Miracle-dz/UniFAR-FTUs huggingface dataset:
Pairwise and Hyper-correlations Based Spatiotemporal Neural Networks for Traffic Speed Predictions

The problem of traffic speed predictions is still very challenging due to the complex and dynamic urban traffic conditions. Many existing works have implied the importance of integrating spatial correlations into models to explore nonlinear spatio-temporal dependencies and make traffic predictions in near future. However, some of the works only consider pairwise correlations and cannot model the hidden information among multiple nodes well, and the others only consider hyper-correlations (that can be shared by more than two nodes) and discount the role of the pairwise ones for propagating spatial dependencies. Therefore, we propose a Spatio-Temporal neural nEtwork based on both Pairwise and Hyper-correlations (STEPH) for traffic speed predictions. It is distinguished primarily by incorporating both types of spatial correlations into temporal information and designing new hybrid spatio-temporal blocks in neural networks to effectively overcome the challenge. Experiments on two real-world traffic datasets demonstrate the effectiveness of the proposed model, and show its superiority performance compared to other state-of-the-art baselines.
Digital Control and Phase Governing of Interleaved Multistage Hybrid Boost Capacitor Charger for Improved Efficiency and Power Density

This paper introduces a digitally controlled multiphase multistage hybrid boost converter (MMHBC) for high voltage capacitor charging applications. Rapid capacitor charging control approach is presented, and topological variations to improve efficiency are examined. The controller provides tight output current regulation, while maintaining real-time current balancing capabilities for superior thermal distribution and converter efficiency. The MMHBC is capable of rapid capacitor bank charging to high voltage by forcing maximum output current at the first stage, which translates into maximal output power during the whole charging period. The controller includes a system governor unit which facilitates additional control aspects for multiphase operation such as phase shedding, soft-start mechanism, and system protection. SMPS operation, rapid capacitor charging, and topological improvements are validated experimentally on a four-phase 12V-to-300V multiphase prototype with a maximum output power of 250W and power density of 2.5kW/Liter.
ML-Based Service Type Priority Decision Method Using Ambient Information for 5GB

5G communications systems offer several types of service groups include enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). The ability to meet various mobile communications requirements of different users is ensured by these service groups. In this paper, a novel service type priority decision method is proposed for 5G and beyond (5GB) systems to determine the most needed service group under a specific region. This approach is useful for the resource allocation planning and optimization through a coverage region. The proposed method is based on the machine learning (ML) usage with several ambient information. Instance-based and model-based techniques are compared for ML. Also, the ensemble methods are tested on the generated synthetic dataset.
Regional and long-haul heavy-duty trucks: energy consumptions and recharging needs

This paper investigates the energy consumption and recharging need of regional and long-haul heavy-duty trucks. To this purpose, a simulation model complete with longitudinal vehicle dynamics, electric drive, storage system and recharging system is used. The simulation model is performed on the driving cycles proposed by the official tool for the evaluation of CO2 emission in the European Union. Specific energy consumption, daily energy needs, and recharging requirement are derived based on the specification of the actual battery electric truck models. Results show that charging during the mandatory driving rest is sufficient to recharge the battery and complete daily profile missions without modifying the truck operation. A charging power of about 1.4 MW is required for long-haul operations.
Sulfides in oxic seawater over the submarine hydrothermal area of Kikai Caldera south of Kyushu Island, Japan

Koschinsky, 2011). Only a few researchers reported on metal sulfide and free sulfide (H2Sgas + HS – + S2–) as a ligand of trace metals in oxic seawater for the Atlantic Ocean (Andreae and Ferek, 1992; Radford-Knoery and Cutter, 1994; Cutter et al., 1999); no new data have been reported since the 1990s. In the present study, we present for the first time depth concentration profiles of free and metal sulfides in oxic seawater from the surface to the bottom over the submarine hydrothermal area of Kikai Caldera, and we estimate the metal sulfides derived from the hydrothermal vent throughout the water column by combining observed sulfide species data with a simple mass balance model.
Enhanced Low-Voltage GaN FETs for e-Mobility Motor Control Improvements

Light e- Mobility plays an important role in the field of transportation. Recent studies show the advantages of adopting gallium nitride transistors (GaN FET) in low-voltage motor drive applications. This paper presents the benefits of choosing GaN FET technology for the electric powertrain. At first, GaN technology features are presented and compared to those of silicon-based (Si) transistors. Then, the typical e-powertrain layout with a GaN FET inverter is investigated through experimental tests. A focus on the DC-bus filter size and the dead-time selection is given. Advantages of using GaN FET have been found in terms of inverter output waveforms quality, system efficiency and power density, not only for the inverter but also for the filters and for the electrical motor. Finally, an overview of the emerging low-voltage motor drive applications is given. This work is useful for understanding how GaN FET operates, especially in motor drive applications in low voltage systems typically of light e-mobility.
An Average Circuit Model of a Single-phase Grid-connected Inverter

