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Trap plays an important role in the charge transportation of three-layer oil-paper and the investigation of trap parameters at different degradation degree is of great significance for further research of the partial discharge (PD) mechanism. This article considers the turn-to-turn structure of on-board traction transformer. A defect model of three-layer oil-paper insulation was designed for PD test. The PD degradation samples are selected from the inception of PD to the breakdown of PD to study the characteristics of surface potential decay on degradation samples by using isothermal surface potential decay method. Given that, the trap distribution at different degradation stages was calculated and its influence on PD characteristics was investigated. The result shows that AC fluctuation voltages caused by inter-harmonic has a great influence on PD distribution, surface charge distribution and trap distribution. With the increase of degradation, the decay rate of surface potential increases and the decay rate of AC fluctuation voltages is larger than that pure AC. And also, the number of shallow trap with AC fluctuation voltages is larger than that with pure AC. The rate of free charge increases with the number of shallow trap increasing. More free electrons involves in the process of PD, accelerating the aging rate of three-layer oil paper samples. Therefore, it can be concluded that surface charge and trap distribution are helpful to clarify PD mechanism.

An electrical tree discharge model of multilayer insulation paper was prepared, and a voltage of 20 kV was used for electrical tree cultivation and initiation tests. Based on the microscopic image information and partial discharge (PD) information during the aging of the electrical tree, the development of electrical tree discharges between layers of paper comprising epoxy resin-impregnated paper insulation was studied. The results show that, by analysing PD information such as the discharge volume phase spectrum and the discharge repetition rate phase spectrum, the development of the electrical tree branch progresses in four stages: initial, growth, stagnation, and bursting. During the growth and stagnation stages, the main discharge paths of the electrical tree are mainly formed, and there are many filamentous fibres on the path edge. During the burst stage, more branches will be formed on the electrical tree, which will cause the greatest damage to the insulation. With the extension of the electrical tree path, the discharge magnitude and discharge times also increase. There are specific discharge patterns in different growth stages of the electrical tree, and the number of discharge events in each positive half-cycle is significantly greater than that in a negative half-cycle. Combining the microscopic image information and PD information during the aging of such an electrical tree, a theoretical basis for evaluating insulation state by PD monitoring is realised. The long-chain molecules of cellulose and epoxy resin break bonds and oxidise, which decompose into a large number of free radicals, monosaccharides, and other low-molecular-weight products.

The low frequency section of frequency domain spectroscopy (FDS) of oil-paper insu- lation system can effectively reflect the ageing state and moisture of the oil-immersed paper. Meanwhile, steady-state insulation resistance is the one of indexes for the condition of transformer. In actual measurement, these two both are time-consuming and poor accessibility, thereby restraining their field applications. Aim at this problem, this paper analyses the polarization process of the oil-paper insulation in transformer, and adopts a low frequency equivalent circuit model for the oil-paper insulation to characterize the polarization process at low frequency. The simulated annealing particle swarm optimization (SAPSO) algorithm is proposed to identify the parameters in equivalent circuit, which are affected by change of insulation state. Compared with the experimental results, the errors of the steady-state insulation resistance and the tan δ below 10-3 Hz, which are calculated by the identified parameters, are less than 2.5%. This method can avoid the influence from external environment and reduce the measurement time. The time and frequency domain parameters calculated can provide reference for the ageing assessment of oil-immersed paper.

Prediction and detection of skin diseases have always been a challenging and critical task for health care professionals. There are many clinics and skin practitioners who are offering their services at exorbitant costs. At the same time, skin condition prevails in our country in every part. In the current scenario majority of the skincare practitioners are using traditional methods to diagnose the disease which may take a considerable amount of time. Skin Diseases are serious problems in recent times as it is a matter of environmental factors, socioeconomic factors, lack of full diet, etc. This paper is a comparative study about understanding various skin diseases related to normal skin issues as well as cosmetology. A comparison between oily, dry, and normal skin is also projected in this study. A survey of different papers is done on basis of technologies used, results with accuracy, ethical conduct, number of diseases diagnosed, datasets, etc are done.

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.

