Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Geological Features and Reservoiring Mode of Shale Gas Reservoirs in Longmaxi Formation of the Jiaoshiba Area

This study is based on the sedimentation conditions, organic geochemistry, storage spaces, physical properties, lithology and gas content of the shale gas reservoirs in Longmaxi Formation of the Jiaoshiba area and the gas accumulation mode is summarized and then compared with that in northern America. The shale gas reservoirs in the Longmaxi Formation in Jiaoshiba have good geological conditions, great thickness of quality shales, high organic content, high gas content, good physical properties, suitable depth, good preservation conditions and good reservoir types. The quality shales at the bottom of the deep shelf are the main target interval for shale gas exploration and development. Shale gas in the Longmaxi Formation has undergone three main reservoiring stages：the early stage of hydrocarbon generation and compaction when shale gas reservoirs were first formed; the middle stage of deep burial and large-scale hydrocarbon generation, which caused the enrichment of reservoirs with shale gas; the late stage of uplift, erosion and fracture development when shale gas reservoirs were finally formed.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Arrhythmia Prediction on Optimal Features Obtained from the ECG as Images

A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid action.The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias.Recent contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal features.Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of arrhythmia.This paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal features.An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.

Research on Feature Extraction and Classification Method of Vibration Signal of Escalator Sprocket Bearing

In order to improve the accuracy of escalator sprocket bearing fault diagnosis,the problem of the feature extraction method of bearing vibration signal is addressed.In this paper,empirical mode is used to decompose the original signal,and the optimal modal component among the multiple modal components is obtained after the optimization decomposition is selected by the envelope spectrum method,and the multi-angle feature measure is introduced to extract the fault characteristic value.According to the vibration characteristics of the bearing vibration signal data,a bearing signal feature group that is more inclined to the fault feature category information is established,which avoids the absolute problem of extracting a single metric feature.The fuzzy C-means clustering algorithm is used to cluster the sample data with similar characteristics into the same cluster area,which effectively solves the problem that a single measurement analysis cannot characterize the complex internal characteristics ofthe bearing vibration signal.

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

A SEMI-OPEN-LOOP CODING MODE SELECTION ALGORITHM BASED ON EFM AND SELECTED AMR-WB+ FEATURES

To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand) semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction) and TCX(Transform Coded eXcitation) classification are investigated.11 classifying features in the AMR-WB+ codec are selected and 2 novel classifying features,i.e.,EFM(Energy Flatness Measurement) and stdEFM(standard deviation of EFM),are proposed.Consequently,a novel semi-open-loop mode selection algorithm based on EFM and selected AMR-WB+ features is proposed.The results of classifying test and listening test show that the performance of the novel algorithm is much better than that of the AMR-WB+ semi-open-loop coding mode selection algorithm.

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.

The geological heritages in Xinjiang, China： Its features and protection

The geological heritage protection and its development at home and abroad reflect the progress in the aspect from the single protection to the coordination between protection and exploitation. The geopark established by UNESCO has closely combined the protection of geological heritages with the promotion of sustainable development of local economy, which has become the best way to protect geological heritages. The geological heritages in Xinjiang, China, are characterized by their large quantity, rich variety and high grade. The complicated geologic-geomorphic environment in Xinjiang contributes to the creation of various geological heritage types and their spatial distribution, and at the same time makes them under control. The main types of the geological heritages in Xinjiang are the geologic-geomorphic landscapes and the water landscapes. The spatial distribution can be divided into five geological heritage districts： Altay, Junggar, Tianshan, Tarim and Kunlun-Altun, among which Tianshan and Kunlun-Altun are most important. Based on the first systematic investigation of the geological heritages in Xinjiang, it is confirmed that the insufficient coordination between protection and exploitation is the primary cause for the backward situation in the geological heritage protection. To solve the problem, this paper proposes 6 major protection steps--from determining the protection types, the protection forms, the protection modes, the protection grades, the protection sequences to determining the＇ protection zones, brings forward the idea of optimal-selection which integrates geoparks with geological heritage protection areas and other protection areas as protection and exploitation reserve list for 209 well as 5 corresponding modes, and makes a important geological heritages in Xinjiang.

