DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

The use of seed texture features for discriminating different cultivars of stored apples

The aim of this study was to identify the textural features of apple seeds with the highest discriminatory power for distinguishing the seeds of different apple cultivars with the use of discriminative classifiers. The seeds of apple cvs. Gala, Jonagold and Idared were scanned with the use of a flatbed scanner, and the acquired images were processed to calculate textural features from color channels: L, a, b, R, G, B, Y, U, V, H, S, I, X, Y and Z. The selected textures were used to develop discriminative models and distinguish the seeds of the examined apple cultivars. The analyses were performed for color spaces and color channels. The seeds of apple cvs. Gala and Idared were discriminated with 100% accuracy in models based on the textures from Lab and YUV color spaces and color channel L for the Naive Bayes, Multilayer Perceptron and Multi Class classifiers. The discriminatory accuracies of the seeds of all analyzed apple cultivars (Gala, Idared and Jonagold) ranged from 72% to 85%. The discriminatory accuracy of the textures selected from Lab color space for the Naive Bayes classifier reached 85%. The seeds of apple cvs. Gala and Jonagold were discriminated with 78–90% accuracy, and the discriminatory accuracy of the textures from Lab color space and color channel b for the Naive Bayes classifier reached 90%. The seeds of apple cvs. Idared and Jonagold were distinguished with 80–94% accuracy. The models based on textures from Lab color space and color channel b for the Naive Bayes classifier were characterized by 94% discriminatory accuracy. The study demonstrated that textural features are useful for discriminating the seeds of different apple cultivars.     

一种电能质量多扰动分类中特征组合优化方法

针对电能质量扰动分类中冗余特征量造成分类器训练困难、分类准确率下降的问题,提出一种基于改进遗传算法的特征组合优化方法。该方法对信号进行小波变换,提取各层的改进小波能量熵作为原始特征,并构造一种基于欧氏距离的适应度函数,采用改进的自适应遗传算法对原始特征进行筛选和优化组合,形成用于电能质量扰动分类的最优特征组合。分别采用二分类-支持向量机法(Binary-SVM)、多标签径向基神经网络(ML-RBF)和多标签K近邻法(ML-KNN)对不同噪声情况下的电能质量单一扰动和混合扰动进行分类,仿真结果验证了所提特征组合优化方法能有效提高分类器的训练速度和分类准确率。     

Topic Tomographies (TopTom): a visual approach to distill information from media streams

In this paper we present Top Tom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user‐generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low‐dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. Top Tom implements a batch processing pipeline able to run both in near‐real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast‐like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.     

Word长文档框架结构的编排技巧

本文介绍了Word长文档框架结构的编排技巧。     

Gas-cooled reactor thermal hydraulic analyses with MELCOR

Pursuant to the Energy Policy Act of 2005, the High Temperature Gas-Cooled Reactor (HTGR) has been selected as the reference design for the Next Generation Nuclear Plant (NGNP). Stemming from a U.S. Nuclear Regulatory Commission (NRC) HTGR research initiative, a need was identified for validation of systems-level computer code modeling capabilities in anticipation of the eventual need to perform licensing analyses. Because the NRC has used MELCOR for light water reactors (LWR) in the past and because MELCOR was recently updated to include gas-cooled reactor (GCR) physics models, MELCOR is among the system codes of interest to the NRC. This paper describes MELCOR modeling of the General Atomics' Modular High Temperature Gas-Cooled Reactor (MHTGR). The MHGTR is a suitable design for demonstration of MELCOR GCR modeling competency for two reasons: 1) the MHTGR is a predecessor to the more advanced General Atomics’ Gas-Turbine Modular High Temperature Reactor (GTMHR), and 2) experimental data useful for benchmark calculations may soon become available. Using the most complete literature references available for the MHTGR design, researchers at Texas A&M University (TAMU) constructed a MELCOR input deck for the MHTGR to partially validate MELCOR GCR modeling capabilities. Normal and off-normal system operating conditions were modeled with appropriate boundary and initial conditions. MELCOR predictions of system response were obtained for steady-state, pressurized conduction cool-down (PCC), and depressurized conduction cool-down (DCC) scenarios. Code results were checked against nominal MHTGR design parameters, physical intuition, and anticipated GCR thermal hydraulic response. No inherent deficiencies in MELCOR modeling capability were observed, suggesting that the newly-implemented GCR models are adequate for systems-level analysis. If and when experimental benchmark data becomes available, further validation activities may proceed given the modeling efforts discussed herein.     

接口控制文件仿真方法研究

针对装甲车辆复杂的电子信息系统的接口控制文件,若要建立其整个系统的仿真模型,不但周期长,而且分工建立起来的各个模型由于思想方案的不同,会给仿真过程带来很多麻烦,因此采用一种根据接口控制文件自动建立其UML系统模型的方法,通过将自动生成的模型联合详细设计模型一起下载到主仿真机,并联合其它相关工具来完成ICD的自动仿真.结果表明该方法是可行的,不仅很好地解决了仿真过程中ICD数据修改后模型需要重新建立的问题,而且实现了ICD仿真的自动化.     

纸箱打样机的嵌入式软件开发

以开发纸箱打样机四轴数控嵌入式软件为设计目标,在ARM主控板和DSP通用控制器双CPU模式的硬件结构上,搭建以μC/OS-II为操作系统的软件开发平台,并在此平台上设计纸箱打样机控制系统的启动代码以及串口驱动程序。纸箱打样机的文件系统、人机界面以及基于Modbus协议的串口通讯等应用程序设计是本文设计的重点,实现了对加工数据的文件管理。给纸箱打样机提供一个友好可定制的人机界面,缩短了开发周期,方便产品升级。     

Soft computing methods applied to combination of one-class classifiers

The paper shows the possibilities of generalizing the two-class classification into multi-class classification by means of a fuzzy inference system. Fuzzy combiner harnesses the support values from classifiers to provide final response having no other restrictions on their structure. We compare proposed combination methods with ECOC and two variations of decision templates, based on Euclidean and symmetric distance. The effectiveness of the proposed combination method based on the fuzzy logic theory is also evaluated via computer experiments carried out on benchmark datasets.