ORCID Profile
0000-0002-3408-982X
Current Organisation
Chongqing University
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Publisher: Association for Computing Machinery (ACM)
Date: 03-07-2019
DOI: 10.1145/3322241
Abstract: Indoor localization is essential for healthcare, security, augmented reality gaming, and many other location-based services. There is currently a wealth of relevant literature on indoor localization. This article focuses on recent advances in indoor localization methods that use spatial context to improve the location estimation. Spatial context in the form of maps and spatial models have been used to improve the localization by constraining location estimates in the navigable parts of indoor environments. Landmarks such as doors and corners, which are also one form of spatial context, have proved useful in assisting indoor localization by correcting the localization error. This survey gives a comprehensive review of state-of-the-art indoor localization methods and localization improvement methods using maps, spatial models, and landmarks.
Publisher: Association for Computing Machinery (ACM)
Date: 04-10-2022
DOI: 10.1145/3472290
Abstract: Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: MDPI AG
Date: 23-11-2020
DOI: 10.3390/S20226698
Abstract: Map-matching is a popular method that uses spatial information to improve the accuracy of positioning methods. The performance of map matching methods is closely related to spatial characteristics. Although several studies have demonstrated that certain map matching algorithms are affected by some spatial structures (e.g., parallel paths), they focus on the analysis of single map matching method or few spatial structures. In this study, we explored how the most commonly-used four spatial characteristics (namely forks, open spaces, corners, and narrow corridors) affect three popular map matching methods, namely particle filtering (PF), hidden Markov model (HMM), and geometric methods. We first provide a theoretical analysis on how spatial characteristics affect the performance of map matching methods, and then evaluate these effects through experiments. We found that corners and narrow corridors are helpful in improving the positioning accuracy, while forks and open spaces often lead to a larger positioning error. We hope that our findings are helpful for future researchers in choosing proper map matching algorithms with considering the spatial characteristics.
Publisher: Hindawi Limited
Date: 06-06-2020
DOI: 10.1155/2020/4142824
Abstract: Partial least squares method has many advantages in multivariate linear regression modeling, but its internal cross-checking method will lead to a sharp reduction of the principal component, thereby reducing the accuracy of the regression equation, and the selection of principal components about the traditional Chinese medicine data is particularly sensitive. This paper proposes a kind of partial least squares method based on deep belief nets. This method mainly uses the deep learning model to extract the upper-level features of the original data, putting the extracted features into the partial least squares model for multiple linear regression and evading the problem that selects the number of principal components, continuously adjusting the model parameters until satisfied well-pleased accuracy condition. Using Dachengqitang experimental data and data sets in the UCI Machine Learning Repository, the experimental results show that the partial least squares analysis method based on deep belief nets has good adaptability to TCM data.
Publisher: Association for Computing Machinery (ACM)
Date: 04-10-2023
DOI: 10.1145/3625819
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: IEEE
Date: 24-10-2020
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/397298
Abstract: Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.
Publisher: Copernicus GmbH
Date: 08-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B2-509-2016
Abstract: Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, existing indoor localization methods that make use of spatial information are either too computationally expensive or too sensitive to the completeness of landmark detection. In this paper, we solve this problem by using the proposed landmark graph. The landmark graph is a directed graph where nodes are landmarks (e.g., doors, staircases, and turns) and edges are accessible paths with heading information. We compared the proposed method with two common Dead Reckoning (DR)-based methods (namely, Compass + Accelerometer + Landmarks and Gyroscope + Accelerometer + Landmarks) by a series of experiments. Experimental results show that the proposed method can achieve 73% accuracy with a positioning error less than 2.5 meters, which outperforms the other two DR-based methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Copernicus GmbH
Date: 14-09-1970
DOI: 10.5194/ISPRS-ANNALS-IV-2-W4-371-2017
Abstract: Abstract. Indoor positioning is a fundamental requirement of many indoor location-based services and applications. In this paper, we explore the potential of low-cost and widely available visual and inertial sensors for indoor positioning. We describe the Visual-Inertial Odometry (VIO) approach and propose a measurement model for omnidirectional visual-inertial odometry (OVIO). The results of experiments in two simulated indoor environments show that the OVIO approach outperforms VIO and achieves a positioning accuracy of 1.1 % of the trajectory length.
