QAR Data Imputation Using Generative Adversarial Network with Self-Attention Mechanism

Quick Access Recorder (QAR), an important device for storing data from various flight parameters, contains a large amount of valuable data and comprehensively records the real state of the airline flight. However, the recorded data have certain missing values due to factors, such as weather and equipment anomalies. These missing values seriously affect the analysis of QAR data by aeronautical engineers, such as airline flight scenario reproduction and airline flight safety status assessment. Therefore, imputing missing values in the QAR data, which can further guarantee the flight safety of airlines, is crucial. QAR data also have multivariate, multiprocess, and temporal features. Therefore, we innovatively propose the imputation models A-AEGAN (“A” denotes attention mechanism, “AE” denotes autoencoder, and “GAN” denotes generative adversarial network) and SA-AEGAN (“SA” denotes self-attentive mechanism) for missing values of QAR data, which can be effectively applied to QAR data. Specifically, we apply an innovative generative adversarial network to impute missing values from QAR data. The improved gated recurrent unit is then introduced as the neural unit of GAN, which can successfully capture the temporal relationships in QAR data. In addition, we modify the basic structure of GAN by using an autoencoder as the generator and a recurrent neural network as the discriminator. The missing values in the QAR data are imputed by using the adversarial relationship between generator and discriminator. We introduce an attention mechanism in the autoencoder to further improve the capability of the proposed model to capture the features of QAR data. Attention mechanisms can maintain the correlation among QAR data and improve the capability of the model to impute missing data. Furthermore, we improve the proposed model by integrating a self-attention mechanism to further capture the relationship between different parameters within the QAR data. Experimental results on real datasets demonstrate that the model can reasonably impute the missing values in QAR data with excellent results

Novel Degree Constrained Minimum Spanning Tree Algorithm Based on an Improved Multicolony Ant Algorithm

Degree constrained minimum spanning tree (DCMST) refers to constructing a spanning tree of minimum weight in a complete graph with weights on edges while the degree of each node in the spanning tree is no more than d (d ≥ 2). The paper proposes an improved multicolony ant algorithm for degree constrained minimum spanning tree searching which enables independent search for optimal solutions among various colonies and achieving information exchanges between different colonies by information entropy. Local optimal algorithm is introduced to improve constructed spanning tree. Meanwhile, algorithm strategies in dynamic ant, random perturbations ant colony, and max-min ant system are adapted in this paper to optimize the proposed algorithm. Finally, multiple groups of experimental data show the superiority of the improved algorithm in solving the problems of degree constrained minimum spanning tree

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection

Cluster-based anomaly detection flow.

<p>Cluster-based anomaly detection flow.</p

Comparison of Adaboost with and without hierarchical structures.

<p>Comparison of Adaboost with and without hierarchical structures.</p

Selection of training and test samples.

<p>Selection of training and test samples.</p

A global framework of CA-AFSA-BP.

<p>A global framework of CA-AFSA-BP.</p

Cluster-based anomaly detection results.

<p>Cluster-based anomaly detection results.</p

CA-AFSA-BP mis-use detection of flow.

<p>CA-AFSA-BP mis-use detection of flow.</p

DR and FR of 4 intrusion detection algorithms.

<p>DR and FR of 4 intrusion detection algorithms.</p