Analysis techniques for detection of IM broken rotor bars after supply disconnection

This paper presents some analysis techniques of the space vector of voltages induced in the stator windings after supply disconnection, to detect broken rotor bars in squirrel-cage induction machines. When the motor is disconnected from the supply no currents flow in the stator windings and the voltages measurable at its terminals are due to flux produced by rotor currents. When the rotor is healthy, the voltages measured at motor terminals are almost sinusoidal because of the symmetry of rotor windings. When there are broken rotor bars, the magnetomotive force generated by rotor windings is distorted, and some particular harmonics, contained in the voltages induced in the stator windings, increase their amplitudes. The diagnostic technique is based on monitoring these voltage harmonics and analyzing the space vector of the voltages induced in the stator windings via MUSIC pseudospectrum and short-time MUSIC (STMUSIC) time-frequency pseudorepresentation. The MUSIC algorithm is based on the eigen analysis of the autocorrelation matrix, and permits us to evidence the principal harmonic frequencies of the signal and decrease the noise influence, thus allowing a better detection of the broken rotor bars. The results obtained using MUSIC and STMUSIC algorithm have been compared experimentally with those obtained by fast Fourier transform (FFT) and short-time FFT, respectively, and two different sized induction motors have been tested, to demonstrate the superiority of the former approach. Differently from most of the diagnostic techniques already proposed in the technical literature, the proposed approach is effective regardless of the load condition of the machine, source characteristics, and iron saturation.     

A fully automatic method for biological target volume segmentation of brain metastases

Leksell Gamma Knife is a mini‐invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator‐dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c‐means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real‐time performance satisfying the physician requirements in a radiotherapy environment.     

Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem

The problem of merging multiple databases of information about common entities is frequently encountered in KDD and decision support applications in large commercial and government organizations. The problem we study is often called the Merge/Purge problem and is difficult to solve both in scale and accuracy. Large repositories of data typically have numerous duplicate information entries about the same entities that are difficult to cull together without an intelligent equational theory that identifies equivalent items by a complex, domain-dependent matching process. We have developed a system for accomplishing this Data Cleansing task and demonstrate its use for cleansing lists of names of potential customers in a direct marketing-type application. Our results for statistically generated data are shown to be accurate and effective when processing the data multiple times using different keys for sorting on each successive pass. Combing results of individual passes using transitive closure over the independent results, produces far more accurate results at lower cost. The system provides a rule programming module that is easy to program and quite good at finding duplicates especially in an environment with massive amounts of data. This paper details improvements in our system, and reports on the successful implementation for a real-world database that conclusively validates our results previously achieved for statistically generated data.     

Highly‐Cyclable Room‐Temperature Phosphorene Polymer Electrolyte Composites for Li Metal Batteries

Despite significant interest toward solid‐state electrolytes owing to their superior safety in comparison to liquid‐based electrolytes, sluggish ion diffusion and high interfacial resistance limit their application in durable and high‐power density batteries. Here, a novel quasi‐solid Li⁺ ion conductive nanocomposite polymer electrolyte containing black phosphorous (BP) nanosheets is reported. The developed electrolyte is successfully cycled against Li metal (over 550 h cycling) at 1 mA cm^?2 at room temperature. The cycling overpotential is dropped by 75% in comparison to BP‐free polymer composite electrolyte indicating lower interfacial resistance at the electrode/electrolyte interfaces. Molecular dynamics simulations reveal that the coordination number of Li⁺ ions around (trifluoromethanesulfonyl)imide (TFSI^?) pairs and ethylene‐oxide chains decreases at the Li metal/electrolyte interface, which facilitates the Li⁺ transport through the polymer host. Density functional theory calculations confirm that the adsorption of the LiTFSI molecules at the BP surface leads to the weakening of N and Li atomic bonding and enhances the dissociation of Li⁺ ions. This work offers a new potential mechanism to tune the bulk and interfacial ionic conductivity of solid‐state electrolytes that may lead to a new generation of lithium polymer batteries with high ionic conduction kinetics and stable long‐life cycling.     

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.     

Parallel Implementation of a Machine Learning Algorithm on GPU

The capability for understanding data passes through the ability of producing an effective and fast classification of the information in a time frame that allows to keep and preserve the value of the information itself and its potential. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. A powerful tool is provided by self-organizing maps (SOM). The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. Because of its time complexity, often using this method is a critical challenge. In this paper we propose a parallel implementation for the SOM algorithm, using parallel processor architecture, as modern graphics processing units by CUDA. Experimental results show improvements in terms of execution time, with a promising speed up, compared to the CPU version and the widely used package SOM_PAK.     

The influence of extent of polymerization on the thermal behavior of an epoxy-based polyether resin

A number of studies have been reported in the literature on the polymerization and thermal decomposition of epoxide resins. Lee¹ and Anderson² have both studied the thermal decomposition of epoxy resins, and they concluded that the characteristic exothermic peak (which can occur anywhere between 300° and 400°C) is caused at least partially by some reaction of the epoxide group. We have been investigating the thermal decomposition of an aromatic polyether resin which is produced by curing the diglycidyl ether of bisphenol A (Epon 825) with the catalytic agent trimethoxyboroxine (Fig. 1). DTA studies of the polyether in an inert atmosphere of N₂ showed exothermic peaks at approximately 390°, 430°, and 470°C, with the major exotherm being the one at 430°C. Our investigation has shown the important role played by low molecular weight epoxides in these exothermic reactions.     

Frying performance of a sunflower/palm oil blend in comparison with pure palm oil

The aim of this study was to test the performance of a vegetable oil blend formulated as alternative to pure palm oil as frying medium. For this purpose, the evolution of many analytical parameters (free acidity, spectrophotometric indices, total polar components, fatty acid composition, short‐chain fatty acids, tocopherol and tocotrienol content and composition, color, flavor evaluated by means of an electronic nose) of the selected blend (sunflower/palm oil 65 : 35 vol/vol) has been monitored during a prolonged frying process (8 h discontinuous frying without oil replenishment) in comparison to pure palm oil. Sensory attributes of the fried food were also evaluated. The blend proved to keep qualitative parameters comparable to those shown by palm oil during the prolonged frying process. Even if some oxidation indices, such as spectrophotometric indices, short‐chain fatty acids and total polar components, increased faster in the blend, it showed a higher tocopherol content and a lower increment in free fatty acids as compared to pure palm oil. Chips fried in the two oils did not show significantly different sensory profiles.     

Aqueous supercapacitors on conductive cotton

Wearable electronics offer the combined advantages of both electronics and fabrics. In this article, we report the fabrication of wearable supercapacitors using cotton fabric as an essential component. Carbon nanotubes are conformally coated onto the cotton fibers, leading to a highly electrically conductive interconnecting network. The porous carbon nanotube coating functions as both active material and current collector in the supercapacitor. Aqueous lithium sulfate is used as the electrolyte in the devices, because it presents no safety concerns for human use. The supercapacitor shows high specific capacitance (˜70–80 F·g⁻¹ at 0.1 A·g⁻¹) and cycling stability (negligible decay after 35,000 cycles). The extremely simple design and fabrication process make it applicable for providing power in practical electronic devices.