New Structural Self-Organizing Fuzzy CMAC with Basis Functions

To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC with Gauss basis functions(GFCMAC) was presented. Moreover, based upon the improvement of the self organizing feature map algorithm of Kohonen, the structural self organizing algorithm for GFCMAC(SOGFCMAC) was proposed. Simulation results show that adopting the Gauss basis functions and fuzzy techniques can remarkably improve the nonlinear approximating capacity of CMAC. Compared with the traditional CMAC,CMAC with general basis functions and fuzzy CMAC(FCMAC), SOGFCMAC has the obvious advantages in the aspects of the convergent speed, approximating accuracy and structural self organizing.

APPLICATION OF FUZZY LOGIC AND SELF-ORGANIZING NETWORK TO TOOL-WEAR CLASSIFICATION

A tool-wear monitoring system for metal turning operations is presented based on the combinative application of fuzzy logic and unsupervised neural network. A group of self-organizing map （SOM） neural networks is established based on the typical cutting condition combinations, and each of networks is corresponding to a typical cutting condition. For a specifie cutting condition, the fuzzy logic method is used to select an optimum trained SOM network. The proposed monitoring system, ealled the Fuzzy-SOM-TWC, is used to classify tool states based on the in-time measurement of force, aeoustic emission（AE）, and motor eurrent signals. An approximate 98%--100% correct classification of tool-wear status is obtained by testing the system with a series data samples under freely selected cutting conditions.

A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique

Determination of homogenous precipitation-based regions is a very important task in effective management of water resources. The present study tried to propose an effective precipitation-based regionalization methodology by conjugating both temporal pre-processing and spatial clustering approaches in a way to take advantage of multiscale properties of precipitation time series. Annual precipitation data of 51 years(1960-2010) for 31 rain gauges(RGs) were collected and used in proposed clustering approaches. Discreet wavelet transform(DWT) was used to capture the time-frequency attributes of the time series and multiscale regionalization was performed by using k-means and Self Organizing Maps(SOM) clustering techniques. Daubechies function(db) was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the approximation(A) and detail(D) coefficients were used to determine the input dataset as a basis of spatial clustering. The proposed model's efficiency in spatial clustering stage was verified using three different indexes namely, Silhouette Coefficient(SC), Dunn index and Davis Bouldin index(DB). Results approved superior performance of k-means technique in comparison to SOM. It was also deduced that DWT-based regionalization methodology showed improvements in comparison to historical-based models. Cross mutual information was used to investigate the RGs of cluster 3's homogeneousness in DWT-k-means approach. Results of non-linear correlation approach verified homogeneity of cluster 3. Verifications based on mean annual precipitation values of rain gauges in each cluster also approved the capability of multiscale approach in precipitation regionalization.

Application of Self-Organizing Map for Exploration of REEs’ Deposition

Varieties of approaches and algorithms have been presented to identify the distribution of elements. Previous researches based on the type of problem, categorized their data in proper clusters or classes. This means that the process of solution could be supervised or unsupervised. In cases, where there is no idea about dependency of samples to specific groups, clustering methods (unsupervised) are applied. About geochemistry data, since various elements are involved, in addition to the complex nature of geochemical data, clustering algorithms would be useful for recognition of elements distribution. In this paper, Self-Organizing Map (SOM) algorithm, as an unsupervised method, is applied for clustering samples based on REEs contents. For this reason the Choghart Fe-REE deposit (Bafq district, central Iran), was selected as study area and dataset was a collection of 112 lithology samples that were assayed with laboratory tests such as ICP-MS and XRF analysis. In this study, input vectors include 19 features which are coordinates x, y, z and concentrations of REEs as well as the concentration of Phosphate (P2O5) since the apatite is the main source of REEs in this particular research. Four clusters were determined as an optimal number of clusters using silhouette criterion as well as k-means clustering method and SOM. Therefore, using self-organizing map, study area was subdivided in four zones. These four zones can be described as phosphate type, albitofyre type, metasomatic and phosphorus iron ore, and Iron Ore type. Phosphate type is the most prone to rare earth elements. Eventually, results were validated with laboratory analysis.

