Performance Guaranteed Inertia Emulation for Diesel-Wind System Feed Microgrid via Model Reference Control

In this paper, a model reference control based inertia emulation strategy is proposed. Desired inertia can be precisely emulated through this control strategy so that guaranteed performance is ensured. A typical frequency response model with parametrical inertia is set to be the reference model. A measurement at a specific location delivers the information of disturbance acting on the diesel-wind system to the reference model. The objective is for the speed of the diesel-wind system to track the reference model. Since active power variation is dominantly governed by mechanical dynamics and modes, only mechanical dynamics and states, i.e., a swing-engine-governor system plus a reduced-order wind turbine generator, are involved in the feedback control design. The controller is implemented in a three-phase diesel-wind system feed microgrid. The results show exact synthetic inertia is emulated, leading to guaranteed performance and safety bounds.Comment: 2017 IEEE PES Innovative Smart Grid Technologies Conferenc

On the stability of the Foschini-Miljanic Algorithm with uncertainty over channel gains

Distributed power control in wireless networks faces challenges related to its stability. When perfect information of channel states and transmitting agents are available, previous work has shown that the stability conditions can be known. When there is uncertainty over the parameter space, stability is not well understood. In this work, we study the impact of parameter uncertainty and network structure on the stability and scalability of a well known distributed power control, namely the Foschini-Miljanic algorithm. More specifically, we derive probabilistic conditions with respect to the parameters of the channel distributions for which the system is stable. Furthermore, we study the effects of these parameters for different node distribution on the plane. Numerical examples validate our theoretical results

Risk Analysis of Prostate Cancer in PRACTICAL Consortium--Response.

D.F. Easton was recipient of the CR-UK grant C1287/A10118. R.A. Eeles was recipient of the CR-UK grant C5047/A10692.This is the author accepted manuscript. The final version is available from the American Association for Cancer Research via http://dx.doi.org/10.1158/1055-9965.EPI-15-100

Low bone density management via capacitively coupled electrical fields and low intensity pulsed ultrasound in hemiparetic cerebral palsy

Osteoporosis is being increasingly recognized in pediatric practice as a consequence of several factors. These include the increasing complexity of chronic conditions and the associated treatments managed by pediatricians. In addition, the improved care provided to children with chronic illness has led to many of them living long enough to develop osteoporosis. Many children with cerebral palsy have diminished bone mineral density and the application of capacitively coupled electrical fields and low intensity pulsed ultrasound aim to improve the formation of bone cells and so may be helpful in the management of such cases. In this study, capacitively coupled electrical fields and low intensity pulsed ultrasound was conducted to investigate its effect on bone mineral density in spastic diaplegic cerebral palsy. Twenty spastic hemiparetic children were the sample of this work. There were divided randomly into two equal groups. Bone mineral densities were measured before and after six months of the application of the treatment program. Group A (control) received the physiotherapy program, while group B (study) received capacitively coupled electrical fields and low intensity pulsed ultrasound in addition to the same treatment program given to group A. Significant improvement were observed in all measuring variables when comparing the posttreatment results in both groups in favor of group B. Conclusion: Capacitively coupled electrical fields and low intensity pulsed ultrasound therapy may be considered as one of the most helpful methods of physiotherapy in management of low bone density in spastic hemiparetic cerebral palsied children.Keywords: Cerebral palsy; Hemi-paresis; Osteoporosis; Capacitively coupled electrical fields; Low intensity pulsed ultrasoun

A distributed framework for sparse convex optimization: algorithms and software tools

