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Fast Transform Kernel Selection Based on Frequency Matching and probability model for AV1

IEEE TRANSACTIONS ON BROADCASTING 2024年第2期70卷 693-707页

作者： Hao, Zhijian Sun, Heming Xu, Guohao Liu, Jiaming Xiong, Xiankui Zhu, Xuanpeng Zeng, Xiaoyang Fan, Yibo Fudan Univ State Key Lab Integrated Chips & Syst Shanghai 200433 Peoples R China Yokohama Natl Univ Fac Engn Yokohama 2400067 Japan ZTE Corp State Key Lab Mobile Network & Mobile Multimedia Shenzhen Peoples R China

As a fundamental component of video coding, transform coding concentrates the energy scattered in the spatial domain onto the upper-left region of the frequency domain. This concentration contributes significantly to Rate-Distortion performance improvement when combined with quantization and entropy coding. To better adapt the dynamic characteristics of image content, Alliance for Open Media Video 1 (AV1) introduces multiple transform kernels, which brings substantial coding performance benefits, albeit at the cost of considerably computational complexity. In this paper, we propose a fast transform kernel selection algorithm for AV1 based on frequency matching and probability model to effectively accelerate the coding process with an acceptable level of performance loss. Firstly, the concept of Frequency Matching Factor (FMF) based on cosine similarity is defined for the first time to describe the similarity between the residual block and the primary frequency basis image of the transform kernel. Statistical results demonstrate a clear distribution relationship between FMFs and normalized Rate-Distortion optimization costs (nRDOC). Then, leveraging these distribution characteristics, we establish Gaussian normal probability model of nRDOC for each FMF by characterizing the parameters of the normal model as functions of FMFs, enhancing the normal model's accuracy and coding performance. Finally, based on the derived normal models, we design a fast selection algorithm with scalability and hardware-friendliness to skip the non-promising transform kernels. Experimental results show that the performance loss of the proposed fast algorithm is 1.15% when 57.66% of the transform kernels are skipped, resulting in a saving of 20.09% encoding time, which is superior to other fast algorithms found in the literature and competitive with the pruning algorithm based on the neural network in the AV1 reference software.

关键词： Transforms Kernel Discrete cosine transforms Computational modeling Standards Quantization (signal) Video coding AV1 transform kernel selection matching factor probability model

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A probability model for Fully Developed Annular Flow in Vertical Pipes: Film Thickness, Interfacial Shear Stress, and Droplet Size Distribution

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JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME 2017年第3期139卷 032001页

作者： Zhang, Ri Liu, Haixiao Dong, Sheng Liu, Mingyang Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China Shanghai Jiao Tong Univ Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Shanghai 200240 Peoples R China

The movement and distribution of each phase in annular flow can be considered as random events at a microscopic level. Hence, a probability analysis is appropriate to estimate the morphological features and mechanical characteristics of annular flow from a macroscopic scale. In the present work, three characteristic parameters including the film thickness, interfacial shear stress, and characteristic droplet size are predicted by a probability model as the statistical results of abundant samples. The film thickness can be directly calculated as one of the solutions to the basic equations of annular flow. The interfacial shear stress is estimated as a combination of the frictional and dragging components. The droplet size distribution is obtained using a method of undetermined coefficients. These characteristic parameters are well verified by comparing with the experimental data available in the literature. It is demonstrated that the probability model can accurately calculate the film thickness and maximum droplet size, but the predictions of the interfacial shear stress and mean droplet size are relatively coarse. Furthermore, the effects on the film thickness and Sauter mean diameter of other parameters are discussed in detail. Finally, some important phenomena observed in experiments are interpreted by the probability model.

关键词： annular flow probability model film thickness interfacial shear stress drop-let size distribution

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A probability model for Grid Faults Using Incomplete Information

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IEEE TRANSACTIONS ON SMART GRID 2017年第2期8卷 956-968页

作者： Al-Kanj, Lina Bouzaiene-Ayari, Belgacem Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Univ Beirut Elec & Comp Engn Beirut Lebanon Princeton Univ Dept Elect Engn Princeton NJ USA

Utilities face the challenge of responding to power outages due to storms and ice damage, but most power grids are not equipped with sensors to pinpoint the precise location of fault causing the outage. Instead, utilities have to depend primarily on phone calls (trouble calls) from customers who have lost power. This paper presents an information model of the grid in the presence of outages;the developed model is used to estimate the probability of power line faults causing the outages. However, the computational complexity of the problem grows exponentially with the number of power lines in the grid. Thus, several methods are proposed for handling the combinatorial growth of events and the behavior is demonstrated using the data of a real-power grid. Performance results show that power line fault detection can be achieved with high accuracy, even with a very low percentage of customers calling to report an outage.

