No abstract

This letter presents a two-dimensional (2-D) system theory based iterative learning control (ILC) method for linear continuous-time multivariable systems. We demonstrate that a 2-D continuous-discrete model can be successfully applied to describe both the dynamics of the control system and the behavior of the learning process. We successfully exploited the 2-D continuous-discrete Roesser's linear model by extending the ILC technique from discrete control systems to continuous control systems. Three learning rules for ILC are derived. Necessary and sufficient conditions are given for convergence of the proposed learning rules. Compared to the learning rule suggested by Arimoto [2], our developed learning rules are less restrictive and have wider applications. The third learning rule proposed in this letter ensures the reference output trajectory can be accurately tracked after only one learning trial. Three numerical examples are used to illustrate the proposed control procedures.

The phishing email is one of the significant threats in the world today and has caused tremendous financial losses. Although the methods of confrontation are continually being updated, the results of those methods are not very satisfactory at present. Moreover, phishing emails are growing at an alarming rate in recent years. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails. In this paper, we first analyzed the email structure. Then, based on an improved recurrent convolutional neural networks (RCNN) model with multilevel vectors and attention mechanism, we proposed a new phishing email detection model named THEMIS, which is used to model emails at the email header, the email body, the character level, and the word level simultaneously. To evaluate the effectiveness of THEMIS, we use an unbalanced dataset that has realistic ratios of phishing and legitimate emails. The experimental results show that the overall accuracy of THEMIS reaches 99.848%. Meanwhile, the false positive rate (FPR) is 0.043%. High accuracy and low FPR ensure that the filter can identify phishing emails with high probability and filter out legitimate emails as little as possible. This promising result is superior to the existing detection methods and verifies the effectiveness of THEMIS in detecting phishing emails.

The wireless remote iterative learning control (ILC) system with random data dropouts is considered. The data dropout is viewed as a binary switching sequence which obeys the Bernoulli distribution. In order to eliminate the effect of data dropouts on the convergence property of output error, the signal at the same time with the lost one but in the last iteration is used to compensate the data dropout at the actuator. With the dropout compensation, the convergence property of output error is analyzed by studying the element values of system transition matrix. Finally, some simulation results are given to illustrate the validity of the proposed method.

New aerial sensors and platforms (e.g., unmanned aerial vehicles (UAVs)) are capable of providing ultra-high resolution remote sensing data (less than a 30-cm ground sampling distance (GSD)). This type of data is an important source for interpreting sub-building level objects; however, it has not yet been explored. The large-scale differences of urban objects, the high spectral variability and the large perspective effect bring difficulties to the design of descriptive features. Therefore, features representing the spatial information of the objects are essential for dealing with the spectral ambiguity. In this paper, we proposed a dual morphology top-hat profile (DMTHP) using both morphology reconstruction and erosion with different granularities. Due to the high dimensional feature space, we have proposed an adaptive scale selection procedure to reduce the feature dimension according to the training samples. The DMTHP is extracted from both images and Digital Surface Models (DSM) to obtain complimentary information. The random forest classifier is used to classify the features hierarchically. Quantitative experimental results on aerial images with 9-cm and UAV images with 5-cm GSD are performed. Under our experiments, improvements of 10% and 2% in overall accuracy are obtained in comparison with the well-known differential morphological profile (DMP) feature, and superior performance is observed over other tested features. Large format data with 20,000 × 20,000 pixels are used to perform a qualitative experiment using the proposed method, which shows its promising potential. The experiments also demonstrate that the DSM information has greatly enhanced the classification accuracy. In the best case in our experiment, it gives rise to a classification accuracy from 63.93% (spectral information only) to 94.48% (the proposed method).

Intravenous leiomyomatosis (IVL), showing unusual growth patterns of uterine leiomyoma, is a rare neoplasm characterized by intravascular proliferation of a histologically benign-looking smooth muscle cell tumor mass, but not invading the tissue. To date, less than 300 cases have been reported and fewer than 100 cases with cardiac involvement. Imaging characteristics of IVL are still not clear so it is usually misdiagnosed before surgery. A 36-year-old woman, who had undergone hysterectomy due to hysteromyoma, presented with shortness of breath after activities. Imaging showed IVL with mass involvement of the left ovarian vein, left renal vein, left external and common iliac vein, as well as within the inferior vena cava (IVC), extending into the right atrium. The operation demonstrated that the mass had no stalk and had well-demarcated borders with the wall of the right atrium and IVC. The patient underwent a one-stage combined multidisciplinary thoraco-abdominal operation under general anesthetic. Subsequently, the pathology report confirmed IVL. IVL should be considered in a female patient presenting with an extensive mass in the right side of the heart. Imaging technology, such as echocardiogram, contrast-enhanced computed tomography and magnetic resonance imaging, can provide important information to reveal the mass, the range and path of the lesion, and relates to the surgical plan decision. Consequently, perfect and exact image examination is very necessary pre-operation.

No abstract

No abstract