Search

This study explores the method of applying surface enhanced Raman scattering (SERS) in diagnosing the aging stage of oil-paper insulation equipment in the power system, which provides access to the on-line mornitoring of electrical equipment. Oil-paper samples in different aging stages were obtained by accelerated aging experiments. Effective Raman signals of insulating oil were collected by using the self-made hexagonal sliver nano-plates SERS substrates and the self-assembled Raman detection platform. For the SERS data of oil-paper insulation samples, kernel entropy component analysis (KECA) was applied to extract the Raman spectral features and support vector machine (SVM) was used to recognize the the aging stage of oil-paper insulation. The results demonstrate that the KECA-SVM method exhibits a good diagnostic capacity and the recognition accuracy of the propsed method reached 81.43 % (57/70). In summary, a new spectral method is proposed to diagnose the aging stage of oil-paper insulation, which lays a foundation for the actual diagnosis of the aging condition in running oil-paper insulation equipment by Raman spectroscopy.

The ReaxFF molecular dynamics simulation and Monte Carlo method were adopted to analyze the pyrolysis process of cellulose in insulating papers from the perspective of microscopic atom. Molecular dynamics failed to continuously describe the motion behavior of an atom. According to this principle, the system can only calculate the atomic state on the node and then move the atom by a time step to continue calculating the atom. This paper would put forward the optimal step setting method of cellulose thermal decomposition in insulating paper: setting one step every other 0.1 fs. Specifically, for small molecules with a simple structure, such as H2O, the step size was set to 0.4 fs or less, while the step size of macromolecules with complex structures (CH2O2) should be set to 0.2 fs or less. In addition, the relationship between the step size and the temperature to which the system was heated was given as well in this paper. In previous literatures, empirical values were used to set the step size. This study would not only provide a theoretical basis for the study on the bond formation and fracture process of cellulose pyrolysis products, but also offer the data and guidance for related fields in the future, thus rendering an efficient simulation process.

Frequency domain spectroscopy (FDS) technique is widely applied in the condition assessment of the oil-paper system in power transformers. However, the synergistic effect generated by moisture and temperature on the FDS data cannot be analyzed by the existing model since the single independent variable (moisture or temperature) is considered in the construction of the model. To quantify such the synergistic effect, a novel method that utilized for normalizing (or standardizing) the FDS curve is reported based on the theory of the power series and fitting analysis. The present findings reveal that the reported method is capable of predicting the dielectric loss (tan $\delta$ ) curve under diverse test conditions, in which the average error is less than 7%. The synergistic effect can be also explored by using the extracted feature parameters. The potential application is then proved to make up for the measurement errors during the FDS test, the findings are expected to promote the moisture analysis of the transformer insulation.

Optical fiber sensing is a novel sensing technology with the advantages of anti-electromagnetic interference, anti-corrosion and high sensitivity suiting for many special applications. In this paper, a Michelson optical fiber voice monitoring system is introduced and a novel sound extraction algorithm is proposed to improve the noise stability of it. The voice demodulated by a traditional algorithm based on $3\times 3$ fiber coupler has unstable background noise intensity with the change of the working point of the interferometer. The unstable noise comes from the shot noise of photodetectors. The sound extraction method proposed in this paper uses the relation between shot noise and light intensity to smooth the unstable noise and does not affect the quality of voice monitoring. Voice monitoring systems using this algorithm have stable monitoring quality without any working point control technology. The fluctuation range of noise intensity in 300~3500 Hz decrease from 10 dB to less than 2 dB compared with the traditional demodulation algorithm. Such a method and system can be widely used in many special voice monitoring application fields such as prison, underground utility tunnel.

Near-infrared spectroscopy (NIR) has become one of the most important methods for counterfeit drugs identification for its low cost, non-destructive, and on-site detection. However, it is often invalid for unknown samples beyond the scope of modeling samples, as well as it is not efficient in conditions of insufficient samples (insufficient number of samples within the class), unbalanced samples (large difference in the number of samples between classes), and sensitivity of identification results (different tolerance for different errors in application scenarios). To solve these problems, this paper proposes a general method for on-site identification of counterfeit drugs based on Siamese-network modeling with near-infrared spectroscopy, which especially constructs the train set and test set, learning the general knowledge of spectral differences to identify the different drugs by a costumed one-dimensional convolution neural network (1D-CNN), and finally answered the question of whether the on-site two spectra are pointed to the same drug. Based on experimental modeling samples of 1314 spectra, which are involved 9 drugs produced by 25 manufacturers, this paper has constructed and fully trained its model. Then, not known at the time of modeling, 4 drugs produced by 9 manufacturers are used for testing in the on-site application, and the accuracy rate amounts to 97.3%. For generalizing consideration, randomly divided into training and testing categories, the 32015 spectra of 135 drugs produced by 391 manufacturers in the spectral library are handled by the same processing. The generalization model is equally applicable, and the accuracy is above 97%. Compared with traditional binary classification identification methods such as SVM, PLS-DA, Auto-encoding, and one class (OC) threshold identification algorithms such as SVM-OC, SIMCA, conformity test (CT), the proposed method has the best identification ability for unknown samples in modeling.

In this paper, the effects of rotor-rotor interaction on the wake structure and thrust generation of a quadrotor unmanned aerial vehicle (UAV) are experimentally investigated in the rotor tip Reynolds number range of 34000 – 54000. The interaction strength is manipulated by varying the number of rotating rotors and the normalized rotor separation distance. A stronger rotor-rotor interaction places the inner tip vortices between rotors closer to each other, forming an upflow region through vortex pairing and intensifying the turbulence intensity between rotors. To comprehensively evaluate the effect of interaction on the wake structure, we propose a modified Landgrebe’s model that accurately describes the wake boundary of UAV, given the number of rotating rotors and the normalized rotor separation distance. The wake analysis based on the model shows that the stronger the rotor-rotor interaction, the less the wake contracts and the closer the vena contracta moves to the rotor-tip path plane. The momentum theory combined with the modified Landgrebe’s model shows that the loss of axial momentum transfer due to the wake inclination is insufficient to account for the thrust loss caused by the rotor-rotor interaction. This paper shows that the shift of the inner tip vortex away from the rotational axis and the corresponding increase of induced axial velocity followed by a decrease in the local effective angle of attack is another important mechanism for the thrust loss.