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2023.Esta tese aborda problemas de otimização convexa distribuída que incorporam uma restrição de esparsidade. Conhecido como Otimização Convexa Esparsa (SCO, na sigla em inglês), esse problema é definido em uma rede de computadores, onde vários agentes trabalham juntos para resolver o problema de otimização de forma colaborativa. Devido à restrição de esparsidade ser uma combinação de um número finito de subespaços, o problema SCO se enquadra na classe de otimização combinatória, que geralmente é considerada NP-difícil. Esta tese desenvolveu algoritmos distribuídos eficientes e ferramentas de software para resolver problemas SCO com dados descentralizados. Os algoritmos foram projetados para funcionar em uma rede computacional ponto a ponto, onde cada nó lida com uma parte específica do problema em paralelo, colaborando com outros nós. Inspirada em avanços anteriores em otimização inteira mista e computação de alto desempenho, esta tese introduz um framework de Programação Inteira Mista (MIP) distribuída para encontrar soluções exatas para problemas SCO. O framework apresenta novos algoritmos e heurísticas distribuídos, que são implementados em uma ferramenta de software chamada Conjunto de Ferramentas para Otimização Convexa Esparsa (SCOT, na sigla em inglês), especificamente projetada para resolver problemas SCO. Em particular, os algoritmos propostos estendem algoritmos de Aproximação Externa de Múltiplas e Únicas Árvores (OA) incorporando um algoritmo totalmente descentralizado chamado Método dos Multiplicadores de Direção Alternada Híbrido Relaxado (RH-ADMM, na sigla em inglês). Isso leva ao desenvolvimento de dois algoritmos distribuídos de programação não linear inteira mista: Aproximação Externa Primal Distribuída (DiPOA, na sigla em inglês) e Aproximação Externa Híbrida Distribuída (DiHOA, na sigla em inglês). Além disso, várias técnicas de reformulação e heurísticas são descritas e analisadas, visando aproveitar a separabilidade de funções não lineares e melhorar o desempenho dos algoritmos.Abstract: This thesis addresses distributed convex optimization problems that incorporate a sparsity constraint. Referred to as Sparse Convex Optimization (SCO), this problem emerges from a network of computing nodes where various agents work together to solve the optimization problem collaboratively. Due to the sparsity constraint being a combination of a finite number of subspaces, the SCO problem falls under the class of combinatorial optimization, which is typically considered NP-hard. This thesis develops efficient distributed algorithms and software tools to solve SCO problems with decentralized data. The algorithms were designed to work on a peer-to-peer computational network where each node handles a specific portion of the problem in parallel while collaborating with other nodes. Inspired by previous advancements in mixed-integer optimization and high-performance computing, this thesis introduces a distributed Mixed-Integer Programming (MIP) framework to find exact solutions for SCO problems. The framework presents novel distributed algorithms and heuristics, which were implemented in a software tool called the Sparse Convex Optimization Toolkit (SCOT), specifically designed to solve SCO problems. In particular, the proposed algorithms extend multi- and single-tree Outer Approximation (OA) algorithms by incorporating a fully decentralized algorithm called the Relaxed Hybrid Alternating Direction Method of Multipliers (RH-ADMM). Such developments led to the design of two distributed mixed-integer nonlinear programming algorithms: Distributed Primal Outer Approximation (DiPOA) and Distributed Hybrid Outer Approximation (DiHOA). Additionally, various reformulation and heuristic techniques were introduced to leverage the separability of nonlinear functions and enhance performance

Endurance exercises versus treadmill training in improving muscle strength and functional activities in hemiparetic cerebral palsy

Weakness of the sound side in hemiparetic cerebral palsy is one of the serious complications which affect these children. Many children with hemiparetic cerebral palsy have diminished muscle power in the neglected sound side, and the application of strengthening exercises aim to improve the muscle strength and function activities and so may be helpful in the management of such cases. In this study, endurance exercises and treadmill training was conducted to investigate its effect on increasing the strength of the quadriceps femoris and hamstring muscles of the sound side in spastic diaplegic cerebral palsy in comparison to the effect of an endurance exercise program. Thirty spastic hemiparetic children were the sample of this work. There were divided randomly into two equal groups. The ratio of peak torque of quadriceps femoris muscle and the hamstring muscle and balance were measured before and after six months of the application of the treatment program. Group A received the physiotherapy program and treadmill training, while group B received endurance exercise in the form of DeLorme resistance exercise in addition to the same physiotherapy program given to group A. Significant improvement were observed in all measuring variables when comparing the post-treatment results in both groups.Keywords: Cerebral palsy; Hemi-paresis; Muscle weakness; Treadmill training; Balanc

Stochastic Signal Processing and Power Control for Wireless Communication Systems

This dissertation is concerned with dynamical modeling, estimation and identification of wireless channels from received signal measurements. Optimal power control algorithms, mobile location and velocity estimation methods are developed based on the proposed models. The ultimate performance limits of any communication system are determined by the channel it operates in. In this dissertation, we propose new stochastic wireless channel models which capture both the space and time variations of wireless systems. The proposed channel models are based on stochastic differential equations (SDEs) driven by Brownian motions. These models are more realistic than the time invariant models encountered in the literature which do not capture and track the time varying characteristics of the propagation environment. The statistics of the proposed models are shown to be time varying, and converge in steady state to their static counterparts. Cellular and ad hoc wireless channel models are developed. In urban propagation environment, the parameters of the channel models can be determined from approximating the band-limited Doppler power spectral density (DPSD) by rational transfer functions. However, since the DPSD is not available on-line, a filterbased expectation maximization algorithm and Kalman filter to estimate the channel parameters and states, respectively, are proposed. The algorithm is recursive allowing the inphase and quadrature components and parameters to be estimated on-line from received signal measurements. The algorithms are tested using experimental data, and the results demonstrate the method’s viability for both cellular and ad hoc networks. Power control increases system capacity and quality of communications, and reduces battery power consumption. A stochastic power control algorithm is developed using the so-called predictable power control strategies. An iterative distributed algorithm is then deduced using stochastic approximations. The latter only requires each mobile to know its received signal to interference ratio at the receiver