Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of Coordinated Frequency Control Technologies for Wind Turbines, HVDC and Energy Storage Systems

Modern grids contain new components, such as renewable power resources, HVDC transmissions, energy storage systems and others. These new components will affect the frequency-response characteristics in power systems. That is, some power electronics-based elements with according control schemes will replace a part of traditional thermal or hydro generators with governor droop control. Thus, novel frequency control technologies for energy storages, HVDC or energy storage systems should be studied. This paper summarized frequency control technologies for the abovementioned components, and discussed about the hybrid energy systems with control schemes. Additionally, some significant trends, including the optimal design of capacity size, adaptive droop or inertia gains of controllers, detailed models for small-signal analyses or grid forming controls, and simplified frequency-response models, were also investigated in this paper.
LC-GVINS: A Robust, Accurate, Monocular Visual-Inertial-GNSS SLAM with Loop Closure

This paper presents a robust, real-time, and highly accurate robot loop closure Visual-Inertial-GNSS navigation system (LC-GVINS) that fully utilizes GNSS raw measurements in loop closure constraints calculation and pose graph optimization. To enhance the system robustness, a uniformization strategy is utilized throughout the feature extraction process, and absolute location is further achieved in complex environments. Loop closure constraints, tightly fused with multiple sensor measurements within a nonlinear optimization framework, with the pose graph performing four degrees of freedom (4-DoF) optimization on the trajectory while fusing loop closure factor constraints, factor constraints, and GNSS constraints. Experiments were conducted to demonstrate the robustness and accuracy of LC-GVINS on public datasets. LC-GVINS exhibits exceptional positioning accuracy on datasets, outperforming SOTA methods in terms of both robustness and accuracy on the dataset.
Correction Control of Needle Deflection Based on Kernel Extreme Learning Machine

Percutaneous puncture surgery is one of the important methods for early diagnosis and treatment of liver tumors. However, due to the interaction between the needle and soft tissue, the tip of the needle receives unbalanced force, resulting in the needle body bending and deviating from the preoperative planned puncture path. We have developed an ultrasound guided puncture robot system and investigated a data -driven needle deflection correction control method. By using a kernel extreme learning machine to calculate the parameters of the needle deviation correction model in real-time, we can achieve accurate puncture deviation correction. Experimental results show that, compared to the no correction control strategy, our proposed needle defleciton correction control strategy has improved the punture accuracy in three aspects: with needle insertion depth 60mm, path following maximum error decreases from 7.193mm to 0.611mm, path following average error decreases from 3.521mm to 0.185mm, and target puncture error decreases from 7.193mm to 0.611mm, greatly improving the accuracy and safety of percutaneous puncture surgery.
An Array Antenna Based on Substrate Integrated Waveguide Antenna For 5G Application

substrate integrated waveguide (SIW)antenna at millimeter wave is suitable technology, many exciting applications have been made with higher frequency for specific kinds of communication, a wide range of applications, services, and technologies, including short-range communication, future mm-wave mobile communication for fifth-generation (5G) cellular networks, and sensor and imaging systems. Because of the short wavelength, mm-wave frequencies allow for physically smaller antennas and circuits and the availability of a significantly broader bandwidth than microwave frequencies. Because of their extreme sensitivity to atmospheric absorption losses, millimeter-wave antennas must be designed with excellent gain characteristics. Furthermore, Millimeter-wave antennas have a wide bandwidth and are ideal for 5G applications. SIW technology has losses at higher frequencies because of vias. This research creates planar antennas with minimal losses and a high-quality factor by controlling vias of substrate-integrated waveguide (SIW) technology. The proposed design results after modifying a single antenna to an array gain increasing to 11.4dB and directivity of 11.7 dB with a wider bandwidth 1.4GHz.
Lens-free microfluidic-based method for detecting circulating tumor cells

Accurate identification of Circulating tumor cells (CTCs) can provide valuable information for early diagnosis and therapeutic monitoring of cancer. Here, we present a method of lens-free imaging combined with microfluidic technology to accomplish label-free classification of CTCs and blood cells (BCs). In this approach, Cell images in a microfluidic chip are recorded and classification of the images is accomplished using a Yolo-V4 convolutional neural network. The recognition precision of CTCs and BCs reached 99.21% and 98.26%, respectively. The 8 microfluidic channels used allow a high throughput of 70,000 cells per minute without clogging. It can be seen that the method has great advantages in detecting rare cells.
Pose Estimation of Household Objects Using RGB-D-NIR Camera

