Electromagnetism-like algorithms for optimized tool path planning in 5-axis flank machining

Optimization of tool path planning using metaheuristic algorithms such as ant colony systems (ACS) and particle swarm optimization (PSO) provides a feasible approach to reduce geometrical machining errors in 5-axis flank machining of ruled surfaces. The optimal solutions of these algorithms exhibit an unsatisfactory quality in a high-dimensional search space. In this study, various algorithms derived from the electromagnetism-like mechanism (EM) were applied. The test results of representative surfaces showed that all EM-based methods yield more effective optimal solutions than does PSO, despite a longer search time. A new EM-MSS (electromagnetism-like mechanism with move solution screening) algorithm produces the most favorable results by ensuring the continuous improvement of new searches. Incorporating an SPSA (simultaneous perturbation stochastic approximation) technique further improves the search results with effective initial solutions. This work enhances the practical values of tool path planning by providing a satisfactory machining quality.     

Mining citizen emotions to estimate the urgency of urban issues

Crowdsourcing technology offers exciting possibilities for local governments. Specifically, citizens are increasingly taking part in reporting and discussing issues related to their neighborhood and problems they encounter on a daily basis, such as overflowing trash-bins, broken footpaths and lifts, illegal graffiti, and potholes. Pervasive citizen participation enables local governments to respond more efficiently to these urban issues. This interaction between citizens and municipalities is largely promoted by civic engagement platforms, such as See-Click-Fix, FixMyStreet, CitySourced, and OpenIDEO, which allow citizens to report urban issues by entering free text describing what needs to be done, fixed or changed. In order to develop appropriate action plans and priorities, government officials need to figure out how urgent are the reported issues. In this paper we propose to estimate the urgency of urban issues by mining different emotions that are implicit in the text describing the issue. More specifically, a reported issue is first categorized according to the emotions expressed in it, and then the corresponding emotion scores are combined in order to produce a final urgency level for the reported issue. Our experiments use the SeeClickFix hackathon data and diverse emotion classification algorithms. They indicate that (i) emotions can be categorized efficiently with supervised learning algorithms, and (ii) the use of citizen emotions leads to accurate urgency estimates. Further, using additional features such as the type of issue or its author leads to no further accuracy gains.     

An iterative method for suboptimal control of linear time-delayed systems

This article presents a new approach for solving the Optimal Control Problem (OCP) of linear time-delay systems with a quadratic cost functional. The proposed method can also be used for designing optimal control time-delay systems with disturbance. In this study, the Variational Iteration Method (VIM) is employed to convert the original Time-Delay Optimal Control Problem (TDOCP) into a sequence of nonhomogeneous linear two-point boundary value problems (TPBVPs). The optimal control law obtained consists of an accurate linear feedback term and a nonlinear compensation term which is the limit of an adjoint vector sequence. The feedback term is determined by solving Riccati matrix differential equation. By using the finite-step iteration of a nonlinear compensation sequence, we can obtain a suboptimal control law. Finally, Illustrative examples are included to demonstrate the validity and applicability of the technique.     

OPAH a model for optimal design of multipurpose small hydropower plants

The investment in small hydropower plants requires the analysis of hydrological, technical, operational, budgetary, economical and financial aspects. The analysis of each possible configuration demands the joint action of several technicians, consuming substantial time and money. During initial design of the project, simplified procedures are usually adopted which may compromise the quality of the base configuration.In this paper we will present a global overview of the OPAH model, which was developed to surpass these limitations. This global model performs the optimization of project configuration. This model uses non-linear programming optimization to analyze the multipurpose operation of the hydropower plant. It uses a numeric simulation model of unsteady flow under pressure to analyze the hydraulic circuit. It uses an economical and financial simulation model that takes in to account the project risk associated to hydrologic and market variability, the financial capacity of the investor and the fiscal aspects.     

Mathematical framework for Bz-based MREIT model in electrical impedance imaging

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality visualizing static conductivity images of a subject by injecting electrical currents (Neumann data) and measuring the induced internal magnetic flux density B using an MRI scanner. Taking advantage of the internal information B, MREIT can deal with the ill-posed characteristics of the inverse problem in electrical impedance tomography (EIT However, the MREIT model at its early stage has technical difficulties in clinical applications mainly due to the requirement of subject rotations for acquiring all of the three components of B = (B_z, B_y, B_z). Lately, a new model so called the B_z-based MREIT model has been proposed to eliminate the subject rotation procedure. In this new MREIT model, we need to measure only one component B_z when the z-axis is the direction of the main magnetic field of the MRI scanner. There have been significant advances in reconstruction algorithms based on the B_z-based MREIT model and experimental studies showed that an excellent contrast resolution can be achievable. Although these advance in B_z-based MREIT, we have not dealt with its rigorous mathematical theory yet.The primary purpose of this work is to provide the rigorous mathematical framework for the B_z-based MREIT model. With this mathematical framework, we obtain the uniqueness in a two-dimensional setting of the B_z-based MREIT model. After introducing an example of the B_z-based MREIT algorithm, we present typical numerical and also experimental results.     

Influence of SiO2 and CaO additions on the microstructure and magnetic properties of sintered Sr-hexaferrite

The effect of simultaneous addition of CaO and SiO₂ on the microstructure and magnetic properties of sintered SrO-excess Sr-hexaferrites was studied. Both additives markedly affect the grain growth behavior and the magnetic properties. CaO-additions promote densification, which results in increased remanence, but due to simultaneuous grain growth the coercivity drops to <100 kA/m. SiO₂ additions are known to suppress grain growth. Simultaneous addition of CaO and SiO₂ is shown to be very beneficial in tailoring a dense microstructure with relatively small grains. The ratio of CaO/SiO₂ was found to be optimum at about 1, and magnets with a remanence of 430 mT and a coercivity of 300 kA/m were obtained. Transmission electron microscopy (TEM) studies and investigations by energy-dispersive analysis of X-rays (EDX) in the scanning TEM (STEM) mode show that both CaO and SiO₂ are concentrated at grain boundaries and grain junctions forming an amorphous secondary phase.     

Parameter estimation approach to the free boundary for the pricing of an american call option

In this paper, we consider a free boundary problem which arises in the pricing of an American call option. The free boundary represents the optimal exercise price as a function of time before a maturity date. We are developing a parameter estimation technique to obtain both the optimal exercise curve of an American call option and its price. For the numerical solution of a forward problem, a time marching finite element method is adopted. Numerical experiment shows the convergence property of the approximation scheme.     

Optimizing random walk search algorithms in P2P networks

In this paper we develop a model for random walk-based search mechanisms in unstructured P2P networks. This model is used to obtain analytical expressions for the performance metrics of random walk search in terms of the popularity of the resource being searched for and the random walk parameters. We propose an equation-based adaptive search mechanism that uses an estimate of the popularity of a resource in order to choose the parameters of random walk such that a targeted performance level is achieved by the search. We also propose a low-overhead method for maintaining an estimate of popularity that utilizes feedback (or lack there-off) obtained from previous searches. Simulation results show that the performance of the equation-based adaptive search is significantly better than the non-adaptive random walk and other straight-forward adaptive mechanisms.