Miracle-dz/UniFAR-FTUs huggingface dataset:
Manipulation with Terahertz Wave Fronts Using Self-Complementary Metasurfaces

Here we numerically demonstrate the possibility to shape a wave front in the terahertz range using a self-complementary metasurface (SCMS) with spatial modulation. A circularly polarized incident wave passing through a SCMS causes two transmitted waves, a co- and cross-polarized. The co-polarized wave, in theory, has a complex transmission coefficient of 1/2 and keeps the same front shape as the incident wave, even if the metasurface is nonuniform. On the contrary, the cross-polarized wave obtains a phase, which can be locally controlled by geometric parameters of unit cells. We apply a phase holographic method to modify the shape of the cross-polarized wave in a desirable way while keeping the co-polarized wave unchanged.
Symbolic Dynamic Filtering for Online Power Quality Anomaly Detection

A methodology for anomaly detection using Symbolic Dynamic Filtering (SDF) is proposed for detection of power quality events in this paper. The methodology overcomes the limitation of bulk data processing by compressing the signature feature information. It improves the sensitivity of early evolving anomaly detection in case of power quality events, which might not be evident by manual inspection. SDF is constructed based on the knowledge of symbolic encoding and finite state automata that generates signature patterns of histograms. Quantification of anomalous condition is done by comparing it with reference conditions, which depicts the deviation from normalcy. The proposed approach is validated on simulated data of various power quality events. The proposed SDF approach is found to be promising for online power quality event detection.
Research on the corrosive effect of inactive sulfur on oil-paper insulation

Sulfur corrosion induced by active sulfur has been proved to significantly decrease the insulation performance of oil-immersed equipment. In order to protect the oil-paper insulation from sulfur corrosion, active sulfur is cleared in the refining process of crude oil. Some inactive sulfur with high-efficiency antioxidant is still retained in the mineral oil to improve the oxidation stability of oil, such as thiophenic compounds. However, the corrosive effect of inactive sulfur on the oil-paper insulation has not yet clear. Therefore, thiophene that is the largest content of sulfide in the insulating oil was selected as the research object, a series of accelerated thermal ageing experiments of oil-paper without and with thiophene under the different high temperature were conducted, scanning electron microscope equipped with an energy-dispersive X-ray spectrometer (SEM/EDX) and inductively coupled plasma atomic emission spectrometry (ICP-AES) were used to characterize the corrosion degree of oil-paper insulation. The results show that no corrosive deposit was observed on the surface of oilpaper insulation without thiophene even at very high temperature, while a few corrosive deposit was accumulated on the surface of oil-paper insulation with 500 mg/kg thiophene. The corrosion degree of oil-paper insulation with thiophene had an uptrend as the increase of temperature (130°C-180°C), preliminarily indicating that thiophene may be activated in the oil-paper system under the action of overheating fault of oil-immersed transformer and posed a corrosion threat to oil-paper insulation.
A Method of Verification in Chisel Based Deep Learning Accelerator Design

Chisel is a new generation of hardware construction language (HCL) for agile development. More and more developers have developed their project in agile design. At the same time, a considerable part of Verilog-based design has also been released in agile design versions. However, there is no comprehensive verification flow for Chisel based design. Due to the difficulties of verification in Chisel based design, it is a tough task to attach Chisel based design on Verilog based design. We purpose a feasible verification flow in chisel-based deep learning accelerator (DLA) design, which is composed by performance equivalence check at module-level and function equivalence check at pin-level. Compared to Universe Verification Method in RTL level codes that cost considerable time and funds, this verification flow improves the verification efficiency and reduce the difficulty of debug.
Plasmon resonance enhanced nontoxic nanoagents for in vivo detection of antibiotic resistant bacteria