This paper describes approaches to the use of wearables and related monitoring and diagnostic tools to glean and present data to patients and their caregivers in the form of Personal Health Dashboard. Raw data from devices such as fitness watches, blood glucose and blood pressure monitors can be analyzed by machine learning algorithms ensembles over the cloud or at the edge, and the resulting patterns can be presented to allow for actionable responses. The advent of low-cost computational, sensing, monitoring and diagnostic tools allow for cost-effective solutions that can have wide adaptability. The paper draws from prior and ongoing research conducted by the team to present these positions in the advancement of healthcare.

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.

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%.

When the inner insulation of oil-impregnated paper bushing breaks down in service, gases decomposed by oil and paper may cause an explosion. The explosion of bushing occurs from time to time because of the lack of an effective monitoring method. In order to provide an effective online monitoring method for the bushing to avoid explosion, this paper presents a method to monitor the PD defect in bushing by measuring the inner pressure of bushing. In this paper, the relationship of variation between characteristic gases content and inner pressure is studied, the effect of inner temperature on inner pressure is analyzed and the method for reducing this effect is also presented, and the monitoring method is proved to be useful by PD tests on a real bushing. The results show that the inner pressure increases obviously due to the generation of characteristic gases by internal discharge in oil-impregnated paper bushing, and the pressure increases with the deterioration of the discharge; although the inner pressure has a great influence on the inner pressure, the influence can be reduced or eliminated greatly by the method of common-mode rejection. According to the results, the internal discharge defect of the oil-impregnated paper bushing can be detected by monitoring the pressure, and the bushing explosion can be avoided.

The aging of the transformer oil-paper insulation distributes spatially, which results in changes in paper resistivity in different regions. This paper establishes an iterative inversion algorithm using the finite element method to calculate the oil-immersed paper resistivity in different regions of a transformer. This algorithm sets the transformer dielectric loss factor tan δ at low frequency, the dielectric parameters of the transformer oil as inputs and the oil-immersed paper resistivity as output. The resistivity obtained from inversion can be used as a reference to access the insulation state of the oil-paper insulation. This paper aims at achieving the nondestructive detection of the partial state of the oil-paper insulation.

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.

The objective of this paper is to study the deterioration of impregnated kraft papers immersed in dielectric liquids under the chemical stress conditions by furan analysis according to ASTM D5837 and IEC 61198. Mineral oil and natural ester (FR3) were liquid used in this study. Kraft paper samples, 3 pieces per each case study, are prepared in rectangle shape with 25.4 ± 0.5 millimeters (1.00 ± 0.02 inches) wide and approximately 254 millimeters (10.0 inches) long according to the ASTM D828 standard. The paper and liquid insulation are heated to dehydrate at 80 °C for 12 hours with 200 mbar pressure, and then they are filled in 200 milliliter bottles and heated with 5 different temperatures being 60 °C, 80 °C, 100 °C, 120 °C and 140 °C respectively. In the case of 120 °C and 140 °C are only conducted with FR3 due to its thermal characteristics. After that, 98% concentration of acetic acid of 2 quantities, 0.04 and 0.08 milliliters, and oxygen (O2) at 1 psi for 1,200 ml, are used for the chemical aging process for 9 days of the test samples before being tested to find furanic compounds. According to the test result, it can be concluded that all 4 factors, temperature, acetic volume, oxygen and moisture, effect on generated furanic compounds of aging paper insulation.

In power transformers in which is dominated the oil-paper insulation system, it is the effort to use innovative insulating materials to achieve greater quality, reliability and longer service life of the transformer. In this paper, we present the results of research into the insulating properties of an oil-paper insulation system in a DC and AC electric field. In the experiments MOL TO 40A transformer oil and two types of paper used in transformers, KREMPEL DPP and insulating paper used in BEZ transformers, were used. The results from the experiments show that KREMPEL insulating paper has a higher electrical strength and the electrical strength of both oil-impregnated insulating papers is greater when applying DC voltage.

The assessment of the aging condition is becoming increasingly important for operators of high voltage assets. Power transformers are especially one of the most important units in the electrical power grid. Due to the fact that oil-paper insulation is still the most economical form of insulation, and is therefore found in the majority of transformers, the condition assessment of the paper insulation is decisive for estimating the remaining service life. Since the determination of the condition via furans is associated with certain fluctuations, several other aging parameters are investigated. Therefore, this study intends to determine how those aging markers of the insulating liquid behave in the absence of insulating paper and how the values change with insulating paper; thus, from the difference between important parameters, indicating the paper aging might be possible. For the investigation, a non-inhibited and an inhibited insulating oil as well as a further inhibited oil with a different composition, are tested as the most common insulating liquids. For this purpose, an open system is reproduced, which has permanent contact with the environment air. Accordingly, the study is carried out for long-term thermal stress to obtain samples with a wide spectrum of aging status. The entire study extended over a period of 14–15 weeks at a temperature of 130 °C with performing the measurements of the parameters at various aging times. The study reveals that the results can be used for an improved condition assessment of transformers.

