We report on an experimental demonstration of graphene-metal ohmic contacts with contact resistance below 100 Ω µm. These have been fabricated on graphene wafers, both with and without hydrogen intercalation, and measured using the transmission line method. Specific contact resistivities of 3 × 10−7 to 1.2 × 10−8 Ω cm2 have been obtained. The ultra-low contact resistance yielded short-channel (source-drain distance of 0.45 µm) HfO2/graphene field effect transistors (FETs) with a low on-resistance (Ron) of 550 Ω µm and a high current density of 1.7 A/mm at a source-drain voltage of 1 V. These values represent state-of-the-art (SOA) performance in graphene-metal contacts and graphene FETs. This ohmic contact resistance is comparable to that of SOA high-speed III–V high electron mobility transistors.

[1] Both the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), the two most rapidly developing areas in eastern China, have suffered from serious air pollution, and thus, numerous investigations were devoted to studying these problems. Other areas in eastern China have received less attention despite similar rapid development in their industries and economy. In this study, we analyzed air-quality data from Kinmen Island (24°27′26″N, 118°19′36″E) located off Fujian Province and between the two above-mentioned deltas. Our results clearly show that the study area is experiencing serious air quality deterioration. Particularly, high levels of suspended particulate matter (PM) were observed during winter, when the northeasterly monsoon prevails. For example, concentrations of wintertime PM 10 (particles ≤ 10 mm in diameter) frequently exceeded 100 mg/m 3 in the last three years. In addition to the air-quality data analysis, aerosol samples were collected between 22 November 2007 and 6 March 2008 and subjected to chemical analyses of various species. Our findings show that the three principal PM components include organic, mineral, and sulfate species with moderate to minor fractions of nitrate, sea salt, elemental carbon, and trace metal oxides. The high PM levels observed over the island may be partly attributed to the transport from a mixed-type industrial area located ∼40 km northeast of Kinmen. Our study could partially fill the air quality data gap between the YRD and PRD regions, and highlight the alarming fact that air pollution has gradually expanded along eastern China's coastal zone.Citation: Hsu, S. C., et al. (2010), High wintertime particulate matter pollution over an offshore island (Kinmen) off

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High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.

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As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new deep learning-based method to conduct semantic segmentation on the point cloud collected by mobile 2D LiDAR. Both online data processing and batch data processing are supported because our method is designed to classify points into meaningful categories of objects scan line by scan line. Local features are important for the success of point cloud semantic segmentation. Thus, we design an iterative point partitioning algorithm and a module named as Spatial Fusion Network, which are two critical components of our method for multi-scale local feature extraction. We evaluate our method on point clouds where sixteen categories of common OCS components have been manually labeled. Experimental results show that our method is effective in multiple object recognition since mean Intersection-over-Unions (mIoUs) of online data processing and batch data processing are, respectively, 96.12% and 97.17%.well record geometric details of surrounding objects [5]. Therefore, it is appropriate to apply MLS systems to OCS inspections. The operational condition of OCS can be acquired by analyzing the MLS point cloud instead of manual measurement. Recognizing the point cloud of OCS as a critical task of the data analysis has been studied in previous studies (e.g., [6][7][8]). Mobile 2D LiDAR is a special kind of MLS system applied to railway inspections. Figure 1 shows an instance of the mobile 2D LiDAR used to scan the OCS infrastructure. As for this kind of MLS system, a point cloud is built up by integrating points at each 2D scan line. Fully understanding the point cloud is significant for automatic inspections and intelligent diagnoses. Thus, this study focuses on multiple OCS component recognition with mobile 2D LiDAR. However, it is a difficult task because the similarities and the various associations among OCS components make scenes complex [8]. In this case, model-driven and data-driven methods become incompetent because rules and features for recognizing specific objects are difficult to be designed by human beings. Fortunately, the success of deep learning-based image recognition (e.g., [9][10][11]) has promoted the fast development of deep learning-based point cloud segmentation, which provides novel means to understand point cloud with semantics.