Showing papers by "Northeastern University (China) published in 2014"
TL;DR: In this article, the chemistry of different solvent extractants and typical configurations for rare earth separations are reviewed. But the choice of extractants is influenced by both cost considerations and requirements of technical performance.
947 citations
TL;DR: The evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigm; and the relation of IT infrastructure and ESs is explored to identified the technological gaps in adopting IoT as an IT infrastructure of ESs.
Abstract: Design and operation of a manufacturing enterprise involve numerous types of decision-making at various levels and domains. A complex system has a large number of design variables and decision-making requires real-time data collected from machines, processes, and business environments. Enterprise systems (ESs) are used to support data acquisition, communication, and all decision-making activities. Therefore, information technology (IT) infrastructure for data acquisition and sharing affects the performance of an ES greatly. Our objective is to investigate the impact of emerging Internet of Things (IoT) on ESs in modern manufacturing. To achieve this objective, the evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigms; and the relation of IT infrastructure and ESs is explored to identify the technological gaps in adopting IoT as an IT infrastructure of ESs. The future research directions in this area are discussed.
595 citations
TL;DR: The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent Neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks and related models.
Abstract: Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized The relationship among stability results in different forms, such as algebraic inequality forms, \(M\) -matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed Concluding remarks and future directions of stability analysis of recurrent neural networks are given
515 citations
TL;DR: A stabilization problem for nonlinear uncertain systems via adaptive backstepping approach is considered, and a designed controller together with the quantizer ensures the stability of the closed-loop system in the sense of signal boundedness.
Abstract: In this paper, we study a general class of strict feedback nonlinear systems, where the input signal takes quantized values. We consider a stabilization problem for nonlinear uncertain systems via adaptive backstepping approach. The control design is achieved by introducing a hysteretic quantizer to avoid chattering and using backstepping technique. A guideline is derived to select the parameters of the quantizer. The designed controller together with the quantizer ensures the stability of the closed-loop system in the sense of signal boundedness.
372 citations
TL;DR: A novel CAFTFTC scheme is proposed to guarantee that all follower nodes asymptotically synchronize a leader node with tracking errors converging to a small adjustable neighborhood of the origin in spite of actuator faults.
Abstract: In this paper, the cooperative adaptive fault tolerant fuzzy tracking control (CAFTFTC) problem of networked high-order multiagent with time-varying actuator faults is studied, and a novel CAFTFTC scheme is proposed to guarantee that all follower nodes asymptotically synchronize a leader node with tracking errors converging to a small adjustable neighborhood of the origin in spite of actuator faults. The leader node is modeled as a higher order nonautonomous nonlinear system. It acts as a command generator giving commands only to a small portion of the networked group. Each follower is assumed to have nonidentical unknown nonlinear dynamics, and the communication network is also assumed to be a weighted directed graph with a fixed topology. A distributed robust adaptive fuzzy controller is designed for each follower node such that the tracking errors are cooperative uniform ultimate boundedness (CUUB). Moreover, these controllers are distributed in the sense that the controller designed for each follower node only requires relative state information between itself and its neighbors. The adaptive compensation term of the optimal approximation errors and external disturbances is adopted to reduce the effects of the errors and disturbances, which removes the assumption that the upper bounds of unknown function approximation errors and disturbances should be known. Analysis of stability and parameter convergence of the proposed algorithm are conducted that are based on algebraic graph theory and Lyapunov theory. Comparing with results in the literature, the CAFTFTC scheme can minimize the time delay between fault occurrence and accommodation and reduce its adverse effect on system performance. In addition, the FTC scheme requires no additional fault isolation model, which is necessary in the traditional active FTC scheme. Finally, an example is provided to validate the theoretical results.
289 citations
TL;DR: A facile green approach for in situ growth of silver nanoparticles (AgNPs) on the surface of graphene quantum dots (GQDs) is reported, which provides a record detection limit of 33 nM for the detection of H2O2 by the AgNPs-based sensing system.
Abstract: We report a facile green approach for in situ growth of silver nanoparticles (AgNPs) on the surface of graphene quantum dots (GQDs). GQDs serve as both reducing agent and stabilizer, and no additional reducing agent and stabilizer is necessary. The GQDs/AgNPs hybrid exhibits a superior absorbance fading response toward the reduction of H2O2. A simple colorimetric procedure is thus proposed for ultrasensitive detection of H2O2 without additional chromogenic agent. It provides a record detection limit of 33 nM for the detection of H2O2 by the AgNPs-based sensing system. This colorimetric sensing system is further extended to the detection of glucose in combination with the specific catalytic effect of glucose oxidase for the oxidation of glucose and formation of H2O2, giving rise to a detection limit of 170 nM. The favorable performances of the GQDs/AgNPs hybrid are due to the peroxidase-like activity of GQDs.
