Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Power Sharing, Voltage Restoration and Stabilization Techniques in Hierarchical Controlled DC Microgrids

In order to overcome the problem of power generation in distributed energy, microgrid(MG) emerges as an alternative scheme. Compared with the ac microgrids, the dc microgrids have the advantages of high system efficiency, good power quality, low cost, and simple control. However, due to the complexity of the distributed generation system, the conventional droop control shows the drawbacks of low current sharing accuracy. Therefore, the improved primary control methods to enhance current sharing accuracy are systematically reviewed, such as particle swarm optimization programming, probabilistic algorithm and voltage correction factor scheme. However, it is difficult to achieve stable and coordinated operation of the dc microgrids by relying on the primary control. Hence, the various secondary control approaches, such as dynamic current sharing scheme, muti-agent system (MAS) control and virtual voltage control methods have been summarized for voltage regulation. Furthermore, the energy management system (EMS), modular-based energy router (MBER) and other coordinated control methods are reviewed to achieve power management. Besides, various control methods to compensate the effect of communication delay are summarized. Moreover, linear matrix inequality (LMI), Lyapunov-Krasovskii functional stability and Takagi–Sugeno model prediction scheme can be adopted to eliminate the influence of communication delay. In addition, due to the constant power loads (CPL) exhibit negative impedance characteristics, which may result in the output oscillation of filter. Thus, various control approaches have been reviewed to match the impedance, such as the nonlinear disturbance observer (NDO) feedforward compensation method, linear programming algorithm, hybrid potential theory and linear system analysis of polyhedral uncertainty. The merits and drawbacks of those control strategies are compared in this paper. Finally, the future research trends of hierarchical control and stability in dc microgrids and dc microgrid clusters are also presented.
Adaptive Convolutional Sparse Coding with Weighted l1 Norm

Convolutional Sparse Coding computes a sparse representation for an entire image by modeling the signal as a sum of several convolutions of dictionary filters and sparse feature maps, instead of the linear combination of dictionary vectors in standard sparse coding. Most of the existing algorithms use a invariant regularization parameter, which can hardly deal well with varying local contrast. In this paper, we propose a new method Adaptive Convolutional Sparse Coding (ACSC) by introducing a set of weight vectors into the ${\mathcal {l}}^{1}$ regularization term to set different penalty coefficients for different sparse feature maps, which can better adapt with the varying local sparse requirements of images. Experimental results show that the proposed ACSC is superior to the existing convolutional sparse coding algorithm in image reconstruction.
A Novel Approach to Speech Signal Segmentation Based on Empirical Mode Decomposition to Assess Human Psycho-Emotional State

A novel technology for speech signal segmentation into voiced, unvoiced and pause sections has been developed. It is based on the adaptive decomposition of short-time fragments of speech signals with a subsequent analysis of the parameters of empirical modes. A segmentation method based on a novel technology with a brief mathematical description is presented. The research has been conducted using the formed base of speech signals, recorded from 220 male and female subjects with signs of psycho-emotional disorders. In accordance with the obtained results, it was concluded that the method based on the developed technology determines the boundaries of informative sections more accurately, and it can be successfully tested at the preprocessing stages in the systems for detection and assessment of human psycho-emotional disorder.
Structural Influence Maximization in Social Networks

With the development and widespread of social applications, large social networks has appeared. Influence maximization problem over social networks has become a popular topic and caused lots of research interests. Given a social network with influence weights on edges and an integer k, the goal of influence maximization problem is to find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation model. Previous information propagation models do not consider the effects of structural information on influences, but only consider the influences between nodes independently. In this paper, a novel definition of information propagation model is proposed and the related influence maximization problems is studied. Theoretical analysis shows that the problem is NP-complete, heuristic algorithms are proposed to solve the influence maximization problem on the new model. Finally, the experimental results show that the algorithm proposed is efficient and effective.
MIMO-Sparse Radars for Enhanced DOA Estimation of Spatio-Temporal Correlated Sources

