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This paper proposes a method of distinguishing the contributions of ionized and injected charges to the charge transport in oil immersed paper in triple-layer and double-layer samples. With the double-layer sample, the apparent mobility including the ions contribution is obtained by the calculation according to the speed of accumulated charge density at the interface. The contribution of ions is excluded by subtracting the accumulated charge density in the triple-layer sample from that in the double-layer sample. It is found that the interface charge mainly comes from the ionization charges under a low field, and the contribution of injected charge takes a more important part at higher electric field.

Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures.

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a “fingerprint” of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch.

Finding relevant papers is a non-trivial problem for scholars due to the tremendous amount of academic information in the era of scholarly big data. Scientific paper recommendation systems have been developed to solve such problem by recommending relevant papers to scholars. However, previous paper recommendations calculate paper similarity based on hand-engineered features which are inflexible. To address this problem, we develop a scientific paper recommendation system, namely VOPRec, by vector representation learning of paper in citation networks. VOPRec takes advantages of recent research in both text and network representation learning for unsupervised feature design. In VOPRec, the text information is represented with word embedding to find papers of similar research interest. Then, the structural identity is converted into vectors to find papers of similar network topology. After bridging text information and structural identity with the citation network, vector representation of paper can be learned with network embedding. Finally, top- $Q$ Q recommendation list is generated based on the similarity calculated with paper vectors. Through the APS data set, we show that VOPRec outperforms state-of-the-art paper recommendation baselines measured by precision, recall, F1, and NDCG.

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 conversion of biowaste to activated carbon (AC) for supercapacitor applications is recently gaining attention due to its extraordinary high specific surface area (SSA), hierarchical pore size distribution, and very good conductivity. Therefore, in this work, we have utilized waste office papers as a precursor to synthesize highly porous carbon to be used as an electrode material for supercapacitors. The sub-nanometre pores in as-synthesized office paper waste-derived activated carbon (OPDAC) material, facilitate easy flow of ions and surprisingly increase the specific capacitance up to 237 F/g at a current density of 1 A/g. Moreover, an all-solid-state symmetric supercapacitor using OPDAC electrodes delivers an ultrahigh energy density of 31 Wh/kg at a power density of 380 W/kg along with a long cycle life of 95% after 3000 charge-discharge cycles. Hence, these remarkable results pave the path for the usage of different biowastes for varying energy storage applications.

Reseachers have investigated numerous factors influencing citation counts of cited papers. One factor investigated has been the number of gained citations, as this could increase the visibility of cited papers and subsequently induce further citations. In this paper, aiming to identify a particular kind of citation that could trigger a rapid growth in the citation counts of cited papers, a concept of "citation promoter" was proposed. We defined citation promoters based on the annual citation rates of the cited papers and the co-citation counts received by the pair of cited and citing papers. The comparative results showed that papers would obtain a sharp rise in citation counts shortly after they were cited by citation promoters. Papers that received citation promoters at an early age outperformed other papers in long-term citation counts. In addition, we developed a classification model for predicting whether a citing paper would be a citation promoter for its cited paper. Since it was a class imbalanced problem (4% positive instances), and there was a lack of content and author features in our dataset, our preliminary models achieved moderate performance with an $F_1$ score slightly higher than 0.5, while the $F_1$ score obtained by random guessing was 0.07. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its Science of Research, Innovation and Enterprise programme (SRIE Award No. NRF2014-NRF-SRIE001-019).

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.

In this work, the extended Debye model or Fourier transform is adopted to convert the polarization and depolarization current test results from the time domain into the frequency domain at low frequencies, thereby shortening the test time but producing large errors when the oil-paper insulation exhibits nonlinear characteristics. The voltage-current response characteristics of samples with three different moisture contents are tested to study the influence of moisture on the nonlinear characteristics. Hammerstein-Wiener (H-W) equivalent mathematical model is proposed for time-frequency domain conversion, and the results from this model and two formal methods are compared. The findings show that the errors of the two conventional methods increase with nonlinearity. The H-W model accurately depicts the nonlinear dielectric response of oil-paper insulation and thus can provide high-precision time-frequency domain conversion data.

