Unscented Particle Filtering Algorithm for Optical-fiber Sensing Intrusion Localization Based on Particle Swarm Optimization

To improve the convergence and precision of intrusion localization in optical-fiber sensing perimeter protection applications, we present an algorithm based on an unscented particle filter (UPF). The algorithm employs particle swarm optimization (PSO) to mitigate the sample degeneracy and impoverishment problem of the particle filter. By comparing the present fitness value of particles with the optimum fitness value of the objective function, PSO moves particles with insignificant UPF weights towards the higher likelihood region and determines the optimal positions for particles with larger weights. The particles with larger weights results in a new sample set with a more balanced distribution between the priors and the likelihood. Simulations demonstrate that the algorithm speeds up convergence and improves the precision of intrusion localization

Research on transverse parametric vibration and fault diagnosis of multi-rope hoisting catenaries

According to application characteristics of the multi-rope friction hoisting catenaries, a linear transverse parametric vibration model of axially moving string was setup with fixed length and inhomogeneous boundary conditions. The Galerkin method was applied to discretize the dynamic governing equations. Using the Newmark method, the coupling coefficient second-order ODEs were solved. The parametric resonance vibrations of catenaries generated by tension variation along with forced boundary excitations were diagnosed with analytical and experimental validations. The transverse vibration amplitudes and frequencies of catenaries measured and analyzed by non-contact video gauge method were consistent with simulation outputs. The simulation outputs were based on practically measured parameters such as boundary displacement excitations and tension variations. The research results indicated that tension imbalance distributions of the catenaries could change their natural frequencies and result in transverse resonance under boundary harmonic displacement excitations. Therefore specific measures should be provided to maintain tension balance in multi-rope hoisting applications

Nonparametric Quasi-likelihood in Longitudinal Data Analysis

This dissertation proposes a nonparametric quasi-likelihood approach to estimate regression coefficients in the class of generalized linear regression models for longitudinal data analysis, where the covariance matrices of the longitudinal data are totally unknown but are smooth functions of means. This proposed nonparametric quasi-likelihood approach is to replace the unknown covariance matrix with a nonparametric estimator in the quasi-likelihood estimating equations, which are used to estimate the regression coefficients for longitudinal data analysis. Local polynomial regression techniques are used to get the nonparametric estimator of the unknown covariance matrices in the proposed nonparametric quasi-likelihood approach. Rates of convergence of the resulting estimators are established. It is shown that the nonparametric quasi-likelihood estimator is not only consistent but also has the same asymptotic distribution as the quasi-likelihood estimator obtained with the true covariance matrix. The results from simulation studies show that the performance of the nonparametric quasi-likelihood estimator is comparable to other methods with given marginal variance functions and correctly specified correlation structures. Moreover, the results of the simulation studies show that nonparametric quasi-likelihood corrects some shortcomings of Liang and Zeger's GEE approach in longitudinal data analysis

Study of gender and power in slash fan fiction: A case study of BBC Sherlock slash fan fiction

My research aims at exploring how gender identity and sexed body in the dominant/submissive pairing are constructed with BBC Sherlock slash fiction as a case study. Because the construction of the dominant/submissive pairing in slash fan fiction is not merely a passive imitation of the heterosexual gender norms but through the analysis of which, one gains understandings of how power relations work on constructing one’s body and identity. Slash fan fiction, commonly accepted as women’s writing for women, provides substantial materials for the study of power and gender. Furthermore, specific interest is put to study how the writing and reading of slash fan fiction challenges the norms and impacts women in real-lives because resistance has been a main part of the study of power and gender. Cultural analysis of texts provides the main methodological framework for my study. The data were collected from online website and analyzed through the signification process of linguistic signs which construct the dominant/submissive slash pairing. The theoretical framework, which consists of Judith Butler’s Gender Performativity (1999) and Michel Foucault’s Microphysic of Power (1978), was employed to discuss the findings and answer the research questions. The findings show that a strong oppositional linguistic choice is used in constructing John and Sherlock’s gender identity and sexed body. This construction reproduces not only the content of masculinity and femininity but also how one is produced and constrained by power relations. My study suggests that the construction of gender identity and sexed body in Sherlock slash fan fiction is the ceaseless enactment and reenactment of gender norms, and through which, the heteronormativity is ultimately confused and denaturalized. My study also indicates that women’s writing and reading of slash fan fiction is, from the perspective of Foucauldian power, the practice of freedom which empowers women to resist the gender normalization that they are always subject to