The use of numerous Single-phase Grid-connected Inverters (SGI) is becoming a reality due to the implementation of smart LV-distribution grid. Power system planners generally use circuit simulation software to test and validate any planned expansion of the grid system. This simulation becomes resource and time consuming when many high switching frequency SGIs are present. To provide them with a simple yet accurate model of SGI, this paper proposes a loss-free impedance-based average circuit model. The development of the model and its experimental and simulation validations are covered in this work. The average circuit model is about 14 times faster than the switching circuit in the simulation test case and maximum error is 1.3 % in steady state.
Robust Design of Perturb & Observe Maximum Power Point Tracking

The perturb & observe (P&O) maximum power point tracking (MPPT) is widely used in photovoltaic (PV) systems due to its simplicity and reliability. To optimize the performance, two parameters should be designed, i.e., step size and perturbation frequency, in practice. However, for the step size, the prior-art design could not perfectly balance the static and dynamic performance of the P&O MPPT. Moreover, its behaviors under dynamic irradiance variations were barely revealed. As such, in this paper, the operation principles and the corresponding characteristics of the MPPT are analyzed. Then, the mathematical model to describe the impact of the design parameters is presented. More importantly, a robust design guideline taking into account both the static and dynamic irradiance conditions is proposed. Finally, the proposal is validated through simulations and hardware-in-the-loop (HiL) tests, where the effectiveness of the theory is verified and great design performance is shown.
A New Indoor Unmanned Vehicle Navigation System Based on The Combination of Lidar and IMU

Under the condition of indoor denial, the indoor navigation of unmanned ground vehicles is facing great challenges. In order to solve this problem, we introduce a loose integrated navigation system based on lidar and inertial measurement unit (IMU), and propose a new hybrid scanning matching algorithm based on lidar to estimate the relative position and direction change of vehicles. The algorithm combines traversal search and mathematical optimization algorithm. Firstly, the fitness function is introduced into the traversal search algorithm to find the optimal estimated value of vehicle attitude, and then the estimated value is used as the initial value of the numerical optimization algorithm for further optimization. Finally, the lidar estimated attitude is loosely combined with the results of DRS prediction through unscented Kalman filter (UKF), so as to reduce the cumulative error of inertial sensor. The actual experiment is carried out on the self-developed unmanned ground vehicle platform. The results show that the indoor unmanned vehicle navigation system proposed in this paper can effectively reduce the error accumulation of DRS, and the positioning error is reduced by 92.4% compared with hectorslam method.
Universal Flux Balancing Control to Suppress Transient DC-Bias of Phase-Shift Modulated Multi-Active-Bridge Converters

As a multiport version of the bidirectional isolated dual-active bridge converter, multi-active bridge (MAB) has promising applications in energy routers for dc microgrids and more electric aircraft. The power flow control can be primarily realized by applying phase shifts between AC voltages of different ports, whereas the duty cycles of the voltages are additional degree of freedom to optimize the power loss. However, in the transient conditions, the varied phase shifts and duty cycles result in transient dc-bias in the transformer winding currents and the magnetizing flux linkage, which not only imposes significant current stress on the semiconductor devices but also may saturate the high-frequency transformer. To address this issue, this paper develops a generic model of the MAB converter using the superposition theorem, and proposes a universal dynamic phase-shift control for the MAB converter to suppress the transient dc-bias. The highlights of the proposed method are 1) applicability in different modulation schemes and various transient conditions, 2) extendability to arbitrary number of ports for the MAB, and 3) simplicity with a unified analytical solution that can be implemented in an open-loop control. The effectiveness of the proposed control method is validated by simulation results of triple-active bridge and penta-active bridge converters.
Driver Fatigue Detection Based On Face Mesh Features Using Deep Learning

The number vehicles and road users increases every year, this also has the potential to increase the risk of traffic accidents. Fatigue is the most dominant cause of accidents compared to several other factors. This research focuses on detecting driver fatigue. We use the mediapipe face mesh model to extract the key points on the face, next is to utilize the deep learning model, Long Short Term Memory (LSTM), which has been trained previously and implemented into mediapipe to detect driver fatigue. The data that is trained is the data point movement of facial features, so that the system can not only process one frame but several frames. The data given by the camera will be processed using the LSTM model’s ability to detect long-term information, dynamically process data, and handle picture data using mediapipe in order to achieve low computational and high accuracy. The LSTM model has better accuracy in predicting facial features than the conventional random forest model.
Data-Driven Computational Algorithms for Predicting Electricity Consumption Missing Values: A Comparative Study