This paper presents a novel, low cost flexible paper substrate suitable for biosensing applications. A microstrip patch antenna consisting of copper tape foil and a cellulose filter paper as a substrate with thickness of 180 μm is fabricated and tested. The substrate has measured dielectric constant (e r ) of 1.9 and losses (tan δ ) of 0.025. For 3.8 GHz resonance, the antenna has size of 29.5 mm x 38 mm on a ground plane of 50 mm x 60 mm. Measurements of the final design are in excellent agreement with simulations. Moreover, measurements of the patch antenna on the proposed substrate (cellulose filter paper) collected at different bending radii and folding angles demonstrate performance consistency.

Methanol in oil can be used to evaluate the aging status of oil-paper insulation system of power transformers. Many scholars have shown that the production of methanol in oil is closely related to the degradation of paper. However, the methanol distributed in oil is affected by the oil-paper adsorption equilibrium. In this paper, the equilibrium distribution of methanol between oil and paper at different temperatures were investigated. The equilibrium experiments were conducted by soaking methanol-oil standard solution with ordinary Kraft paper at 20°C, 45°C and 60°C respectively. The results showed that temperature obviously affects the adsorption capacity of oil and paper for methanol: with the increase of temperature, the equilibrium time of methanol between oil and paper is accelerated. Within a certain temperature range, the adsorption capacity of paper for methanol will decrease with the increase of temperature. However, when the temperature was near the boiling point of methanol, the content of methanol in oil declined, which might be related to the existence of gaseous methanol at high temperature. At this time, the equilibrium distribution model of methanol changed from the two-phase model of oil-paper system to the three-phase model of oil-paper-gas system.

This paper presents a set of modeling procedures which are substantial for developing a data-driven soft sensor for paper strength parameters in production environment. Special focus is put on describing methods for efficient data aggregation, data cleaning and feature selection, which were employed to appropriately model the inherently complex process of papermaking and handle the challenging nature of collected data. The resulting soft sensor for tensile index deployed at a paper machine shows solid performance. Moreover, good quality of calculated real-time predictions is also confirmed by means of experiment-based validation.

Paper-based digital microfluidic biochip (PB-DMFB) technology provides a promising solution to many biochemical applications. However, PB-DMFB manufacturing process may suffer from potential security threats. For example, both Trojan insertion and man-in-the-middle attack may affect the functionality of PB-DMFBs. To ensure the correct functionality of PB-DMFBs, we propose a watermarking scheme to hides information in the PB-DMFB layout, which allows users to check design integrity and authenticate the source of the PB-DMFB design. As a result, it serves as a first countermeasure against both Trojan insertion and man-in-the-middle attacks for PB-DMFBs.

The degree of polymerization is a useful technique to monitor insulation capabilities of oil impregnated paper. In present work, accelerated thermal ageing experiments have been conducted by taking Kraft and thermal upgraded papers along with two types of dielectric oil (mineral oil and vegetable oil) at different temperatures $(90^{\circ}C, 110^{\circ}C$ and $130^{\circ}C)$. An improved 2nd order kinetic model has been developed based on experimental results which utilized to develop a relationship between temperature and final degree of polymerization. The proposed model can be used to analyze and monitor thermal deterioration of oil paper insulation at any temperature. Moreover, the proposed novel mathematical modelling can be employed to predict premature failure as well as end of life criteria of power transformers.

In order to understand the aging process of paper used in high voltage transformers, transformer grade Kraft paper samples were aged in a fan oven in order to access the full range of DP values from 1100 (new) to ~200 (end of life). The absence of significant oxidation was verified by infrared spectroscopy and the mechanical and dielectric properties were assessed as a function of DP. Whilst the dielectric properties (in the absence of water) were unaffected by aging, the tensile strength was reduced. This confirms studies in the literature which show that most transformer breakdowns occur through mechanical failure of the paper and crucially, provides a mechanism of providing paper samples of known DP for subsequent exposure to oil flow.

