Key Laboratory of Agri information Service Technology

Beijing, China

Key Laboratory of Agri information Service Technology

Beijing, China
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Chen W.,Chinese Academy of Agricultural Sciences | Chen W.,Key Laboratory of Agri Information Service Technology | Li Z.,Chinese Academy of Agricultural Sciences | Li Z.,Key Laboratory of Agri Information Service Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

Social tagging becomes prevailing with the emergence of Web 2.0 communities recently. By utilizing this additional valuable information from user-created tags, it is convenient to understand users’ interests and behavior so that we can provide good user experience in applications of various domains. Therefore, the definition of profile is crucial for tagging systems. Furthermore, it is important to have recommendations for various groups of users. In recipe recommendations, older people typically have different needs compared with young users. In this paper, we first focus on the definitions of user profile, item feature and how to derive semantics from these sources. Afterwards, we design the framework of the tag-based multimedia recipe recommendation system (MRRS). Finally, we conduct preliminary experimental study of recipe recommendations for different age groups. The result shows that older people concern more about the nutrition aspects of recipes. © Springer International Publishing AG 2017.


Chen W.,Chinese Academy of Agricultural Sciences | Chen W.,Key Laboratory of Agri Information Service Technology | Li Z.,Chinese Academy of Agricultural Sciences | Li Z.,Key Laboratory of Agri Information Service Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

As the development of information technology and intelligent tutoring, web-based learning has been widely used nowadays to help people acquire knowledge in a flexible way. Especially, web-based learning services can greatly facilitate farmers to gain knowledge on farming. It is observed that learning goals and preferences tend to be localized for farming knowledge due to similar climate and soil conditions in an area. Therefore, geographical information can be utilized to recommend agricultural learning resources. In this paper, an agricultural learning resource recommendation approach is proposed using agent-based simulation that takes geographical information into account. The agent simulation environment is introduced. A distance-aware agent reputation model is presented. A multi-agent collaborative recommendation approach is proposed. Simulation experiments are conducted for the evaluation of the proposed approach. The results show good performance of it. © Springer International Publishing AG 2017.


Meng X.-X.,Chinese Academy of Agricultural Sciences | Li J.,China National Institute of Standardization | Su X.-L.,Chinese Academy of Agricultural Sciences | Su X.-L.,Key Laboratory of Agri Information Service Technology | And 2 more authors.
Journal of Integrative Agriculture | Year: 2012

The key activity to build semantic web is to build ontologies. But today, the theory and methodology of ontology construction is still far from ready. This paper proposed a theoretical framework for massive knowledge management - the knowledge domain framework (KDF), and introduces an integrated development environment (IDE) named large-scale ontology development environment (LODE), which implements the proposed theoretical framework. We also compared LODE with other popular ontology development environments in this paper. The practice of using LODE on management and development of agriculture ontologies shows that knowledge domain framework can handle the development activities of large scale ontologies. Application studies based on the principle of knowledge domain framework and LODE was described briefly. © 2012 Chinese Academy of Agricultural Sciences.


Chen W.,Chinese Academy of Agricultural Sciences | Chen W.,Key Laboratory of Agri information Service Technology | Zhao X.,Beijing Research Center for Information Technology in Agriculture | Zhao X.,Chinese National Engineering Research Center for Information Technology in Agriculture
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

In current big data era, there has been an explosive growth of various data. Most of these large volume of data are non-structured or semi-structured (e.g., tweets, weibos or blogs), which are difficult to be managed and organized. Therefore, an effective and efficient classification algorithm for such data is essential and critical. In this article, we focus on a specific kind of non-structured/semi-structured data in our daily life: recipe data. Furthermore, we propose the document model and similarity-based classification algorithm for big non-structured and semistructured recipe data. By adopting the proposed algorithm and system, we conduct the experimental study on a real-world dataset. The results of experiment study verify the effectiveness of the proposed approach and framework. © Springer International Publishing Switzerland 2016.


Xiao S.,China University of Mining and Technology | Jiang H.,China University of Mining and Technology | Zhuang J.,Chinese Academy of Agricultural Sciences | Zhuang J.,Key Laboratory of Agri Information Service Technology
Journal of Computational and Theoretical Nanoscience | Year: 2015

Wireless sensor networks (WSNs) have attracted a lot of research attention. WSNs contain a large number of nodes that are capable of sensing, processing and transmitting environmental information. Based on these capabilities, object detection algorithm in WSNs has been studied in this paper. By detecting persistent object and ephemeral object, we investigate in detail the fundamental relationship of object detection probability and detection delay with different nodes density, sensing range and duty cycle. For Wireless Sensor Networks are composed of power-restrained nodes, so energy-efficiency is a key concern in WSNs. Balancing object detection performance and network lifetime is a challenge in WSNs. Base on the theoretical analysis, we propose a novel energy-aware wake up algorithm that significantly prolongs the life of WSNs and maintain the detection performance. Simulation results confirm with the theoretical analysis and demonstrate the advantage of EAS over previous proposed methods. Copyright © 2015 American Scientific Publishers All rights reserved.