Data availability is a significant issue and barrier for modeling and analyzing low voltage networks. This paper develops, implements, and compares several prediction algorithms for finding missing values in energy usage for commercial consumers. Four predictive machine learning models, such as random forest regression, linear regression, multi-layer perceptron, and decision trees, are utilized in this paper. Four commercial users from a regional city in Australia are selected as a dataset based on 30-minute intervals. Firstly, the obtained data is analyzed and pre-processed and then utilized for model training and testing. RMSE and MAE measures are used to compare the effectiveness of each machine learning model. This paper concludes that the multi-layer perceptron model provides better performance than that of random forest, decision tree, and linear regression. The RMSE and MAE of MLP model are 4.7472 and 4.2103, respectively, when using individual users as a training set.
A Primary-Side Monitoring Method for Coupling Coefficient and Receiver Resonant Frequency in SS-Compensated Wireless Charging Systems with Relay Coil

A primary-side monitoring approach is proposed to estimate the coupling coefficient and receiver resonant frequency in a series-series compensated wireless power transfer (WPT) system in which the coupling coefficient and receiver’s resonator parameters can be unknown. A relay coil, coupled with the receiver coil but decoupled from the transmitter coil, is designed to obtain the phase information of the receiver current. Two frequency sweeping processes are included to find the frequency points where the output voltage is in phase or out of phase with the input voltage. Only one zero-crossing detector is required in the process. A detailed analysis of the proposed approach is conducted. In the experiment, the estimation errors are less than 6% for the coupling coefficient and 1% for the receiver resonant frequency. Real-time implementation of the proposed method confirms that the estimation process is extremely fast. The whole estimation process only takes around ten milliseconds.
Small-Signal Stability Analysis of Consensus Algorithm-Based Distributed Control for DC Microgrids Under Multiple Time Delays

Time delay problem is one of the most critical issues in the application of distributed control for DC microgrids (MGs). This paper focuses on a continuous time consensus algorithm based distributed coordinated control for DC MG considering multiple time delays. Firstly, the small-signal model of a DC MG with primary and secondary control is established. Secondly, this paper considers multiple time delay by a set of delay differential equations (DDEs). Thirdly, this paper proposes a critical eigenvalue tracking method to solve the transcendental characteristic equation of the DDEs. Afterwards, the stability of the DC MG considering time delay is analyzed based on the established model. Finally, the obtained stability margin is verified by simulation results.
Smooth Switching Control Strategy of Parallel-type Power Conversion System Off-grid

The power conversion system (PCS) based on droop control is voltage control in parallel. The island and grid-connected operation of the PCS are controlled by turning off and on the grid-side static transfer switch (STS). When the grid fails suddenly, the STS breaking command is issued. The PCS runs in island through the breaking process, but a longer time voltage shock, power and frequency oscillation will occur after breaking process is over. In order to solve above problems and achieve smooth transition of parallel PCS from grid-connected to island mode, a new type feedback link of voltage and current controller is designed in this paper. It combines the differential and integral controllers with droop controller to achieve smooth switching from grid-connected to island mode. In addition, the stability of the improved system is analyzed through root locus. The simulation results verify that the proposed control strategy can achieve a smooth transition from grid-connection to island mode.
Characteristics analysis and loss optimization of the turn-on clamp circuit for IGCT based DC transformer

Integrated Gate Commutated Thyristors (IGCT) has a natural advantage in high voltage and large capacity power electronic equipment due to its high current processing capability. However, due to the positive feedback in the IGCT turn on process, the turn-on clamp circuit must be added to limit the switching speed which reduces system efficiency. This paper firstly introduces the working principle of opening clamp circuit. Secondly, the bus current in Modular Multilevel Converter (MMC) and Double Active Bridge (DAB) are compared, and pointed that the backflow power of DAB will significantly increase the loss of clamped circuit. In order to reduce the loss if clamped circuit and further improve the efficiency of IGCT-DAB, reducing the inductance and buffer capacitor in parallel with the IGCT were proposed, and their feasibility was analyzed. Finally, a DAB prototype of 1.5kV/3MW was constructed in MATLAB/Simulink to verify the correctness of the theory.
Distributed method for Economic Dispatch Problem with the representation of renewable resources

