Optimized Partition Scheme for Prefabricated Underpass with Large Cross Section

With the acceleration of urbanization in China, more underpasses will be constructed in big cities to alleviate the great traffic pressure. The prefabricated and assembly construction method has been introduced to replace the traditional cast-in-place method to achieve quick construction. However, for a fully prefabricated and assembled underground structure (PAUS) with large cross section, the structure must be cut into segments in transverse direction to reduce the size and weight for easy transportation and assembly. Therefore, how to develop an optimal partition scheme is a new problem to be studied. Firstly, three preliminary partition schemes were proposed based on the internal force distribution and completed engineering practices. Then, the three schemes were compared in terms of bending moment, shear force, and axial force. The construction efficiencies were also compared with special emphasis on difference of the build period. Finally, an optimal partition scheme was determined and successfully applied in the real project. Furthermore, the construction period of this partition scheme was 1/3 of the traditional cast-in-place method. The results of the current paper can provide some design guidance to large cross-sectional underpasses and other underground structures in the partition stage.

Pose-Guided Part-Based Adaptive Pyramid Features for Occluded Person Reidentification

Reidentifying an occluded person across nonoverlapping cameras is still a challenging task. In this work, we propose a novel pose-guided part-based adaptive pyramid neural network for occluded person reidentification. Firstly, to alleviate the impact of occlusion, we utilize pose landmarks to generate pose-guided attention maps. The attention maps will help the model focus on the nonoccluded regions. Secondly, we use pyramid pooling to extract multiscale features in order to address the scale variation problem. The generated pyramid features are then multiplied by attention maps to achieve pose-guided adaptive pyramid features. Thirdly, we propose a pose-guided body part partition scheme to deal with the alignment problem. Accordingly, the adaptive pyramid features are divided into partitions and fed into individual fully connected layers. In the end, all the part-based matching scores are fused with a weighted sum rule for person reidentification. The effectiveness of our method is clearly validated by the experimental results on two popular occluded and holistic datasets, i.e., Occluded-DukeMTMC and the Market-1501.

A Fast Method of Visually Lossless Compression of Dental Images

A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

Collaborative Intelligence: Accelerating Deep Neural Network Inference via Device-Edge Synergy

With the development of mobile edge computing (MEC), more and more intelligent services and applications based on deep neural networks are deployed on mobile devices to meet the diverse and personalized needs of users. Unfortunately, deploying and inferencing deep learning models on resource-constrained devices are challenging. The traditional cloud-based method usually runs the deep learning model on the cloud server. Since a large amount of input data needs to be transmitted to the server through WAN, it will cause a large service latency. This is unacceptable for most current latency-sensitive and computation-intensive applications. In this paper, we propose Cogent, an execution framework that accelerates deep neural network inference through device-edge synergy. In the Cogent framework, it is divided into two operation stages, including the automatic pruning and partition stage and the containerized deployment stage. Cogent uses reinforcement learning (RL) to automatically predict pruning and partition strategies based on feedback from the hardware configuration and system conditions so that the pruned and partitioned model can better adapt to the system environment and user hardware configuration. Then through containerized deployment to the device and the edge server to accelerate model inference, experiments show that the learning-based hardware-aware automatic pruning and partition scheme can significantly reduce the service latency, and it accelerates the overall model inference process while maintaining accuracy. Using this method can accelerate up to 8.89× without loss of accuracy of more than 7%.

Performance Assessment of ERA5 Wave Data in a Swell Dominated Region

The present paper deals with a performance assessment of the ERA5 wave dataset in an ocean basin where local wind waves superimpose on swell waves. The evaluation framework relies on observed wave data collected during a coastal experimental campaign carried out offshore of the southern Oman coast in the Western Arabian Sea. The applied procedure requires a detailed investigation on the observed waves, and aims at classifying wave regimes: observed wave spectra have been split using a 2D partition scheme and wave characteristics have been evaluated for each wave component. Once the wave climate was defined, a detailed wave model assessment was performed. The results revealed that during the analyzed time span the ERA5 wave model overestimates the swell wave heights, whereas the wind waves’ height prediction is highly influenced by the wave developing conditions. The collected field dataset is also useful for a discussion on spectral wave characteristics during monsoon and post-monsoon season in the examined region; the recorded wave data do not suffice yet to adequately describe wave fields generated by the interaction of monsoon and local winds.

