Miracle-dz/UniFAR-FTUs huggingface dataset:
Responses of Preview-based Vehicle Suspension System on Discrete Impact Roads

The preview-based suspension system improves the performance of the vehicle chassis by adjusting the dynamic characteristics of the suspension in real-time according to the roughness information of the road ahead. In order to reduce the impact load on the vehicle when passing through a discrete impact road, this study proposes a preview suspension control system and control method by combining vehicle dynamics and computer vision. The proposed preview suspension control system acquires the characteristic information of the discrete impact road in real-time. As the main controller, a semi-active suspension controller integrates the real-time road information of the vehicle and performs hybrid state control on the suspension, is proposed. As the front-end previewer, a discrete impact road previewer based on a convolutional neural network is proposed with a YOLO v5s-based discrete impact road target detection algorithm, which can detect and measure the typical characteristic road ahead through computer vision. A simulation platform based on MATLAB/Simulink is established and the ride comfort simulation and analysis of preview control suspension on discrete impact road are conducted. The results show that the vision-based road preview method can accurately detect the characteristic information of discrete impact road surfaces, and the proposed preview suspension control system and method can effectively improve ride comfort and suspension hysteresis under typical road surfaces.
FINITE-DIFFERENCE BISECTION ALGORITHMS FOR FREE BOUNDARIES OF AMERICAN OPTIONS

This paper presents two algorithms based on the Jamshidian equation which is from the Black-Scholes partial differential equation. The first algorithm is for American call options and the second one is for American put options. They compute numerically free boundary and then option price, iteratively, because the free boundary and the option price are coupled implicitly. By the upwind finite-difference scheme, we discretize the Jamshidian equation with respect to asset variable s and set up a linear system whose solution is an approximation to the option value. Using the property that the coefficient matrix of this linear system is an M-matrix, we prove several theorems in order to formulate a bisection method, which generates a sequence of intervals converging to the fixed interval containing the free boundary value with error bound h. These algorithms have the accuracy of O(k + h), where k and h are step sizes of variables t and s, respectively. We prove that they are unconditionally stable. We applied our algorithms for a series of numerical experiments and compared them with other algorithms. Our algorithms are efficient and applicable to options with such constraints as r ＞ d, r ≤ d, long-time or short-time maturity T.
Collision Avoidance Pathfinding of Multiple AGVs Considering Motion Uncertainties*

The multiple automatic guided vehicles (AGVs) pathfinding methods that do not consider the uncertainties of the future motion state may lead to large-scale congestion. To address this problem, this paper proposes an improved conflict-based search (CBS) algorithm with a load-information map. Firstly, the grid map with load information and the evaluation criteria of collision-free pathfinding is constructed. Then, an improved CBS algorithm is designed under a set of constraints on the motion states. This is achieved by performing a search on the constrained conflict tree between individual robots using load-information-map at the high level while exploring efficient single-robot searches with modified penalty function at the low level. Finally, the proposed improved CBS algorithm is tested through simulation and experiment. The results show that it not only finds a collision-free path for multiple AGVs under the motion uncertainties but also obtains a comprehensive optimal solution that considers the evaluation criteria and map load simultaneously.
ZnO Nanoparticle Printing for UV Sensor Fabrication

In this work, a novel microfabrication-compatible production process is demonstrated and used to fabricate UV photoresistors made from ZnO nanoparticles. It comprises a simple room-temperature production method for synthesizing and direct-writing nanoparticles. The method can be used on a wide range of surfaces and print a wide range of materials. Here, it is used to synthesize a ZnO photoresistor for the first time. The sensor shows a two orders of magnitude lower resistance under UV-C exposure compared to darkness. The low cost and simplicity of this synthesis method enables cheap integration of UV-C sensors for human exposure monitoring or UV-output monitoring of light sources.
Position Tracking and Disturbance Rejection for Motion Control System Using Equivalent Input Disturbance Approach and Feedforward Control

This paper presents a new method to achieve fast and precise positioning performance for a motion control system with an exogenous disturbance and plant uncertainty. The equivalent-input-disturbance approach is employed to reject the disturbance and attenuate the uncertainty in an inner-loop way. The feedforward control is designed to improve transient response considering the nominal model of the system. The feedback control is devised to guarantee the stability of the closed-loop system. Performance analysis reveals the mechanism of disturbance rejection and uncertainty attenuation. Simulation results of a numerical example demonstrate the validity of the presented method.
Novel IoT-Based E-Health System: Hospital Management, Telemedicine and Quarantine Management for COVID-19

