Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Interarea Modes From Ambient Data of Phasor Measurement Units Using an Autoregressive Exogenous Model

Various modal identification methods for interarea modes have been developed to improve identification accuracy by overcoming the measurement noise in ambient data of phasor measurement units (PMUs). In this study, a modal identification method that is insensitive to measurement noise is proposed by introducing bandpass filters, which extract a modal signal from an autocorrelation function of PMU ambient data. The bandwidth of the filters is set to be adequately narrow such that the noise can be rejected sufficiently. To reduce the computational burden of the proposed method, the filters are designed by transforming a reference lowpass filter. An autoregressive exogenous (ARX) model of interarea modes is applied to the extracted modal signal. The parameters of the ARX model are estimated for modal identification via the least squares method. The identification accuracy of the proposed method is compared with those of conventional modified extended Yule Walker and discrete Fourier transform methods with respect to the signal-to-noise ratio and modal damping by using synthetic ambient data. Finally, the feasibility of the proposed method is demonstrated by identifying interarea modes of Kundur’s two-area four-machine system and two real power systems in South Korea.
Multiple input Fuzzy controlled DC-DC Converter for harvesting renewable energy

Several renewable energy sources are integrated to supply the load continuously with multi input converters (MIC). Instead of using several single input converters for each energy source for distributing power in micro and nano-grids it is advantageous to use multiple input converters which has simple structure, less devices and low cost. MIC is used for energy harvesting in hybrid system. In this paper a common switching function (CSF) with common duty ratio(CDR) can be generated by frequency division at high frequency for a MIC. The duty ratio of individual switch in the input source will be an integer multiple of CDR and independent of number of input sources. CDR is taken as control variable for regulating the output voltage of MIC. This switching strategy simplifies controller design and transforms the converter into a buck-boost converter with multiple inputs. The operating point of the converter is regulated by fuzzy controller. The results obtained from simulation and experiment shows the effectiveness of the switching strategy.
Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information

Bayesian Maximum Entropy was used to estimate the probabilities of occurrence of soil categories in the Netherlands, and to simulate realizations from the associated multi‐point pdf. Besides the hard observations (H) of the categories at 8369 locations, the soil map of the Netherlands 1:50 000 was used as soft information (S). The category with the maximum estimated probability was used as the predicted category. The quality of the resulting BME(HS)‐map was compared with that of the BME(H)‐map obtained by using only the hard data in BME‐estimation, and with the existing soil map. Validation with a probability sample showed that the use of the soft information in BME‐estimation leads to a considerable and significant increase of map purity by 15%. This increase of map purity was due to the high purity of the existing soil map (71.3%). The purity of the BME(HS) was only slightly larger than that of the existing soil map. This was due to the small correlation length of the soil categories. The theoretical purity of the BME‐maps overestimated the actual map purity, which can be partly explained by the biased estimates of the one‐point bivariate probabilities of hard and soft categories of the same label. Part of the hard data is collected to describe characteristic soil profiles of the map units which explains the bias. Therefore, care must be taken when using the purposively selected data in soil information systems for calibrating the probability model. It is concluded that BME is a valuable method for spatial prediction and simulation of soil categories when the number of categories is rather small (say < 10). For larger numbers of categories, the computational burden becomes prohibitive, and large samples are needed for calibration of the probability model.
Surface EMG vs. High-Density EMG: Tradeoff Between Performance and Usability for Head Orientation Prediction in VR Application

Head orientation prediction is one of the solutions to reduce end-to-end latency on Virtual Reality (VR) systems and is important since it can alleviate negative effects like motion sickness. This study compared head orientation prediction models from two different electromyography (EMG) systems: surface EMG (sEMG) and High-Density EMG (HD-EMG). The deep learning method was used to train the prediction model, and the results showed that the model with input from the pre-processed sEMG + IMU sensor outperformed the model with input from the HD-EMG + IMU sensor. However, the decreasing performance from HD-EMG was compensated by its comfort and the ease of use of its electrode. This tradeoff between performance and usability with sEMG compared to HD-EMG should be a consideration for users who want to choose between performance and ease of use for head orientation prediction purposes. Comparison with state-of-the-art head prediction methods proved that the sEMG-based model offers better performance in predictions when users change their head directions, which was quantified by calculating the dt peaks. In other words, our sEMG-based prediction model is suitable for VR applications, which require the user to perform high-intensity or abrupt movements, such as in FPS games or exercise/sports games.
Design and Analysis of a Combined FBG Sensor for the Measurement of Three Parameters

