Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

A new analytical model of single-stage microleverage mechanism in resonant accelerometer

Microleverage mechanism which is widely applied in microelectromechanical systems (MEMS) transfers and amplifies force or displacement from input to output. In this work, one-stage microleverage mechanism is integrated into a biaxial micro resonant accelerometer to improve sensitivity. Force amplification factor of the microleverage is analyzed and deduced by integral method. The results from theoretical model match well with the ones from finite element method (FEM) simulation, which proves that the proposed model is relatively accurate and the width of lever beam is a quite important parameter in design. The resonant accelerometer is successfully fabricated by MEMS technology. Preliminary experiments are conducted and demonstrate differential sensitivity of 71 Hz/g for the accelerometer with resonant frequency of 267.726 kHz.     

A quantitative optimisation model for a horizontal MEMS solid propellant thruster with experimental verification

This paper studies the effect of multiple geometric parameters of a micronozzle on a MEMS solid propellant thruster (MSPT) and establishes a quantitative optimisation model. Influences of the structural parameters of the micronozzle on performance are accurately calculated. The significance and contribution ratio of each parameter are evaluated by F distribution. Correspondingly, a horizontal MSPT is fabricated and tested to prove the optimisation model. In the implementation, a complete microigniter is formed by a single layer of Au, achieving an ultra-low ignition voltage of 2.1 V with an extremely low ignition resistance of 4 Ω.     

Theoretical analysis of the slip flow effect on gas-lubricated micro spherical spiral groove bearings for machinery gyroscope

The performance of a gas-lubricated micro spherical spiral groove bearing (MSSGB) with slip flow effect is investigated. A modified Reynolds equation incorporated with Barber’s first-order slip flow model is proposed to investigate the flow characteristics of gas in MSSGBs. Parameter transformation and oblique coordinate transformation are applied to eliminate the curve effect on the calculation domain. An improved finite difference method (FDM) based on Green’s formula is used to solve the Reynolds equation. The perturbation method is adopted to determine the dynamic coefficients. The effects of slip flow and bearing parameters, including the groove depths, rotor speeds, and eccentricity ratios, on the bearing characteristics are investigated and discussed. Prediction results show that the slip flow effect on MSSGB performance is significant. Moreover, the groove depth at micro clearance has a crucial influence on bearing performance.     

An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse

One of the challenging problems in order picking is how to deal with the congestion happens in warehouse with multiple pickers. In this paper, we consider an ant colony optimization (ACO)-based online routing method to find picking routes for multiple order pickers under nondeterministic picking time. Here, a default route is formed by ACO for each single picker. Then, we coordinate these routes to alleviate congestion by dedicated rules based on indoor positioning and information sharing technologies, during order pickers serve the picking task. Our results indicate that the proposed method can achieve a reduction in the order service time primarily by coping with the congestion. We conclude that the new method is particularly effective in multiple-block picker-to-parts warehouses.     

A simple robust control for global asymptotic position stabilization of underactuated surface vessels

Global asymptotic stabilization of underactuated surface vessels is generally achieved only by designing complicated controllers. This paper proposes a very simple control law that globally asymptotically stabilizes the position of underactuated surface vessel to a desired constant location and its velocities to zero. The proposed controller is independent of velocity signals and is robust to model parameters. It neither includes an observer nor an adaptive/sliding‐mode law. Controller development and stability analysis rely on a novel Lyapunov function and LaSalle's theorem. Furthermore, by extending the proposed control strategy, a saturated control law is also obtained ensuring the semiglobal asymptotic stability of position error system. Effectiveness of the proposed control schemes is demonstrated by simulation examples. Copyright © 2017 John Wiley & Sons, Ltd.     

Continuous output‐feedback control for a class of switched high‐order planar systems and its application

This paper deals with the problem of continuous output‐feedback stabilization for a class of switched high‐order planar systems under arbitrary switchings. Based on the common Lyapunov function design method, by using the adding a power integrator technique and designing an implementable observer, a continuous output‐feedback controller is constructed such that the closed‐loop system is global stabilization and the output can be regulated to the origin. As an application, the developed strategy is utilized to the control design for the continuous stirred tank reactor with two modes feed stream. The simulation results verify the efficiency of the proposed design scheme. Copyright © 2017 John Wiley & Sons, Ltd.     

Robust object tracking via multi-scale patch based sparse coding histogram

There are many visual tracking algorithms that are based on sparse representation appearance model. Most of them are modeled by local patches with fixed patch scale, which make trackers less effective when objects undergone appearance changes such as illumination variation, pose change or partial occlusion. To solve the problem, a novel appearance representation model is proposed via multi-scale patch based sparse coding histogram for robust visual tracking. In this paper, the appearance of an object is modeled by different scale patches, which are represented by sparse coding histogram with different scale dictionaries. Then a similarity measure is applied to the calculation of the distance between the sparse coding histograms of target candidate and target template. Finally, the similarity score of the target candidate is passed to a particle filter to estimate the target state sequentially in the tracking process. Additionally, in order to decrease the visual drift caused by partial occlusion, an occlusion handling strategy is adopted, which takes the spatial information of multi-scale patches and occlusion into account. Based on the experimental results on some benchmarks of video sequences, our tracker outperforms state-of-the-art tracking methods.     

An AR based fast mode decision for H.265/HEVC intra coding

As a critical technique to raise coding efficiency in H.265/HEVC (H.265/High Efficiency Video Coding), the number of prediction modes for intra coding is much larger than that in H.264/AVC. However, the large number of prediction modes also leads to high complexity. In this paper, an AR (Auto Regression) based fast mode decision method for H.265/HEVC intra coding is proposed, including an AR based fast mode selection and an MPM (Most Probable Mode) joint fast mode selection method. Considering the correlation between SATD (Sum of Absolute Transformed Differences) cost and RD (Rate-Distortion) cost, we establish a selective function controlled by an adaptive parameter which is acquired by an AR model. According to the distribution of the SATD cost, the selective function can decide which modes can be discarded and which modes can be used for the FMD (Fine Mode Decision). In addition, in order to further reduce the number of candidate modes for FMD, an MPM joint fast mode selection method is presented based on the relationship between MPMs and candidate modes. Experimental results show that just in PU layer, the proposed AR based fast intra mode decision method can save encoding time by 32 % with negligible coding loss in all-intra main configuration compared to the intra mode decision of HM13.0.     

Conditional random field with the multi-granular contextual information for pixel labeling

To make full use of the contextual information object recognition and scene understanding, a multi-granular context conditional random field (MGCCRF) model is presented to combine context information in a variety of scales. It is efficiently implemented through extending the pairwise clique to the multi-granular context windows. In the fine-granular context window, the label consistency of similar features can be obtained with the probability of the label transferring between two adjacent pixels. At the same time, the spatial relationships among different classes in the coarse-granular context window are explicated in details. To train the MGCCRF model, a piecewise training method with the bound optimization algorithm is designed to improve the performance. Experiments on two real-world image databases show that compared with other methods, the modified conditional random field model is more competitive and effective in terms of the quantitative and qualitative labeling performance.