MULTI-LAYER TRACK FUSION ALGORITHM BASED ON SUPPORTING DEGREE MATRIX

The random noises of multi-sensor and the environment make observations uncertain and correlative, so the performance of fusion algorithms is reduced by using observations directly. To solve this problem, a multi-layer track fusion algorithm based on supporting degree matrix is proposed. Combined with the track fusion algorithm based on filtering step by step, it uses multi-sensor observations to establish supporting degree matrix and realize multi-layer fusion. Simulation results show its estimation precision is higher than the original algorithm and is increased by 20% around. Therefore, it solves the problem of target tracking further in the distributed track fusion system.

7元旋转对称2-弹性函数的构造

基于旋转对称轨道的数对分布矩阵的性质,给出了所有7元旋转对称2-弹性函数的具体构造。结果表明,在F_(2)^(7)上有且仅有280个非线性旋转对称2-弹性函数。进一步地,对于任意的奇数k,在F_(2)^(7k)上至少有280个非线性旋转对称2-弹性函数。

基于改进YOLOv8和多元特征的对虾发病检测方法

[目的/意义]对虾病害严重危害对虾养殖业。针对对虾病害发病快、死亡率高等特点,高密度的工厂化养殖等模式需要一种高效率对虾发病检测方法替代传统人工检查方法,实现对虾发病的及时预警。[方法]提出一种基于改进YOLOv8(You Only Look Once)和多元特征的对虾发病检测方法。首先利用改进YOLOv8网络从对虾夜间水面红外图像中进行前景提取,再利用Farneback光流法和灰度共生矩阵(Gray Level Co-occurrence Matrix,GLCM)提取对虾视频片段的运动特征与图像纹理特征,利用提取到的特征参数构建训练数据集,训练支持向量机(Support Vector Machine,SVM)作为分类器用于检测对虾视频片段,实现对正常与发病的对虾视频片段的检测分类。[结果和讨论]训练好的SVM分类器在300个测试样本上的表现为检测准确率平均值为83%,检测效果达到设计要求。检测误差主要是将发病片段错误地检测为正常片段。该误差主要受水面对虾数量和视频影响。[结论]本研究实现了对对虾发病的检测,提供了一种基于计算机视觉的检测方法。但受条件限制,仅在工厂化养殖环境下进行了实验,尚不能适用于多种养殖环境,仍有改进空间。

公路梁桥支座脱空度预测模型及优化算法应用研究

桥梁支座脱空度是桥梁结构的常见病害,对桥梁的安全性和使用寿命产生严重影响。因此,准确获取支座的脱空信息成为一个重要的研究课题。该研究提出了一种两阶段的支座脱空检测方法,第一阶段,基于柔度矩阵对角差值比指标(flexibility diagonal matrix change rate,FDMCR)实现脱空支座定位,第二阶段采用BP(back propagation)神经网络进行支座脱空度的预测。研究针对单跨简支梁桥和三跨连续梁桥,通过有限元模拟实现了梁桥理论振型、自振频率的获取,并利用激光多普勒测振系统对室内单跨梁桥开展模态测试,获取实测数据,验证了所提方法的可行性。此外,针对单跨梁桥,分析了单目标粒子群优化算法、混合蛙跳算法、人工蜂群算法(artificial bee colony,ABC)优化算法和多目标非支配排序遗传算法(NSGA-II)优化所构建BP模型的效果。研究结果表明,两阶段的方法可以有效地实现脱空定位和脱空度预测,而优化算法则能够提高预测模型的预测效果,尤其是ABC算法在单跨梁桥脱空度预测模型上表现出更低的预测误差,同时基于支座位置属性构建多目标函数能够缓解单目标中各支座预测效果不均匀的问题。该研究对于预测公路梁桥支座脱空度具有重要的实际意义,并为类似问题的解决提供了新的思路和方法。

改进高斯云模型及其在装备保障体系能力评估中的应用

针对云模型在评估方法使用过程容易出现雾化特性差、评估结果不能直接使用的问题,提出一种改进的高斯云模型。该模型针对相同指标的专家评估结果,利用相似关系矩阵对专家进行聚类并赋权,求解每类专家的评估高斯云模型,并将其与权重结合形成该评估指标的评估云模型;综合所有指标的评估云模型形成综合评估云;将综合评估云与云标尺进行比对得出评估结果。运用该方法对装备保障体系能力进行评估,并与传统云模型所得评估结果进行对比,证明了改进高斯云模型的可行性和有效性,为其在评估方法中的运用提供参考。

