Mixture Kernel Density Estimation and Remedied Correlation Matrix on the EEG-Based Copula Model for the Assessment of Visual Discomfort

Since electroencephalogram (EEG) signals can directly provide information on changes in brain activity due to behaviour changes, how to assess visual discomfort through EEG signals attracts researchers’ attention. However, previous assessments based on time-domain EEG features lack sufficient consideration of the dependence among EEG signals, which may affect the discrimination to visual discomfort. Although the copula model can explore the dependence among variables, the EEG-based copula models still have the following deficiencies: (1) the methods ignoring the fine-grained information hidden in EEG signals could make the estimated marginal density function improper, and (2) the approaches neglecting the pseudo-correlation among data may inappropriately estimate the correlation matrix parameter of the copula density function. The mixture kernel density estimation (MKDE) and remedied correlation matrix (RCM) on the EEG-based copula model are proposed to mitigate the mentioned shortcomings. The simulation experiments show that MKDE can not only better estimate the marginal density function but also explore fine-grained information. The RCM can be closer to the real correlation matrix parameter. With the favourable quality of the proposed EEG-based model, it is used to extract time-domain EEG features to assess visual discomfort further. To our best knowledge, the extracted features present better discrimination to visual discomfort compared with the features extracted by the state-of-the-art method.

Cyclotrons as Drivers for Precision Neutrino Measurements

As we enter the age of precision measurement in neutrino physics, improved flux sources are required. These must have a well defined flavor content with energies in ranges where backgrounds are low and cross-section knowledge is high. Very few sources of neutrinos can meet these requirements. However, pion/muon and isotope decay-at-rest sources qualify. The ideal drivers for decay-at-rest sources are cyclotron accelerators, which are compact and relatively inexpensive. This paper describes a scheme to produce decay-at-rest sources driven by such cyclotrons, developed within the DAEδALUS program. Examples of the value of the high precision beams for pursuing Beyond Standard Model interactions are reviewed. New results on a combined DAEδALUS—Hyper-K search for CP violation that achieve errors on the mixing matrix parameter of 4° to 12° are presented.

Application of PSO Algorithm in PMSM Sensorless Control System

Aiming at difficulty of determining the noise matrix parameter, PSO algorithm is applied to the optimization of noise parameter matrix. the fitness function is the time integral of the absolute value of the deviation between actual value and estimated value of the motor speed, the position of the particle, which makes the value of fitness function the smallest, is ultimately determined through constantly adjusting the position of the particle in the space, thereby computing matrix with the smallest deviation. The results show that the precision of speed estimation is obviously improved after noise matrix parameters of the system are optimized by PSO algorithm. And optimized waveform pulse of the motor speed is diminishes, speed-governing is more stable.

Increasing parameter certainty and data utility through multi-objective calibration of a spatially distributed temperature and solute model

To support the goal of distributed hydrologic and instream model predictions based on physical processes, we explore multi-dimensional parameterization determined by a broad set of observations. We present a systematic approach to using various data types at spatially distributed locations to decrease parameter bounds sampled within calibration algorithms that ultimately provide information regarding the extent of individual processes represented within the model structure. Through the use of a simulation matrix, parameter sets are first locally optimized by fitting the respective data at one or two locations and then the best results are selected to resolve which parameter sets perform best at all locations, or globally. This approach is illustrated using the Two-Zone Temperature and Solute (TZTS) model for a case study in the Virgin River, Utah, USA, where temperature and solute tracer data were collected at multiple locations and zones within the river that represent the fate and transport of both heat and solute through the study reach. The result was a narrowed parameter space and increased parameter certainty which, based on our results, would not have been as successful if only single objective algorithms were used. We also found that the global optimum is best defined by multiple spatially distributed local optima, which supports the hypothesis that there is a discrete and narrowly bounded parameter range that represents the processes controlling the dominant hydrologic responses. Further, we illustrate that the optimization process itself can be used to determine which observed responses and locations are most useful for estimating the parameters that result in a global fit to guide future data collection efforts.