Yinwei Zhang

Yinwei Zhang

(He/Him)

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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This photo is taken at D.C at March, 2021, during the cherry blossom.

publications

talks

Effective and Efficient Moving Object Detection by a Moving Camera

Published:

Detecting and tracking moving targets with video surveillance systems is challenging, especially when using a moving camera. Conventional algorithms using projective transformation and frame differencing are inaccurate and slow. A new algorithm that combines a type of optical flow and color features is proposed to improve both detection accuracy in and tracking speed. Case studies of a variety of complex scenarios were conducted to demonstrate its effectiveness and efficiency.

Functional Data Analytics for Detecting Bursts in Water Distribution Systems

Published:

It is crucial to develop effective and efficient algorithms to detect bursts in water distribution systems from spatially and temporally correlated hydraulic measurements. Traditional anomaly detection methods based on basis expansion may be applicable, falling short of accurate estimations of burst magnitude and starting time. This research proposes a spatio-temporal decomposition based burst detection method, which estimates the ST profile parameters according to the expected magnitude of spars burst. The effectiveness of the proposed method is demonstrated by a simulation case study.

Investigation of Curing Process Heterogeneity from Raman Spectrum via CP Decomposition

Published:

Monitoring the Raman Spectrum Fluence in the Ultraviolet 3D Curing process is essential in the optical science. Traditional method based on SEM scanning suffers from complexity and time-consuming. Wavelet decomposition based method encounters difficulty in frequency selection and interpretation. This research proposes a novel unsupervised learning algorithm based on CP decomposition, which clusters cured samples via heterogeneous features extracted from Raman spectrum fluence data. The effectiveness of the proposed method is demonstrated by a real case study.

teaching

SIE 406/506 Quality Engineering

Undergraduate/graduate course, The University of Arizona, Systems and Industrial Engineering Dept, 1900

  1. Introduce statistical tools and concepts that are useful for product/process quality improvements
  2. Demonstrate the procedures of implementation of the quality engineering tools in various applications

SIE 433/533 Fundamentals of Data Science for Engineers

Undergraduate/graduate course, The University of Arizona, Systems and Industrial Engineering, 1900

This course will provide senior undergraduate and graduate students from a diverse engineering disciplines with fundamental concepts, principlesand toolsto extract and generalize knowledge from data. Students will acquire an integrated set of skills spanning data processing, statistics andmachine learning,along with a good understanding of the synthesis of these skills and their applications to solving problem. The course is composed of a systematic introduction of the fundamentaltopics of data science study, including: (1) principles of data processing and representation, (2) theoretical basis and advances in data science,(3) modeling and algorithms,and (4) evaluation mechanisms. The emphasis in the treatment of these topics will be given to the breadth, rather than the depth. Real-world engineering problems and data will be used as examples to illustrate and demonstrate the advantages and disadvantages of different algorithms and compare their effectiveness as well as efficiency,and help students to understand and identify the circumstances under which the algorithms are most ap