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Automatic detection of follicle ultrasound images based on improved Faster R-CNN

Journal of Physics: Conference Series 2019年第4期1187卷

作者： Tianlong Zeng Jun Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 P. R. China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 P. R. China

Follicle Ultrasonic images detection technology plays an important role in the monitoring of bull-follicle. Because of follicle ultrasound image containing lots of speckle noise and fuzzy edges, the traditional image detection algorithm is difficult to get better detection results on the ultrasonic image, and the traditional image detection algorithm needs to carry out sample feature extraction for each image, which is time-consuming and time-consuming and labor-intensive. According to the characteristics of the cattle follicle ultrasound image sets, this paper proposes a model of image detection based on improved deep learning Faster R - CNN to automatically detect cattle ovarian follicles, through joint VGG-16 different network layer characteristic figure to replace single deepest characteristic figure, retain the deep semantic characteristics at the same time, also keep the shallow characterization information. The experimental results show that this method has a better effect on the ultrasonic image detection of bovine follicle.

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Anatomy of an online misinformation network

arXiv

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arXiv 2018年

作者： Shao, Chengcheng Hui, Pik-Mai Wang, Lei Jiang, Xinwen Flammini, Alessandro Menczer, Filippo Ciampaglia, Giovanni Luca College of Computer National University of Defense Technology China School of Informatics Computing and Engineering Indiana University Bloomington United States MOE Key Laboratory of Intelligent Computing and Information Processing Xiangtan University China Indiana University Network Science Institute Bloomington United States

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network. Copyright © 2018, The Authors. All rights reserved.

关键词： Social networking (online)

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Synchronization of Switched Neural Networks with Communication Delays via the Event-Triggered Control

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IEEE Transactions on Neural Networks and Learning Systems 2017年第10期28卷 2334-2343页

作者： Wen, Shiping Zeng, Zhigang Chen, Michael Z. Q. Huang, Tingwen Guangdong Province Key Lab of Digital Manufacturing Equipment Key Laboratory of Image Processing Intelligent Control of Education Ministry of China School of Automation Guangdong HUST Industrial Technology Research Institute Huazhong University of Science Technology Wuhan Hubei430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong Texas A M University at Qatar DohaTX5825 United States

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results. © 2012 IEEE.

关键词： Synchronization

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Predicting commercial activeness over urban big data

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017年第3期1卷 1-20页

作者： Yang, Su Wang, Minjie Wang, Wenshan Sun, Yi Gao, Jun Zhang, Weishan Zhang, Jiulong Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University 825 Zhangheng Rd Shanghai201203 China Department of Software Engineering China University of Petroleum Qingdao266580 China School of Computer Science Xian University of Technology Xian710048 China

This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of visitors, the region functions, the population and buying power of local residents, the average price of services, and the rating scores of customers, which are computed from heterogeneous data including taxi GPS trajectories, point of interests, geographical data, and user-generated comments. Then, we apply sparse representation to discover the impactor factor of each variable of the social contexts in terms of predicting commercial activeness of UCDs under a linear predictive model. The experiments show that a linear correlation between social contexts and commercial activeness exists for Beijing and Shanghai based on an average prediction accuracy of 77.69% but the impact factors of social contexts vary from city to city, where the key factors are rich life services, diversity of restaurants, good shopping experience, large number of local residents with relatively high purchasing power, and convenient transportation. This study reveals the underlying mechanism of urban business ecosystems, and promise social context-aware business planning over heterogeneous urban big data. © 2017 Association for Computing Machinery.

关键词： Ecosystems

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Image retrieval based on image entropy and regional expansion

International Journal of Control and Automation

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International Journal of Control and Automation 2016年第6期9卷 411-422页

作者： Shuying, Chen Zhiyong, An Key Laboratory of Intelligent Information Processing Universities of Shandong Shandong Institute of Business and Technology China

This paper uses the image entropy directly on the color image to express the integral color image information and color spatial distribution of the neighborhood, then the image is divided into blocks, for each block we use the Pelaez Aggregation to determine the seed, after that we use the regional expansion method for color image blocks to extract the color characteristic of color image. The centroid was extracted from the segmented image and combined with variance as the similarity metric standard. Result shows that, this method is not only simple and efficient, but also improves the performance of image retrieval. © 2016 SERSC.