The use of vegetable oil (natural ester) in electrical devices like power transformers is increasing due to their high biodegradability and better safety. The lifespan of power trans- formers is mainly defined by cellulose insulation condition, which usually works together with dielectric oil as electrical insulation and also as mechanical winding protector and compactor. That is why the aim and results of this research shows us not only the dielectric parameters evolution, but also the relationship between the mechanical factors and the moisture content of thermal accelerated ageing processes, with commercial vegetable oil, of Kraft paper and Diamond Dotted Paper (DDP). These are two of the most common insulating materials in electric power transformers. In addition, the new tests have been done by a different method of paper ageing analysis. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement Nº 823969 - BIOTRAFO.

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.

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.

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.

Machine learning (ML) has increasingly been adopted in safety-critical systems such as autonomous vehicles (AVs) and industrial robotics. In these domains, reliability and safety are important considerations, and hence it is critical to ensure the resilience of ML systems to faults and errors. On the other hand, soft errors are becoming more frequent in commodity computer systems due to the effects of technology scaling and reduced supply voltages. Further, traditional solutions for masking hardware faults such as Triple-Modular Redundancy (TMR) are prohibitively expensive in terms of their energy and performance overheads. Therefore, there is a compelling need to ensure the resilience of ML applications to soft errors on commodity hardware platforms.We first experimentally assess the resilience of safety-critical ML applications to soft errors. We demonstrate through fault injection experiments that even a single bit flip due to a soft error can lead to misclassification in Deep Neural Network (DNN) applications deployed in AVs, leading to safety violations. However, not all the errors in an DNN will result in serve consequences such as safety violations, and hence it is sufficient to protect the DNN from the ones that do. Unfortunately, finding all possible errors that result in safety violations is a very compute intensive task. We propose BinFI, a fault injection approach that efficiently injects critical faults that are highly likely to result in safety violations, based on the unique properties of DNNs. Finally, we propose Ranger, an approach to protect DNNs from critical faults with minimal performance overheads and no accuracy loss. We will conclude by presenting some of our ongoing work, and the future challenges in this area.

The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context. info:eu-repo/semantics/publishedVersion

Even today, oil-paper insulation is one of the most important insulation systems for high-voltage components, which is mainly used in transformers. Due to an increase of regulations and obligations for transformer operators, alternative insulating liquids are becoming more and more important, especially esters, as these liquids are characterized as an environmentally friendly and low flammability substitute. So far there is only little experience on the ageing behaviour of ester-paper insulations and on the formation of ageing products with this mixed dielectric. The aim of this study is to investigate the influence of a synthetic based ester, a rapeseed-based and a soya-based natural ester on the ageing behaviour of a Kraft paper insulation. In this context, the correlation of ageing markers, such as the acid value, the moisture content etc., is measured in the liquid and the current condition of the paper insulation is investigated. It could be observed that the ageing behaviour of studied samples is different to mineral oil based liquids and also differences between synthetic and natural esters could be seen.

Basic repair and maintenance tasks of industrial machines are usually performed by local service personnel at the location of the component to be maintained. Complex maintenance tasks require an expert who travels to the customer to solve a specific problem. This process is time and cost intensive. Augmented Reality (AR) Remote Assistance offers means to support newcomers on site by guiding personnel through the maintenance process with novel visualizations. This paper describes an Augmented Reality based remote assistance tool for industrial purposes. We present the technology and our implementation approach, as well as the challenges and limitations we have encountered. The potential of the tool was evaluated and analyzed in an industry oriented use case for repairing an industrial PC. The results were compared with a maintenance procedure that was carried out using standard paper repair instructions. Our approach enabled the users to perform complex tasks faster and with fewer errors. Overall, the error rate decreased significantly due to the AR-based remote support. We also found that paper-based instructions are still more efficient in solving simple tasks.