关键词： Distribution system fault grid fault power line fault power outage probability model probability of fault trouble call

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A probability model for fully developed annular flow in vertical pipes: Prediction of the droplet entrainment

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2015年 84卷 225-236页

作者： Zhang, Ri Liu, Haixiao Liu, Mingyang Tianjin Univ Sch Civil Engn Tianjin 300072 Peoples R China

The phase distributions and mechanical properties of annular flow can be regarded as random states. Hence the probability analysis is an appropriate method to investigate the possibilities of the relevant events and the statistic results of some characteristic parameters. In the present work, a probability model for fully developed annular flow in vertical pipes is proposed to predict the phase distributions and mechanical characteristics. The probability model works in three mechanisms. First, a vortex generation theory on energy transfer from vortexes to droplets is supposed to describe the atomization process. Second, a random walk theory is applied to track the droplet deposition on the liquid film. Third, the atomization and deposition rates are respectively related to the probabilities of droplet generation and elimination by analyzing the interaction between vortexes and droplets. Based on the knowledge of dynamic equilibrium between atomization and deposition processes, a balance equation is established to close the equation set and the representative parameters of annular flow can be solved. The new model is a statistical method and almost links all the parameters involved in annular flow. By comparing the predicted droplet entrainment with experimental data available in the literature, the present model is well verified and demonstrates advantages both in accuracy and in convenience. Furthermore, the effects on the entrainment of many parameters, including the gas flow rate, liquid flow rate, gas density, liquid density, gas viscosity, surface tension and pipe diameter, are discussed in detail. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Annular flow probability model probability analysis Vortex generation theory Random walk theory Droplet entrainment

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Topic Propagation Prediction Based on Dynamic probability model

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IEEE ACCESS 2019年 7卷 58685-58694页

作者： Wang, Jing Zhao, Hui Liu, Zhijing Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

As social networks play an increasingly important role in people's lives, people are more likely to discuss hot topics on social networks. Predicting the spread of hot topics, known as topic propagation prediction is an important task. Due to the unpredictability of the users and topics in social networks, predicting the topic propagation trend is still a major challenge. Different users play different roles in topic propagation. However, existing studies have not utilized user role analysis. In this paper, we propose a topic propagation prediction method (TPP) based on user role analysis and dynamic probability model. First, we describe our user role analysis, which incorporates four user-factors to characterize user attributes along two dimensions. Second, we combine dynamic probability model with user role analysis to accurately predict the topic propagation trend. Finally, we prove the efficiency of TPP by experiments.

关键词： probability model user role analysis topic propagation prediction

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Statistical characteristics of driver acceleration behaviour and its probability model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2022年第2-3期236卷 395-406页

作者： Liu, Rui Zhao, Xuan Zhu, Xichan Ma, Jian Changan Univ Sch Automobile Xian Peoples R China Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

Naturalistic driving data were applied to study driver acceleration behaviour, and a probability model of the driver was proposed. First, the question of whether the database is large enough is resolved using kernel density estimation and Kullback-Liebler divergence. Next, the convergence database is utilised to achieve the bivariate acceleration distribution pattern. Subsequently, two probability models are proposed to explain the pattern. Finally, the statistical characteristics of the acceleration behaviours are studied to verify the probability models. The longitudinal and lateral acceleration behaviours always approximate a similar Pareto distribution. The braking, accelerating, and steering manoeuvres become more intense at first and then less intense as the velocity increases. These behaviours characteristics reveal the mechanism of the quadrangle bivariate acceleration distribution pattern. The bivariate acceleration behaviour of the driver will never reach a circle-shaped pattern. The bivariate Pareto distribution model can be applied to describe the bivariate acceleration behaviour of the driver.

关键词： Naturalistic driving data driver behaviour statistical characteristics probability model Pareto distribution

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Sociability-based Influence Diffusion probability model to evaluate influence of BBS post

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NEUROCOMPUTING 2018年 293卷 18-28页

作者： Li, Lei Lin, Xin Zhou, MengChu Fu, Lili Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Beijing Union Univ Business Coll Beijing 100025 Peoples R China

A Bulletin Board System (BBS) yields user-generated posts, which has enjoyed fast spreading speed. Significant events are often revealed by a post. It may then spread widely, thereby producing large influence in some specific social circles and sometimes the whole society. Hence, evaluating post influence becomes important. It can help Web service providers locate quickly those influential posts, users or communities, place right advertisements, expand an event's influence, and explode a hot topic's discussions. Recently, BBS has grown to have some new features, e.g., sociability. The existing studies use an Influence Diffusion model (IDM) and its expanded versions for the analysis of influence. However, they suffer from such drawbacks as identical treatment of every comment or reply, and complete ignorance of relationships among users, thereby leading to the inaccurate assessment of post influence. To overcome the limitations, inspired by our prior user model for user participation in virtual communities, we propose a behavioral model for user participation in a post and give a Sociability-based Influence Diffusion probability model (S-IDPM) by utilizing user relationship and reply-chains to measure the responses of different users and evaluate post influence. Experiments with real data collected from a popular BBS. Our results show that S-IDPM outperforms IDM and its expanded version called Influence Diffusion probability model (IDPM). S-IDPM can be helpful to achieve better post influence diffusion evaluation than IDM and IDPM do. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Bulletin Board System (BBS) Post influence Influence diffusion model Sociability probability model

