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Feeding is an important and also a heavy work in river crab farming, uniform feeding is necessary for raising river crab on the whole pond. The overall scheme of automatic bait casting system carried by paddlewheel catamaran is proposed based on RTK-GPS navigation positioning device. The feeding device equipped with the controllable flow velocity of bait feeder, distance adjustable thrower and bait weight measurable device which could perform variable feeding is designed to solve the problem of nonuniform distribution of the feed. The hardware and software system of automatic feeding device are designed. Then the calculation and simulation for the modeling of the feeding device are pefromed. The test results show that the system linear track positioning accuracy is less than 30 cm, and automatic continuous feeding speed is up to 240 m2/min. The results of this study provide technology reference for further study of automatic uniform feeding on the whole pond in river crab aquaculture.

Linear orbit tracking algorithm of navigation for aquaculture paddle boat. Based on the motion coordinate system, after the establishment of the paddle boat hydrodynamic model, the transfer function and state equation of the paddle boat yaw angle system, the sliding mode control method is adopted to achieving high-precision track tracking for the paddle boat. In order to save the energy, reduce the active frequency of the control system caused by the disturbance, and extend the duration of sailing of the paddle boat, the Kalman filter is proposed to reduce the influence of the disturbance on the track tracking. The simulation and experimental verification show that the sliding mode control method based on Kalman filter can realize high-precision linear track tracking, and Kalman filter can effectively reduce the influence of disturbance.

To identify knowledge of combination therapy for chronic diseases, high-quality manually curated corpora are required. In this study, we describe the construction work of a corpus for chemical disease semantic extraction in Chinese (RCorp) from a collection of Chinese biomedical abstracts. During the RCorp corpus construction, we used domain-existing guidelines as references, and incorporated annotation tools to the standard annotation process. The resulting corpus consists of 339 Chinese biomedical articles with 2,367 annotated chemicals, 2,113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. RCorp gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. The result analysis of the corpus proves the quality of the corpus for the combination therapy of chronic diseases knowledge discovery task.

This paper introduced a LDA-based pseudo relevance feedback (PRF) model for cross language information retrieval. To validate the performance of PRF techniques in CLIR task, we conducted cross language query expansion experiments based on a self-constructed CLIR system, the LDA-based PRF model was applied before or after the query translating process, namely the pre-translation-PRF, the post-translation-PRF, and the combined-PRF strategy. We also compared this model with the classical VSM-based PRF algorithm. Experiment results showed that the proposed LDA-based PRF method was effective for improving the performance of CLIR.

Studies of Community-based question and answer services (cQA) have grown to be one of the emerging trends in Web information services. And one of the main tasks is to retrieve similar questions. To identify the fact that some questions with different expressions though may indeed have the same, or very similar, meaning, the similar questions are defined in syntactic, semantic and pragmatic aspects according to user retrieval intention. Five models including Language Model, Translation-based Language Model, Parser-based model, LDA and WordNet source-based model are selected as baselines. Integrated models which linearly combine WordNet, Stanford Parser and Language Model and further weighted by syntactic feature are proposed to integrate features of the three aspects. Experiment results show that our integrated models perform better than the basic models, especially when the linear combination of the WordNet model and the Stanford Parser model is weighted by syntactic information with proper noun phrases as representative. The integrated models are further verified by logistic analysis.