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Rapid and low-cost 3D scanning system becomes a common solution for prosthetic designers and engineers to acquire accurate 3D geometrical model of the involved patient. Multiple Kinect sensor fusion solutions have been proposed but still, there is a lack of fusion scheme within a single workstation and a systematic reconstruction error analysis. The objective of the present study was to develop a new Kinect sensor fusion scheme running on a single workstation and an error compensation strategy. Architecture of a 3D scanning system for the lower residual limb was proposed. Four Kinect sensors were used to develop the fusion scheme. An error compensation approach based on linear regression method was performed. Then, an iterative rigid multi-set registration method was proposed. Different primitive geometries (cylindrical objects, box) with well-known physical dimensions were designed, fabricated and used for the evaluation process. Obtained results showed that the error compensation strategy leads to a reduction of around 40% of 3D geometrical reconstruction error for the residual stump-like structure. The total scanning and processing time is 25.4 ± 2.5 seconds. Moreover, our novel iterative rigid multi-set registration method leads to an improvement of accuracy of 60% compared to conventional registration method. Our proposed 3D scanning system using multiple Kinect cameras with error compensation strategy is expected to reduce the design cost and improve the design accuracy. As perspective, this 3D scanning system with fusion scheme will be integrated into a Computer-Aided Design (CAD) system with soft tissue deformation feedbacks for lower limb prostheses.

Multiple types of Demand Response Programs (DRPs) are offered within the same infrastructure. Multiple objectives of technical, economic and marketing order are targeted. Defining a model with weights for the various objectives and the related Key Performance Indicators (KPIs) is of a high importance. A structured model can be achieved through the assessment of an adequate market design with adequate dynamic pricing schemes to be offered to consumers, coupled with reducing costs at various levels and optimizing the technical indices while using business intelligence and big data. The originality of this paper resides in the simulation of a model combining multiple DRPs with multiple objectives and their assigned weights, hence the elasticity of dynamic parameters and criteria selection to achieve the compromise. It includes the proposal of dynamic pricing schemes for consumers’ detected clusters while optimizing costs and considering technical constraints. The optimal solution is the result of combining an assessment process based on historical data coupled with intensive what if scenarios to serve the planning and optimization phase, thus its application will be guaranteed. The validation is preformed through a simulation on Matlab using multi-objective optimization clustering based with assigned weights.

Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction.

Energy produced by wind turbines is characterized by its uncertainty which causes high fluctuations. Two types of fluctuations exist: the first one is represented by the slow power variation measured during large period; the second is the fast power variation measured from minute to minute that is known by the ramping rate. In this paper, the second type is studied. The high level of penetration of these fluctuations may affect the power network quality and may cause voltage and frequency instability. Power smoothing that is defined in this paper by ramping rate control is an important issue to avoid these fluctuations and to maintain the power network stability. Conventional methods based on storage system and on delta control of wind turbines are published in literature for this purpose. Unfortunately, storage systems are either small or expensive and delta control reduces yield energy. Another approach consists of adding to the delta control the power consumption control of heating and ventilation air conditioning (HVAC) systems installed in large buildings. This would provide positive and negative electrical power reserve for the compensation of wind power fluctuations. Unfortunately, this technique is still limited by a small margin of control since neither the temperature nor the humidity of the air-conditioned zones should be affected. This paper suggests an improvement of this margin of control by integrating in the regulation process the supply fan with the return fan. A numerical study based on real data shows that the proposed method is more advantageous than using slow response heat storage systems or expensive storage batteries or DC controllers. Moreover, this method reduces wind power losses and keeps the temperature variation unperceived, although an important reduction in electrical power fluctuation is achieved.

In large photovoltaic plants (PV farms), the partial shading phenomenon due to clouds leads to a decrease in the total power of the installation, and consequently a decrease in profitability. For improving the plant yield although partially shaded, a reconfiguration of the panel's connection is performed. Several reconfiguration algorithms are published, and a switch matrix is controlled providing panels interconnectivity. However, these algorithms are based on measurements made on each panel of the farm providing the solar radiation on it, its output voltage, and current. This conventional method needs a complex and expensive installation of a great number of instruments, it requires also regular maintenance. This research aims to find a low-cost and spacesaving reconfiguration method using the least possible measuring devices. A feed-forward neural network is developed to estimate the shading rate of each PV zone using meteorological data and sun position. It is found that training a regression model responds better to the problem. Also, training a feed-forward neural network with the Levenberg-Marquardt algorithm proves to be efficient in terms of stability, speed, and convergence with a small error.

The prosthetic socket provides the critical interface between the prosthetic device and the patient’s residual limb. Since each stump is unique in terms of morphology and mechanics, each socket should be patient specific. Computer-aided design solutions have been proposed in the literature. However, there is a lack of an efficient solution able to modify local information based on soft tissue deformation feedback to enhance the design process. The objective of the present work was to develop and evaluate a computer-aided design approach with real-time soft tissue deformation feedback and an inverse approach to optimize the stump–socket interaction. A computer-aided parametric socket design workflow was proposed. Soft tissue deformation was performed using a novel formulation of the mass-spring system. An inverse approach was proposed to estimate and optimize the stump–socket interaction. An interactive parametric design tool was also developed and evaluated. The proposed approach was applied on a CT-based dataset. Finally, the obtained design outcomes were compared with FE simulation outcomes for evaluation purpose. As results, a virtual socket prototype of the CT-based stump model was designed and illustrated by an interactive process. The comparison of stump–socket interaction behavior with FE simulation outcomes showed a very good agreement with a pressure absolute deviation error ranging from 1.44 ± 2.13 to 3.66 ± 4.56 kPa. Moreover, the contact pressures are below the pain-threshold curves, confirming the comfortability of the designed sockets according to the predefined criteria. The present study proposed a computer-aided socket design solution to locally enhance the socket geometry and mechanics. This opens new avenues to increase the design accuracy, reduce the design cost and give the involved patient a geometrically and mechanically fitted socket device. As perspectives, this process will be integrated with our available visual sensor fusion toward a complete computer-aided socket design system for lower limb prosthetic design and fabrication.

