Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Objective Optimization for Enhancing System Coordination Restoration by Placement of Fault Current Limiters on an Active Distribution System with System Reliability Considerations

Nowadays, customer satisfaction relies on not only the power delivery, but also in the system reliability. It results a number of measures adopted by utility for reliability improvement. Moreover, distributed generators (DGs) are also enhanced to integrate to distribution system because DGs improve system reliability and capability. However, DGs can lead to mis-operation of protective devices and wider outage areas in power system due to increase in fault level. Moreover, system outage is the vital cause which decreases the system reliability. To mitigate escalation of fault level, fault current limiters (FCLs) are installed to limit incremental fault generated by DGs and improve system reliability. However, to enhance effectiveness of FCLs installation, non-dominated sorting particles swarm optimization (NSPSO) is adopted to find the optimal placement of FCLs. Moreover, novel binary particle swarm optimization and continuous particle swarm optimization are performed optimization simultaneously for selecting installed locations. Therefore, three objectives are considered in optimization process consist of to minimize total cost of fault current limiters installed, to minimize total different of margin operating time of main and backup relay pairs and to maximize system reliability. The optimal results are the set of various solutions conform with multi-objective placed in pareto optimal front, and the result show effectiveness of combined continuous-binary NSPSO. The optimization process is carried out on IEEE 33-bus and IEEE-69 bus radial system.
RepGuide: Reputation-Based Route Guidance Using Internet of Vehicles

Future route guidance application will make use of wireless traffic information exchange between vehicles and infrastructure roadside units (RSUs) to form a ubiquitous connected network known as the Internet of (connected) Vehicles (IoV). Current standards, such as Wireless Access for Vehicular Environment and ETSI Intelligent Transport Systems, for vehicular networking recommend elliptic-curve-based security algorithms to reliably share traffic information and provide defense against network attacks. However, data integrity attacks cannot be detected by the security algorithms proposed in the standards and can degrade the accuracy of an IoV route guidance application. RSUs have limited transmission range and rely on traffic information reported by vehicles within their range to estimate traffic density data in their vicinity. Malicious vehicles can transmit wrong traffic information, resulting in non-optimal route decisions. We propose a reputation algorithm known as RepGuide that enables reliable route guidance using IoV. RepGuide finds outliers in the traffic density information and identifies them as suspicious data. The algorithm then verifies the suspicious traffic information by multihop communication exchange with vehicles outside the transmission range of the RSU. Any suspicious data that cannot be verified is not used for finding optimal routes. Our performance evaluation results show that RepGuide reduces the travel time by 18 percent for an IoV route guidance application.
A Survey and Implementation on Neural Network Visualization

With the rising demand of learning neural network technology and analysis in recent years, how to diffuse the knowledge from sophisticated researchers to the general audience has become an essential issue. We have conducted a short survey on neural network visualization. Regarding the problem that existing tools for the neural network development provide limited visualization for model editing, we have implemented an easy-to-use neural network development platform prototype with full visualization on both model designing and results analysis. Users can now design, edit and analyze in a user-friendly browser-based environment, while training the model on one-click running through the web-generated program.
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images

Reduction of mixed noise is an ill posed problem for the occurrence of contrasting distributions of noise in the image. The mixed noise that is usually encountered is the simultaneous presence of additive white Gaussian noise (AWGN) and impulse noise (IN). A standard approach to denoise an image with such corruption is to apply a rank order filter (ROF) followed by an efficient linear filter to remove the residual noise. However, ROF cannot completely remove the heavy tail of the noise distribution originating from the IN and thus the denoising performance can be suboptimal. In this paper, we present a variational step to remove the heavy tail of the noise distribution. Through experiments, it is shown that this approach can significantly improve the denoising performance of mixed AWGN-IN using well-established methods.
Joint Resource Allocation and User Association for Heterogeneous Services in Multi-Access Edge Computing Networks

Multi-access edge computing (MEC) has emerged as a promising technique for low-latency services, in light of its proximity to users and embedded cloud computing capability. In order to improve the network efficiency and fairness, it is crucial and challenging to jointly optimize resource management and user association mechanism with the consideration of heterogeneous services and network conditions. Moreover, the existing studies always neglect the heterogeneities of services on the requirements of both computation capability and storage capability. To solve this issue, we derive a strategy to improve the overall delay-aware performance of heterogeneous services with the MEC capability of computation and storage and the choices of the users’ association. Accordingly, a coalition-game-based algorithm is proposed to form user coalitions for association scheme and resource sharing policy. In particular, we mathematically present that the proposed algorithm is capable of convergence and optimality. The simulation results also show that our algorithm gets a good performance efficiently. Furthermore, it reduces the weighted sum of delays of users by average 27.8% and 82.1%, while continuously improving delay-aware fairness, compared with those of the priority-based assignment scheme and the nearest assignment scheme, respectively.
Analysis of Downlink Channel Estimation Based on Parametric Model in Massive MIMO Systems