The need to cover the required load across the power grid as efficiently as possible has led to the development of methods for solving a task known as the Economic Dispatch Problem (EDP). As the possible number of agents in the network grew, so did the modularity of the entire network, the dynamic changes in its topology were occurring more and more often. This fact led to the development of decentralized methods. In this paper, a gradient distributed algorithm is introduced where models for solar and wind power plants are used. They are also incorporated into the algorithm. They are utilized for recalculation from weather data, specifically the power of the wind and the power of sunlight, to the provided energy output. Power plants based on renewable sources are hence considered in the representation of the given network topology. The described algorithm is subsequently validated on two examples.
A Bidirectional Current-Fed Isolated MMC with Zero-Current Switching for High Step Ratio DC-DC Applications

To effectively integrate distributed energy resources and non-traditional loads into the grid, medium-voltage direct current (MVDC) systems have gained attention in recent years. A key enabling technology for MVDC distribution is high step ratio dc-dc converters with bidirectional capability, performing a role similar to ac distribution transformers. The current shaping modular multilevel converter (CS-MMC) has been proposed for this type of application because of its ability to operate at high-effective frequencies by eliminating internal string inductors, thereby minimizing module capacitance, size, and cost. Until now, published CS-MMC work has focused on unidirectional, voltage-fed operation. To enable bidirectional operation, a novel current-fed operational approach is proposed in this paper that is fundamentally different from earlier works. The circuit topology, operating principles, and experimental results are presented. A peak measured efficiency of 95.8% is achieved when transferring 2.2 kW from 100V to 1kV for a 6 module, 2.7 kW, 1kV/100V prototype.
Single-Inductor Dual-Input Buck Converter Without Leakage Current for PV Power Optimizer Applications

A modified single-inductor dual-input buck converter is proposed for PV power optimizer applications. The leakage current issue, which cannot be overcome by existing dual-input buck converters, is solved with the proposed converter. The leakage current analytical model is analyzed in detail. The stray capacitors between the PV array and the ground are clamped by modifying the position of the filter inductor in the converter. Experimental tests have been conducted to verify the effectiveness of the proposed dual-input buck converter.
A Mobile Manipulation System for Automated Replenishment in the Field of Unmanned Retail

Automated replenishment is one of the most challenging in-store logistics tasks, which has not been well-addressed due to the complex coordination of a mobile base and a manipulator. In this paper, we build a complete mobile manipulation system to enable automated replenishment in the field of unmanned retail. Specifically, the designed mobile manipulation system consists of a UR5 robotic arm and an automated guided vehicle (AGV). For the manipulation part, we propose a 6-DoF target-oriented grasp detection method for stably picking products in the cluttered scenes; For the mobile part, we develop a navigation method to plan a collision-free path for efficiently delivering the goods. We evaluate the designed system via real-world experiments and the results show the efficiency and reliability of our mobile manipulation system in finishing the replenishment tasks. The video demonstration can be found at https://youtu.be/g6WrF-I7tNw
An Improved Point Cloud Segmentation Method Based on Multi-scale Fusion Strategy

With the rapid development of 3D sensors, point cloud semantic segmentation methods have gradually become important components of 3D scene understanding. Considering the limitation of the local receptive field, we design dilate sampling, which improves the segmentation performance without increasing the computation. Furthermore, we fuse information from multiple scales in the decoder to recognize objects of various sizes. After experiments, we obtain comparable results on the S3DIS dataset and Toronto3D dataset.
GaN-on-Si Power HEMTs for Automotive: Current Status and Perspectives

In this paper we present a comprehensive review and outlook of gallium nitride power devices for next-generation power electronics, with focus on the emerging automotive field. The analysis of different markets available GaN-on-Si HEMTs will draw the present state of this technology for power applications and automotive, highlighting the different available topologies and their key characteristics, together with a comparison with Si and SiC. Furthermore, we will investigate the present and future of GaN devices in the different building blocks composing an automotive system (power train inverter, on board charger, off board charger, DC/DC converters, ADAS, infotainment). In the end, we will highlight the present reliability issues and limits of GaN-on-Si power devices (threshold voltage instabilities, dynamic on-resistance, breakdown limitation).
Safe height estimation of deformable objects for picking robots by detecting multiple potential contact points

Object sorting in logistics warehouses is still carried out manually, and there is a great need for automation with arm robots. It is desirable that target objects be carefully placed in situations where careful handling of products is important. We propose a method for estimating the height of picked object with a single depth camera to achieve precise placing of items such as stacking, especially for objects that are deformable, e.g., bags. The proposed method detects multiple potential contact points of a picked object to estimate the appropriate height to place the object using the point-cloud difference before and after picking. The validity of the proposed method was verified using 26 cases in which deformable objects were placed inside a container, and it was confirmed that object-height estimation is possible with an average error of 3.2 mm.
Prospect theory in the health domain : A quantitative assessment 1

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Prospect theory in the health domain: A quantitative assessment Arthur E. Attema, Werner B.F. Brouwer, Olivier L’haridon