Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Step Model Predictive Direct Thrust Control Method for Linear Synchronous Motors Driven by Three-Level NPC Inverters

The standard direct thrust control (DTC) method is not suitable for multi-level inverters and is difficult to deal with complex constraints. In this article, a new prediction model is proposed. It has low computational complexity. Based on this prediction model, a new multi-step model predictive direct thrust control (MP-DTC) method for high-power linear synchronous motors (LSM) is designed. In order to reduce the switching frequency and control the neutral point voltage of three-level neutral-point-clamped (NPC) inverters, new constraint conditions and value functions are designed. Compared to the traditional MP-DTC, the proposed two-step MP-DTC strategy proposed in this article can reduce the switching frequency by 43%. In addition, this method can effectively suppress the neutral point voltage unbalance of three-level NPC inverters in a convenient way. The mean absolute deviation is 1.88%.
A Global and Locally Enhanced Water Cycle Algorithm for Dynamic Economic Dispatch of Power Systems

The dynamic economic dispatch problem of the power system has the characteristics of high dimensionality, strong non-linearities, and multiple constraints. In this study, a global and locally enhanced water cycle algorithm is proposed to solve the problem. Firstly, distance control parameters and adaptive t-distribution non-linear adjustment strategies are used to balance the global exploration and local exploitation capabilities of the algorithm. Secondly, a quadratic interpolation operator is introduced in the later stages of the algorithm's search to further improve the speed and accuracy of the algorithm's search. Finally, a constraint repair technique is used to deal with the constraints of dynamic economic dispatch. To verify the effectiveness of the improved water cycle algorithm, a 10-generator power system including a wind farm is used as a test case for simulation experiments and compared with current advanced optimization algorithms. The results show that the improved water cycle algorithm is effective and superior in solving the dynamic economic dispatch problem of power systems.
Research on Secondary Frequency Regulation of Thermal Power Unit Assisted by PV Storage System

The massive access to new energy sources has brought tremendous challenges to the frequency regulation capability of the power grid. By using photovoltaic energy storage system to assist traditional generating units such as thermal power, secondary frequency regulation can be achieved to improve the frequency situation of the power system. Then, a new control strategy combining energy storage control strategy with inverter control strategy is proposed. The inverter control strategy is a kind of non-differential droop control with frequency compensated secondary control strategy. At last, the effectiveness and superiority of the new control strategy are proved by using the Matlab/Simulink simulation software.
Frugal Love wave acoustic sensor full plaform for in situ operation in liquids

This work presents a Love wave (LW) sensor platform based on the concept of “frugality”. The implementation of the whole “frugal” platform used conventional and cheap components like an electronic circuit made with PCB manufacturing, an Arduino microcontroller, a PDMS microfluidic mold made by additive manufacturing, or by xurography. The main innovation of this work lies in the use of a transducer with a biofilm based on exopolymers (EPS) released by microalgae and obtained by simple centrifugation and ultrafiltration of their culture medium. It was shown that the EPS adhered easily to the surface of the LW sensor which allows their deposition using a single layer. The acoustic characterization showed the validity of such a LW wave sensor platform to be operational for biochemical detection in liquid medium. The implementation of the entire “frugal” platform as a portable mobile laboratory was demonstrated. This preliminary work opens the way to new frugal platforms, usable for a wider audience and which would be of great interest for the popularization of sensors in environmental monitoring.
Constructing the State Estimation Algorithms Based on Decomposition for the Fractional Dynamic Systems

The paper deals with the statement of the nonlinear task of estimating the dynamic system state, unknown parameters, and fractional order. The solution uses the decomposition approach, which includes Kalman filters, wavelets, fuzzy systems, neural networks, and their combinations. An example illustrates the problem solution with estimating algorithms accuracy and speed.
Design and Analysis of Magnetic Field Modulation Magnetic Lead Screw for Wave Power Generation

In order to solve the problems of large volume and low power density of the direct drive wave generator, a high-speed magnetic screw for wave power generation is designed in this paper. First, the basic working principle of the magnetic field modulation magnetic lead screw is introduced. The conditions for satisfying the magnetic field modulation are given. The force and torque of magnetic lead screws with different numbers of pole pairs are calculated. Secondly, in order to further increase the power density of the generator, the electromagnetic performance of the magnetic lead screw with different transmission ratios is calculated when the inner permanent magnet has one pair of poles. The influence law of the lead on the distortion rate of the torque waveform and the transmission ratio is obtained. Finally, the power density and electromagnetic performance were comprehensively considered to determine the basic parameters of the magnetic lead screw for wave power generation. The magnetic density distribution of the designed magnetic field modulation magnetic screw is calculated, which verifies the rationality of the design. This design provides a reference for further improving the power density of current wave generators.
Exploring Android Apps Using Motif Actions