In this paper, a modified COMSOL Multiphysics model with artificial stability is adopted, and the boundary condition developed in the previous paper are applied to the calculation of the photoionization rate. The main processes involved in air discharge were simulated by 39 plasma chemical reactions composed of 12 kinds of particles. The characteristics of low-temperature sub-atmospheric pressure air discharge under unipolar square wave pulse voltage with 13kV applied amplitude, 25 kHz-50 kHz frequency and a duty cycle of 50%-75% are discussed. The results show that: When the duty cycle increases, the average density of electron, N2+, N4+, O2+, O4+, and N2O2+ show an increasing trend, and the average electron temperature changes little; When the frequency increases, the average electron density, the average density of N4+ and O4+ show a downward trend, the average density of N2+, O2+, N2O2+ change little; The average electron temperature shows an increasing trend, but the duration of high electron temperature becomes shorter. When discussing a single period, it is found that the potential difference between the electrode and the plasma determines the direction of the electric field in the space. For the photoionization reaction, even if its influence on the negative streamer is not important, it still needs to be considered in the simulation. The effect of temperature on discharge is also taken as the research emphasis, it includes the following aspects: Firstly, at low temperature, the collision reaction and attachment reaction rates are increased, while the recombination reaction rate is almost unaffected; Secondly, low temperature results in the decrease of secondary electron emission coefficient, which indirectly increases the electric field gradient and the peak value of electric field; Finally, at low temperature, the mobility of carriers, including electrons, positive and negative ions, is increased, but the streamer velocity is decreased.

Electrochemical impedance spectroscopy (EIS) is a widely used means for characterization of the dynamics of batteries and electrochemical energy conversion systems in general. EIS is useful for on-line condition monitoring since it contains valuable information on the internal condition of the batteries. The conventional approach to the EIS of batteries relies on their successive excitation with mono-component sinusoidal signals at the pre-defined frequencies. When inferring about the battery’s state-of-health, the low-frequency part of the impedance characteristic is of particular interest. Excitation in the low-frequency region can take an excessively long time. Moreover, maintaining stable experimental conditions over long time intervals i na way that the external disturbances will not affect the estimated impedance, might be demanding, especially in the in-field applications. To alleviate the said limitations, and to minimally intrude the process operation, in this paper we apply broadband electrochemical impedance spectroscopy based on a discrete random binary sequence for perturbation of the battery input. The impedance is evaluated by processing voltage and current signals with continuous Morse wavelet transform. The main contributions of the paper refer to (i) the accurate evaluation of the impedance spectra from $\mu $ Hz to kHz range with high-frequency resolution (more than 200 points per decade) and (ii) the evaluation of the uncertainty region of the impedance characteristic. The entire characterisation takes only a fraction of the time required by the classical sine-based electrochemical impedance spectroscopy. The algorithm is successfully demonstrated on a commercial Li-ion battery, which, together with the all datasets are available for download at https://repo.ijs.si/gnusev/supplementary_material.git.

A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery's charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models.

In recent years, methanol has been proposed as a chemical indicator to assess the aging condition of cellulose insulation in oil-immersed power transformers. However, the formation mechanisms of methanol during cellulose degradation are not clear enough. In this paper, such formation mechanisms were studied using molecular dynamics simulation. Three main formation pathways of methanol were found and discussed. Analysis of the Mayer bonds of cellulose revealed that each atom of cellulose contributes differently to methanol formation. The methylol groups on the 5-carbon atom of the 1-pyran ring and 4-pyran ring of cellulose are more readily detached to form methanol molecules. In addition, changes in the amount of methanol molecule with simulation time were investigated. The results indicated that the separate existence of initial moisture and oxygen has no significant effect on the formation of methanol, while the co-existence of initial moisture and initial oxygen catalyzes the conversion of cellulose degradation products to methanol. The findings reported in this paper can provide a valuable theoretical basis for further studies on methanol as an indicator to evaluate the residual life of cellulose insulation.

The art of paper folding also known as Origami plays an important role in various research areas of different scientific fields. Origami structures possess many useful characteristics such as reconfigurability, flexibility, deployability, compactness, and multi-functionality. These attributes are inevitable for modern communication systems. Additionally, they provide antenna engineers an extra degree of freedom while blueprinting next-generation designs. One of the desirable features of origami is its folding capability. Using this property, it can be transformed into a two-Dimensional (2D) sheet from a three-Dimensional (3D) structure and vice versa through external stimulus. In recent years, researchers have proposed numerous smart materials to optimize folding behavior. These smart materials extend the functionality of origami technology for designing self-folding structures required in space systems, small scale devices, and self-assembly systems. Origami structures feature many useful characteristics pertinent to modern day design challenges of communication systems such as reconfiguration, flexibility, compactness, and multifunctionality. In this work, a state of the art review is presented on conceptualization, design challenges, and fabrication of light-weight and low-cost deployable origami antennas. Furthermore, to provide critical insights deployment challenges and possible solutions are also provided. It is believed that this work will not only help peers to understand the basic working principles of different origami structures but will also lay the foundation for future research in the evolving field of origami-based antennas. Moreover, the design methodology proposed in this paper will also provide pragmatic solutions for origami antennas suitable for the harsh and rigid environments.

The purpose of this paper is to design a broadband and very thin absorber with periodic structure using magneto-dielectric particles. A theoretical method has been developed for the analysis of periodic structures. This method has the ability to compute the field, reflection, transmission, and absorption for different number of layers and particles with different shapes and dimensions. In this paper, in addition to electric and magnetic dipole moments, quadrupole equivalences of particles are considered as well. This will help the analytical approach to calculate the fields more accurately. It is shown how these changes, will help to design a broadband absorber in microwave regime. Then a step-by-step method for design an absorber is presented based on this theoretical method that is called planar analysis method . In addition, due to the properties of magneto-dielectric materials that can have electrical and magnetic effects together and often have high losses; we can have broadband absorbers using these materials. In this paper, an absorber based on rectangular cube particles composing magneto-dielectric material is designed. The simulation results show that the absorber has 104% bandwidth for absorption above 90%. The thickness of this absorber with respect to center of absorption bandwidth is $\lambda /12$ .