Large-signal model of 2DFETs: compact modeling of terminal charges and intrinsic capacitances

We present a physics-based circuit-compatible model for double-gated two-dimensional semiconductor-based field-effect transistors, which provides explicit expressions for the drain current, terminal charges, and intrinsic capacitances. The drain current model is based on the drift-diffusion mechanism for the carrier transport and considers Fermi–Dirac statistics coupled with an appropriate field-effect approach. The terminal charge and intrinsic capacitance models are calculated adopting a Ward–Dutton linear charge partition scheme that guarantees charge conservation. It has been implemented in Verilog-A to make it compatible with standard circuit simulators. In order to benchmark the proposed modeling framework we also present experimental DC and high-frequency measurements of a purposely fabricated monolayer MoS2-FET showing excellent agreement between the model and the experiment and thus demonstrating the capabilities of the combined approach to predict the performance of 2DFETs.

Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

As a typical landmark in human lungs, the detection of pulmonary fissures is of significance to computer aided diagnosis and surgery. However, the automatic detection of pulmonary fissures in CT images is a difficult task due to complex factors like their 3D membrane shape, intensity variation and adjacent interferences. Based on the observation that the fissure object often appears as thin curvilinear structures across 2D section images, we present an efficient scheme to solve this problem by merging the fissure line detection from multiple cross-sections in different directions. First, an existing oriented derivative of stick (ODoS) filter was modified for pulmonary fissure line enhancement. Then, an orientation partition scheme was applied to suppress the adhering clutters. Finally, a multiple section model was proposed for pulmonary fissure integration and segmentation. The proposed method is expected to improve fissure detection by extracting more weak objects while suppressing unrelated interferences. The performance of our scheme was validated in experiments using the publicly available open Lobe and Lung Analysis 2011 (LOLA11) dataset. Compared with manual references, the proposed scheme achieved a high segmentation accuracy, with a median F1-score of 0.8916, which was much better than conventional methods.

Fluid Structure Interaction Analysis of Liquid Tanks by the Coupled SPH - FEM Method with Experimental Verification

The paper presents the comparison of the results between the numerical model developed for the simulation of the fluid-structure interaction problem and the experimental tests. The model is based on the so called “partition scheme” in which the equations governing the fluid’s pressures and the equations governing the displacement of the structure are solved separately, with two distinct solvers. The SPH (Smoothed Particle Hydrodynamics) method is used for the fluid and the standard FEM (Finite Element Method), based on shell elements, is used for the structure. Then, the two solvers are coupled to obtain the coupled behaviour of the fluid structure system. The elasto plastic material model for the structure includes some important nonlinear effects like yielding in compression and tension. Previously experimentally tested (on a shaking table) rectangular tanks with rigid and deformable walls were used for the verification of the developed numerical model. A good agreement between the numerical and the experimental results clearly shows that the developed model is suitable and gives accurate results for such problems. The numerical model results are validated with the experimental results and can be a useful tool for analyzing the behaviour of liquid tanks of larger dimensions.

Exposing Video Compression History by Detecting Transcoded HEVC Videos from AVC Coding

The analysis of video compression history is one of the important issues in video forensics. It can assist forensics analysts in many ways, e.g., to determine whether a video is original or potentially tampered with, or to evaluate the real quality of a re-encoded video, etc. In the existing literature, however, there are very few works targeting videos in HEVC format (the most recent standard), especially for the issue of the detection of transcoded videos. In this paper, we propose a novel method based on the statistics of Prediction Units (PUs) to detect transcoded HEVC videos from AVC format. According to the analysis of the footprints of HEVC videos, the frequencies of PUs (whether in symmetric patterns or not) are distinguishable between original HEVC videos and transcoded ones. The reason is that previous AVC encoding disturbs the PU partition scheme of HEVC. Based on this observation, a 5D and a 25D feature set are extracted from I frames and P frames, respectively, and are combined to form the proposed 30D feature set, which is finally fed to an SVM classifier. To validate the proposed method, extensive experiments are conducted on a dataset consisting of CIF ( 352 × 288 ) and HD 720p videos with a diversity of bitrates and different encoding parameters. Experimental results show that the proposed method is very effective at detecting transcoded HEVC videos and outperforms the most recent work.