This paper investigates an enhanced direction-of-arrival (DOA) estimation problem of spatio-temporal correlated sources when a multiple-input multiple-output (MIMO) sparse radar is deployed. A virtual array with increased degrees-of-freedom (DOF) is realized within two steps by combining MIMO radar with the Khatri-Rao (KR) product approach. Since the final virtual array is a uniform linear array (ULA), a well-known spatial smoothing algorithm is applied to reinforce the rank of its covariance matrix. Meanwhile, the enhanced DOF results in a higher spatial resolution of the radar in the context of DOA estimation.
Beamforming and Transceiver Optimization with Phase Noise for mmWave and THz Bands

This paper investigates the impact of phase noise on the transceiver signal processing originating from the local oscillators (LOs). We explore the impact of these effects with different transceiver architectures, illustrate the beam shape properties, and quantify their impact on the system performance for different modulation schemes in terms of error rates. Specifically, we model the phase noise both as Wiener and Gaussian distributed with covariance structure depending on the architecture used to share the LO signals.
Asynchronous Motor Control Algorithm with Parameter Identification

This paper proposes a method for vector control of drives with asynchronous motors. The method improves the motor dynamics by adapting the control system to changes in rotor resistance and primary inductance. It presents equations for the asynchronous motor voltages, which are used to synthesize the dynamic processes, as well as differential equations for the vector control errors. Based on the voltage equations, the paper proposes a procedure to optimally configure the magnetizing-current circuits and the load-current circuits. To ensure the robustness of dynamic processes, internal virtual dissipation circuits are added to the current control circuitry. Adaptive identification of rotor resistance and primary static inductance in an asynchronous motor is based on monitoring the stator currents and rotor speeds. Adaptive identification uses functions of vector control error currents. It is based on synthesizing control circuits that uses integral controllers, thanks to which the a priori estimates of rotor resistance and primary static inductance of asynchronous motors approach the true values. Constructing adaptive identification circuits requires data on the stator winding currents and rotor speeds, as well as the relative short-circuit inductance values. The quality of a priori data on the relative short-circuit inductance value is crucial for the accuracy of adaptive identification of rotor resistance and primary static inductance of an asynchronous motor.
An Active Learning Framework Based on Query-By-Committee for Sentiment Analysis

Social networks are the main resources to obtain information about people’s opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion. However, it cost too much time to label the abundantly unlabeled data. To dramatically reduce annotation cost, this paper creatively apply an Active learning framework for Sentiment analysis called Active learning for Sentiment analysis (AL4SA) to naturally integrate active learning and machine learning into a single framework. First, AL4SA train a committee with multiple weak learners. Then, committee members are used to select the most informative samples from the unlabeled data by the Vote entropy strategy and enhance the model’s performance by incorporating newly samples in each iteration. Our experiments were compared with baseline machine learning methods such as KNN, Naïve Bayes, Gradient Boosting Decision Tree (GBDT) and Support Vector Machine (SVM) on the hotel review corpus, the result shows that the cost of the labeled data can be cut by at least half and the performance is optimal than those baseline machine learning methods due to the most informative samples were selected by committee members.
A Temperature-Insensitive CMOS-MEMS Resonator Utilizing Electrical Stiffness Compensation

A temperature-insensitive micromechanical resonator that utilizes an electrical stiffness compensation technique was demonstrated. In particular, a 2.92-MHz free-free beam (FF-beam) resonator based on a $0.35-\mu \mathrm{m}$ 2-poly-4-metal CMOS-MEMS process platform cancels the first-order temperature coefficient of frequency $(TCF_{1})$, from − 70.03 ppm of an uncompensated version to +0.44 ppm, and a $22\times$ lower overall frequency drift from the uncompensated 2120 ppm to 95 ppm from 25°C to 55°C. In contrast to the previously demonstrated electrical stiffness compensated resonator of [1], for which the resonator consists of an out-of-plane overhanging temperature-compensating electrode realized by a CMOS-incompatible poly-Si surface micromachining process, this work employs an in-plane U-shaped compensating electrode design that allows the use of a standard CMOS-MEMS process platform for achieving the device. The FEA-derived thermal expansion of the compensating electrode is used to predict the temperature coefficient of frequency. The measured results is compared with a finite element analysis.
A Study of Sentiment Analysis Task and It's Challenges