A novel feature extraction method for ship-radiated noise

To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.

Vibration-based feature extraction of determining dynamic characteristic for engine block low vibration design

In order to maintain vibration performances within the limits of the design, a vibration-based feature extraction method for dynamic characteristic using empirical mode decomposition (EMD) and wavelet analysis was proposed. The proposed method was verified experimentally and numerically by implementing the scheme on engine block. In the implementation process, the following steps were identified to be important:1) EMD technique in order to solve the feature extraction of vibration signals; 2) Vibration measurement for the purpose of confirming the structural weak regions of engine block in experiment; 3) Finite element modeling for the purpose of determining dynamic characteristic in time region and frequency region to affirm the comparability of response character corresponding to improvement schemes; 4) Adopting a feature index of IMF for structural improvement based on EMD and wavelet analysis. The obtained results show that IMF of signal is more sensitive to response character corresponding to improvement schemes. Finally, examination of the results confirms that the proposed vibration-based feature extraction method is very robust, and focuses on the relative merits of modification and full-scale structural optimization of engine, together with the creation of new low-vibration designs.

Authentication based on feature of hand-written signature

The typical features of the coordinate and the curvature as well as the recorded time information were analyzed in the hand-written signatures.In the hand-written signature process 10 biometric features were summarized:the amount of zero speed in direction x and direction y,the amount of zero acceleration in direction x and direction y,the total time of the hand-written signatures,the total distance of the pen traveling in the hand-written process,the frequency for lifting the pen,the time for lifting the pen,the amount of the pressure higher or lower than the threshold values.The formulae of biometric features extraction were summarized.The Gauss function was used to draw the typical information from the above-mentioned biometric features,with which to establish the hidden Markov mode and to train it.The frame of double authentication was proposed by combing the signature with the digital signature.Web service technology was applied in the system to ensure the security of data transmission.The training practice indicates that the hand-written signature verification can satisfy the needs from the office automation systems.

Radiographic Features of Osteogenesis Imperfecta about a Female Sibship

Osteogenesis imperfecta (OI) belongs to a group of congenital osteoporosis which hallmark feature is “affecting skeleton, increasing bone fragility that fracture easily and decreasing bone density due to quantitative and/or qualita-tive abnormalities”. We report a female sibling’s involvement in 3 cases with probable recessive inheritance pattern. Only female aged between 5 and 13 years were affected with skeletal lesions in the lower limbs. The boy of this family had no skeletal or extra-skeletal lesions. Their parents had no affection and no bond of consanguinity. The observed malformations can be classified as type V or VI according to Sillence’s clinical classification. Lack of genetic test in our context has limited accuracy of the diagnosis as new data evoke a genetic classification into 12 types that leading an effective therapeutic management.

MOLECULAR DYNAMICS SIMULATION ON VIERATIONAL SPECTROSCOPIC FEATURES OF HYDROGEN BONDS IN CRYSTALLINE POLYMERS

Introduction The molecular dynamics simulation technique has recently proved to be a suitable alternative approachfor simulation of vibrational spectroscopy. In this study, molecular dynamics was utilized to understandlow frequency vibrations in highly ordered poly(ρ-phenylene terephthalmide) (PPTA). A key structuralfeature of this polymer is the presence of hydrogen bonds. There is little question that this strong localized

Market Features in 2008

Luxury Brands Enter 2nd Tier Cities Event: In October 2008, a new LV franchised store was launched in MASION MODE in Urumchi in Xinjiang Province, which is the

