An Online Data-Driven Fault Diagnosis Method for Air Handling Units by Rule and Convolutional Neural Networks.

The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system's historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.

Duopoly Price Competition in Wireless Sensor Network-Based Service Provision.

The Internet of Things (IoT) is emerging as a new communication paradigm and has attracted a significant amount of attention from both academic and engineering communities. In this paper, we consider an IoT market where three roles exist: Wireless Sensor Networks (WSNs), two service providers (SPs) and end users. The WSNs are responsible for sensing and providing data to the two SPs. Based on the sensed data from WSNs, the two SPs compete to provide services to the end users. We model the relationship between the two SPs and end users as a two-stage Stackelberg game, where the two SPs set the prices for their services firstly, and then the end users decide which SP to choose. Specifically, we consider two price-competition scenarios of the two SPs, which are engaged in two games, one is a noncooperative strategic game (NSG) where the two SPs set the prices for services simultaneously, the other is a Stackelberg game (SG) where SP1 who sets the price first is the leader and SP2 who sets the price after is the follower. Each user decides whether and which SP to purchase services from based on prices and service rates. An equilibrium is achieved in each of the two scenarios. Numerical results are conducted to verify our theoretical analysis.

Novel fractional-order decentralized control for nonlinear fractional-order composite systems with time delays.

A novel decentralized non-integer order controller applied on nonlinear fractional-order composite system(NFOCS) is proposed. In addition, some novel results for the asymptotic stabilization are shown with fractional parameter α∈0,1. First, we derive certain novel results useful for the Mittag-Leffler function. Then, we design a new decentralized fractional-order controller for the NFOCS according to the novel results applied to Mittag-Leffler function. Next, this novel asymptotic stabilization condition has been proposed. Compared with other controllers our controller has wider control gain range and weaker requirements. Moreover, we solve the asymptotic stabilization problem of the NFOCS with time delays via the novel controller. In the end, four general examples are performed to show the progressiveness of the new fractional-order decentralized controller.

Sintering conditions recognition of rotary kiln based on kernel modification considering class imbalance.

Accurate sintering condition recognition (SCR) is an important precondition for optimal control of rotary kilns. However, the occurrence probability of abnormal conditions in the industrial field is much lower than normal, resulting in imbalanced class sintering samples in general. This significantly deteriorates the effectiveness of existing recognition models in abnormal condition detection. In this paper, an integrated framework considering class imbalance is proposed for sintering condition recognition. In the proposed framework, after analysing the characteristics of thermal signals by the Lipschitz method, four discriminant features are extracted to comprehensively describe different sintering conditions. In addition, focusing on the class imbalance of sintering samples, the kernel modification method is introduced to enhance the optimal marginal distribution machine (ODM), and a novel recognition model kernel modified the ODM (KMODM) is proposed for SCR. By constructing a new conformal transformation function to modify the ODM kernel function, KMODM optimizes the spatial distribution of training samples in the kernel space, thereby alleviating the detection accuracy deterioration of the minority class. The experimental results on real thermal signals and standard datasets show that the KMODM model can effectively handle imbalanced data. Based on this, the proposed SCR framework can reduce the misjudgement of abnormal conditions and balance the recognition accuracy of each condition.

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).