A high accuracy modeling scheme for dynamic systems: spacecraft reaction wheel model

Reaction wheels are crucial actuators in spacecraft attitude control subsystem (ACS). The precise modeling of reaction wheels is of fundamental need in spacecraft ACS for design, analysis, simulation, and fault diagnosis applications. The complex nature of the reaction wheel leads to modeling difficulties utilizing the conventional modeling schemes. Additionally, the absence of reaction wheel providers’ parameters is crucial for triggering a new modeling scheme. The Radial Basis Function Neural Network (RBFNN) has an efficient architecture, alluring generalization properties, invulnerability against noise, and amazing training capabilities. This research proposes a promising modeling scheme for the spacecraft reaction wheel utilizing RBFNN and an improved variant of the Quantum Behaved Particle Swarm Optimization (QPSO). The problem of enhancing the network parameters of the RBFNN at the training phase is formed as a nonlinear constrained optimization problem. Thus, it is proposed to efficiently resolve utilizing an enhanced version of QPSO with mutation strategy (EQPSO-2M). The proposed technique is compared with the conventional QPSO algorithm and different variants of PSO algorithms. Evaluation criteria rely upon convergence speed, mean best fitness value, stability, and the number of successful runs that has been utilized to assess the proposed approach. A non-parametric test is utilized to decide the critical contrast between the results of the proposed algorithm compared with different algorithms. The simulation results demonstrated that the training of the proposed RBFNN-based reaction wheel model with enhanced parameters by EQPSO-2M algorithm furnishes a superior prediction accuracy went with effective network architecture.

Cost-effective Network Disintegration through Targeted Enumeration

We live in a hyperconnected world---connectivity that can sometimes be detrimental. Finding an optimal subset of nodes or links to disintegrate harmful networks is a fundamental problem in network science, with potential applications to anti-terrorism, epidemic control, and many other fields of study. The challenge of the network disintegration problem is to balance the effectiveness and efficiency of strategies. In this paper, we propose a cost-effective targeted enumeration method for network disintegration. The proposed approach includes two stages: searching candidate objects and identifying an optimal solution. In the first stage, we use rank aggregation to generate a comprehensive node importance ranking, upon which we identify a small-scale candidate set of nodes to remove. In the second stage, we use an enumeration method to find an optimal combination among the candidate nodes. Extensive experimental results on synthetic and real-world networks demonstrate that the proposed method achieves a satisfying trade-off between effectiveness and efficiency. The introduced two-stage targeted enumeration framework can also be applied to other computationally intractable combinational optimization problems, from team assembly, via portfolio investment, to drug design.

The Time of Patriarchs Peter IV and Damian As the Nodal Point of the Genesis of the Coptic Church: Problems and Proposed Solutions

Introduction. The article examines a recently put forward hypothesis that the time of the Coptic Church’s final genesis was the period of the Alexandrian anti-Chalcedonian Patriarchs Peter IV (576–578) and Damian (578–607). Methods. A comparative research method and factor analysis are used. The main research task is to identify all the factors that contributed to the making of full-fledged ecclesiastical structures by the Theodosians (one of the trends of the Egyptian Miaphysites), and a correlation of these factors with each other to single out the main of them. Analysis. The successful establishment of the Miaphysites (Theodosian) episcopate resulted from the configuration of objective and subjective factors. Objective factors include the following: the weakening of control by the central authorities over the structures of the Miaphysites after Justinian I (482/483–565), the increasing regionalization of the empire and the strengthening of the role of local elites in the provinces, the growing importance of the Coptic language in secular and clerical office work. Subjective factors include the victory of the Miaphysite Patriarch Peter IV over his rival Theodore and the appearance of Damian as Peter’s successor. Results. On the whole, the proposed hypothesis quite thoroughly explains the emergence of the Coptic Church during the period. However, it has several disadvantages, which open up a number of prospects for further researches. Firstly, there is almost no explanation for the success of Damian’s personnel policy. Secondly, insufficient attention was paid to the Egyptian anti-Chalcedonian monasticism. From the author’s point of view, Egyptian Miaphysite monks, suffering from the pressure of the central and local authorities after the Chalcedonian schism, managed to establish an effective network functioned as a “rhizome”, on which the episcopate risen during Peter’s and Damian’s time relied primarily in rebuilding stable ecclesiastical structures in Egypt.

Lithium-ion power battery grouping: A multisource data fusion based clustering approach and distributed deployment

Consistence of lithium-ion power battery significantly affects the life and safety of battery modules and packs. To improve the consistence, battery grouping is employed, assembling batteries with similar electrochemical characteristics to make up modules and packs. Therefore, grouping process boils down to unsupervised clustering problem. Current used grouping approaches include two aspects, static characteristics based and dynamic based. However, there are three problems. First, the common problem is underutilization of multi-source data. Second, for the static characteristics based, there is grouping failure over time. Third, for the dynamic characteristics based, there is high computational complexity. To solve these problems, we propose a distributed multisource data fusion based battery grouping approach. The proposed approach designs an effective network structure for multisource data fusion, and a self supervised scheme for feature extraction from both static and dynamic multisource data. We apply our approach on real battery modules and test state of health (SOH) after charging-discharging cycles. Experimental results indicate that the proposed scheme can increase SOH of modules by 3.89%, and reduce the inconsistence by 68.4%. Meanwhile, with the distributed deployment the time cost is reduced by 87.9% than the centralized scheme.