Prosthetic technology is rapidly advancing but there's a catch. Regardless the technology or the material used, a minimum cost is still high. One of the problems relates to the fact that the conventional socket fabrication process is still used. This method is based on subjective estimations of the involved specialists and feedbacks of the patients. This process consumes remarkable amount of time, manpower and materials. Research works are needed to design new efficient and low-cost alternative techniques for the socket design. This technique should definitely be based on CAD-CAM methods. Therefore, the first step toward this objective is to establish an accurate numeric model for evaluating and optimizing the design process. In this present work, we developed a new approach to simulate the stump soft tissue deformation and stump-socket interaction using Mass-Spring System (MSS) approach and a point-to-surface contact formulation. A novel Mass-Spring System with corrective spring (MSS-CS) model was developed and evaluated. A node-to-surface contact formulation was also integrated and evaluated. The MSS-CS model and contact formulation were evaluated with primitive geometrical object and a stump-socket model. Moreover, a finite element model of the stump-socket interaction was also developed using Abaqus to evaluate the proposed approach. Obtained results show that the proposed contact formulation has a very good precision level and the contact pressures on the interface between the elastic and rigid bodies are very close to the analytical solutions. The comparison with Abaqus showed a qualitative concordance for the contact pressure. However, quantitative deviation remains high [25-50]% at the peak contact pressure due to different contact formulations. In particular, our MSS-CS approach is more efficient than Abaqus simulation in term of computational time and cost. A novel approach was proposed to model soft tissue deformation and stump-socket interaction in an efficient and accurate manner. As perspectives, this present approach for a real-time simulation of the stump-socket interaction could be used in a real-time CAD-CAM platform to provide a cost-effective socket manufacturing process.

Assessment and qualification of consumers’ behavior play an important role in the selection of the suitable Demand Response Programs (DRPs) achieving their engagement and satisfaction. Targeting the assessed consumers’ clusters with adequate and elastic pricing schemes ensures their participation. The model offers the full visibility on the dynamicity of pricing and demands for the various simultaneous clusters’ DRPs operating in a synchronized manner. The originality of this paper resides in the classification of consumers’ behaviors according to the criteria and the elasticity of the multiple dynamic offered pricing schemes for the clusters along with their impacts on each other. An optimal solution is reached through assessment, qualification, planning, benefits’ visibility on various proposals and relocation. Intensive what if scenarios assist in the decision making process of the optimal selection in a planned phase. Thus, contracts’ terms and conditions suiting the consumers and the electrical utility are arranged. The method is validated through a simulation on Matlab using clustering and multi-objective optimization.

Prediction on electrical consumption of clients and their selection for appropriate demand response programs (DRPs), are a major challenge for the stability of the distribution network. Prediction accuracy of patterns and their matching with reality plays an important role in DRPs modeling for better consumers’ participation. Existing Approaches studied several aspects of prediction and baselines in DRPs environment. The originality of this work consists in demonstrating a prediction based approach, studying its accuracy and continuously adding new patterns to the training set. The variation versus planning phase is kept minimal and adapted to customers’ variations. It is achieved at the individual and aggregated profiles, thus customers’ classification and engagement are ensured. The approach is validated through a simulation on Matlab. Objectives include the customer prequalification and the effects of prediction accuracy on the balance generation/demand.

Integration of the renewable energy sources (RES) in distribution systems is accompanied with a lot of challenges to power system protection, control and operation. This integration can cause reverse power flow that affects the voltage stability of the power system. To avoid the power system instability, the monitoring of the integration of RES is necessary. In this paper a hybrid algorithm is proposed to monitor reverse power flow, and then it was tested on the IEEE-39 and IEEE-118 node test feeders.

Multiphase induction generators are gaining popularity in wind energy systems for their robustness, reliability and fault-tolerant behavior when compared to the famous three-phase ones. This work proposes an advanced robust nonlinear control strategy for a multiphase induction generator in a variable speed wind energy conversion system. The considered structure in this paper consists of a six-phase induction generator and two conventional three-phase two-level voltage source converters. The stator currents of the structure are regulated using the proposed method that consists of a time delay estimation-based modified super-twisting. On one hand, and a proportional-integral regulates the speed in an outer control loop. On the other hand, the proposed method regulates the stator currents in an inner control loop. The stability analysis of the closed-loop error dynamics is proved using a quadratic Lyapunov function. Finally, numerical simulations have been carried out on a model of a real six-phase induction generator to show the good features of the designed method.

The increased penetration level of Electric Vehicles (EVs) on the Distribution Network (DN) will cause severe problems on the network especially when all EVs tend to charge when the electricity price is low, even in the presence of demand response programs. The main problem is caused by the topology of the existing DN, in which the network is decentralized in terms of controlling and scheduling the loads. The Distribution System Operator (DSO) has no authority to control the loads of the endusers due to security and privacy issues. To overcome this situation, we propose a novel Programmable Smart Transformer, in which the DSO can program to perform certain tasks and to control the total load-demand on the transformer without intervening in controlling the particular loads of the end-users. Therefore, the security and privacy issues are solved, and both DSO and end-users are satisfied.