Track Changes In Parameters Research Articles

UAV group control protocol with adaptive consensus

SummaryIn this paper, a modified consensus protocol algorithm is proposed for controlling a group of identical unmanned aerial vehicles (UAVs), which have been subjected to interfering signals during coordinate information exchange, using an unknown parameter in the consensus protocol. The maximum levels of interfering signals in the proposed protocol were adjusted by incorporating a hysteresis function with a dead zone consistent with the initial coordinates and the interfering signal levels. An adaptation algorithm is proposed to address a priori uncertainty regarding the consensus parameters, involving the correction of an unknown parameter by eliminating control signals exhibiting false sign changes. This correction relies on the coordinates in the phase plane, indicating that the delay in maneuver execution occurs at the beginning of the maneuver. Furthermore, by modeling synchronized motion, UAV group consensus is demonstrated for an ideal case devoid of a priori uncertainty regarding control protocol parameters, interfering signals, or consequences. The convergence of the adaptation algorithm was assessed by defining a vector function to track parameter changes during tuning. The monotonically decreasing nature of the resulting curve, along with the finite duration of the tuning process, provides confirmation of the convergence of the adaptation algorithm.

Deep Petri nets of unsupervised and supervised learning

Artificial intelligence is one of the hottest research topics in computer science. In general, when it comes to the needs to perform deep learning, the most intuitive and unique implementation method is to use neural network. But there are two shortcomings in neural network. First, it is not easy to be understood. When encountering the needs for implementation, it often requires a lot of relevant research efforts to implement the neural network. Second, the structure is complex. When constructing a perfect learning structure, in order to achieve the fully defined connection between nodes, the overall structure becomes complicated. It is hard for developers to track the parameter changes inside. Therefore, the goal of this article is to provide a more streamlined method so as to perform deep learning. A modified high-level fuzzy Petri net, called deep Petri net, is used to perform deep learning, in an attempt to propose a simple and easy structure and to track parameter changes, with faster speed than the deep neural network. The experimental results have shown that the deep Petri net performs better than the deep neural network.

Approximate filtering of conditional intensity process for Poisson count data: Application to urban crime

The primary focus is a sequential data assimilation method for count data modelled by an inhomogeneous Poisson process. In particular, a quadratic approximation technique similar to the extended Kalman filter is applied to develop a sub-optimal, discrete-time, filtering algorithm, called the extended Poisson–Kalman filter (ExPKF), where only the mean and covariance are sequentially updated using count data via the Poisson likelihood function. The performance of ExPKF is investigated in several synthetic experiments where the true solution is known. In numerical examples, ExPKF provides a good estimate of the “true” posterior mean, which can be well-approximated by the particle filter (PF) algorithm in the very large sample size limit. In addition, the experiments demonstrate that the ExPKF algorithm can be conveniently used to track parameter changes; on the other hand, a non-filtering framework such as a maximum likelihood estimation (MLE) would require a statistical test for change points or implement time-varying parameters. Finally, to demonstrate the model on real-world data, the ExPKF is used to approximate the uncertainty of urban crime intensity and parameters for self-exciting crime models. The Chicago Police Department’s CLEAR (Citizen Law Enforcement Analysis and Reporting) system data is used as a case study for both univariate and multivariate Hawkes models. An improved goodness of fit measured by the Kolmogorov–Smirnov (KS) statistics is achieved by the filtered intensity. The potential of using filtered intensity to improve police patrolling prioritisation is also tested. By comparing with the prioritisation based on MLE-derived intensity and historical frequency, the result suggests an insignificant difference between them. While the filter is developed and tested in the context of urban crime, it has the potential to make a contribution to data assimilation in other application areas.

Nonlinear Dynamic Characteristics of the Railway Vehicle

AbstractThe nonlinear dynamic characteristics of a railway vehicle are checked into thoroughly by applying two different wheel-rail contact model: a heuristic nonlinear friction creepage model derived by using Kalker ’s theory and Polach model including dead-zone clearance. This two models are matched with the quasi-static form of the LuGre model to obtain more realistic wheel-rail contact model. LuGre model parameters are determined using nonlinear optimization method, which it’s objective is to minimize the error between the output of the Polach and Kalker model and quasi-static LuGre model for specific operating conditions. The symmetric/asymmetric bifurcation attitude and stable/unstable motion of the railway vehicle in the presence of nonlinearities which are yaw damping forces in the longitudinal suspension system are analyzed in great detail by changing the vehicle speed. Phase portraits of the lateral displacement of the leading wheelset of the railway vehicle are drawn below and on the critical speeds, where sub-critical Hopf bifurcation take place, for two wheel-rail contact model. Asymmetric periodic motions have been observed during the simulation in the lateral displacement of the wheelset under different vehicle speed range. The coexistence of multiple steady states cause bounces in the amplitude of vibrations, resulting instability problems of the railway vehicle. By using Lyapunov’s indirect method, the critical hunting speeds are calculated with respect to the radius of the curved track parameter changes. Hunting, which is defined as the oscillation of the lateral displacement of wheelset with a large domain, is described by a limit cycle-type oscillation nature. The evaluated accuracy of the LuGre model adopted from Kalker’s model results for prediction of critical speed is higher than the results of the LuGre model adopted from Polach’s model. From the results of the analysis, the critical hunting speed must be resolved by investigating the track tests under various kind of excitations.