In the real world, people heavily rely on haptic or touch to manipulate objects. In emerging systems such as assistive devices, remote surgery, self-driving cars and the guidance of human movements, visual or auditory feedback can be slow, unintuitive and increase the cognitive load. Skin stretch devices (SSDs) that apply tangential force to the skin via a tactor can encode a far richer haptic space, not being limited to force, motion, direction, stiffness, indentation and surface geometry. This paper introduces novel hand-worn hydraulic SSDs that can induce 3-axis tangential forces to the skin via a tactor. The developed SSDs are controlled by new soft microtubule muscles (SMMs) which are driven by hydraulic pressure via custom miniature syringes and DC micromotors. An analytical model is developed to characterize the responses of SMM output in terms of force and elongation. A kinematic model for the motion of the 3-axis SSD is also developed. We evaluate the capability of the tactor head to track circular reference trajectories within different working spaces using an optical tracking system. Experimental results show that the developed SSDs have good durability, high-speed, and can generate omnidirectional shear forces and desired displacement up to 1.8 N and 4.5 mm, respectively. The developed SMMs and SSDs created in this paper will enable new forms of haptic communication to augment human performance during daily activities such as tactile textual language, motion guidance and navigational assistance, remote surgical systems, rehabilitation, education, training, entertainment, or virtual and augmented reality.

Multiple-target localization is extensively applied in wireless connected networks. However, sensor location uncertainty is known to degrade significantly the target localization accuracy. Fortunately, calibration emitters such as unmanned aerial vehicles (UAV) with known location can be used to reduce the loss in localization accuracy due to sensor location errors. This paper is devoted to the use of UAV calibration emitters for time differences of arrival (TDOA) and frequency differences of arrival (FDOA) positioning of multiple targets. The study starts with deriving the Cramér-Rao bound (CRB) for TDOA/FDOA-based target location estimate when several UAV calibration signals are available. Subsequently, the paper presents an iterative constrained weighted least squares (ICWLS) estimator for multiple-target joint localization using TDOA/FDOA measurements from both target sources and UAV calibration emitters. The newly proposed method consists of two stages. In the first phase, the sensor locations are refined based on the calibration measurements as well as the prior knowledge of sensor locations. The second step provides the estimate of multiple-target locations by combining the measurements of target signals as well as the estimated values in the first phase. An efficient ICWLS algorithm is presented at each stage. Both the two algorithms are implemented by using matrix singular value decomposition (SVD), which is able to provide a closed-form solution and update the weighting matrix at every iteration. Finally, the convergence behavior and estimation mean-square-error (MSE) of the new estimator are deduced. Both theoretical analysis and simulation results show that the developed method can improve the TDOA/FDOA localization accuracy obviously with the help of UAV calibration emitters.

High penetration of renewable energy brings economic and environmental benefits to the grid, but also causes challenges to the distribution system flexibility due to its uncertainty. As a new variable load, electric vehicles (EVs) can increase system's flexibility through interactions with the grid and promote the consumption of renewable energy. This paper studies the flexibility evaluation of distribution system considering the interaction between EVs and the grid. Firstly, the charging and discharging control strategies of EVs have been analyzed to reduce the impact of renewable energy output fluctuations on the distribution system flexibility, such as the G2V (Grid to vehicle) and V2G (Vehicle to grid) modes. Secondly, the battery capacity, driving plan, and transportation network are modeled to study the impact of the EVs connected to the grid on the distribution system flexibility. Thirdly, the flexibility evaluation method of distribution network is proposed based on the feasibility analysis of uncertain region. Finally, the analyses of the example show that the proposed method can realize the evaluation of the time series flexibility of the distribution system, which proves the effectiveness of the method proposed in this paper.

A microrobot with untethered control in 3D space is a good choice to be applied in the fields of biomedicine with in small and confined workspace. In this paper, an electromagnetic actuation system (EMA) which combined with Helmholtz coil and Maxwell coil for the microrobot 5 DOF locomotion in 3D space is built. The magnetic field analysis of the proposed 3D EMA system was analyzed by finite-element-method (FEM) with multi-physics COMSOL software. The proposed EMA system can produce magnetic field with different characteristics such as a controllable uniform gradient magnetic field, a rotating magnetic field and a oscillating magnetic field in a three-dimensional space by independently changing the current in each coil. In this paper the 3D motion dynamic equation model of microrobot was established. A novel control method for the gravity compensation for the wireless locomotive microrobot was proposed. The proposed method has the property that the direction of magnetic flux and the locomotion path of the microrobot are independent. Meanwhile, it can achieve the horizontal motions or nearly horizontal motions and overcome the gravity well at the same time. It has been verified by experiments in 3D liquid environment. With the proposed method, the microrobot shows good performance in horizontal motions as well as various motions in the 3D space.

Telemedicine, as a new technical means and medical model, can truly realize the sharing and monitoring of telemedicine information, and ultimately ensure that everyone has equal access to medical and health resources. Based on the research on the status of telemedicine application and wireless communication technology, this paper proposes a multi-physical parameter wireless telemedicine health monitoring system solution, and analyzes the overall structure and functional requirements of the system. Human physiological parameters of the wireless remote medical system for health monitoring include body temperature, respiration, blood oxygen saturation, pulse, blood pressure, and electrocardiogram. In this paper, fabric electrodes are used to extract human bioimpedance signals, discrete Fourier transform algorithm is used to detect human respiratory signals, and respiratory rate is detected based on dynamic differential threshold peak detection technology. The reflection type photoelectric sensor is used to realize the reflection of the human pulse signal, and the continuous measurement of the cuff-free blood pressure based on the pulse wave conduction time is combined with the ECG (Electrocardiogram) data. A self-learning threshold algorithm based on near-infrared photo plethysmo graphy signal trough detection is designed on the reflective blood oxygen saturation calculation algorithm. The difference threshold method is used to extract the QRS band feature points. We tested the overall operation of the system. The results show that the collected human physiological signal data is accurate. After a series of tests, the validity and reliability of the collected physiological signals have been proven.

