Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data

Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.

City level CO_(2) and local air pollutants co-control performance evaluation: A case study of 113 key envir on mental protection cities in China

‘Co-control',or synergistically reducing CO_(2)and local air polluta nt emissions,is an important strategy for cities to achieve'low carb on'and'blue sky'simultaneously.However,there were few studies to evaluate and compare the level of co-control of CO_(2) and local air pollutants in cities year.The present study proposed qualitative and quantitative methods to evaluate the level of co-control of CO_(2)and three local air pollutant(SO_(2).NOX,and particulate matter PM)emissions in key environmental protection cities in China over two periods(2012-2015 and 2015-2018).Statistical analysis found that,though three local air pollutant emissions positively correlated with CO_(2) emission,no significantly positive correlation was found between local air pollutants emission reductions and CO_(2) emission reductions,indicating that co-control effects were poor in general.By using the co-control effect coordinate system,qualitative evaluation showed that less than half of the sample cities could achieve co-control of the total amount of CO_(2) and local air pollutants.By employing the indicator of emission reduction equivalence(EReq),quantitative evaluation showed that the co-control level of the sample cities improved in 2015-2018 compared with 2012-2015.Further regression analysis showed that,reducing coal consumption and economic development significantly enhanced the co-control performance of the observed cities.The present case study proved that the proposed methods for evaluation and comparison of the city co-control performance works well,and can be used in other countries and regions to promote global cities racing to carbon and local air pollutants co-control.

Mode decomposition and source localization performance for the short vertical array in shallow water

In this paper, by using the fast iterative method of mode decomposition[12], source range-depth localization performance of MMP for three kinds of vertical array (short, sparse and short-sparse arrays) in shallow water with a downward refraction sound-speed profile in the surnmertime is discussed; the accuracy of mode decomposition is measured by its rootmean-square error, RMS. The numerical results illustrate that the accuracy of source range and depth estimation are raised and the sidelobes are effectively suppressed. The short-sparse vertical array not only has shorter length and fewer hydrophones, but also can be applied to the different sea areas with various depth, so it is a practical type of vertical arrny in the engineering project of the passive source localization.