Automated Android testing approaches often fail to interact properly with complex UIs consisting of multiple related elements. For instance, to trigger a state transition in a form-based UI, one has to first fill out all input fields and then click on the submit button, but test generators would usually interact with the fields and button in arbitrary order, struggling to trigger the corresponding state transition, and resulting in overall lower code coverage. One way to overcome this problem is to define motif actions, which allow test generators to interact not just with individual UI elements, but with combinations of UI elements related through common patterns of interaction sequences. We designed 12 such motif actions for common scenarios and integrated them into the Android test generation tool Mate.Our experiments demonstrate that these motif actions are applicable to a wide range of apps (86.5 % out of a sample of 551 apps). Motif actions are particularly useful on complex apps, where our experiments on 109 such apps demonstrate an average increase of 2.19 % activity coverage and 2% line coverage.
High-frequency Oscillation Suppression Strategy Based on Arm Current Feedforward Virtual Damping in SVG for Photovoltaic Plants

With the large-scale grid connection of renewable energy, there exists the oscillation problem, causing equipment damage and large power loss. Recently, some oscillation suppression strategies do not consider the influence of SVG on stability. However, a high-frequency oscillation problem caused by SVG is found in an actual PV plant. Therefore, this paper proposes a high-frequency oscillation suppression strategy based on arm current feedforward virtual damping in SVG for PV plants. The proposed strategy can eliminate the oscillation problem without affecting the original dynamic performance. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed oscillation suppression strategy.
eMinD: Efficient and Micro-Flow Independent Detection of Distributed Network Attacks

Network infrastructures are critical and, therefore, subject to harmful attacks against their operation and the availability of their provided services. Detecting such attacks, especially in high-performance networks, is challenging considering the detection rate, reaction time, and scalability. Attack detection becomes even more demanding concerning networks of the future facing increasing data rates and flow counts. With eMinD, we present an approach that scales well to high data rates and large amounts of data flows. eMinD investigates aggregated traffic data, i.e., it is not based on micro-flows and their inherent scalability problems. We evaluate eMinD with real-world traffic data, compare it to related work, and show that eMinD outperforms micro-flow-based approaches regarding the reaction time, scalability, and the detection performance. We reduce required state space by 99.97%. The average reaction time is reduced by 90%, while the detection performance is even increased, although highly aggregating arriving traffic. We further show the importance of micro-flow-overarching traffic features, e.g., IP address and port distributions, for detecting distributed network attacks, i.e., DDoS attacks and port scans.
Influence of the source/drain doping region on the reconfigurability of BESOI MOSFET

This paper studies experimentally the influence of the N+ doped source/drain regions on the back enhanced with separate contacts SOI MOSFET (BESOI SC MOSFET) reconfigurability. The reconfigurable FET (RFET) proposed in this paper resulted in a drain current level in the same order of magnitude for both types of configurations (nMOS and pMOS). These results present a significant balanced drain current compared with the others BESOI device fabrications versions. In BESOI SC MOSFET, with programming gate voltage of |VGB|=30V and drain voltage |VD|=0.1V, the BESOI drain current reaches a value of, approximately, −1.4 μA and 1.6 μA, working as pMOS and nMOS respectively, which can be considered very well balanced.
Studying Non-Eliminated Harmonics in the Current Spectrum of a Three-Level Converter with Programmed PWM

The distortion of voltage generated by the powerful pulse-width modulation converters (PWM) when operating on the low semiconductor module switching frequencies presents a very relevant issue. The Pre-Programmed Pulse Pattern PWM (PPWM) method preventively eliminates or reduces certain converter’s voltage harmonics as well as the current spectrum. This assures that the voltage quality standards are maintained. The paper studies how the primary 50 individual non-eliminated harmonics behave in the voltage spectra of a three-level converter within the modulation index changing range of 0 to 1.15 with a step of 0.01 for six different PPWMs. The study results can be used in PWM algorithms of power converters.
Dark Current and Electron Activation Energy in Quantum Brag Mirror Detectors (QBMDs)

This work presents a study on the dark current of Quantum Bragg Mirrors Detectors based on In0.53Ga0. 47As/In0.52Al0. 48As heterostructures. Three devices were investigated, with the first one having a single quantum well that serves as a reference. The other two have symmetric superlattices acting as quantum Bragg mirrors. The dark current was measured for each device as a function of the applied bias voltage, at temperatures ranging from 100K to 300K. The influence of quantum Bragg mirrors on the dark current and the estimation of activation energy of each device were analysed using Levine’s model.
TransVAT: Transformer Encoder with Variational Attention for Few-Shot Fault Diagnosis

