Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
Currently, positioning, navigation, and timing information is becoming more and more\nvital for both civil and military applications. Integration of the global navigation satellite system\nand /inertial navigation system is the most popular solution for various carriers or vehicle\npositioning. As is well-known, the global navigation satellite system positioning accuracy will\ndegrade in signal challenging environments. Under this condition, the integration system will fade\nto a standalone inertial navigation system outputting navigation solutions. However, without\nouter aiding, positioning errors of the inertial navigation system diverge quickly due to the noise\ncontained in the raw data of the inertial measurement unit. In particular, the micromechanics\nsystem inertial measurement unit experiences more complex errors due to the manufacturing\ntechnology. To improve the navigation accuracy of inertial navigation systems, one effective\napproach is to model the raw signal noise and suppress it. Commonly, an inertial measurement\nunit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play\nan important role in the accuracy of the inertial navigation systemâ??s navigation solutions.\nMotivated by this problem, in this paper, an advanced deep recurrent neural network was\nemployed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a\ndeep long short term memory recurrent neural network and a deep gated recurrent unitâ??recurrent\nneural network were combined together to construct a two-layer recurrent neural network for\nnoise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset\nfrom a micromechanics system inertial measurement unit was employed in the experiments. The\nresults showed that, compared to the two-layer long short term memory, the three-axis attitude\nerrors of the mixed long short term memoryâ??gated recurrent unit decreased by 7.8%, 20.0%, and\n5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed\n15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the\nperformance of designed method, specifically, the mixed deep recurrent neural networks\noutperformed than the two-layer gated recurrent unit and the two-layer long short term memory\nrecurrent neural networks....
The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable\nhealthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have\npaid attention for predicting individualsâ?? influential factors for adoption of wearable healthcare devices.Theproposed research aimed\nat determining the key factors which impact an individualâ??s intention for adopting wearable healthcare devices. The extended\ntechnology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical\napproach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from\nthe structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the\ndecision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results\nobtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is\none of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the\nwearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing\nunit for increasing actual usersâ?? continuous adoption intention and potential usersâ?? intention to use wearable devices....
The progress of technology on energy and IoT fields has led to an increasingly complicated\nelectric environment in low-voltage local microgrid, along with the extensions of electric vehicle,\nmicro-generation, and local storage. It is required to establish a home energy management system\n(HEMS) to efficiently integrate and manage household energy micro-generation, consumption and\nstorage, in order to realize decentralized local energy systems at the community level. Domestic\npower demand prediction is of great importance for establishing HEMS on realizing load balancing\nas well as other smart energy solutions with the support of IoT techniques. Artificial neural networks\nwith various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are\nwidely utilized on energy predictions. However, the selection of network configuration for each\nresearch is generally a case by case study achieved through empirical or enumerative approaches.\nMoreover, the commonly utilized network initialization methods assign parameter values based\non random numbers, which cause diversity on model performance, including learning efficiency,\nforecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP)\nmethod is proposed to achieve a population of well-performing networks with proper combinations\nof configuration and initialization automatically. In the experimental study, power demand\npredictions of multiple households are explored in three application scenarios: optimizing potential\nnetwork configuration set, forecasting single household power demand, and refilling missing data.\nThe impacts of evolutionary parameters on model performance are investigated. The experimental\nresults illustrate that the proposed method achieves better solutions on the considered scenarios.\nThe optimized potential network configuration set using EENNP achieves a similar result to manual\noptimization. The results of household demand prediction and missing data refilling perform better\nthan the naïve and simple predictors....
Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a\nnovel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and\npalmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides\nmore robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition\nand evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing\ncomputational complexity which is a necessary requirement in real-life scenarios....
The development of object detection in infrared images has attracted more attention\nin recent years. However, there are few studies on multi-scale object detection in infrared street\nscene images. Additionally, the lack of high-quality infrared datasets hinders research into such\nalgorithms. In order to solve these issues, we firstly make a series of modifications based on Faster\nRegion-Convolutional Neural Network (R-CNN). In this paper, a double-layer region proposal\nnetwork (RPN) is proposed to predict proposals of different scales on both fine and coarse feature\nmaps. Secondly, a multi-scale pooling module is introduced into the backbone of the network to\nexplore the response of objects on different scales. Furthermore, the inception4 module and the\nposition sensitive region of interest (ROI) align (PSalign) pooling layer are utilized to explore richer\nfeatures of the objects. Thirdly, this paper proposes instance level data augmentation, which takes into\naccount the imbalance between categories while enlarging dataset. In the training stage, the online\nhard example mining method is utilized to further improve the robustness of the algorithm in\ncomplex environments. The experimental results show that, compared with baseline, our detection\nmethod has state-of-the-art performance....
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