Buckling Optimization of Curved Grid Stiffeners through the Level Set Based Density Method

Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.

A survey of density based clustering algorithms

Density based clustering algorithms(DBCLAs)rely on the notion of density to identify clusters of arbitrary shapes,sizes with varying densities.Existing surveys on DB-CLAs cover only a selected set of algorithms.These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms.In this paper we present a comprehensive survey of various DB-CLAS over last two decades along with their classification.We group the DBCLAs in each of the four categories:density definition,parameter sensitivity,execution mode and nature of*data and further divide them into various classes under each of these categories.In addition,we compare the DBCLAs through their common features and variations in citation and conceptual dependencies.We identify various application areas of DBCLAS in domains such as astronomy,earth sciences,molecular biology,geography,multimedia.Our survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.

A Novel Method of Deinterleaving Radar Pulse Sequences Based on a Modified DBSCAN Algorithm

A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the pulses efficiently.Other PDW information such as rise time,carrier frequency,pulse width,modulation on pulse,fall time and direction of arrival are not required.To identify the valid PRIs in a set of interleaved pulses,an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement.The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF.Furthermore,without specifying any input parameter,the proposed method can deinterleave radar pulses while up to 30%jitter is present in the associated PRI.The accuracy and efficiency of the proposed method are verified by computer simulations and real data results.Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered.So,the simulation results can be of practical significance.

An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

Radar false alarm plots elimination based on multi-feature extraction and classification

Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.

Geoacoustic inversion based on reflection model of effective density fluid approximation

In order to obtain the physical and geoacoustic properties of marine sediments,an inverse method using reflection loss of different grazing angles is presented.The reflection loss is calculated according to the reflection model of effective density fluid approximation.A two-step hybrid optimization algorithm combining differential evolution and particle swarm optimization along with Bayesian inversion is employed in estimation of porosity,mean grain size,mass density and bulk modulus of grains.Based on the above physical parameters,geoacoustic parameters,including sound speed and attenuation,are further calculated.According to the numerical simulations,we can draw a conclusion that all the parameters can be well estimated with the exception of bulk modulus of grains.In particular,this indirect inverse method for bottom geoacoustic parameters performs high accuracy and strong robustness.The relative errors are 0.092%and 17%,respectively.Finally,measured reflection loss data of sandy sediments at the bottom of a water tank is analyzed,and the estimation value,uncertainty and correlation of each parameter are presented.The availability of this inverse method is verified through comparison between inverse results and part of measured parameters.

A novel tin-graphite dual-ion battery based on the sodium-ion electrolyte with high energy density

Subject Code:E02With the support by the National Natural Science Foundation of China and the Chinese Academy of Sciences,the research team led by Prof.Tang Yongbing(唐永炳)at the Functional Thin Films Research Center,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,developed a novel tin-graphite dual-ion battery based on sodium-ion electrolyte with high energy density,which

Doping Effect of Poly(vinylidene fluoride)on Carbon Nanofibers Deduced by Thermoelectric Analysis of Their Melt Mixed Films

The effect of temperature on the electrical conductivity(σ)and Seebeck coefficient(S)of n-type vapor grown carbon nanofibers(CNFs)and poly(vinylidene fluoride)(PVDF)melt-mixed with 15 wt%of those CNFs is analyzed.At 40°C,the CNFs show stable n-type character(S=-4.8μV·K^(-1))with anσof ca.165 S·m^(-1),while the PVDF/CNF composite film shows anσof ca.9 S·m^(-1)and near-zero S(S=-0.5μV·K^(-1)).This experimental reduction in S is studied by the density functional tight binding(DFTB)method revealing a contact electron transfer from the CNFs to the PVDF in the interface.Moreover,in the temperature range from 40°C to 100°C,theσ(T)of the CNFs and PVDF/CNF film,successfully described by the 3D variable range hopping(VRH)model,is explained as consequence of a thermally activated backscattering mechanism.On the contrary,the S(T)from 40°C to 100°C of the PVDF/CNF film,which satisfactorily matches the model proposed for some multi-walled carbon nanotube(MWCNT)doped mats;however,it does not follow the increase in S(T)found for CNFs.All these findings are presented with the aim of discerning the role of these n-type vapor grown carbon nanofibers on theσand S of their melt-mixed polymer composites.

Identification and characterization of irregular consumptions of load data

The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore,identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.