281 citations
TL;DR: It is shown that silent synapse-based remodeling of the two major mPFC-to-NAc projections differentially regulated the progressive increase in cue-induced cocaine seeking after withdrawal (incubation of cocaine craving), which may provide substrates for utilizing endogenous antirelapse mechanisms to reduce cocaine relapse.
281 citations
TL;DR: In this article, a method to control chaotic behavior of a typical Smart Grid based on generalized fuzzy hyperbolic model (GFHM) is presented, which is designed by solving a linear matrix inequality (LMI).
Abstract: This paper presents a method to control chaotic behavior of a typical Smart Grid based on generalized fuzzy hyperbolic model (GFHM). As more and more distributed generations (DG) are incorporated into the Smart Grid, the chaotic behavior occurs increasingly. To verify the behavior, a dynamic model which describes a power system with DG is presented firstly. Then, the simulation result shows that the power system can lead to chaos under certain initial conditions. Based on the universal approximation of GFHM, we confirm that the chaotic behavior could be suppressed by a new controller, which is designed by means of solving a linear matrix inequality (LMI). This approach could make a good application to suppress the chaos in Smart Grid. Finally, a numerical example is given to demonstrate the effectiveness of the proposed chaotic suppression strategy.
280 citations
TL;DR: New design conditions for the three type of output feedback controllers are introduced in terms of unified linear matrix inequality (LMI) representations, which guarantee the prescribed H∞ performances of the closed-loop systems.
Abstract: This note investigates the problem of output feedback H∞ control for linear discrete-time systems. Three types of H∞ controllers are considered, namely, static output feedback controllers, dynamic output feedback controllers, and observer-based output feedback controllers. New design conditions for the three type of output feedback controllers are introduced in terms of unified linear matrix inequality (LMI) representations, which guarantee the prescribed H∞ performances of the closed-loop systems. In contrast to the existing LMI conditions for designing the output feedback H∞ controllers, the improvement of the proposed results over the existing ones is shown by theoretical proof and numerical example.
255 citations
TL;DR: Considering the misalignment of gear root circle and base circle and accurate transition curve, an improved mesh stiffness model for a healthy gear pair is proposed and validated by the finite element method as mentioned in this paper.
245 citations
TL;DR: An improved differential evolution algorithm with a real-coded matrix representation for each individual of the population, a two-step method for generating the initial population, and a new mutation strategy are proposed to solve the SCC scheduling problem.
Abstract: This paper studies a challenging problem of dynamic scheduling in steelmaking-continuous casting (SCC) production. The problem is to re-optimize the assignment, sequencing, and timetable of a set of existing and new jobs among various production stages for the new environment when unforeseen changes occur in the production system. We model the problem considering the constraints of the practical technological requirements and the dynamic nature. To solve the SCC scheduling problem, we propose an improved differential evolution (DE) algorithm with a real-coded matrix representation for each individual of the population, a two-step method for generating the initial population, and a new mutation strategy. To further improve the efficiency and effectiveness of the solution process for dynamic use, an incremental mechanism is proposed to generate a new initial population for the DE whenever a real-time event arises, based on the final population in the last DE solution process. Computational experiments on randomly generated instances and the practical production data show that the proposed improved algorithm can obtain better solutions compared to other algorithms.
TL;DR: The highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.
TL;DR: IoT and cloud computing are proposed to help a conventional assembly modeling system evolve into an advanced system, which is capable to deal with complexity and changes automatically.
Abstract: After the technologies of integrated circuits, personal computers, and the Internet, Internet of Things (IoT) is the latest information technology (IT) that is radically changing business paradigms. However, IoT's influence in the manufacturing sector has yet been fully explored. On the other hand, existing computer-aided software tools are experiencing a bottleneck in dealing with complexity, dynamics, and uncertainties in their applications of modern enterprises. It is argued that the adoption of IoT and cloud computing in enterprise systems (ESs) would overcome the bottleneck. In this paper, the challenges in generating assembly plans of complex products are discussed. IoT and cloud computing are proposed to help a conventional assembly modeling system evolve into an advanced system, which is capable to deal with complexity and changes automatically. To achieve this goal, an assembly modeling system is automated, and the proposed system includes the following innovations: 1) the modularized architecture to make the system robust, reliable, flexible, and expandable; 2) the integrated object-oriented templates to facilitate interfaces and reuses of system components; and 3) the automated algorithms to retrieve relational assembly matrices for assembly planning. Assembly modeling for aircraft engines is used as examples to illustrate the system effectiveness.
