Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Driven Angular Sampling for Reliable and Efficient SPECT Imaging

Low radiopharmaceutical dose and reduced scan time for molecular medical tomographic imaging are pursued for wider and safer medical applications. Towards this goal we propose an analytical approach to optimally reduce scanning duration or radiopharmaceutical dose for Single Photon Emission Computed Tomographic (SPECT) techniques, while not compromising on reconstructed image accuracy and reconstruction stability. In addition, we provide statistical guarantees to ensure generalization. This is achieved by: (a) utilizing the observation model and Fisher information driven scan strategy, (b) coordinating scanning with point spread function and prior of the reconstruction algorithm, and (c) providing statistical guarantees on reconstructed image variance through the Cramer-Rao bound. Our approach distributes the given total scanning duration optimally across scan angles to minimize Mean Square Error for a given image reconstruction algorithm. It coordinates the duration at each scan angle to ensure optimal information flow to the chosen reconstruction algorithm. For maximum likelihood (ML) estimators we derive a globally optimal closed form equation for angular sampling, and for maximum a posteriori (MAP) estimators we show the optimization problem is a difference of convex functions which can be efficiently optimized. The efficacy of the proposed scanning strategy is quantified through Monte Carlo simulations using real SPECT images and synthetic phantoms. The proposed algorithm achieves more than 2 dB PSNR improvement over conventional uniform scanning approach for real SPECT images. This improvement could be traded in to achieve more than 50% reduction in scan duration.
Research and Prospect of Cyber-Attacks Prediction Technology for New Power Systems

With the rapid advancement of new power systems construction and the rapid development of cyber-attack technology, the cyber-attack has become the main threat to the safe and stable operation of new power systems. And, cyber-attack detection has been unable to meet the actual needs of the new power system security protection. Reliable cyber-attack behavior prediction provides an important reference for attack blocking, attack counterattack, and post-disaster steady-state recovery, which is the core component of building a new power system in-depth security defense system. Firstly, this paper analyzes the possible cyberattack threats to the new power system and gives a brief overview of the importance and feasibility of attack behavior prediction. Then, the research methods for new power system cyberattack prediction are summarized and compared. Finally, specific solutions are given for the challenges faced by the new power system.
Modeling and Simulation of Open Phase Stator Winding Fault in Pentagon connected Five Phase Induction Motor Drive using Back-EMF

This paper is aimed at the analysis of operation of five phase induction machine (FPIM) with one stator phase winding open. It presents different parameters of the machine with stator winding connected in pentagon configuration. Back-EMF approach is adopted for the modeling of fault and the same is simulated in MATLAB-SIMULINK environment. The primary objective is to build a mathematical model for the simulation of the motor drive system in the aforesaid fault condition and to apply a suitable fault tolerant control strategy (FTCS) that reduces the ripple in the electromagnetic torque post fault condition. Reference currents for hysteresis controller are obtained by modifying the x-y components after fault occurrence without affecting the indirect field oriented control (IFOC). A quantitative analysis on electromagnetic torque ripple under healthy, during fault and post fault conditions is presented along with the percentage derating. The simulation results obtained are in accordance with the conditions that are pertaining to the fault. This strategy can be applied to pentacle configuration as well.
Modeling and Design of Triple Transistor Isolation-based Bidirectional Zeta-SEPIC DC-DC Converter

The use of Bidirectional DC-DC converters is increasing significantly in dual directional power flow (forward and backward) requirements such as uninterrupted power supply (UPS) systems, Electric Vehicles (EVs), and renewable energy-based arrangements. A new converter topology is proposed by synthesizing Zeta and SEPIC converters and it has been verified for its performance by State Space Average (SSA) modeling and simulation. The proposed converter reduces the steady state maximum voltage stress on both forward input side switches up to forward input voltage during the Zeta mode of operation. The converter is designed for buck operation in forward Zeta mode and boost operation in backward SEPIC mode. The simulation results confirm both modes of operations in the closed loop with reduced output voltage/current ripples.
Research on the Influence of Hydrodynamic Analysis to Dynamic Modeling of Underwater Manipulator

Underwater manipulator has been widely used in underwater environment to perform underwater engineering construction, marine resource exploration, underwater target salvage and other tasks. Aiming at unstructured environment in the application of underwater manipulator, this paper summarizes the calculation methods of inertia and viscous hydrodynamics of underwater manipulator, analyzes the dynamic model of underwater manipulator from the strip theory and non-strip theory respectively based on hydrodynamics, and finally studies how to optimize the dynamic model of underwater manipulator from the impact of underwater manipulator hydrodynamics on dynamic modeling, The new research directions in the future are pointed out.
Detection of Rice Seed Hole Seeding Amount Based on Deep Learning

