Yi Chai, Yungang Wang, Yuansheng Wang, Le Peng and Lvyuan Hou
At present, the statistics of human error events in domestic civil aviation are limited, and the analysis indicators are difficult to quantify. The purpose of this study is to…
Abstract
Purpose
At present, the statistics of human error events in domestic civil aviation are limited, and the analysis indicators are difficult to quantify. The purpose of this study is to reduce the incidence of human error events and improve the safety of civil aviation.
Design/methodology/approach
In this paper, a safety prevention evaluation method combining analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) is proposed. The risk factors of civil aviation safety are identified through questionnaire survey and calculated by MATLAB software.
Findings
The results of the study are as follows: a safety risk evaluation index system including 4 first-level indicators and 16 second-level indicators is constructed; the AHP is used to calculate the weight of the influencing factors of human error and sort them; and the FCE method is used to quantitatively evaluate the safety prevention of civil aviation human error and put forward the countermeasures.
Research limitations/implications
This study also has some limitations. While it provides an overall quantitative identification of civil aviation safety risk factors, the research methods chosen, such as the questionnaire survey method and the AHP, involve individual subjectivity. Consequently, the research results may have errors. In the preliminary preparation of the follow-up study, we should analyze a large number of civil aviation accident investigation reports, more accurately clarify the human error factors and completely adopt the quantitative analysis method in the research method.
Practical implications
This study identifies the risk factors of civil aviation safety and conducts a reasonable analysis of human error factors. In the daily training of civil aviation, the training can be focused on previous man-made accidents; in view of the “important” influencing factors, the aviation management system is formulated to effectively improve the reliability of aviation staff; according to the evaluation criteria of human error in civil aviation, measures to prevent and control accidents can be better formulated.
Social implications
In view of these four kinds of influencing factors, the corresponding countermeasures and preventive measures are taken according to the discussion, so as to provide the basis for the prevention of aviation human error analysis, management and decision-making, prevent the risk from brewing into safety accidents and improve the safety of aviation management.
Originality/value
Based on the questionnaire survey, this study creatively applies the safety prevention evaluation method combining AHP and FCE to the study of civil aviation human error, integrates the advantages of qualitative and quantitative methods, flexibly designs qualitative problems, objectively quantifies research results and reduces subjective variables. Then, by discussing civil aviation safety management measures to avoid risk factors, reduce the incidence of human error events and improve the safety of civil aviation.
Details
Keywords
Bailing Zhang, Yungang Zhang and Wenjin Lu
The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities. There have been many intrusion detection schemes proposed, most…
Abstract
Purpose
The task of internet intrusion detection is to detect anomalous network connections caused by intrusive activities. There have been many intrusion detection schemes proposed, most of which apply both normal and intrusion data to construct classifiers. However, normal data and intrusion data are often seriously imbalanced because intrusive connection data are usually difficult to collect. Internet intrusion detection can be considered as a novelty detection problem, which is the identification of new or unknown data, to which a learning system has not been exposed during training. This paper aims to address this issue.
Design/methodology/approach
In this paper, a novelty detection‐based intrusion detection system is proposed by combining the self‐organizing map (SOM) and the kernel auto‐associator (KAA) model proposed earlier by the first author. The KAA model is a generalization of auto‐associative networks by training to recall the inputs through kernel subspace. For anomaly detection, the SOM organizes the prototypes of samples while the KAA provides data description for the normal connection patterns. The hybrid SOM/KAA model can also be applied to classify different types of attacks.
Findings
Using the KDD CUP, 1999 dataset, the performance of the proposed scheme in separating normal connection patterns from intrusive connection patterns was compared with some state‐of‐art novelty detection methods, showing marked improvements in terms of the high intrusion detection accuracy and low false positives. Simulations on the classification of attack categories also demonstrate favorable results of the accuracy, which are comparable to the entries from the KDD CUP, 1999 data mining competition.
Originality/value
The hybrid model of SOM and the KAA model can achieve significant results for intrusion detection.
Details
Keywords
Liang Gong, Hang Dong, Xin Cheng, Zhenghui Ge and Liangchao Guo
The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.
Abstract
Purpose
The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.
Design/methodology/approach
This study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.
Findings
The experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.
Originality/value
This study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.