Gabor特征结合快速HOG特征的行人检测

针对行人检测中HOG特征提取速度慢且易忽视细节特征的问题,提出了一种Gabor特征结合快速HOG特征的行人检测算法.首先对输入图像进行小波变换,并引入积分图思想和主成分分析算法快速提取图像HOG特征;其次融合Gabor小波变换得到的Gabor特征,最后采用混合特征训练分类器,实现行人的有效检测.测试集上的实验结果表明,在使用相同分类器的情况下,该混合特征提取方法比单一特征提取方法的检测正确率最多可提高7.37％,因此所提出的算法可以有效地提高行人检测的精度.     

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats. In this paper, the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared; These two IDS dataset is preprocessed, then Auto Cryptographic Denoising (ACD) adopted to remove noise in the feature of the IDS dataset; the classifier algorithms, K-Means and Neural network classifies the dataset with adam optimizer. IDS classifier is evaluated by measuring performance measures like f-measure, recall, precision, detection rate and accuracy. The neural network obtained the highest classifying accuracy as 91.12% with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset. Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.     

Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves, when the destination and source nodes are not in range of coverage. Because of its wireless type, it has lot of security concerns than an infrastructure networks. Wormhole attacks are one of the most serious security vulnerabilities in the network layers. It is simple to launch, even if there is no prior network experience. Signatures are the sole thing that preventive measures rely on. Intrusion detection systems (IDS) and other reactive measures detect all types of threats. The majority of IDS employ features from various network layers. One issue is calculating a huge layered features set from an ad-hoc network. This research implements genetic algorithm (GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks. For attack detection, the reduced feature set was put to a fuzzy logic system (FLS). The performance of proposed model was compared with principal component analysis (PCA) and statistical parametric mapping (SPM). Network performance analysis like delay, packet dropping ratio, normalized overhead, packet delivery ratio, average energy consumption, throughput, and control overhead are evaluated and the IDS performance parameters like detection ratio, accuracy, and false alarm rate are evaluated for validation of the proposed model. The proposed model achieves 95.5% in detection ratio with 96.8% accuracy and produces very less false alarm rate (FAR) of 14% when compared with existing techniques.     

基于分形布朗运动和Ada Boosting的多类音频例子识别

提出了一种基于分形布朗运动的音频特征提取和识别方法．这种方法使用分形布朗运动模型计算出音频例子的分形维数，并作为其分形特征．针对音频分形特征符合高斯分布的特点，使用Ada Boosting算法进行特征约减．然后分别使用Ada-加权高斯分类器和支持向量机对约减特征后的音频分类，并在两类分类的基础上构造多类分类的模型．实验表明，经过特征约减后的音频分形特征在音乐和语音的分类中都优于其他音频特征．     

一种融合KPCA和KDA的人脸识别新方法

核判别分析（KDA）和核主成分分析（KPCA）分别是线性判别分析（LDA）和主成分分析（PCA）在核空间中的非线性推广,提出了一种融合KDA和KPCA的特征提取方法并应用于人脸识别中,该方法综合利用KDA和KPCA 的优点来提高人脸识别的性能。此外,还提出了一种广义最近特征线(GNFL)方法来构造有效的分类器。实验结果证明：提出的方法获得了更好的识别结果。     

基于DDCT与TCSVD的人脸特征提取与识别算法

提出一种基于分块离散余弦变换(DCT)与奇异值分解阈值压缩(TCSVD)的人脸特征提取与识别算法。该算法对人脸图像进行分块DCT变换,根据图像块位置和能量分布选择不同的DCT高低频分量构建特征矩阵,通过对每个图像块的特征矩阵进行SVD阈值压缩和特征组合来构建人脸鉴别特征,并利用分类器进行特征分类与识别。AR人脸库上的实验结果验证了该算法的有效性。     

A novel neural-network approach of analog fault diagnosis based on kernel discriminant analysis and particle swarm optimization

Kernel principal component analysis (KPCA) and kernel linear discriminant analysis (KLDA) are two commonly used and effective methods for dimensionality reduction and feature extraction. In this paper, we propose a KLDA method based on maximal class separability for extracting the optimal features of analog fault data sets, where the proposed KLDA method is compared with principal component analysis (PCA), linear discriminant analysis (LDA) and KPCA methods. Meanwhile, a novel particle swarm optimization (PSO) based algorithm is developed to tune parameters and structures of neural networks jointly. Our study shows that KLDA is overall superior to PCA, LDA and KPCA in feature extraction performance and the proposed PSO-based algorithm has the properties of convenience of implementation and better training performance than Back-propagation algorithm. The simulation results demonstrate the effectiveness of these methods.     