A new fiber Bragg grating (FBG) sensor that can measure temperature, vibration, and strain is proposed in this article. The structure of this sensor is designed and optimized via theoretical analysis and the finite-element method (FEM). With an innovatively designed L-shaped cantilever (LSC), this designed sensor can better balance the vibration measurement sensitivity and the resonant frequency. The strain measurement structure is designed as a hollow square so that the reaction force of the sensor for the measured object is only <inline-formula> <tex-math notation="LaTeX">$2.76\times 10^{-3}$ </tex-math></inline-formula> N/<inline-formula> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula>, which ensures that the strain distribution of the measured object is not affected. The designed sensor can significantly amplify the measured strain with a measurement sensitivity (5.44 pm/<inline-formula> <tex-math notation="LaTeX">$\mu \varepsilon$ </tex-math></inline-formula>) that is four times greater than that of the bare FBGs. This article uses single-mode FBGs inscribed by a femtosecond laser (FS-FBG) to make the sensors, and the sensing performance is analyzed. The results show that this FS-FBG-based sensor can withstand temperatures up to 1100 °C.
Channel Models for Performance Evaluation of Wireless Systems in Railway Environments

In the automotive and rail domains, vehicles are entering the era of full automation thanks to wireless sensors and communication systems, shifting control functions from a human driver to computers. High data rate, robustness, high reliability and ultra-low latency wireless communications are required in the context of autonomous train and safety critical applications. Today, the Future Railway Mobile Communication System (FRMCS) is under development at European level within the International Union of Railways (UIC). This system will answer all the current and future needs of rail. It will be IP based, multi-bearer and resilient to technology evolution. In the context of the development of different FRMCS prototypes by industry, it is crucial to be able to test them in representative Railway radio environments thanks to laboratory tools. Characterization of radio channels in railway environments, by measurements or simulations, is a very active field. In this article, based on broad literature survey, we show that not all the published models are suitable for performance evaluation. Then, we propose a selection of typical Tapped-Delay-Line channel models to be implemented in an original hardware and software testing platform capable to reproduce the effect of representative Railway environments in laboratory, with real time emulation at RF (Radio Frequency) level. Preliminary results in Hilly 3 taps and Cutting 5 taps channel models are presented as a proof of concept of a “zero on site testing” approach, allowing for time and cost savings in the validation of railway communication systems.
Relationship between Incident Angle of Wind on Rotor Blade and Output of a Drag-type Multi-blade Vertical-Axis Wind Turbine with Stationary Multi-vanes

Drag-type vertical-axis wind turbines have a large starting torque and excellent self-starting performance, and are non-directional with respect to wind. In addition, because the maximum output occurs at a tip speed ratio of 1 or less, these turbines can be operated in a lower speed range compared with lift-type wind turbines. However, drawback of drag-type wind turbines is that their output is lower per unit sectional area than that of the lift-type wind turbines. Therefore, we have been studying the characteristics of a multi-blade drag-type vertical-axis wind turbine with stationary vanes functioning as wind direction plates. Our experimental results so far have shown that the wind turbine output is increased by using stationary vanes. In this paper, a method for calculating the output power of a multi-blade drag-type vertical-axis wind turbine with stationary vanes based on the characteristics of a single rotor blade was studied and compared with the results of wind turbine characteristics measured in a wind tunnel. In addition, the relationship between the wind incident angle of the rotor blade and the wind turbine output was examined using the proposed method for calculating wind turbine characteristics, assuming that the angle of installation of the stationary vanes are variable.
IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.
Modelling the Frequency Dependence of the Open-Circuit Voltage of a High-Tc Superconducting Dynamo

A high-Tc superconducting (HTS) dynamo enables the injection of large DC currents into a superconducting circuit, without the requirement for current leads. In this work, we attempt to explain the frequency dependence of such dynamos/flux pumps reported in the literature, where it is observed that the rate at which the open-circuit DC voltage increases reduces with increasing frequency, in contrast to the expected linear behaviour. Heat generated in the HTS wire has been the common explanation given to date for this phenomenon. Here we offer an alternative explanation: the interaction between and current flow in the different layers of the HTS wire as the frequency of the dynamo increases. Our claim is based on numerical analysis using a segregated H-formulation finite-element model of the HTS dynamo benchmark problem that is extended to include the full HTS wire architecture and coupled with a thermal model. This framework enables us to efficiently model the relative movement between the rotating room-temperature permanent magnet and the stationary HTS wire and to study the impact of the frequency of rotation and temperature on the open-circuit DC voltage of the dynamo.
Smart Home Security with Dual Modes

