QSRR Study on the Components of Styrax Japonicus Sieb Flowers Using Improved Molecular Electronegativity-distance Vector (I-MEDV)

Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization（MSC） method called improved molecular electronegativity-distance vector（I-MEDV） was developed. It was used to describe the structures of 37 compounds of styrax japonicus sieb flowers. Through multiple linear regression（MLR）,a QSRR model was built up. The correlation coefficient（R1） of the model was 0.980. Then,4 vectors were selected to build another model through the method of stepwise multiple regression（SMR） ,and the correlation coefficient（R2） of the model was 0.975. Moreover,all the two models were evaluated by performing the crossvalidation with the leave-one-out（LOO） procedure and the correlation coefficients（Rcv） were 0.948 and 0.968,respectively. The results show that the I-MEDV could successfully describe the structures of organic compounds. The stability and predictability of the models were good.

Quantitative Structure-retention Relationship Study of Polychlorinated Dibenzothiophenes by Molecular Electronegativity Distance Vector(MEDV)

Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.

Multi-Class Support Vector Machine Classifier Based on Jeffries-Matusita Distance and Directed Acyclic Graph

Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes＇ sequence in the graph. DAG is equivalent to guided operating SVM on a list, and the classification performance depends on the nodes＇ sequence in the graph. Jeffries-Matusita distance （JMD） is introduced to estimate the separability of each class, and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method, numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile, comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the orooosed JMD-DAG method.

Classifying Data Sets Using Support Vector Machines Based on Geometric Distance

Support vector machines （SVMs） are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM （GDB-SVM）. It takes the distance between a point and classified hyperplane as classification rule,and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C ＋＋ 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all （OAA）. Three predicted results of five of GDB-SVM are better than those of the method of one against one （OAO）. Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO, but highly scalable for large data sets while generating high classification accuracy.

Distance Estimation and Material Classification of a Compliant Tactile Sensor Using Vibration Modes and Support Vector Machine

Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negotiating obstacles.A bionic tactile sensor used in the present work was inspired by the antenna of the stick insects.The sensor is able to detect an obstacle and its location in 3 D(Three dimensional) space.The vibration signals are analyzed in the frequency domain using Fast Fourier Transform(FFT) to estimate the distances.Signal processing algorithms,Artificial Neural Network(ANN) and Support Vector Machine(SVM) are used for the analysis and prediction processes.These three prediction techniques are compared for both distance estimation and material classification processes.When estimating the distances,the accuracy of estimation is deteriorated towards the tip of the probe due to the change in the vibration modes.Since the vibration data within that region have high a variance,the accuracy in distance estimation and material classification are lower towards the tip.The change in vibration mode is mathematically analyzed and a solution is proposed to estimate the distance along the full range of the probe.

RSSI/TDOA/DV-Distance组合定位算法

由于目前单一定位算法具有各自的优缺点及适用范围,大规模复杂无线传感器网络则需采用多种定位技术。针对实际应用需求,提出了一种基于接收信号强度指示(RSSI)、到达时间差(TDOA)和矢量距离(DV-Distance)的组合定位算法。该算法扩大了TDOA算法的覆盖范围,提高了成功率,并修正了DV-Distance算法中定位节点与信标节点间有效距离信息。试验表明,该算法满足了复杂定位系统的差异化需求,提高了大规模无线传感器网络的整体定位精度和稳定性。

Seizure detection using earth movers' distance and SVM in intracranial EEG

Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

On Graphs with Same Distance Distribution

In the present paper we investigate the relationship between Wiener number W, hyper-Wiener number R, Wiener vectors WV, hyper-Wiener vectors HWV, Wiener polynomial H, hyper-Wiener polynomial HH and distance distribution DD of a (molecular) graph. It is shown that for connected graphs G and G*, the following five statements are equivalent:?;and if G and G* have same distance distribution DD then they have same W and R but the contrary is not true. Therefore, we further investigate the graphs with same distance distribution. Some construction methods for finding graphs with same distance distribution are given.

