Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic Vibration Characteristics Analysis of Permanent Magnet Synchronous Linear Motor for Electromagnetic Launch

To analyze the electromagnetic vibration characteristics of a permanent magnet synchronous linear motor under high-speed, high-thrust transient emission conditions, this paper first establishes a magnetic field analysis model. Based on this model, an analytical method is employed to analyze the motor’s electromagnetic characteristics. Analytical expressions for air gap flux density and electromagnetic force waves within the range of permanent magnet length are derived. The amplitude, frequency, and order of the electromagnetic force waves are analyzed. Subsequently, the spatial and frequency orders of the electromagnetic force waves are investigated using two-dimensional Fourier transforms in the time-space domain. The correspondence between spatial and frequency orders is clarified. Finally, the accuracy of the analytical calculations is verified through finite element simulation. This study provides valuable insights for understanding the non-periodic transient electromagnetic vibration mechanism of permanent magnet synchronous linear motors.
Assessing Distributed Energy Resource Hosting Capacity Under Different Grid-Tie Inverter Control Strategies

Despite the potential benefits that the integration of distributed energy resources (DERs) can bring to the system, it may cause problems related to power quality constraints, such $as$ reverse power flow in substation and overvoltages. An effective approach to address these problems involves the adoption of reactive and active power control in grid-tie inverters associated with DER. Therefore, this paper assesses the impacts of grid-tie inverter control modes, including both Volt-Var and Volt-Watt strategies, on the DER hosting capacity. In order to improve the overall system operation, modifications in the Volt-Var and Volt-Watt curves were proposed. It is noteworthy that these control strategies can have adverse effects on certain distribution system performance indicators, such $as$ voltage deviation and power losses; for this reason, these indicators are also evaluated in this study. A stochastic approach was adopted to deal with the uncertainties associated with DERs and loads. Finally, from tests conducted in the IEEE 33-bus test system, it was concluded that the proper adjustment of the Volt-Var and Volt-Watt control curves significantly influences DER hosting capacity, $as$ well $as$ voltage deviation and power losses.
Implementation of Face Detection Using OKAO Vision Technology for NAO Robot System

In order for the system to recognize faces, it must have the capability to identify the distinct features of a face, allowing the camera to differentiate between facial features and non-facial features. In This paper we present an intelligent system of face detection recognition using the ALFaceDetection function which is used in the “face reco” and “face detection” boxes, and tracking using the “face tracker” box in the graphical user interface of Choregraphe on NAO robots. The system utilizes advanced algorithms for accurate detection of faces and identification based on stored facial features, to track faces. He detects positioning information and updates it according to the new position of the face. The robot is able to act based on the position of a person's face captured by the camera and adjust its motion accordingly.
DC-Link Voltage Control for Series-Parallel Wireless Motor without Communication Considering System Nonlinearity

This paper proposes a dc-link voltage control (DVC) strategy without communication between primary and secondary sides for a series-parallel wireless motor (SPWM) system. Communication devices, such as Bluetooth, can be removed, which can eliminate the influence of wireless communication failure on the voltage feedback control. In the DVC strategy, the time-frequency domain hybrid (TFDH) modeling approach is proposed for dc-link voltage estimation. Since the nonlinearity of the diode rectifier and all the electric power components are considered, the accuracy of the voltage estimation can be improved compared with the FHA-based method. Finally, the effectiveness of the proposed DVC strategy is verified by simulation for the SPWM system.
A Motor-Parameter-Free Model Predictive Voltage Control for PMSM Drives Based on Incremental Prediction Model

The traditional model predictive voltage control (T-MPVC) approach employed for permanent magnet synchronous motor (PMSM) has a strong parameter dependence. Therefore, with the intention to decrease the parameter sensitivity of PMSM, a motor-parameter-free model predictive voltage control approach is suggested in this paper. First, an incremental voltage expression is established to alleviate the impacts of flux-linkage and resistance parameters. Second, an integral controller is devised for obtaining the aggregate parameter information containing the inductance and incorporate it into the already established incremental voltage expression, thus implementing the motor-parameter-free model predictive voltage control. Then, the computational complexity is further reduced by utilizing the fast vector selection approach. Finally, the conclusions of the simulation demonstrate the validity of the suggested approach.
Analysis and promotion of multi-source ship shore power system based on prospect theory

In the context of energy saving and emission reduction and building green ports, ship-to-shore power technology is one of the important strategies favored by governments to solve port pollution. This paper describes the development background of marine shore power technology, introduces the composition and power supply mode of multi-source shore power system, analyzes the port multi-source ship shore power based on the prospect theory, the practical value and application prospects of multi-source marine shore power systems are described.
A PCB Defect Detection Algorithm Based on Improved Yolov7-tiny