Self-governing small regions of power systems, known as “microgrids”, are enabling the integration of small-scale renewable energy sources (RESs) while improving the reliability and energy efficiency of the electricity network. Microgrids can be primarily classified into three types based on their voltage characteristics and system architecture; 1) AC microgrids, 2) DC microgrids, and 3) Hybrid AC/DC microgrids. This paper presents a comprehensive review of stability, control, power management and fault ride-through (FRT) strategies for the AC, DC, and hybrid AC/DC microgrids. This paper also classifies microgrids in terms of their intended application and summarises the operation requirements stipulated in standards (e.g., IEEE Std. 1547-2018). The control strategies for each microgrid architecture are reviewed in terms of their operating principle and performance. In terms of the hybrid AC/DC microgrids, specific control aspects, such as mode transition and coordinated control between multiple interlinking converters (ILCs) and energy storage system (ESS) are analysed. A case study is also presented on the dynamic performance of a hybrid AC/DC microgrid under different control strategies and dynamic loads. Hybrid AC/DC microgrids shown to have more advantages in terms of economy and efficiency compared with the other microgrid architectures. This review shows that hierarchical control schemes, such as primary, secondary, and tertiary control are very popular among all three microgrid types. It is shown that the hybrid AC/DC microgrids require more complex control strategies for power management and control compared to AC or DC microgrids due to their dependency on the ILC controls and the operation mode of the hybrid AC/DC microgrid. Case study illustrated the significant effects of microgrid feeder characteristics on the dynamic performance of the hybrid AC/DC microgrid. It is also revealed that any transient conditions either in the AC or DC microgrids could propagate through the ILC affecting the entire microgrid dynamic performance. Additionally, the critical control issues and the future research challenges of microgrids are also discussed in this paper.

Each joint of hydraulic drive legged robot adopts highly integrated electro-hydraulic servo system, and the system has two common forms, namely, valve-controlled cylinder system and pump-controlled cylinder system. The former has a large energy loss, which restricts the endurance of the legged robot in the field. Although the latter has the advantages of energy saving and high efficiency, the overall response ability of control system is unsatisfied. In this paper, a novel pump-valve compound drive system (PCDS) is designed. It combines the advantages of pump-controlled cylinder system and valve-controlled cylinder system, which can not only effectively reduce the energy loss of the system, but also ensure the electro-hydraulic servo system of the robot joint has the ideal response ability. This paper consists of three parts. In the first part, the mathematical model of nonlinear force control of PCDS is established, which takes into account the pressure-flow nonlinearity of servo valve, the asymmetry of servo cylinder and the complexity and variability of load. So this model has a high accuracy. In the second part, On the basis of the above model, a force control method combining tracking error compensation and load compensation control is designed. And this control method can improve the control precision of the robot. In the third part, the performance of the designed control method is verified by experiments. The experiments show that the designed force control method can effectively reduce the influence of external position disturbance on the tracking accuracy of the force control system and improve the control accuracy of the robot joint movement.

Healthcare sector is probably the most benefited from the applications of nanotechnology. The nanotechnology, in the forms of nanomedicine, nanoimplants, nanobiosensors along with the internet of nano things (IoNT), has the potential to bring a revolutionizing advancement in the field of medicine and healthcare services. The primary aim of this paper is to explore the clinical and medical possibilities of these different implementations of nanotechnology. This paper provides a comprehensive overview of nanotechnology, biosensors, nanobiosensors, and IoNT. Furthermore, multilevel taxonomies of nanotechnology, nanoparticles, biosensors, nanobiosensors, and nanozymes are presented. The potential medical and clinical applications of these technologies are discussed in details with several examples. This paper specifically focuses on IoNT and its role in healthcare. In addition to describing a general architecture of IoNT for healthcare, the communication architecture of the IoNT is also explained. The challenges in the successful realization of IoNT are also discussed critically, along with a special discussion on internet of bio-nano things (IoBNT) and its potential in making IoNT more compatible to human body.

This paper exhibits a low profile dual-polarized MIMO antenna with high isolation to meet the requirements of 5G smartphones. The integration between a vertically polarized slot and a horizontally polarized slot is investigated and applied for 28 GHz dual-polarized smartphone antenna. The antenna is combined with metasurface (MTS) to achieve high gain and more directivity. In order to design the metasurface, the characteristic mode analysis is used to investigate the performance of MTS at 28 GHz. The proposed antenna achieves high isolation coefficients better than 40 dB and cross polarization lower than -40 dB from simulated and measured results. The isolation between elements is achieved without any additional decoupling techniques. The proposed MTS slot antenna operates with 4 GHz bandwidth (26-30 GHz) with a realized gain of 11 dBi and efficiency of 90%. Four antennas (with eight ports) are positioned orthogonally at the corners of the mobile PCB to serve MIMO for 5G applications. The effect of MIMO antenna on the human is taken into consideration in power density term. Furthermore, the housing and components of smartphones are taken into our consideration in this paper.

Programmable metasurfaces have garnered significant attention as they confer unprecedented control over the electromagnetic (EM) response of any surface. Such feature has given rise to novel design paradigms such as Software-Defined Metamaterials (SDM) and Reconfigurable Intelligent Surfaces (RIS) with multiple groundbreaking applications. However, the development of programmable metasurfaces tailored to the particularities of a potentially large application pool becomes a daunting task because the design space becomes remarkably large. This paper aims to ease the design process by proposing a methodology that employs a semi-analytical formulation to model the response of a metasurface and, then, derives performance scaling trends as functions of a representative set of design and application-specific variables. Although the methodology is amenable to any EM functionality, this paper explores its use for the case of beam steering at 26 GHz for 5G applications. Conventional beam steering metrics are evaluated as functions of the unit cell size, number of unit cell states, and metasurface size for different incidence and reflection angles. It is shown that metasurfaces 5¿×5¿ or larger with unit cells of ¿/3 and four unit cell states ensure good performance overall. Further, it is demonstrated that performance degrades significantly for angles larger than ¿>60o and that, to combat this, extra effort is needed in the development of the unit cell. These performance trends, when combined with power and cost models, will pave the way to optimal metasurface dimensioning. This work was supported in part by the European Commission (VISORSURF) under Grant H2020-FETOPEN-736876, and in part by Catalan Institution for Research and Advanced Studies (ICREA) through the ICREA Academia Programme. Odysseas Tsilipakos acknowledges support by the Stavros Niarchos Foundation within the framework of the project Advancing Young Researchers Human Capital in Cutting Edge Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges (ARCHERS).

