Nowadays vehicle type classification is a fundamental part of intelligent transportation systems (ITSs) and is widely used in various applications like traffic flow monitoring, security enforcement, and autonomous driving, etc. However, vehicle classification is usually used in supervised learning, which greatly limits the applicability for real ITS. This article proposes a semisupervised vehicle type classification scheme via ensemble broad learning for ITS. This presented method contains two main parts. In the first part, a collection of base broad learning system (BLS) classifiers is trained by semisupervised learning to avoid time-consuming training process and alleviate the increasingly unlabeled samples burden. In the second part, a dynamic ensemble structure constructed by trained classifier groups with different characteristics obtains the highest type probability and determine which the vehicle belongs, so as to achieve superior generalization performance than a single base classifier. Several experiments conducted on the pubic BIT-Vehicle dataset and MIO-TCD dataset demonstrate that the proposed method outperforms single BLS classifier and some mainstream methods on effectiveness and efficiency.

Crack is the earlier indication of concrete structural severe damage, it plays an important role in structure health monitoring (SHM) of industrial civil infrastructures (such as building, bridge, road and dam, etc.). Crack damage classification is the first and critical stage for concrete SHM. However, commonly used human visual classification is costly, labor intensive and unreliable, and other machine learning based classification methods also have some drawbacks. To address these problems, this paper proposes a cascade broad neural network (CBNN) architecture for concrete surface structural crack damage automated classification, which generates an effective and efficient framework with much less hyper-parameters than deep neural networks, and sufficiently explores the advantages of multi-level cascades of classifier ensemble. Experimental results on 4 challenging datasets demonstrate that its performance is quite more excellent than current mainstream state-of-the-art classification methods (both in testing accuracy and training time)

Sensors equipped in the high-speed trains can produce a lot of data with normal working condition and different faults occurred. In recent years, many data-driven methods were developed for fault detection and diagnosis (FDD). However, inaccurate diagnosis and costly computation are still great challenges existed. In order to address those issues, this paper developed a FDD architecture using statistic feature and the improved broad learning system (BLS) to promote performance. It uses statistic feature to capture inherit discrimination of normal data and fault data, then adopts the improved BLS model to achieve accurate and fast fault diagnosis without time-consuming training and mathematical models of high-speed trains. In validation, the proposed FDD scheme is firstly conducted on software-based fault-injection simulation platform, it can give guiding significance for the subsequent hardware-in-the-loop simulation platform. All results show that the presented FDD framework achieves state-of-the-art performance than other mainstream FDD methods.

Heterogeneous Multi-Agents System (MAS) has been attracting increasing attention in many application areas, but the safety and reliability of MAS are still challenging issues. Fault diagnosis is a necessary technology to ensure the safety and reliability of heterogeneous MAS. According to the characteristics of high dispersion in MAS, strong local perception ability and weak global perception ability, this paper proposes a distributed hybrid knowledge-based and data-driven fault diagnosis, which realizes dynamic re-construction of data and knowledge through reinforcement learning and fuzzy broad learning. In the meantime, we also consider communication network topology to realize distributed collaborative diagnosis, which can effectively improve the diagnostic performance. Then, we develop a high-fidelity heterogeneous MAS software-in-the-loop and hardware-in-the-loop fault simulators to simulate different types of failures (i.e., actuator failure, sensor and communication failure). Finally, through the cross-validation on the above developed simulators, this work verifies the effectiveness of the proposed distributed intelligent fault diagnosis.

A heterogeneous multiagent system (MAS) can easily experience unpredictable faults due to its complex structure and involvement of different individuals. However, existing approaches have several issues, including complicated network architecture, insufficient feature extraction, and poor generalization ability. This study proposes a novel framework called prior-knowledge-augmented broad reinforcement learning (PK-BRL) to effectively diagnose faults in a heterogeneous MAS. First, we construct a highly realistic visualized heterogeneous MAS and perfom fault injection. Second, we present a novel fault diagnosis framework based on broad reinforcement learning with prior knowledge that effectively integrates offline reinforcement learning and broad learning into the fault diagnosis process. The interaction between heterogeneous multiagents and the constructed environment enables us to learn a superior fault diagnosis strategy. Finally, experiments conducted on software-in-the-loop and hardware-in-the-loop platforms demonstrate that the proposed PK-BRL framework has a state-of-the-art diagnostic accuracy for the heterogeneous MAS, which offers valuable theoretical and practical significance for real-world application.

Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of the margin of an ensemble-based classification and selects the smallest margin instances as support vectors. Our experimental results show that our method reduces training set size significantly without degrading the performance of the resulting SVMs classifiers.