Search

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

Failures of bushing caused by corrosive sulfur has gradually appeared in recent years. Apart from copper and sulfur, certain amount of aluminum was also found both in oil and paper after disassembling. The corrosion mechanism of the aluminum foil and its effect on oil-paper insulation were unknown. In this study, series of sulfur corrosion experiments were taken to investigate the sulfur corrosion of aluminum foil. The results showed the aluminum foil could certainly be corroded in oil containing corrosive sulfur (DBDS). Thermal products of insulting paper triggered the corrosion by removing the alumina film on the surface. Then, the contact corrosion occurred between aluminum and DBDS. In addition, the mechanisms of corrosion products generation both in oil and on paper were studied. The corrosion products in oil was DBDS-Al(3,4) and that on paper was RCOOAln. It caused the decrease of electrical properties of oil-paper. Particularly, the dielectric loss of oil and paper were dramatically enhanced.

Oil-immersed insulation paper (OIP) bushing is one of the most important equipment in power transmission and distribution systems. Moisture is one of the most serious factors that affect the insulation status of OIP bushing. In this paper, the OIP bushing was assumed to be in a stable and simplicity condition, and then the transient moisture distribution of capacitor core and its effects on the electric field were studied. First, by dissecting a 110 kV OIP bushing, the specific sizes for modelling were obtained. Afterwards, a moisture diffusion model for OIP bushing was established based on COMSOL Multiphysics software, and it was also verified by moisture diffusion experiment. Then, the moisture distribution for different conditions were obtained and analyzed. Finally, a model for the electric field of OIP bushing was established, and then the effects of moisture on the electric field were studied.

Studying the moisture diffusion in non-oil-immersed insulation paper is helpful to optimize the drying process and improve the drying efficiency of oil-immersed equipment. Moisture diffusion factor (MDF) is the key parameter to describe the moisture diffusion in non-oil-immersed insulation paper. Thermal aging affects the moisture absorption capacity of insulation paper obviously, thus, this paper intends to modify the empirical expression of MDF by considering the effects of thermal aging. The transient moisture contents of non-oil-immersed insulation paper with different thicknesses were measured at 40 °C and 60 °C, respectively. Then, the experimental values of MDF were extracted, and the empirical expression of MDF was modified by considering the effects of thermal aging. Finally, the modified expression of MDF was verified. The following conclusion can be obtained: 1) the equilibrium time constant of non-oil-immersed insulation paper increases with thermal aging; 2) the moisture absorption rate of non-oil-immersed insulation paper decreases with thermal aging, and; 3) the modified expression of MDF is reasonable and effective.

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%.

Kevlar pulp treated with low-temperature plasma and nano cellulose fibrils (NCFs) have been introduced as reinforcements in the preparation for composite insulating paper with low relative permittivity, dielectric loss, good mechanical properties, and thermal stability. More polar groups of Kevlar fibers could be exposed after plasma treatment. Results showed that the relative permittivity of the composite paper containing 20% Kevlar pulp, 10% NCF, and 70% softwood pulp is decreased by 29.6% and the dielectric loss is decreased by 43.2% at 50 Hz, 25°, compared with the conventional paper formed by pure softwood pulp. The change was due to the introduction of Kevlar pulp with intrinsic low dielectric constant and loss, which decreases the whole polarizability of the paper. The mechanical properties of the composite paper became worse after introducing 20% Kevlar pulp. About 10% NCF can work as a mechanical reinforcement to compensate for the tensile and Yang's modulus loss. Scanning electron microscopy results showed that NCF could fill the void defects and improve the interface bonding of the fibers to enhance the paper mechanical properties. The composite paper also had better insulating and thermal properties than the conventional paper. It has been concluded that the Kevlar pulp/NCF/softwood pulp composite paper has the potential to replace the pure softwood paper for the application of high-voltage ac transformer insulation.

