Convolutional neural network: Deep learning-based classification of building quality problems

The rapid development of the construction industry in China has introduced unprecedented quality-related problems in the country’s building industry. In response to this issue, the government has established various complaint channels to report quality problems. Therefore, building quality complaints (BQCs) need to be classified and solved by respective agencies or departments rapidly for avoiding adverse impact on the safety, health, and well-being of people. However, the current process of classifying BQCs is labor intensive, time consuming, and error prone. An automatic complaint classification is required to improve the effectiveness and efficiency of complaint handling, but studies on this issue are limited. Prevailing text classification research in construction has focused on utilizing conventional shallow machine learning. By contrast, this study explores a novel convolutional neural network (CNN)-based approach that incorporates a deep-learning method to automatically classify the short texts contained within BQCs. The presented approach enables capturing the semantic features in BQC texts and automatic classification of the BQCs into predefined categories. After the model optimization, tests are conducted to examine the practical application of the text classification approach compared with Bayes-based and support vector machine classifiers. Results indicate that the developed CNN-based approach performs well in the Chinese BQC classification with limited manual intervention and few complicated feature engineering.     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

面向任务口语理解研究现状综述

口语理解是对话系统重要的功能模块，语义槽填充和意图识别是面向任务口语理解的两个关键子任务。近年来，联合识别方法已经成为解决口语理解中语义槽填充和意图识别任务的主流方法，介绍两个任务由独立建模到联合建模的方法，重点介绍基于深度神经网络的语义槽填充和意图识别联合建模方法，并总结了目前存在的问题以及未来的发展趋势。     

Study on the long-term behaviour of glass fibre in the tensile stress field

Glass fibre is a random network structure composed of [SiO₄] tetrahedra. The structure contains a large number of defects, which act as crack initiation points. Under tensile stress, cracks undergo crack initiation, stable propagation, failure propagation, and fracture, and the stress that begins after unstable propagation is called the critical fracture stress. When the stress is less than the critical value, the crack is subject to the force of chemical bonds during the crack propagation process, and crack arrest occurs. When the stress is greater than the critical value, the glass fibre will undergo destructive fracture. In this paper, long-term tensile tests were carried out on glass fibre and a glass fibre composite under different constant tensile stress conditions. The fracture times of the glass fibre and glass fibre composite under different tensile stresses were obtained, the critical fracture stress of glass fibre was inferred, and the fracture mechanism was explained.     

Microwave irradiated Carrageenan-Guar gum micro-porous IPN: a novel material for isotropic tissue scaffolding

In this study, a novel micro porous honeycomb structured Crg-GG-IPN material was incepted to be applicable as scaffold and accomplished. The hydrophilicity was confirmed by FT-IR and OCA. Amorphous nature and micro-rough surface were confirmed by XRD and AFM. Void fraction was 0.61?±?0.04. Void space, hemocompatibility and platelet adhesion were captured by SEM. Degradability of the material was confirmed by in-vitro degradation study. Incision method using mice model was a clear evidence for cell attachment and non-toxicity and was confirmed from hematology and histopathology. Thus, it appears that Crg-GG scaffolds can be useful as wound healing material for clinical applications.     

GreyFan:一种Wi-Fi隐蔽信道攻击框架

针对现实网络中诸如侧信道攻击、HID 攻击等传统的物理攻击，物理隔离被认为是一种较为彻底的抵御网络攻击的安全防护手段。2018 年，业界首次提出了一种物理隔离环境下的 Wi-Fi 隐蔽信道方法——Ghost Tunnel，即在Wi-Fi尚未连接的状态下，无线AP可成功将数据传给发起连接请求的计算机。提出了一种基于Ghost Tunnel方法的攻击框架—— GreyFan，利用该攻击框架攻击者可以对未连接Wi-Fi的用户实施无感知攻击，如文件隐蔽传输、任意代码执行等，并分析了相应的防御技术。     

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model.     

网络匿名扫描系统设计及优化

针对网络扫描工具在进行扫描时面临的溯源问题，提出了一种匿名网络扫描系统。首先将匿名系统与网络扫描工具结合以实现匿名扫描；然后在现有匿名系统的基础上实现了该系统的本地私有化；接着通过流量分析发现，Nmap的多进程扫描因为代理链的原因会变成单进程扫描而导致其扫描扫描性能较低；最后提出了一种基于多Namp进程并发的性能优化方案，将总体扫描任务分割为多个扫描任务，并分配给多个单独的Nmap进程并行运行。实验结果表明，该性能优化方案的扫描时延接近正常扫描情况下的时延，达到了提高匿名扫描系统性能的目的。因此，该优化后的网络匿名扫描系统在阻碍溯源的同时提升了扫描效率。     

车辆自组织网络中新型NP-CSMA随机多址协议研究分析

智能交通的出现使得车辆自组织网络受到越来越多的关注。车辆自组织网络的动态拓扑结构变化非常剧烈,这对网络的吞吐率、传输速率等性能提出了很高的要求。提出1种具有握手机制协议的自适应多通道双时钟NP-CSMA随机多址接入协议。该协议首先区分2种P-CSMA协议,握手机制成功解决了隐藏的终端问题,双时钟机制减少了平均空闲时间,多通道机制增加通道数量和划分用户优先级的同时提高了系统吞吐率,自适应机制能够使系统在高负载下保持稳定吞吐率。还对该协议的传输速率进行了分析,得出该协议的传输速率相对较高的结论。通过平均周期方法推导吞吐率和传输速率的计算公式,仿真结果与理论推导一致。     

Adaptive neural network command filtered backstepping control of pure‐feedback systems in presence of full state constraints

An adaptive neural network (NN) command filtered backstepping control is proposed for the pure‐feedback system subjected to time‐varying output/stated constraints. By introducing a one‐to‐one nonlinear mapping, the obstacle caused by full stated constraints is conquered. The adaptive control law is constructed by command filtered backstepping technology and radial basis function NNs, where only one learning parameter needs to be updated online. The stability analysis via nonlinear small‐gain theorem shows that all the signals in closed‐loop system are semiglobal uniformly ultimately bounded. The simulation examples demonstrate the effectiveness of the proposed control scheme.