Hidden Markov Models and Self-Organizing Maps Applied to Stroke Incidence

Several studies were devoted to investigate the effects of meteorological factors on the occurrence of stroke. Regression models had been mostly used to assess the correlation between weather and stroke incidence. However, these methods could not describe the process proceeding in the back-ground of stroke incidence. The purpose of this study was to provide a new approach based on Hidden Markov Models (HMMs) and self-organizing maps (SOM), interpreting the background from the viewpoint of weather variability. Based on meteorological data, SOM was performed to classify weather patterns. Using these classes by SOM as randomly changing “states”, our Hidden Markov Models were constructed with “observation data” that were extracted from the daily data of emergency transport at Nagoya City in Japan. We showed that SOM was an effective method to get weather patterns that would serve as “states” of Hidden Markov Models. Our Hidden Markov Models provided effective models to clarify background process for stroke incidence. The effectiveness of these Hidden Markov Models was estimated by stochastic test for root mean square errors (RMSE). “HMMs with states by SOM” would serve as a description of the background process of stroke incidence and were useful to show the influence of weather on stroke onset. This finding will contribute to an improvement of our understanding for links between weather variability and stroke incidence.

Complexity and Self-Organized Criticality of Solid Earth System(Ⅰ)

The author puts forward the proposition of Complexity and Self Organized Criticality of Solid Earth System in the light of: (1) the science of complexity studies the mechanisms of emergence of complexity and is the science of the 21st century, (2) the study of complexity of the earth system would be one of the growing points occupying a strategic position in the development of geosciences in the 21st century. By the proposition we try to cogitate from a new viewpoint the ancient yet ever new solid earth system. The author abstracts the fundamental problem of the solid earth system from the essence of the generalized geological systems and processes which reads: the complexity and self organized criticality of the global nature, structure and dynamical behavior of the whole solid earth system emerging from the multiple coupling and superposition of non linear interactions among the multicomponents of the earths material and the multiple generalized geological (geological, geophysical, and geochemical) processes . Starting from this cognizance the author proposes eight major themes and the methodology of researches on the complexity and self organized criticality of the solid earth system.

Formation of Self-organization InAs Quantum Dots on (001) InP Substrate by As/P Exchange Reaction in MOCVD

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Crude Extract of Detergent⁃Like Gleditsia sinensis lam Exhibiting Self⁃Organization for Protection of Mild Steel in Harsh Hydrochloric Acid Solution:How to Seek Crude Natural Plant Extracts as Green Corrosion Inhibitors

This study proposes a thought to employ detergent⁃like renewable low⁃cost crude extract of Gleditsia sinensis lam(GSL)as green corrosion inhibitor for mild steel in HCl solution.Crude Gleditsia sinensis lam extract(GSLE)was gained at mild conditions by simply refluxing in ethanol with a Soxhlet extractor.The target GSLE extract exhibited regular self⁃organization in mixed solvents of organic solvents/H2O such as ethanol/H2 O(v/v,50/50)at room temperature,which was evidenced by different means including scanning electron microscopy,transmission electron microscopy,and dynamic light scattering.The study demonstrates that the yielded assemblies of the crude extract of GSLE displayed chemical adsorption on the studied mild steel sample surfaces.Furthermore,the formed stable crude extract assemblies of GSL presented outstanding anti⁃corrosion capability in 1.0 mol/L HCl aqueous solution based on electrochemical measurements including polarization curves and impedance spectroscopy.It is discovered that the maximal corrosion inhibition efficiency could reach approximate 95%.The molecular modeling was performed to acquire the nature of frontier orbitals of the main representative chemical components of crude GSLE for deep understanding of chemical interactions with iron.The results presented herein would guide us to seek sustainable environmentally friendly low⁃cost detergent⁃like plant crude extracts for corrosion inhibition of various metals in aggressive acid environments.

Metallogenic Districts of Yangtze Cratonic Rim at the Edge of Chaos

Combining the science of complexity with ore geology, the author puts forward a new theory of metallogenesis: “complexity and self organized criticality of metallogenic dynamic systems”, and three fundamental theories are raised for it. The ore genesis and regularity of ore formation of four metallogenic districts around the Yangtze craton in China are studied with this theory. It is found that “metallogenic districts of Yangtze cratonic rim are all at the edge of chaos”. This proposition is expounded by four determinative criteria of the edge of chaos for metallogenic districts of Yangtze cratonic rim.