The internet of Things (IoT) is a technology that has shown impact to revolutionize individual live. In the Integration Era in year 2005-2018, the researcher and experts have started to combine the security system with the IoT technology. One of the main reasons is, the IoT being used as the source of data storage. Same as Closed-Circuit Television (CCTV), as one of the main component in security system; the data storage is being upgraded from a digital video recorder (DVR) which is a kind of memory card to an endless network video recorder (NVR), which is internet cloud data storage. The NVR camera system is widely known as Internet Protocol Camera (IP Camera). This project is mainly focusing on the security system which could be implemented by community, either in-house or workplace, as the system has dual modes of operation (security mode and calibration mode). The improvement in term of notification, user friendly system is further discussed in this paper. From the notification, the users shall be aware with the location of the burglar. This study shall provide the development of new Smart Home Security, focusing on CCTV system technology.
Evaluation of Different Materials to Design 3D Printed Horn Antennas for Ku-Band

The evolution of new satellite services in the Ku-band drives the development of new antenna technologies. For systems like this, the high performance and the antenna’s cost are critical aspects to consider. 3D printing technology is a possible solution, as it provides both fast and low cost prototyping, while ensuring high precision features. In this article two 3D printed horn antennas are presented. They followed different production approaches in order to evaluate distinct materials. The first prototype was printed with PLA and then covered with copper tape, while the second one was merely printed with a conductive PLA. Although the first one presented better efficiency, the second one achieved better bandwidth.
Emotion Recognition in Public Speaking Scenarios Utilising An LSTM-RNN Approach with Attention

Speaking in public can be a cause of fear for many people. Research suggests that there are physical markers such as an increased heart rate and vocal tremolo that indicate an individual’s state of wellbeing during a public speech. In this study, we explore the advantages of speech-based features for continuous recognition of the emotional dimensions of arousal and valence during a public speaking scenario. Furthermore, we explore biological signal fusion, and perform cross-language (German and English) analysis by training language-independent models and testing them on speech from various native and non-native speaker groupings. For the emotion recognition task itself, we utilise a Long Short-Term Memory - Recurrent Neural Network (LSTM-RNN) architecture with a self-attention layer. When utilising audio-only features and testing with non-native German’s speaking German we achieve at best a concordance correlation coefficient (CCC) of 0.640 and 0.491 for arousal and valence, respectively – demonstrating a strong effect for this task from non-native speakers, as well as promise for the suitability of deep learning for continuous emotion recognition in the context of public speaking.
Analysis of Hybrid Solenoid Coupler as Transmitter with Rectangular and Circular Coupler as Receiver for Wireless Power Transfer

Wireless Power Transfer (WPT) is one of the promising technologies which could address the charging issues of Electric Vehicles (EVs). Inductive wireless charging of EV batteries is possible by using transmission coil and receiver coil. Design of couplers has been a crucial part of WPT systems aiding efficient power transfer. In this paper, a coil with rectangular geometry with solenoid at the centre is taken as Transmitter (Tx) and two different receivers of rectangular (Rxr) and circular (Rxc) geometry are analysed and compared in terms of misalignment tolerance, coupling coefficient and change in inductance of coils. Additionally, we also show that the coupling coefficient is improved when a solenoid is placed at the centre in series with the transmitter coil. A comprehensive comparison of these combinations reveal that a rectangular pad is more suitable for this type of transmitter.
Identification of Early-Stage Cervical Cancer Tissue Using Metamaterial Terahertz Biosensor With Two Resonant Absorption Frequencies

