Research on data fusion algorithm and anti-collision algorithm based on internet of things

Cui, Yongfeng; Ma, Yuankun; Zhao, Zhongyuan; Li, Ya; Liu, Wei; Shu, Wanneng

doi:10.1016/j.future.2018.03.016

Cited by 20 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is proposed that service quality is an important factor for online retail enterprises to successfully attract customers. ese services include product purchase service, distribution service, and after-sales service [11]. e model of consumers' shopping behavior in e-commerce environment is established and tested with examples, and the conclusion is drawn that consumers' behavior is closely related to consumption risk, shopping convenience, and other factors [12].…”

Section: Related Workmentioning

confidence: 99%

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Liao

Tsai

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The comprehensive B2C online marketing is analyzed, and the current situation and shortage of comprehensive B2C online marketing strategies are summarized. Then, based on the relevant theories of consumer behavior and online marketing, the model of influencing factors in the purchasing decision-making process of online consumers is preliminarily constructed, the online purchasing behavior of consumers is studied by means of questionnaire survey, and the model is revised and improved through data collection and verification. Finally, based on the model, the online marketing strategy is discussed from the aspects of comprehensive B2C online marketing construction, product positioning, price strategy, channel construction, website design, and so on. It has important guiding significance to comprehensive B2C online marketing practice. Aiming at the B2C online marketing problem of multimodel fusion with multiobservation samples, a new multimodel fusion B2C online marketing algorithm based on LS-SVM is proposed, which is suitable for multiobservation samples. In each B2C online marketing of multimodel fusion, the mode of B2C online marketing to be multimodel fusion is represented by the multiobservation sample set. Firstly, the label of the multiobservation sample set is assumed, and this assumption condition is taken as the constraint condition of the optimization problem in LS-SVM. Thus, the B2C online marketing error of multimodel fusion is obtained. The category of multiobservation samples was determined by comparing the B2C online marketing errors of multimodel fusion under two assumptions. The B2C network marketing prediction method, stacking integrated learning method based on multimodel fusion, is adopted to build a multimachine learning algorithm embedded into the stacking integrated learning B2C network marketing prediction model. Through verification, it shows that the lower the correlation degree, the better the model prediction effect. Compared with the traditional single-model prediction, the B2C network marketing prediction method based on multimodel fusion stacking integrated learning method has higher prediction accuracy. The model prediction effect is better.

show abstract

Section: Related Workmentioning

confidence: 99%

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Liao

Tsai

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In the above parametric environment, the 3D point data in the video are extracted using the Google tracking-related API of Kinect SDK [27,28], the video mainly includes the gymnast's competition video and Weizmann human behavior database; the ratio of training data and test data is about 1 : 1, where the relevant gymnastic postures are mainly walking, jumping, prone, squatting, standing, and raising hands.…”

Section: Building System Test Environmentmentioning

confidence: 99%

Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data

Zhang

2021

Security and Communication Networks

View full text Add to dashboard Cite

The existing motion recognition system has a low athlete tracking recognition accuracy due to the poor processing effect of recognition algorithm for edge detection. A machine vision-based gymnast pose-tracking recognition system is designed for the above problem. The software part mainly optimizes the tracking recognition algorithm and uses the spatiotemporal graph convolution algorithm to construct the sequence graph structure of human joints, completes the strategy of label subset division, and completes the pose tracking according to the change of information dimension. The results of the system performance test show that the designed machine vision-based gymnast posture tracking recognition system can enhance the accuracy of tracking recognition and reduce the convergence time compared with the original system.

show abstract

“…Several innovative applications of research related with IoT is shown in Table 1 below. Perception and recognition technology are the basis of the IoT and data fusion and anti-collision technology are the sensor network and radio frequency identification research focus [4]. Because current definitions of IoT invariably imply a similar approach to the high-level architecture of a system, the ubiquitous use of the term IoT refer to the use of digital technologies in industry is unhelpful as it hinders the analysis of alternative system architectures, including the location and the nature of the data or information processing and associated performance and security issues [5].…”

Section: Literature Review 21 Internet Of Thingsmentioning

confidence: 99%

Smart Door Lock

Erwan¹,

Alfian²,

Adenan³

2021

ijortas

View full text Add to dashboard Cite

Internet of Things (IoT) has become a phenomenon in many researchers. A smart digital automated system based on IoT plays major role which helps in reducing human work by applying interaction technology in daily life. This research introduces the IoT Smart Door Lock where smartphone uses Wireless Fidelity (Wi-Fi) to activate the door lock. By using Android software, the smartphone will automatically activate the door lock within a specific range. Data transmission will be done using Wi-Fi technology. So, the user does not have to worry whether the door has been locked or not because user can control it by using their smartphone within a specific range. The user also does not have to worry about losing the door key anymore because there is no device required except user’s smartphone. This project consists of hardware and software development. The main purpose on the design is to prevent the situation where the user is unable to unlock the door when the key is missing also to give a solution for users to monitor door status when they forget the door has been locked or not. This system aims to eliminate the use of keys to access the door, where the daily struggle faced to bring along a bunch of keys anywhere. Besides that, the door locking system must function efficiently. The prototype of this system is successfully done, the procedure and the software and hardware requirement has been compiled in this report.

show abstract

Research on data fusion algorithm and anti-collision algorithm based on internet of things

Cited by 20 publications

References 8 publications

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Research on the B2C Online Marketing Effect Based on the LS-SVM Algorithm and Multimodel Fusion

Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data

Smart Door Lock

Contact Info

Product

Resources

About