DBLAR:A DISTANCE-BASED LOCATION-AIDED ROUTING FOR MANET

In location-aided routing of Mobile Ad hoc NETworks(MANET),nodes mobility and the inaccuracy of location information may result in constant flooding,which will reduce the network performance.In this paper,a Distance-Based Location-Aided Routing(DBLAR) for MANET has been proposed.By tracing the location information of destination nodes and referring to distance change between nodes to adjust route discovery dynamically,the proposed routing algorithm can avoid flooding in the whole networks.Besides,Distance Update Threshold(DUT) is set up to reach the balance between real-time ability and update overhead of location information of nodes,meanwhile,the detection of relative distance vector can achieve the goal of adjusting forwarding condition.Simulation results reveal that DBLAR performs better than LAR1 in terms of packet successful delivery ratio,average end-to-end delay and routing-load,and the set of DUT and relative distance vector has a significant impact on this algorithm.

An improved Mahalanobis distance-based colour segmentation method for rural building recognition

Aiming at the rapid identification of rural buildings in complex environments from high-spatialresolution images, an improved Mahalanobis distance colour segmentation method(IMDCSM) is proposed and realised in Red, Green and Blue(RGB) space. Vector sets of a lower discrete degree are obtained by filtering the colour vector sets of the building samples, and a standard ellipsoid equation can be constructed based on these vector sets. The threshold of interested colour range can be flexibly and intuitively selected by changing the shape and size of this ellipsoid. Then, according to the relationship between the location of the image pixel colour vector and the ellipsoid, all building information can be extracted quickly. To verify the effectiveness of the proposed method, unmanned aerial vehicle(UAV) images of two areas in the suburbs of Chengdu city and Deyang city were utilised as experimental data for image segmentation, and the existing colour segmentation method based on the Mahalanobis distance was selected as an indicator to assess the effectiveness of this method. The experimental results demonstrate that the completeness and correctness of this method reached 95% and 83.0%, respectively, values that are higher than those of the Mahalanobis distance colour segmentation method(MDCSM). In general, this method is suitable for the rapid extraction of rural building information, and provides a new threshold selection method for classification.

ANALYSIS OF THE GENOMIC DISTANCE BETWEEN BAT CORONAVIRUS RATG13 AND SARS-COV-2 REVEALS MULTIPLE ORIGINS OF COVID-19

The severe acute respiratory syndrome COVID-19 was discovered on December 31,2019 in China.Subsequently,many COVID-19 cases were reported in many other countries.However,some positive COVID-19 samples had been reported earlier than those officially accepted by health authorities in other countries,such as France and Italy.Thus,it is of great importance to determine the place where SARS-CoV-2 was first transmitted to human.To this end,we analyze genomes of SARS-CoV-2 using k-mer natural vector method and compare the similarities of global SARS-CoV-2 genomes by a new natural metric.Because it is commonly accepted that SARS-CoV-2 is originated from bat coronavirus RaTG13,we only need to determine which SARS-CoV-2 genome sequence has the closest distance to bat coronavirus RaTG13 under our natural metric.From our analysis,SARS-CoV-2 most likely has already existed in other countries such as France,India,Netherland,England and United States before the outbreak at Wuhan,China.

Finding the Asymptotically Optimal Baire Distance for Multi-Channel Data

A novel permutation-dependent Baire distance is introduced for multi-channel data. The optimal permutation is given by minimizing the sum of these pairwise distances. It is shown that for most practical cases the minimum is attained by a new gradient descent algorithm introduced in this article. It is of biquadratic time complexity: Both quadratic in number of channels and in size of data. The optimal permutation allows us to introduce a novel Baire-distance kernel Support Vector Machine (SVM). Applied to benchmark hyperspectral remote sensing data, this new SVM produces results which are comparable with the classical linear SVM, but with higher kernel target alignment.

Indexing the bit-code and distance for fast KNN search in high-dimensional spaces

Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curse of dimensionality. Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD). BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector, the key managed by a B+-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD out- performs the existing index structures for KNN search in high-dimensional spaces.

Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.

Metric Learning with Relative Distance Constraints:A Modified SVM Approach

Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.

