A Stochastic Simulation Optimization-Based Range Gate Pull-Off Jamming Method

Contributors

Residual and stratified branching particle filters

Irregularities of the track are a main cause of train-induced ground vibration, and track maintenance is of great importance. Although geometric irregularities at the wheel-rail contact are widely used, other types of irregularities, such as stiffness irregularities, irregularities from different track positions and irregularities in the wave propagation, were analysed in the present study. The track behaviour was investigated by a multi-beam-on-soil model. This track model is coupled with a vehicle model to calculate the vehicle–track interaction. The track model was also used for the track filtering, which transfers a track support error to the equivalent rail irregularity or, conversely, the sharp axle pulse on the rail to a smoother pulse on the soil. In the case in which this filtering varies randomly along the track, the pulses of the moving static load induce a certain ground vibration component (“the scatter of axle pulses”). This effect was calculated by the superposition of axle pulses in the frequency domain and by a stochastic simulation. Simultaneous vehicle, track and soil measurements at a certain site were used to evaluate the different excitation and ground vibration components. The agreement between calculations and axle-box and soil measurements is good. The ground vibrations calculated from rail irregularities and corresponding dynamic loads, however, clearly underestimate the measured ground vibration amplitudes. Only the static load that is moving over a varying track support stiffness can produce the important mid-frequency ground vibration component by the scatter of axle pulses.

We propose a framework that combines simulation optimization with Bayesian decision analysis to evaluate the worth of hydraulic conductivity data for optimal groundwater resources management in ecologically sensitive areas. A stochastic simulation optimization management model is employed to plan regionally distributed groundwater pumping while preserving the hydroecological balance in wetland areas. Because predictions made by an aquifer model are uncertain, groundwater supply systems operate below maximum yield. Collecting data from the groundwater system can potentially reduce predictive uncertainty and increase safe water production. The price paid for improvement in water management is the cost of collecting the additional data. Efficient data collection using Bayesian decision analysis proceeds in three stages: (1) The prior analysis determines the optimal pumping scheme and profit from water sales on the basis of known information. (2) The preposterior analysis estimates the optimal measurement locations and evaluates whether each sequential measurement will be cost‐effective before it is taken. (3) The posterior analysis then revises the prior optimal pumping scheme and consequent profit, given the new information. Stochastic simulation optimization employing a multiple‐realization approach is used to determine the optimal pumping scheme in each of the three stages. The cost of new data must not exceed the expected increase in benefit obtained in optimal groundwater exploitation. An example based on groundwater management practices in Florida aimed at wetland protection showed that the cost of data collection more than paid for itself by enabling a safe and reliable increase in production.

Network-on-Chip (NoC) has been developed as an interconnect patterns in state-of-the-art multiprocessors. Core mapping and identifying suitable NoC platforms have a substantial effect on NoC system performance and corresponding costs. In this research work, investigated core mapping and optimization of NoC platforms by using an efficient algorithm. Consider two performance metrics (reliability and delay) and two cost metrics (area and power). The proposed mapping algorithm was assessed by applying it to different NoC benchmarks. The simulation results expose that the suggested mapping technique outperformed the other mapping techniques in terms of performance and cost metrics.

Evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation

Medical images exchanged over vulnerable networks are sensitive to modifications or tampering which could lead to misdiagnosis, risking the lives of the patients. Therefore, to provide safe and secure transmission of medical images, key security requirements such as authenticity, integrity, and confidentiality must be addressed. In this paper, a novel and efficient algorithm is proposed which provides security services for transmitted medical images in tele-medicine applications. The proposed algorithm employs joint encryption/watermarking procedures to provide the required security services, as well as to provide control access privileges at the receiving side. The algorithm is based on reversible data hiding principles that guarantee the extraction of the hidden data while restoring the original image unaffected. The embedded data is basically two watermarks; a spatial domain watermark and an encrypted domain watermark. The dual watermarks allow the authenticity and integrity of the transmitted medical images to be verified in the spatial and encrypted domains. The proposed algorithm has been extensively evaluated using three performance metrics; embedding capacity, visual image quality, and entropy. The ability of the algorithm to fulfill the security, reversibility, and separability requirements has been validated.

