Security-Oriented Indoor Robots Tracking: An Object Recognition Viewpoint

Indoor robots, in particular AI-enhanced robots, are enabling a wide range of beneficial applications. However, great cyber or physical damages could be resulted if the robots’ vulnerabilities are exploited for malicious purposes. Therefore, a continuous active tracking of multiple robots’ positions is necessary. From the perspective of wireless communication, indoor robots are treated as radio sources. Existing radio tracking methods are sensitive to indoor multipath effects and error-prone with great cost. In this backdrop, this paper presents an indoor radio sources tracking algorithm. Firstly, an RSSI (received signal strength indicator) map is constructed based on the interpolation theory. Secondly, a YOLO v3 (You Only Look Once Version 3) detector is applied on the map to identify and locate multiple radio sources. Combining a source’s locations at different times, we can reconstruct its moving path and track its movement. Experimental results have shown that in the typical parameter settings, our algorithm’s average positioning error is lower than 0.39 m, and the average identification precision is larger than 93.18% in case of 6 radio sources.

Statistical properties of a stochastic model of eddy hopping

Statistical properties are investigated for the stochastic model of eddy hopping, which is a novel cloud microphysical model that accounts for the effect of the supersaturation fluctuation at unresolved scales on the growth of cloud droplets and on spectral broadening. Two versions of the model, the original version by Grabowski and Abade (2017) and the second version by Abade et al. (2018), are considered and validated against the reference data taken from direct numerical simulations and large-eddy simulations (LESs). It is shown that the original version fails to reproduce a proper scaling for a certain range of parameters, resulting in a deviation of the model prediction from the reference data, while the second version successfully reproduces the proper scaling. In addition, a possible simplification of the model is discussed, which reduces the number of model variables while keeping the statistical properties almost unchanged in the typical parameter range for the model implementation in the LES Lagrangian cloud model.

Statistical properties of a stochastic model of eddy hopping

Statistical properties are investigated for the stochastic model of eddy hopping, which is a novel cloud microphysical model that accounts for the effect of the supersaturation fluctuation at unresolved scales on the growth of cloud droplets and on spectral broadening. It is shown that the model fails to reproduce a proper scaling for a certain range of parameters, resulting in a deviation of the model prediction from the reference data taken from direct numerical simulations and large-eddy simulations (LESs). Corrections to the model are introduced so that the corrected model can accurately reproduce the reference data with the proper scaling. In addition, a possible simplification of the model is discussed, which may contribute to a reduction in computational cost while keeping the statistical properties almost unchanged in the typical parameter range for the model implementation in the LES Lagrangian cloud model.

Electric Vehicle Charger Static and Dynamic Modelling for Power System Studies

Electric Vehicles (EVs) are becoming increasingly available and are expected to be a large part of the load in future power systems. EV chargers are a relatively new type of load and are mainly interfaced with the grid through power electronics. It is therefore important to investigate the impact they have on power system dynamic behaviour. In this paper, two detailed EV charger models (representing a typical slow and fast charger) were investigated. The aim was to test the capability of standard static—and more importantly, dynamic—load models, commonly used in power system studies, to represent the static and dynamic behaviour of EV chargers. Different control parameter settings for two types of EV chargers were investigated, as were the limits of standard power system dynamic load model structures’ accurate representation. Typical parameter sets have also been provided for cases where proper representation was possible.

Size matters: optimal mask diameter and box size for single-particle cryogenic electron microscopy

Recently it has been demonstrated that single-particle cryogenic electron microscopy (cryo-EM) at 200 keV is capable of determining protein structures, including those smaller than 100 kDa, at sub-3.0 Å resolutions, without using significant defocus or a phase plate. However, the majority of near-atomic resolution cryo-EM structures has been determined using 300 keV. Consequently, many typical parameter settings for the cryo-EM computational image processing steps, especially those associated with the contrast transfer function, are based on the accumulated experience of 300 kV cryo-EM. We have therefore revised these parameters, established theoretical bases for criteria to find an optimal mask diameter and box size for a given dataset irrespective of acceleration voltage or protein size, and proposed a protocol. Considering the defocus distributions of the datasets, merely optimizing the mask diameters and box sizes yielded meaningful resolution improvements for the reconstruction of < 200 kDa proteins using 200 kV cryo-EM.

Analysis of Counting Bloom Filters Used for Count Thresholding

A bloom filter is an extremely useful tool applicable to various fields of electronics and computers; it enables highly efficient search of extremely large data sets with no false negatives but a possibly small number of false positives. A counting bloom filter is a variant of a bloom filter that is typically used to permit deletions as well as additions of elements to a target data set. However, it is also sometimes useful to use a counting bloom filter as an approximate counting mechanism that can be used, for example, to determine when a specific web page has been referenced more than a specific number of times or when a memory address is a “hot” address. This paper derives, for the first time, highly accurate approximate false positive probabilities and optimal numbers of hash functions for counting bloom filters used in count thresholding applications. The analysis is confirmed by comparisons to existing theoretical results, which show an error, with respect to exact analysis, of less than 0.48% for typical parameter values.

New character of a laser channel for squeezed chaotic state

We in this paper report a new character of laser channel, namely, during squeezed chaotic state evolving in the laser channel. Its density operator keeps form-invariant, only two newly found typical parameters evolve with time in a well-regulated way, so they can describe the features of this physical process well. The typical parameter is comparable to the variance of normal distribution.

Latin Hypercube Designs with Branching and Nested Factors for Initialization of Automatic Algorithm Configuration

The configuration of algorithms is a laborious and difficult process. Thus, it is advisable to automate this task by using appropriate automatic configuration methods. The [Formula: see text] method is among the most widely used in the literature. By default, [Formula: see text] initializes its search process via uniform sampling of algorithm configurations. Although better initialization methods exist in the literature, the mixed-variable (numerical and categorical) nature of typical parameter spaces and the presence of conditional parameters make most of the methods not applicable in practice. Here, we present an improved initialization method that overcomes these limitations by employing concepts from the design and analysis of computer experiments with branching and nested factors. Our results show that this initialization method is not only better, in some scenarios, than the uniform sampling used by the current version of [Formula: see text], but also better than other initialization methods present in other automatic configuration methods.

Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark

This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.