According to characteristics of perpendicularity error evaluation of planar lines, Particle Swarm Optimization (PSO) is proposed to evaluate the minimum zone error. The evolutional optimum model and the calculation process are introduced in detail. Compared with conventional optimum methods such as simplex search and Powell method, it can find the global optimal solution, and the precision of calculating result is very good. Then, the objective function calculation approaches for using the Particle Swarm Optimization algorithm to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and genetic algorithm (GA), indicate that the proposed method does provide better accuracy on perpendicularity error evaluation, and it has fast convergent speed as well as using computer and popularizing application easily.
With the continuous development and gradual progress of Internet of Things (IoT) in human society, people are becoming increasingly diverse in terms of user preferences and things choices. In this situation, several new cultures or social phenomena have been emerging including the so-called Crossdressing culture. As a special group of humans, cross-dressers are often very sensitive to their nonmainstream identities. Therefore, they are often confronted with more difficulties when using some modern information techniques such as Content-of-Interest (COI) search. Motivated by this fact, we introduce some advanced information retrieval and privacy protection techniques into the cross-dressing domain and further propose a privacy-aware COI search and recommendation solution for cross-dressers, named PCSR. First, PCSR uses fastText tool to transform the cross-dressers' input keywords and the candidate webpages into corresponding vectors with less private content associated with cross-dressers. Afterwards, we use vector similarity calculation techniques to make privacy-preserving COI search and recommendation. At last, we validate the effectiveness of PCSR through a set of experiments. We believe that our proposed PCSR solution can benefit the cross-dressers significantly when performing COI search and recommendation in IoT while protecting sensitive information of cross-dressers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.