Fault diagnosis plays a critical role in ensuring safety and minimizing downtime across various industries. However, due to the difficulties in acquiring fault signals in practical engineering systems, labeled samples are often scarce. To address this issue, few-shot learning has emerged as a promising approach for bearing fault diagnosis in recent years. Recent studies with promising results have demonstrated the effectiveness of using Transformer and variational attention in this field. Compared to conventional methods, the Transformer has demonstrated superior performance in feature extraction and classification. Variational attention, on the other hand, permits a distribution of attention weights and enhances the interpretability of models. This method can identify pertinent features and offer perceptions of the root causes of faults. Therefore, the proposed model, TransVAT, is based on the relation network of few-shot learning and replaces the dot-product attention in the Transformer encoder with variational attention for feature extraction. The experimental findings demonstrate that the model performs well with limited data, especially on the one-shot task.
Research on Vibration of Converter Transformer Under DC Bias Magnetism

During operation, the converter transformer may experience DC bias, leading to increased vibration. In order to solve this problem, this paper comprehensively considers the electromagnetic field theory and Solid mechanics theory, and establishes a Multiphysics simulation coupling model of converter transformer. Based on this model, simulate and analyze the changes in magnetic flux density, stress, and displacement parameters of the converter transformer core and winding during the process of increasing the DC bias coefficient from 0 to 1.5, in order to explore the vibration characteristics of the converter transformer under the influence of DC bias. The results indicate that with the addition of DC components, the magnetic flux density, stress, and displacement of the core and winding of the converter transformer increase, indicating that the overall vibration of the converter transformer intensifies. In terms of specific performance, the increase in magnetic flux density of the iron core and winding decreases as the iron core approaches saturation. In terms of stress, the trend of core stress change is the same as that of magnetic flux density change, but the winding stress maintains a stable growth with the increase of DC component.
Continuous Domain Input Abstraction and Fault Detection Capability in Combinatorial Testing

Combinatorial Testing (CT) is a fundamental approach for testing varying applications ranging from mobile applications to autonomous driving. Input modeling is a prerequisite to CT. When the input domain is continuous, abstraction is necessary to generate a CT test suite. In this study, we discuss three novel, work-in-progress research avenues. With these avenues, we aim to maximize the fault detection capability of CT test suites through continuous domain input abstraction optimization. We motivate our research on abstraction by demonstrating how it affects the fault detection capability of CT test suites on three audio plugin implementations. Overall, continuous domain input abstraction optimization remains a promising open challenge.
ServerRCA: Root Cause Analysis for Server Failure using Operating System Logs

The development of the information technology industry has made servers an essential infrastructure for enterprises. Server failure may result in significant economic losses. Therefore, it is essential to conduct root cause analysis (RCA) on server failure to improve server reliability. However, existing RCA approaches suffer from limitations in analysis granularity, adaptation difficulties, and data acquisition constraints. To overcome the limitations, we propose ServerRCA, an automated solution that utilizes operating system (OS) logs for accurate and efficient root cause analysis of server failures. OS logs provide detailed information and are easily accessible. Firstly, ServerRCA employs log parsing to transform raw logs into log templates. Next, we propose a hierarchical matching approach that leverages the hierarchical structure of fault logs to accurately identify fault events. Furthermore, we also introduce a human-in-the-loop feedback mechanism to enhance the ability of ServerRCA. Finally, ServerRCA constructs the fault propagation chain using the fault events identified earlier. Extensive experiments on real server failures demonstrate the effectiveness of ServerRCA, achieving significant improvements in F1-score, HR@1, and HR@3 over comparative methods. Our work contributes to the automated RCA of server failures using OS logs and provides a novel framework for accurate fault event identification in server failure analysis.
An entropy clustering method for blades biofouling detection of marine current turbine under variable marine current speeds

Marine current turbine (MCT) attracts extensive attention as a renewable energy power generation equipment. However, accurate and reliable blades biofouling detection for MCT remains a significant challenge, especially under variable marine current speed. This paper proposes an entropy clustering method to improve the detection accuracy of blades biofouling for MCT under variable marine current speed. Firstly, the stator current signal is decomposed based on variational mode decomposition (VMD) to enlarge the feature of blades biofouling. Secondly, to eliminate the influence of swell effect, fluctuating features caused by swell effect are divided into each subspace based on the proposed entropy clustering method. At the last stage, principal component analysis models (PCA) are established in each feature subspace to detect blades biofouling. To verify the effectiveness of the proposed method, experiments are carried out on a 230W MCT platform. The results show that the proposed method has satisfactory performance for blades biofouling detection under variable marine current speed.
Tuberculosis Screening with Cough Sounds Using the Deep Learning Models

