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( Jianhua Wang ),( Yubin Lan ),( Shilei Lu ),( Lianglun Cheng ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.3
Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value. Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper. The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams. Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology. At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.
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An Efficient Complex Event Detection Algorithm based on NFA_HTS for Massive RFID Event Stream
Wang, Jianhua,Liu, Jun,Lan, Yubin,Cheng, Lianglun The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.2
Massive event stream brings us great challenges in its volume, velocity, variety, value and veracity. Picking up some valuable information from it often faces with long detection time, high memory consumption and low detection efficiency. Aiming to solve the problems above, an efficient complex event detection method based on NFA_HTS (Nondeterministic Finite Automaton_Hash Table Structure) is proposed in this paper. The achievement of this paper lies that we successfully use NFA_HTS to realize the detection of complex event from massive RFID event stream. Specially, in our scheme, after using NFA to capture the related RFID primitive events, we use HTS to store and process the large matched results, as a result, our scheme can effectively solve the problems above existed in current methods by reducing lots of search, storage and computation operations on the basis of taking advantage of the quick classification and storage technologies of hash table structure. The simulation results show that our proposed NFA_HTS scheme in this paper outperforms some general processing methods in reducing detection time, lowering memory consumption and improving event throughput.
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An Efficient Complex Event Detection Algorithm based on NFA_HTS for Massive RFID Event Stream
Jianhua Wang,Jun Liu,Yubin Lan,LiangLun Cheng 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.2
Massive event stream brings us great challenges in its volume, velocity, variety, value and veracity. Picking up some valuable information from it often faces with long detection time, high memory consumption and low detection efficiency. Aiming to solve the problems above, an efficient complex event detection method based on NFA_HTS (Nondeterministic Finite Automaton_Hash Table Structure) is proposed in this paper. The achievement of this paper lies that we successfully use NFA_HTS to realize the detection of complex event from massive RFID event stream. Specially, in our scheme, after using NFA to capture the related RFID primitive events, we use HTS to store and process the large matched results, as a result, our scheme can effectively solve the problems above existed in current methods by reducing lots of search, storage and computation operations on the basis of taking advantage of the quick classification and storage technologies of hash table structure. The simulation results show that our proposed NFA_HTS scheme in this paper outperforms some general processing methods in reducing detection time, lowering memory consumption and improving event throughput.
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An Improved Classification Model Based on Feature Fusion for Orchid Species
Wang Jianhua,Wang Haozhan,Long Yongbing,Lan Yubin 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
Orchid is a kind of terrestrial herb and it has elegant fower posture, quiet fower fragrance, rich colors and noble moral, therefore it has high ornamental value and is deeply loved by people. There are many kinds of orchids, and some of them are similar in shape, texture and color, which make people difcult to quickly and correctly distinguish them. As the existing classifcation model of orchid species have the problems of low accuracy rate and long classifcation time because of the inter species similarities and intra species diferences in orchid species, thus infuencing its wide application. In order to solve the problem above, in this paper, an improved classifcation model based on feature fusion is proposed for orchid species. The achievement of the paper lies in the fact that we successfully developed a classifcation model based on feature fusion to realize the high-efcient classifcation for orchid species. Specifcally, in our scheme, frstly we obtained 12 orchid image sets with number of 12,227 images by network and feld photography; Secondly we analyzed and studied the semantic relationship of diferent scale features from acquired orchid images above; Thirdly we designed an improved classifcation model based on feature fusion on the basis of the semantic relationship above; At last, we used the classifcation model above to realize the high-efcient classifcation for 12 orchid species. The experimental results showed that our proposed classifcation model based on feature fusion in this paper can realize 92.98% classifcation accuracy rate compared with classifcation models without using feature fusion technology, which can greatly improve the classifcation efciency for orchid species.
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