This paper presents a wavelet texture analysis of microscopy images acquired from artificially-aged samples of power transformer winding insulation paper. Oil-impregnated Kraft paper samples are prepared for experimental tests where they are thermally stressed in an oven at temperatures above what would normally occur in operation. This accelerated ageing test arrangement is used to produce a set of paper samples with varying levels of insulation deterioration. From the paper samples, microscopy images of the paper surface are obtained using a standard optical microscope. These microscopic images are analyzed using a texture analysis method that utilizes a two-dimensional wavelet transform to extract detailed information from the wavelet decomposition coefficient matrices. These features are analyzed to assess how the texture of paper changes in response to thermal deterioration and ageing. The results suggest that the texture features are correlated to the commonly-used degree of polymerization test. Additionally, statistical classification is performed on the wavelet texture features using both supervised and unsupervised machine learning methods. The results demonstrate that differentiation between oil-impregnated paper samples with different levels of thermal degradation using wavelet texture analysis is effective.

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.

Diagnosis of the aging stage of oil-paper insulation is an important technical means for preventing aging failure of oil-paper insulation equipment. This article proposes a Raman spectroscopy diagnostic model to assess the aging status of transformer oilpaper insulation by analyzing the Raman spectra of the insulating oil. Oil-paper samples in different aging stages were obtained from electrical and thermal accelerated aging tests in the lab. The Raman spectra of insulating oil were collected in the range from 400 to 3000 cm−1, which covers the Raman signals of the aging products of oilpaper insulation. Spectral peak contour features were extracted based on Voigt fitting of the Raman spectra and studied using the grid search optimized one-against-one multi-class support vector machine (OAO MCSVM) for diagnosis of the aging stage. The results show that the diagnostic accuracy of the diagnostic model based on the spectral peak contour features was 92% (46/50). Spectral peak contour features demonstrate an ability to reflect the aging process, and the proposed diagnostic method shows potential for monitoring oil-paper insulation.

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.

This report demonstrates the fabrication of SnS/paper based low-cost and flexible pressure sensor, employing a facile solvothermal synthesis followed by vacuum filtration, for artificial electronic skin and touchpad applications. The fabricated pressure sensor displayed a high sensitivity of 3.18 kPa $^{- {\sf 1}}$ and a response time $\sim 300$ msec. Inspired by the high value of sensitivity and an appreciable flexibility, a large-area pressure sensor array ( ${\sf 3} \times {\sf 3}$ ) was fabricated and integrated onto a human arm for demonstrating real-time applications. The fabricated sensor array was able to consistently detect and track random localized points of contact, the shape of an unknown object placed on top of it, and an index finger trajectory along the surface with high accuracy and precision. The fabricated device showed a negligible change in the performance over 500 bending cycles which indicates the highly robust nature of the device. Successful fabrication of such a cost-effective large-area pressure sensor array is a major step in flexible electronics, and holds immense potential in artificial electronic skin, security, personal healthcare applications.

Over the last decade, the rapid development in health care industry and security has driven innovations in the field of wearable electronics and smart sensors. This report demonstrates the fabrication of molybdenum diselenide (MoSe2) by a facile, low-cost and scalable hydrothermal synthesis method followed by a vacuum filter deposition on cellulose paper. The fabricated device is utilized as a pressure and strain sensor. The fabricated device is passivated using polydimethylsiloxane (PDMS), and also, PDMS film enhances the flexibility of the device. The calculated sensitivity of pressure sensor is ~0.3 kPa−1, which is comparable and relatively better than the pressure sensor fabricated using sophisticated techniques. Furthermore, the device exhibited significant of merit for strain sensing with a gauge factor of ~6.84. The fabricated device showed excellent resilience in 500 cycle bending tests, which promises the robustness of the device. As an application, the fabricated sensor is used to develop a wireless keyboard by integrating the sensor on fingertips of an individual and a real-time human motion monitoring application in which the data are transmitted wirelessly to a smartphone with a dedicated android application. The successful fabrication of MoSe2/paper-based cost-effective, flexible, and biodegradable pressure-strain sensor shows tremendous applications in future intelligent user interface systems, human security, and personal health care monitoring.

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.

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 eight papers in this special section were presented at the IEEE International Solid-State Circuits Conference (ISSCC)that was held in San Francisco, California, from February 16 to February 20, 2020.

