Design for CNN Templates with Performance of Global Connectivity Detection

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectivitydetection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing thecorresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binarypatterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns isgiven.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.

Re-scaling and adaptive stochastic resonance as a tool for weak GNSS signal acquisition

Weak global navigation satellite system（GNSS） signal acquisition has been a limitation for high sensitivity GPS receivers. This paper modifies the traditional acquisition algorithms and proposes a new weak GNSS signal acquisition method using re-scaling and adaptive stochastic resonance（SR）. The adoption of classical SR is limited to low-frequency and periodic signals. Given that GNSS signal frequency is high and that the periodic feature of the GNSS signal is affected by the Doppler frequency shift, classical SR methods cannot be directly used to acquire GNSS signals. Therefore, the re-scaling technique is used in our study to expand its usage to high-frequency signals and adaptive control technique is used to gradually determine the Doppler shift effect in GNSS signal buried in strong noises. The effectiveness of our proposed method was verified by the simulations on GPS L1 signals. The simulation results indicate that the new algorithm based on SR can reach-181 d BW sensitivity with a very short data length of 1 ms.

LLSDA: Design and Implementation of Lightning Location Data Analysis and Visualization

Visualizing lightning location data is necessary in analyzing and researching lightning activity patterns.This article uses C#and the cross-platform.NET framework to develop a lightning location data analysis class library and the data-driven client to help lightning researchers improve work efficiency by avoiding repeated wheel invention.Lightning Location System Data Analyzer(LLSDA)is a suite of software tools that includes a.NET class library for software developers and a desktop application for end users.It supports a wide range of lightning location data formats,such as the University of Washington Global Lightning Location System(WWLLN)and Beijing Huayun Dongfang ADTD Lightning Location System data format,and maintains scalability.The class library can easily read,parse,and analyze lightning location data,and combined with third-party frameworks can realize grid analysis.The desktop application can be combined with MeteoInfo(a GIS open-source project)for secondary development.