The rapid progress in the development of methods for the study of biological systems determines the interest in synthesis of optical labels for effective marking of pathogens. The conjugation of magnetic gold-coated nanoparticles and quantum dots was performed using polymeric coatings, which are applied to design of brighter agents and can be used for detection and eradication of pathogens.
Optimal parameters for laser stabilization via self-injection locking to high-Q resonator

Self-injection locking of a laser is an effective method for laser frequency stabilization. We performed fourparametric optimization of a diode laser self-injection locked to high-quality-factor whispering gallery mode resonator. The influence of Kerr nonlinear effect to the optimal state were analysed. The solution to reduce the effect is suggested.
Research on Local Consensus Algorithm Based on Wireless Sensor Network of Power Monitoring System

In the power monitoring system, it is necessary to carry out long-term continuous monitoring of the current and voltage of the monitored equipment, and the wind speed, temperature, humidity and other parameters in the monitoring environment, usually using wireless sensor networks as the communication method. Affected by factors such as power consumption, bandwidth, transmission distance, it is very important to select the appropriate data synchronization algorithm in the wireless sensor network. Inspired by the blockchain consensus algorithm, the local consensus algorithm proposed in this paper focuses on the analysis of the data synchronization mechanism between network nodes, which can reduce unnecessary data interaction between nodes and increase the reliability, real-time and adaptability of the network.
Economical and Dependable Time Sources for Distributed Energy Resources

Computing devices are required to have time synchronization to enable meaningful logging, fault finding, and forensics. In an automation environment, there are additional requirements for SCADA. In a power environment there are specific requirements and the accuracy requirements are exacting. The solutions used in substations may be excessively complex and expensive for small distributed generation sites and roadside cabinets. A solution is identified for sites in the absence of network connectivity, which is inexpensive, highly available, and includes detection of component failure and attack.
A Tensor Approach for Activity Recognition and Fall Detection Using Wearable Inertial Sensors

Tensors are high order muti-way arrays which extend vectors and matrices to higher, greater than three, dimensions. Rather than vectorizing data, tensors allow its representation in its original raw form, hence offering better understanding and interpretation of the underlying data structure. In this paper, we propose an activity recognition and fall detection approach which makes use of tensors as the mean for wearable inertial sensors data representation. To reduce the tensor size and only keep important information, we project the tonsorial data into a lower dimensional space using multilinear principle component analysis. To evaluate the proposed approach, we carry out experiments on a publicly available large fall and activities dataset. The results show the defectiveness of tensorizing data compared to its processing through concatenated vectors.
Exposure to Traffic-Related Air Pollutants in the Perinatal Period of Life in Relation to Respiratory Health in Infancy

The role of air pollution exposure on the development of asthma remains unclear. This can only be answered by prospective cohort studies. The authors give an overview of cohort studies focusing mainly on the role of early exposure, investigating the association between outdoor exposure early in life and respiratory or allergy symptoms in the growing children. Based on those studies, there is a suggestive evidence for a causal association between childhood asthma symptoms and exposure to traffic-related air pollutants.
A comparative analysis of ECG denoising methods

A common problem in ECG analysis is the removal of unwanted noise that corrupt the signal while recording and can hinder the correct interpretation. To provide high-quality ECG records, many approaches have been developed. This paper introduces a comprehensive survey of filtering methods to cope with the noise artifacts in the ECG signals in order to find the best-suited filter for each type of noise. For this purpose, we used 47 half hour signals coming from adding artifacts to clean ECG records taken from real clinical databases. We calculated the Signal to Noise Ratio (SNR) and the Root Mean Square Error (RMSE) after filtering and then a statistical test was used to select the most efficient method. The noises that were filtered are Power Line Interference (PLI), Baseline Wander (BW), Electrod Motion Artifacts (EM) and Muscle Artifacts (MA). Empirical Mode Decomposition was the best method to filter PLI noise. BW, MA and EM were removed to a large extent using Wavelet+Adaptive filtering method.
Communication for patients with disorders of consciousness with a vibro-tactile P300 brain-computer interface