Pose estimation is a technique used to predict the pose of an object, i.e., its orientation and its position. This technique is needed by the robot to pick up objects that are placed somewhere. In general, this technique is developed with visual input from a camera. The challenge that is usually faced in this technique is the lighting conditions that are not fixed, where the stability of the estimation results in this environment is highly expected. In this study, we first use an RGB-D-NIR camera to provide color (RGB), depth (D), and near-infrared (NIR) inputs which are expected to complement each other. Second, by utilizing data from the camera, we combine it with Guided Filtering Fusion and Deep learning methods. As a preliminary result of the study, we conducted experiments in normal and dark lightning conditions on household objects, with various input combinations; and obtained quite accurate results in dark conditions.
Enhancement of Pattern Depth in Plasmonic Lithography for Practical Application

A hybrid plasmonic waveguide (HPW) patterning system, which is composed of the plasmonic BNA-PR layer-silver reflector, has been proposed to solve the shallow pattern issue in the plasmonic lithography. Theoretical calculations show that line array patterns in sub-20 nm-feature can be achieved with high aspect ratios and pattern uniformity, mainly due to the forward and reflected waves can remove the exponential decay and allow a higher wave vector to participate the patterning process. Eventually, the general criteria of the proposed MIM waveguide patterning system is also discussed, which can broaden the application of the plasmonic lithography in nanoscale fabrication.
Design requirements for a modular framework of industrial surface defect detection system designs in the context of machined, automotive workpieces

The planning phases of industrial surface defect detection systems for machined, automotive workpieces are time-consuming and require project teams comprised of experts from different disciplines in order to achieve optimal systems regarding the prioritized objectives. To master this challenge current research is aiming for flexible inspection system concepts that are not application-specific and thus can be used for a range of industry-dependent specifications without the necessity to complete each planning phase from the beginning. This paper presents a thorough analysis of the influencing factors at the beginning of the inspection planning process of an industrial surface defect detection system for machined, automotive workpieces. Based on this analysis, design requirements for a modular framework for industrial surface defect detection system designs in this sector are derived and contextualized with available system components. Finally, an outlook for future research is presented with the goal to further increase the flexibility of industrial surface defect detection systems.
Filter-less Single-Stage Resonant AC/DC Converter Employing Push-Pull Transformer

In this article, we present filterless single-stage resonant ac/dc converter. By using the push-pull transformer itself, with the primary-side switching modulation fixed at 0.5, the proposed ac/dc converter accomplishes theoretically zero input-current ripple and so does not need the extra input filter. Semi-bridgeless configuration reduces the conduction loss associated with an input full-bridge diode rectifier. The resonant neutral-point-clamped (NPC) structure much reduces the voltage stress at secondary-side active power components; this aspect enables use of inexpensive low-voltage-rated active power components. Therefore, the proposed converter features low cost, high power density, and high efficiency. A prototype with 1-kW output power is implemented to verify the superior properties of the proposed ac/dc converter.
Coordinated Control Strategy of Grid-Forming Wind Power Generation System with Energy Storage System for Primary Frequency Regulation

This paper proposes a coordinated control strategy for wind power generation systems equipped with energy storage systems (ESSs) to achieve primary frequency regulation (PFR) control, which takes into account the interrelation between the ESS’s capability and the frequency support ability. The frequency deviation and the state of charge (SOC) of ESSs are divided into multiple ranges to output maximum power in the optimized range of SOC, and consider various control objectives and constraints for the ESS under different operating conditions. Then, the required active power compensation corresponding to the frequency deviation can be calculated and distributed to the machine-side converter (MSC) according to the virtual inertia control (VIC). Simulation results demonstrate that the proposed coordinated control strategy achieves the system's PFR control performance and keeps the ESS healthy and safe.
A Web-Based Platform for Real-Time Speech Emotion Recognition using CNN

This pilot study presents a web-based real-time speech emotion recognition platform using a convolutional neural network algorithm. The study aims to develop a reliable tool for predicting emotions in speech with a user-friendly design to enable easy access and display of recognition results. The platform recognizes seven emotions (angry, disgust, fear, happy, neutral, sad, and surprise) and has two functionalities: static and real-time speech signals analysis. The static analysis allows users to upload pre-recorded audio files for analysis, while the real-time analysis provides continuous audio processing as it is being recorded. The study also focuses on developing a reliable model with minimal features to predict emotions while accurately identifying various emotions detected in speech. The algorithmic performance of the model was evaluated using publicly available datasets (RAVDESS, TESS, and SAVEE). It achieved an accuracy of 86.46% in static analysis using the selected spectral feature: i.e., MFCC. The performance of the real-time analysis was validated through a user study involving 20 participants. It achieved an accuracy of 65% in recognizing emotions in real-time due to possible known factors. An interesting finding was the discrepancy between how individuals perceived their emotions and those detected by the ML model. The accuracy of the ML model was higher in pre-recorded audio recognition and about the same in real-time recognition compared to previous works. The user-friendly design and CNN algorithm make it a promising solution to address challenges in emotion recognition and highlight the importance of further research in this field.
Aircraft Controllability Through High-Lift Propellers