Evolutionary game dynamics is a combination of game theory and dynamical systems. Using dynamical theory, we investigate chaotic behavior in asymmetric Rock-Paper-Scissors games under imitative dynamics with two different populations. Our theoretical analysis and numerical simulations demonstrate that the dynamical system can give rise to chaotic behavior in zero-sum and positive-sum asymmetric games. However, chaos cannot occur in a negative-sum asymmetric game. In particular, the numerical simulations show that there is a limit cycle in a negative-sum asymmetric game.

In forensic document analysis, the authenticity of a document must be properly checked in the context of suspected forgery. Hyperspectral Imaging (HSI) is a non-invasive way of detecting fraudulent papers in a multipage document. The occurrence of a forged paper in a multi-page document may have a substantial difference from rest of the papers in its age, type, color, texture, and so on. Each pixel in an HSI data can be used as the material fingerprint for the spatial point it corresponds to. Hence, hyperspectral data of paper samples made of the same substance have similar characteristics and can be grouped into a single cluster. Similarly, paper samples made of different substances have different spectral properties. This paper relies on this heuristic and proposes a tensor based clustering framework for hyperspectral paper data, with an application to detect the forged papers in multi-page documents. Information embedded in the hyperspectral patches of the papers to be clustered is arranged into individual lateral slices of a third-order tensor in this framework. Further, this work employs the self-expressiveness property of submodules and an objective function is formulated to extract self-expressive representation tensor with low multirank and f-diagonal structure. Objective function of the proposed method incorporates $l_{\frac {1}{2}}$ -induced Tensor Nuclear Norm (TNN) and $l_{\frac {1}{2}}$ regularization to impart better low rankness and f-diagonal structure to the representation tensor. Experimental results of the proposed method were compared to the state-of-the-art subspace clustering approaches. The results demonstrate improved performance of the proposed method over the existing clustering algorithms.

Currency recognition has always been a troublesome task for blind and visually impaired people (BVIP). The problem is more severe in developing countries such as India, where there is still a lack of robust currency recognition systems. BVIP primarily relies on size variations and patterns such as intaglio printings for recognizing the underlying currency denominations. Most of the current Indian legal tenders resemble in size, thus making the identification process more strenuous. Also, the engraved patterns are not as distinctive as BVIP standards, and they fade over time. For an automated paper currency recognition system, issues such as folded or partial views, uneven illumination, and background clutter make it non-trivial and challenging. This paper ventures to present an end-to-end and robust framework for assisting BVIP in recognizing the Indian paper currency denomination. This paper presents a lightweight network, IPCRNet, useful in a resource-constrained environment such as low/medium level smartphones. The proposed network is based on Dense connection, Multi-Dilation, and Depth-wise separable convolution layers. Additionally, we congregated one of the most diversified Indian paper currency image dataset with more than 50,000 images belonging to almost all denominations in circulation. A customized and publically available android application, “Roshni-Currency recognizer”, has also been introduced. The experimental results on multiple datasets demonstrate the superiority of the proposed model. IPCRNet improves the classification accuracy by more than 2% on the proposed dataset compared to the state-of-the-art networks.

In this era, research and development are becoming a continuous and accelerating process because technology changes rapidly with a short lifecycle. As a result, various methodologies are being developed to monitor these rapidly changing research trends; In particular, clustering method-related studies in science and technology documents are being developed with a variety of approaches. However, previous studies on document clustering methods focus on a specific field or language but do not take into consideration certain important pieces of information in science and technology documents. Therefore, this study proposes an embedding methodology that uses important content from scientific and technical documents. We took into consideration the importance of information containing core structures in science and technology documents and proposed a clustering methodology that analyzes structured and unstructured data, such as textual information, author information, and citation information. The proposed method combines both textual and structural data from the paper, using a method that focuses on screening important information by sections in science and technology documents. Then, Girvan-Newman clustering and Louvain clustering models are applied to generate embedding vectors and show evaluation results through the clustering indices. As a practical example, we applied the proposed methodology using paper data from the field of hydrogen cell vehicles. The results of this study will be effective in identifying gaps in technology for new technological development, identifying technology trends, and presenting directional information for future technology development.