The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AUC) is widely used to evaluate the performance of a scoring function in machine learning, but is studied in algorithmic fairness less than other performance metrics. Due to the pairwise nature of the AUC, defining an AUC-based group fairness metric is pairwise-dependent and may involve both intra-group and inter-group AUCs. Importantly, considering only one category of AUCs is not sufficient to mitigate unfairness in AUC optimization. In this paper, we propose a minimax learning and bias mitigation framework that incorporates both intra-group and inter-group AUCs while maintaining utility. Based on this Rawlsian framework, we design an efficient stochastic optimization algorithm and prove its convergence to the minimum group-level AUC. We conduct numerical experiments on both synthetic and real-world datasets to validate the effectiveness of the minimax framework and the proposed optimization algorithm.

Timetable rescheduling of metro network during the last train period

The path planning and tracking problem of the multi-robot system (MRS) has always been a research hotspot and applied in various fields. In this article, a novel multi-robot path planning and tracking model (MPPTM) is proposed, which can carry out online path planning and tracking problem for multiple mobile robots. It considers many issues during this process, such as collision avoidance, and robot failure. The proposed approach consists of three parts: a neural dynamic path planner, a hyperbolic tangent path optimizer, and an error-driven path tracker. Assisted by Ultra-wideband positioning system, the proposed MPPTM is a low-cost solution for online path planning and high-accurate tracking of MRS in practical environments. In the proposed MPPTM, the proposed path planner has good time performance, and the proposed path optimizer improves tracking accuracy. The effectiveness, feasibility, and better performance of the proposed model are demonstrated by real experiments and comparative simulations.

Database systems allow quick creation of performance problems. The goal of database systems is to allow the computer-illiterate to write complex and complete applications. It is the job of the syst...

Risk measure index tracking model

In this paper, we present a power control algorithm in cognitive radio multiple-input–multiple-output (MIMO) system over time-varying fading channels based on the utility-based framework. In particular, a more general objective function framework is proposed under a long- or short-term transmitting and interference power constraints for secondary users, which results the optimization problem is nonconvex. A stochastic optimization algorithm is proposed based on an improved quantum-behaved particle swarm optimization (IQPSO), which can solve the nonconvex optimization problem efficiently. The acceleration coefficients are adjusted with time-varying equations which can help the particles jump out the local optimum. Due to the characteristics of wave function of quantum Delta potential well model, IQPSO can get better optimal solutions in search space for the stochastic optimization problem. To show the efficiency of the proposed algorithm, the utilities of the MIMO CR system got by IQPSO are compared with other approaches in the literature in different case studies, which show that the proposed algorithm can solve the nonconvex problem efficiently and achieve significant throughput.

Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems

Linear optimization is many times algorithmically simpler than nonlinear convex optimization. Linear optimization over matroid polytopes, matching polytopes, and path polytopes are examples of problems for which we have simple and efficient combinatorial algorithms but whose nonlinear convex counterpart is harder and admits significantly less efficient algorithms. This motivates the computational model of convex optimization, including the offline, online, and stochastic settings, using a linear optimization oracle. In this computational model we give several new results that improve on the previous state of the art. Our main result is a novel conditional gradient algorithm for smooth and strongly convex optimization over polyhedral sets that performs only a single linear optimization step over the domain on each iteration and enjoys a linear convergence rate. This gives an exponential improvement in convergence rate over previous results. Based on this new conditional gradient algorithm we give the first algorithms for online convex optimization over polyhedral sets that perform only a single linear optimization step over the domain while having optimal regret guarantees, answering an open question of Kalai and Vempala and of Hazan and Kale. Our online algorithms also imply conditional gradient algorithms for nonsmooth and stochastic convex optimization with the same convergence rates as projected (sub)gradient methods.