Great advances have been made with the lattice Boltzmann (LB) method for complicated fluid phenomena and fundamental thermal processes over the past three decades. This paper presents a systematic overview of the LB method from 1990 to 2018, based on bibliometric analysis and the Science Citation Index Expanded (SCI-E) database. The results show that China took the leading position in this field, followed by the USA and UK. The Chinese Academy of Sciences had the most publications, while the Los Alamos National Laboratory was first as far as highest average citation per paper and h-index are concerned. Physical Review E was the most productive journal and “Mechanics” was the most frequently used subject category. Keyword analysis indicated that recent research has focused on the natural convection and heat transfer of nanofluid or multiphase flow in complex porous media. Hydrothermal treatment of nanofluid with shape factor on the conditions, such as variable magnetic fields, thermal radiation and slipping boundary, were the research hotspots. Further research perspectives mainly explore the multiscale models for coupling multiple transport phenomena, morphology optimization of porous parameters, new nanoparticles with shape factor, multicomponent LB method considering Knudsen diffusion effect, LB-based hybrid methods, radiation performance or boiling-heat transfer of nanofluid, and the active control of droplets, may continue to attract more attention. Moreover, some new applications, such as phase change of metal foam, erosion induced by nanaofluid, anode circulating, 3D modeling in thermal systems with vibration, and magnetohydrodynamics microfluid devices, could be of interest going forward.

The micro capacitive sensing and electrostatic drive control system (front-end electronics, FEE) is the core component of inertial sensor in space gravitational wave detection. The FEE requires high-precision displacement detection, high-stability electrostatic drive, and stable system control to achieve an acceleration resolution of 10-15 m/s2/Hz1/2 in the low-frequency range of 0.1 mHz-1 Hz. Based on the requirements of the future Chinese space gravitational wave detection task (Taiji Program), this paper conducted key technical research of the FEE using differential capacitance detections and electrostatic drives. The structure and working principle of the FEE were also introduced. The structural parameters of the entire system, working parameters, and electrostatic control system model were provided, and the performance of the PID controller was analyzed. Finally, using the torsion pendulum to overcome the influence of gravity on the earth, the FEE multi-degree of freedom control function was verified on the vibration isolation marble platform, the measurement range and power conversion coefficient were calibrated, and the noise level under current conditions was measured. Experimental results show that the FEE developed in this paper can achieve stable control in multiple degrees of freedom, the acceleration range is larger than 10-3 m/s2, the electric force conversion factor is 4.8 × 10-3 m/s2/V, and the measured acceleration resolution is 9.6 × 10-6 m/s2/Hz1/2. After optimizing the sensitive structure parameters, the acceleration resolution can be estimated at 3.3 × 10-15 m/s2/Hz1/2. These results satisfy the Taiji Program requirements. This paper provides a solid foundation for the future exploration of space gravitational waves in China and clears the optimization direction for the next step.

This paper presents a high precision adaptive slope compensation circuit for for DC-DC converter in wearable devices. Compared with the traditional adaptive slope compensation circuit, the comparator is used to sample the output voltage and input voltage, which greatly improves the accuracy.In this paper, the circuit is designed in UMC 0.18- $\mu \text{m}$ CMOS Technology and verified by Virtuoso Spectre Circuit Simulator. The simulation results show that the accuracy of the adaptive slope compensation circuit in this paper can reach more than 96%.

The Byzantine general problem is the core problem that consensus algorithms are trying to solve, which is at the heart of the design of blockchains. As a result, we have seen numerous proposals of consensus algorithms in recent years, trying to improve the level of decentralization, performance, and security of blockchains. In our opinion, there are two most challenging issues when we consider the design of such algorithms in the context of powering blockchains in practice. First, the outcome of a consensus algorithm usually depends on the underlying incentive model, so each participant should have an equal probability of receiving rewards for its work. Secondly, the protocol should be able to resist network failures, such as cloud services shutdown, while maintaining high performance otherwise. We address these two critical issues in this paper. First, we propose a new metric, called fair validity, for measuring the performance of Byzantine agreements. Intuitively, fair validity provides a lower bound for the probability of acceptances of honest nodes' proposals. This is a strong notion of fairness, and we argue that it is crucial for the success of a blockchain in practice. We then show that any Byzantine agreement could not achieve fair validity in an asynchronous network, so we will focus on synchronous protocols. This leads to our second contribution: we propose a fair, responsive, and partition-resilient Byzantine agreement protocol able to tolerate up to 1/3 corruptions. As we will show in the paper, our protocol achieves fair validity and is responsive in the sense that the termination time only depends on actual network delay, as opposed to arbitrary, pre-determined time-bound. Furthermore, our proposal is partition-resilient. Last but not least, experimental results show that our Byzantine agreement protocol outperforms a wide variety of state-of-art synchronous protocols, combining the best from both theoretic and practical worlds.

Solar Photovoltaic System (SPV) is one of the growing green energy sources having immense penetration in the national grid as well as the off-grid around the globe. Regardless of different solar insolation level at various regions of the world, SPV performance is also affected by several factors: conversion efficiency of PV cell technology, ambient temperature and humidity, soiling and seasonal/weather patterns. The rise in PV cell temperature and soiling is found to be detrimental issues regarding power plant performance and life expectancy leading alterations in the levelised cost of energy (LCoE). In this paper, authors present a short glance about factors affecting the performance of photovoltaic modules and re-discuss their usability in cleaning intervention decision-making models. With some highlights on the essence of cleaning to mitigate the soiling issues in PV power plants, this paper presents the existing cleaning techniques and practices along with their evaluations. The need for an optimal cleaning intervention by using advanced scientific tools rather than by visual inspection is drawing the attention of PV experts. The authors finally suggest a schematic of a decision-making model which involves the use of probable parameters, data processing techniques and machine learning tools. The implementation of data science and machine learning in a solar PV panel cleaning system could be a remarkable advancement in the field of renewable energy.