Tuberculosis (TB) is an infectious respiratory disease. Interferon-gamma release assays, tuberculin skin tests, and recombinant Mycobacterium tuberculosis fusion protein are the three most commonly used methods for tuberculosis screening. However, it is difficult to achieve a balance between accuracy and cost. In this study, we propose a model to screen patients with TB using cough sounds. We collected 345 samples from the hospital, of which 141 were from patients with tuberculosis, 52 were from patients with other respiratory diseases, and 152 were from healthy subjects. The model had two branches. One branch employed a parallel feedforward neural network with 39-dimensional Mel-Frequency Cepstral Coefficients as its input. The other branch uses two different convolutional networks with Mel spectrograms as input and then incorporates the Ultra-Lightweight Subspace Attention Mechanism to extract features from two different pooling layers. Finally, the features extracted from the two branches were combined. The experimental results showed that the AUC value and F1 score of our model were 0.9375 and 91.74%, respectively. This method of using cough sounds to classify diseases is contactless, affordable, and has the potential to serve as an excellent tool for tuberculosis screening.
Research on Network Traffic Feature Map Generation and Evaluation Methods

With the continuous development of machine learning, it has been widely used in the fields of image processing and computer vision. However, it needs to be transformed into images before feeding network traffic data into a deep learning model for training. The existing methods for directly converting network traffic data into images demonstrate unfavorable results. Therefore, this paper proposes a method for generating network traffic feature maps. This method first uses information entropy and classical k-nearest neighbors estimation algorithm to calculate the entropy values for discrete and continuous variables respectively, then the entropy values are used to filter network traffic. Finally, feature maps are generated through an encoder. Experiments on the UNSW_NB15 and CIC-IDS2017 datasets show that the proposed method outperforms other methods in terms of metrics such as PSNR and SSIM.
Design Optimization of a Linear Wound Field Flux Switching Machine for Dry Gravity Energy Storage

In this study, a linear wound field flux switching machine (LWF-FSM) is optimized for use in dry gravity energy storage. Energy storage technologies can be used to mitigate uncertainties on the grid due to the increased share of renewable energy sources (RES). In the newly proposed linear electric machine dry gravity energy storage system (LEM-GESS), LEMs are used to raise and lower a 50 ton hexagonal piston iron mass in a shaft to store and discharge energy. A designed LWF-FSM is optimized for power factor and efficiency using a non-dominated sorting genetic algorithm (NSGA-II). Other than the low power factor, the results indicate that the LWF-FSM can offer good performance as main drive unit in the dry gravity energy storage system. It is also observed that by step-positioning, the thrust ripple can be further reduced.
One-Bit Matrix Completion With Time-Varying Sampling Thresholds

We explore the impact of coarse quantization on matrix completion in the extreme scenario of generalized one-bit sampling, where the matrix entries are compared with time-varying threshold levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number of one-bit samples, generated as a result of these comparisons. To recover the low-rank matrix from its highly-quantized known entries, we first formulate the one-bit matrix completion problem with time-varying thresholds as a nuclear norm minimization problem, with one-bit sampled information manifested as linear inequality feasibility constraints. We then modify the popular singular value thresholding (SVT) algorithm to accommodate these inequality constraints, resulting in the creation of the One-Bit SVT (OB-SVT). Our findings demonstrate that incorporating multiple time-varying sampling threshold sequences in one-bit matrix completion can significantly improve the performance of the matrix completion algorithm. We perform numerical evaluations comparing our proposed algorithm with the maximum likelihood estimation method previously employed for one-bit matrix completion, and demonstrate that our approach can achieve a better recovery performance.
Timing-Aware ROS 2 Architecture and System Optimization

ROS 2 is a framework consisting of software libraries for developing robot systems, such as autonomous driving systems, that consist of multiple interacting components. In ROS 2, each component is implemented as a node, which contains time-triggered and event-triggered tasks. These tasks communicate with each other via ROS 2 topics or shared memory, and are scheduled by a ROS 2 executor. In ROS 2 systems, the system configuration and callback execution can have a significant impact on system performance, including end-to-end latencies, message loss, and memory usage. In this paper, we provide a bound on the timer period of ROS 2 timers to prevent sensor undersampling, and a subscription buffer size limit to prevent message loss and minimize memory usage. Furthermore, we explain the occurrence of message loss and high end-to-end latencies in ROS 2 systems, which are caused by the system configuration and subscription buffer size choice. Based on our observations, we propose a callback-prioritization heuristic to reduce end-to-end latencies and subscription buffer sizes. We demonstrate our findings using case studies based on Autoware.Universe and provide further evaluation to highlight the benefits of our heuristic.