Long-Term Recurrent Merge Network Model for Image Captioning

2018 ◽  
Author(s):  
Yang Fan ◽  
Jungang Xu ◽  
Yingfei Sun ◽  
Ben He
Keyword(s):  
Network Model ◽  
Image Captioning

Grey RBF Neural Network Forecasting on LV under the View of Government Responsibility

2012 ◽  
Vol 452-453 ◽  
pp. 700-704
Author(s):  
Feng Rong Zhang ◽  
Annik Magerholm Fet ◽  
Xin Wei Xiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Development Policy ◽  
Rbf Neural Network ◽  
Good Method ◽  
Government Responsibility ◽  
Long Period ◽  
The Government

At present, the domestic research on the scale of macroscopic logistics has yet belonged to the blankness, therefore, this research tries using LV in circulation and LV in stock to measure the logistics volume and forecasting it in a long period. In order to overcome the phenomenon of “floating upward” in long-term period, this paper establish the improved Grey RBF to forecast the LV next 5-10 year in Jilin province of China. The results show that the increased circulation of goods is the main reason leading to increased logistics volume, and the simulation also shows that the improved gray RBF neural network model is a good method for the government to establish the logistics development policy.

scholarly journals Designing Incentive Mechanisms for Mobile Crowdsensing with Intermediaries

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yatong Chen ◽  
Huangxun Chen ◽  
Shuo Yang ◽  
Xiaofeng Gao ◽  
Yunhe Guo ◽  
...  
Keyword(s):  
Network Model ◽  
Large Scale ◽  
Incentive Compatibility ◽  
Rapid Development ◽  
Sensor Technology ◽  
Incentive Mechanisms ◽  
Mobile Crowdsensing ◽  
Network Applications ◽  
Sensing Platform

In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, and transportation. However, most existing mobile crowdsensing systems lack vast user bases and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale and provide long-term guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform and provide a solution to cope with the problem of user overlapping among intermediaries. We theoretically prove the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency. Furthermore, we evaluate our incentive mechanisms by extensive simulations. Evaluation results validate the effectiveness and efficiency of our proposed mechanisms.

Research of the System Based on BP Network for the Prediction of Grape Disease

2013 ◽  
Vol 462-463 ◽  
pp. 476-480
Author(s):  
Feng Bao ◽  
Juan Wang ◽  
Zhen Hui Ren
Keyword(s):  
Influencing Factors ◽  
Network Model ◽  
Network Structure ◽  
Good Effect ◽  
Bp Network ◽  
Disease Occurrence ◽  
Sample Data ◽  
Long Term Prediction ◽  
Selection Of

The text introduced a system based on BP network for the prediction of grape disease. It included the design of a network structure, the selection of parameter for network study, the processing of sample data etc. With the use of BP network model, this system can forecast the extent of grape disease, so it is applicable to the conditions which have many influencing factors, complicated relationship, difficulty of analyze quantitatively and requirement of long-term prediction. Using this system to the prediction of grape disease in Zhuo Lu area Zhang Jia Kou city, the authors obtained a good effect, which is of value to the prediction of grape disease occurrence.

Analysis of a long-term pumping and tracer test using the channel network model

1998 ◽  
Vol 32 (3-4) ◽  
pp. 203-222 ◽  
Author(s):  
B Gylling ◽  
L Birgersson ◽  
L Moreno ◽  
I Neretnieks
Keyword(s):  
Network Model ◽  
Tracer Test ◽  
Channel Network

scholarly journals Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system

2018 ◽  
Vol 5 (3) ◽  
pp. 172092 ◽  
Author(s):  
Xiangyun Gao ◽  
Shupei Huang ◽  
Xiaoqi Sun ◽  
Xiaoqing Hao ◽  
Feng An
Keyword(s):  
Granger Causality ◽  
Network Model ◽  
Stock Price ◽  
Financial System ◽  
Diffusion Path ◽  
Complex Network Theory ◽  
The Real Estate ◽  
Diffusion Paths ◽  
Equilibrium Relationships

Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.

scholarly journals Boosting Memory with a Persistent Memory Mechanism for Remote Sensing Image Captioning

2020 ◽  
Vol 12 (11) ◽  
pp. 1874
Author(s):  
Kun Fu ◽  
Yang Li ◽  
Wenkai Zhang ◽  
Hongfeng Yu ◽  
Xian Sun
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
External Memory ◽  
Image Captioning ◽  
Time Step ◽  
Current Time ◽  
Input Information ◽  
Memory Mechanism ◽  
Persistent Memory

The encoder–decoder framework has been widely used in the remote sensing image captioning task. When we need to extract remote sensing images containing specific characteristics from the described sentences for research, rich sentences can improve the final extraction results. However, the Long Short-Term Memory (LSTM) network used in decoders still loses some information in the picture over time when the generated caption is long. In this paper, we present a new model component named the Persistent Memory Mechanism (PMM), which can expand the information storage capacity of LSTM with an external memory. The external memory is a memory matrix with a predetermined size. It can store all the hidden layer vectors of LSTM before the current time step. Thus, our method can effectively solve the above problem. At each time step, the PMM searches previous information related to the input information at the current time from the external memory. Then the PMM will process the captured long-term information and predict the next word with the current information. In addition, it updates its memory with the input information. This method can pick up the long-term information missed from the LSTM but useful to the caption generation. By applying this method to image captioning, our CIDEr scores on datasets UCM-Captions, Sydney-Captions, and RSICD increased by 3%, 5%, and 7%, respectively.

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