Patients commonly visit to hospitals for monitoring and treatment of their chronic diseases especially in COVID-19 epidemic ultimately increases the patients and hospitals' burden. The foremost advancement with respect to examining a patient's critical condition in such pandemic is remote patient monitoring and providing treatment via telemedicine. The main objective of this paper is to provide a novel Internet of Things (IoT) based system for continuous remote monitoring of patients' location, health statistics related to COVID-19 infection, telemedication and maintaining E-health record database. The system monitors oxygen levels and heart-rate signals using MAX30100 (Heart Rate and Pulse Oximeter sensor) and temperature via LM-35 module interfaced with the ESP8266 WI-FI module for web-monitoring. The healthcare sector involving a web server database controlled by cPanel will be used by consultants to have patients' data remotely for telemedicine. Besides, the database is also used as an electronic health record for hospital management system to maintain E-files and history of patients' complications. Moreover, the device monitors the real time location of infected patients using GPS and alerts the medical officials if the patients breach the quarantine norms. The real time location of infected patients also enables the medical authorities to investigate about the total number of COVID-19 cases in any particular area. However, the android application is developed for patients' family/relatives so that they can also monitor the patient condition to take the necessary actions before the worst condition arises. The developed system is efficient in providing integrated services to assist healthcare officials, minimize cost, maintains security and upgrade disease diagnosis speed in less time.
Load Optimized Gate Driving for Charge Pumps

This paper presents a method for efficiency optimization for charge pumps using a negative feedback loop to control the gate driving voltage in each transistor. Switching losses can be significantly lowered by reducing the gate voltage swing while only slightly increasing the conduction losses, thus improving the performance of the switched capacitor dc-dc converter. By setting a target output voltage it is possible to use a feedback loop that controls the voltage swing in each power switch where, by reducing their driving voltage, the output resistance is increased, regulating the output voltage to the target value. The proposed methodology is used to optimize the efficiency of a power management unit (PMU) designed by Silicongate in the scope of the pAvIs project.
Clock Shift and Sampling Delay Effects on Stability in Digitally Controlled Cascaded DC-DC Converters

Interactive digital control using PMBus protocol can significantly improve performance, efficiency and reliability in high frequency low voltage DC (LVDC) microgrid applications using online optimization through shared clock and data. Modeling and analysis of digitally controlled standalone DC-DC converters, considering finite sampling delays, have been well reported in the past; however, modeling and analysis of fast scale stability and performance limits, under finite sampling delay and clock shift, have not been investigated so far in digitally controlled LVDC microgrids. This paper considers an intermediate bus architecture (IBA), consisting of an intermediate bus converter (IBC) followed by a point-of-load (PoL) converter, under clock-synchronized digital current mode control (DCMC). Considering the IBA dynamics, a discrete-time (DT) framework is proposed, and DT small-signal models are derived for stability and performance analysis. It is shown that the clock shift between individual converters and the sampling delays of their corresponding digital controllers have significant impacts on the fast-scale stability of the overall IBA system, which may lead to severe fast-scale instability with complex nonlinear phenomena and resulting in much higher (inductor) current ripple and RMS quantities. Simulation case studies are presented, and the stability boundaries are found to be consistent with the analytical predictions. The proposed framework will be helpful to design stable digital control in DC microgrids.
A Hybrid Method for Hour-ahead PV Output Forecast with Historical Data Clustering

An accurate forecast of the photovoltaic (PV) output can indispensably enhance the stability of power supply. However, the effectiveness of prediction results highly depends on many factors and the data preparations are also an essential process to be considered. The focus of this paper is to classify PV data types by density-based spatial clustering of applications with noise (DBSCAN). Then, the procedure for historical data clustering and for hour-ahead PV output forecast implemented by back propagation neural network (BPNN) model is present. Forecasted results show that data clustering provided by DBSCAN can efficiently classify the PV data types for input to BPNN to achieve better accuracy.
Constrained model-predictive control of the electron density profile in ITER using two pellet injectors

On contemporary tokamaks, the control of the density is mainly performed with gas injection or with the injection of single type pellets (mm-sized body of frozen fuel). On future reactors, pellet injection is expected to be the main actuator available for core density control as gas injection is foreseen to be ineffective. It is anticipated that multiple pellet types (e.g., with varying size, injection velocity, composition) will be used. Hence, a control strategy that can control the density with multiple actuators is required. This work presents a model predictive controller (MPC) which can regulate the electron density profile of a tokamak fusion plasma using multiple pellet injectors and accounts for operational limits using constraints. This is the first MPC controller developed with the aim to control the density profile in a fusion reactor. Results of simulations with continuous and discrete pellet representations demonstrate the performance and robustness of the controller in the presence of plant-controller model mismatches.
Physical conditions in photodissociation regions: M17 northern bar