The current era of electrification of the aerospace industry brings many challenges, but also brings forward radically new aircraft designs. The new eVTOL and eSTOL concepts were enabled by the distributed electric propulsion. Even though eVTOL and eSTOL projects are gaining a lot of attention, another concept is also notable. The so-called High-Lift propulsion is based on the increase of airflow over the wing, thus reducing landing speed. Most notable example is NASA’s X-57 Maxwell. However, this concept can be used to control the aircraft as well. This paper introduces new approach of aircraft control using distributed High-Lift propulsion concept. The controller uses differential thrust to completely control the aircraft states and therefore eliminates the need for traditional control surfaces.
AI-based Cavitation Detection in Process Valves

Process valves are important components of many industrial plants. They are used to control dosing or tempering systems for beverages, hygienic products or chemicals for industrial production processes. At certain combinations of pressure and flowrates the flow phenomena of cavitation occurs inside the valve. It leads to a faster deterioration of the valve by abrasion and causes unwanted effects such as vibrations and distortions of the control process. State-of-the-art solutions for cavitation detection are based on vibration or acoustic signals measured at the valves outlet. However, those sensors have to be mounted separately behind the valve and the signals are often analysed by hand since they are dependent on the valve type and the operating conditions of the plant. Current AI-based approaches on the other hand require powerful setups. This paper proposes KNN, SVM, Decision Tree, Tree Bagger and LSTM approaches based on acceleration sensors directly placed on the valve that can be implemented on less powerful embedded devices. The results are compared to recent researches of similar model types. All approaches achieved state-of-the-art results in Model-in-the-Loop tests with KNN scoring best. Due to their small memory size and good test results, LSTMs are integrated on an embedded device for online classification. During the system test, the LSTMs reached an accuracy of up to 87 % with a precision of 0.7 and recall of 0.95.
Background Music Removal Using Deep Learning

Due to the increasing popularity of video-sharing websites and social networking services, videos are frequently posted to the Internet. However, users must be careful when posting copyright materials, e.g., music. Thus, the purpose of this paper is to investigate a method to remove music from audio data that include a mixture of various sounds, e.g., driving noises, human conversation, and music. Although speech enhancement techniques can be utilized to remove music, such methods also remove noise other than music, which reduces the realism of the content. Sound source separation can also be utilized for music removal, however, it has to select non-music sounds after separation, which increases the process. Thus, we conducted a preliminary experiment using a deep learning method in order to remove only music from audio data that include a mixture of speech and music sounds in a monaural audio signal, maintaining original noises and without sound source separation. We used Conv-TasNet for the network structure and the parameters were updated such that the RMSE value between the output of the model and the original speech sound was small. The results showed that accuracy tended to improve with longer sample lengths in the range of 50–1,000 ms. The music removal results demonstrated that music components were reduced overall.
An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx

The present study introduces a novel methodology that utilizes Light Gradient Boosting Regressors to predict engine-out emissions of NOx, HC, and CO. The accuracy of the proposed models is evaluated on different types of homologation cycles. The dataset used in this study is derived from a set of 47 experimental driving cycles, including RDE, WLTC, NEDC, ECE, US06, and HWFET. The experimental driving cycles are performed on a roll bench using a spark-ignited, naturally aspirated, V12 engine-equipped vehicle. A three-second sliding window is incorporated in the models to capture the dynamic behavior of pollutant emissions. The performance of the LightGBR models is assessed using the mean absolute percentage error (MAPE) on the total pollutant mass, which is found to be 5.2% for CO, 5.7% for HC, and 6.8% for NOx. The results demonstrate the efficacy of the proposed methodology, which can be used to estimate the impact of powertrain calibration changes on pollutant emissions in a virtual environment, thereby reducing the number and the cost of the experimental tests.
Simplified Class E Inverter for 13.56MHz Low Power Wireless Power Transfer Applications

Wireless power transfer systems have been widely studied, especially at 13.56MHz. However, the power level is usually high, and applications up to 1W have not received too much attention, although there are relevant applications emerging in the market, such as Near Field Communication (NFC) charging. Therefore, in this paper, a low-power wireless power transfer system operating at 13.56MHz is designed and its efficiency for power levels below 1W is analyzed. The prototype system, based on a class E inverter, allows the receiver to move freely on a surface of 8cm x 4cm with an efficiency between 50% and 61% for a delivered power between 250mW and 1W. In addition, it is proposed to adopt a method to measure more accurately the impedance during the power transfer.
Multi Pulse Controller For Increasing Power Density In Fully Soft-Switching Quasi-Resonant Converters