TL;DR: It is shown that the proposed method improves the existing FD techniques and achieves a better FD performance as the additional reference input sensitivity for faulty cases is considered.
Abstract: This paper is concerned with the fault detection (FD) problem in finite frequency domain for continuous-time Takagi-Sugeno fuzzy systems with sensor faults. Some finite-frequency performance indices are initially introduced to measure the fault/reference input sensitivity and disturbance robustness. Based on these performance indices, an effective FD scheme is then presented such that the generated residual is designed to be sensitive to both fault and reference input for faulty cases, while robust against the reference input for fault-free case. As the additional reference input sensitivity for faulty cases is considered, it is shown that the proposed method improves the existing FD techniques and achieves a better FD performance. The theory is supported by simulation results related to the detection of sensor faults in a tunnel-diode circuit.
TL;DR: It was concluded that the Cu content affects the Cu existence and the Cu ion release behavior, which in turn influences the antibacterial property.
TL;DR: An online adaptive policy learning algorithm (APLA) based on adaptive dynamic programming (ADP) is proposed for learning in real-time the solution to the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in the H∞ control problem.
Abstract: The problem of H∞ state feedback control of affine nonlinear discrete-time systems with unknown dynamics is investigated in this paper. An online adaptive policy learning algorithm (APLA) based on adaptive dynamic programming (ADP) is proposed for learning in real-time the solution to the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in the H∞ control problem. In the proposed algorithm, three neural networks (NNs) are utilized to find suitable approximations of the optimal value function and the saddle point feedback control and disturbance policies. Novel weight updating laws are given to tune the critic, actor, and disturbance NNs simultaneously by using data generated in real-time along the system trajectories. Considering NN approximation errors, we provide the stability analysis of the proposed algorithm with Lyapunov approach. Moreover, the need of the system input dynamics for the proposed algorithm is relaxed by using a NN identification scheme. Finally, simulation examples show the effectiveness of the proposed algorithm.
03 Nov 2014
TL;DR: A system AutoCog is presented to automatically assess description-to-permission fidelity of applications and outperforms other related work on both performance of detection and ability of generalization over various permissions by a large extent.
Abstract: The booming popularity of smartphones is partly a result of application markets where users can easily download wide range of third-party applications. However, due to the open nature of markets, especially on Android, there have been several privacy and security concerns with these applications. On Google Play, as with most other markets, users have direct access to natural-language descriptions of those applications, which give an intuitive idea of the functionality including the security-related information of those applications. Google Play also provides the permissions requested by applications to access security and privacy-sensitive APIs on the devices. Users may use such a list to evaluate the risks of using these applications. To best assist the end users, the descriptions should reflect the need for permissions, which we term description-to-permission fidelity. In this paper, we present a system AutoCog to automatically assess description-to-permission fidelity of applications. AutoCog employs state-of-the-art techniques in natural language processing and our own learning-based algorithm to relate description with permissions. In our evaluation, AutoCog outperforms other related work on both performance of detection and ability of generalization over various permissions by a large extent. On an evaluation of eleven permissions, we achieve an average precision of 92.6% and an average recall of 92.0%. Our large-scale measurements over 45,811 applications demonstrate the severity of the problem of low description-to-permission fidelity. AutoCog helps bridge the long-lasting usability gap between security techniques and average users.
TL;DR: In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively and a new solution generating method is developed to enhance accuracy and convergence rate of the algorithm.
Abstract: This paper presents an improved fruit fly optimization (IFFO) algorithm for solving continuous function optimization problems. In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively. A new solution generating method is developed to enhance accuracy and convergence rate of the algorithm. Extensive computational experiments and comparisons are carried out based on a set of 29 benchmark functions from the literature. The computational results show that the proposed IFFO not only significantly improves the basic fruit fly optimization algorithm but also performs much better than five state-of-the-art harmony search algorithms.
TL;DR: A novel iterative two-stage dual heuristic programming (DHP) algorithm is developed to solve the Hamilton-Jacobi-Bellman equation of the switched system with the saturating actuator and the convergence and optimality are strictly proven.