The intent of this work is to evaluate the influence of cellulose insulation paper on gassing tendency of transformer oil under electrical discharge conditions. For comprehensive enumeration, insulation oil was thermally stressed at 115°C as per ASTM D 1934 with a controlled thermal aging history. The thermally aged oils were considered for gassing tendency evaluation as per ASTM D 6180 (5 hours, 10 kV AC) in the presence of cellulose. Different cellulose Kraft paper proportions including 0, 10, 20, and 30% of aged oil were introduced in to discharge cell. Different cellulose proportions and aging conditions were considered to understand the impact of cellulose while understanding the decomposition aspects of insulation. Diagnostic measurements with those including dissolved gas analysis, moisture, interfacial tension, turbidity, dissolved decay contents, acidity, and dissipation factor were performed before and after gassing tendency evaluation. It was found that, cellulose paper aids significantly in increasing dissolved gases and moisture in oil during electrical discharge. Importantly, diagnostic measurements have shown the ability of paper to absorb certain decomposition products during the discharge process. [ABSTRACT FROM AUTHOR]

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

Oil-paper insulation will inevitably endure impulse voltage with a superimposed dc voltage (superimposed voltage) because of introduced lightning or switching, which can induce charge accumulation and trigger flashover during the operation of a converter transformer. Temperature rise is also a nonnegligible factor that can significantly influence the performances of oil-paper insulation. This paper aims to reveal the effects of ambient temperature and superimposed voltage on surface charge and flashover behaviors in an oil-paper insulation system. Surface potential decay (SPD) experiments were performed under superimposed voltage with various polarity and amplitude combinations and at the temperature ranging from 20 °C to 80 °C. The results show that the initial surface potential and decay rate increase as the amplitude of the impulse voltage increases at 20 °C. The initial surface potential declines, and the process of SPD is accelerated at higher temperatures because of the increased concentration of shallow traps. Subsequently, flashover tests were conducted under superimposed voltage. It is found that flashover occurs after applying superimposed voltages even with a rather low dc voltage. In addition, the dc voltage component is primarily responsible for flashover. Weibull results also indicate that it is prone to trigger flashover at higher temperatures, because thermal excitation helps more carriers to escape from deep traps and participate in the process of flashover. Accordingly, the insulation characteristics of oil-paper system can be significantly influenced by superimposed voltage and temperature, which should draw great concern among the power grid.

With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations.

The total/partial oil change treatment can affect the furfural partitioning in oil-paper-pressboard insulation, resulting in poor accuracy of furfural analysis. Moreover, due to the high cost of total oil change, partial oil change is more practical during transformer operation. This paper investigates the furfural partitioning in the oil-paper-pressboard insulation under partial oil change conditions. First, the initial furfural partitioning equation with non-oil change is proposed based on the obtained furfural content in oil, paper, and pressboard. Then, the modified furfural partitioning model under partial oil change condition is established by introducing a correction factor. Moreover, a preliminary correction scheme for the furfural partitioning model considering the influence of moisture and acid is provided. Finally, furfural content in oil-Kraft paper and oil-thermally upgraded Kraft (TUK) paper insulation are discussed, and results indicate that the proposed modified furfural partitioning model is not suitable for the oil-TUK paper insulation. This work can provide theoretical support for furfural analysis of oil-immersed transformers. [ABSTRACT FROM AUTHOR]

The 2020 and 2021 Special Sections on Invited Papers on Emerging Topics in the Power and Energy Society (PES) proved to be of broad interest to diverse readers in the PES community, and the publication of this new section marks the third year in a row for the special invited section. After a rigorous peer-review process, a total of 13 articles were accepted for publication in the 2022 invited section of the IEEE Open Access Journal of Power and Energy (OAJPE).