Construction of University Online Examination System Based on Cloud Computing Technology

With cloud computing's powerful computing power, many end users can create a variety of effective network applications using the cloud's services without having to worry about computing technology or access methods. Based on CC technology's on-demand service characteristics and unlimited dynamic expansion capability, this article designs and implements a shared network examination system. In the Web mode, the functions of receiving and distributing examination data, identity verification, online examination, and examination result collection can be realized using the SaaS deployment structure, MVC three-tier architecture, Java modeling language, XFIE, JSON, web service, DES, and other technologies combined with MySQL database. At the same time, the improved parallel genetic annealing algorithm (IPGAA) is proposed as a CC resource scheduling strategy. The IPGAA has better adaptability in the CC system with various cloud resources because it combines the fast global search ability of the genetic algorithm (GA) with the local search ability of the simulated annealing algorithm. Simulation tests show that the IPGAA is effective.

Low-Light Image Enhancement Based on Generative Adversarial Network

Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.

Self-organization of in vitro neuronal assemblies drives to complex network topology

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of electrophysiological signals recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the neuronal network topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular community topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of communities. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

A Survey on the Implementation and Management of Secure Virtual Private Networks (VPNs) and Virtual LANs (VLANs) in Static and Mobile Scenarios

Nowadays, the demand for connection between the remote offices of a company, or between research locations, and constantly increasing work mobility (partly due to the current pandemic emergency) have grown hand in hand with the quality and speed of broadband connections. The logical consequence of this scenario is the increasingly widespread use of Virtual Private Network (VPN) connections. They allow one to securely connect the two ends of a connection via a dedicated network, typically using the Internet and reducing the costs of Content Delivery Network (CDN) lines (dedicated connections). At the same time, Virtual Local Area Networks (VLANs) are able to decrease the impact of some scalability issues of large networks. Given the background above, this paper is focused on overviewing and surveying the main progresses related to VPNs and VLANs in wireless networks, by collecting the most important contributions in this area and describing how they can be implemented. We state that security issues in VLANs can be effectively mitigated through the combination of good network-management practices, effective network design and the application of advanced security products. However, obviously, the implementation of VPNs and VLANs poses specific issues regarding information and network security; thus some good solutions are also surveyed.

SEFPN: Scale-Equalizing Feature Pyramid Network for Object Detection

Feature Pyramid Network (FPN) is used as the neck of current popular object detection networks. Research has shown that the structure of FPN has some defects. In addition to the loss of information caused by the reduction of the channel number, the features scale of different levels are also different, and the corresponding information at different abstract levels are also different, resulting in a semantic gap between each level. We call the semantic gap level imbalance. Correlation convolution is a way to alleviate the imbalance between adjacent layers; however, how to alleviate imbalance between all levels is another problem. In this article, we propose a new simple but effective network structure called Scale-Equalizing Feature Pyramid Network (SEFPN), which generates multiple features of different scales by iteratively fusing the features of each level. SEFPN improves the overall performance of the network by balancing the semantic representation of each layer of features. The experimental results on the MS-COCO2017 dataset show that the integration of SEFPN as a standalone module into the one-stage network can further improve the performance of the detector, by ∼1AP, and improve the detection performance of Faster R-CNN, a typical two-stage network, especially for large object detection APL∼2AP.

Advances in Regression Kriging-Based Methods for Estimating Statewide Winter Weather Collisions: An Empirical Investigation

Winter conditions create hazardous roads that municipalities work hard to maintain to ensure the safety of the travelling public. Targeting their efforts with effective network screening will help transportation managers address these problems. In our recent efforts, regression kriging was found to be a viable and effective network screening methodology. However, the study was constrained by its limited spatial extent making the reported results less conclusive and transferrable. In addition, our previous work implemented what has long been adopted in most of conventional studies—the Euclidean distance; however, use of the road network distance would, intuitively, result in further improving kriging estimates, especially when dealing with transportation problems. Therefore, this study improves upon our previous efforts by developing a more advanced kriging model; namely, network regression kriging using the entire state of Iowa with the significantly expanded road network. The transferability of the developed models is also explored to investigate its generalization potential. The findings based on various statistical measures suggest that the enhanced kriging model vastly improved the estimation performance at the cost of greater computational complexity and run times. The study also suggests that regional semivariograms better represent the true nature of the local variances, though an overall model may still function adequately if higher fidelity is not required.