We present results of long-slit spectroscopy, from 2.0 to 2.5 μm, of the photodissociation region in the northern bar of the H II region M17. The emission from ionized material peaks to the south of that from molecular gas, in agreement with previous measurements. The diagnostic 1−0 S (1) to 2−1 S(1) line ratio is approximately 3, larger than the ratio for pure fluorescence, implying that the H 2 is slightly thermalized. The ratio does not change along the extent of the molecular emission which fills the slit (approximately 60 arcsec), suggesting that the collisional de-excitation rate is constant. We take this to mean that the physical conditions are constant across the region, implying that in the northern bar of M17 we are viewing the face of the molecular cloud
Dynamic Visual SLAM Based on Efficient Two-Stage Statistical Model

Dynamic objects usually break the static environment assumption which is a crucial principle of visual Simultaneous Localization and Mapping(SLAM), leading to bad tracking quality and high computation burden to improve performances. To promote visual SLAM performances in dynamic environment with less extra time consumption, we propose an efficient robust dynamic landmark removal method based on a two-stage statistical model using static score propagation and conditional random field (CRF) based on ORB-SLAM 2 framework. To detect obviously dynamic landmarks, the static score is firstly estimated according to statistical feature of re-projection error and propagated along multiple observing frames. Then the CRF is constructed to refine the initial static/dynamic labelling results in consideration of neighboring motion and long-term observation quality. With the accurate static/dynamic labels, only static landmarks are employed in tracking process to eliminate disturbance from dynamic objects. Evaluations on TUM RGB-D dynamic datasets indicate that our method improves visual SLAM performances in highly dynamic environment with accuracy at the same level as state-of-the-art methods and a significant advantage on time efficiency.
2.1 kW 3 MHz Capacitive Power Transfer with Sleeve-Type Coupler for Rotary Applications

This paper designed a 2.1 kW 3 MHz capacitive power transfer (CPT) system with a sleeve-type coupler for rotary applications. A new M1-SS CPT topology is proposed with a compact receiver circuit. On the receiver side, only a small series compensation inductor is used while a mutual inductive coupling link is used on the transmitter side to improve the power transfer capability and achieve load-independent constant-voltage (CV) output. To verify the proposed design, a 3 MHz CPT prototype is implemented. With a wide load range, the maximum output power achieves 2.122 kW. Meantime, the experimental results also validate the load-independent CV output and the zero-phase-angle (ZPA) property. When the output power is within 1.54 kW, the measured dc-ac efficiency achieves above 92%, and the maximum efficiency of 93.19% is achieved at 755W output.
Potentials and Limits of Using Preconfigured Spatial Beams as Bandwidth Resources: Beam Selection Versus Beam Aggregation

This letter studies how to use spatial beams preconfigured in a legacy spatial division multiple access (SDMA) network as bandwidth resources via the implementation of non-orthogonal multiple access (NOMA). Two different beam management schemes, namely beam selection and beam aggregation, are developed to improve the overall system throughput without consuming extra spectrum or changing the performance of the legacy network. Analytical and simulation results are presented to show that the two schemes realize different tradeoffs between system performance and complexity.
Influence of pressure gradient on streamwise skewness factor in turbulent boundary layer

The paper shows an effect of favourable and adverse pressure gradients on turbulent boundary layer. The skewness factor of streamwise velocity component was chosen as a measure of the pressure gradient impact. It appears that skewness factor is an indicator of convection velocity of coherent structures, which is not always equal to the average flow velocity. The analysis has been performed based upon velocity profiles measured with hot-wire technique in turbulent boundary layer with pressure gradient corresponding to turbomachinery conditions. The results show that the skewness factor decreases in the flow region subjected to FPG and increases in the APG conditions. The changes of convection velocity and skewness factor are caused by influence of large-scale motion through the mechanism called amplitude modulation. The large-scale motion is less active in FPG and more active in APG, therefore in FPG the production of vortices is random (there are no high and low speed regions), while in the APG the large-scale motion drives the production of vortices. Namely, the vortices appear only in the high-speed regions, therefore have convection velocity higher than local mean velocity. The convection velocity affects directly the turbulent sweep and ejection events. The more flow is dominated by large-scale motion the higher values takes both the convection velocity of small-scale structures and sweep events induced by them.
Machine learning based optimization of a ceramic bushing manufacturing process