A multi-pulse controller for increasing power density of flying capacitor based fully soft-switching quasi-resonant converters [1], [2] is introduced. The presented controller increases the rms inductor current, without increasing its peak value while maintaining soft switching on all switching edges. This is achieved by creating a periodic sequence of successively increasing current pulses, whose peak current is the same as for the original solution [1]. The operation is verified with an experimental prototype with input voltage ranging from 3 V to 5 V, output voltage regulated at 12 V with inductor and flying capacitor vaues as low as 105 nH and 33 nF, supporting load currents from 10 mA to 500 mA and demonstrating high efficiency curves of up to 92 %.
Tracking Control for Serial Spherical Robotic Manipulators with Uncertain Dynamics

The serial spherical robotic manipulators are highly nonlinear and strongly coupled systems. Considering the system uncertainty and disturbance, achieving high-precision control of the manipulators is a challenge. This paper studies tracking control method of serial spherical robotic manipulators with uncertain dynamics and disturbance torque on joints. The disturbance observer and sliding mode control are combined to realize the high-precision joint angle position tracking of 3-DOF serial spherical robotic manipulators. The simulation results demonstrate the effectiveness of the adopted method.
LRS4DP: Location Recommendation System for Destination Prediction

Destination prediction based on the partial trajectory of a moving vehicle is vital for urban mobility applications. Recent research efforts focus on improving the prediction accuracy by incorporating more spatio-temporal semantics through complex model architectures, which inevitably impact the generalization and scalability due to ad-hoc hyper-parameters and heavier computations. In the present study, we propose a novel Location Recommendation System for Destination Prediction, LRS4DP. Through an integrated design of several technologies (map-matching, deep learning and recommender system), LRS4DP provides an end-to-end solution for destination prediction based on input trajectories and road network configurations. By adopting a node-based spatial discretization scheme through map-matching, LRS4DP is able to adapt according to the local road network density and generalize to different urban layouts. As compared to the state-of-the-art algorithms, our proposed Top-K formulation based on individual road nodes leads to fundamentally better spatial precision and prediction accuracy even with simple model architectures. We further designed the offline training and online serving as a location recommendation system to achieve better scalability and flexible trade-off between performance and run-time. The experimental evaluation of two real-world taxi datasets demonstrates the generalization of LRS4DP under different urban scales and layouts. The LRS4DP framework is also generically applicable for location prediction tasks (e.g., next location and passing-by location predictions) and capable to support various downstream transportation and location-based service applications.
Segmentation of Skeleton Ultrasound Images Based on MMA-SUISNet

When exploring the use of ultrasound to provide real-time, radiation-free 3D imaging for fracture surgery, A skeleton ultrasound image segmentation network based on the fusion of mixed multiple attention (MMA-SUISNet) was proposed to solve the problems of excessive noise, small skeleton features, and difficult boundary division in the ultrasound image. The model uses the Squeeze Exception (SE) module to complete the encoding function, constructs cross-layer connections, and improves the ability to identify small targets; By adding Convolutional Block Attention Module (CBAM) to the encoder, the model can adaptively adjust the weights of channels and positions to better extract features and reduce the impact of noise; By adding Attention Gates (AG) to the decoder, features are adaptively emphasized and transmitted, allowing the network to focus on skeleton boundary information. For the collected skeleton ultrasound images, this paper shows through segmentation, ablation, and generalization experiments that the proposed model has improved Dice, IoU, and F1-Score indicators by 13.87%, 10.01%, and 13.80% compared to the original U-Net model, respectively.
Open-circuit Fault Diagnosis and Fault-tolerant Control Strategy for Parallel Three-phase Rectifiers

In this paper, an open-circuit fault diagnosis and fault-tolerant control strategy for parallel three-phase rectifier are proposed. The different characteristics of three-phase rectifier under normal condition, single switch open-circuit fault and double switch open-circuit fault are analyzed in depth. A fault diagnosis method based on the harmonic content of the switching bridge arm is studied, and a method based on neural network is designed to analyze the harmonic content of the bridge arm to locate the fault switch of three-phase rectifier. The power quality characteristics of three-phase traction substation are analyzed, and a comprehensive compensation control strategy is proposed, which combines the positive sequence and negative sequence double closed-loop control with predictive current control. The correctness and effectiveness of the fault diagnosis method and integrated compensation control method are verified by simulation and experiment.
Genetic Testing for Cardiac Ion Channelopathies Effective : February 1 , 2018

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