Methanol is regarded as an available indicator to analyze the aging condition of cellulose insulation. However, the diffusion mechanism of methanol molecules in the paper-oil insulation system is still unclear, since it cannot be explored by using only experiments. Given this issue, the molecular dynamics simulations were performed to investigate the diffusion mechanism of methanol molecules. In the current work, the mean square displacement of methanol during the diffusion process was calculated and analyzed, as well as its trajectory tracking. Then, the research on interaction energies and hydrogen bonds in paper/oil system were conducted. Finally, the effect of temperature on the diffusion of methanol molecules was also studied. Findings revealed that the diffusion of methanol in cellulose insulation is faster than that in insulating oil, besides, the presence of hydrogen bonds affects the diffusion rate of methanol molecules in cellulose insulation and insulating oil. Thus, the present findings could support the investigation of the diffusion mechanisms of methanol molecules, by which the aging condition evaluation of cellulose insulation could be also promoted.

The interest in developing and testing dielectric nanofluids for electrotechnical purposes has increased significantly in the last ten years. A number of works have been published reporting dielectric and thermal properties for these liquids that enhance those of conventional insulating fluids. Some authors propose that the application of dielectric nanofluids to transformer insulation could improve the reliability of transformers and open new possibilities for transformer design. However, this will not be feasible until thorough studies on the interaction of these materials with other elements of the transformer, specially the solid insulation, are carried out. In this work, the effect of the presence of nanoparticles on the dielectric response of oil and oil-paper insulation is analysed in laboratory conditions. Specimens have been prepared to emulate the insulation system of transformers and the dielectric response in the frequency domain of these specimens has been characterized. The dependence of nanoparticle concentration on the dielectric response has been analysed. Proposing physical mechanisms that explain the experimental results observed over the oil and the paper samples. Some conclusions are extracted in this work that may be of interest if nanofluids are considered as liquid insulation for transformers.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Flexible In2O3 nanowire transistors gated by microporous SiO2-based solid electrolytes are fabricated on paper substrates at room temperature. Low-voltage (1.0 V) operation of these devices is realized owing to the large electric-double-layer capacitance of (1.73 μF/cm2 at 20 Hz) of the microporous SiO2 solid electrolytes, which were deposited at room temperature. The subthreshold swing, current on/off ratio, and field-effect mobility of the paper-based nanowire transistors are estimated to be 74 mV/decade, 1.7×106, and 218.3 cm2/V·s, respectively. These low-voltage paper-based nanowire transistors show promise for use in portable flexible paper electronics and low-cost portable sensors.

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

A Midwestern pulp and paper mill had arc-flash ratings around 100 cal/cm2 on 50 480-V unit substations. New relays were installed to replace the existing protection and add light detection to improve arc-flash detection times. During a couple of one-day outages, sample installations took place. During the cold mill outage, 44 installations were completed over 12 days by working around the clock. Old relays were removed, new plates were mounted and wired in, and fiber sensors were routed through the buswork and into the breaker cubicles. Updated arc-flash studies show an average arc-flash rating reduction of 90%. [ABSTRACT FROM AUTHOR]

Electrical power quality is a vital aspect when designing or assessing the operation of all modern power systems and forms an important part of the ongoing energy transition to more efficient and multi-vector systems. However, the ongoing proliferation and changing functionality of power electronic devices, coupled with new grid operating paradigms, such as renewable energy sources integration, microgrids, low-voltage dc distribution networks, and the large-scale integration of electric vehicles, present unique opportunities and challenges for grid operators the world over and require new assessment methods and fresh perspectives on the role of power quality.

Accurate modeling and simulation of electric field transients in HVDC cables is an important support to optimize insulation system design and to evaluate the influence of voltage transients and steady-state conditions on accelerated ageing mechanisms and insulation reliability. Traditionally, field models considering time-independent permittivity and conductivity are used, but this approach neglects polarization mechanisms and charge trapping-detrapping phenomena. This article includes polarization dynamics in the field model and shows that its impact on transient electric field simulations in HVDC paper-insulated cables can be significant. A method is presented to infer the model parameters from experimental polarization and depolarization current measurements.