Voice disability is a barrier to effective communication. Around 1.2% of the World's population is facing some form of voice disability. Surgical procedures namely laryngoscopy, laryngeal electromyography, and stroboscopy are used for voice disability diagnosis. Researchers and practitioners have been working to find alternatives to these surgical procedures. Voice sample based diagnosis is one of them. The major steps followed by these works are (a) to extract voice features from voice samples and (b) to discriminate pathological voices from normal voices by using a classifier algorithm. However, there is no consensus about the voice feature and the classifier algorithm that can provide the best accuracy in screening voice disability. Moreover, some of the works use multiple voice features and multiple classifiers to ensure high reliability. In this paper, we address these issues. The motivation of the work is to address the need for non-invasive signal processing techniques to detect voice disability in the general population. This paper conducts a survey related to voice disability detection methods. The paper contains two main parts. In the first part, we present background information including causes of voice disability, current procedures and practices, voice features, and classifiers. In the second part, we present a comprehensive survey work on voice disability detection algorithms. The issues and challenges related to the selection of voice feature and classifier algorithms have been addressed at the end of this paper.

Despite the importance of the desalination process in removal of water salinity to produce fresh drinking water, it is an energy-consuming process. Therefore, the use of solar energy as an alternative and free energy source has an important role in enhancing the efficiency of the desalination process. This paper presents an experimental investigation using solar chimney to enhance the desalination process, where a modification to the conventional solar stills has been made. A mathematical model describing air flow inside the solar chimney has been constructed using Computational Fluid Dynamics (CFD). The commercial CFD package fluent release 6.3 has been used in this paper to express the air flow and temperature distribution inside solar chimney. Furthermore, the process parameters such as seasonal temperature, salt concentration, water depth and type of solar desalination unit have been examined to find the best operating conditions. The desalination process has been recommended to be carried out under permissible operating conditions in sunny days, with water depths ranging from (1-2 cm) and low salt concentration ranging from (3.5-8%). The modeled data has been verified with experimental data and the verification results have been estimated at 14%. Simulation results showed that water desalination has been improved by 30%.

Water leaks in the distribution network produce significant losses and cause serious economic inconvenience especially in areas with water shortage. In this paper, the operational aspects of the most popular offline detection technologies, ground penetrating radars (GPR's), infrared (IR) cameras, and acoustic detectors, were compared. The authors also studied the potential of using the recent Terahertz imaging technology for the same application. Acoustic detectors were found the most suitable technology for the atmosphere in UAE, where the levels of humidity and, consequently, soil moisture are high, because both of GPRs and IR cameras operational capability to detect leaks tend to decrease sharply as soil moisture increases. On the other side, a conventional acoustic detector has very limited scope of detection. This paper presents a method of expanding the sensing component of acoustic detectors by connecting acoustic sensors through a digital communication system using the 3G/4G networks to a monitoring center with an acoustic spectrum analyzer. The novelty of this system is its ability to provide offline detection of leakages in the underground water pipelines remotely without deforming the surrounding environment or adjusting the acoustic detector's analyzing system. Simulation results proves the ability of the system to reconstruct the input noise signal at the end of the proposed network which is to be connected to the acoustic analyzer.

In this paper, a detailed analysis of multi-input multi-output (MIMO) cross-layer secure communication algorithms in information-physical systems is investigated employing an interference strategy. A three-stage data-assisted channel estimation method is proposed in this paper for the acquisition of channel state information for complex jamming channels in large-scale MIMO two-layer systems. To implement the data-assisted scheme, assuming that there are no errors and no delay in the system, if the data detection and decoding data sequences are completed at a small cell base station, they are sent to the macro base station via a wired backhaul. Due to the sparsity of the channel at the macro base station after user grouping, a channel estimation algorithm based on optimal block orthogonal matching tracking(s) is proposed in the case where the downlink channel at the macro base station utilizes the decoded uplink data and known training sequences. The simulation results show that the data-assisted method proposed in this paper is effective in improving channel estimation accuracy. A machine learning algorithm is directly used to classify the channel difference or channel matrix to obtain the authentication results. In this paper, the scheme is first simulated using channel data from dynamic communication scenarios, its feasibility is analyzed, and the parameters in the scheme are compared, and the optimal scheme is the bagging tree authentication scheme using a 128-dimensional channel matrix as input. To address the interference problem caused by the dense arrangement of SAPs in heterogeneous networks and the unbalanced network load, large-scale MIMO techniques are introduced to reduce the downlink interference caused by the microcell boundary expansion in heterogeneous networks.

According to maritime image histograms' statistic and analysis, the histogram of pure maritime image obeys Gaussian distribution approximately, thus Three Adaptive Sub-histograms Equalization (TASHE) algorithm for maritime image enhancement is proposed in this paper. First, the characteristics of pure maritime image's histogram are studied, then the adaptive threshold's optimal selection strategy for the histogram's division is discussed, finally the implement of three sub-histograms is described. This paper employs visible gray maritime image, visible color maritime image and infrared maritime image to verify the enhancement algorithm's effectiveness and robustness, the experimental results show that TASHE algorithm can not only keep the maritime image's mean brightness and naturalness, but also improve the maritime image's contrast without the noise and artifacts. The objective image quality assessment also indicates that TASHE algorithm can improve the original maritime image's Enhancement Measure by Entropy (EME) value, furthermore, when a maritime image is pre-processed by TASHE algorithm, the maritime target's Detection Rate (DR) can be improved.

This paper presents the design of an extended parameterisations of H ∞ controller for off grid operation of a microgrid. The microgrid consists of distributed generation units, filters and local loads. The filters are used to achieve accurate sinusoidal output voltage. However, loads which are connected to the microgrid are parametrically uncertain. Hence, it undergoes with unknown loads uncertainties. These unknown loads may create unknown loads harmonics, non-linearities which may reduce the voltage and current profile of the microgrid. As a result, the sudden rise and fall of voltage current profile damages the domestic and commercial loads. The proposed controller provides robust stability against various unknown loads and uncertainties. The design of the controller is presented using linear matrix inequality approach and satisfies the Lyapunov stability criterion. Moreover, it provides lower closed-loop H ∞ norm and has better tracking accuracy than other. For justification, several load conditions have been tested in MATLAB/SimPowerSystem Toolbox to ensure the robust stability of the proposed controller. All the results presented in the paper indicate high performance of the controller.

Molecular communication is a novel communication paradigm, which has prospective potential applications in many fields. Obtaining knowledge of the diffusive molecular channel is important for the design of the molecular communication systems as well as proper working of many application systems. In this paper, we focus on a kind of molecular communication system with flow drift, which is modeled as an inverse Gaussian distributed channel. The maximum likelihood estimation method is applied to obtain the estimator of parameters such as medium velocity, propagation distance, and diffusion coefficient. This paper also derives the closed-form expressions of the Cramer-Rao lower bounds. The performances of our channel parameter estimators are validated in MATLAB and the results confirm its effectiveness.

Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors' knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a ten-fold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5”) and 48 mm (1.9”), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5”) and 67 mm (2.1”), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with single-sensor.

Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.

Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs’ charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner’s subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra’s shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability.

Energy storage plays a significant role in improving the stability of distributed energy, improving power quality and peak regulation in the micro-grid system, which is of great significance to the sustainable development of energy. In grid-connected mode, energy storage is mainly used to reduce the operating costs of micro-grid. Real-time price arbitrage is an important source of energy storage revenue. It is feasible to design arbitrage strategies using Q-learning algorithm. Due to the overestimation of the Q learning algorithm, this paper proposes an arbitrage strategy method based on Double-Q learning. Compared with Q-learning algorithm, Double-Q learning can avoid overestimation and provide more stable and accurate arbitrage strategy for energy storage systems. Since the source of arbitrage in previous studies was limited to electricity prices alone, this paper considers joint arbitrage of electricity and carbon prices. The simulation results show that if adding fluctuate carbon prices to arbitrage sources, the arbitrage profits will increase by more than 110%.

The electrical tree initiation and propagation behaviors are a key issue for the polyethylene-based cable insulation. This paper focuses on the effect of crystalline morphology on electrical tree growth characteristics of high-density and low-density polyethylene (HDPE/LDPE) blend insulation. In this paper, the electrical tree growth characteristics of HDPE/LDPE blends with HDPE mass fractions of 0, 10, 15, 20 wt% are investigated under repetitive impulse voltage at 40, 60, 80 °C. It is found that with the rise of HDPE content from 0 to 15 wt%, the growth rate and accumulated damage of electrical tree decreases, with the morphology of electrical tree tending to bush tree. The increasing of voltage amplitude improves the energy of injected charge while the temperature rise leads to the relaxation of molecular chain, which both result in the promotion of collision ionization, thus increasing the density of electrical tree. Compared with branch tree, bush tree illustrates better inhibition of discharge tracks due to uniform electric field at the end of branches. The crystalline characteristics of HDPE/LDPE blends indicate that the crystallinity increases with the addition of HDPE, and the blend comprising 15 wt% HDPE in an LDPE matrix apparently reduces the average size of spherulites and improves the distribution of spherulites evenly. The upsurge of crystal-amorphous interface leads to the increase of deep trap energy level density and the decrease of carrier mobility. Carrier injection and migration at the interface between crystalline and amorphous regions are restrained, thus inhibiting the growth of electrical tree. It is concluded that the crystalline morphology modified by polymer blending has a significant effect on the electrical tree growth characteristics.

Combined Cooling, Heating, and Electrical (CCHP)-based microgrids meet various energy demands at the same time. Risk analysis of such CCHP-based microgrids under environmental and economic constraints is a challenging issue. Furthermore, high penetration of various renewable energy sources in such CCHP-based microgrids clouds the issue. Thus, Downside risk constraints technique (DRCT) is employed in this paper to manage and minimize the risk associated with various uncertainties in the studied sample CCHP-based microgrid under environmental and economic constraints. The uncertainties of solar radiation, wind speed, market tariff and cooling, heating, and electrical demands are considered, and the stochastic optimization approach is used to model the mentioned uncertainties. The effect of the Real-Time Pricing rate of the Demand Response Program (RTP-DRP) has been studied in this paper. By analyzing the obtained results, it can be found that with considering DRCT, the risk-in-cost (RC) level can plunge to 0 respectively by paying 2.61% and 1.68% without and with RTP-DRP.

Three-component fiber optic seismometers are widely used in the detection of seismic signals. The arctangent approach of phase-generated carrier (PGC-Arctan) demodulation algorithm is one of the commonly used demodulation techniques in FOISs. Nonlinear factors such as phase modulation depth(C) variation and light intensity disturbance (LID) have an important impact on performance of traditional PGC demodulation algorithms. This paper introduces a new demodulation algorithm called Kalman filtering ellipse (PGC-Arctan-EKF) demodulation algorithm, which can improve the robustness of the fitting. In order to meet the requirements of high performance and low power consumption of seafloor sensing, Tri-Component fiber optic seismometer based on SoC(ARM +FPGA) is proposed in this paper. The phase self-noise of the system is better than -115 dB re rad/Hz1/2,and the large signal can reach 66 dB re rad/Hz1/2 @220Hz with the THD is -61.22 dB.

Clean and sustainable hydrogen production is key to the establishment of zero-carbon hydrogen energy system in response to the global warming challenge. This paper is aimed to analyze China's potential of hydrogen production from wind and solar power. To achieve this goal, this paper firstly used the SBM model with undesirable output to measure the efficiency of green hydrogen production at the provincial level. Then, the efficiency of green hydrogen production and the installed capacity of wind and solar power are integrated to construct a comprehensive indicator. Lastly, China's potential of green hydrogen production from both provincial and regional perspectives are simulated by entropy method and the dynamic change of the potential from 2017 to 2030 is also studied. From the results, we can find that: (1) the efficiency of the hydrogen production from wind power is significantly higher than that of the hydrogen production from solar power; (2) the efficiency and potential of green hydrogen production for each province of China in 2030 are improved compared with that in 2017; (3) the potential of green hydrogen production in the Northwest and North China is significantly higher than other regions; (4) there is a certain inconsistency in the development of the supply side and demand side of hydrogen energy, specially for the Southern China and northwest. Finally, based on the results above, some policy implications are provided to facilitate the high-quality development of the hydrogen energy industry.

The Internet of Things (IoT) is shaping the current and next generation of the Internet. The vision of IoT is to embed communication capabilities with a highly distributed, ubiquitous and dense heterogeneous devices network. This vision includes the adaptation of secure mobile networks, anytime, anywhere, by anyone or anything with new intelligent applications and services. Many efforts have been made to review the literature related to the IoT for the benefit of IoT development. However, many issues need to be addressed to overtake the full potential of the IoT. Therefore, this paper aims to classify and standardize IoT research areas by considering review papers that were published between 2010 and 2019. This paper analyzes a total of 95 related reviews, which were manually selected from databases based on 6 chosen areas. This paper presents the trends and classification of IoT reviews based on 6 research areas, namely, application, architecture, communication, challenges, technology, and security. IoT communication research has been dominating the trends with 21% of total reviews and more than 100% research growth in the last 10 years. Hence, this paper can provide useful insights into specific emerging areas of IoT to assist future research.