This paper investigates downlink channel estimation in massive multiple-input multiple-output (MIMO) systems. To reduce the overhead of downlink channel estimation and uplink feedback in FDD systems, cascaded precoding has been used in massive MIMO such that only a low-dimensional effective channel needs to be estimated and fed back. In this paper, we analyze a strategy for downlink channel estimation based on the parametric model in massive MIMO with cascaded precoding. The path delays of uplink channels are first estimated and quantized at the base station, then fed forward to the user equipment (UE) through a dedicated feedforward link. In this manner, the UE can obtain the a priori knowledge of the downlink channel in advance since it has been demonstrated that the downlink and the uplink channels can have identical path delays. Our analysis and simulation results show that the proposed approach can achieve near-MMSE performance.
Quadratic Programming for Multirobot and Task-Space Force Control

We have extended the task-space multiobjective controllers that write as quadratic programs (QPs) to handle multirobot systems as a single centralized control. The idea is to assemble all the “robots” models and their interaction task constraints into a single QP formulation. By multirobot, we mean that whatever entities a given robot will interact with (solid or articulated systems, actuated, partially or not at all, fixed-base or floating-base), we model them as clusters of robots and the controller computes the state of each cluster as an overall system and their interaction forces in a physically consistent way. By doing this, the tasks specification simplifies substantially. At the heart of the interactions between the systems are the contact forces; methodologies are provided to achieve reliable force tracking by our multirobot QP controller. The approach is assessed by a large panel of experiments on real complex robotic platforms (full-size humanoid, dexterous robotic hand, fixed-base anthropomorphic arm) performing whole-body manipulations, dexterous manipulations, and robot–robot comanipulations of rigid floating objects and articulated mechanisms, such as doors, drawers, boxes, or even smaller mechanisms like a spring-loaded click pen.
An Enhanced Registration and Display Algorithm for Medical Augmented Reality

Augmented Reality (AR), combines real world and supplemental aids, helps in better user perception which can be very useful inaccurate decision making especially in medical applications. AR process includes registration, display, and tracking. AR system must be of a very high accuracy and should have a clear standard way of saving metadata and various information for an AR standard display. A lot of registration techniques may lack in accuracy, and display techniques may not have a standard display on different browsers and platforms. This paper handles the above shortcomings by proposing a marker-less medical AR system that uses both of intensity based detection to detect lesions in the liver and augmented reality markup language (ARML) for a standard AR display. Morphological operations are applied to make detection easier and to enhance the scene. Detection will be then followed by AR registration technique which is implemented using a mutual based information algorithm. In all system phases including preprocessing, we will consider saving metadata and information in ARML tags. Finally tracking process will be considered by applying previous steps to consequent video frames. Results of the proposed system were found to enhance physician perception for targeted objects with high accuracy. In addition; the usage of ARML for saving metadata guarantees standard AR display, it also enhances patient treatment and follow up.
Electromagnetic Transient Modeling and Simulation of Power Converters Based on a Piecewise Generalized State Space Averaging Method

Large-scale photovoltaic (PV) power plants connected to the grid leads to the complex calculations in modeling and simulation of the power system, especially in the electromagnetic transient progress. Common averaging methods are studied for modeling a grid-connected converter to simplify the calculation and balance accuracy and efficiency in the simulation. The piecewise technique is applied to the generalized state space averaging model of converters, which combine time segment with similar operating characteristic. In addition, a new piecewise generalized state space averaging (P-GSSA) model is derived and a multi time scale modeling is achieved for the grid-connected converters in PV systems. The model error and accuracy of the P-GSSA model are analyzed, the physical significance and the influence factors in the model error of P-GSSA model are discussed. The proposed P-GSSA model is flexible to be used in a PVs system simulation, and it can accurately reflect the steady-state and transient characteristics of a converter. The research shows that P-GSSA model improved the simulation efficiency over 20% by the index of consumed time, meanwhile, the model error analysis is under 5%. The analysis of the P-GSSA model is verified by simulating a DC/AC converter and a photovoltaic system.
A High Performance Framework for Large-Scale 2D Convolution Operation on FPGA