Steady-State Performance Analysis and Step-Size Selection for LMS-Adaptive Wideband Feedforward Power Amplifier Linearizer

Balancing between power amplifier (PA) linearity and power efficiency is one of the biggest implementation challenges in radio communication transmitters. Among various linearization methods, the feedforward linearization technique is a fairly established principle offering a good tradeoff between linearity and power-efficiency even under wideband operation. Moreover, adaptive techniques for such linearizer have been proposed in literature to track parameter changes in the main PA and other circuitry. Among those, least mean squares (LMS) method for adapting signal cancellation loop (SCL) and error cancellation loop (ECL) coefficients is an attractive low-complexity alternative. In this paper, we carry out extensive closed-form performance analysis of the achievable intermodulation distortion (IMD) reduction of the overall LMS-adaptive feedforward linearizer, as a function of the used step-sizes and essential waveform statistics. Such analysis is currently missing from the state-of-the-art literature. Both memoryless nonlinearities and Wiener-Hammerstein type PA memory models are studied for which IMD suppression expressions are derived. Comprehensive computer simulations are also provided to illustrate the accuracy of the analysis when practical OFDM waveforms are used. Design examples are given as well where the analysis results are used to choose proper linearizer step-sizes to meet given transmitter spectral mask specifications.

Optimizing Supply-Chain Planning

Semiconductor manufacturing is a very capital-intensive endeavor that can return substantial revenues. The production planning process must deliver a build schedule that makes efficient use of Intel’s capital resources while satisfying as much demand as possible. This schedule should comprehend the flexibility of production resources, the dynamic nature of supply and demand within Intel’s supply chain, as well as the timing of new product releases and production facility improvements. Previous planning processes relied on spreadsheets for heuristic manual decision making with localized data. With the growing complexity of Intel’s products and manufacturing processes, these methods had become inadequate and unsustainable. Upgrading the planning process required better decision algorithms, improved data management, as well as more automated and integrated planning processes. New tools based on Mathematical Programming were implemented in multiple divisions and stages of Intel’s supply chain. The development team worked closely with the users to understand their business and capture their operating logic to create automated decision systems. These tools balance requirements to satisfy demand, achieve inventory targets, and remain within production capacity to reduce costs and satisfy demand across Intel’s supply chain. They have been developed to evolve the planning process while maintaining visibility to the logic and data flow to facilitate continuous improvement. Advances in data management were required to complement decision algorithm improvements. The new tools integrate directly into source data systems while providing planningand optimization-specific functionality, including mechanisms to track parameter changes and supply dynamic reporting capabilities. These advances allow planners to more easily identify data issues and to better understand the planning recommendations from the tools. The robust data management infrastructure enables tighter integration of organizations, increased scalability, and more consistent implementation of solutions across business units. Advances in decision algorithms, data management, and system automation led to improvements in solution quality, data health, and productivity. The new applications allow planners to rapidly perform analyses on multiple business scenarios to produce better solutions and improve collaboration with other organizations. While results reported by the business users over the past four years have proven the stability and value of this decision support technology, there is still work to be done. Plans for extensions and continuous improvement are provided in the last section of this paper.

A fault detection method for unknown systems with unknown input and its application to hydraulic turbine monitoring

Based on a recently developed approach to the design of unknown input observers, this paper presents a fault detection method for unknown systems with unknown input. By exploiting the properties of an assumed model structure, a residual signal is formed which is independent of the unknown input but dependent on the measurements and the unknown system parameters. With this signal, an identification algorithm with a residual-based resetting technique for the covariance matrix is obtained and used to track parameter changes caused by system faults. The method is demonstrated through its application to a simulated hydraulic turbine generating unit.

Sequential estimation of a time dependent advertising effectiveness model

A commonly used model for the relationship between advertising awareness and weight of advertising is that of Simon Broadbent (1979), One way TV advertisements work. In the paper Short-term modelling of advertising effectiveness: A confirmation of Broadbent's Model (Jex, 1985), it was found that from the very general class of Box-Jenkins Transfer Function models the Broadbent type model was indicated as being the most appropriate for National Girobank data. The present paper extends this work to cover the possibility that the parameters of the Broadbent model may change in time. Discount Weighted Estimation has been used to track parameter changes. These changes may be slow, due to decay of effectiveness or ‘ageing’ of a campaign, or they may be sudden, due for example to competitor activity.