Abstract: In this paper, a novel iterative two-stage dual heuristic programming (DHP) is proposed to solve the optimal control problems for a class of discrete-time switched nonlinear systems subject to actuators saturation. First, a novel nonquadratic performance functional is introduced to confront control constraints of the saturating actuator. Then, the iterative two-stage DHP algorithm is developed to solve the Hamilton-Jacobi-Bellman (HJB) equation of the switched system with the saturating actuator. Moreover, the convergence and optimality of the two-stage DHP algorithm are strictly proven. To implement this algorithm efficiently, there are two neural networks used as parametric structure to approximate the costate function and the corresponding control law, respectively. Finally, simulation results are given to verify the effectiveness of the proposed algorithm.
TL;DR: The proposed design methods not only suit for a standard form of the fuzzy filter but also give more relaxed design conditions, which guarantees a prescribed H∞ performance of the filtering error system.
Abstract: This paper is concerned with the problem of nonfragile H∞ filtering for continuous-time Takagi-Sugeno (T-S) fuzzy systems. The filter to be designed is assumed to have two types of multiplicative gain variations. First, two relaxed H∞ filtering analysis conditions are proposed based on useful linear matrix inequality preliminaries. Whereafter, the results are exploited to derive sufficient conditions for designing a nonfragile H∞ filter, which guarantees a prescribed H∞ performance of the filtering error system. Compared with the existing results, the proposed design methods not only suit for a standard form of the fuzzy filter but also give more relaxed design conditions. Finally, simulation examples will be given to show the efficiency of the proposed design methods.
TL;DR: The magnetic Ag3PO4/TiO2/Fe3O4 heterostructured nanocomposite was synthesized and was found to exhibit markedly enhanced photocatalytic activity, cycling stability, and long-term durability in the photodegradation of acid orange 7 (AO7) under visible light.
Abstract: Silver orthophosphate (Ag3PO4) is a low-band-gap photocatalyst that has received considerable research interest in recent years. In this work, the magnetic Ag3PO4/TiO2/Fe3O4 heterostructured nanocomposite was synthesized. The nanocomposite was found to exhibit markedly enhanced photocatalytic activity, cycling stability, and long-term durability in the photodegradation of acid orange 7 (AO7) under visible light. Moreover, the antibacterial film prepared from Ag3PO4/TiO2/Fe3O4 nanocomposite presented excellent bactericidal activity and recyclability toward Escherichia coli (E. coli) cells under visible-light irradiation. In addition to the intrinsic cytotoxicity of silver ions, the elevated bactericidal efficiency of Ag3PO4/TiO2/Fe3O4 can be largely attributed to its highly enhanced photocatalytic activity. The photogenerated hydroxyl radicals and superoxide ions on the formed Ag/Ag3PO4/TiO2 interfaces cause considerable morphological changes in the microorganism’s cells and lead to the death of the bacteria.
TL;DR: A robust adaptive NN output feedback control scheme is developed and it is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics.
Abstract: This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.
TL;DR: A real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram, electromyogram, and electrooculogram signals is presented and is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.
Abstract: This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.
TL;DR: A risk decision analysis method based on cumulative prospect theory (CPT) is proposed to solve the risk decision-making problem in emergency response by aggregating the prospect value and the cost of each response action.
TL;DR: In this article, the photonic crystal fiber based surface plasmon resonance (PCF-SPR) chemical sensors were intensively reviewed, and the principles, superiorities and problems of the PCF-SRS sensors were also discussed in detail.
Abstract: Research developments of the photonic crystal fiber based surface plasmon resonance (PCF-SPR) chemical sensors were intensively reviewed Photonic crystal fibers, such as the microstructured optical fiber, the photonic bandgap fiber and the Bragg fiber with various structures were applied to the SPR sensors, including fuse-tapered fiber structure, D-type fiber structure and cladding-off fiber structure Those sensors were classified as three kinds of configurations which were respectively based on the inner metal layer, the metallic nanowire and the outer metal film What's more, the principles, superiorities and problems of the PCF-SPR sensors were also discussed in detail
TL;DR: A novel k-step fault-estimation observer is proposed to construct the k-1)th fault error dynamics and a dynamic output feedback fault tolerant controller is designed to compensate the fault effects on the closed-loop fuzzy system.