Convolutional neural network (CNN), as the focus in the artificial intelligence field, has attracted more and more attention resent years. Various accelerators based on FPGA platform have been proposed because of the high performance and high energy efficiency. However, limited to the on-chip memory resources, only small networks can be accelerated by these accelerators. Due to the fact that 2D convolution operation is the most computing-intensive part, accelerating large-scale 2D convolution operation has become the key to accelerating large-scale CNNs on FPGA platforms. This paper presents a method of rotary data-storage and a design of the new PE (processing unit). Compared to the traditional Z-type PE, it performs non-redundant calculations when the stride of convolution operation is greater than 1, which significantly reduces the calculating time. At the same time, in order to reduce the external memory bandwidth with limited on-chip resources, two optimization architectures and a blockcalculation method have been proposed, which can reduce the usage of on-chip memory resources. As a case study, we select some convolution layers of ResNet-34 and compare it with a previous accelerator. Under the condition of same consumption of resources, the result shows that the proposed framework needs only 29.9% time and 51% external memory bandwidth of the previous accelerator.
A Pedestrian Dead-Reckoning System for Walking and Marking Time Mixed Movement Using an SHSs Scheme and a Foot-Mounted IMU

A pedestrian dead-reckoning (PDR) system based on micro-inertial technology usually uses a sensor integrated with a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and is attached to the person’s body (usually on the foot) to acquire the trajectory of the pedestrian. Although a variety of PDR system solutions have been proposed, the PDR solution for marking time and walking hybrid motions has not been formally proposed and solved. Therefore, in this paper, we propose the PDR system using a step-and-heading system scheme for marking time and walking hybrid motion. The main contributions include: 1) an adaptive gait partitioning method for walking and marking time hybrid motions is proposed; 2) a motion classification algorithm that uses a multi-layer perceptron to classify whether each step is walking or marking time is proposed; and 3) a new step length estimator based on several currently proposed models is proposed. Finally, an extended Kalman filtering algorithm combining heuristic heading reduction, flat-ground hypothesis, and cardinal heading aided inertial navigation techniques is used for heading estimation. We evaluate the effectiveness of the proposed algorithm through experiments, and the experimental results show that the average position error of the two groups of test experiments based on marking time and walking hybrid motion is 0.42% and 0.6%, respectively.
Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm

In non-destructive testing and evaluation, the microwave-based synthetic aperture radar (SAR) imaging have shown great potential in the detection of defects on buried objects such as pipes. However, due to the pipe curvature and high stand-off distance when inspecting an insulated pipe, the useful defect information used to characterize the pipe image is lost as a result of low signal-to-noise ratio (SNR), resulting in a blurred and unfocused image. In this paper, we proposed a robust microwave-based SAR imaging using autofocus range-Doppler algorithm (RDA) for the inspection of an insulated pipe. Singular value decomposition is used to mitigate the effect of the insulation layer by removing dominant singular values that characterize the insulation layer, and the autofocus RDA is designed to refocus the SAR image using RDA residual refocusing. The SNR, improvement factor (IF), and squared error are used to evaluate the qualitative image information of the defect on the pipe. Experimental results showed the efficacy of the method in detecting defects on an insulated pipe, in particular, a significant reduction in the noise content of the image compared to the known SAR Omega-k algorithm. It was found that the autofocus RDA gave higher values of SNR and IF (3 dB and 6 dB) compared to the Omega-k algorithm (−1 dB and 2 dB), respectively.
Learning Proximal Operators with Gaussian Processes

Several distributed-optimization setups involve a group of agents coordinated by a central entity (coordinator), altogether operating in a collaborative framework. In such environments, it is often common that the agents solve proximal minimization problems that are hidden from the central coordinator. We develop a scheme for reducing communication between the agents and the coordinator based on learning the agents’ proximal operators with Gaussian Processes. The scheme learns a Gaussian Process model of the proximal operator associated with each agent from historical data collected at past query points. These models enable probabilistic predictions of the solutions to the local proximal minimization problems. Based on the predictive variance returned by a model, representative of its prediction confidence, an adaptive mechanism allows the coordinator to decide whether to communicate with the associated agent. The accuracy of the Gaussian Process models results in significant communication reduction, as demonstrated in simulations of a distributed optimal power dispatch application.
A Micro-Phenomenological Lens for Evaluating Narrative Visualization

Narrative visualizations engage audience in data stories, evoking emotions by using narrative patterns, rhetoric, visual design, and content among other strategies. How these elements combine to influence user experiences is complex and difficult to measure using empirical methods. This is partly due to the fact that narrative visualizations influence audiences affectively and implicitly [1]–[3]. Evaluations of narrative visualizations that aim to better understand these mechanisms should capture this rich complexity by focusing on gathering descriptions of lived experience. Micro-phenomenology, a rigorous set of methods developed for soliciting descriptions of experiences, has empirically been shown to improve recollection of otherwise implicit aspects of experience [4]. Building on work using micro-phenomenological interviews to evaluate static visualizations [5], we apply these methods to interactive narrative visualizations. We conducted a small study to explore the potential of these methods in this context. Our findings reveal how narrative patterns and designs influence affective states, how they support various forms of exploratory analysis, and how they can facilitate or hinder non-analytical reflection such as the imagining of stories described within visualizations. These types of insights can inform future designs and help researchers understand how techniques employed in narrative visualizations influence users in specific and often implicit ways.
Slow Gas Heating Process in an Atmospheric- Pressure Streamer Discharge Visualized by High-Speed Schlieren Method

