Volume 15, Number 6, 2018
In the recent years, deep learning models have addressed many problems in various fields. Meanwhile, technology development has spawned the big data in healthcare rapidly. Nowadays, application of deep learning to solve the problems in healthcare is a hot research direction. This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records (EHR), electrocardiography (ECG), electroencephalogram (EEG), community healthcare, data from wearable devices, drug analysis and genomics analysis. The scope of this paper does not cover medical image processing since other researchers have already substantially reviewed it. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.
Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of contour is a difficult task, especially when the contour is incomplete or unclosed. In this paper, the existing contour detection approaches are reviewed and roughly divided into three categories: pixel-based, edge-based, and region-based. In addition, since the traditional contour detection approaches have achieved a high degree of sophistication, the deep convolutional neural networks (DCNNs) have good performance in image recognition, therefore, the DCNNs based contour detection approaches are also covered in this paper. Moreover, the future development of contour detection is analyzed and predicted.
The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.
The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation (PID) controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization (MCIWO) and particle swarm optimization (PSO) algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.
A model predictive controller based on a novel structure selection criterion for the vapor compression cycle (VCC) of refrigeration process is proposed in this paper.Firstly, those system variables are analyzed which exert significant influences on the system performance.Then the structure selection criterion, a trade-off between computation complexity and model performance, is applied to different model structures, and the results are utilized to determine the optimized model structure for controller design.The controller based on multivariable model predictive control (MPC) strategy is designed, and the optimization problem for the reduced order models is formulated as a constrained minimization problem.The effectiveness of the proposed MPC controller is verified on the experimental rig.
Obvious resonance peak will be generated when parallel photovoltaic grid-connected inverters are connected to the weak grid with high grid impedance, which seriously affects the stability of grid-connected operation of the photovoltaic system.To overcome the problems mentioned above, the mathematical model of the parallel photovoltaic inverters is established.Several factors including the impact of the reference current of the grid-connected inverter, the grid voltage interference and the current disturbance between the photovoltaic inverters in parallel with the grid-connected inverters are analyzed.The grid impedance and the LCL filter of the photovoltaic inverter system are found to be the key elements which lead to existence of resonance peak.This paper presents the branch voltage and current double feedback suppression method under the premise of not changing the topological structure of the photovoltaic inverter, which effectively handles the resonance peak, weakens the harmonic content of the grid current of the photovoltaic grid-connected inverter and the voltage at the point of common coupling, and improves the stability of the parallel operation of the photovoltaic grid-connected inverters in weak grid.At last, the simulation model is established to verify the reliability of this suppression method.
Adaptive motion/force tracking control is considered for a class of mobile manipulators with affine constraints and under-actuated joints in the presence of uncertainties in this paper.Dynamic equation of mobile manipulator is transformed into a controllable form based on dynamic coupling technique.In view of the asymptotic tracking idea and adaptive theory, adaptive controllers are proposed to achieve the desired control objective. Detailed simulation results confirm the validity of the control strategy.
This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances.First, the full order system will be controlled using ε-dependent control law.The corresponding Lyapunov equation is ill-conditioned due to the presence of slow and fast phenomena.Secondly, a time-scale decomposition of the Lyapunov equation is carried out using singular perturbation method to avoid the numerical stiffness.A composite control law based on local controllers of the slow and fast subsystems is also used to make the control law ε-independent.The designed fault tolerant control guarantees the robust stability of the global closed-loop singularly perturbed system despite loss of effectiveness of actuators.The stability is proved based on the Lyapunov stability theory in the case where the singular perturbation parameter is sufficiently small.A numerical example is provided to illustrate the proposed method.
This paper presents a new approach to estimate the true position of an unmanned aerial vehicle (UAV) in the conditions of spoofing attacks on global positioning system (GPS) receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system (INS) and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter (PSOF) to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle′s experiences, application of PSOF algorithm in the INS/GPS integration system leads to more precise positioning compared with general particle filter (PF) and adaptive unscented particle filer (AUPF) in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estimating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error (RMSE) test.
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