OBSERVER/FILTER STRUCTURE BASED ADAPTIVE FRICTION COMPENSATION FOR HIGH PRECISION TURNTABLE

Two adaptive friction compensation schemes are developed for a high precision turntable system with nonlinear dynamic friction to handle two types of parametric uncertainties in the friction. Both schemes utilize a nonlinear observer/filter structure to compensate for uncertainties in corresponding friction parameters associated with the turntable system. Moreover, in the second scheme, adjustable gains are introduced into the dual nonlin- ear filters and they can be tuned to improve the position tracking performance. In both cases, a Lyapunov-like argument is provided for the global asymptotic stability of the closed-loop system. Simulation results demonstrate the effectiveness of the proposed schemes.

FPGA Implementation of Deep Leaning Model for Video Analytics

In recent years,deep neural networks have become a fascinating and influential research subject,and they play a critical role in video processing and analytics.Since,video analytics are predominantly hardware centric,exploration of implementing the deep neural networks in the hardware needs its brighter light of research.However,the computational complexity and resource constraints of deep neural networks are increasing exponentially by time.Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics.But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of handling the videos in the hardware.Field programmable Gate arrays(FPGA)is thought to be more advantageous in implementing the convolutional neural networks when compared to Graphics Processing Unit(GPU)in terms of energy efficient and low computational complexity.But still,an intelligent architecture is required for implementing the CNN in FPGA for processing the videos.This paper introduces a modern high-performance,energy-efficient Bat Pruned Ensembled Convolutional networks(BPEC-CNN)for processing the video in the hardware.The system integrates the Bat Evolutionary Pruned layers for CNN and implements the new shared Distributed Filtering Structures(DFS)for handing the filter layers in CNN with pipelined data-path in FPGA.In addition,the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity.The extensive experimentations are carried out using CASIA video datasets with ARTIX-7 FPGA boards(number)and various algorithms centric parameters such as accuracy,sensitivity,specificity and architecture centric parameters such as the power,area and throughput are analyzed.These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25%better performance than the existing architectures.

Robust state-space realizations of digital filters against finite word length errors

This article deals with two important issues in digital filter implementation： roundoff noise and limit cycles. A novel class of robust state-space realizations, called normal realizations, is derived and characterized. It is seen that these realizations are free of limit cycles. Another interesting property of the normal realizations is that they yield a minimal error propagation gain. The optimal realization problem, defined as to find those normal realizations that minimize roundoff noise gain, is formulated and solved analytically. A design example is presented to demonstrate the behavior of the optimal normal realizations and to compare them with several well-known digital filter realizations in terms of minimizing the roundoff noise and the error propagation.

High efficiency filtering using two-dimensional photonic crystal coupled cavity structures

Transmission spectra of coupled cavity structures （CCSs） in two-dimensional （2D） photonic crystals （PCs） are investigated using a coupled mode theory, and an optical filter based on CCS is proposed. The performance of the filter is investigated using finite-difference time-domain （FDTD） method, and the results show that within a very short coupling distance of about 3λ, where ）, is the wavelength of signal in vacuum, the incident signals with different frequencies are separated into different channels with a contrast ratio of 20 dB. The advantages of this kind of filter are small size and easily tunable operation frequencies.

Combined centralized and decentralized control for high-precision spacecraft

The improved structural filter combined with Positive Position Feedback(PPF) controller is investigated for high-precision attitude control of flexible spacecraft which consists of rigid central body and flexible appendages.PPF controller is adopted for high frequency vibration suppression,while the improved structural filter is used for suppression of low frequency vibration.After introducing PPF controller,the vibration frequencies are changed.In view of the frequency uncertainties,an improved structural filter is designed,and the stability study for the centralized control system is conducted.The simulation results show that the performance of spacecraft control system is improved,and the control inputs remain unchanged.

Model-Based Fault Detection of a Battery System in a Hybrid Electric Vehicle

Recently, a new type of IMM （interacting multiple model） method was introduced based on the relatively new SVSF （smooth variable structure filter）, and is referred to as the IMM-SVSF. The SVSF is a type of sliding mode estimator that is formulated in a predictor-corrector fashion. This strategy keeps the estimated state bounded within a region of the true state trajectory, thus creating a stable and robust estimation process. The IMM method may be utilized for fault detection and diagnosis, and is classified as a model-based method. In this paper, for the purposes of fault detection, the IMM-SVSF is applied through simulation on a simple battery system which is modeled from a hybrid electric vehicle.

Optimized guided mode resonant structure as thermooptic sensor and liquid crystal tunable filter

Applicability of guided mode resonant structures to tunable optical filtering and sensing is demonstrated using nematic liquid crystals. As a sensor, a minimum refractive index detectivity of 10^-5 is demonstrated while as a tunable filter, tunability range of few tens of nanometers with 2-nm bandwidth is presented. The optimum design is achieved by maximizing the evanescent field region in the analyte which maximizes the overlap integral. The device can be operated in reflection or transmission modes at normal incidence. It can also be operated at a single wavelength by measuring the angular profile of the light beam.

Electrospun Nanocomposite Fibrous Membranes for Sustainable Face Mask Based on Triboelectric Nanogenerator with High Air Filtration Efficiency

Air pollution caused by the rapid development of industry has always been a great issue to the environment and human being’s health.However,the efficient and persistent filtration to PM_(0.3) remains a great challenge.Herein,a self-powered filter with micro-nano composite structure composed of polybutanediol succinate(PBS)nanofiber membrane and polyacrylonitrile(PAN)nanofiber/polystyrene(PS)microfiber hybrid mats was prepared by electrospinning.The balance between pressure drop and filtration efficiency was achieved through the combination of PAN and PS.In addition,an arched TENG structure was created using the PAN nanofiber/PS microfiber composite mat and PBS fiber membrane.Driven by respiration,the two fiber membranes with large difference in electronegativity achieved contact friction charging cycles.The open-circuit voltage of the triboelectric nanogenerator(TENG)can reach to about 8 V,and thus the high filtration efficiency for particles was achieved by the electrostatic capturing.After contact charging,the filtration efficiency of the fiber membrane for PM_(0.3) can reach more than 98%in harsh environments with a PM_(2.5) mass concentration of 23,000µg/m^(3),and the pressure drop is about 50 Pa,which doesn’t affect people’s normal breathing.Meanwhile,the TENG can realize self-powered supply by continuously contacting and separating the fiber membrane driven by respiration,which can ensure the long-term stability of filtration efficiency.The filter mask can maintain a high filtration efficiency(99.4%)of PM_(0.3) for 48 consecutive hours in daily environments.

A cost driven predictive maintenance policy for structural airframe maintenance

Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next scheduled maintenance stop.With progress in sensor technology and data processing techniques,structural health monitoring（SHM） systems are increasingly being considered in the aviation industry.SHM systems track the aircraft health state continuously,leading to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule.This paper builds upon a model-based prognostics framework that the authors developed in their previous work,which couples the Extended Kalman filter（EKF） with a firstorder perturbation（FOP） method.By using the information given by this prognostics method,a novel cost driven predictive maintenance（CDPM） policy is proposed,which ensures the aircraft safety while minimizing the maintenance cost.The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance.A numerical case study simulating the maintenance process of an entire fleet of aircrafts is implemented.Under the condition of assuring the same safety level,the CDPM is compared in terms of cost with two other maintenance policies：scheduled maintenance and threshold based SHM maintenance.The comparison results show CDPM could lead to significant cost savings.