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The relative humidity is an important parameter reflecting the performance of proton exchange membrane (PEM) fuel cells, which is often accompanied by changes in heat and temperature. In order to study the different humidification effects on the performance of PEM fuel cells, a temperature and heat transfer (T&HT) model is presented. The innovation of this paper is to study the performance of fuel cell (FC) from the perspective of temperature heat transfer by asymmetric humidification. Firstly, symmetrical humidification experiments are performed at three operating temperatures. After that, a three‐dimensional (3D) structure is built using fluent and T&HT model is imported through custom functions. Secondly, the asymmetric humidification experiment is put into practice with 60 °C operating temperature. Furthermore, the Taguchi method is used to optimize the performance of fuel cells in the crossover experiment. Finally, the experimental and numerical results are compared by the contours and polarization curves. The results show that T&HT model is in agreement with the experiment, and asymmetric humidification is more reasonable and flexible than symmetrical humidification. When the cathode relative humidity is 50% and the anode relative humidity is 75%, the maximum optimization rate of system efficiency is 17%.

Abstract. As there usually exist widespread crack, decay, deformation and other damages in the wooden architectural heritage (WAH). It is of great significance to detect the damages automatically and rapidly in order to grasp the status for daily repairs. Traditional methods use artificial feature-driven point clouds and image processing technology for object detection. With the development of big data and GPU computing performance, data-driven deep learning technology has been widely used for monitoring WAH. Deep learning technology is more accurate, faster, and more robust than traditional methods.In this paper, we conducted a case study to detect timber-crack damages in WAH, and selected the YOLOv3 algorithm with DarkNet-53 as the backbone network in the deep learning technology according to the characteristics of the crack. A large timber-crack dataset was first constructed, based on which the timber-crack detection model was trained and tested. The results were analyzed both qualitatively and quantitatively, showing that our proposed method was able to reach an accuracy of more than 90% through processing each image for less than 0.1s. The promising results illustrate the validity of our self-constructed dataset as well as the reliability of YOLOv3 algorithm for the crack detection of wooden heritage.

Abstract. Plane segmentation from the point cloud is an important step in various types of geo-information related to human activities. In this paper, we present a new approach to accurate segment planar primitives simultaneously by transforming it into the best matching issue between the over-segmented super-voxels and the 3D plane models. The super-voxels and its adjacent topological graph are firstly derived from the input point cloud as over-segmented small patches. Such initial 3D plane models are then enriched by fitting centroids of randomly sampled super-voxels, and translating these grouped planar super-voxels by structured scene prior (e.g. orthogonality, parallelism), while the generated adjacent graph will be updated along with planar clustering. To achieve the final super-voxels to planes assignment problem, an energy minimization framework is constructed using the productions of candidate planes, initial super-voxels, and the improved adjacent graph, and optimized to segment multiple consistent planar surfaces in the scenes simultaneously. The proposed algorithms are implemented, and three types of point clouds differing in feature characteristics (e.g. point density, complexity) are mainly tested to validate the efficiency and effectiveness of our segmentation method.

ABSTRACT:Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.

<p><strong>Abstract.</strong> Suffered from the environmental changes and human’s influence, Chinese paintings are facing a worrying situation in documentation, which leads urgent into establishing digital archives to realize the permanent preservation of its surface information. Non-destructive identification of pigment types using hyperspectral techniques is the key to ensure the colour restoration scientifically. At present, one commonly used method for spectral identification is to calculate the similarities between unknown spectra and standard spectra in spectral library. The purpose of this study is to establish a spectral library of typical pigments specifically for the information preservation and pigment types identification on surface of Chinese paintings. The main works of the research are: (1) collecting the standard spectra in laboratory and the pigment spectra on surface of real cultural relics as the data source of the spectral library; (2) adding the expert knowledge of traditional pigments and normative descriptions of spectra measurement as part of the spectral library; and (3) constructing the spectral library website with the Browser/Server structure, and by using MySQL database for storage, SSM framework for background building and JSP page for displaying. The significance of the design and construction of the Typical Pigments of Chinese Paintings (TPCP) spectral library is not only to establish a standard spectral library for pigment types identification, but also a digital archive of pigment information on the surface of real cultural relics, to achieve permanent preservation, and provide spectral data sources and pigment information references for conservation workers and researchers in related fields for further research.</p>

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