Publisher: Hindawi Limited
Date: 24-08-2020
DOI: 10.1155/2020/9634308
Abstract: Purpose . The purpose of this article is to predict the topic popularity on the social network accurately. Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP). Design/Methodology/Approach . Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up. Findings . The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%. The training speed is higher and the prediction precision is higher. Practical Implications . The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities . Moreover, its future popularity can be predicted by deep learning methods. At the same time, a new application field of deep learning technology has been further discovered and verified. Originality/Value . This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-04-2019
Publisher: IOP Publishing
Date: 31-03-2021
Abstract: Visible light positioning (VLP) is a promising technique to bring location-based service for numerous Internet of Things applications. Recent advances in VLP have shown that machine learning (ML)-based positioning algorithms show satisfying performance in physical environments under highly noisy and interference-rich conditions. With so many ML methods proposed, one major concern is that trained models could fail due to environmental heterogeneity. In this paper, we propose AdVLP, a novel adversarial training method for VLP based on deep neural networks, to address the issue of the vulnerability of data-driven approaches, which happens when channel parameters change. Our proposed method, which is inspired by generative adversarial networks, manages to adapt the two domains of the source dataset and the target dataset. The channel parameters, e.g. the Lambertian order of light-emitting diode (LED) lights and a photodiode, the LED power as well as the location error of the lights, were discussed. We observed in experiments that the accuracy of a neural network method would decrease as the bias of the parameters became larger. Secondly, a higher dimension of the parameter change would make the neural network method more vulnerable. However, our proposed approach could achieve substantial improvement in positioning accuracy.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 04-12-2015
DOI: 10.3390/S151229821
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: No publisher found
Date: 2017
DOI: 10.3390/S17061272
Publisher: MDPI AG
Date: 26-10-2015
DOI: 10.3390/S151027251
Publisher: Informa UK Limited
Date: 04-02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Elsevier BV
Date: 06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Elsevier BV
Date: 07-2021
Publisher: American Society for Microbiology
Date: 24-02-2021
DOI: 10.1128/JVI.01590-20
Abstract: Encephalomyocarditis virus is an important pathogen that can cause encephalitis, myocarditis, neurological diseases, and reproductive disorders. It also causes huge economic losses for the swine industry worldwide.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Copernicus GmbH
Date: 14-09-2017
DOI: 10.5194/ISPRS-ANNALS-IV-2-W4-335-2017
Abstract: Abstract. Maps are the foundation of indoor location-based services. Many automatic indoor mapping approaches have been proposed, but they rely highly on sensor data, such as point clouds and users’ location traces. To address this issue, this paper presents a conceptual framework to represent the layout principle of research buildings by using grammars. This framework can benefit the indoor mapping process by improving the accuracy of generated maps and by dramatically reducing the volume of the sensor data required by traditional reconstruction approaches. In addition, we try to present more details of partial core modules of the framework. An ex le using the proposed framework is given to show the generation process of a semantic map. This framework is part of an ongoing research for the development of an approach for reconstructing semantic maps.
Publisher: Hindawi Limited
Date: 28-08-2020
DOI: 10.1155/2020/4904829
Abstract: This paper aims at the dynamic properties of the proposed globally planned economic systems named after CPE proposed by Loo-Keng Hua who is one of the worldwide famous Chinese mathematicians. First, we give new existence conditions of growth balanced solution to the model. Second, we lead into the concept of stability for balanced output and carry out a theorem that deals with some equivalent conditions for judging a solution of output starting from the fact that any initial input can whether approach the existing balanced solution or not. Third, a new dynamic price system related to interest factors is proposed here and it is demonstrated that the new price equation is a much generalized one in comparison with the original price one which is only a special case of this new price equation. Also, relationships of the balanced solutions between the price and the output equation are investigated and the stability analysis is studied as well for the new price system. Finally, two ex les are employed to illustrate the technical operation of input-output method and some new contributions of this article.
Publisher: IEEE
Date: 06-2011
Publisher: Wiley
Date: 28-08-2023
DOI: 10.1002/ROB.22242
Abstract: Simultaneous localization and mapping (SLAM) is required in many areas and especially visual‐based SLAM (VSLAM) due to the low cost and strong scene recognition capabilities conventional VSLAM relies primarily on features of scenarios, such as point features, which can make mapping challenging in scenarios with sparse texture. For instance, in environments with limited (low‐even non‐) textures, such as certain indoors, conventional VSLAM may fail due to a lack of sufficient features. To address this issue, this paper proposes a VSLAM system called visual SLAM that can adaptively fuse point‐line‐plane features (PLPF‐VSLAM). As the name implies, it can adaptively employ different fusion strategies on the PLPF for tracking and mapping. In particular, in rich‐textured scenes, it utilizes point features, while in non‐/low‐textured scenarios, it automatically selects the fusion of point, line, and/or plane features. PLPF‐VSLAM is evaluated on two RGB‐D benchmarks, namely the TUM data sets and the ICL_NUIM data sets. The results demonstrate the superiority of PLPF‐VSLAM compared to other commonly used VSLAM systems. When compared to ORB‐SLAM2, PLPFVSLAM achieves an improvement in accuracy of approximately 11.29%. The processing speed of PLPF‐VSLAM outperforms PL(P)‐VSLAM by approximately 21.57%.