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Estimation of switching characteristics in quantum- dot cellular automata using probability model

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MICRO & NANO LETTERS 2014年第1期9卷 46-49页

作者： Chen Xiangye Cai Li Jia Kaixiang Yao Xiaokuo Zhang Mingliang Air Force Engn Univ Coll Sci Xian 710051 Peoples R China Xian Univ Sci & Technol Polytech Coll Xian 710054 Peoples R China

Quantum-dot cellular automata (QCA) is a novel computational paradigm which utilises the quantum mechanism to encode and process bit information. The switching characteristics of the QCA majority gate are evaluated by using the proposed probability model. All kinds of probabilities are achieved by solving the time-independent Schrodinger equation. The simulation results reveal that the majority gate switches at a higher correct probability when the cell distance and the size decrease. Cell miniaturisation would raise a significant implication in how to realise reliable QCA fabrication. In addition, the results also illuminate that majority output has different probabilities when the inputs change. Therefore, to analyse the QCA circuit reliability by using the probability model, all the input combinations must be considered.

关键词： cellular automata circuit reliability probability quantum dots Schrodinger equation switching switching characteristics quantum-dot cellular automata probability model time-independent Schrodinger equation cell miniaturisation circuit reliability

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Character Recognition Algorithm Based on Fusion probability model and Deep Learning

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COMPUTER JOURNAL 2021年第11期64卷 1705-1714页

作者： Liu, Zhijun Pan, Xuefeng Peng, Yuan Wuchang Shouyi Univ Coll Informat Sci & Engn Nanli Rd Wuhan 430064 Hubei Peoples R China Wuhan Qingchuan Univ Coll Comp Donghu New Technol Dev Zone Yuping AveZhonghua Sci & Technol Ind Pk Wuhan 430204 Hubei Peoples R China Army Engn Univ PLA Ordnance NCO Acad Luoyu East Rd Wuhan 430075 Hubei Peoples R China

The authors proposed a character recognition algorithm to improve the recognition efficiency and recognition accuracy in character recognition. The algorithm is based on a deep belief network classifier. In the character recognition, a comprehensive feature model is established firstly by combining the histogram Gabor feature, grid level feature and gray level co-occurrence matrix. Subsequently, the deep belief network trains the feature model. Finally, the probability model is used to judge the recognition symbols. The algorithm is tested with 74 k data set and is compared and analyzed from three indexes: false acceptance rate, false rejection rate and accuracy. The data set simulation and comparison with other algorithms indicate that the recognition system based on the probability model and depth learning has higher accuracy and better performance.

关键词： character recognition joint feature probability model classifier deep belief network

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A new probability model for insuring critical path problem with heuristic algorithm

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NEUROCOMPUTING 2015年 148卷 129-135页

作者： Li, Zhenhong Liu, Yankui Yang, Guoqing Hebei Univ Coll Math & Comp Sci Baoding 071002 Hebei Peoples R China

In order to obtain an adequate description of risk aversion for insuring critical path problem, this paper develops a new class of two-stage minimum risk problems. The first-stage objective function is to minimize the probability of total costs exceeding a predetermined threshold value, while the second-stage objective function is to maximize the insured task durations. For general task duration distributions, we adapt sample average approximation (SAA) method to probability objective function. The resulting SAA problem is a two-stage integer programming model, in which the analytical expression of second-stage value function is unavailable, we cannot solve it by conventional optimization algorithms. To avoid this difficulty, we design a new hybrid algorithm by combining dynamic programming method (DPM) and genotype-phenotype-neighborhood based binary particle swarm optimization (GPN-BPSO), where the DPM is employed to find the critical path in the second-stage programming problem. We conduct some numerical experiments via a critical path problem with 30 nodes and 42 arcs, and discuss the proposed risk averse model and the experimental results obtained by hybrid GPN-BPSO, hybrid genetic algorithm (GA) and hybrid BPSO. The computational results show that hybrid GPN-BPSO achieves the better performance than hybrid GA and hybrid BPSO, and the proposed critical path model is important for risk averse decision makers. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Critical path probability model Stochastic optimization Minimum risk criterion Hybrid algorithm

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