The heat dissipation factor of conventional epoxy impregnated paper bushings is a subject of concern due to the large quantities of power in a High Voltage Direct Current (HVDC) system. The present work deals with the selection of better insulation as a replacement to the conventional resin impregnated material employing nano-hexagonal-Boron Nitride and nano-hexagonal-Boron Nitride added with nano-cellulose-fiber. The bushing of the converter transformer is designed using the Finite Element Method (FEM), and the electrothermal analysis is performed at the loaded working condition. Besides, numerous optimization schemes are also presented for adapting the structure of the thermal conductor enclosed in the inner conductor. The electrothermal performances of the above materials with the optimized structure are compared and an advanced scheme is proposed. Further, the results obtained from the designed system are employed in the form of an Artificial Neural Network to simplify the process of thermal computation characterized by the selected scheme. The internal parameters of the neural network are tuned implementing a hybrid amalgamation of Particle Swarm optimization - Grey Wolf Optimiser and the performance is compared against the actual values. The supremacy of the implemented algorithm is justified by a comparative analysis with other well-established algorithms using various statistical parameters.

Tight assembly of stator windings with no insulation paper damage is a manufacturing challenge. We evaluate different sets of parts according to the following parameters: magnet wire thickness, stator slot smoothness, length of the straight magnet wire after the slot end, and type and amount of insulation cap at the end of the slot. These parameters have discrete values with small differences between them. The damage criterion is the decrease of the insulation paper breakdown voltage after assembly/disassembly of parts, assembled in a small set of designed experiments. Parameter values, i.e., levels at individual experiments are set by an orthogonal experiment matrix. Repetition of each experiment provides statistical significance. Data analysis shows that the additive model alone is not sufficient due to the high correlation of the parameters' influences. We extend the model to include interparameter influences, which we model by adding a virtual parameter. The extended additive model generates parameter values that do not degrade the insulation paper breakdown voltage within the manufacturing process. These values are verified by repetitions of the control experiment.

This paper presents the design and analysis of a paper-based chipless RFID sensor for wetness content measurement in plant leaves. The sensor is designed using a passive microwave resonator to measure the ambient physical parameter. The resonator is designed with a rectangular loop inductively coupled with an interdigital capacitor. The experimental analysis of the designed sensor is illustrated here to prove its efficacy. The resonator provides adequate backscattered signal for wireless measurement in the microwave frequency band. When interrogated with a couple of UWB antennas and a VNA, it yields accurate leaf wetness response of the plant. The commercial competence of this sensing device is validated through its experimental analysis on three different types of leaf samples. This sensor is suitable for plant health monitoring in any small-scale gardening applications such as nurseries and greenhouses.

We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers selected from all papers published between October 1, 2019, and September 30, 2022, in OAJPE and its preceding journal, IEEE Power and Energy Technology Systems Journal (PETS-J).

Novel designs of paper-based bandstop frequency-selective surfaces (FSSs) are presented in this article. The characteristics of the paper-based honeycomb structure are investigated first. One folded dipole resonator with first-order bandstop response is analyzed and further printed on the sidewalls of the honeycomb structure accordingly. A second-order response with two cascade FSSs is proposed with controllable bandwidth by the coupling coefficient of the resonators with the aid of the equivalent circuit model. Two paper-based FSS samples were fabricated and measured to demonstrate the validity of the proposed design strategies. Benefiting from the merits of lightweight and strong mechanical properties, the proposed paper-based FSS is a potential candidate for application in radar systems and aircraft. [ABSTRACT FROM AUTHOR]

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design, and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing different natural advantages of complementary quantum systems and for engineering new functionalities. This review article focuses on the current frontiers with respect to utilizing magnons for novel quantum functionalities. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant-rich variety of physics and material selection enable exploration of novel quantum phenomena in materials science and engineering. In addition, the ease of generating strong coupling with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we focus on the recent progress of magnon–magnon coupling within confined magnetic systems. Next, we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.