Publisher: Human Kinetics
Date: 11-2016
Abstract: Two years on from the inaugural Active Healthy Kids Australia (AHKA) Physical Activity Report Card, there has been little to no change with the majority of Australian children still insufficiently active. The 2016 AHKA Report Card was developed using the best available national- and state-based physical activity data, which were evaluated by the AHKA Research Working Group using predetermined weighting criteria and benchmarks to assign letter grades to the 12 Report Card indicators. In comparison with 2014, Overall Physical Activity Levels was again assigned a D - with Organized Sport and Physical Activity Participation increasing to a B (was B -) and Active Transport declining to a C - (was C ). The settings and sources of influence again performed well ( A - to a C +), however Government Strategies and Investments saw a decline ( C + to a D ). The traits associated with physical activity were also graded poorly ( C - to a D ). Australian youth are insufficiently active and engage in high levels of screen-based sedentary behaviors. While a range of support structures exist, Australia lacks an overarching National Physical Activity Plan that would unify the country and encourage the cultural shift needed to face the inactivity crisis head on.
Publisher: Wiley
Date: 19-08-2020
DOI: 10.1111/TGIS.12664
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-01-2023
Publisher: SAGE Publications
Date: 2015
DOI: 10.1155/2015/109642
Abstract: Indoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness and high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K-nearest neighbors (WKNN), which calculates K-nearest neighboring points to a mobile user. However, existing WKNN cannot effectively address the problems that there is a difference in observed AP sets during offline and online stages and also not all the K neighbors are physically close to the user. In this paper, similarity coefficient is used to measure the similarity of AP sets, which is then combined with radio signal strength values to calculate the fingerprint distance. In addition, isolated points are identified and removed before clustering based on semi-supervised affinity propagation. Real-world experiments are conducted on a university c us and results show the proposed approach does outperform existing approaches.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Hindawi Limited
Date: 29-09-2020
DOI: 10.1155/2020/5138135
Abstract: Research on ecological total-factor energy efficiency (ETFEE) is conducive to energy conservation, emission reduction, and ecological protection. This paper focuses on the measurement and decomposition of the ETFEE in the Beijing-Tianjin-Hebei (BTH) region in China. In order to measure the ETFEE values, the window technology is combined with a nonradial and nonoriented SBM-undesirable model considering undesirable outputs to overcome the defect of insufficient data of research objects and ensure the calculation process to be implemented. The findings show that Beijing and Tianjin are DEA-efficient, while Hebei is not. The technological progress rates of Beijing and Tianjin reach up to 11.92% and 14.96%, while that of Hebei retrogresses by 4.47%. The scale efficiencies of Beijing, Tianjin, and Hebei are 97.75%, 86.60%, and 93.81%, respectively, which means that there are potentials for further optimization in the energy structures. The impulse response results between the energy structure and the ETFEE show that the proportions of coal and petroleum have negative effects on the ETFEE, while that of natural gas has a positive effect. The research results can provide reference for decision makers to formulate regional development plans.
Publisher: Copernicus GmbH
Date: 08-06-2016
DOI: 10.5194/ISPRS-ARCHIVES-XLI-B2-509-2016
Abstract: Abstract. Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, existing indoor localization methods that make use of spatial information are either too computationally expensive or too sensitive to the completeness of landmark detection. In this paper, we solve this problem by using the proposed landmark graph. The landmark graph is a directed graph where nodes are landmarks (e.g., doors, staircases, and turns) and edges are accessible paths with heading information. We compared the proposed method with two common Dead Reckoning (DR)-based methods (namely, Compass + Accelerometer + Landmarks and Gyroscope + Accelerometer + Landmarks) by a series of experiments. Experimental results show that the proposed method can achieve 73% accuracy with a positioning error less than 2.5 meters, which outperforms the other two DR-based methods.
No related grants have been discovered for Fuqiang Gu.