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Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

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In mobile phone supply chains, consumers can choose handsets and telecommunication services as a bundled package or buy handsets and services independently. This article develops a multi-agent simulation model to explore price, subsidy and bundling decisions for competing mobile phone supply chains with network externality, where each chain includes one mobile phone manufacturer and one service operator. There are two bundling strategies: free or bundled. The results indicate that: 1) if the impact of network externality is not too small, then competitive differentiation can be formed when one party adopts a bundled one. If bundled scenario is adopted by both, the total profit is reduced. Consequently, both service operators and manufacturers choose (accept) different bundling strategies; 2) network externality and consumer heterogeneity both increase the advantage of bundled scenario in the asymmetric setting; 3) when the effect of network externality is sufficiently small, free scenario is dominant; and 4) consumer heterogeneity can alleviate competition in the symmetric settings.

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<p>Health monitoring and disease mitigation of trees are essential to ensure the sustainability of wood industry, safety of ecosystems, and maintenance of climatic conditions. Several non-destructive testing methods have been applied to monitor and detect the decays inside the trunks. Among them, ground penetrating radar (GPR) has gained recognition due to its high efficiency and good resolution. However, due to the wide beam width of the antenna pattern and the complicated scattering caused by the trunk structure, the recorded GPR profile is far from the actual geometry of the tree trunk. Moreover, the irregular contour of the tree trunk makes traditional data processing algorithms difficult to be performed. Therefore, an efficient migration algorithm with high resolution, as well as a high accuracy survey-line positioning method for curved contour of the trunk should be developed.</p> <p>In this paper, a combined approach is proposed to image the inner structures inside the irregular-shaped trunks. In the first step, the 3D contour of the targeted tree trunk is built up by a 3D point cloud technique via photographing around the trunk at various angles. Subsequently, the 2D irregular contour of the cross-section of trunk at the position of the GPR survey line is extracted by the Canny edge detection method to locate the accurate position of each GPR A-scans [1]. Thirdly, the raw GPR profile is pre-processed to suppress undesired noise and clutters. Then, an RTM algorithm based on the zero-time imaging condition is applied for image reconstruction using the extracted 2D contour [2]. Lastly, a denoising method based on the total variation (TV) regularization is applied for artifact suppression in the reconstructed images [3].</p> <p>Numerical, laboratory and field experiments are carried out to validate the applicability of the proposed approach. Both numerical and laboratory experimental results show that the RTM can yield more accurate and higher resolution images of the inner structures of the tree cross section than the BP algorithm. The proposed approach is further applied to a diseased camphor tree, and an elliptical decay defect is found the in the migrated GPR image. The results are validated by a visual inspection after the tree trunk was sawed down.</p> <p><img src="" alt="" width="621" height="221" /></p> <p><strong>Fig. 1</strong> Field experiment. (a) Geometric reconstruction result using point cloud data, (b) migrated result by the RTM algorithm and (c) bottom view of the tree trunk after sawing down. The red and yellow ellipses indicate the cavity and the decay region in the trunk, respectively.</p> <p>References:</p> <p>[1] Canny, "A Computational Approach to edge detection," <em>IEEE Transactions on Pattern Analysis and Machine Interllgent, </em>vol. PAMI-8, no. 6, pp. 679-698, 1986, doi: 10.1109/TPAMI.1986.4767851.</p> <p>[2] S. Chattopadhyay and G. A. McMechan, "Imaging conditions for prestack reverse-time migration," <em>Geophysics, </em>vol. 73, no. 3, pp. S81-S89, 2008, doi: 10.1190/1.2903822.</p> <p>[3] L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," <em>Physica D, </em>vol. 60, pp. 259-268, 1992, doi: 10.1016/0167-2789(92)90242-F.</p>