双向准Z源逆变器驱动永磁同步电机的快速有限集模型预测控制

双向准Z源逆变器驱动永磁同步电机(PMSM)系统在兼具Z源逆变器和PMSM优点的同时,还可实现能量的双向流动。针对双向准Z源逆变器驱动PMSM系统的特点,提出一种快速矢量选择有限集模型预测电流控制(FCS\|MPCC)策略。由双向准Z源网络直流链电压闭环与PMSM电磁功率前馈生成电感电流参考值,由电机转速闭环生成电机电流参考值。通过预测直通(ST)及非直通(NST)状态下的电感电流值并引入电感电流子代价函数以确定是否选择ST状态,进而实现对直流链电压的控制。在NST状态下,结合空间电压矢量脉宽调制策略,仅使目标电压矢量所在扇区的4个矢量参与PMSM定子电流预测和代价函数计算,以选择最优的开关状态,在实现对PMSM转速控制的同时减小在线计算量。仿真结果表明,所提控制策略可实现对双向准Z源逆变器的升压及PMSM牵引或制动工况下的转速控制,系统具有良好的稳态及动态性能。     

基于局部线性嵌入和支持向量机回归的TBM施工参数预测

依托吉林引松工程开展隧道掘进机（TBM）施工参数预测研究,提出TBM施工数据分段提取算法,提取上升段前30 s的总推进力、刀盘转速、推进速度、刀盘扭矩、刀盘转速电位器设定值、推进速度电位器设定值、贯入度、贯入度指数(FPI)、扭矩切深指数(TPI)9个参数作为输入;通过局部线性嵌入（LLE）完成对上升段数据特征的降维;基于支持向量机回归（SVR）建立TBM施工控制参数（推进速度、刀盘转速）和负载参数（总推进力、刀盘扭矩）预测模型. 分析是否结合前一掘进循环的FPI、TPI指数进行预测对预测效果的影响. 结果表明,上述方法在推进速度、刀盘转速、总推进力、刀盘扭矩的预测中均取得了较好的预测效果,平均预测绝对百分比误差均小于15%,验证了该预测方法的有效性,该方法可以为TBM现场施工提供指导.     

基于GWO-SVM的下肢假肢穿戴者骑行相位识别

针对下肢假肢穿戴者骑行相位识别的问题,提出基于灰狼算法优化的支持向量机(GWO-SVM)分类模型. 建立下肢多源信息系统,采集膝关节、踝关节的加速度信号以及膝关节角度信号. 应用奇异值分解,对采集到的信号进行降噪处理. 在对信号进行降噪处理之后,为了避免单一信号不确定的影响,从数据冗余角度,选取各信号的特征点,开展归一化处理,组成多维特征向量,作为SVM分类模型的输入. 为了能够进一步提高分类精度,加强全局优化能力,利用GWO算法对核参数进行优化. 通过与PSO-SVM分类模型、GA-SVM分类模型对比表明,基于GWO优化的SVM分类模型对骑行相位的识别率为94%,高于其他方法优化的SVM分类模型.     

基于数据融合的ABC-SVM社区疾病预测方法

针对现有社区医疗服务中的疾病预测方法存在数据利用率低、疾病分析类型单一、自动化程度差、疾病预测效果不理想等不足,提出在物联网大数据环境下可用于社区医疗的健康数据融合及疾病预测方法. 通过主成分分析(PCA)和聚类分析对社区中居民的生理指标数据进行特征提取;结合人工蜂群(ABC)算法构造支持向量机(SVM)非线性分类器对数据进行特征级融合分析并预测潜在疾病. 实验结果表明,所提方法的疾病识别准确率达到93.10%,相较于传统SVM方法和BP神经网络方法分别提高17.24% 和72.41%. 该方法能够在提高数据利用率、降低计算资源消耗的前提下有效识别多种潜在疾病,可实现疾病早发现、早预防、早治疗;可广泛应用于社区健康管理、老年社区监护甚至临床医疗.     

基于邻域彩色变化矢量场的图像边缘检测技术研究*

首先进行了边缘检测系统结构设计,建立了图像邻域彩色变化矢量场的数理模型,提出了用图像邻域彩色变化方向锐度描述图像边缘,进而应用模糊聚类自适应检测边缘.实验表明:与基于梯度的边缘检测技术相比,该方法在噪声抑制以及边缘准确定位上均取得了好的效果,是一种应用广泛的优秀边缘检测算法.     

Adenovirus-Mediated Inducible Expression of a PD-L1 Blocking Antibody in Combination with Macrophage Depletion Improves Survival in a Mouse Model of Peritoneal Carcinomatosis

Immune checkpoint inhibitors (ICIs) have demonstrated remarkable efficacy in a growing number of malignancies. However, overcoming primary or secondary resistances is difficult due to pharmacokinetics issues and side effects associated with high systemic exposure. Local or regional expression of monoclonal antibodies (mAbs) using gene therapy vectors can alleviate this problem. In this work, we describe a high-capacity adenoviral vector (HCA-EFZP-aPDL1) equipped with a mifepristone-inducible system for the controlled expression of an anti-programmed death ligand 1 (PD-L1) blocking antibody. The vector was tested in an immune-competent mouse model of colorectal cancer based on implantation of MC38 cells. A single local administration of HCA-EFZP-aPDL1 in subcutaneous lesions led to a significant reduction in tumor growth with minimal release of the antibody in the circulation. When the vector was tested in a more stringent setting (rapidly progressing peritoneal carcinomatosis), the antitumor effect was marginal even in combination with other immune-stimulatory agents such as polyinosinic-polycytidylic acid (pI:C), blocking mAbs for T cell immunoglobulin, mucin-domain containing-3 (TIM-3) or agonistic mAbs for 4-1BB (CD137). In contrast, macrophage depletion by clodronate liposomes enhanced the efficacy of HCA-EFZP-aPDL1. These results highlight the importance of addressing macrophage-associated immunoregulatory mechanisms to overcome resistance to ICIs in the context of colorectal cancer.     

Quadratic optimization fine tuning for the Support Vector Machines learning phase

This work presents a comparative analysis of specific, rather than general, mathematical programming implementation techniques of the quadratic optimization problem (QP) based on Support Vector Machines (SVM) learning process. Considering the Karush–Kuhn–Tucker (KKT) optimality conditions, we present a strategy of implementation of the SVM-QP following three classical approaches: (i) active set, also divided in primal and dual spaces, methods, (ii) interior point methods and (iii) linearization strategies. We also present the general extension to treat large-scale applications consisting in a general decomposition of the QP problem into smaller ones, conserving the exact solution approach. In the same manner, we propose a set of heuristics to take into account for a better than a random selection process for the initialization of the decomposition strategy. We compare the performances of the optimization strategies using some well-known benchmark databases.     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.