Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Ridesplitting Service Using Optimization Procedures on Shareability Network: A Case Study of Chengdu, China

Ridesourcing services play a crucial role in metropolitan transportation systems and aggravate urban traffic congestion and air pollution. Ridesplitting is one possible way to reduce these adverse effects and improve transport efficiency. This paper aims to explore the potential of ridesplitting in peak hours using empirical ridesourcing data of Chengdu, China provided by DiDi Chuxing. A ridesplitting trip identification algorithm based on a shareability network is developed to quantify the potential of ridesplitting. Then, we evaluate the gap between the potential and actual scales of ridesplitting, which the literature has not yet reported. We compare the potential of ridesplitting under three different objectives. The results show that the objective of minimizing the total travel cost produces better performance than the objectives of maximizing shared trips and time savings. Under the objective of maximizing cost savings, the percentage of potential cost savings is 18.47% with an average delay of 4.76 minutes, whereas the actual percentage is 1.22% with an average delay of 9.86 minutes. The potential percentage of shared trips is 90.69%, while the actual percentage is 7.85%. Furthermore, the potential time savings can reach 25.75%, while the actual time savings are 2.38% in the real world. The findings of this study can help transportation management agencies and ridesourcing companies develop sensible policies to improve ridesplitting services.
Urban Parking System Based on Dynamic Resource Allocation in an Era of Connected and Automated Vehicles*

With the advent of automated vehicles, centralized online resource allocation offers opportunities in urban parking management to alleviate parking problem in the era of internet of things. In this paper, we propose a new parking system for large-scale resource management in an environment of mixed automated and human-driven vehicles. The system is formulated as a dynamic resource allocation (DRA) problem of mixed-integer linear programming (MILP) at each decision point with the objective of minimizing the total cost for users. Based on a tree representation of the solution space for the matching pairs, we combine Monte Carlo tree search (MCTS) and some heuristic rules to find a nearly global-optimal matching order (parking users and resources) within a short time. Simulation results show that this strategy can keep a good tradeoff between performance and computation efficiency, and this online resource allocation system can significantly alleviate urban parking problem in a mixed traffic environment.
Recognition of Activities of Daily Living via Hierarchical Long-Short Term Memory Networks

In order to offer optimal and personalized assistance services to frail people, smart homes or assistive robots must be able to understand the context and activities of users. With this outlook, we propose a vision-based approach for understanding activities of daily living (ADL) through skeleton data captured using an RGB-D camera. Upon decomposition of a skeleton sequence into short temporal segments, activities are classified via a hierarchical two-layer Long-Short Term Memory Network (LSTM) allowing to analyse the sequence at different levels of temporal granularity. The proposed approach is evaluated on a very challenging daily activity dataset wherein we attain superior performance. Our main contribution is a multi-scale, temporal dependency model of activities, founded on a comparison of context features that characterize previous recognition results and a hierarchical representation with a low-level behaviour-unit recognition layer and a high-level units chaining layer.
A Practical HfO2-based OxRAM Memristor Model Suitable for Circuit Design and Simulation

CMOS technologies are attending their limits due to its continuous shrinking process, which has an impact on various aspects, accurately on the size, performance and power consumption of the device. One of the promising devices known as memristor is under investigation in order to be used together with deep nanometer CMOS, that has the ability to solve technologies problems. It has emerged in several domains due to its nonlinear behavior, nonvolatility, low power consumption, high density and compatibility with CMOS. Several memristor models have been developed so far. Whereas, a compact model should be flexible and sufficiently accurate. In this paper, an analysis of a memristor model using Verilog-A and SPICE is discussed, in order to elaborate an appropriate model for implementation in CMOS-based circuit applications. It is shown that the proposed Verilog-A model is flexible, accurate and efficient, verified by both electrical simulations and experimentally measured results. It also carries the desired nonlinear memristor fingerprint, an adjustable threshold voltage and the applicability to fit and simulate different switching behaviors. Hence, it is the most suitable for low-power and high-density applications at the industrial levels, which takes advantage compared to other approaches based on SPICE models.
In-depth Analysis of Autonomous Vehicle Collisions in California

Autonomous Vehicles (AVs) are an inevitable aspect of future transportation systems. Major carmakers in addition to many technology companies are developing and testing new technologies to add self-driving cars to their portfolio. Under the autonomous vehicle testing regulations of California, many of these companies have already begun testing their AVs on the streets of California. As mentioned in this regulation, each company is obligated to report any incident within ten days after occurrence. Based on the collision reports, this paper provides a comprehensive overview and an in-depth analysis regarding all types of accidents which were documented as a part of testing regulations and in which at least one AV was involved. All data gathered from the collision reports are analyzed to identify the root cause of the accidents and to determine whether the AV was the cause of the accident, or on the contrary, it could have potentially avoided the accident. This paper highlights also the existing challenges and improvement potentials of autonomous systems based on current technologies and testing methods.
An Active Power Sink to Emulate Residential Loads in Secondary Distribution System