We propose a simple method for fast and label-free identification of early-stage cervical cancerous tissues using a metamaterial (MM) terahertz (THz) biosensor with two resonant absorption frequencies at 0.286 THz and 0.850 THz, respectively. Because the two resonant absorption frequencies are very sensitive to the ambient change of dielectric properties, the cervical cancerous tissues can be distinguished from the normal tissues by analyzing the resonant frequency shift. The calculated sensitivity of the low resonant frequency and that of the high resonant frequency are 29 and 74 GHz/RIU, respectively. In the experiment, the cervical cancerous tissue samples were taken from different patients, and the cancerous and normal parts were determined by hematoxylin-eosin (HE) stain. Compared with the traditional method of directly detecting cancer tissue without the sensor, this method has the advantages of being simple, label free and fast. In addition, compared with biosensors which have a single resonant absorption frequency, the biosensors with double resonant absorption frequencies have higher accuracy. This method potentially can be applied for the detection and diagnosis of many other cancer tissues.
Target Localization Geometry Gain in Distributed MIMO Radar

In this paper, we analyze the accuracy of target localization in multiple-input multiple-output (MIMO) radars with widely-separated antennas. The relative target-antennas geometry plays an important role in target localization. We investigate the optimal placement of transmit and receive antennas for coherent and non-coherent processing, based on maximizing the determinant of the Fisher information matrix (FIM), which is equivalent to minimizing the error ellipse area. The square root of the average determinant of the FIM can be expressed as a product of three parameters, namely the equivalent single radar gain, coherency gain and geometry gain. It is shown that the coherency gain of coherent MIMO radar is greater than the non-coherent one, while the geometry gain of coherent MIMO radar is always smaller than or equal to the non-coherent case. The maximum value of the geometry gain for a MIMO radar system with <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> transmit and <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> receive antennas is proportional to <inline-formula><tex-math notation="LaTeX">$MN$</tex-math></inline-formula> for coherent while it is <inline-formula><tex-math notation="LaTeX">$\sqrt{2}MN$</tex-math></inline-formula> for the non-coherent case.
SMFuse: Multi-Focus Image Fusion Via Self-Supervised Mask-Optimization

In this paper, a novel self-supervised mask-optimization model, termed as SMFuse, is proposed for multi-focus image fusion. In our model, given two source images, a fully end-to-end Mask-Generator is trained to directly generate the binary mask without requiring any patch operation or postprocessing through self-supervised learning. On the one hand, based on the principle of repeated blur, we design a Guided-Block with guided filter to obtain an initial binary mask from source images, narrowing the solution domain and speeding up the convergence of the binary mask generation, which is constrained by a map loss. On the other hand, as the focused regions in source images show richer texture details than the defocused ones, i.e., larger gradients, we also design a max-gradient loss between the fused image and source images as a follow-up optimization operation to ensure the fused image to be all-in-focus, forcing our model to learn a more accurate binary mask. Extensive experimental results conducted on two publicly available datasets substantiate the effectiveness and superiority of our SMFuse compared with the current state-of-the-art. Our code is publicly available online.11[Online]. Available: https://github.com/jiayi-ma/SMFuse.
Spectral Graph-based Cyber Worm Detection Using Phantom Components and Strong Node Concept

Innovative solutions need to be developed to defend against the continued threat of computer worms. We propose the spectral graph theory worm detection model that utilizes trafﬁc dispersion graphs, the strong node concept, and phantom components to create detection thresholds in the eigenspectrum of the dual basis. This detection method is employed in our proposed model to quickly and accurately detect worm attacks with different attack characteristics. It also intrinsically identiﬁes infected nodes, potential victims, and estimates the worm scan rate. We test our model against the worm-free NPS2013 dataset, a modeled Blaster worm, and the WannaCry CTU-Malware-Capture-Botnet-284-1 and CTU-Malware-Capture-Botnet-285-1 datasets. Our results show that the spectral graph theory worm detection model has better performance rates compared to other models reviewed in literature.
Specialized CNT-based Sensor Framework for Advanced Motion Tracking

In this work, we discuss the design and development of an advanced framework for high-fidelity finger motion tracking based on Specialized Carbon Nanotube (CNT) stretchable sensors developed at our research facilities. Earlier versions of the CNT sensors have been employed in the high-fidelity finger motion tracking Data Glove commercialized by Yamaha, Japan. The framework presented in this paper encompasses our continuing research and development of more advanced CNT-based sensors and the implementation of novel high-fidelity motion tracking products based on them. The CNT sensor production and communication framework components are considered in detail and wireless motion tracking experiments with the developed hardware and software components integrated with the Yamaha Data Glove are reported.