面向应用型本科的“计算机控制系统”课程教学改革与探索

应用型本科院校以培养高素质应用型人才作为培养目标。为达到预期的培养目标,需要在专业培养方案设置、课程知识体系、课程教学模式与方法等各个环节进行有针对性的优化设计。《计算机控制系统》课程兼有深入的理论基础和面向实际应用的特点,因此在教学改革时需要把握好理论与实践的关系。针对理论教学,在优化调整讲授内容的基础上,通过引入工程案例法将核心知识点串联起来,并制作“慕课”形式的教学视频作为线下教学的补充;针对实验教学,设置更多的设计性和综合性实验并引入三级训练项目作为补充。改革后的教学内容与工程实践的结合更加紧密,教学素材与方法也更加丰富,极大地提升了学生的学习自主性,更有利于学生分析和解决实际问题能力的培养。     

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.     

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to figure out whether the OCT image is a CSCR case or not. 150 images are checked for this research work (75 abnormal from Optical Coherence Tomography Image Retinal Database, in-house clinical database, and 75 normal images). This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized. The total precision is 90 percent obtained from the Two Class Support Vector Machine (TCSVM) classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale (SNNWPB) classifier using the MATLAB 2019a program.     

Automated construction of Wi-Fi-based indoor logical location predictor using crowd-sourced photos with Wi-Fi signals

Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes.     

A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classi f ier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thor oughly tested and compared with other methods for various faults on HVDC transmission lines.     

基于生成对抗网络的知识蒸馏数据增强

在图像分类和工业视觉检测过程中,缺陷样本量少导致神经网络分类器训练效率低及检测精度差,直接采用原始的离散标签又无法使网络分类器学习到不同类别间的相似度信息。针对上述问题,在区域丢弃算法的基础上,提出一种基于生成对抗网络的知识蒸馏数据增强算法。使用补丁对丢弃区域进行填补,减少区域丢弃产生的非信息噪声。在补丁生成网络中,保留生成对抗网络的编码器-解码器结构,利用编码器卷积层提取特征,通过解码器对特征图上采样生成补丁。在样本标签生成过程中,采用知识蒸馏算法中的教师-学生训练模式,按照交叉检验方式训练教师模型,根据教师模型生成的软标签对学生模型的训练进行指导,提高学生模型对特征的学习能力。实验结果表明,与区域丢弃算法相比,该算法在CIFAR-100、CIFAR-10数据集图像分类任务上的Top-1 Err、Top-5 Err分别降低3.1、0.8、0.5、0.6个百分点,在汽车转向器轴承数据集语义分割任务上的平均交并比和识别准确率分别提高2.8、2.3个百分点。     

TBM试验台支撑推进节能系统设计与仿真分析

针对全断面硬岩隧道掘进机(hard rock tunnel boring machine,简称TBM)在撑靴以设定压力撑紧围岩后支撑推进系统存在较大流量损失的问题,设计出一种具有负载敏感、恒压控制和蓄能器辅助支撑功能的支撑推进(简称LSCPGT)系统。利用AMESim软件搭建了LSCPGT系统模型,仿真分析了LSDRGT系统在变推进负载下的压力流量响应,并对比分析了在支撑工况下LSCPGT系统和恒压控制泵型支撑(constant pressure gripper,CPG)系统,以及在推进工况下LSCPGT系统与负载敏感泵型推进(load-sensing thrust,LST)系统和定量泵型推进(ration thrust,RT)系统的压力流量响应.结果表明:LST系统和LSCPGT系统在推进过程中都没有流量损失;CPG系统在支撑工况下存在流量损失,而LSCPGT系统由于蓄能器的保压作用没有流量损失;相对于LST系统+CPG系统的支撑推进系统,LSCPGT系统在撑靴达到设定压力后效率至少可提高43.5%。所设计的LSCPGT系统在满足支撑推进要求的同时,避免了流量损失,具有较好的节能效果。     

Automated detection of glioblastoma tumor in brain magnetic imaging using ANFIS classifier

This article proposes a novel and efficient methodology for the detection of Glioblastoma tumor in brain MRI images. The proposed method consists of the following stages as preprocessing, Non‐subsampled Contourlet transform (NSCT), feature extraction and Adaptive neuro fuzzy inference system classification. Euclidean direction algorithm is used to remove the impulse noise from the brain image during image acquisition process. NSCT decomposes the denoised brain image into approximation bands and high frequency bands. The features mean, standard deviation and energy are computed for the extracted coefficients and given to the input of the classifier. The classifier classifies the brain MRI image into normal or Glioblastoma tumor image based on the feature set. The proposed system achieves 99.8% sensitivity, 99.7% specificity, and 99.8% accuracy with respect to the ground truth images available in the dataset.     

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian’s is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian’s behavior detection. Kalman filter has been used to detect pedestrian’s using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.