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29. Research on E-commerce Data Mining and Managing Model in The Process of Farmers' Welfare Growth

Author

Ye Lei and Song Ying

Subjects
Apriori algorithm, Association rule learning, Computer science, business.industry, Online analytical processing, E-commerce, Customer relationship management, Recommender system, computer.software_genre, Resource (project management), Data mining, Cluster analysis, business, computer
Abstract

To establish customer resource database for agriculture and manage customer relationship scientifically, this paper introduces the current development of CRM system and related technical trends, and analyzes the demand of introducing customer relationship management into agricultural products e-commerce system. Then it puts forward the establishment of customer relationship management model based on OLAP and data mining technology under Web. The principle of association rule algorithm and clustering algorithm is explained, and the classical algorithm Apriori algorithm of association rules is analyzed and calculated. A framework of e-commerce recommendation system is also proposed and the improved algorithm is used to analyze the evaluation information of agricultural products of an e-commerce website. The clustering analysis of customer interest is performed finally. The experimental data of the improved algorithm are obtained, and the effectiveness of the improved algorithm is verified.

Published
2021

33. Ultra-Low-Noise MIMO Distributed Acoustic Sensor Using Few-Mode Optical Fibers.

Author

Lu, Bin, Gu, Jinfeng, Wang, Zhaoyong, Ye, Lei, Liu, Yifan, Yang, Junqi, Wu, Bingyan, Ye, Qing, Qu, Ronghui, and Cai, Haiwen

Abstract

Distributed optical fiber acoustic sensing technology (DAS) based on Rayleigh backscattering (RBS) is experiencing a rapid development because of the unique capability of measuring acoustic waves along long-distance optical fiber with high spatial resolution, that has broad prospects of applications, including geophysical prospecting, seismology, pipeline security, and train tracking. Despite the numerous applications, DAS currently suffers from high noise level and insufficient signal-to-noise ratio (SNR) due to inherent low RBS coefficient (∼ −70 dB/m) in fiber, and cannot meet the application requirements of weak signal detection. Here, an ultra-low-noise MIMO DAS system using few-mode fibers (FMF) is proposed. FMF is characterized by higher non-linear effect threshold and larger capture fraction of RBS light, and through MIMO technology, the system can realize higher diversity scale and obtain more SNR gain. In addition, ‘frequency label’ method is proposed to address the signal aliasing problem in transmission modes. In preliminary experiments, a 4×4 MIMO Φ-OTDR system is realized, where the minimum noise floor is −88 dB rad2/Hz with corresponding strain resolution of 0.15 $p\varepsilon /\sqrt {Hz} $. These results pave the way for advanced acoustic applications of DAS, such as acoustic emission and marine acoustics. [ABSTRACT FROM AUTHOR]

Published
2022
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36. Building Social Capital for a Proactive Environmental Strategy: A Multidisciplinary Perspective.

Author

Zhuang, Yiming and Ye, Lei

Abstract

Environmental issues are becoming critical drivers of firm's competitive advantages. In response, many firms are adopting proactive environmental strategies. A proactive environmental strategy requires a substantial investment of resources from firms, and social capital provides the necessary resources for firms to adopt a proactive environmental strategy. In this article, we explore how social capital contributes to proactive environmental strategy from different perspectives, including marketing, human resources, operations, supply chain, and big data analytics, with the focus on how this issue affects managerial decision-making. [ABSTRACT FROM AUTHOR]

Published
2022
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37. A Tensor-Based Catheter and Wire Detection and Tracking Framework and Its Clinical Applications.

Author

Ma, YingLiang, Zhou, Diwei, Ye, Lei, Housden, R. James, Fazili, Ansab, and Rhode, Kawal S.

Subjects
CATHETERS, CLINICAL medicine, THREE-dimensional imaging, X-ray imaging, IMAGE reconstruction
Abstract

Objective: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. Method: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. Results: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved. Conclusion: A novel and fully automatic detection framework and its clinical applications are developed. Significance: The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Practical Performance Enhancement of DAS by Using Dense Multichannel Signal Integration.

Author

Wang, Zhaoyong, Yang, Junqi, Gu, Jinfeng, Liu, Yifan, Lu, Bin, Ying, Kang, Ye, Lei, Ye, Qing, Qu, Ronghui, and Cai, Haiwen

Abstract

Distributed fiber acoustic sensing (DAS) technology can measure acoustic waves along long-distance optical fiber with high spatial resolution. Due to its unique advantages, DAS has been applied in wide fields. However, DAS currently still suffers from limited sensitivity and severe signal fading. Due to the inherent random nature and low signal intensity of Rayleigh backscattering, it is a challenge to essentially eliminate these fundamental limitations. Here a DAS system using its distributed sensing feature is demonstrated with dense multichannel signal integration, complete elimination of signal fading and 100-fold improvement of signal-to-noise ratio (SNR) are experimentally demonstrated. These results provide a simple and effective method to improve DAS performance in practical applications. As far as we know, it is the first time that the distributed sensing characteristic is utilized to tackle fading and sensitivity for DAS, simultaneously. It is believed that this method will promote the process of DAS large-scale applications and help DAS to attract widespread attentions, especially in intrusion detection, acoustic emission flaw detection, near-surface dynamics and characterization, etc. [ABSTRACT FROM AUTHOR]

Published
2021
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50. A LSTM Based Bus Arrival Time Prediction Method

Author

Li Fengxi, Ye Lei, Han Qing-wen, He Guangyan, Chen Lidong, and Zeng Lingqiu

Subjects
050101 languages & linguistics, Computer science, 05 social sciences, Real-time computing, Training (meteorology), 02 engineering and technology, Span (engineering), Arrival time, Long short term memory, Traffic conditions, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, State (computer science), Intelligent transportation system
Abstract

Bus arrival time prediction not only provides convenience for passengers, but also helps to improve the efficiency of intelligent transportation system. However, the low prediction accuracy becomes one of great puzzle. Considering both historic data and real-time traffic condition, in this paper, a new bus arrival time prediction method is proposed. A LSTM training model is used to get historic cruising speed, while two traffic factors are defined to illustrate real-time traffic state. Then a bus arrival time prediction is established based on speed values. Validation experiment results show that proposed method could predict the bus arrival time in special time span accurately.

Published
2019

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