Cultivating rice seedlings by using seedling tray sowing is an important part of large-scale industrialized rice planting. Rice seedling raising is to sow after germination. During the seed cultivation process, it will produce different characteristics and thus affect the seedling development or sowing effect. In order to improve the accuracy and efficiency of rice seed target detection of rice pot floppy disk hole seeding amount, this paper proposes a method of rice pot floppy disk hole seeding amount detection based on depth learning. The production data set should use the pictures taken from the seeded floppy disk on the top to train the network on the GPU computing platform by using the data set through the YOLOv5 neural network model. The analysis of the training results and the verification results show that the trained neural network model is a very suitable network model for the detection of the amount of rice seeds planted in holes in terms of real-time and other comprehensiveness. This method can automatically learn and extract the characteristics of rice seeds in the picture of the bowl floppy disk, and realize the real-time automatic detection of 0-7 and more rice seeds planted in holes in the bowl floppy disk, which is conducive to migration to embedded platforms.
Conductivities consistent with Birkeland currents in the AMPERE‐driven TIE‐GCM

The Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) satellite mission has offered for the first time global snapshots of the geomagnetic field‐aligned currents with unprecedented space and time resolution, thus providing an opportunity to feed an acknowledged first‐principles model of the Earth's upper atmosphere such as the National Center for Atmospheric Research Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (NCAR TIE‐GCM). In the first step, Marsal et al. (2012) used AMPERE data in the current continuity equation between the magnetosphere and the ionosphere to drive the TIE‐GCM electrodynamics. In the present work, ionospheric conductivities have been made consistent with enhanced upward field‐aligned currents, which are assumed to correspond to electrons plunging as a result of downward acceleration by electric fields built up along the geomagnetic field lines. The resulting conductance distribution is reasonably commensurate with an independent model that has tried to quantify the ionizing effect of precipitating particles onto the auroral ionosphere. On the other hand, comparison of geomagnetic observatory data with the ground magnetic variations output by the model only shows a modest improvement with respect to our previous approach.
Combination of the Synchronverter with Droop Control for Hybrid OFF-grid System

In this paper, the synchronverter for hybrid off-grid based on a solar photovoltaic system and synchronous generator to improve the power system stability (frequency and voltage) at the point of common coupling (PCC) is presented. The proposed configuration consists of the PV solar with MPPT, a battery bank, a three phases inverter, and a diesel generator. The three-phase inverter combined with a PV solar mimic the behavior of a synchronous generator with a moment of inertia and is compared with a traditional droop controller. Additionally, the system design of the battery energy storage system (BESS) supports virtual inertia during disturbances. However, the virtual inertia helps to reduce the impact on the frequency deviation during sudden load and solar irradiance variation and faults. For validation, simulation results using MATLAB/Simulink is realized to evaluate the performance of the proposed control strategy.
Estimation of Equivalent Circuit Model Parameters for a Generic Battery Using Least-Square Method

A generic battery represents a generalized battery model. This battery does not use the parameters of the battery, and only works with the maximum charging point, breakpoint, and nominal point. In this paper, two widely used electrical equivalent circuit models (ECMs), i.e., 1-RC and 2-RC are constructed to represent the generic battery model to analyze battery characteristics easily. So, ECM can be used as a generalized battery model, and it is the widely used modeling technique for State-of-Charge (SOC), State-of-Health (SOH) estimation, etc. of the battery. ECM and generic battery models are charged/discharged using the same current profile. The parameters of the ECM are estimated using the simulated current and voltage profile of the generic battery by the least-square method. This paper shows a detailed calculation for the ECM parameters estimation, and ECM can replace the generic battery. The comparison of estimated SOC with generic battery SOC for the same testing condition has been done to validate the modeling of the battery. Furthermore, A lithium-iron-phosphate (LFP) battery voltage profile is compared with ECM and generic battery voltage profile to validate the estimated parameters of the ECM.
Hierarchical Action Embedding for Effective Autonomous Penetration Testing

Penetration testing is an efficient technique in cyber-security. Using reinforcement learning to enhance the automation and accuracy of penetration testing is a promising approach. However, intricate network systems and the lack of a cyber-security knowledge base remain obstacles to this approach. Here, we propose a hierarchical action embedding that represents penetration testing action space. It helps improve the tactic of re-inforcement learning agents in complicated network scenarios by indicating the relation between actions using MITRE ATT&CK knowledge. The results of three testing configurations s how that the hierarchical action embedding improves the effectiveness of reinforcement learning compared to previous algorithms.
Bifurcation Free Coil Design for Multi Power Level Wireless Charging of Electric Vehicles