In this publication we present a novel P300 speller that could be used for communication with DOC patients. The stimulation for the P300 is done tactile on seven different body locations. Test on four healthy users have been done and resulted in an average accuracy of 95% when answering simple "yes" versus "no" questions. We further implemented a statistical test, that provides additionally a so called zero-class. If the classification result "yes" or "no" is rather uncertain, and therefore maybe wrong, the speller provides the so called zero-class as output. The answer is therefore considered ambiguous. After adding the zero-class calculations to the analysis of the same data, the percentage of correct answers decreased from 95% to 87.5%, but with the big advantage of no single wrong classification.
DDA-ENS: Dominance Degree Approach based Efficient Non-dominated Sort

In Pareto based multi- and many-objective evolutionary algorithms (MOEAs and MaOEAs), non-dominated sorting is an important step that divides the set of solutions into different disjoint non-dominated fronts. In the last 20 years, there have been various approaches proposed for non-dominated sorting. Recently, an approach known as Dominance Degree Approach for Non-Dominated Sorting (DDA-NS) has been proposed. This approach guarantees that the number of floating-point comparisons is always bounded by ${\mathcal{O}}(MN\log N)$ for N solutions and M objectives, which is not true for various approaches. However, the worst-case time complexity of DDANS is recently proved to be Θ(MN2 + N3). In this paper, we develop an approach on the top of DDA-NS, which also guarantees ${\mathcal{O}}(MN\log N)$ floating-point comparisons and its worst-case time complexity is proved to be Θ(MN2) as opposed to Θ(MN2 + N3) of DDA-NS.
Contamination Identification and Classification on Composite Insulator by Visible Light Images

Pollution flashover occurs more easily on contaminated transmission line insulators, which causes huge losses to the power system. In order to prevent the pollution flashover, the first step is to identify the contamination grade of insulator. This paper identifies the pollution grade through the color characteristic of visible light image. Firstly, the image segmentation method based on the randomized Hough transform method is adopted to achieve the surfaces of the red composite insulators with different contamination grades in the transformer substations of Heilongjiang Power Grid. Then, a total of 36 kinds of characteristic of $R,\ G,\ B,\ H,\ S$ and $V$ component images are calculated. According to Fisher criterion, the mean and median of S component, which can significantly represent the contamination grade, are selected. Finally, a support vector machine for classification decision is designed. Experimental results show that the identification accuracy of the composite insulators reaches 97.5%, which proves this method can be used for identification of composite insulator contamination grade.
A Driving Fatigue Dataset of Driver's Facial Features and Heart Rate Features

Aiming at the problems of single eigenvalue type and not-real fatigue evaluation in the current published driving fatigue dataset, and conforming to the research development trend of facial and heart rate features fusion, this paper studies a set of simulated fatigue driving experiment scheme using low intrusive camera and smart watch as fatigue information collection equipment, and combined with self-evaluation and expert-evaluation of fatigue state. According to this experiment, a fatigue driving dataset including driver's facial features, heart rate features and more realistic fatigue evaluation is obtained. Facial features are five categories of 51 dimensions, including head posture, gaze direction, facial micro expression, eye opening and mouth opening. Heart rate features include 7 dimensions such as instantaneous-HR, mean-HR, SDNN, RMSSD, LFP, HFP and LFP/HFP. The dataset is composed of 2400 sample data of 20 experimenters, and the training set and test set are randomly divided into 7:3, which can be used for the training and test of the corresponding driving fatigue detection model.
A Grid Feeding Voltage Source Inverter with an Advanced Control Scheme for Solar PV System

Nowadays, grid-tied multilevel inverters (MLIs) with closed loop control strategy has carried a great attention due to its superior performance in the superconducting energy system and solar photovoltaic (PV) system. In this paper, an improved PI +LLC (lead-lag compensator control) technique is proposed with a Neutral point clamped (NPC) voltage source MLI fed grid tied PV system. The controller is a proportional integral (PI) controller with the feedback of LLC. It shows excellent performance to minimize total harmonic distortion (THD), steady state error, and almost zero phase shift.
Modelling Privacy of Smart Meter Data