This paper reviews the modeling of high-temperature superconductors (HTS) using the finiteelement method (FEM) based on the H-formulation of Maxwell's equations. This formulation has become the most popular numerical modeling method for simulating the electromagnetic behavior of HTS, especially thanks to the easiness of implementation in the commercial finite-element program COMSOL Multiphysics. Numerous studies prove that the H-formulation is able to simulate a wide scope of HTS topologies, from simple geometries such as HTS tapes and coils, to more complex HTS devices, up to large superconducting magnets. In this paper, we review the basics of the H-formulation, its evolution from 2D to 3D, its application for calculating critical currents and AC losses as well as magnetization of HTS bulks and tape stacks. We also review the use of the H-formulation for large-scale HTS applications, its use to solve multi-physics problems involving electromagnetic-thermal and electromagnetic-mechanical couplings, and its application to study the dynamic resistance of superconductors and flux pumps.

This paper proposed a novel polarimeter for full-Stokes polarization imaging without using any kind of micro-polarizer array. The key components of the polarimeter are two grooved birefringent crystal plates with deviate fast axis, which are called Binary Birefringent Crystals (BBCs) in this paper. Mounting the jointed BBCs in the focal plane of an imaging detector, we manage to obtain the four components of Stokes vector simultaneously through the method proposed in this paper. We demonstrate the basic principle of the proposed polarimeter and verify it through numerical analysis. And one of the best-optimized configurations for the polarimeter is also presented. The proposed polarimeter is simultaneous, compact, easier in fabrication and capable of full-stokes polarization imaging. It is expected to be of potential use in a variety of applications.

This paper proposes a dynamic programming (DP)-based operational strategy for a biofuel distributed generation system with hybrid energy storage (BDG-HES). The proposed system consists of a biofuel-powered engine and a gen-set, six battery packs, a super-capacitor module, and four power electronic converters. It is primarily designed to meet the electrical demand of a typical UK family while the energy storage system can reduce the operating duration of the engine so as to improve the fuel efficiency. The DP, as an optimization strategy, is used to determine the operational scheme for the BDG-HES. In this paper, four case studies are carried out based on real-world measurement data. The test results have confirmed the effectiveness of the proposed methodology. The overall system efficiency is improved by approximately 19% over the operational range compared to a conventional gen-set based power system.

Many neurological disorders are characterized by the focal and anatomically definable lesions within the brain parenchyma. Traditional treatment may introduce major trauma in neurosurgery and conventional medical devices can only trace straight trajectories. To overcome these problems, a design and optimization method for a patient-specific concentric tube robot (CTR) satisfying the constraints of anatomy, surgical tasks and follow-the-leader (FTL) deployment is proposed in this paper. CTR is a tentacle like continuum robot that can work inside confined and complex biological chambers with the ability of tracking complex 3D trajectories. It consists of pre-curved superplastic tubes with hollow cavities to accommodate the surgical tools. These merits make the CTR well suitable for minimally invasive surgeries. This paper introduces a design framework that utilizes preoperative MRI data to configure patient-specific CTR for single and multiple tasks with the minimum number of tubes. A constant curvature circular arc model is built to solve the problem of inverse kinematics. Two iterative optimization methods for single and multiple tasks are proposed to optimize the parameters of the CTR. Initial waypoints of the CTR are produced based on the FTL deployment. The waypoints are then refined using a Follow Shape Rapidly-exploring Random Tree algorithm (FSRRT) for cases that the initial configurations of the CTR cannot completely satisfy the FTL deployment. Simulations and experiments are carried out on a human brain model to validate the proposed methods. The parameters of CTR including the entire length, curvature, radius angle, number, diameter, arc length and the waypoints are obtained. The errors of the FTL deployment are found to be within 2.1mm.

Soft modular robots have the advantages of both bionic continuum robots and modular self-reconfigurable robots. They have potential application for working in narrow space and uneven terrain. The outstanding abilities of the soft modular robots are infinite degrees of freedom and a changeable configuration. However, these advantages also incur difficulty in mathematical modeling and continuous motion control, especially for docking, which is the key to realizing a changeable configuration. Therefore, the development of a soft modular robot requires a modeling method to guide the module's motions accurately. In this paper, we design and manufacture a pneumatic soft modular robot with a novel connecting mechanism to achieve docking. A nonlinear dynamic model of our soft module is established. In particular, the analytic solutions of the module plane docking are presented. Based on the nonlinear dynamic modeling, three experiments are performed under different conditions. The experimental results pertaining to module plane bending agree with the theoretical values, which verify the accuracy of the modeling method. Moreover, fixed point docking and two modules docking experiments demonstrate the validity of the modeling method and the docking ability. The modeling method proposed in this paper can well guide the docking of soft modular robots.

This paper addresses reading failures of a barcode scanner interfered by light emitting diode (LED) lamps. It quantifies the reading performance in terms of timing signal-to-interference ratio (TSIR), in particular, as a function of modulation depth and frequency of the interference from modulated LED lighting. At the decision variable, the interference typically generated in the LED driver by a switched mode power supply (SMPS) is neither additive nor Gaussian. It has frequencies up to several MHz that can seriously affect the barcode reading performance. To calculate the signal and interference power for TSIR, the laser scanner physical channels are analyzed, including the laser beam path and the LED interference path. Since barcode scanners usually use peak or edge detection, the reading reliability is subject to the first and second derivatives of the LED inference, respectively. We validate the proposed TSIR for predicting scanner reading performance by experiments. For instance, we found that typical scanners reach a specified sensitivity at a TSIR value of around 50. We further investigate the reading performance under multi-frequency interference, e.g., interference from the harmonics of the SMPS control signal. In general, this paper presents a simple, yet realistic model to quantify the reading performance in terms of TSIR for barcode scanner subject to single-frequency LED interference, and it proposes an empirically verified flicker interference metric (FIM) for multi-frequency interference.