基于光纤环网的隧道火灾监控系统研究

文章对基于光纤环网的隧道群火灾报警系统进行研究,分析了其优缺点,提出了解决办法,并完成改造,应用于实际.     

F-BIDS: Federated-Blending based Intrusion Detection System

The rapid development of network communication along with the drastic increase in the number of smart devices has triggered a surge in network traffic, which can contain private data and in turn affect user privacy. Recently, Federated Learning (FL) has been proposed in Intrusion Detection Systems (IDS) to ensure attack detection, privacy preservation, and cost reduction, which are crucial issues in traditional centralized machine-learning-based IDS. However, FL-based approaches still exhibit vulnerabilities that can be exploited by adversaries to compromise user data. At the same time, meta-models (including the blending models) have been recognized as one of the solutions to improve generalization for attack detection and classification since they enhance generalization and predictive performances by combining multiple base models. Therefore, in this paper, we propose a Federated Blending model-driven IDS framework for the Internet of Things (IoT) and Industrial IoT (IIoT), called F-BIDS, in order to further protect the privacy of existing ML-based IDS. The proposition consists of a Decision Tree (DT) and Random Forest (RF) as base classifiers to first produce the meta-data. Then, the meta-classifier, which is a Neural Networks (NN) model, uses the meta-data during the federated training step, and finally, it makes the final classification on the test set. Specifically, in contrast to the classical FL approaches, the federated meta-classifier is trained on the meta-data (composite data) instead of user-sensitive data to further enhance privacy. To evaluate the performance of F-BIDS, we used the most recent and open cyber-security datasets, called Edge-IIoTset (published in 2022) and InSDN (in 2020). We chose these datasets because they are recent datasets and contain a large amount of network traffic including both malicious and benign traffic.     

Japanese educational and learning systems in the post-industrial society: A value chain of educational and learning systems

Japan's educational system has some major problems. The most important among these concerns is the basic concept of the educational process and the goal of education. The old concept of public educational systems has become outdated in today's Japanese society, although this concept had supported social and spiritual faith, economic success and selfless devotion to one's country for more than 100 years. Now, Japanese people need a new concept of the educational process and the goal of education for the twenty-first century. The paper proposes a value chain of educational and learning systems aimed at building a network consisting of multiple fields for fostering future human resources.     

计算机信息管理系统课程的实践教学体系

本文结合我校教学的实际情况,探讨了计算机信息管理系统课程实践教学的改革,主要包括"三阶段"的技术要求、"企业化"的分组管理、"过程化"的项目运行和"合理化"的考核评价办法。     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

一类多输入多输出系统的冗余设计

分析了多输入多输出多变量系统在某环节失效时系统的整体性,给出了针对开环传递函数矩阵以及解耦后新对象均为本质稳定对象时系统的冗余设计方法.该方法有效解决了某环节失效后对系统稳定性的影响,实现了复杂系统的容错控制.仿真结果表明,该冗余设计方法可行,能够实现系统的容错控制.     

Space and time localization for the estimation of distributed parameters in a finite element model

In this paper we study the problem of estimating the possibly non-homogeneous material coefficients inside a physical system, from transient excitations and measurements made in a few points on the boundary. We assume there is available an adequate Finite Element (FEM) model of the system, whose distributed physical parameters must be estimated from the experimental data.We propose a space–time localization approach that gives a better conditioned estimation problem, without the need of an expensive regularization. Some experimental results obtained on an elastic system with random coefficients are given.     

Pricing schemes for digital content with DRM mechanisms

In this paper, utilizing game-theoretic model, we examine the impact of collaborative structure, content quality, and network environment on the development of pricing scheme and DRM protection policy of digital content. DRM protection level decreases and pirating activities becomes relatively tolerable as the content provider and platform provider operate collaboratively. Depending on the market structure, higher content quality may strengthen or weaken the adoption of DRM. However, it would seem that, as the network environment becomes more decentralized and uncontrolled, weaker DRM protection should be a better strategy.     

Time-local formulation and identification of implicit Volterra models by use of diffusive representation

We present a time-continuous identification method for nonlinear dynamic Volterra models of the form HX=f(u,X)+v with H, a causal convolution operator. It is mainly based on a suitable parameterization of H deduced from the so-called diffusive representation, which is devoted to state representations of integral operators. Following this approach, the complex dynamic nature of H can be summarized by a few numerical parameters on which the identification of the dynamic part of the model will focus. The method is validated on a physical numerical example.     

Stochastic system transformation using generalized orthonormal basis functions with applications to continuous-time system identification

This paper studies the system transformation using generalized orthonormal basis functions that include the Laguerre basis as a special case. The transformation of the deterministic systems is studied in the literature, which is called the Hambo transform. The aim of the paper is to develop a transformation theory for stochastic systems. The paper establishes the equivalence of continuous and transformed-discrete-time stochastic systems in terms of solutions. The method is applied to the continuous-time system identification problem. It is shown that using the transformed signals the PO-MOESP subspace identification algorithm yields consistent estimates for system matrices. An example is included to illustrate the efficacy of the proposed identification method, and to make a comparison with the method using the Laguerre filter.