Smart meter data form the backbone of various data driven distribution system applications ranging from demand response, outage management, power theft detection, capacitor bank switching to distribution network planning and asset management. However, a consumer’s concern for privacy often limits the usage of smart meter data. Privacy preserving algorithms have been proposed to address privacy concern of the consumers. However, lack of a privacy model inhibits estimation of privacy preserved by these algorithms. Privacy pricing can be an alternate approach to encourage smart meter data sharing by incentivizing consumers. However, price estimation of privacy is a challenging task due to the lack of a mathematical privacy model. Unavailability of a privacy model makes it difficult to quantity the privacy of smart meter data. This paper proposes a mathematical model for objective assessment of privacy of smart meter energy data. At first, the possible applications of privacy model of smart meter data are presented. Then, the notion of smart meter data privacy is discussed. Based on this notion, a mathematical model is proposed to quantity the privacy of a time series of energy data. Privacy of practical smart meter data is evaluated using proposed model. The model is also used to study the impact of metering interval and time series length on privacy. The results demonstrate the efficacy of the proposed model.
Plant Growth and Trace-Element Uptake on Acidic Coal Refuse Amended with Lime or Fly Ash

Two commonly used revegetation species, ‘Kentucky 31’ tall fescue (Festuca arundinacea Schreb.) and ‘Lincoln’ smooth brome (Bromus inermis Leyss.), were grown for 60 days in pots containing coarse coal mine refuse (referred to as gob, pH = 3.5) amended with either lime or alkaline powerplant fly ash. Both species were also grown in pots containing a silt loam surface soil as a control. Morphological growth parameters were measured over time; dry weights and shoot/root ratios were determined at harvest. Concentrations of Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, V, and Zn in the plant shoots were determined by atomic absorption spectrophotometry. Plant growth of both species was not as good on either lime- or fly ash-amended gob as it was on surface soil; however, more vigorous growth occurred on lime-amended gob than on fly ash-amended gob. Significant differences (p <0.05) in the tissue concentrations of Cd, Co, Fe, Hg, Mn, Pb, V, and Zn were found among the plants grown on the three substrates. Except for Hg and Pb, these elements were higher in plants grown on at least one of the amended-gob substrates than in plants grown on surface soil. Significant substrate differences were not observed for Al, As, Cr, Cu, Ni, and Se. The tissue concentrations of some elements—notably Al, Cu, Fe, Mn, V, and Zn—were high enough in plants from one or more of tbe substrates to either approach or exceed concentrations which have been reported to be associated with toxic effects in some plant species.
Photovoltaically Self-Charging Battery

For many low-power applications, solar cells are used as an environmentally friendly power supply. In order to provide electrical power also in the absence of solar radiation, we invented a device which unifies hoth a solar cell and a rechargeable battery in one unit. The main components are a photoactive layer on a charge-storage layer. Thus. this new device represents a flat battery, which charges itself on illumination. In principle it can be produced cheaply, e.g., by screen printing. The characteristics of charging and discharging are presented with special consideration of the variation of light intensity and ion concentration in the electrolyte. A relatively high reverse reaction at the WO 3 electrode still takes place. After charging for I h under an illumination of 1000 W/m 2 , the first samples yielded 1.8 C/cm 2 when discharged in the dark.
Using deep learning to detect early signs of cognitive disease