In order to solve the problems of PCB surface defect detection, such as slow speed, long detection time and missed detection, a PCB surface defect detection algorithm based on improved Yolov7-tiny was proposed. The REPVGG structure is introduced into the conv, and the multi-channel structure of the training network is transformed into the single-channel structure of the inference network by the thought of network re-parameterization, so as to improve the inference speed of the model. Se attention, a visual channel attention mechanism, is incorporated into conv, and a 1 × 1 × c weight matrix is obtained by extrusion and excitation operations, enhance the ability of model precision extraction. In CBL Network Module, PRelu activation function is used instead of LeakyRelu to optimize the extraction precision. The experimental results show that the MAP@0.5 of the model can reach 98.6% on the open source PCB defect data set of Peking University.
Ensemble Learning-Based Classification for Acroporidae and Pocilloporidae

The two main families of hard corals Acroporidae and Pocilloporidae are the primary species responsible for hermatypic corals. By assessing their population, the health of coral reefs is assessed. Despite their distinct characteristics, it is difficult to identify these two species based on single external features, particularly in different underwater environments and from varying angles of observation. Even with deep learning methods such as Convolutional Neural Network (CNN), satisfactory identification results were not obtained. Thus, we proposed an ensemble learning classification based on multiple features to distinguish Acroporidae from Pocilloporidae. We used several weak classifiers such as the growth range of coral tips and color channel transformations. An XGBoost-based [6] ensemble learning approach was employed to construct a strong classifier with an accuracy rate exceeding 80%.
Approaches to CAD Development for Specialized VLSI

This paper discusses various approaches to the development of computer-aided design systems for specialized very large scale integration (VLSI) circuits. Based on a hierarchical approach to VLSI design, the main stages that need to be implemented by the CAD system are identified, and the tasks performed by the system at each stage are considered. The functionality accelerating changes in the design are proposed for each stage. The main approaches used in the development of this kind of system, their pros and cons, are also considered.
Suspension Force Model of Bearingless Multi-Sector Surface-Mounted Permanent Magnet Synchronous Motor Based on Maxwell Stress Tensor Method

Considering motor load and eccentricity, the time-domain model of suspension force of a bearingless multisector permanent magnet synchronous motor is obtained. The model is suitable for the bearingless motor with multi-sector winding connections and can be used to calculate the magnitude and direction of the suspension force vector on the rotor of the bearingless motor in real time. Finally, Ansys Maxwell simulation results are compared with the calculated results to verify the correctness of the time-domain model. Furthermore, based on the time-domain model, the decoupling control strategy of the bearingless motor is proposed.
An Embedding-Based Approach to the Ontology Matching Problem

Ontology matching plays a crucial role in the realm of ontology engineering, specifically in ontology development and integration. It serves the purpose of resolving the issue of heterogeneous semantics between ontologies. The alignment of ontologies is a critical step in enabling interoperability of systems and data, which is particularly important in a world where data is increasingly numerous and diverse. This paper proposes a fully automatic ontology matching approach, which utilizes machine learning algorithms. By combining a neural embedding approach and a mapping neural network, this approach achieves a precise and effective mapping between ontologies. The experimental findings on the well-known ontology benchmark ‘anatomy dataset’ are shown to be quite superior to previous matching tools.
Hydro-Pump/Generator-based Hydrogen-Pressure Energy Storage for Power Grid-Forming Support

This paper presents a method to design water-compressed hydrogen energy storage system (WCH-ESS) and its active regulation function for the power grid. First, it proposed the system architecture of water-compressed hydrogen ESS station. Then, the station operation depends on the variable-speed constant frequency (VSCF) motor/generator as the core function. With the station and core function, the design method of active support function of power grid based on the WCH-ESS is proposed. Finally, the layout, function and risk avoidance design of the WCH-ESS based on an energy island is analyzed. This method can take the discarded wind/photovoltaic energy as the surplus electric energy to drive the motor to compress the hydrogen when the load of the power grid is low. For high-value electric energy, the system adopts the WCH-ESS to generate more power, which can participate in the frequency regulation of power grid to stabilize the power system. It can also be used as emergency backup to improve the consumption rate of wind power and photovoltaic power generation.
Robust Sclera Segmentation for Skin-Tone Agnostic Face Image Quality Assessment

Face image quality assessment (FIQA) is crucial for obtaining good face recognition performance. FIQA algorithms should be robust and insensitive to demographic factors. The eye sclera has a consistent whitish color in all humans regardless of their age, ethnicity and skin-tone. This work proposes a robust sclera segmentation method that is suitable for face images in the enrolment and the border control face recognition scenarios. It shows how the statistical analysis of the sclera pixels produces features that are invariant to skin-tone, age and ethnicity and thus can be incorporated into FIQA algorithms to make them agnostic to demographic factors.
A Novel Stator Dual-PM Flux-Switching Machine with Improved Utilization of High-Order Harmonics and Reduced Flux Leakage