Wireless charging of electric vehicles (EVs) can solve all the cable-related issues from vehicle charging, making it a more convenient and safe way to charge EVs. However, the performance of wireless charging is affected by the coupling coefficient and load variation due to the nonlinear behavior of wireless charging systems. Due to the dominant effect of bifurcation, the same transmitter cannot charge the vehicle at different charging rates. Therefore, the designing individual charging systems for different power levels is one of the major concerns with the wireless charging of EVs. Consequently, it increases the infrastructural cost of the wireless charging systems. Hence, this paper proposes a tapping-based transmitter coil with switched series compensation for multi-power level wireless charging of the EVs. Furthermore, detailed coil design steps are given to design bifurcation-free wireless charging at the required power levels. The effectiveness of the designed coil-set in terms of safety and field emissions has been validated on ANSYS Maxwell software. The bifurcation-free wireless charging of EVs with the proposed coil-set at level-l (3.7 kW) and level-2 (7.7 kW) has been validated through LTspice and MATLAB/Simulink results.
An Active Clamped Boost Converter based system for Wireless Charging Applications

This paper introduces an inductive power transfer (IPT) system for various charging applications. An actively clamped boost converter is presented, capable of generating high-frequency AC signals for transferring power wirelessly. The SS (series-series) compensation is employed as it is load-independent and maximizes the power transfer without any bifurcation issues. The clamping capacitors help to minimize the voltage across the switches, and the boost inductor reduces the input EMI filter requirements. Moreover, the diode bridge rectifier on the receiver side is employed to charge the battery. Design aspects and mathematical analysis is explained in detail also the feasibility of the proposed converter is verified by simulation on MATLAB/Simulink platform for a 1.2 KW system and the small-scale laboratory prototype for a 500W system.
An Improved Fault-Tolerant Cascaded H-bridge Multilevel Inverter

Cascaded H-bridge multilevel inverter is one of the most commonly used converters in renewable energy applications. Despite some significant benefits, conventional cascaded H-bridge inverter suffers from reliability problems due to no redundant states. In this paper, a new fault-tolerant solution for cascaded H bridge multilevel inverter is proposed to enhance reliability. The proposed topology uses an additional switched capacitor-based boost module in place of one of the H bridge modules in the cascaded H bridge MLI, which generate the same number of levels and magnitude of output voltage in case of switch and source faults. The simulation is done using the MATLAB/Simulink and experimental results are shown to validate the proposed topology.
Risk Evaluation of the Destination Port Logistics based on Self-Organizing Map Computing

We consider a destination port logistics service provider (DPLSP), which wants to improve its service quality by reducing risk of delivery time delay. This paper diagnoses potential risk factors that estimate the performances of the DPLSP who provides services only after the arrival of freight, with the intention of reducing supply chain risk and improve supply chain performance through creative computing approach. Self-organizing feature map (SOFM) computing is a type of artificial neural network based on an unsupervised learning algorithm. We propose the approach of SOFM computing for the purpose of clustering risk data of DPLSPs from a less subjective perspective and then rank the cluster results into different levels based on the total risk value of each cluster. Numerical studies to test the effectiveness of this model would be carried out using air import logistics lead-time reports from a large DPLSP. The results illustrate that the proposed approach could successfully cluster and rank the risk data according to their values.
Survey paper on the Forward Kinematics Solution of a Stewart Platform

Over the past few decades, researchers have become increasingly interested in the Stewart Platform, a parallel manipulator with six degrees of freedom (DOF) that offers remarkable functionality over serial manipulators. The main objection to the implementation of closed loop control in Stewart Platform is its difficulty in solving the forward kinematics. In the forward kinematics problem, the pose and orientation of the moving platform are determined from the leg lengths. This is a high degree nonlinear problem with multiple solutions. Even though many studies were conducted about the kinematics of parallel robots, forward kinematics continues to be a hindrance to its practical application. Consequently, the real-time solution of forward kinematics is gaining interest. To address the direct kinematics problem, several approaches were presented, including numerical techniques, analytical methods, sensor-based methods, methods using neural networks, and observer-based methods. In this paper, the different approaches and limitations of the forward kinematics problem on Stewart Platform are analyzed.
Model Predictive Control of MMC-based Medium Voltage Microgrid for Grid Connected and Islanded Operation

This study presents a model predictive control (MPC) of modular multilevel converter (MMC)-based microgrid for grid connected mode (GCM) and islanded mode (ISM) operations with seamless transition capabilities. Submodule (SM) capacitor voltage control of the MMC is the major challenge in improving the efficiency and reliability of the MMC system. Therefore, a model predictive current control (MPCC) scheme is developed to balance the SMs' capacitor voltage and supply the power generated by the renewable energy sources to load and the utility grid. In the ISM, model predictive voltage control (MPVC) is developed to achieve constant voltage across the load terminals and control the SMs' capacitor voltage. The developed MPC techniques estimate the future behavior of control variables and uses a cost function to produce optimal switching sequence for the MMC. Thus, in comparison to the classical d-q frame control, the developed MPC techniques avoid the use of pulse width modulation schemes, proportional integral controllers and rigorous tuning efforts. Moreover, for seamless transition between ISM to GCM and vice-versa, a phase-locked loop (PLL)-based seamless transition sequence is devised. The performances of the MPCC and MPVC are validated against the conventional d-q control via simulation results under different operating conditions.
Similarity Analysis in Data Element Matching based on Word2vec