基于EWT-SVM的雨量识别方法

为了从雨声信号中识别出雨量的大小,提出了一种基于经验小波变换和支持向量机的雨量识别算法。对于采集到的雨声信号先进行去噪,接着对信号进行经验小波变换分解,分解后得到数个经验小波函数分量,然后通过Matlab编程对各个经验小波函数分量进行特征提取,在时域和频域范围内组成评价特征矩阵,最后通过SVM对特征矩阵进行分类识别。通过仿真实验发现,对于同一个信号,经验小波函数相较于经验模态分解有更好的自适应性并且克服了经验模态分解的混叠现象和端点效应。实验结果表明基于经验小波变换和支持向量机的雨量识别方法在雨量识别领域具有良好的效果,研究方法为雨量识别、智能雨量计的发展奠定了良好的基础。

基于L_(2)灵敏度最小化的数字滤波器状态空间实现稀疏化方法

为解决传统数字滤波器在有限精度实现时因有限字长(Finite Word Length,FWL)效应导致滤波器性能下降的问题,提出一种L_(2)灵敏度最小化的数字滤波器状态空间实现稀疏化方法.推导前向差分算子数字滤波器结构传输函数及其等效状态空间实现,根据可控及可观格莱姆矩阵得到基于相似变换矩阵的L_(2)灵敏度表达式,并进行稀疏化校准,将L_(2)灵敏度最小化问题转换为凸函数求最值问题,求导得到L_(2)灵敏度最小化表达式,代回即得前向差分算子数字滤波器的稀疏化状态空间实现.仿真结果表明,所提方法设计的数字滤波器具有更好的抗FWL效应.

Classification using wavelet packet decomposition and support vector machine for digital modulations

To make the modulation classification system more suitable for signals in a wide range of signal to noise rate （SNR）, a feature extraction method based on signal wavelet packet transform modulus maxima matrix （WPTMMM） and a novel support vector machine fuzzy network （SVMFN） classifier is presented. The WPTMMM feature extraction method has less computational complexity, more stability, and has the preferable advantage of robust with the time parallel moving and white noise. Further, the SVMFN uses a new definition of fuzzy density that incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types （i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK, and OQPSK）. Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability （success rates are over 98% when SNR is not lower than 0dB）, and it adapts to engineering applications.

基于正则化正交非负矩阵分解的旋转目标检测方法

小样本的旋转目标检测是指在样本数少的情况下进行旋转目标检测模型的训练,深度学习在旋转目标检测领域往往需要庞大的样本数和计算算力。现有的基于机器学习的旋转目标检测方法大多有着对目标尺度和姿态敏感的缺点。因此提出一种基于正则化正交非负矩阵分解的旋转目标检测方法,来解决小样本的旋转目标检测难题。首先,针对样本不具有各种角度的图片,对样本进行旋转后进行背景填充,这样便于更好的表征学习。其次,提出一种基于正则化正交非负矩阵分解算法对旋转样本的梯度直方图特征进行表征学习。最后,为了测试算法在特征学习后的有效性,利用支持向量机对特征提取后的数据进行训练和测试。实验结果表明本文的目标检测方法在多个数据集中可以取得不错的效果。

Norea矮塔斜拉桥索力优化

为确定柬埔寨Norea三跨双塔单索面混凝土矮塔斜拉桥的最优成桥索力,基于有限元模型,分别采用刚性支撑连续梁法、最小弯曲能法、影响矩阵法对成桥索力进行了分析和优化。结果表明:三种优化方法中以影响矩阵法得到的索力值效果最好,将最终优化索力值与原设计值对比,分析了斜拉桥的内力、位移以及应力变化,在恒荷载或恒荷载+附加荷载组合作用下,优化后的索力分布更加均匀,结构整体的弯矩、位移以及应力均有所减小,矮塔斜拉桥整体受力和线形更加理想。

Kernel matrix learning with a general regularized risk functional criterion

Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.

Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information;PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm.

Stiffness identification of four-point-elastic-support rigid plate

As the stiffness of the elastic support varies with the physical-chemical erosion and mechanical friction, model catastrophe of a single degree-of-freedom(DOF) isolation system may occur. A 3-DOF four-point-elastic-support rigid plate(FERP) structure is presented to describe the catastrophic isolation system. Based on the newly-established structure, theoretical derivation for stiffness matrix calculation by free response(SMCby FR) and the method of stiffness identification by stiffness matrix disassembly(SIby SMD)are proposed. By integrating the SMCby FR and the SIby SMD and defining the stiffness assurance criterion(SAC), the procedures for stiffness identification of a FERP structure(SIFERP) are summarized. Then, a numerical example is adopted for the SIFERP validation, in which the simulated tested free response data are generated by the numerical methods, and operation for filtering noise is conducted to imitate the practical application. Results in the numerical example demonstrate the feasibility and accuracy of the developed SIFERP for stiffness identification.

