NUMERICAL STUDIES FOR A MODEL DESCRIBING COMPLEXITY

A simple model based on the discussion for infinite dimensional system is introduced to investigate the dynamical complexity for continous system.By using numerical. methods,we show the dynamical behaviors of the maodel model appear tocorrespond to universal language and context-senitive language.

A new class of spectrally arbitrary complex sign pattern

Assume that S is an nth-order complex sign pattern.If for every nth degree complex coefficient polynomial f(λ)with a leading coefficient of 1,there exists a complex matrix C∈Q(S)such that the characteristic polynomial of C is f(λ),then S is called a spectrally arbitrary complex sign pattern.That is,if the spectrum of nth-order complex sign pattern S is a set comprised of all spectra of nth-order complex matrices,then S is called a spectrally arbitrary complex sign pattern.This paper presents a class of spectrally arbitrary complex sign pattern with only 3n nonzero elements by adopting the method of Schur complement and row reduction.

Complex bursting patterns in Van der Pol system with two slowly changing external forcings

This paper investigates the generation of complex bursting patterns in Van der Pol system with two slowly changing external forcings. Complex bursting patterns, including complex periodic bursting and chaotic bursting, are presented for the cases when the two frequencies are commensurate and incommensurate. These complex bursting patterns are novel and have not been reported in previous work. Based on the fast-slow dynamics, the evolution processes of the slow forcing are presented to reveal the dynamical mechanisms undedying the appearance of these complex bursting patterns. With the change of ampli- tudes and frequencies, the slow forcing may visit the spiking and rest areas in different ways, which leads to the generation of different complex bursting patterns.

Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains.One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment.In this work,we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum（MPS） through a multi-scale morphology analysis procedure.The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves.Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

Proactive Personalized Services Through Fog-Cloud Computing in Large-Scale IoT-Based Healthcare Application

With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing(CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named Fog Cep Care. Experimental result shows that Fog Cep Care is superior to the traditional IoT-based healthcare application.

PROBE:NOISE-AND-ROTATION RESISTANCE OF HOPFIELD NEURAL NETWORK IN IMAGED TRAFFIC SIGN RECALL

This paper examines the noise and rotation resistance capacity of Hopfield Neural Network （HNN） given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio （SNR）, recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.

Pitch perception of harmonic complex tones based on excitation patterns

Fundamental frequency difference limens were measured to study whether pitch perception of medium-rank harmonic complex tones depends on the resolvability of the compo- nents and to study the effect of masker tone on discrimination performance. Target tone was presented alone, or mixed with the masker, which were filtered into the same bandpass frequency region （low, medium, or high） to obtain different resolvability. There were five kinds of funda- mental frequency difference and four kinds of phase combination between target and masker. Five young subjects participated in experiments, all of whom had normal hearing （thresholds ≤ 15 dB HL）. Results found fundamental frequency difference limens were increased with up-shift frequency region of the harmonics. The fundamental frequency difference between target and masker had a significant impact on the performance, while phase effects were small. Analysis suggested that resolvability of harmonics had a significant impact on the fundamental frequency difference limens, but pitch perception of medium-rank harmonics was not based on the resolv- ability. Analysis also suggested that most results of pitch perception of target-masker mixture were closely correlated with peaks on the excitation patterns.

Symptom clustering in chronic gastritis based on spectral clustering

OBJECTIVE: Apply spectral clustering to analyze the patterns of symptoms in patients with chronic gastritis(CG).METHODS: Based on 919 CG subjects, we applied mutual information feature selection to choose the positively correlated symptoms with each pattern.Then, we used the Shi and Malik spectral clustering algorithm to select the top 20 correlated symptoms.RESULTS: We ascertained the results of six patterns.There were three categories for the pattern of accumulation of damp heat in the spleen-stomach(0.00332). There were six categories for the pattern of dampness obstructing the spleen-stomach(0.02466). There were two categories for the pattern of spleen-stomach Qi deficiency(0.013 89).There were three categories for the pattern of spleen-stomach deficiency cold(0.009 15). There were five categories for the pattern of liver-Qistagnation(0.01910).There were four categories for the pattern of stagnant heat in the liver-stomach(0.00585).CONCLUSION: Most of the spectral clustering results of the symptoms of CG patterns were in accordance with clinical experience and Traditional Chinese Medicine theory. Most categories suggested the nature and/or location of the disease.