Editorial: special issue on quantum precision measurement

With the rapid development of cold atom physics and quantum optics,Quantum Precision M easurement(QPM)is becoming a more and more active research field.An extreme high measurement precision and sensitivity could be

Restraint of mid-spatial frequency error in magnetorheological finishing (MRF) process by maximum entropy method

In order to restrain the mid-spatial frequency error in magnetorheological finishing (MRF) process, a novel part-random path is designed based on the theory of maximum entropy method (MEM). Using KDMRF-1000F polishing machine, one flat work piece (98 mm in diameter) is polished. The mid-spatial frequency error in the region using part-random path is much lower than that by using common raster path. After one MRF iteration (7.46 min), peak-to-valley (PV) is 0.062 wave (1 wave =632.8 nm), root-mean-square (RMS) is 0.010 wave and no obvious mid-spatial frequency error is found. The result shows that the part-random path is a novel path, which results in a high form accuracy and low mid-spatial frequency error in MRF process.

Frequency-domain analysis of computer-controlled optical surfacing processes

Mid-high spatial frequency errors are often induced on optical surfaces polished by computer-controlled optical surfacing (CCOS) processes. In order to efficiently remove these errors, which would degrade the performances of optical systems, the ability of a CCOS process to correct the errors have been investigated based on the convolution integral model in view of the availability of material removal. To quantify the ability, some conceptions, such as figure correcting ability and material removal availability (MRA), have been proposed. The research result reveals that the MRA of the CCOS process to correct a single spatial frequency error is determined by its tool removal function (TRF), and it equals the normalized amplitude spectrum of the Fourier transform of its TRF. Finally, three sine surfaces were etched using ion beam figuring (IBF), which is a typical CCOS process. The experimental results have verified the theoretical analysis. The employed method and the conclusions of this work provide a useful mathematical basis to analyze and optimize CCOS processes.

Hybrid Particle Swarm Optimization Algorithm Based on Entropy Theory for Solving DAR Scheduling Problem

An efficient task-scheduling algorithm in the Digital Array Radar(DAR) is essential to ensure that it can handle a large number of requested tasks simultaneously. As a solution to this problem, in this paper, we propose an optimization model for scheduling DAR tasks using a hybrid approach. The optimization model considers the internal task structure and the DAR task-scheduling characteristic. The hybrid approach integrates a particle swarm optimization algorithm with a genetic algorithm and a heuristic task-interleaving algorithm. We introduce the chaos theory to optimize initialized particles and use entropy theory to indicate the diversity of particles and adaptively adjust the inertia weight, the crossover probability, and the mutation probability. Then, we improve both the efficiency and global exploration ability of the hybrid algorithm. In the framework of the swarm exploration algorithm, we include a heuristic task-interleaving scheduling algorithm, which not only utilizes the wait interval to transmit or receive subtasks, but also overlaps the receive intervals of different tasks. In a large-scale simulation,we demonstrate that the proposed algorithm is more robust and effective than existing algorithms.

Multi-sensing paradigm based urban air quality monitoring and hazardous gas source analyzing:a review

Effectively monitoring urban air quality,and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions.Previous works,such as long-term obser-vations by monitoring stations,cannot provide customized data services and in-time emergency response under urgent situations(gas leakage incidents).Therefore,we first review the up-to-date approaches(often machine learning and optimization methods)with respect to urban air quality monitoring and hazardous gas source anal-ysis.To bridge the gap between present solutions and practical requirements,we design a conceptual framework,namely MAsmed(Multi-Agents for sensing,monitoring,estimating and determining),to provide fine-grained concentration maps,customized data services,and on-demand emergency management.In this framework,we leverage the hybrid design of wireless sensor networks(WSNs)and mobile crowdsensing(MCS)to sense urban air quality and relevant data(e.g.traffic data,meteorological data,etc.);Using the sensed data,we can create a fine-grained air quality map for the authorities and relevant stakeholders,and provide on-demand source term estimation and source searching methods to estimate,seek,and determine the sources,thereby aiding decision-makers in emergency response(e.g.for evacuation).In this paper,we also identify several potential opportunities for future research.