Slow or non-healing wounds in chronic diabetic foot diseases are often associated with co-morbid circumstances, inevitably leading to lower limb amputations. The wound bed in chronic foot ulcers are prone to bacterial infections, with Staphylococcus aureus ( S. aureus ) acting as the major culprit in resisting the normal wound healing process. This study reports the development of a flexible screen-printed wound dressing based on a composite of Zeolitic Imidazolate Framework (ZIF 67) and carbon nitride (C3 N4) conjugated with S. aureus probe DNA for the detection of its corresponding target DNA. The nano-wound dressing was assessed using Electrochemical Impedance Spectroscopy (EIS) for the quantification of S. aureus target DNA within 1 fM to $10~\mu \text{M}$ . A direct proportionality between the charge transfer resistance ( $\text{R}_{\text {ct}}$ ) and S. aureus target DNA concentration. Circuit simulations detailing the quantifications of interfacial parameters, involved in the recognition event, were performed by curve fitting of Nyquist spectra in order to explain the variation of $\text{R}_{\text {ct}}$ with target DNA concentrations. The Limit of Detection (LoD), sensitivity and response time of the developed wound dressing-based sensor were found to be 0.46 fM, 0.25 $\text{k}\Omega $ /fM/mm2 and ~ 10 s respectively at 21.04 kHz, while being selective only towards S. aureus target DNA. The sensor was further validated in human serum samples and was shown to possess a shelf life of 35 days, thereby exhibiting tremendous potential to be implemented in connected health applications.

Conductivity is an important parameter which has definite correlation with the amount of moisture present within the transformer insulation. However, in existing methods conductivity is usually estimated from time domain (PDC) measurement, which is inadvertently noise prone and time consuming. Considering this issue, in this article presents a novel technique for conductivity estimation for moisture sensing of oil-paper insulation using frequency domain spectroscopy measurement (FDS). To this end, FDS measurement of oil-paper insulation was performed from 50mHz to 1kHz to obtain the imaginary complex permittivity $\varepsilon ''$ ( $\omega$ ), which is a function of conductivity ( $\sigma$ ) as well as imaginary complex susceptibility $\chi ''$ ( $\omega$ ). From the knowledge of frequency variation of both $\sigma $ and $\chi ''$ ( $\omega$ ), we developed an electrical equivalent model of the insulation. The equivalent model parameters were optimized until $\varepsilon ''$ ( $\omega$ ) computed from the proposed model and that obtained from original FDS measurement were almost identical. The conductivity values obtained from the equivalent model showed good agreement when compared with PDC measurement. To validate the practicability of the proposed method, experiments were conducted on various test samples and also on a few real life transformers. Most importantly, the measurement time was found to be reduced by 78.5%, enabling diagnosis of transformer insulation at a significantly reduced time.

This study investigates the effect of moisture content on space charge characteristics based on the dynamic space charge response and conductivity characteristics of oilpaper insulation under a DC electric field. Moisture contents ranging from 0.34–2.89% are considered under an electric field of up to 20 kV/mm. The results show that homocharge accumulates inside the oil-paper insulator near both electrodes, with the amount of charge gradually increasing as the moisture content rises. It may be related to the decreased charge injection barrier at the electrode-sample interface as the moisture level increases. The conductivity increases with the rise in moisture level. However, as the conductivity increases, the corresponding space charge accumulation during polarization does not decrease, but increases significantly. This indicates that space charge accumulation may be related to both charge injection and conductivity. The electric field distortion inside the oil-paper insulator increases as the moisture content rises, with the maximum electric field distortion rate going from 16.9 to 92.9% at 5 kV/mm and from 29.7 to 62.5% at 20 kV/mm.

This paper presents the design, fabrication, and characterization results of a paper-based, low-cost, easy-to-use, comfortable and wearable tactile sensor array for human health monitoring applications. Paper substrate and spray-deposited metallic electrodes and traces are employed to achieve a low fabrication cost. Capacitive sensing scheme employing a deformable triangular PDMS sensing membrane is chosen due to its high sensitivity and zero DC power dissipation. Trade-offs among different sensor array designs are investigated to achieve an optimal design, which consists of a $1\times 5$ sensor array occupying an area of 10 mm $\times10$ mm. Each sensor in the array achieves a nominal capacitance value and sensitivity of approximately 1 pF and 30 fF/mmHg, respectively. Fabricated sensor array can be comfortably attached to an individual’s temporal region and ankle area to monitor real-time blood pressure (BP) pulse waveform with high fidelity, from which heart rate and heart rate variability information can be accurately obtained. In addition, the prototype sensor array demonstrates its capability of detecting atrial fibrillation (AFib) from BP pulse waveform measured from an AFib patient. [2020-0179]