Relationship between pressure drop and cell resistance as a diagnostic tool for PEM fuel cells

An increase in pressure drop, particularly on the cathode side of PEM fuel cell, is a reliable indicator of PEM fuel cell flooding, while an increase in cell resistance is a reliable indicator of fuel cell drying. By monitoring both pressure drop and cell resistance in an operational fuel cell stack it was possible to diagnose either flooding or drying conditions inside the stack. These parameters may be used for making decisions on corrective actions.     

Nacre-Inspired,Liquid Metal-Based Ultrasensitive Electronic Skin by Spatially Regulated Cracking Strategy

The realization of liquid metal-based wearable systems will be a milestone toward high-performance, integrated electronic skin. However, despite the revolutionary progress achieved in many other components of electronic skin, liquid metal-based flexible sensors still suffer from poor sensitivity due to the insufficient resistance change of liquid metal to deformation. Herein, a nacre-inspired architecture composed of a biphasic pattern (liquid metal with Cr/Cu underlayer) as “bricks” and strain-sensitive Ag film as “mortar” is developed, which breaks the long-standing sensitivity bottleneck of liquid metal-based electronic skin. With 2 orders of magnitude of sensitivity amplification while maintaining wide (>85%) working range, for the first time, liquid metal-based strain sensors rival the state-of-art counterparts. This liquid metal composite features spatially regulated cracking behavior. On the one hand, hard Cr cells locally modulate the strain distribution, which avoids premature cut-through cracks and prolongs the defect propagation in the adjacent Ag film. On the other hand, the separated liquid metal cells prevent unfavorable continuous liquid-metal paths and create crack-free regions during strain. Demonstrated in diverse scenarios, the proposed design concept may spark more applications of ultrasensitive liquid metal-based electronic skins, and reveals a pathway for sensor development via crack engineering.     

混合动力特种车APU控制系统开发

本文设计的辅助动力单元(APU)控制系统采用恒压控制的结构.首先优化了柴油发动机工作区域,之后设计了发动机转速变增益PI控制器和发电机调压器.最终在APU试验台架上的试验结果验证了发动机转速控制器的能够对发动机实际工作点有很好的控制效果,并且APU的功率响应特性和输出电压的稳定性均能满足车载特种用电设备的要求.     

Directional-stability-aware brake blending control synthesis for over-actuated electric vehicles during straight-line deceleration

For the purpose of both energy regeneration and directional stability enhancement, regenerative and hydraulic blended braking control of an over-actuated electric vehicle equipped with four individual on-board motors during normal straight-line deceleration is studied. System models which include the vehicle dynamics, tire, electric powertrain, and hydraulic brake models are developed. Mechanisms of directional instability of the electric vehicle during straight-line braking are analyzed. To improve the electric vehicle's safety and performance, novel compensation methods through blended braking are studied. On the basis of half-shaft torque estimation, two new regenerative braking control algorithms are proposed. Simulations of the developed control algorithms are carried out during normal straight-line braking maneuvers. The results and discussions demonstrate that the developed approaches are advantageous when compared with the conventional baseline strategy, with respect to both the directional stability and regeneration efficiency, thus validating the feasibility and effectiveness of the controller synthesis.     

IPC-Net: Incomplete point cloud classification network based on data augmentation and similarity measurement

Existing point cloud classification researches are usually conducted on datasets with complete structure and clear semantics. However, in real point cloud scenes, the occlusion and truncation may destroy the completeness of objects affecting the classification performance. To solve this problem, we propose an incomplete point cloud classification network (IPC-Net) with data augmentation and similarity measurement. The proposed network learns the feature representation of incomplete point clouds and the semantic differences compared to the complete ones for classification. Specifically, IPC-Net adopts a random erasing-based data augmentation to deal with incomplete point clouds. IPC-Net also introduces an auxiliary loss function weighted by attention scores to measure the similarity between the incomplete and the complete point clouds. Extensive experiments verify that IPC-Net has the ability to classify incomplete point clouds and significantly improves the robustness of point cloud classification under different completeness.     