基于局部形状特征和Bag-of-Feature模型的磨粒图像形状特征提取

以磨粒图像为研究对象,提出了基于局部形状特征和Bag-of-Features模型的磨粒形状特征提取方法。首先构建磨粒区域的骨架,根据骨架端点和分支得到磨粒的轮廓基元和区域基元,不同数量的相邻的轮廓和区域基元组合构成局部轮廓和局部区域,提取局部轮廓和区域的形状特征,两者融合得到磨粒的局部形状特征集合。然后根据Bag-of-Features模型的思想,以训练集所有磨粒样本的局部形状特征集合为基础,构建视觉词典,经过特征编码、特征汇集和归一化,得到磨粒形状特征的编码向量表示。最后根据形状特征的编码向量,采用多级支持向量机的方法对磨粒类型进行识别。实验结果表明,基于提出的磨粒形状特征方法能够有效、准确地识别磨粒类型。

基于动响应数据特征的桥梁结构损伤识别

介绍了本征正交分解(Proper Orthogonal Decomposition,POD)的基本原理,探讨了POD在桥梁结构损伤识别中的应用。提出了基于动响应数据特征的桥梁结构损伤识别方法,该识别方法基于POD技术对桥梁结构在不同位置、不同时刻收集到的位移快照矩阵(Snapshot Matrix)进行本征正交分解,得到结构的本征正交模态(POMs),进而构造出损伤指标来识别结构的损伤位置及程度,实现了对桥梁结构损伤的多工况识别。并以保定黄花沟桥为例,通过数值模拟试验,验证了该方法的有效性,结果表明POD能够从空心板桥结构的振动响应数据中提取出结构的本质特征,并且提取过程简单、快捷,可为桥梁结构提供一种有效的损伤识别方法。

国家级创新创业教育实践基地建设特征探索——基于首批国家级双创基地建设内容的文本分析

国家级创新创业教育实践基地作为高校实施创新创业教育的实践载体,在创新创业教育体系中发挥着重要作用。该文以100所国家级创新创业教育实践基地资料为研究对象,使用内容分析法探究不同类别高校基地的建设内容特点。结果显示综合类、理工类、职业类、特色类高校基地在建设内容特点上存在差异,并结合具体案例总结出依托优势平台、校企合作、结合特色产业三种基地建设模式,以期为高校创新创业教育提供新思路。

基于OFMD和FSC的滚动轴承复合故障诊断

针对滚动轴承的复合故障诊断问题,深入研究了一种基于优化特征模态分解和快速谱相关的复合故障诊断方法。首先,通过理论分析,提出脉冲能量因子指标来实现特征模态分解的参数选择以及最优分量的选取;然后,基于快速谱相关原理设计谱相关相对强度曲线和改进快速谱相关图,用于确定不同故障调制后对应的最优载波,对最优载波进行包络处理,从而分离轴承的复合故障特征,最终实现复合故障的准确性诊断。通过模拟故障试验和工程案例分析结果表明,该文所提方法相比于经验模态分解能够有效滤除噪声干扰,具有良好的鲁棒性,同时,避免了解卷积方法设定参数的缺陷,且与Autogram方法相比,能够有效分离复合故障特征,避免复合故障特征成分耦合。

基于特征图像组合与改进ResNet-18的电能质量扰动识别方法

针对传统电能质量扰动(power quality disturbance,PQD)识别体系中单一图像特征信息受限与算法识别能力不足等问题,依据特征融合的思想,提出一种基于特征图像组合与改进ResNet-18的PQD识别方法。首先,对PQD信号进行变分模态分解(variational mode decomposition,VMD)得到一系列固有模态函数(intrinsic mode functions,IMFs)与残差分量;其次,将IMFs、残差分量、原始扰动信号与Subtract分量纵向拼接成分量矩阵,利用信号-图像转化方法生成特征分量彩色图;再次,对原始扰动信号进行连续小波变换(continuous wavelet transform,CWT)生成小波时-频图;最后,将特征分量彩色图与小波时-频图组合输入改进的六通道ResNet-18中训练学习并完成扰动识别。通过仿真对PQD识别方法进行分析并将其与目前常用识别体系进行比较。结果表明,所提方法具有较好的抗噪性能并且能够更好地提取PQD特征信息,达到更高的识别准确率。