Abstract: This paper is concerned with the problem of robust fault estimation and fault-tolerant control for a class of Takagi–Sugeno (T–S) fuzzy systems with time-varying state delay and actuator faults. Based on the ( $k-1$ )th fault estimation information, a novel $k$ -step fault-estimation observer is proposed to construct the $k$ th fault error dynamics. The obtained fault estimates via $k$ -step fault-estimation can practically better depict the size and shape of the faults. Then, based on the information of online $k$ -step fault-estimation, a dynamic output feedback fault tolerant controller is designed to compensate the fault effects on the closed-loop fuzzy system. Furthermore, some less conservative delay dependent sufficient conditions for the existence of fault estimation observers and fault tolerant controllers are given in terms of solution to a set of linear matrix inequalities. Finally, simulation results of two numerical examples are presented to show the effectiveness and merits of the proposed methods.
TL;DR: In this article, a one-step electrochemical exfoliation method to afford partially exfoliated graphene electrode (Ex-GF) with graphene sheets standing on graphite foil (GF) matrix stably.
TL;DR: In this article, an electro-codeposition method was used to synthesize a high performance negative electrode composed of a vanadium oxide (V2O5) and polyaniline (PANI) composite.
Abstract: To meet the increasing demand for high energy density supercapacitors, it is crucial to develop positive and negative electrodes with comparable energy density. Previous studies have primarily focused on the development of positive electrodes, while negative electrodes are relatively less explored. Here we report an electro-codeposition method to synthesize a high performance negative electrode composed of a vanadium oxide (V2O5) and polyaniline (PANI) composite. Scanning electron microscopy revealed that the composite film is composed of one-dimensional polymer chains. Energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD) confirmed successful incorporation of V2O5 into PANI chains. Significantly, the V2O5–PANI composite nanowires exhibited a wide potential window of 1.6 V (between −0.9 and 0.7 V vs. SCE) and a maximum specific capacitance of 443 F g−1 (664.5 mF cm−2). The flexible symmetric supercapacitor assembled with this composite film yielded a maximum energy density of 69.2 W h kg−1 at a power density of 720 W kg−1, and a maximum power density of 7200 W kg−1 at an energy density of 33.0 W h kg−1. These values are substantially higher than those of other pure V2O5 or PANI based supercapacitors. Moreover, the assembled symmetric supercapacitor device showed an excellent stability with 92% capacitance retention after 5000 cycles. The capability of synthesizing high performance composite electrodes using the electro-codeposition method could open up new opportunities for high energy density supercapacitors.
TL;DR: An effective discrete artificial bee colony (DABC) algorithm that has a hybrid representation and a combination of forward decoding and backward decoding methods for solving the HFS problem with the makespan criterion is presented.
Abstract: The hybrid flowshop scheduling (HFS) problem with the objective of minimising the makespan has important applications in a variety of industrial systems. This paper presents an effective discrete artificial bee colony (DABC) algorithm that has a hybrid representation and a combination of forward decoding and backward decoding methods for solving the problem. Based on the dispatching rules, the well-known NEH heuristic, and the two decoding methods, we first provide a total of 24 heuristics. Next, an initial population is generated with a high level of quality and diversity based on the presented heuristics. A new control parameter is introduced to conduct the search of employed bees and onlooker bees with the intention of balancing the global exploration and local exploitation, and an enhanced strategy is proposed for the scout bee phase to prevent the algorithm from searching in poor regions of the solution space. A problem-specific local refinement procedure is developed to search for solution space that is unexplored by the honey bees. Afterward, the parameters and operators of the proposed DABC are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is conducted, with the best performing algorithms presented for the HFS problem under consideration, and with adaptations of some state-of-the-art metaheuristics that were originally designed for other HFS problems. The results show that the proposed DABC performs much better than the other algorithms in solving the HFS problem with the makespan criterion.
TL;DR: This review is structured to cover active plasmonic devices from two aspects: functionalities and driven methods and hopes it would provide basic knowledge for a new researcher to get familiar with the field, and serve as a reference for experienced researchers to keep up with the current research trends.
Abstract: Liquid crystals are a promising candidate for development of active plasmonics due to their large birefringence, low driving threshold, and versatile driving methods. We review recent progress on the interdisciplinary research field of liquid crystal based plasmonics. The research scope of this field is to build the next generation of reconfigurable plasmonic devices by combining liquid crystals with plasmonic nanostructures. Various active plasmonic devices, such as switches, modulators, color filters, absorbers, have been demonstrated. This review is structured to cover active plasmonic devices from two aspects: functionalities and driven methods. We hope this review would provide basic knowledge for a new researcher to get familiar with the field, and serve as a reference for experienced researchers to keep up the current research trends.