This paper proposes a novel stator dual-permanent magnet flux-switching (SDPM-FS) machine with enhanced torque density compared to the conventional flux-switching permanent-magnet (FSPM) machines. The SDPM-FS machine features a unique arrangement of PMs located on the stator tooth region. A set of radially magnetized PMs is added into the dummy slots, and the side teeth on the stator are removed to enhance the 4th-order harmonic of the stator PM excited magnetomotive (MMF). The utilization of 2 pole-pair working harmonic of air-gap flux density is significantly improved while the flux leakage of spoke-type PMs is simultaneously reduced. Consequently, the phase flux linkage increases, and the no-load phase back EMF is further enlarged. In this paper, the working principle of the proposed SDPM-FS machine is analytically introduced by using a simplified magnetomotive force (MMF)-permeance model. Subsequently, a non-dominated sorting genetic algorithm II (NSGA-II) is used to conduct the global optimization. Furthermore, the performance of the proposed SDPM-FS machine is comprehensively compared with a conventional multi-tooth FSPM machine using finite element analysis (FEA). The results demonstrate that the SDPM-FSM exhibits notable enhancements in torque density and efficiency.
Research on Nonlinear Process Fault Detection Based on Generative LLE and SVDD

In the modern chemical industry, the process data collected are high-dimensional and complex. Aiming at the nonlinear problem of chemical process, this paper proposed a fault detection method based on Generative Locally Linear Embedding (GLLE) and Support Vector Data Description (SVDD). Firstly, GLLE is used to reduce the dimension of the data to retain more nonlinear features, and the corresponding mapping matrix is obtained by local linear regression. Then, to avoid the influence of noise on the traditional statistics, SVDD is used to obtain the corresponding "center" and "radius", and then the statistics and control limits are calculated. Finally, the effectiveness of the proposed method was verified by the simulation study of Tennessee Eastman (TE) process.
Research on IPMSM Online Parameter Identification with Low Carrier Ratio Considering Nonlinearity of NPC Inverter

Cross-coupling saturation caused by low carrier ratio is critical for interior permanent magnet synchronous machine (IPMSM) control. In order to realize the online parameter identification of IPMSM under low carrier ratio and improve the control performance, this paper proposes a disturbance-based online parameter identification strategy. The high-frequency response signal is obtained by introducing a small current disturbance in the synchronous coordinate system, and the corresponding observer is designed to realize the online parameters identification. In addition, considering the nonlinear characteristics introduced by NPC inverter and the influence of the harmonic current signal under low carrier ratio, the corresponding inverter compensation strategy and resonant controller are designed. Finally, the proposed online parameter identification strategy is verified in simulation.
An Advanced Subdomain Model for Predicting Dynamic Demagnetization in a Surface-Mounted Permanent-Magnet Field-Modulated Motor

This work aims at constructing an advanced subdomain model (SDM) suitable for predicting the dynamic demagnetization in a multi-phase surface-mounted permanent-magnet field-modulated motor (SM-PMFMM). Executing as a hybrid method that integrates the traditional pure-analytical SDM with the pure-numerical finite difference method (FDM), the advanced SDM not only has a low computational burden but also allows full consideration into the effects of both eddy current and magnetic nonlinearity on the magnet condition. Effectiveness of the advanced SDM in demagnetization analysis for the studied SM-PMFMM is verified by both simulation and experimental results.
Robotic Grasping Based on Deep Learning: A Survey

The progress achieved in the robotics field in the last decade made the deployment of robots possible in real - world. One of the essential skills required to do these tasks is object grasping and placing. Grasping is the process of lifting an object without dropping it by the end effector of a robot manipulator. To accomplish the grasping task, a robot has to automatically identify the grip region of a specific item via tactile or visual information. Deep learning, a branch of machine learning developed by simulating human brain activities, can be beneficial in achieving robot grasping tasks. In addition, the development in hardware computational abilities and data availability helped Deep Learning succeed in many fields such as computer vision, image segmentation, and object detection. Derived by this success, many researchers developed Deep Learning-based methods for robot object grasping and achieved promising results. This survey focuses on data-driven (or sometimes called learning-based) approaches based on Deep Learning and used for vision-based robotic grasping. The purpose of this review paper is to provide an overview of object grasping and placing based on Deep Learning, it's used in this field with exploring the most recent works.
Sensorless Control of Five-Phase Flux-Intensifying Permanent Magnet Motor Based on Gradient Descent Adaptive Filter SMO

