Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.     

地铁站场景下的可行驶区域检测

在室内环境下的机器人视觉导航任务中,可行驶区域检测是不可或缺的一部分,这是保证自动驾驶任务实现的基础.目前较多的解决方法是对数据集中出现过的障碍物进行识别来检测可行驶区域,缺乏灵活性,因此本文提出了一种针对地铁站等室内平坦地面的可行驶区域检测方法,提高实用性.本文采用经典的MobileNetV3网络对采集到的前方图像进行分类,判断是否为地面区域.由于室内地面的地标、箭头等贴纸的影响,因此需要对非地面区域进一步判断,与常规的立体障碍物进行区分.本文利用连续帧之间的特征点匹配获得相机移动距离,并利用直线拟合计算斜率的方法达到区分立体障碍物与平面地标的目的.实验表明,本文提出的方法能较好地检测机器人前方可行驶区域,具有较高的实用价值.     

Design of a 2-DOF-PID controller using an improved sine–cosine algorithm for load frequency control of a three-area system with nonlinearities

This paper proposes an improved sine–cosine algorithm (ISCA) based 2-DOF-PID controller for load frequency control. A three-area test system is built for study, while some physical constraints (nonlinearities) are considered for the investigation of a realistic power system. The proposed method is used as the parameter optimizer of the LFC controller in different scenarios. The 2-DOF-PID controllers are used because of their capability of fast disturbance rejection without significant increase of overshoot in set-point tracking. The 2-DOF-PID controllers’ efficacy is observed by examining the responses with the outcomes obtained with PID and FOPID controllers. The simulation results with the suggested scheme are correlated with some of the existing algorithms, such as SCA, SSA, ALO, and PSO in three different scenarios, i.e., a disturbance in two areas, in three areas, and in the presence of physical constraints. In addition, the study is extended to a four-area power system. Statistical analysis is performed using the Wilcoxon Sign Rank Test (WSRT) on 20 independent runs. This confirms the supremacy of the proposed method.     

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.     

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.     

从法律视角看网络泄密

随着互联网用户的剧增,网络安全成为困扰中国互联网健康发展的重要问题之一。与此同时,日趋严重的网络泄密问题已逐渐成为与黑客入侵、病毒袭击、垃圾邮件、流氓插件并行的第五大网络安全隐患．从法律角度来看,网路泄密实质上是对网络隐私权的侵犯,笔者通过对网路隐私权的分析,从现行法律制度与未来法律制度来解读网络泄密。     

网络教学平台在计算机语言类课程教学中应用的反思

根据教学实践,文章深入分析计算机语言类课程的教学现状;提出网络教学平台作为传统教学方式的一种重要的辅助工具,在丰富教学内容、提高教学质量、拓展教学空间和时间、促进教学改革等方面具有明显的优势;同时也指出在当前网络教学平台的利用过程中存在着网络资源匮乏、利用率低等不足之处,并提出了相应解决对策和措施。     

Approximate dynamic programming solutions with a single network adaptive critic for a class of nonlinear systems

Approximate dynamic programming (ADP) formulation implemented with an adaptive critic (AC)-based neural network (NN) structure has evolved as a powerful technique for solving the Hamilton-Jacobi-Bellman (HJB) equations. As interest in ADP and the AC solutions are escalating with time, there is a dire need to consider possible enabling factors for their implementations. A typical AC structure consists of two interacting NNs, which is computationally expensive. In this paper, a new architecture, called the ‘cost-function-based single network adaptive critic (J-SNAC)’ is presented, which eliminates one of the networks in a typical AC structure. This approach is applicable to a wide class of nonlinear systems in engineering. In order to demonstrate the benefits and the control synthesis with the J-SNAC, two problems have been solved with the AC and the J-SNAC approaches. Results are presented, which show savings of about 50% of the computational costs by J-SNAC while having the same accuracy levels of the dual network structure in solving for optimal control. Furthermore, convergence of the J-SNAC iterations, which reduces to a least-squares problem, is discussed; for linear systems, the iterative process is shown to reduce to solving the familiar algebraic Ricatti equation.     

医院网络安全的技术实现与改进

随着信息科技的高速发展,网络在越来越多地为人们生活提供便利的同时,也为医院节省了大量的人力和物力,如何在现有的条件下,搞好局域网络的安全,是医院内网络管理人员的一个重要课题.本文从系统安全、网络运行安全和内部网络安全分析,并应用到实践中,以保证网络安全.