Machine learning (ML) has shown great promise in a variety of domains in recent years. ML models are known to require large amounts of labeled training data, keeping small to medium-sized business from utilizing them. This paper presents ML based approach to optimize a ceramic bushing manufac-turing process, by predicting the employed press-fit process as a function of press punch position. Accurate predictions would ensure optimal process configuration, guaranteeing quality and reducing waste. Models are trained in a supervised manner to predict the press-fit process and the ceramic defect probabilities as functions of press punch position. We were able to predict the press-fit process with a mean correlation of 0.996 and assess whether the process would damage the ceramic with a mean precision of 96.7%. Our results exemplify how ML can be used to predict and optimize highly specialised processes even with small datasets.
Global Localization based on Road-centric 3D Point Cloud Descriptor in Urban Environments

Global localization is essential for unmanned vehicle navigation in a prebuilt map especially in weak or erroneous GNSS signals areas. Unlike images containing rich features, point clouds are sparse and almost contain pure geometric information which makes global localization using a single scan still a challenging problem. Based on Scan Context framework, we propose a road-centric 3D point cloud descriptor for global localization in urban environment. Rather than generate the descriptor in the raw sensor coordinate, we incorporate road measurements and topological information to transform the point cloud into a road-centric coordinate and generate the descriptor which makes it theoretically rotation-invariant and translation-invariant. In addition, we can obtain more precise initial pose to align point cloud with the prebuilt map through the transformation between raw sensor coordinate and road-centric coordinate. Evaluation on KITTI dataset shows that our proposed method has better performance in global localization and the success rate is 26% higher than Scan Context.
Active Haptic Assistance Control based on Expert Driver Behavior Analyzation

To improve novice driver's path following performance and reduce driver workload, an active haptic assistance controller in consideration of expert driver characteristics is proposed in this paper. By collecting drive data and recognizing expert driver's characteristics, we establish an expert driver model and apply it to the active haptic assistance controller using sliding mode control (SMC) to generate optimized steering input. We evaluate the performance of the expert driver model and the controller in hardware-in-loop experiments which conducted in Matlab/Simulink platform with CarSim® vehicle model and Prescan environment. The results show that the controller can help novice drivers steer as expert without extra knowledge.
Inspection of the Loss Reduction Effect of Three-Phase Inverter by Using a New Single-Phase PWM Control Method

This paper presents the loss reduction effect of a three-phase inverter using a new single-phase Pulse Width Modulation (PWM) control method. The proposed modulation method turns on or off only one of the three phase legs in one PWM-carrier period. In this method, the number of switching cycles can be reduced to one-third compared to a three-Phase modulation method. Also, the control algorithm can be simplified in comparison with a conventional single-phase modulation method. The simulation analysis was conducted to indicate the operating principle and evaluate its performance. As a result, it is confirmed that the proposed single-phase modulation method has control characteristics equal to the conventional single-phase modulation method and the switching loss can be reduced compared to the three-phase modulation method. In addition, the experimental results obtained by using a 2-kW, 100-V setup are shown to verify the operating principle and its performance. The conversion efficiency of the proposed system achieved 95.3%. Furthermore, the same effect as the conventional single-phase modulation method was also achieved.
Development of road environment identification system using YOLO V5

The detection and recognition of road traffic environment is a key technology for smart cars, and it is also an important task of environmental perception in the vehicle’s automatic driving system. It is the basis for the subsequent path planning, decision-making and control of the vehicle. As the advancement of autonomous driving technology is becoming more and more mature, advanced driver assistance systems (ADAS) systems can complete partial semi-autonomous driving with human assistance, such as automatic cruise system (ACC), when fully autonomous driving technology has not yet been completed. The completion of fully autonomous driving technology is extremely important for road environment recognition. Therefore, this study uses the object detection technology YOLO V5 algorithm as the road environment identification. In the research, it is divided into two parts, the first part is the part of road environment image training, which mainly deals with image processing, establishing database and generating weight files for testing. The second part is the test system. When the specified image or image is input into the road detection system, data processing and identification are performed, and the identification result is labeled and output. The research results show that objects in the road environment can be marked through the YOLO V5 system as road environment recognition.
Quasi-Resonant DC-DC Converter Single-Switch for Single-Input Bipolar-Output Applications

In this paper, a new quasi-resonant DC-DC converter topology is presented, which is the result of combining simpler configurations of single input and single output. The proposed topology is a combination of the Zeta and Buck-Boost resonant configurations, which allows a bipolar output voltage to be generated using a single power switch. In addition, the combination converter can operate in step-up and step-down modes and is a transformerless converter, allowing a reduction in size and an increase of the power density. A complete analysis of the proposed converter has been carried out and subsequently verified through simulation. The results obtained show that this new topology is suitable for multiple applications requiring bipolar voltage and high-power density.