This study conducts thermal aging treatment to study the effect of thermal aging on the PD degradation process of the oil-paper insulation. The partial discharge inception voltages, extinction voltages, carbon mark, and microscopic morphology of samples are analyzed. The results show that thermal aging does not reduce the partial discharge inception voltages of oil-paper but can make the spark produced in the oil-paper more difficult to extinguish. At the initial stage, thermal aging has little effect on the development of partial discharge in normal direction of samples, while in later stages, the effect begins to appear; thermal aging reduces the insulation performance between layers of oil impregnated paper and accelerates the carbon trace growth rate in the horizontal direction. In this model, the first layer is most severely bombarded by electrons, and this layer is most obviously affected by the electric field in the normal direction at the contact point between the electrode and the insulating paper. The lower layer is affected by the horizontal and vertical electric fields; therefore, a large area of fiber looseness occurs on the surface of the oil impregnated paper. The degree of deterioration is proportional to the endurance time and aging time, and the damage degree of oil impregnated paper decreases with the increase of the number of layers. [ABSTRACT FROM AUTHOR]

In this article, we put forward the substantial challenges in cyber resilience in the domain of autonomous systems and outline foundational solutions to address these challenges. These solutions fall into two broad themes: resilience-by-design and resilience-by-reaction. We use several application drivers from autonomous systems to motivate the challenges in cyber resilience and to demonstrate the benefit of the solutions. We focus on some autonomous systems in the near horizon (autonomous ground and aerial vehicles) and also a little more distant (autonomous rescue and relief). For resilience-by-design, we focus on design methods in software that are needed for our cyber systems to be resilient. In contrast, for resilience-by-reaction, we discuss how to make systems resilient by responding, reconfiguring, or recovering at runtime when failures happen. We also discuss the notion of adaptive execution to improve resilience, execution transparently and adaptively among available execution platforms (mobile/embedded, edge, and cloud). For each of the two themes, we survey the current state, and the desired state and ways to get there. We conclude the paper by looking at the research challenges we will have to solve in the short and the mid-term to make the vision of resilient autonomous systems a reality. This article came out of discussions that started at the NSF-sponsored Grand Challenges in Resilience Workshop held at Purdue in 2019 with the co-authors contributing to going into the depth of the issues and then this article.

This Special Issue is assembled from a selection of highly-rated technical papers presented at the 2020 IEEE Electron Devices Technology and Manufacturing Conference that took place April 6-21, 2020.

This paper describes the design, fabrication and characterization of a three phase floating electrodes electroportation system for dust confinement, compatible with industrial optical quality control systems. The proposed Floating Electrode Electronic Curtain (FE-EC) is composed of floating parallel wire electrodes framed by a two-layer PCB. The floating electrodes are connected to one of the synchronized three channels to generate an electric field traveling wave. The separation between the floating wire electrodes has been determined by minimizing the number of trajectories crossing the FE-EC using a numerical simulation tool. It is shown that the effect of the FE-EC in the luminosity of an industrial led luminaire can be compensated by an 1% increment in the led supply voltage. The performance of the fabricated FE-EC prototype is experimentally quantified, showing efficiencies higher than 95% in simulated relevant conditions.

Software fault prediction (SFP) is a research area that helps development and testing process deliver software of good quality. Software metrics are of various types and are used in SFP for measurements. Inheritance is a prominent feature, which measures the depth, breadth, and complexity of object-oriented software. A few studies exclusively addressed the efficacy of inheritance in SFP. This provokes the need to identify the potential ingredients associated with inheritance, which can be helpful in SFP. In this paper, our aim is to collecting, organizing, categorizing, and investigating published fault prediction studies. Findings include identification of 54 inheritance metrics, 78 public datasets with various combinations of 10 inheritance metrics, 60% use of method level & use of private datasets, an increased number of studies using machine learning approaches. This study will facilitate scholars to studying previous literature on software fault prediction having software metrics, with their methods, public data sets, performance evaluation of machine learning algorithms, and findings of experimental results in a comfortable, and efficient way, emphasizing the inherited aspect specifically.