Temporal variations in the gas density gradient occurring in an atmospheric-pressure streamer discharge are visualized by the schlieren method to investigate the effects of the background gas species on the temporal variations of the gas heating process after the discharge. The streamer discharge is generated in needle-to-plane electrode configurations in dry-nitrogen, humid-nitrogen, dry-air, and humid-air conditions. The discharge characteristics are measured by the voltage and current waveforms and the spatial distribution of the discharge emission of light. The results of the schlieren visualization show that the gas density gradient increases after the discharge and its rate of increase is accelerated by the presence of water molecules. In addition, the degree of the increase in the density gradient is higher in humid-nitrogen discharges than in humid-air discharges. These characteristics are subsequently explained by the vibrational to the translational energy-transfer process of nitrogen, oxygen, and water molecules.
New Fuzzy Control of Photovoltaic Conversion Cascade Based Three Levels Inverter for Stand-Alone Applications

This paper presents a new control scheme based on Fuzzy Logic Controller (FLC) of output three levels inverter voltages for photovoltaic conversion stand alone two stages cascade. The error between the root mean square value of output voltage and it to reference and the change of error constitute the two inputs of FLC. The error of modulation index represents the output of FLC. The boost converter is controlled by a FLC in order to extract the maximum power from PVG. In order to have a symmetrical and stable output voltage of our cascade, the input DC voltages must be of equal values. An algorithm for stabilizing these two voltages using the redundant configurations of the inverter vectors is introduced into the control loop. The simulation results show that the rms output voltage is inside the band of +/-10V and with low total harmonic distortion despite irradiation change.
Utterance and Syllable Level Prosodic Features for Automatic Emotion Recognition

This paper describes an automatic emotion recognition (AER) system that combines prosodic features extracted at utterance level and syllable level to recognize its emotional content. The prosodic features are extracted after identifying speech/non-speech intervals, followed by syllable level segmentation. Prosodic features chosen include parameters for representing dynamics of pitch and energy, along with duration information. Two separate classifiers are built using Deep Neural Networks (DNN). The decision scores based on both levels are fused to identify the emotion of a test utterance from the German Emotion Database (Emo-DB) which contains seven emotions, namely anger, boredom, disgust, fear, happiness, sadness and neutral. The proposed system gives a Weighted Average Recall (WAR) of 58.88 % for both utterance level and syllable level prosodic features. Fusion of scores by merely adding the scores gives an overall WAR of 61.68%.
A Novel Spark Plug with High Field Intensity

A novel spark plug with high field intensity has been designed with resonant frequency of 2.45 GHz in this paper, which is applied to auto microwave ignition for replacing traditional spark plugs. According to the electric coupling feeding theory, the electric field intensity on the top of the inner conductor was calculated as $1.25\times 10^{6}$ through the finite element method with input power of 1 W. In that case, the threshold of plasma within the cylinder being $2.25\times 10^{7}\mathrm{V}/\mathrm{m}$ will be satisfied if the input power is within 400 W. Besides, its bandwidth is 9 M, much wider than traditional microwave spark plug, which is vital for frequency drift when igniting.
Improved Design of Artificial Neural Network for MPPT of Grid-Connected PV Systems

This paper addresses the efficiency increase of grid-connected photovoltaic (PV) systems. One of the affecting factors to increase the efficiency is the accurate tracker of maximum power point. Some conventional methods, such as the perturb-and-observe and the incremental conductance (INC), are used for maximum power point tracking (MPPT). The artificial intelligence is exploited to produce a superior MPPT control system. In this paper, the design of artificial neural network (ANN) for MPPT system is improved to maximize the power output of grid-connected PV system. Most of the ANN based MPPT, in the literature, used the solar irradiance and the temperature as inputs, where in this paper alternative inputs are proposed, which effectively ameliorate the behavior of the ANN as a MPPT system. The second amelioration proposed is employing a genetic algorithm (GA) optimization to design the ANN topology, e.g., the number of neurons in the hidden layer, the type of learning algorithm and the type activation function of each neuron. The simulation results are presented and compared to demonstrate the privilege of the performance of the improved ANN design for MPPT.