Adaptive Streaming in Mobile Network

Mobile ecommerce is immersed rapid familiar to other high-flying Internet markets. With the fast developing of wireless connections and Internet, electronic commerce more and more moves to mobile environment. Streaming, as a rapid growing application in Internet, will be more used in mobile ecommerce. In this paper, we’ll review the network protocol used in mobile ecommerce and streaming technology. An optimized architecture is given based on MPEG-4 and Mobile Ipv6. The core streaming protocol used in this architecture is RTSP/RTP proposed by IETF. This system gives one possible implementation of streaming over wireless network. Two key bottlenecks we found in this project are wireless bandwidth and mobile client power. To avoid the two problems, self-adaptive methodology is used. Let streaming application be adaptive to the wireless network environment to improve streaming performance

ENAT-PT: An Enhanced NAT-PT Model

NAT-PT would allow IPv4 nodes to communicate with IPv6 nodes transparently by translating the IPv6 address into a registered V4 address. However, NAT-PT would fall flat when the pool of V4 addresses is exhausted. NAPT-PT multiplexes the registered address’ ports and will allow for a maximum of 63K outbound TCP and 63K UDP sessions per IPv4 address, but it is unidirectional. We present in this paper a novel solution ENAT-PT（an enhanced NAT-PT），which will allow for a great number of inbound sessions by using a single V4 address. By using ENAT-PT, we can visit V6 networks from a V4 network with a small address pool

Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model

Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model

Automated bridge crack detection method based on lightweight vision models

Abstract Deep learning-based bridge crack detection methods have advantages over traditional methods. We proposed an automated bridge crack detection method using lightweight vision models. First, our study applied the You Only Look Once 4th version (YOLO v4) (Bochkovskiy et al. in Yolov4: Optimal speed and accuracy of object detection. arXiv:200410934, 2020) to bridge surface crack detection. Then, to achieve model acceleration, some lightweight networks were used to replace the feature extraction network in YOLO v4, which reduced the parameter numbers and the backbone layers. The lightweight design can reduce the computational overhead of the model, making it convenient to deploy on edge platforms with limited computational power. The experimental results showed that the lightweight network-based bridge crack detection model required significantly less storage space at the expense of a slight reduction in precision. Therefore, an improved YOLO v4 crack detection method was proposed to meet real-time running without sacrificing accuracy. The precision, recall, and F1 score of the proposed crack detection method are 93.96%, 90.12%, and 92%, respectively. And the model only required 23.4 MB of storage space, and its frames per second could reach 140.2 frames. Compared with existing bridge crack detection methods, the proposed method showed precision, speed, and model size advantages

Crack Extension Analysis of Atmospheric Stress Corrosion Based on Peridynamics

Based on peridynamics, an atmospheric stress corrosion model was proposed. In this model, the role of hydrogen and stress in anodic-dissolution-dominated stress corrosion cracking was considered, and atmospheric corrosion was characterized by the change in liquid film thickness on the metal surface in the atmospheric environment. The near-field kinetic anodic dissolution model and the atmospheric corrosion model were coupled by varying the liquid film thickness. The thickness of the liquid film depended on factors such as the temperature, relative humidity, and hygroscopic salts. We validated the model using stress corrosion behavior from the literature for 304 stainless steel in a simulated atmospheric environment. The results of the model captured the crack expansion process. The obtained crack expansion direction and branching behavior agreed well with the experimental results in the literature

Crack Extension Analysis of Atmospheric Stress Corrosion Based on Peridynamics

Based on peridynamics, an atmospheric stress corrosion model was proposed. In this model, the role of hydrogen and stress in anodic-dissolution-dominated stress corrosion cracking was considered, and atmospheric corrosion was characterized by the change in liquid film thickness on the metal surface in the atmospheric environment. The near-field kinetic anodic dissolution model and the atmospheric corrosion model were coupled by varying the liquid film thickness. The thickness of the liquid film depended on factors such as the temperature, relative humidity, and hygroscopic salts. We validated the model using stress corrosion behavior from the literature for 304 stainless steel in a simulated atmospheric environment. The results of the model captured the crack expansion process. The obtained crack expansion direction and branching behavior agreed well with the experimental results in the literature

Application of Deep Convolution Neural Network in Crack Identification

The surface crack of structure is an important sign to evaluate the safety of structure. In order to ensure the safety and reliability of the building structure, it is necessary to detect and monitor the surface cracks of the structure. Traditional artificial surface inspections are time-consuming because inspectors have different experience and knowledge, which can lead to misjudgments. Based on the basic framework of four deep convolution neural networks, their classifiers are reconstructed. To fully train these networks and simulate crack images taken in various situations in life, image enhancement techniques are used to extend the dataset. After training, compared with the established shallow network structure, they can learn the feature information in the image more fully, and finally improve the accuracy. After further verification, it is found that one of the models can achieve an accuracy of 96.5%. To verify the universality and validity of the model, two cross-datasets experiments were performed. The experimental results show the validity of the model, and the diagnostic precision is 98.23% and 99.04%, respectively

The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection

The real-time detection of cracks is an important part of road maintenance and an important initiative to reduce traffic accidents caused by road cracks. In response to the lack of efficiency of current research results for the real-time detection of road cracks and the low storage and computational capacity of edge devices, a new automatic crack detection algorithm is proposed: BT–YOLO. We combined Bottleneck Transformer with You Only Look Once (YOLO), which is more conducive to extracting the features of small cracks than YOLOv5s. The introduction of DWConv to the feature extraction network reduced the number of parameters and improved the inference speed of the network. We embedded the SimAM (Simple, Parameter-Free Attention Module) non-parametric attention mechanism to make the crack features more prominent. The experimental results showed that the accuracy of BT–YOLO in crack detection was increased by 4.5%, the mapped value was increased by 8%, and the parameter amount was decreased by 24.9%. Eventually, we deployed edge devices for testing. The frame rate reached 89, which satisfied the requirements of real-time crack detection

Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model