Harnack Inequality for Distribution Dependent Stochastic Hamiltonian System

The dimension free Harnack inequality is established for the distribution dependent stochastic Hamiltonian system, where the drift is Lipschitz continuous in the measure variable under the distance induced by the H\"{o}lder-Dini continuous functions, which are $\beta (\beta>\frac{2}{3})$-H\"{o}lder continuous on the degenerate component and square root of Dini continuous on the non-degenerate one. The results extend the existing ones in which the drift is Lipschitz continuous in the measure variable under $L^2$-Wasserstein distance.Comment: 23 page

Enhancement of nitrate removal at the sediment-water interface by carbon addition plus vertical mixing

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Chemosphere 136 (2015): 305-310, doi:10.1016/j.chemosphere.2014.12.010.Wetlands and ponds are frequently used to remove nitrate from effluents or runoffs. However, the efficiency of this approach is limited. Based on the assumption that introducing vertical mixing to water column plus carbon addition would benefit the diffusion across the sediment–water interface, we conducted simulation experiments to identify a method for enhancing nitrate removal. The results suggested that the sediment-water interface has a great potential for nitrate removal, and the potential can be activated after several days of acclimation. Adding additional carbon plus mixing significantly increases the nitrate removal capacity, and the removal of total nitrogen (TN) and nitrate-nitrogen (NO3--N) is well fitted to a first-order reaction model. Adding Hydrilla verticillata debris as a carbon source increased nitrate removal, whereas adding Eichhornia crassipe decreased it. Adding ethanol plus mixing greatly improved the removal performance, with the removal rate of NO3--N and TN reaching 15.0-16.5 g m-2 d-1. The feasibility of this enhancement method was further confirmed with a wetland microcosm, and the NO3--N removal rate maintained at 10.0-12.0 g m-2 d-1 at a hydraulic loading rate of 0.5 m d-1.The present work was supported by the State Oceanic Administration of China (Demonstration project of coastal wetland restoration, north coast of Hangzhou Wan bay), the National Science Foundation of China under Grant No. 51378306 and 41471393, and Science and Technology Planning Project of Zhejiang Province No.2014F50003

Screw lifetime prediction based on wavelet neural network and empirical mode decomposition

To predict residual lifetime of ball screw, screw lifetime prediction technology based on wavelet neural network (WNN) and empirical mode decomposition (EMD) is proposed. Screw accelerated lifetime test platform is introduced. Accelerometers are installed to monitor ball screw lifetime. With the method of principal component analysis (PCA), high dimension features are mapped to low dimensional space and stored into sample library together with screw expected remaining lifetime. Training samples and testing samples are randomly selected from the sample library to train and test the WNN. Then EMD is used to extract output tendency of WNN. Finally, screw lifetime prediction model can be obtained. The experimental results show that the maximum error of the training samples is 602 hours while the maximum error of the testing samples is 652 hours, which meet the need of screw lifetime prediction

Adaptive Learning for the Resource-Constrained Classification Problem

Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufacturing settings under a limited inspection budget. Typical classification algorithms treat the learning process and the resource constraints as two separate and sequential tasks. Here we design an adaptive learning approach that considers resource constraints and learning jointly by iteratively fine-tuning misclassification costs. Via a structured experimental study using a publicly available data set, we evaluate a decision tree classifier that utilizes the proposed approach. The adaptive learning approach performs significantly better than alternative approaches, especially for difficult classification problems in which the performance of common approaches may be unsatisfactory. We envision the adaptive learning approach as an important addition to the repertoire of techniques for handling resource-constrained classification problems

Control integrated studies on high speed permanent magnetic generators system

The power converter and its control system has great impact on the behavior of the high speed permanent magnet generator system (HSPGS) due to the high operating frequency, and therefore should be investigated in the design and research on the HSPGS. This paper investigates a 3kW 80,000 rpm HSPGS with the control system and evaluates machine performance. A study model composed of three dimensional finite element analyses and control system is proposed, and the magnetic density distribution in machine is investigated via FEA and power converter control directly coupled research. From the comparisons of simulation results of generator operating with or without control system, the effects of control system current time harmonics on the three dimensional flux distribution, the vector eddy current in rotor sleeve, and the iron loss of the machine are explored. By using the harmonics analyses, both the space flux harmonics and the voltage current time harmonics are obtained. The tested waveforms and voltage and current value of machine under different working conditions verify the calculated results. The obtained results could provide reference for system level design and investigation for HSPGS

RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition

Emotion recognition in conversation (ERC) has received increasing attention from researchers due to its wide range of applications. As conversation has a natural graph structure, numerous approaches used to model ERC based on graph convolutional networks (GCNs) have yielded significant results. However, the aggregation approach of traditional GCNs suffers from the node information redundancy problem, leading to node discriminant information loss. Additionally, single-layer GCNs lack the capacity to capture long-range contextual information from the graph. Furthermore, the majority of approaches are based on textual modality or stitching together different modalities, resulting in a weak ability to capture interactions between modalities. To address these problems, we present the relational bilevel aggregation graph convolutional network (RBA-GCN), which consists of three modules: the graph generation module (GGM), similarity-based cluster building module (SCBM) and bilevel aggregation module (BiAM). First, GGM constructs a novel graph to reduce the redundancy of target node information. Then, SCBM calculates the node similarity in the target node and its structural neighborhood, where noisy information with low similarity is filtered out to preserve the discriminant information of the node. Meanwhile, BiAM is a novel aggregation method that can preserve the information of nodes during the aggregation process. This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters. On both the IEMOCAP and MELD datasets, the weighted average F1 score of RBA-GCN has a 2.17$\sim$5.21\% improvement over that of the most advanced method