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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.

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.

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.

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%.

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.

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.

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.

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.

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.

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.

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

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.

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.

Our proposed method focuses on offline friends who are acquainted with a target user in the real world. We use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to distinguish between online friends of a target user who are also offline friends and online friends who are acquainted with the target user only in cyberspace. By utilizing the probability of friendship as a function of the geographical distance between users’ home locations, our proposed method achieves an estimation accuracy 1.7 percentage point better than that of a conventional method. We have also been able to verify the effectiveness of an optimization method that narrows down candidate areas for a target user’s home location.

The present work presents a hybrid identification of recursive least squares with a metaheuristic called radial movement optimization (RMO) applied to the joints of a cylindrical robotic manipulator. The main contribution of the research is to consider the covariance matrix with RMO. At the end a comparative analysis will be made with some classic methods. The results of the identifications were tested with the RMSE. A covariance matrix has also been generated from manipulator identifications.

In the power transformer, the oil-impregnated paper is used for providing insulation between live conducting parts, which comprises of transformer oil and insulating paper (either Kraft paper/Pressboard). These insulating papers are made up of cellulose fibers and it gets easily degraded at higher hot-spot temperature with increase in aging periods. So this affects the lifetime of solid insulation and further transformer life. In this research work, the latest advanced solid insulation named “Nomex paper” made up of aramid polymers is considered as a solid insulating paper material for analyzing the aging behavior. Furthermore, the electrical properties such as breakdown voltage of both oil and paper, flash point and fire point, viscosity have been measured to study the change in characteristics during aging. Also, the level of degradation of solid insulation has been identified by measuring Breakdown voltage of paper under all aging times. On looking standard IEEE C57.91, all these samples were analyzed at the maximum hot-spot temperature of about $130^{\mathbf {\circ }}$C. The results clearly show that the solid insulation gets affected due to deterioration and decomposition.

In HVDC converter transformers, the potential DC component in electric stresses imposed on valve windings would unfavorably cause space charge accumulation inside oil-paper insulation. Insulation degradation and aging may have great effects on space charge behaviors in oil-paper insulation. This paper focuses on the effect of the low molecular weight (LMA) and the moisture on space charge phenomenon in oil impregnated-paper insulation. Pulsed electro-acoustic (PEA) technique was used in this study to measure space charge behaviors in 400 $\mu$ m thick, dry and moist single-layer paper sample impregnated in mineral oil prepared with different amounts of formic acid. The measurements were carried out at $30 ^ { \circ } \mathrm { C }$ under a field strength of 20 kV/mm. It was found that LMA greatly strengthens the homo charge accumulation. The combination of LMA and moisture results in even more active space charge accumulations. In dry samples, the existence of LMA causes slower charge decaying rate. However, the introduction of the moisture accelerates the charge dissipation.

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach., Comment: 5 pages, 4 figures

A paper based simple, flexible, and perfect resonant electromagnetic wave absorber is proposed in this article. The absorber consists of the conducting grid patterns where unit cell of the pattern is a two-dimensional grid based structure and the bottom is a conducting ground plane. The grid based top conducting patterns are created by inkjet printing over a thin paper having conducting layer at the bottom. The proposed absorber undoubtedly attains a cost-effective and eco-friendly solution for designing a flexible electromagnetic absorber. The experimental and simulation results are found to be in excellent agreement. The proposed absorber exhibits perfect absorption of 99.99% at 10.84 GHz and possesses an overall thickness of 0.531 mm.

Paper-based digital microfluidic biochips (PB-DMFBs) provide a promising solution for microfluidic bioassays. Due to the low-cost substrate material and low demand for complicated manufacturing equipment, PB-DMFBs can be fabricated without foundry. On the flip side, convenience of fabrication allows PB-DMFBs to be fabricated everywhere, which makes it is difficult to manage production and distribution of IP (bioassays). As a result, PB-DMFBs are vulnerable to security threats. IP and its creator, the biocoders, may suffer from infringement. To ensure IP protection, in this paper, we proposed the first Digital Rights Management (DRM) scheme to protect IPs of PB-DMFBs from security threats. A chip-level synthesis algorithm is also presented to realize not only complex biochemical operations but also the demand of DRM.

The insertion of mica barrier between coaxial electrodes results in a larger reduction of leakage current than the use of kraft paper insertion. The reduction is influenced by the barrier position and the testing voltage level. When using the kraft paper, there is a certain voltage level beyond which the characteristic of leakage current as a function of barrier position is relatively the image of that under the voltage level. At this level, the leakage current is almost the same wherever the barrier position. The use of mica insertion tends to shift the symmetry axis of the characteristic curves, meaning higher leakage current level.

International audience; As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing photomicrographs of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.