关键词： Color

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Massive MIMO Channel Modeling Using Map-based Ray Tracing Method

Massive MIMO Channel Modeling Using Map-based Ray Tracing Me...

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2017 7th IEEE International Symposium on Microwave, Antenna, Propagation, and EMC Technologies

作者： Junliang Yao Haipeng Ren Qing Liu Shaanxi Key Laboratory Complex System Control and Intelligent Information Processing Xi'an University of Technology

Massive multiple-input multiple-output(MIMO), with giant array size and multi-dimension array structure, has been widely considered as a key physical layer technique in future wireless communications. With regarding the large number of antenna elements, some new challenges and issues are arising, e.g. modeling the near-field effects in MIMO channel and the high computational complexity. To solve these problems, a statistical channel model is proposed based on the cluster delay line(CDL) framework. The proposed model focuses on the spherical wavefront theory, thus can be applied to the massive MIMO system. Furthermore, the map-based ray tracing algorithm with low complexity is used to compute the statistical parameters, such as pathloss, delay spread, etc., The numerical analysis results show that the proposed channel model is enable to describe the main characteristics of massive MIMO channel.

关键词： large scale MIMO wireless channel modeling ray tracing algorithm path loss delay spread

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Super Dye Adsorption Capability of Natural Superfine Down Particles Derived from Solid Wastes

Super Dye Adsorption Capability of Natural Superfine Down Pa...

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textile Bioengineering and Informatics Symposium Proceedings

作者： Feng-Xiang Chen Hui-Yu Yang Ya-Ling Wang Xin Liu Wei-Lin Xu Beijing Advanced Innovation Center for Biomedical Engineering and Key Laboratory of Bio-inspired Smart Interfacial Science and Technology of Ministry of Education School of Chemistry Beihang University State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile University

Natural superfine down particles(NSDP) were successfully prepared and evaluated as an efficient adsorbent for the removal of acid brilliant scarlet 3R, direct fast scarlet 4BS and vat scarlet R from aqueous solution. The adsorbent was characterized by scanning electron microscopy(SEM), N2 adsorption and Fourier transform infrared(FTIR) spectroscopy. The adsorption behavior of NSDP for these three dyes was investigated as a function of *** results showed that the NSDP surface has an abundance of functional groups(carboxyl, hydroxyl and aminegroups). The adsorption capacity of NSDP was strongly pH-dependent. The maximum absorption capacity of the dyes onto the NSDP was observed at pH 3(108.84, 87.06, and 67.51mg/g). The results indicated that NSDP can be used as an efficient and economic adsorbent to remove dyes in the textile effluents.

关键词： Natural Superfine Down Particle textile Wastewater Dye Ddsorption pH

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Social cognitive optimization with tent map for combined heat and power economic dispatch

arXiv

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arXiv 2018年

作者： Sun, Jiaze Li, Yang School of Computer Science and Technology Xi 'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi 'an University of Posts and Telecommunications Xi 'an710121 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China

Combined Heat and Power Economic Dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel Social Cognitive Optimization algorithm with Tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, Adaptive Constraints Relaxing (ACR) rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by carrying out two typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state-of-the-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Functions

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The Ball Mill Load Measuring algorithm though Grinding tone signal based on GA

The Ball Mill Load Measuring algorithm though Grinding tone ...

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2017 International Seminar on Artificial Intelligence, Networking and Information technology

作者： Yingmin Yi Haichuan Yang Lu Sun Xiaoli Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

For a high energy loss and complex system of ball mill,this paper provide a ball mill load detection method based on genetic algorithm optimizing BP neural *** effective frequency range of mill sound signal is *** soft measurement model of mill load based on mill sound signal is *** order to solve the problem which converge slowly and easily reach minimal value,the global optimization of GA(genetic algorithm) local optimization of BP neural network will be combined to improve the BP neural *** with the detected mill load error generated from existing BP neural network and RBF neural network based on *** experiments results show that the proposed algorithm has better precision.

关键词： Ball mill load Grinding tone signal Genetic algorithm RBF neural network

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