This article adds more information to the problem of natural ester use as an electroinsulating fluid in cellulose paper-oil systems, e.g., for use in power transformers. An accelerated thermal aging (120, 140 and 160 °C) of samples consisting of cellulose paper (transformerboard), a treated natural ester (filtered, basic Al2O3, 0.53% DBPC), a copper plate and moisture was studied. Research was especially focused on the impact of moisture on the aging of the above system. Samples with three different oil moisture levels (80, 143 and 305 ppm water content) were subjected to thermal aging to display the change in a selection of properties (tanδ, acid number, water content). For a study of the polarization effects, broadband dielectric spectroscopy (BDS) was performed to observe the complex permittivity and conductivity dependencies on the temperature and frequency (±30 °C, 0.01-107 Hz). Havriliak-Negami (H-N) diagrams were used to further understand the results. The degradation products during thermal aging were recognized by Fourier transform infrared (FT-IR) spectroscopy. Hydrolysis and hydrogenation occurred in the insulating system with a natural ester aged by elevated temperatures, which is in contrast to CO2 formation in the case of electric aging. The results showed an increase in the acid number, dissipation factor, and number of hydrolysis and hydrogenation products. Furthermore, changes in polarization were observed as a consequence of the thermal aging of paper-natural ester insulation system with the presence of copper and moisture.

Due to the shortcomings of electrical properties and furfural in characterizing the aging characteristics of paper insulation and the limitations of field application, this paper focuses on the feasibility of evaluating the polymerization degree of insulating paper by the ethanol (EtOH) content in transformer oil. The accelerated thermal aging test is conducted, during which ethanol is produced by degradation of insulating paper. For each oil-paper samples, with different ethanol concentrations are prepared by controlling the aging time. In this paper, a gas chromatograph equipped with a mass spectrometer (GC-MS) measurement method with headspace injection is applied to obtain a fitting curve between ethanol content in oil and average degree of polymerization (DPv), which is a method for judging the degree of paper insulation aging condition. Then, showing the influence of temperature on ethanol generated in this experiment. At last, it is indicated that it is feasible to evaluate the polymerization degree of insulating paper by using the ethanol content in transformer oil.

In this paper, we study the state-dependent Gaussian wiretap channel with action encoder and noncausal state at the transmitter, which is also called the action-dependent dirty paper wiretap channel. Two lower bounds on the secrecy capacity of this model are provided, and we find that the two bounds have their advantages and disadvantages, i.e., none of the two always performs better than the other. In addition, we find that taking action on the state helps to increase the achievable secrecy rate of the dirty paper wiretap channel.

Disposable low-cost sensors are essential for the detection of urea for environmental and healthcare applications. In this paper, we proposed a hand-drawn electrode paper chip sensor by utilising the enzyme modified cellulose fibre as a sensing and substrate material. Further modified cellulose fibres increase the impedance measurement stability and facilitate for performing detection. By demonstrating detection of the low-level concentration of urea in the micromolar range, it shows that developed paper chip has excellent potential as a low-cost disposable sensor.

Flexible supercapacitors are considered as a promising candidate for power supplementation in wearable electronics due to their high power density. However, effectively fabricate flexible and low-cost supercapacitor electrodes in a big scale is still a key challenge. Herein we demonstrate a scalable fabrication method for hierarchical electrodes via metalizing air-laid paper and loading with MnO2 as cathode active materials. To be specific, we coat a thin layer of Ni on air-laid paper by magnetron sputtering, then deposit Ni nanocone arrays (NNAs) on the Ni sputtered paper and finally deposit MnO2 on the NNAs to obtain the NNAs@MnO2 paper electrode. The as-prepared paper-based electrode possesses high conductivity and fine wettability, which facilitates the electrons and ions transporting through the conductive network. Additionally, this electrode provides large specific surface area with a hierarchical architecture. Thus the electrode shows high capacitance (451 F/g) and favorable cycle performance (92.9% capacity retention after cycling for 5000 times). By coupling with activated carbon (AC) coated on the NNAs conductive paper as anode, an NNAs paper-based asymmetric supercapacitor is constructed. Benefiting from the high mechanical durability and the 3D hierarchical architecture of the electrodes, the asymmetric supercapacitor exhibits excellent mechanical flexibility and high energy density (26.9 μWh/cm2 at 1.08 mW/cm2). This method can be easily scaled up to produce lightweight and low-cost conductive paper electrodes, making it promising for the application of flexible supercapacitors in wearable electronics.

To realize the detection of pesticide, this study developed a paper-based chip involving a 2D to 3D flow channel and automatic time delay for detecting pesticides. The time delay was controlled by different pullulan concentrations. The 3D channel was fabricated by hot embossing (optimized printing parameters were 28 kg at 80°C, and Advantec No. 1 filter paper was used) and transfer printing the hydrophobic material into the paper. Finally, the limit of the detected concentration of the pesticide was 4 ng/mL, which was achieved using 250 U/mL of acetylcholinesterase (AChE) and 2.5% pullulan solution.