Converter transformer is the important part of high voltage direct current transmission system (HVDC), whose safety reliability is related to the safety of HVDC. Compared to the traditional AC transformer, there is a complex AC-DC electric field inside the converter transformer, which is more likely to cause a decline in insulation performance, after the internal oil-paper insulation is dampened. In this paper, a test platform for insulation breakdown strength of oil-paper insulation was set up according to the standard in IEC60243. This paper analyzed the change regulation of moisture content of oil-paper insulation. The difference of breakdown strength of oil-paper insulation, which contain different percentage of moisture, were studied under AC, DC, different ratio of AC/DC voltage and polarity reversal voltage, and combined with double-layer dielectric theory, the effect of moisture content and voltage type on breakdown characteristics of oil-paper insulation was analyzed. The analysis showed that most of the moisture was concentrated in the insulating paper, with the increase of moisture content, the increasing amplitude of electrical conductivity of insulating paper is larger than that of insulating oil. As a result, the electric field strength on the insulation paper decreases and the electric field strength on the insulation oil increases, which made the insulation gap easier to breakdown

This paper focuses on the potential and practice of alternative forms of assessment to the conventional written exam. Readers will be interested in issues such as why would a staff member want to replace the written exam and how can I be sure that the replacement is equally effective at capturing student attainment. The paper summarises a trial at the University of Sheffield where the written exam was replaced by a computer quiz which was marked automatically.

International audience; The design of a dipole antenna printed on a paper substrate is presented in this paper. The antenna which integrates a compact balun is devoted for dual-band 2.4/5 GHz WLAN applications. The antenna is based on a double-side printed multilayer paper substrate and is fed with a coaxial cable for the testing. The simulated results of the whole structure are also presented in detail and compared with the measured performances.

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.

Blockchains (BCs) received significant attentions from FinTeh in recent years. Many proof-of-concepts (POCs) projects and experiments have been carried out to explore the strengths and weaknesses of constructing financial market infrastructures upon BCs. The experiments showed that considerable improvements are still necessary to meet financial requirements such as safety, efficiency, reliability, scalability, security and transparency. The Principles for Financial Market Infrastructure (PFMI), issued by the Committee on Payment and Settlement System (CPSS) and International Organization of Securities Commissions (IOSCO), is a key standard for designing Financial Market Infrastructures (FMIs) so that global financial markets could be more robust and stable, especially, could withstand financial shocks. It is thus necessary to design BC-based FMIs according to PFMI. This position paper analyzes financial system requirements and the challenges, and discusses the design issues of BCs in meeting PFMI.

Virtual Reality (VR) and 360 film have caught the attention of audiences and content creators and emerged as a new media, however, the market penetration of VR and head mounted hardware has remained slow despite the availability of more affordable mobile options. This has resulted in some audiences turning to VR cinemas, festivals and out-of-home exhibitions. Creating affordable, scalable VR cinemas presents a number of challenges and many of the decisions taken in both developing and facilitating these curated exhibitions directly impact audience's reception of spatial audio soundtracks. This workshop position paper looks to discuss the potential issues and future solutions in the use of current synchronous exhibition applications, the competing formats, standardised Head Related Transfer Functions, headphone build/colourisation, and the on-boarding process.

Usability and the use of automated static analysis tools in the software development process have been an evolving subject of research in the last decades. Several studies shed light on issues like high false positive rates and low comprehensibility, which hinder tool adoption for even software engineers. Yet, the tools' perceived usefulness and ease of use play a much larger role when it comes to untrained software developers, as is usually the case in scientific software development. In this paper, we outline a multi-stage interview study to learn more about how scientists come to accept and use static analysis tools.

Even though AI/ML is of strategic importance for many companies, it is not easy to come up with an AI life cycle in a multi-cloud world that is being increasingly embraced. In this paper, we present our position on an AI strategy that is future proof and does not force vendor lock in.

Optimal linear transmitter beamformers in multi-antenna multi-user systems are of the Minimum Mean Squared Error (MMSE) type (dual uplink MMSE receivers). MMSE designs make an optimal compromise between noise enhancement and interference suppression and reduce to matched filters at low SNR and zero-forcing at high SNR. We consider a realistic scenario of user channels of varying attenuation and constrain the beamformers to either zero-force or ignore each interference term. This leads to a reduced-order zero-forcing (RO-ZF) design in which the number of interference sources being zero-forced increases with SNR. We apply a simple large systems analysis (applicable to Massive MIMO) to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs. RO-ZF designs lead to variable reductions of computational complexity and channel state information (CSI) requirements (esp. in future multi-cell extensions), both important considerations in Massive MIMO systems.

Bitcoin blockchain is growing big, yet its transaction capacity hit its limits in 2017. One solution heavily discussed within the bitcoin community is to let it grow faster by increasing size of the blocks. However, making transactions smaller instead of blocks bigger is more sustainable and also brings other additional benefits. In this paper, we focus on space wasted by non-cryptographic use of hashes and uncompressed numbers within transaction inputs. In our analyses, we show that transactions can be made approximately 16% smaller, depending on their complexity.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

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.