We report our recent progress in quantitative aging assessment of the oil-impregnated-paper (OIP) equipment (i.e., degree of polymerization, DP) by near infrared spectroscopy (NIRS). The NIRS database is built by incorporating 8 types of insulating paper and total 478 differently aged samples. We propose an improved PCA-RBF-NN model to address the nonlinear correlation between DP of insulating paper and spectra, and hence strengthening the prediction accuracy for field assessment. In the improved model, the principle component analysis (PCA) and the filtering layer are two essential procedures for eliminating the noises and unrelated information from the spectra. The field practices show that the improved PCA-RBF-NN model owns better performance than the classic PLS model and general RBF-NN model on the disassembled bushing (RMSE: 56 vs 109 vs 124) and transformer (RMSE: 50 vs 237 vs 244), respectively. The NIRS powered by the improved algorithm can provide a rapid solution to the aging condition assessment of the OIP power equipment in the field. [ABSTRACT FROM AUTHOR]

The performance of oil-impregnated paper significantly affects the safety and reliability of transformers. Previous studies on water reducing the breakdown strength of oil-impregnated paper have been mainly due to increasing charge carriers and conductivity. However, the influence of water on the cellulose crystal structure of oil-impregnated paper is still not clear; in particular, its role on breakdown strength remains ambiguous. In this study, the AC breakdown strength and crystal structure of vegetable and mineral oil-impregnated paper with different moisture contents were investigated. The results show that with increasing moisture content, the cellulose crystallinity and crystal size increases linearly, while the breakdown strength decreases. The breakdown strength of vegetable oil-impregnated paper is higher than that of mineral oil-impregnated paper and is less affected by moisture. Herein, a model is proposed on the effects of water on the crystal structure of oil-impregnated paper and the correlation of breakdown strength with crystal structure is reported. [ABSTRACT FROM AUTHOR]

Origami structures held promise for their flexibility, simultaneous responsiveness, and adaptability, yet they are vulnerable due to the hygroscopic property when exposed to the humid environments. However, the patterning of hydrophobic material along the design enhances the controllability of paper for self-folding to robotics locomotion. The capillary action of sprayed water molecules is localized through the number or sizes of hydrophilic channels defined by the printed wax patterns resulting in large deformation to form various 3D geometries. The patterned paper performed folding actuation with water and unfolding behavior with evaporation without being mechanically manipulated by external forces or moments. The paper actuators further enable the sequential self-folding along the multiple hinges for the cube and diamond structures. Advances in soft robotics for practical applications, the utilization of different swelling/shrinking properties with water and kirigami-inspired programmable structures interact with the lightweight and non-planar objects. The anisotropic properties of paper in response to water spraying combined with folding/unfolding deformations permit the fold-to-pop-up robots for biomimetic locomotion on the plane or water. Two robots deform their body to move forward through bending-relaxation gait. [2020-0083] [ABSTRACT FROM AUTHOR]

In this paper, a method of aging diagnosis under different oil-paper ratios is proposed by investigating the accelerated thermal aging data with different oil-paper ratios. Aged oil-paper insulation samples with oil-paper ratios of 10:1, 15:1 and 20:1 are obtained by accelerated thermal aging tests. The Raman spectra of the transformer oil samples are measured by a Raman spectrometer, and screened by random forest algorithm to extract the features. A modified model is established to correcting the features of the aged oil samples under the conditions of 15:1 and 20:1 oil-paper ratio to features under the condition of 10:1 oil-paper ratio. An grid search optimized support vector machine (SVM) classification model is established, and verified by the corrected features. The experimental results show that the proposed method can effectively identify the aging of oil-paper insulation with different oil-paper ratios. [ABSTRACT FROM AUTHOR]

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.