基于风险矩阵和层次分析法的现浇箱梁模板支撑体系施工安全风险评估

在桥梁施工过程中,现浇箱梁模板支撑体系作为危险性较大的分部分项工程,施工过程中稍有不慎,极易造成各类安全事故,甚至是体系坍塌事故,引发群死群伤。文章结合相关规范及全生命周期理论,对模板支撑体系施工过程分析,并参考过往事故案例分析致因因素,运用L·S和AHP对各影响因素指标进行权重计算,借助实际项目施工案例对模板支撑体系安全程度进行定量安全评价,将所有致因因素排序,评估不同致因因素的风险等级,并结合实际,制定针对性对策措施,以期为模板支撑体系施工过程风险管控提出合理化建议。

Flatness intelligent control via improved least squares support vector regression algorithm

To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method.

固有无序蛋白与结合配体作用位点的分析与预测

固有无序蛋白(简称IDPs)在生理条件下不具有稳定的二级或三级结构,但是在生物体内通过与结合配体相互作用来发挥重要的生物学功能,故研究固有无序蛋白与配体的相互作用,对理解这些蛋白的功能具有重要的生物学意义。本文基于IDPsBind数据库,获得固有无序蛋白与5类配体分子(DNA,RNA,金属离子,肽,小分子)结合的结合位点,然后对这些结合位点处残基出现在5类结合位点的倾向性进行分析,结果发现:5类配体分子的结合位点处氨基酸的分布是不一样的。然后,利用滑动窗口中心残基的结合配体类型,建立5类结合配体的结合位点数据集,并提取四种特征参数:位置特异性矩阵(PSSM),20种氨基酸组分(AAC),以及残基的疏水性(HP)和溶剂可及表面积(SASA)特征,结合机器学习算法对5类结合位点进行分类识别,在5折交叉检验结果中预测准确率(Acc)最高达到87%,当特征融合后,预测准确率(Acc)达到88.3%。该研究结果对固有无序蛋白与结合配体相互作用的分析提供了很好的参考。

Using the improved position specific scoring matrix and ensemble learning method to predict drug-binding residues from protein sequences

Identification of the drug-binding residues on the surface of proteins is a vital step in drug discovery and it is important for understanding protein function. Most previous researches are based on the structural information of proteins, but the structures of most proteins are not available. So in this article, a sequence-based method was proposed by combining the support vector machine (SVM)-based ensemble learning and the improved position specific scoring matrix (PSSM). In order to take the local environment information of a drug-binding site into account, an improved PSSM profile scaled by the sliding window and smoothing window was used to improve the prediction result. In addition, a new SVM-based ensemble learning method was developed to deal with the imbalanced data classification problem that commonly exists in the binding site predictions. When performed on the dataset of 985 drug-binding residues, the method achieved a very promising prediction result with the area under the curve (AUC) of 0.9264. Furthermore, an independent dataset of 349 drug- binding residues was used to evaluate the pre- diction model and the prediction accuracy is 84.68%. These results suggest that our method is effective for predicting the drug-binding sites in proteins. The code and all datasets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Ensem_DBS.zip.

基于LBP特征匹配算法的红外人脸图像表情识别技术

红外人脸图像表识别过程中易受到光照不均匀、角度多变、亮度差异大等问题的影响,导致红外人脸图像表情识别效率较差,为解决上述问题,提出基于LBP特征匹配算法的红外人脸图像表情识别方法。首先通过局部优化保留摄影法对红外人脸图像实行降维处理,获取优化后的图像,然后采用多角度分水岭法分割图像,保留图像的细节信息。并采用LBP算子提取预处理后的图像纹理特征,将提取的纹理特征输入支持向量机中,计算出特征的类内比重,完成红外人脸图像表情的识别。实验结果表明,所提方法的平均识别准确率为92%,识别100张人脸图像表情耗时129 s识别效率高、识别效果好、稳定性强。

基于T-GLCM和Tamura融合特征的纹理材质分类

虚拟现实力触觉再现对于图像纹理特征提取的要求越来越高,纹理因素复杂且无规律,单一的纹理提取算法并不能准确地描述图像纹理的特点.因此提出基于GLCM(灰度共生矩阵)和Tamura融合特征的纹理材质分类算法.此外,本文对传统灰度共生矩阵GLCM进行优化,提出了改进的GLCM(T-GLCM)算子,提升了GLCM的旋转不变性并减少了大量的冗余信息.利用Tamura纹理特征对图像进行量化,然后将各特征区域量化后级联成一组特征向量,融合T-GLCM的纹理特征,通过支持向量机(SVM)对纹理材质进行分类.实验结果表明,相比传统纹理特征提取算法,本文算法具有更高的分类精度且鲁棒性更好.