In recent years, several deep learning networks are proposed to segment 2D or 3D bio-medical images. However, in liver and lesion segmentation, the proportion of interested tissues and lesions are tiny when contrasting to the image background. That is, the objects to be segmented are highly imbalanced in terms of the frequency of occurrences. This makes existing deep learning networks prone to predict pixels of livers and lesions as background. To address this imbalance issue, several loss functions are proposed. Since no researches are having made a comparison among those proposed loss functions, we are curious about that which loss function is the best among them? At the same time, we also want to investigate whether the combination of several different loss functions is effective for liver and lesion segmentation. Firstly, we propose a novel deep learning network (cascade U-ResNets) to produce liver and lesion segmentation simultaneously. Then, we investigate the performance of 5 selected loss functions, WCE (Weighted Cross Entropy), DL (Dice Loss), WDL (Weighted Dice Loss), TL (Teverskry Loss), WTL (Weighted Teversky Loss), with our cascade U-ResNets. We further assemble all cascade U-ResNets trained with different loss functions together to segment livers and lesions jointly on the liver CT (Computed Tomography) volume. Experimental results on the LiTS dataset 1 showed our ensemble model can achieve much better results than every individual model for liver segmentation.

For the rotating machinery system, it is a challenge to explore fault detection and diagnosis for multiple-faults condition, which simultaneously contains faulty bearing components and faulty gear components. In the study, a fault feature separation and extraction approach is proposed for the bearing-gear fault condition through combining empirical mode decomposition (EMD), Hilbert transform (HT), principal component analysis (PCA), independent component analysis (ICA) techniques. Firstly, EMD is implemented to decompose the single sensor signal to obtain multiple sub-band signals termed as the intrinsic mode functions (IMFs). Secondly, the most relevant IMFs to bearing and gear fault features are selected to construct multiple-channels model with the help of the simulated bearing and gear fault signals. Thirdly, HT is utilized to compute marginal Hilbert spectrum (MHS) for each IMF in multiple-channels model, to construct an MHS matrix. Finally, some statistically independent components are obtained by decomposing the MHS matrix with PCA and ICA, and multiple fault features are identified from these components. The experimental application of the proposed method is put into a bearing-belt-gearbox union machinery system to evaluate its validity. The experimental analysis results indicate that the proposed method is effective to separate and extract a bearing fault and a gear fault for two types of compound bearing-gear fault conditions. INDEX TERMS Empirical mode decomposition (EMD), Hilbert transform (HT), independent component analysis (ICA), marginal Hilbert spectrum (MHS), multiple faults detection. XUELI ZHU received the B.Sc. degree in construction industry enterprise automation from Construction Industry Enterprise Automation, in 1982, and the Ph.D. degree in heating, gasing, ventilation, and air conditioning engineering from the

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As an effective way of improving traffic efficiency, vehicle platoon control has attracted extensive interest recently. Communication between vehicles tends to be affected by communication noises. Aimed at improving communication efficiency, an event-triggered vehicle platoon control under random communication noises is studied in this paper. First, for vehicle platoons with linear third-order dynamics, a time-varying consensus gain c(t) is introduced to reduce the effects of the communication noises. Second, with the introduction of the algebraic graph theory and matrix analysis theory, conditions for internal stability and l p -string stability under random additive communication noises are derived. Third, by utilizing the Lyapunov approach and Itô stochastic differential equations, the consensus of vehicle platoon under random additive communication noises is proved. Last, to reduce the frequent communication between vehicles, an event-triggered mechanism is introduced, and the design for the triggering parameter is derived. The effectiveness of the proposed method is verified with some numerical simulations.

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This paper proposes a sliding mode control (SMC) strategy to solve the problem of independent control of temperature and humidity in air conditioners; the complexity increases due to the need to make full use of the wind side for cooling. First, dynamics of indoor temperature and humidity are determined based on mathematical models. Second, in order to reduce the chattering effect of SMC and coordinate indoor conditions to satisfy people’s needs, the independent control of temperature and humidity is realized via a novel fuzzy sliding mode control strategy based on reaching laws. Finally, simulation results are provided to verify the effectiveness of the proposed method.

In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Great efforts have been made by using Deep Neural Networks to recognize multi-label images. Since multi-label image classification is very complicated, people seek to use the attention mechanism to guide the classification process. However, conventional attentionbased methods always analyzed images directly and aggressively. It is difficult for them to well understand complicated scenes. In this paper, we propose a global/local attention method that can recognize an image from coarse to fine by mimicking how humanbeings observe images. Specifically, our global/local attention method first concentrates on the whole image, and then focuses on local specific objects in the image. We also propose a joint max-margin objective function, which enforces that the minimum score of positive labels should be larger than the maximum score of negative labels horizontally and vertically. This function can further improve our multi-label image classification method. We evaluate the effectiveness of our method on two popular multilabel image datasets (i.e., Pascal VOC and MS-COCO). Our experimental results show that our method outperforms stateof-the-art methods.