Microfluidic paper-based analytical devices (μPADs) have recently attracted the attention of researchers and industry because they are small, easily fabricated, inexpensive, and require small sample volumes. However, it is difficult to simplify complex assays on such devices because there are limited means of fluid control. Therefore, in the present study we developed a thermoresponsive valve membrane by polymerizing N-isopropylacrylamide (NIPAAm) in a porous membrane. PNIPAAm is a thermoresponsive polymer; it becomes hydrophilic below, and hydrophobic above, the lower critical solution temperature (LCST; 32 °C). We used a green dye solution to investigate flow in a three-dimensionally stacked, T-shaped paper network containing the thermoresponsive valve. The valve membrane was able to prevent the passage of green dye at 25 °C ( LCST). The result demonstrates that our thermoresponsive valve could be used in µPADs. Moreover, it has the potential to automatically enable complex immunoassays in µPADs and improve their versatility.

To evaluate the performance of Nanofibrillated cellulose (NFC) modified insulating paper under long term running process, 10 wt. % NFC modified insulating paper and ordinary insulating paper were subjected to accelerate thermal aging for 720 hours in a 130°C thermal aging chamber. The aging characteristics and electrical performance were measured during the aging process. Results show that NFC modification can delaying the thermal aging rate of oil immersed insulating paper and enhancing the electrical strength of oil immersed insulating paper during thermal aging process. NFC makes oil-impregnated insulating paper more compact during thermal aging process, resulting in higher polymerization degree and crystallinity. NFC modification can increase the relative permittivity and dielectric loss of the oil-impregnated paper during thermal aging process slightly. The AC and DC breakdown strength of the NFC modified paper are better maintained than the conventional oil-impregnated paper.

Moisture in transformer paper insulation is one of the most important parameters to be monitored in order to assess the useful life of the transformer. Time domain spectroscopy based on polarization and depolarization current measurements is one of the methods that can be used for detection of moisture content in the transformer paper insulation. However, these measurements could be influenced by thermal transient during the application of voltage, which may lead to an inaccurate interpretation of the insulation condition. In this paper, a novel method is proposed in order to avoid this thermal transient effect by using the high frequency component of the of depolarization current acquired during the measurement. Preliminary results show a correlation between the high frequency component of the depolarization current waveform and the moisture content in the paper insulation within a moisture content range of about 3 %. At higher moisture content, a resistive leakage current seems to have a negative effect on the measured depolarization current., 2019 IEEE 20th International Conference on Dielectric Liquids (ICDL), June 23-27, 2019, Roma, Italy

© 2013 IEEE. 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.

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, 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.

Paper influence analysis is essential technology of the literature management system. In recent years, research on influence analysis has received crucial attention from the industry and academia. In this field, there are many new challenges in how to evaluate valuable papers in the paper citation network. Therefore, in this paper, we have proposed an influence model in paper citation networks with integration features of PageRank and Hyperlink-Induced Topic Search (HITS). First, three ranking features are extracted with the combination of PageRank value, hub, and authority. Second, more features are added to our ranking classification method. Third, the ranking-based classification with $P$ ageRank and HITS is proposed to analyze the paper propagation. An influence score is computed to evaluate the importance of a paper. The experiments show that our ranking-based influence model is effective for paper propagation.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

The paper mills are one of the main industries on which the economy of India is based. The paper production process is very complex and comprised of several stages. One of them is the head box which is the key sub process of this entire system. It distributes the stock uniformly over the wire section of the paper mill. The automatic control system design is the focus of this paper. The considered process is very challenging for control system design due the presence of dead time which makes the system unstable. So, a very popular technique, IMC (internal model control), is attempted. Its performance is analyzed. Also, a modern approach, ANFIS (adaptive neuro fuzzy inference system) is adopted for the control system design for the same process. Finally, the control performance of both the approaches has been compared. For simulations, MATLAB is used in this work.