Improved clustering method based on artificial immune

An improved clustering method based on artificial immune is proposed. To obtain the better initial solution, the initial antibody network is introduced by self organizing map （SOM） method. In the process of the clustering iteration, a series of optimization and evolution strategies are designed, such as clustering satisfaction, the threshold design of scale compression, the learning rate, the clustering monitoring points and the clustering evaluations indexes. These strategies can make the clustering thresholds be quantified and reduce the operator’s subjective factors. Thus, the local optimal and the global optimal clustering simultaneously are proposed by the synthesized function of these strategies. Finally, the experiment and the comparisons demonstrate the proposed method effectiveness.

Morphological self-organizing feature map neural network with applications to automatic target recognition

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Hidden Markov Models to Estimate the Lagged Effects of Weather on Stroke and Ischemic Heart Disease

The links between low temperature and the incidence of disease have been studied by many researchers. What remains still unclear is the exact nature of the relation, especially the mechanism by which the change of weather effects on the onset of diseases. The existence of lag period between exposure to temperature and its effect on mortality may reflect the nature of the onset of diseases. Therefore, to assess lagged effects becomes potentially important. The most of studies on lags used the method by Lag-distributed Poisson Regression, and neglected extreme case as random noise to get correlations. In order to assess the lagged effect, we proposed a new approach, i.e., Hidden Markov Model by Self Organized Map (HMM by SOM) apart from well-known regression models. HMM by SOM includes the randomness in its nature and encompasses the extreme cases which were neglected by auto-regression models. The daily data of the number of patients transported by ambulance in Nagoya, Japan, were used. SOM was carried out to classify the meteorological elements into six classes. These classes were used as “states” of HMM. HMM was used to describe a background process which might produce the time series of the incidence of diseases. The background process was considered to change randomly weather states, classified by SOM. We estimated the lagged effects of weather change on the onset of both cerebral infarction and ischemic heart disease. This fact is potentially important in that if one could trace a path in the chain of events leading from temperature change to death, one might be able to prevent it and avert the fatal outcome.

Mini Review: Chemical Mesoscopics Notions in the Explanation of Polymeric Materials Modification Mechanism with Participation of Metal Carbon Mesocomposites

The paper is dedicated to the consideration of the chemical mesoscopics notions application for the explanation of polymeric materials modification mechanism by the metal carbon mesoscopic composites.The main peculiarities of these nanosized particles are following:a)the presence of unpaired electrons on the carbon cover;b)the structure of carbon cover consists from poly acetylene and carbine fragments;c)the atomic magnetic moment of inner metal is equaled to more than 1,3μB.The metal carbon mesocomposites activity depends on the medium and conditions influence because of the possible changes of the phase coherency and quantization of negative charges.

Self-organization Mechanism during the Bedform Process

This paper investigates the functional and morphological self organization phenomena that occur during the bedform process in river systems. The fluvial process has architecturally functional self organization actions that serve to self adjust the river regime. The bedform (or sand waves) process is part of the functional self organization at the middle level of the geometrical scale. By increasing the resistance of the mobile bed and simultaneously decreasing the capacity carrying sediment, the bedform serves to self adjust the river system. The morphological self organization of the bedform process is the basis for the functional self organization. The concept of the water sand interaction region is suggested, and a nonlinear model is constructed to describe the complex interaction among water flow, bed load transport, and local bed deformation, i e , the sand waves. A numerical simulation was developed based upon this model. The primary results show that the model is able to repeat many important phenomena in the bedform process, especially the critical phase transition.