Heuristics for periodical batch job scheduling in a MapReduce computing framework

Task scheduling has a significant impact on the performance of the MapReduce computing framework. In this paper, a scheduling problem of periodical batch jobs with makespan minimization is considered. The problem is modeled as a general two-stage hybrid flow shop scheduling problem with schedule-dependent setup times. The new model incorporates the data locality of tasks and is formulated as an integer program. Three heuristics are developed to solve the problem and an improvement policy based on data locality is presented to enhance the methods. A lower bound of the makespan is derived. 150 instances are randomly generated from data distributions drawn from a real cluster. The parameters involved in the methods are set according to different cluster setups. The proposed heuristics are compared over different numbers of jobs and cluster setups. Computational results show that the performance of the methods is highly dependent on both the number of jobs and the cluster setups. The proposed improvement policy is effective and the impact of the input data distribution on the policy is analyzed and tested.This work is supported by the National Natural Science Foundation of China (No. 61272377) and the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120092110027). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "RESULT - Realistic Extended Scheduling Using Light Techniques" (No. DPI2012-36243-C02-01) partially financed with FEDER funds.Xiaoping Li; Tianze Jiang; Ruiz García, R. (2016). Heuristics for periodical batch job scheduling in a MapReduce computing framework. Information Sciences. 326:119-133. https://doi.org/10.1016/j.ins.2015.07.040S11913332

Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects

[EN] Workers with different levels of experience and knowledge have different effects on job processing times. By taking into account 1) the sum-of-processing-time; 2) the job-position; and 3) the experience of workers, a more general learning model is introduced for scheduling problems. We show that this model generalizes existing ones and brings the consideration of learning and forgetting effects closer to reality. We demonstrate that some single machine scheduling problems are polynomially solvable under this general model. Considering the forgetting effect caused by the idle time on the second machine, we construct a learning-forgetting model for the two-machine permutation flow shop scheduling problem with makespan minimization. A branch-and-bound method and four heuristics are presented to find optimal and approximate solutions, respectively. The proposed heuristics are evaluated over a large number of randomly generated instances. Experimental results show that the proposed heuristics are effective and efficient.This work was supported in part by the National Natural Science Foundation of China under Grant 61572127 and Grant 61272377, in part by the Key Research and Development Program in Jiangsu Province under Grant BE2015728, in part by the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province under Grant 12KJA630001, and in part by the Collaborative Innovation Center of Wireless Communications Technology. The work of R. Ruiz was supported by the Spanish Ministry of Economy and Competitiveness through Project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" under Grant DPI2015-65895-R. This paper was recommended by Associate Editor A. Janiak.Li, X.; Jiang, Y.; Ruiz García, R. (2018). Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 48(5):743-754. https://doi.org/10.1109/TSMC.2016.2616158S74375448

Analysis of significant factors on cable failure using the Cox proportional hazard model

This paper proposes the use of the Cox proportional hazard model (Cox PHM), a statistical model, for the analysis of early-failure data associated with power cables. The Cox PHM analyses simultaneously a set of covariates and identifies those which have significant effects on the cable failures. In order to demonstrate the appropriateness of the model, relevant historical failure data related to medium voltage (MV, rated at 10 kV) distribution cables and High Voltage (HV, 110 kV and 220 kV) transmission cables have been collected from a regional electricity company in China. Results prove that the model is more robust than the Weibull distribution, in that failure data does not have to be homogeneous. Results also demonstrate that the method can single out a case of poor manufacturing quality with a particular cable joint provider by using a statistical hypothesis test. The proposed approach can potentially help to resolve any legal dispute that may arise between a manufacturer and a network operator, in addition to providing guidance for improving future practice in cable procurement, design, installations and maintenance