A home contains a variety of active/passive and linear/non-linear loads. The measured data from several rural and semi-urban homes show that a contemporary home draws a wide range of harmonics from the secondary distribution grid. These harmonics are significant in magnitude. To study its impact on the existing secondary distribution system, a home is emulated using power electronics module, which is capable of drawing a wide range of harmonics. This module is connected to a distribution system test bed to study its impact in the laboratory. A hysteretic current controlled current fed rectifier is adopted as a power electronics module. Modelling and analysis of this module, in the presence of harmonics, are investigated and various operating constraints are also discussed. Its extensive ability is verified experimentally on a 110 V, 200 VA prototype.
Analysis of Multi-Pickup Inductive Power Transfer System with LCC Compensation for Maglev Train

This paper presents a multi-pickup inductive power transfer system with double-sided LCC compensation for stationary MAGLEV charging. The primary side employs a parallel line as a wireless power rail and the secondary side has multiple pickups. Due to the high power demand of the MAGLEV train, the current ratio should be limited to minimize the losses in the semiconductors and wires. A single turn power rail without ferrite is designed as the primary coil and rectangular pad with integrated compensation inductance is employed for the secondary side. A finite element model of the coupler is simulated. The circuit analysis is performed to evaluate the double-sided LCC compensation for a multi-pickup application. The simulation results of the current of the inverter and the primary winding are shown, and a prototype is built.
An Asymmetrical Fault Current Iterative Algorithm of Droop-controlled Inverter

As a voltage-controlled inverter, droop-controlled inverter will provide large inrush fault current when subjected to short-circuit faults, causing unplanned disconnection from the utility grid or even power devices damage. Therefore, to ensure the reliability of protection system, it is very important to figure out the fault current characteristics. This paper proposes an asymmetrical fault current iterative algorithm of droop-controlled inverter by using systems’ symmetrical components. It is found that the fault current of each phase is composed of an attenuated periodic component and a dc component. The periodic component is mainly determined by the control parameters, grid equivalent voltage and equivalent line impedance. While the dc component is only related to grid parameters and fault moment. Comparisons between calculation results and simulation results or experimental results verified the correctness and feasibility of proposed method.
Target Detection Algorithm Based on MMW Radar and Camera Fusion*

Environmental perception is a key technology for Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, and thus a crucial prerequisite for traffic situation assessment and reasonable decision making. Sensor fusion can make full use of the characteristics of multi-sensors to achieve complementary advantages to improve the target recognition precision in various weather conditions. In this paper, a robust target detection algorithm based on millimeter wave (MMW) radar and camera fusion is proposed. Firstly, the images captured by the camera with low visibility in foggy weather are defogged. Then, the effective target is filtered by MMW radar, and mapped to the camera image to obtain the corresponding regions of interest (ROIs). Finally, the method of weighted is used to combine camera vision network recognition results with the radar target estimation results, and obtain the final result of the ROIs. The simulation results indicates that the detection precision through fusion of MMW radar and camera significantly outperforms that of single sensor.
Effect of Cutting Maneuvers on Center of Foot Pressure Movement in University Tennis Players

The knee anterior cruciate ligament (ACL) injury is caused by abnormal turning force to knee joint. In particular, cutting maneuvers often cause ACL injury. Therefore, analyze cutting maneuvers is important for preventing ACL injury. In the previous study, there are many things used force plates to measure foot pressure, motion capture using marker, and analyze electromyographic. Till now, there is no study to analyze center of foot pressure (COP). In the measurement of COP distribution, we use the insole type measuring device in this study. The insole type can be attached to the shoes used daily and measured in various environments. Therefore, it can be performed in various places in which the competition is carried out, and it can deal with various action. The purpose of this study is to analyze the motion of cutting maneuvers. The changes of COP distribution by changing angle of cutting maneuvers are clarified. In this study, we defined parameter and compared cutting maneuvers from the values. Our measurement system has 6-axis inertial sensor that attached to the instep. We estimated the timing of direction change and running cycle more accurately by measurement data. The angle of the cutting operation is increased, or non-dominant foot or female, the value of the parameter is large.
Evolutionary Learning in Decision Making for Tactical Lane Changing

Decision making for lane change is an important challenge for automated vehicles, especially in complex traffic environments. In recent years, there have been studies that utilize reinforcement learning for lane change applications. However, such an approach requires high computational costs and is difficult to implement by parallel computing. To overcome the problem, an evolutionary learning approach is put forward for the decision-making application of autonomous driving. By deploying the parallel workers, making parameters of the neural network mutate and recombining the well-behaved off-springs during the evolutionary learning process, the Evolution Strategy (ES) agent learns to make decisions for lane-change maneuvers. At the same time, safety verification is performed, which ensures driving safety and simplifies the learning process. To test the performance of the proposed method, a highway simulation environment is established. The results show that the combination of the high-level evolutionary learning and low-level safety verification jointly achieve efficient driving behavior control.
Analysis of Spatial Equity in Taxi Services: A Case Study of New York City*