Low-cost inkjet printing of a Frequency Selective Surface (FSS) paper poster to reduce the drop of 5G signals within an indoor environment is introduced in this paper. An array of square loop elements is employed for this application. This low-cost FSS operates at a central frequency of 25.2 GHz while covering the 24 GHz band used in 5G systems. CST Microwave Studio™ was employed to simulate the FSS. The array structure was fabricated using a household Inkjet Printer on a photo paper using nanoparticle silver ink filled in refillable ink cartridges. Simulation transmission responses compared well with measurements. Inkjet printing could print tracks with fine details required for the mm-wave applications and offered reliable conductivity for the filtering performance. Printing of low-cost posters can help in improving the strength of 5G signals.

This paper describes a user identification method conducted across online social networks (OSNs) using information regarding friends’ locations, in contrast to a conventional method based on the similarity of two display names. This latter method encounters the problem of decreased identification accuracy if a user registers different display names across OSNs. Our proposed method aims at addressing this issue by utilizing information on friends’ locations. We convert location information extracted from an OSN to that of such geographic administrative units as country, state and city, and calculate the weighted occurrence frequency of a location pair on the basis of distance between the pair. This is based on the hypothesis that a friend list relatively rarely includes an account pair whose locations are extremely distant from one another. We calculate a similarity score between two accounts’ weighted occurrence frequencies for each geographic administrative unit. Finally, we identify a user on the basis of individual similarity scores or weighted average scores for a given geographic administrative unit. Evaluation experiments show that our proposed method improves performance over that of a conventional method (accuracy: 83.1%$\rightarrow$91.2%).

International audience; Internet of Things (IoT) is a network where several and various interconnected, smart objects continuously generate alarge amount of data transmitted over Internet. Several efforts and research works in IoT focused on hardware constraints, lowcost, application design, as well as good communication technologies. However, due to the intrinsic characteristics of diversity,heterogeneity, large-scale, dynamic and large volume of data generated and various IoT applications needs, using traditionaldatabase management mechanisms and analytics architectures is not generally suitable. Then, it is challenging to provideefficient IoT data storage and query processing mechanisms for satisfying IoT application needs. This paper identifies the mainspecifications and mechanisms of data and query management for IoT and classifies them. Moreover, this work presentsadvances on data and query management mechanisms on IoT, shows their advantages and limits and discusses the challengingopen research issues that need to be addressed for providing guidelines for further contributions.

Porous microneedles (MNs) are considered as promising methods to collect biosamples for painless and minimal invasive diagnosis of diseases such as diabetes. MNs have microscale size and interconnected pores and thus they can penetrate through epidermis without pain and collect interstitial fluid. These properties can be utilized for a new method for glucose sensing for diagnosis of diabetes. We thus focus on an integrated porous polydimethylsiloxane (PDMS)-based MNs (PDMS MN) with paper-based glucose sensor to realize the device for colorimetric diagnosis of diabetes. Disc-shaped PDMS MN layer consisting of 21 microneedles within an area of 1.7 cm2 was prepared. The microneedle showed a length of 800 μm and a porosity of 70 %. In this integrated system, porous media connects needles and sensor via a microfluidic channel. We successfully demonstrated the qualitative andquantitative analysis of glucose in the sample solution collected by porous MNs.

Several applications can benefit from recording information about the places a mobile entity visits and the length of time it spends there (e.g., shoppers, employees, buses, movable equipment, autonomous robots, etc.). In this paper, we present our approach to recording spatio-temporal information in a secure and inviolable way using a Distributed Ledger Technology. So far in this work, we observe that, although sending data to the blockchain is a heavy process, there are ways that this transaction time does not disrupt the flow of new data such as adding these data to a queue. However, this makes the sending process overall longer. We intend to continue this study by testing different blockchain services and comparing their performances.

We report on THz metamaterials for chemical sensing fabricated on low-loss, plastic, flexible substrates. The metamaterial is bonded to a paper sheet, which is saturated with the liquid to be analysed. Liquids in contact with the metamaterial surface with different refractive indices result in a shift of the metamaterial resonant frequency. This shift in frequency can be detected by measuring its complex transmission using a THz vector network analyser, in the frequency range 0.75–1.1 THz. The sensitivity of the metamaterial is 0.0015 refractive index units for a spectral resolution of 1 GHz which is well achievable with the VNA system.

The problem of joint information transmission and input signalling estimation over a state-dependent channel is considered. The general single-letter upper and lower bounds of the optimal capacity-distortion trade-offs are provided. For the Gaussian dirty paper channel, we calculate the bounds by choosing parameters carefully, and obtain a computable form eventually. In such a way, we obtain a complete characterization for this problem setting.