With the increasing demand for computer-aided big data processing, deep learning has gradually become an effective means to help big data processing. There are often many redundant database fields between different departments. These fields are often completely equivalent, but there are certain differences in field names, which brings trouble to data element matching. To this end, we propose a more targeted approach - ‘MetaMatch’ to handle database fields, combining $W$ ord2vec with a high-performance database. To measure the effectiveness of the proposed method, we propose a $W$ ord2vec-based data element matching method. The method performs semantic segmentation on key fields of the database and trains word vectors. Then, we perform tokenization processing on each training case. According to the result of word segmentation, the corresponding word vector is constructed. We use this method to implement data element matching for big data systems in our experiments and design a validation experiment to evaluate the matching accuracy. The matching accuracy rate reached 79.3%.
Model Predictive Speed Control of Permanent Magnet Synchronous Motor

Energy efficient control of electric drives is of prime importance in the industrial applications. This is the reason why Permanent Magnet Synchronous Motors (PMSM) are being used popularly in these applications. PMSMs are known for the advantages like higher efficiency, higher torque to weight ratio, lower maintenance, etc. Independent speed and torque control of the machine has a major role in PMSM based drives. Control algorithms for this purpose vary from simple PI control to the intelligent control techniques, Sliding Mode Control (SMC), etc. Model Predictive Control (MPC) is revisited after its invention in 1970s, for the control of PMSM. In this work, MPC based speed control of PMSM is presented. It is observed that MPC based control not only improves the drive performance but also improves robustness and stability of motor drive system. This is shown by the conclusion and results of MATLAB/ Simulink based simulations.
A Position Sensorless Brushless DC Motor Drive with Inherent Torque Control for EV Application

This paper presents a new position sensorless approach to permanent magnet brushless DC (PMBLDC) motor drive based on torque control for electric vehicle (EV) application. This method introduces a sector defined voltage vector-based switching logic in a constant torque region compared to conventional six-step trapezoidal control. The selection of voltage vectors, depends on the rotor position and the torque error, which eventually reduces the torque ripple. Position information is obtained using stator flux linkage function, which shows superior dynamic performance at low speeds than the traditional back-EMF based method. Due to frequent commutation in the PMBLDC motor drive, the Lissajous pattern (locus) of the stator flux linkage deviates from ideal circle as in the case of a permanent magnet synchronous motor (PMSM), affecting rotor position estimation. Stator flux is kept constant while focusing only on the torque makes the controller computation simpler and faster. This approach keeps the stator flux linkage amplitude close to sinusoid and the position estimation tends to be more accurate. Low frequency oscillation in torque is minimized with this control, which is an advantage to EV or traction drive. The concept is well proved using detailed simulation analysis
TTAG+R: A Dataset of Google Play Store's Top Trending Android Games and User Reviews

Context: Android games are gaining wide attention from users in recent years. However, the existing literature reports alarming statistics about banning popular and top-trending Android apps. The popular gaming apps have been removed from Google Play Store due to various user concerns. Objectives: The goal of this work is twofold: (i) to assist the researchers and practitioners in identifying the state-of-the-art challenges, constraints, and compliments about Android apps for future Android-specific studies, and (ii) to encourage active users' perspectives on the Android development process because usability remains a core deciding factor about the success or failure of Android apps. Method: To accomplish this, we introduce a novel open-source dataset, Top Trending Android apps with their user Reviews (TTAG+R) in GitHub. Results and Contributions: Briefly, TTAG+R presents information about 245 top trending Android Free Games, 97 top trending Android Grossing Games, and 52 top trending Android Paid Games with a total of 8,423 user reviews in 12 different. csv files. The main contributions of this paper are: (i) provides one-place comprehensive data on Android Apps, (ii) describes various features of Android apps and their user reviews, (iii) reports the updated and latest knowledge about Android apps, and (iv) provides the data in an unfiltered form so that researchers may not find difficulty in using this dataset in their data-driven experimentation. From a research implication viewpoint, the dataset supports: (i) understanding the usability characteristics of Android apps, (ii) discovering current trends and pitfalls in Android apps, and (iii) analyzing the Android financial market. Conclusion and Future Work: Thus, TTAG+R is freely available to the research community, and useful for future enhancements in the Android domain. In the future, we plan to keep the data up-to-date with the most recent information for the continued usage of the dataset.