Reviews act as a valuable source of information for decision making. Online e-commerce sites has provided their users to make their opinion about products and services. Huge amount of such opinions are publicly available in the form of reviews. Manufacturers, retailer as well as customers have great interest in customer reviews. A customer has interest in such reviews to determine which product or a particular brand to buy. Manufacturers can analyze the improvement area in their product from such opinionated reviews. Due to large number of reviews available on internet for analysis, it is not cost worthy to read these manually. To optimize this time consuming task there is a need of an automated system which provides summarized result of user sentiments. Sentiment analysis (SA) is the field of study that analyzes people’s sentiments or opinion from reviews or opinionated text. In sentiment analysis process machine learning is used to analyze sentiments, emotions and produce a summarized result for decision making. Sentiment analysis can be viewed as a natural language processing task, the task is to develop a system that understands the people's language. Sentiment analysis is a difficult task due to ambiguous nature of human languages( like English). In this paper we present a comprehensive study of sentiment analysis tasks and the challenges for sentiment analysis system. As the aspect based sentiment analysis is an active research area in SA, we will cover this level of sentiment analysis in detail.
A Multi - platform Cooperative Localization Method Based on Dead Reckoning and Particle Filtering

A multi-node cooperative localization method is proposed based on dead reckoning and particle filter, which can enhance the robustness and accuracy of the whole localization network. Only one reference node is needed to conduct the position estimation of the unknown node. A cooperative navigation network is constructed using the navigation information of each node, such as unmanned aerial vehicles, ground vehicles, unmanned platforms and even dismounted soldiers etc. Each node is required to share navigation and positioning information. And when some of the nodes are disabled under circumstances such as GPS failure or system malfunction, other nodes can provide them with cooperative localization capability to improve the navigation robustness of the whole localization system.
Structure/controller Co-design of Multi-motor Driving System with Fixed-time Dynamic Surface Backstepping Control

A structure/controller co-design scheme with a fixed-time dynamic surface backstepping control is presented in this paper. The co-design scheme is developed to optimize the structure parameter and the controller parameter simultaneously so that the overall system optimality can be guaranteed. The nested optimization strategy and the improved cuckoo search (CS) algorithm is employed to resolve the raised co-design problem. The fixed-time dynamic surface controller is developed to improve the dynamic performance of the system. The presented controller can converge in a fixed-time regardless of the initial state of the system. The tracking control and the synchronization control are both considered in the proposed control scheme. Experimental results demonstrate the efficacy of the proposed approach.
Analysis and Modeling of LLC Resonant Converter Used in Electric Vehicle

In this paper, analysis and modeling of an LLC resonant converter applied for an electric vehicle, are presented. The converter parameters, such as resonant tank elements, are chosen in order to improve the overall efficiency of the converter and to reduce the switching losses. Thus, to make a practical use of this topology, the small signal modeling technique based on the generalized state space averaging method is applied to the LLC converter. Finally, simulation results are presented to prove that the circuit operation is verified for these specifications: Input voltage 24V, output voltage 120V and output power 500W. Keywords: Resonant converter, switching losses, planar transformer, phase shift, average model, electric vehicle.
Multimodal biometric verification system: Evaluation of various score level fusion rules

