weakly identified with the organization. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Fault-tolerant sensor systems using evolvable hardware

This paper describes a system that is robust with respect to a sensor failure. The system utilizes multiple sensor inputs (three in this case) connected to a programmable device that averages the outputs from the sensors. The programmable device is programmed using evolvable hardware techniques. If one or more of the input sensors fail, then the controller detects the failure and initiates a reprogramming of the circuit. The system then continues to operate with a reduced number of sensors. The failure detection is accomplished by comparing the actual system output with a Kalman-filter estimate of the output. If the actual output and the filter estimate differ by an amount greater than the system uncertainty, then a failure is noted. The system is robust to several different failure modes: sensor fails as open circuit, sensor fails as short circuit, partial failures, multiple sensors fail, field programmable analog array input amplifier failure. This paper describes the experimental setup as well as results using actual temperature sensors. For all failure types, the system was able to recover to within 2% of the target value.     

Asymptotic Dynamic Time Warping calculation with utilizing value repetition

Dynamic Time Warping (DTW) is a popular method for measuring the similarity of time series. It is widely used in various domains. A major drawback of DTW is that it has a high computational complexity. To address this problem, pruning techniques to calculate the exact DTW distance, as well as DTW approximation methods, have become important approaches. In this paper, we introduce Blocked Dynamic Time Warping (BDTW), a new similarity measure which works on run-length encoded time series representation. BDTW utilizes any repetitive values (zero and nonzero) in time series to reduce DTW computation time. BDTW closely approximates DTW distance, and it is significantly faster than traditional DTW for time series with high levels of value repetition. Moreover, BDTW can be combined with time series representation methods which provide constant segments, to serve as a close approximation method even for the time series without value repetition. Constrained BDTW, BDTW upper bound and BDTW lower bound are discussed as variations of BDTW. BDTW upper bound and BDTW lower bound are presented as a new DTW upper bound and lower bound which can be efficiently applied on time series with high levels of value repetition for pruning unhopeful alignments and matches in the exact DTW calculation. We show the effectiveness of BDTW and its variations on different applications using the following datasets: Almanac of Minutely Power, Refit Smart Homes, as well as the 85 datasets from the University of California, Riverside time series classification archive (UCR archive).