基于多种LBP特征集成学习的人脸识别*

单一的特征与分类器只能对限定条件下的人脸进行较好的识别,当在非限定条件下(如光照、背景等发生变化时)将出现人脸识别率较低问题,针对该问题,提出了一种基于多种局部二进制特征集成学习的人脸识别算法。首先,使用监督梯度下降法 (SDM)对人脸特征点定位,应用中心对称局部二进制(CSLBP)算子提取每个特征点邻域特征,将所有人脸特征点邻域特征合成为精细的纹理特征;同时运用分区LBP直方图算法提取人脸区域的微观空间结构特征;然后,使用K最近邻算法(KNN)和支持向量机(SVM)分别训练这两种特征,得到类别排序列表和投票决策矩阵;最后,利用加权求和的规则融合决策矩阵,构成最优集成分类器,从而得到输出类别。通过在非限制性人脸库LFW上实验结果表明,所提算法采用集成的方法明显优于单一的特征和分类器。     

Decision tree based light weight intrusion detection using a wrapper approach

The objective of this paper is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. Therefore in this work, the design of IDS is investigated from these three perspectives. The goals of this paper are (i) removing redundant instances that causes the learning algorithm to be unbiased (ii) identifying suitable subset of features by employing a wrapper based feature selection algorithm (iii) realizing proposed IDS with neurotree to achieve better detection accuracy. The lightweight IDS has been developed by using a wrapper based feature selection algorithm that maximizes the specificity and sensitivity of the IDS as well as by employing a neural ensemble decision tree iterative procedure to evolve optimal features. An extensive experimental evaluation of the proposed approach with a family of six decision tree classifiers namely Decision Stump, C4.5, Naive Baye’s Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern has been introduced.     

Recognition of control chart patterns using an intelligent technique

Control chart patterns (CCPs) are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. This paper presents a novel hybrid intelligent method for recognition of common types of CCP. The proposed method includes three main modules: the feature extraction module, the classifier module and optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module multilayer perceptron neural network and support vector machine (SVM) are investigated. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, improved bees algorithm is proposed for selecting of appropriate parameters of the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy.     

Dynamic human contact prediction based on naive Bayes algorithm in mobile social networks

Human contact prediction is a challenging task in mobile social networks. The existing prediction methods are based on the static network structure, and directly applying these static prediction methods to dynamic network prediction is bound to reduce the prediction accuracy. In this paper, we extract some important features to predict human contacts and propose a novel human contact prediction method based on naive Bayes algorithm, which is suitable for dynamic networks. The proposed method takes the ever-changing structure of mobile social networks into account. First, the past time is partitioned into many periods with equal intervals, and each period has a feature matrix of all node pairs. Then, with the feature matrixes used for classifiers training based on naive Bayes algorithm, we can get a classifier for each time period. At last, the different weights are assigned to the classifiers according to their importance to contact prediction, and all classifiers are weighted combination into the final prediction classifier. The extensive experiments are conducted to verify the effectiveness and superiority of the proposed method, and the results show that the proposed method can improve the prediction accuracy and TP Rate to a large extent. Besides, we find that the size of time interval has a certain impact on the clustering coefficient of mobile social networks, which further affects the prediction accuracy.     

Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

脑电信号多维特征融合与分类算法研究

针对目前运动想象脑电信号特征提取单一,分类识别准确率低等现象,结合卷积神经网络分类器,提出了一种多维度特征加权融合的特征融合算法来提高运动想象脑电识别率。对预处理后的脑电信号进行小波包变换,提取其共空间特征、能量特征、边际谱熵特征以及非线性动力学特征,然后加权融合,使用卷积神经网络分类器分类。为验证算法的合理性,使用BCI-IV Dataset 2a数据集对提出的特征融合算法进行验证分析,结果表明,所提出的加权特征融合算法结合CNN分类器可以有效提高运动想象识别准确率。实验中,9位志愿者平均分类准确率达到75.88%,平均Kappa系数为0.70。     

基于复杂网络特性的带钢表面缺陷识别

针对带钢表面缺陷识别问题,提出一种基于动态演化复杂网络特性的特征描述方法, 这些特征同时具有位移、旋转不变性、大小不变性、较强的抗干扰能力和鲁棒性,为 缺陷识别提供良好的分类特征;为了提高分类器的效率,应用主成分分析法 (Principal component analysis, PCA) 对复杂网络特 征向量进行特征降维处理;采用最优有向无环图支持向量机 (Directed acyclic graph support vector machine, DAG-SVM)算法进行缺陷分类.结果表明该方法识别率高而且识别速度快.     

An energy-efficient geographic routing protocol design in vehicular ad-hoc network

The combination of vehicles and wireless communication has resulted in a promising future for vehicular ad hoc networks (VANETs). The VANET is a kind of network that can be built randomly, quickly and temporarily without any infrastructure. The routing of data in a VANET is a challenging task due to the high dynamics, unstable connection environments and transfer direction limit (real road planning) involved. However, it has been discussed that radio obstacles, as found in urban areas, have a significant negative impact on the performance of location based routing. Therefore, the design of a suitable routing protocol for VANETs is an important issue, especially in regard to inter-vehicle communication applications. This paper proposes a energy-efficient geographic routing algorithm that uses the direction, density and distance between nodes in the crossroad routing strategy, to improve the link stability. We compare the novel scheme, ad hoc on-demand distance vector, and dynamic source routing (DSR) for packet loss rate and average end-to-end delay in VANETs to reduce the power consumptions. The simulation results are obtained which demonstrate the power effectiveness of our proposed routing strategy.     

改进的脉冲耦合神经网络人脸特征提取方法

针对基于子空间分解的人脸特征提取方法对人脸图像在采集过程中的光照、旋转、位置等变化较为敏感的问题,提出了一种改进的脉冲耦合神经网络人脸特征提取方法。该方法模拟生物视觉的感知过程,将人脸图像分解成由若干二值图像组成的认知序列,计算序列中的每幅二值图像的熵作为人脸特征,基于支持向量机实现分类与识别;同时克服了标准的脉冲耦合神经网络模型参数过多的缺点,识别率也有所改善。理论与实验结果表明,该方法与现有的基于子空间分解的人脸特征提取方法相比,对人脸图像在采集过程中的光照、旋转、位置等变化有较强的鲁棒性,而且具有较低的维数。     

基于相对速度的802.11p车载网络自适应退避算法*

IEEE 802.11p协议是针对车载环境应用制定的物理层与MAC层标准.车载网络中节点的高移动性和拓扑结构的频繁变更导致网络丢包率增加.为解决车辆移动特性引发的信道访问不公平性问题及降低网络丢包率,提出一种基于相对速度的自适应退避算法RSBA,依据移动节点间相对速度差异优化退避机制.通过NCTUns模拟实验表明,RS...     

基于经验模式分解和匹配追踪的人脸检测

提出了一种新的正面人脸检测算法。该方法利用经验模式分解和匹配追踪算法来提取人脸特征，训练Bayes分类器来进行分类判决。在FERET人脸库中与特征脸(Eigenfaces)方法进行了比较，实验结果表明，该算法的计算效率和检测精度均优于特征脸方法。     

一种基于旋转森林的集成协同训练算法

集成协同训练算法（ensemble co-training）是将集成学习（ensemble learning）和协同训练算法（co-training）相结合的半监督学习方法,旋转森林（rotation forest）是利用特征提取来构造基分类器差异性的集成学习方法,在对现有的集成协同训练算法研究基础上,提出了基于旋转森林的协同训练算法——ROFCO,该方法重在利用未标记数据提高基分类器之间的差异性和特征提取效果,使基分类器的泛化误差保持不变或下降的同时,能保持甚至提高基分类器之间的差异性,提高集成效果。实验结果表明该方法能取得较好效果。