This paper presents a sensorless control strategy of five-phase flux-intensifying interior permanent magnet (FPFT-IPM) motor based on sliding mode observer (SMO). Due to the back-EMF harmonics caused by inverter nonlinearity and motor secondary saliency, the position estimation errors will be increased. Thus, an adaptive filter (AF) based on gradient descent (GD) algorithm is proposed for improving estimated accuracy of rotor position. The feasibility of the algorithm is verified by simulation and experiment.
Torque Ripple Analysis and Suppression of Dual-rotor Axial Flux Permanent Magnet Synchronous Motor

Axial flux permanent magnet synchronous motor is superior to other types of motors in terms of efficiency, torque and size. However, torque ripple is a problem that cannot be ignored in axial flux permanent magnet synchronous motor, which further increases the vibration and noise of the motor and reduces the control accuracy. Therefore, this paper will suppress torque ripple by optimizing the method of permanent magnet tilt and chamfering. Taking the 16-pole 24-slot dual-rotor single-stator axial flux permanent magnet synchronous motor as an example, the finite element method is used to simulate and analyze different tilt angles, and the most suitable tilt angle that does not affect the performance of the motor is determined. The optimized motor model is simulated and analyzed, and the torque ripple is reduced by 64%, which verifies the feasibility of the method.
Active Disturbance Rejection Control Combined with Current-Stress-Optimization for Dual Active Bridge DC-DC Converter

Due to irregular load switching, the bus voltage fluctuations in dual active bridge (DAB) converter are generated. In this work, active disturbance rejection control (ADRC) based dual-phase-shift (DPS) modulation strategy is proposed to improve the dynamic response of DAB converter and relive the current stress of the converter. Firstly, under the DPS modulation strategy, the transmission power and current stress are analyzed. The minimum current stress under each working condition and the corresponding shift combination is obtained. Then, an extended state observer (ESO) is established, and the transmission power change caused by the sudden load change and DC bus voltage are regarded as the total disturbance. In this paper, an ADRC combined with current stress optimization control is proposed, which has better operating characteristics than traditional proportion integration (PI). At last, the superiority of the control strategy is verified by experiments.
MPPT Control Of Photovoltaic Panel Based On Artificial Neural Network

To benefit from the electrical power provided by the photovoltaic (PV) panels, the development of effective control strategies plays a key role. These strategies are known as the maximum power point tracking (MPPT) methods. This task presents a challenging problem given the great dependence of the PV panels even when to the changes atmospheric conditions and its nonlinear demeanor. This paper proposes, a MPPT based on artificial neural network (ANN) technique. Python software has been employed to simulate the considered method. Simulation results have been compared to the classical control method, which is Perturb and Observe (P&O) technique. To further prove the effediversess of the proposed approach the latter has been implemented on Raspberry Pi 3-bases embedded board.
Research and Classification of Eating Behavior Patterns of Cattle Using a Convolutional Neural Network

The study provides an overview of evidence on the use of feeding characteristics in cattle to identify health and welfare problems. A review of machine learning algorithms suitable for classifying the feeding behavior of cattle is presented and the use of a convolutional neural network (CNN) is justified. The use of intragastric bolus and undertightening as sources of input data for CNN is shown. To solve the problems of practical implementation of the developed CNN, a compromise between CNN performance, computational complexity and required hardware resources was identified, a search for hyperparameters was performed, and the CNN was optimized.
Multi-timescale Coordinated Operation Optimization of Integrated Energy System

Energy and environmental issues have made the integrated energy system (IES) a research focus. The IES promotes the complementary operation of the electric energy flow (EEF) and the thermal energy flow (TEF). However, the timescale differences between two energy flows make the IES operation optimization difficult. To handle the problem, this study proposes a multi-timescale coordinated operation optimization method for electricity-heat coupled IESs. The proposed method defines decision variables and constraints at different timescales to characterize the operation characteristics of EEF and TEF. The coordinated optimization mechanisms are established between two energy flows based on the energy coupling relationship. The optimization method can not only ensure the real-time power balance of EEF, but also meet the cold/heat energy demands over a long-time interval. Simulation results show that the proposed method reduces the operation cost by 7.7% compared to a tradition operation strategy. Furthermore, the proposed method provides a more effective way than the single timescale optimization method to deal with the volatility of renewable generation and electric load.
Real-Time System for Detection Hidden and Visible Keypoints of Rainbow Trout

In this paper, we describe the experience of applying deep learning methods to the detection of eight keypoints of rainbow trout. Using the two loss functions combination and hyperparameters tuning, our solution achieves performance comparable with state-of-the-art results of human keypoints detection. The obtained result enables the digital transformation of aquaculture industries and the automation of production processes, through the implementation of a wide range of video analysis algorithms providing such capabilities as non-contact fish counting, weighing, behavior analysis, and diagnosis of diseases.