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All Journal JABM JOURNAL of ACCOUNTING - BUSINESS & MANAGEMENT Indonesian Journal of Electrical Engineering and Computer Science
Jun Sun
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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African-American Accountants Then and Now: A Longitudinal Study of Factors Influencing Perceptions of the Workplace Tammi C. Redd; Glen D. Moyes; Jun Sun
Journal of Accounting, Business and Management (JABM) Vol 18 No 2 (2011): October
Publisher : STIE Malangkucecwara

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This study addresses the question of whether African-American accounting professionals perceive levels of job satisfaction and other work attributes differently over time as they gain experience in accounting practice. We examine how the elements that influence job satisfaction and perceptions of the workplace have changed over time for these accounting professionals. We contend that the evolution of the workforce and work itself have set forth contemporary workplace attitudes challenging Herzbergs (1959, 1966) Two-Factor Theory. Archival and newly collected data are combined to generate a longitudinal perspective on the African-Americans perception of job satisfaction and other work attributes specific to the field of accounting. The results reveal significant increases in the level of workload job stress and the overall level of job satisfaction, countered with significant decrease in professional-family conflict and discrimination applied to promotion.
A New Image Segmentation Algorithm and It’s Application in lettuce object segmentation Jun Sun; Yan Wang; Xiaohong Wu; Xiaodong Zhang; Hongyan Gao
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 3: July 2012
Publisher : Institute of Advanced Engineering and Science

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Lettuce image segmentation which based on computer image processing is the premise of non-destructive testing of lettuce quality. The traditional 2-D maximum entropy algorithm has some faults, such as low accuracy of segmentation, slow speed, and poor anti-noise ability. As a result, it leads to the problems of poor image segmentation and low efficiency. An improved 2-D maximum entropy algorithm is presented in this paper. It redistricts segmented regions and furtherly classifies the segmented image pixels with the method of the minimum fuzzy entropy, and reduces the impact of noise points, as a result the image segmentation accuracy is improved. The improved algorithm is used to lettuce object segmentation, and the experimental results show that the improved segmentation algorithm has many advantages compared with the traditional 2-D maximum entropy algorithm, such as less false interference, strong anti-noise ability, good robustness and validity. DOI: http://dx.doi.org/10.11591/telkomnika.v10i3.618
Co-Authors Glen D. Moyes Hongyan Gao Tammi C. Redd Xiaodong Zhang Xiaohong Wu Yan Wang