Persistent variation in spatial behavior affects the structure and function of interaction networks

The function of a network is affected by its structure. For example, the presence of highly interactive individuals, or hubs, influences the extent and rate of information spread across a network. In a network of interactions, the duration over which individual variation in interactions persists may affect how the network operates. Individuals may persist in their behavior over time and across situations, often referred to as personality. Colonies of social insects are an example of a biological system in which the structure of the coordinated networks of interacting workers may greatly influence information flow within the colony, and therefore its collective behavior. Here I investigate the effects of persistence in walking patterns on interaction networks us- ing computer simulations that are parameterized using observed behavior of harvester ants. I examine how the duration of persis- tence in spatial behavior influences network structure. Furthermore, I explore how spatial features of the environment affect the relationship between persistent behavior and network structure. I show that as persistence increases, the skewness of the weighted degree distribution of the interaction network increases. However, this relationship holds only when ants are confined in a space with boundaries, but not when physical barriers are absent. These findings suggest that the influence of animal personalities on network structure and function depends on the environment in which the animals reside [Current Zoology 61 （1）： 98-106, 2015].

Clustering Seismic Activities Using Linear and Nonlinear Discriminant Analysis

Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.

Process Intensifi cation by CO_(2)for High Quality Carbon Nanotube Forest Growth:Double-Walled Carbon Nanotube Convexity or Single-Walled Carbon Nanotube Bowls?

Introduction of CO_(2)is a facile way to tune the growth of vertically aligned double-or single-walled carbon nanotube(CNT)forests on wafers.In the absence of CO_(2),a double-walled CNT convexity was obtained.With increasing concentration of CO_(2),the morphologies of the forests transformed first into radial blocks,and finally into bowl-shaped forests.Furthermore,the wall number and diameter distribution of the CNTs were also modulated by varying the amount of CO_(2).With increasing CO_(2)concentration,CNTs with fewer wall number and smaller diameter were obtained.The addition of CO_(2)is speculated to generate water and serve as a weak oxidant for high quality CNT growth.It can tune the growth rate and the morphologies of the forests,prevent the formation of amorphous carbon,and reduce the wall number of the CNTs.

Synoptic mode of Antarctic summer sea ice superimposed on interannual and decadal variability

In contrast to decreased Arctic sea ice extent,Antarctic sea ice extent shows a somewhat increased trend.There is a large interannual variability of Antarctic sea ice,especially in the Pacific sector of the Southern Ocean.The change and variability of Antarctic sea ice in synoptic timescales in the recent decades remain unclear.We identify synoptic modes of variability of Antarctic summer sea ice by applying the Self Organizing Map(SOM)technique to daily sea ice concentration data for the period 1979–2018.Nearly 40%of the variability is characterized by opposite changes between sea ice cover in the Bellingshausen,Amundsen and western Ross Seas and in the rest of the Antarctic seas,and another 30%by meridional asymmetry in the Weddell,Amundsen,and Ross Seas.Most of these spatial patterns may be explained by the dynamics and thermodynamic processes associated with anomalous atmospheric circulations related to the Southern Annular Mode(SAM)with a structure of strong zonal asymmetry.The interannual variability of the sea ice modes appears to have little connection to SAM,and only a weak relation to ENSO.The annual frequencies of SOM node occurrences also show a great decadal variability.Node 9 appears mainly prior to 1990;while node 1 occurs mainly after 1990.The decadal variability of nodes 1 and 9 is associated with the asymmetrical SAM,which results from two wavetrains excited over northern Australia and the southeastern Indian Ocean.These results further highlight the importance of understanding the role of southern mid-to-high latitude atmospheric intrinsic variability in predicting Antarctic summer sea ice variations from synoptic to decadal timescales.

Mechanism and Patterns of Control for Complex Systems with Human Participation

Many systems, such as transportation systems, the information super highway and urban systems, lie between natural systems and man made systems. These kinds of systems, which have both the self organization phenomena of natural systems and the organization of man made systems, are called complex systems with human participation (CSHP). The mechanism of control for CSHP is essentially the self organization/organization cooperation. This paper discusses the concept of CSHP and the mechanism and patterns of control for CSHP. Finally, a case study of control for transportation systems, the typical CSHP, is given to properly illustrate the mechanism and the serial pattern of control for CSHP.

Competitive Learning Approach to GIS Based Land Use Suitability Analysis

This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms （CLG-LUSA）. It uses Kohonen＇s Self Organ- ized Maps （SOM） and Linear Vector Quantization （LVQ） among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision＇s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a mu- nicipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making.