In this paper, biometric verification system is built adopting five physiological traits. We have opted to choose score level fusion, due to its ease in accessing and combining scores generated by different matchers. In our work, we have explored simple transformation rules (min, max, sum), dynamic weighting (DLC, WF) and t-norms (Frank, hamacher and Sugeno weber) score level fusion schemes. All these score level fusion schemes are checked with different desirable parameters such as accuracy, scalability and robustness in declaring the most efficient method for both unimodal and multimodal biometric system.
Design and Performance of Unlicensed NB-IoT

Due to the characteristics of wide coverage and low power consumption, the Narrow Band Internet of Things (NB-IoT) is believed to be a promising candidate technology to enable massive IoT in the upcoming 5G. Generally, the cellular NB-IoT is deployed only in licensed spectrum which is expensive and resource-constrained, thus causing limitations for operators. In view of these challenges, it is envisioned to opportunely operate NB-IoT in unlicensed spectrum, also termed as NB-IoT-U. In this paper, we present a specific framework for NB-IoT-U system with the restriction from the Federal Communications Commission (FCC) regulations, in the Sub-1GHz band. To verify the feasibility of this technology, we provide a comprehensive performance analysis of NB-IoT-U in terms of coverage and battery life.
Image Significance Region Detection Based on Global Color Clustering and Contrast

in this paper, an image significance region detection algorithm combining global color clustering and color contrast is proposed. Firstly, by clustering the image color, the color of each pixel in the image is replaced by the color of the closest clustering center. Then, the clustering color contrast is used to calculate the significance region of the image, which enhances the significance region and reduces the influence of nonsignificant objects. Experimental results show that this method has higher precision and better recall rate, and significantly reduces the influence of complex texture on the calculation of significance region.
Tip-enhanced Raman Spectroscopy of Graphene

This paper presents an optical spectroscopy system engineered to overcome the diffraction limit, describing its building blocks and its application in the characterization of graphene. Based on a technique know as Tip-enhanced Raman Spectroscopy (TERS), the system is capable of obtaining images and spectral information from nanostructures smaller then the minimal length imposed by the diffraction limit of light through the use of optical nanoantennas designed to convert local evanescent radiation to propagating radiation and vice-versa. The system is described with focus on the optical components, including tip-light focus alignment, and on software to handle the massive amount of data generated in hyperspectral imaging. The system is utilized to extract rich information from a graphene nanofiake, with comments on important aspects to generate such spectral analysis.
Probabilistic Model of the Effect of a Ground Discontinuity on the Transmission of a Microstrip Interconnect

This paper presents a probabilistic model for the prediction of the transmission performance of a miscrostrip link in presence of a ground discontinuity. The proposed model is based on a machine learning approach. Specifically, it combines the least-squares support vector machine regression with a Gaussian process regression with the aim of predicting the first resonance frequency of a miscrostrip structure for different geometries and positions of a rectangular slit in the ground plane. The model is trained from a small set of costly electromagnetic simulations generated via a latin hypercube sampling scheme and provides also the confidence intervals of its predictions. The accuracy and the capability of the proposed modeling approach are demonstrated by comparing the model predictions and their relative confidence intervals with the results provided by a parametric full-wave electromagnetic simulation.
Adaptive Controller Design for Overhead Cranes With Moving Sliding Surface

An adaptive sliding mode control (ASMC) method with moving sliding surface (MSS) is proposed for anti-swing and positioning control of two-dimensional underactuated overhead crane in this paper. The designed moving sliding surface can change its slope in real time according to displacement error and speed error, thereby tracking performance of the trolley is improved. At the same time, a dynamic exponential term is added into the moving sliding surface so as to drive initial state variables into the sliding surface quickly to ensure the global robustness of the crane system. Finally, the controller switching gain is adjusted by an adaptive control law, which can effectively reduce chattering phenomenon occurring in the phase of sliding mode control. The controller can realize rapid positioning of the trolley and anti-swing control of the load under the condition of rope length changing. The global stability analysis of the suggested controller is given in the paper. The final simulation results confirm the effectiveness of the proposed controller.
A Fully-Differential CMOS-MEMS DETF Resonator Design with Extended Mass and Electrodes to Enable High Power Handling