Tian R.-Y.,Chinese Academy of Agricultural Sciences | Tian R.-Y.,Key Laboratory of Agri information Service Technology | Zhang X.-F.,Chinese Academy of Agricultural Sciences | Zhang X.-F.,Key Laboratory of Agri information Service Technology | Liu Y.-J.,CAS Institute of Policy and Management
Physica A: Statistical Mechanics and its Applications | Year: 2015

SIR model is a classical model to simulate rumor spreading, while the supernetwork is an effective tool for modeling complex systems. Based on the Opinion SuperNetwork involving Social Sub-network, Environmental Sub-network, Psychological Sub-network, and Viewpoint Sub-network, drawing from the modeling idea of SIR model, this paper designs super SIC model (SSIC model) and its evolution rules, and also analyzes intervention effects on public opinion of four elements of supernetwork, which are opinion agent, opinion environment, agent's psychology and viewpoint. Studies show that, the SSIC model based on supernetwork has effective intervention effects on rumor spreading. It is worth noting that (i) identifying rumor spreaders in Social Sub-network and isolating them can achieve desired intervention results, (ii) improving environmental information transparency so that the public knows as much information as possible to reduce the rumors is a feasible way to intervene, (iii) persuading wavering neutrals has better intervention effects than clarifying rumors already spread everywhere, so rumors should be intervened in properly in time by psychology counseling. © 2015 Elsevier B.V.


Li D.,CAS Institute of Software | Zhao J.,CAS Institute of Software | Zhang H.,CAS Institute of Software | Qiao P.,CAS Institute of Software | And 2 more authors.
Proceedings - International Conference on Natural Computation | Year: 2016

An Improved Many Worlds Quantum Genetic Algorithm (IMWQGA) was proposed aiming at the shortcomings of the Quantum Genetic Algorithm, such as the multimodal function optimization problems easily falling into the local optimum and vulnerability to premature convergence. Using the concept of Many Worlds and the derivative way of parallel worlds' parallel evolution, we propose to update the population according to the main body and adopt the transition methods, such as parallel transition, backtracking, travel forth and so on. In addition, the quantum training operator and the combinatorial optimization operator as new operators of quantum genetic algorithm were also proposed. © 2015 IEEE.


Li G.,Chinese Academy of Agricultural Sciences | Li G.,Key Laboratory of Agri Information Service Technology | Li Z.,Chinese Academy of Agricultural Sciences | Li Z.,Key Laboratory of Agri Information Service Technology
Emerging Economies, Risk and Development, and Intelligent Technology - Proceedings of the 5th International Conference on Risk Analysis and Crisis Response, RACR 2015 | Year: 2015

It is critical for futures market risk quantitative management to effectively estimate the extreme risk. At present the indicator of Value at Risk (VaR) has been widely used to measure market risk. However, VaR estimation techniques are not in-depth enough to efficiently estimate the complicated agricultural futures market risks. Thus, taking the futures market of soybean oil, rapeseed oil and palm oil for example, this study analyzed summary statistics of futures market price returns during given sample period, established ARMA-GARCH models to estimate downside VaRs with different residual assumptions at the 95%, 97.5% and 99% confidence level respectively, and compared the accuracy of VaR estimation based on residual assumptions of standard normal distribution, Student-t distribution and generalized error distribution. These finding are observed: (i) Significant volatility clustering can be found for futures market returns. Estimating VaR using ARMA-GARCH approach can effectively depict the distribution and volatility of vegetable oil futures market return. (ii)At any confidence level of 95%, 97.5% and 99%, the VaR model based on t distribution is easier to overestimate the futures market risk. (iii) The accuracy of estimated VaR based on GED is better at a higher confidence level. At the confidence level of 95%, VaR model based on standard normal distribution and GED are almost the same in statistical sense. However, at the 97.5% and 99% confidence level, the accuracy of risk measure based on GED is better than that based on standard normal distribution method. © 2015 Taylor & Francis Group, London.


Li Z.-M.,Chinese Academy of Agricultural Sciences | Xu S.-W.,Key Laboratory of Agri information Service Technology | Cui L.-G.,Key Laboratory of Agri information Service Technology | Li G.-Q.,Key Laboratory of Agri information Service Technology | And 2 more authors.
Advanced Materials Research | Year: 2013

After analyzing and reviewing the short-term forecasting methods research of pork price at home and abroad, a chaotic neural network model based on genetic algorithm (CNN-GA) was put forward according to the nonlinear characteristics of pork price,which established on the base of the chaotic theory and the neural network technology. Chosen the daily retail price data of the pork (streaky pork) from January 1, 2008 to June 11, 2012,we designed the basic structure of CNN-GA, and thentrainedit in order to attain the trained CNN-GA model. Finally, the trained CNN-GA model was used to predict the 20 days' (from June 12, 2012 to July 1, 2012) retail price of pork (streaky pork) and then compared the predicted price with the real price to test the model's forecast accuracy and application ability.The result shows that the model has high prediction precision, good fitting effect and hasan important reference and practical significance for the short-term price forecasting of the pork market. © (2013) Trans Tech Publications, Switzerland.


Li S.J.,Key Laboratory of Agri information Service Technology | Zhu Y.P.,Key Laboratory of Agri information Service Technology
Applied Mechanics and Materials | Year: 2014

The low water and nitrogen utilization rates and environmental pollution as a result of the excessive irrigation and fertilization had been paid more and more attention. Adopting the simulation model based on physiological processes to study the optimization method and control technology of agricultural production and management is important to water-saving, rational fertilization and healthy environment. The maize (Zea mays L) is taken as material to explore the integration method of crop simulation with wireless sensing data. First the time scale and temporal scale of wireless sensing data will be unified with simulation step of crop model when the parameters are used as model inputs. Then the relationship between nitrogen balance and soil moisture will be analyze to construct maize production and management monitoring and control system based on wireless sensing data. The system can not only simulate the real-time and dynamic maize growth and development, but also provide the irrigation and fertilization schedule to distribute the annual natural resources depend on the user's production goal. The expected forecast results will offer theoretical basis and technological support for intelligent control, water and fertilizer utilization, and management of agricultural production departments. © (2014) Trans Tech Publications, Switzerland.

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