Data fusion with flexible message composition in Driver-in-the-Loop vehicular CPS

This work addresses a unique data fusion problem in Vehicular Cyber-Physical Systems (VCPS) arising from Human Factors (HF) considerations. Typically, a VCPS message intended for human drivers is composed of many data elements (DEs), and different messages can be fused by the sender before transmission e.g., by eliminating identical (or redundant) DEs in order to save transmission bandwidth in the wireless network. Still, not all distinct DEs can be received properly due to the limited transmission resources available to the sender and/or transmission errors. Subsequently, some of the messages intended for a driver cannot be delivered. On the other hand, a partially delivered message may still be beneficial (in terms of generating some utility) to a driver. More specifically, when considering HF, the DEs can be grouped into two distinct parts: essential and auxiliary. While a partially reconstructed message missing even a single essential DE fails to produce any benefit (or utility) for a driver, each auxiliary DE can independently produce an additional utility so long as all the essential DEs of the message are also available.In this paper, we deal with a new Driver-in-the-Loop Data Fusion Problem (DDFP) with the primary issue being: given a list of out-going messages and a limit on the number of DEs that can be transmitted, how does the sender choose which DEs (each carrying a different utility) to transmit, in order to maximize the system-wide utility at the receiver. We formulate DDFP mathematically, and prove it to be NP-Complete. We study DDFP in both ideal and lossy communication networks, and propose several efficient algorithms for them. Besides the Single-Sender-Single-Receiver model, we also look into DDFP in Multiple-Sender-Single-Receiver and Single-Sender-Multiple-Receiver models with several practical considerations. Numerical results from large scale simulations are also presented.     

Feature generation based on relation learning and image partition for occluded person re-identification

In order to solve the challenging tasks of person re-identification(Re-ID) in occluded scenarios, we propose a novel approach which divides local units by forming high-level semantic information of pedestrians and generates features of occluded parts. The approach uses CNN and pose estimation to extract the feature map and key points, and a graph convolutional network to learn the relation of key points. Specifically, we design a Generating Local Part (GLP) module to divide the feature map into different units. Based on different occluded conditions, the partition mode of GLP has high flexibility and variability. The features of the non-occluded parts are clustered into an intermediate node, and then the spatially correlated features of the occluded parts are generated according to the de-clustering operation. We conduct experiments on both the occluded and the holistic datasets to demonstrate its effectiveness.     

A fuzzy multicriteria model for comparing energy projects

A fuzzy set approach to multicriteria modelling is presented for selecting the best alternative solution to a large-scale engineering project. The problems of making expert recommendations, amidst conflicting views of different people and institutions, is handled by this technique which incorporates the viewpoints of different interest groups, both quantitative and qualitative. The efficacy of the methodology is demonstrated by applying it to the real world problem of selecting the best site for the Bay of Fundy Tidal Power Project where emphasis is placed on socio-economic aspects. The direct impacts of developing tidal power energy on good living, transportation, psychological and political factors are studied by using fuzzy binary relations to model preferences.     

Electronic-structure calculation of point defects in silicon

Density-functional theory allows studies of ground-state properties of a many-electron system from first principles. Using Green-function technique and the local density (or local spin-density) approximation, it is possible to calculate charge (ion) states, total energies, donor- and acceptor levels, and spin-densities of point defects in semiconductors. Focussing on existing open questions and controversies, we present results of recent theoretical studies of single substitutional and interstitial chalcogens and 3d transition-metal impurities in silicon. For the chalcogen point defects S, Se and Te we identify their positions in the silicon lattice as the substitutional site. For 3d impurities in silicon we obtain spin-multiplicities and donor and acceptor levels in good agreement with available experimental data. The early 3d interstitial and the late 3d substitutional ions are predicted to have low-spin ground-states which is in conflict with the generally accepted model of Ludwig and Woodbury (LW). A large number of predictions will be offered to be tested by future experimental studies.