We propose a novel differential drive and sense double-ended tuning fork (DETF) with wing electrodes to reduce the motional resistance ($\boldsymbol{R_{m}}$) while achieving excellent power handling. The devices are fabricated using back end of line (BEOL) materials of $0.18\boldsymbol{\mu}\mathbf{m}$ 1P6M CMOS process. The wing electrodes are added to the high-velocity region to achieve high transduction efficiency. This increases the device mass ($\boldsymbol{m_{r}}$) which requires increased stiffness ($\boldsymbol{k_{r}}$) to maintain the same resonance frequency. The proposed design has resonance at 1 MHz in vacuum under a dc bias ($\boldsymbol{V_{P}}$) of 30V with $\boldsymbol{R_{m}}$ of $250\ \mathbf{k}\boldsymbol{\Omega}$ for a $\boldsymbol{Q}$ of 2,300. The device can sustain the input power of 438 nW delivered to the resonator, corresponding to −5 dBm of the drive power from the network analyzer (NA), which is around 2000X higher as compared to traditional capacitive MEMS resonator design.
An Improved Harmonics Mitigation Scheme for a Modular Multilevel Converter

Modular Multilevel Converters (MMC) are gaining importance because of their flexible structure, re-configurable property, and simplicity of operation. The operation of MMC at a low-switching frequency (LSF) helps in enhancing the performance of the converter. This paper proposes an improved harmonics mitigation scheme for a Multilevel DC-Link Inverter (MLDCLI), which is a variant of MMC. The proposed scheme is a modified version of the conventional Nearest Level Modulation (NLM) scheme, termed as modified Nearest Level Modulation (mNLM) Scheme. The proposed scheme is effective compared to NLM because of the choice of the switching angles obtained by the use of the algorithm proposed. The MLDCLI topology is operated for twelve different configurations, and mNLM is implemented on all the configurations. Using MATLAB software, the simulation results are validated, and the same is extended to a hardware prototype. The simulation and experimental results of NLM and mNLM schemes are compared. The effectiveness of the proposed scheme is evident by the reduced voltage THD, increased rms voltage, increased rms current, and increased output power.
Development and Design of AIVBOT for Intelligent Manufacturing System Education Factory

The mobile robot is one important element that makes factories more autonomous. With autonomous mobile robots in factories, it can increase the productivity and flexibility of the production line. In previous work, we introduced an AIV, an Autonomous Intelligent Vehicles with hub motors embedded in Mecanum wheels. This paper presents a mechanism design called AIVBOT (Autonomous Intelligent Vehicles with robot) that improves wheel idling and reduces the position error during navigation. The AIVBOT with GMT manipulator on the application layer can grab various workpieces from Processing Areas on Intelligent Manufacturing System Education Factory to the Precise Measurement Areas. With the ArUco marker and Intel Realsense d435 depth camera on the end effect, AIVBOT can precisely put the workpieces on CMM (coordinate measuring machine). The AIVBOT can handle different tasks on Intelligent Manufacturing System Education Factory and is suitable for automated production lines.
RHNet: Lightweight Dilated Convolutional Networks for Dense Objects Counting

In this paper, we propose a lightweight network called RHNet to achieve a faster counting method for dense objects counting. RHNet is composed of two major components. The first part contains four convolution layers and two max-pooing layers, which is designed to extract features mainly. The following second part is a structure named dilated special pyramid pooling, which is aimed at understanding the multiscale information. Compared to other published excellent networks, RHNet is a lightweight network because it has only 0.03 million parameters. Importantly, we demonstrate RHNet a decent accuracy on famous ShanghaiTech crowd dataset, WorldExpo’10 crowd dataset, UCF-QNRF dataset and an extended dataset with more kinds of dense objects.