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41.
This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa. 相似文献
42.
本文介绍“风险矩阵法”进行风险分级工作的基本思路,结合水泥厂的生产特点通过危险有害因素辨识,获得危险源分布情况,采用风险矩阵法对风险进行评估,按风险值将风险等级划分为重大风险、较大风险、一般风险和低风险,为水泥生产企业的安全风险分级工作提供参考。 相似文献
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Xuan-Tao Ji Si-Yuan Luo Yu-He Huang Kun Zhu Jin Zhu Xiao-Yu Peng Min Xiao Xiao-Dong Wang 《核技术(英文版)》2022,33(1):13-23
The muon radiography imaging technique for high-atomic-number objects(Z) and large-volume objects via muon transmission imaging and muon multiple scattering imaging remains a popular topic in the field of radiation detection imaging. However, few imaging studies have been reported on low and medium Z objects at the centimeter scale. This paper presents an imaging system that consists of three layers of a position-sensitive detector and four plastic scintillation detectors. It acquires data by co... 相似文献
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Farzaneh Khorasani Morteza Mohammadi Zanjireh Mahdi Bahaghighat Qin Xin 《计算机系统科学与工程》2022,40(3):1085-1098
With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed. 相似文献
48.
Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE). 相似文献
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Suisui Zhang Jingying Li Yan Nie Luyao Qiang Boyang Bai Zhiwei Peng Xiaoxun Ma 《中国化学工程学报》2022,42(2):236-244
HFC-134a is a widely used environment-friendly refrigerant. At present, China is the largest producer of HFC-134a in the world. The production of HFC-134a in China mainly adopts the calcium carbide acetylene route. However, the production route has high resource and energy consumption and large waste emission, and few of the studies addressed on the environmental performance of its production process. This study quantified the environmental performance of HFC-134a production by calcium carbide route via carrying out a life cycle assessment (LCA) using the CML 2001 method. And uncertainty analysis by Monte-Carlo simulation was also carried out. The results showed that electricity had the most impact on the environment, followed by steam, hydrogen fluoride and chlorine, and the impact of direct CO2 emissions in calcium carbide production stage on the global warming effect also could not be ignored. Therefore, the clean energy (e.g., wind, solar, biomass, and natural gas) was used to replace coal-based electricity and coal-fired steam in this study, showing considerable environmental benefits. At the same time, the use of advanced production technologies could also improve environmental benefits, and the environmental impact of the global warming category could be reduced by 4.1% via using CO2 capture and purification technology. The Chinese database of HFC-134a production established in this study provides convenience for the relevant study of scholars. For the production of HFC-134a, this study helps to better identify the specific environmental hotspots and proposes useful ways to improve the environmental benefits. 相似文献
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