Handwriting and hand drawings have historically been used as a proxy metric to evaluate psychological and cognitive traits, in addition to fine motor skills. Detecting onset of cognitive diseases early is a very challenging task due to the expertise required to evaluate each individual subject, as well as the time that the process entails. In this work, we evaluated the application of state-of-the-art deep learning and transfer learning models to determine if the author of a copied hand drawing of a template displays signs of cognitive disease. Compared to expert cognitive evaluation, our best performing method yielded a mean accuracy of 67.60% and area under ROC of 0.595 in determining if an undiagnosed subject displays signs of cognitive disease. Our results suggest that state of the art techniques in deep learning have the potential to help alleviate the difficulty of screening for early signs of cognitive disease. Results also suggest that transfer learning did not perform as well as a purpose-built network architecture trained from scratch. Lastly, our results indicate that models which consider all drawings made by a subject outperform models that look at drawing-evaluation pairs independently.
GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences

Traffic flow prediction is an important foundation for intelligent transportation systems. The traffic data are generated from a traffic network and evolved dynamically. So spatio-temporal relation exploration plays a support role on traffic data analysis. Most researches focus on spatio-temporal information fusion through a convolution operation. To the best of our knowledge, this is the first work to suggest that it is necessary to distinguish the two aspects of spatial correlations and propose the two types of spatial graphs, named as geographic graph and semantic graph. Then two novel stereo convolutions with irregular acceptive fields are proposed. The geographic-semantic-temporal contexts are dynamically jointly captured through performing the proposed convolutions on graph sequences. We propose a geographic-semantic-temporal graph convolutional network (GST-GCN) model that combines our graph convolutions and GRU units hierarchically in a unified end-to-end network. The experiment results on the Caltrans Performance Measurement System (PeMS) dataset show that our proposed model significantly outperforms other popular spatio-temporal deep learning models and suggest the effectiveness to explore geographic-semantic-temporal dependencies on deep learning models for traffic flow prediction.
Fine-Grained Context-aware Ad Targeting on Social Media Platforms

One of the most important sources of revenue for social media platforms, like, Twitter, Facebook, Reddit, etc., is advertising. An effective social media advertising plan moves people from awareness and interest in desire and action. Despite the potentiality, campaigns and marketing strategies should be improved. One of the challenges is to identify the right target audience at the right time, considering both communities of interests and locations and the development of these conditions along the timeline. This is crucial to create the right communication strategy and the right advertising message. This paper proposes a context-aware ad-targeting methodology using time, locations, and inferring users’ interests by analyzing published content. The method relies on a fuzzy extension of Triadic Formal Concept Analysis for identifying Location-based and Content-based communities of users. Then, a task of community fusion takes place, named Join, for matching a target audience. The matching may be tuned for identifying a wide or narrow community and implementing a fine-grained ad targeting. Experimental results are given.
A Fast Extraction of Positive Sequence Components with Noise Immunity in Unbalanced Conditions

Fast and accurate acquisition of voltage components are the basic requirement for grid-connected inverters to realize various control under unbalanced grids. In view of this situation, a delay sampling period filter (DSPF) algorithm, which rapidly separates each voltage component by delaying two sampling periods of voltage components under dq frame, is proposed. Besides, in view of the noise interference in the grid voltage, a delay operation period filter (DOPF) algorithm is proposed to scale the operation period and limit the noise level to an acceptable range. Finally, the corresponding model is built in MATLAB and physics experiment platform to verify the feasibility and priority of this method.
SIMULATION OF MULTIPLE STEADY STATES IN A POROUS CATALYST DUE TO PHASE TRANSITION

From numerical calculations, two of the four multiple steady states (branches A and C) obtained on a biporous Pt/Al2O3 catalyst surrounded by reacting mixture containing saturated vapor of cyclohexene reported in the preceding paper6) can be attributed to the behavior of the catalyst filled with vapor mixture and condensed liquid mixture in its micropores. Stability analysis based onheat generation and heat removalcurveswithout considering phase change in the catalyst interior showedonly a single steady state, i.e. the rate of vapor-filled catalyst, while incorporating the effect of phase change on the heat generation curve yielded two steady states which gave good agreement with those of experiments within experimental error.