Lattice dynamical wavelet neural networks implemented using particle swarm optimisation for spatio-temporal system identification

Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNN), is introduced for spatiotemporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimisation (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the orthogonal projection pursuit algorithm, significant wavelet-neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated waveletneurons are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be redundant. In the second stage, an orthogonal least squares (OLS) algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet-neurons from the network. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet-neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework

Web-enabled, Real-time, Quality Assurance for Machining Production Systems

AbstractIn order to maintain the close control of production quality, frequent measurement and process parameter adjustments are desirable. In the discrete parts industry, part inspection is intended to be a metric for the process quality but quality control is typically done long after the part has been machined. The long latency between machining and quality assessment makes it difficult to incorporate quality feedback into production. Quality assurance relies on continuous real–time quality feedback, which is not a complex concept. However, the collection and representation of the necessary process data and quality measurement data is challenging. This paper discusses Web-enabled, real-time quality data and statistics based on the integration of two manufacturing open specifications: MTConnect and Quality Measurement Results (QMResults). A pilot implementation that integrates the two technologies and produces Web-enabled, real-time quality results in a standard XML representation from Computer Numerical Control (CNC) machine tool inspections will be discussed

Comment on ``Local dimer-adatom stacking fault structures from 3x3 to 13x13 along Si(111)-7x7 domain boundaries''

Zhao et al. [Phys.Rev.B 58, 13824 (1998)] depicted several atomic structures of domain boundaries on a Si(111) surface and criticized the article by the present author and the co-workers. I will point out that their criticism is incorrect and their structure models have no consistency.Comment: 2 pages. Physical Review B, to appea

Development of a multiplex event-specific PCR assay for detection of genetically modified rice

Global rice supplies have been found contaminated with unapproved varieties of genetically modified (GM) rice in recent years, which has led to product recalls in several of countries. Faster and more effective detection of GM contamination can prevent adulterated food, feed and seed from being consumed and grown, minimize the potential environmental, health or economic damage. In this study, a simple, reliable and cost-effective multiplex polymerase chain reaction (PCR) assay for identifying genetic modifications of TT51-1, Kemingdao1 (KMD1) and Kefeng6 (KF6) rice was developed by using the event-specific fragment. The limit of detection (LOD) for each event in the multiplex PCR is approximately 0.1%. Developed multiplex PCR assays can provide a rapid and simultaneous detection of GM rice

Cosmological models with linearly varying deceleration parameter

We propose a new law for the deceleration parameter that varies linearly with time and covers Berman's law where it is constant. Our law not only allows one to generalize many exact solutions that were obtained assuming constant deceleration parameter, but also gives a better fit with data (from SNIa, BAO and CMB), particularly concerning the late time behavior of the universe. According to our law only the spatially closed and flat universes are allowed; in both cases the cosmological fluid we obtain exhibits quintom like behavior and the universe ends with a big-rip. This is a result consistent with recent cosmological observations.Comment: 12 pages, 7 figures; some typo corrections; to appear in International Journal of Theoretical Physic

Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154530/1/bjd18026.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154530/2/bjd18026_am.pd