基于改进麻雀搜索算法的无线传感器网络定位研究

针对距离矢量跳(DV-Hop)算法在无线传感器网络中存在的定位误差大的问题,提出了一种基于改进麻雀搜索的无线传感器网络定位算法。首先,针对传统DV-Hop算法,修正了节点平均跳距和节点最小跳数。其次,引入Sine混沌映射、自适应惯性权重和双样本学习策略提高算法的搜索能力;最后,利用MATLAB构建仿真模型进行性能对比实验。实验结果表明:与其他节点定位算法相比,所提算法可有效提高对未知节点的定位精度和收敛速度。

基于情绪向量的隐半马尔可夫模型股市拐点预测方法

股市的情绪化倾向是股票市场具有高度不确定性的主要原因,直接利用历史数据的股票趋势预测方法难以适应市场情绪的多变性,在实际应用中效果不理想。文章针对市场情绪的不稳定性导致股市拐点难以预测的问题,提出一种基于情绪向量的隐半马尔可夫模型股市拐点预测方法(hidden semi-Markov model stock turning point prediction method based on sentiment vector,SV-HSMM)。针对市场情绪不可观察性,选取与市场情绪相关的主要特征,使用马尔可夫毯融合成市场情绪;利用隐半马尔可夫模型建模市场环境,构建市场情绪、市场状态和状态持续时间之间的结构关系;引入情绪向量平滑情绪的多变性,并利用Kullback-Leibler(KL)距离量化情绪热度;利用隐半马尔可夫模型的动态推理实现股市拐点预测。结果表明情绪向量方法具有更好的预测效果。

基于氢谱修饰指数的润滑油添加剂抗磨损性能的预测研究

润滑油添加剂对保障机械运行的稳定性和安全性、延长设备的使用寿命、节约能耗具有重要作用,由于通过实验获得数据费时费力,故用定量结构摩擦性能关系研究方法,来获取抗磨损性能数据成为一种简便而高效的方法.为研究酰肼类及磷酸类润滑油添加剂抗磨损性能与结构的关系,定义并建构了新的氢谱修饰指数^(0)H,并计算36个酰肼类及磷酸类分子的电性距离矢量,筛选了M_(2)、M_(3)、M_(21)和M_(59),将这5个结构描述符作为BP神经网络的输入变量,磨损体积量度V s作为神经网络输出变量,用5-3-1的神经网络结构,构建了神经网络法预测模型,模型相关系数0.944,磨损体积量度V s的预测值与文献实验值的平均相对误差为2.61%,优于文献方法结果.结果表明:酰肼类及磷酸类润滑油添加剂抗磨损性能与氢谱修饰指数有良好的非线性关系,影响抗磨损性能的主要因素是分子中原子特性、基团片段的—CH_(3)、—CH_(2)—、>CH—、—O、—N—和—P—等.该研究对指导合成新型抗磨损性能好的化合物分子有理论意义.

一种改进的群组机器人网络路由算法

针对群组机器人系统在应急场景下移动频繁、能量有限的特点,提出了一种基于能量与速度的分簇自组织按需距离矢量协议(Clustered Ad hoc On-Demand Distance Vector Protocol Based on Energy and Speed,ESC-AODV),以延长群组机器人网络运行时间,提高通信可靠性。用路由性能代替跳数作为路由判据,目的节点在重复接收到路由请求(Route Request,RREQ)数据包时,若路由性能更小,则回复路由应答(Routing Reply,RREP)数据包,以此选择更好的路由,引入分簇结构,通过簇头和网关组成的骨干网络减少广播洪泛次数。实验结果证明,节点数量多时,改进的ESC-AODV协议在延长网络生存时间的同时,平均端到端时延、数据包投递率、吞吐量和路由开销均优于AODV以及基于能量、负载和速度的AODV路由协议(AODV Routing Protocol Based on Energy,Load and Speed,ELS-AODV)。ESC-AODV协议能够节约网络能量,提高可靠性,获得更优的网络性能。

结构化最大间隔双支持向量机在股票预测中的应用

股票价格受政策、宏观经济以及公司经营状况等多方因素的影响,且各因素之间存在较高的相关性,因此股票数据存在的高噪声、非平稳等特性使得股票预测充满困难。为了减少数据中存在的噪声对股价预测准确性的影响,基于马氏距离的类间隔可分性,提出了结构化最大间隔双支持向量机,其分别针对正类样本和负类样本,寻找两个非平行的超平面,使每一类样本离本类样本的欧式距离尽可能小,同时离异类超平面的马氏距离尽可能大。8组基准数据集的实验结果表明,该方法在含噪声数据的分类问题上具有稳定的准确率,从而提升了模型的预测性能和抗噪能力。同时将其应用到股票涨跌趋势预测中,通过对上证综指、上证A指、上证380指数以及中国平安等14只股票实证分析的结果表明,相较于其他对比模型,结构化最大间隔双支持向量机表现出了较好的预测结果,具有一定的实用价值。