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A Model Stacking Algorithm for Indoor Positioning System using WiFi Fingerprinting
JinQuan Wang,YiJun Wang,GuangWen Liu,GuiFen Chen 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.4
With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.
Fabing Su,Qing Liu,Fangna Gu,Ziyi Zhong,Guangwen Xu 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.5
The effect of preparation method on the catalytic performance of V-promoted Ni/Al2O3 catalysts for synthetic natural gas (SNG) production via CO methanation has been investigated. The Ni-V/Al2O3 catalysts were prepared by co-impregnation (CI) method, deposition precipitation (DP) method as well as two sequential impregnation (SI) methods with different impregnation sequence. Among the prepared catalysts, the one prepared by CI method exhibited the best catalytic performance due to its largest H2 uptake and highest metallic Ni dispersion. In a 91h-lifetime test, this catalyst showed high stability at high temperature and weight hourly space velocity. This work demonstrates that the catalytic performance of the V-promoted Ni/Al2O3 catalysts can be improved by carefully controlling the preparation method/conditions.
Visual Tracking with Online Incremental Deep Learning and Particle Filter
Shuai Cheng,Yonggang Cao,Junxi Sun,Guangwen Liu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
To solve the problem of tracking the trajectory of a moving object and learning a deep compact image representation in the complex environment, a novel robust incremental deep learning tracker is presented under the particle filter framework. The incremental deep classification neural network was composed of stacked denoising autoencoder, incremental feature learning and support vector machine to achieve the feature-extracting and classification of particle set. Deep learning is successfully taken to express the image representations obtained effectively. Unsupervised feature learning is used to learn generic image features and transfer learning transforms knowledge from offline training to the online tracking process. The incremental feature learning was consisted of adding features and merging features to online learn compact feature set. Linear support vector machine increases the discretion for target with similar appearance and is further tuned to adapt to appearance changes of the moving object. Compared with the state-of-the-art trackers in the complex environment, the results of experiments on variant challenging image sequences show that incremental deep learning tracker solves the problem of existent trackers more efficiently, it has better robust and more accurate, especially for occlusions, background clutter, illumination changes and appearance changes.