Many people criticize the unequal distribution of traditional taxis (TT) service and believe the advent of e-hailing taxis (ET) can bring equity to the public. However, this have not been rigorously studied by existing research. This paper aims to explore the spatial equity of taxi services and how the advent of ET affects the equity. Trip data of TT and ET in New York City in 2010 and 2017 was used for a case study. The spatial autoregressive models were used to estimate the spatial dependence of taxi service pick-ups, which could indicate the existence of inequality in taxi services. It was found that the spatial dependence of TT in 2010 was the highest and the spatial dependence of ET was the lowest in 2017 with other contributing factors like population and employment controlled. When ET appeared, the spatial dependence of the total taxis (TT+ET) trips in 2017 decreased compared with that in 2010, suggesting that the advent of ET increased the spatial equity of taxi services.
Optimization and Experiments of Lateral Semi-Superjunction Device Based on Normalized Current-Carrying Capability

A lateral double diffused metal oxide semiconductor transistor with the semi-superjunction (semi-SJ LDMOS) is optimized based on the normalized current-carrying capability (CC) and experimentally realized in this letter. The device is fabricated based on an optimized equivalent substrate and the semi-SJ is introduced near the source. The semi-SJ increases the local doping concentration in the N regions meanwhile reduces the current path area and carrier mobility, resulting in the variation of CCs before and after the introduction of the semi-SJ. The normalized CC factor <inline-formula> <tex-math notation="LaTeX">$\eta _{\text {C}}$ </tex-math></inline-formula> is proposed to evaluate this variation, with which the minimum specific on-resistance <inline-formula> <tex-math notation="LaTeX">${R} _ {\text {ON}, \text {sp}}$ </tex-math></inline-formula> is predicted by the condition of <inline-formula> <tex-math notation="LaTeX">$\eta _{\text {C}}= {1}$ </tex-math></inline-formula>. The experiments of the semi-SJ LDMOS exhibit a minimum <inline-formula> <tex-math notation="LaTeX">${R} _ {\text {ON}, \text {sp}}$ </tex-math></inline-formula> of 25.5 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>cm<sup>2</sup> under a breakdown voltage <inline-formula> <tex-math notation="LaTeX">${V} _{{\text {B}}}$ </tex-math></inline-formula> of 464.3 V. This represents a reduction in <inline-formula> <tex-math notation="LaTeX">${R} _ {\text {ON}, \text {sp}}$ </tex-math></inline-formula> by 37.7% when compared with the theoretical <inline-formula> <tex-math notation="LaTeX">${R} _ {\text {ON}, \text {sp}}$ </tex-math></inline-formula> of triple RESURF devices.
A road hazard state prediction method based on BP neutral network

This work addresses the task of road hazard state evaluation in traffic scenarios with MEC(multiple access edge computing) available information. Here we concentrate on inter-vehicle energy relationship, and devote to propose a prediction method, which could predict both regional overall hazard state and hazard distribution characteristics. A classical learning network, BP neutral network, is employed here for corresponding prediction process, while Ising model is used to explain road hazard state. The network training progress is discussed in detail. A validation data set is used to verify the effectiveness of proposed approach. Simulation results show that proposed method is able to make accuracy prediction of road hazard state. Resulting hazard values could be utilized to help driver avoid potential dangers and generate valid cruising trajectory.
Single DC-Link Three-phase AC-DC-AC Converter With Shared Legs

This paper proposes a six-leg three-phase AC-DC-AC converter which has three of its legs shared between grid and load sides. The proposed converter presents double of voltage generation capability at load side compared with grid side. Therefore, the converter may operate with high modulation index at both load and grid sides when the amplitude of load voltage is twice the amplitude of grid voltage. Hence, this operation allows to optimize the converter voltage waveform quality and to employ all voltage generation capability at both sides simultaneously. Pulse Width Modulation (PWM) and control strategy are presented. Several analysis are performed considering DC-link voltage, harmonic distortion, average switching frequency and semiconductor losses. Finally, simulation and experimental results are shown for validation purposes.
Vision-Based Trajectory Planning via Imitation Learning for Autonomous Vehicles

Reliable trajectory planning like human drivers in real-world dynamic urban environments is a critical capability for autonomous driving. To this end, we develop a vision and imitation learning-based planner to generate collision-free trajectories several seconds into the future. Our network consists of three sub-networks to conduct three basic driving tasks: keep straight, turn left and turn right. During the planning process, high-level commands are received as prior information to select a specific sub-network. We create our dataset from the Robotcar dataset, and the experimental results suggest that our planner is able to reliably generate trajectories in various driving tasks, such as turning at different intersections, lane-keeping on curved roads and changing lanes for collision avoidance.
New Gate Driver for online adjustable switching behavior of Insulated Gate Bipolar Transistors (IGBTs)

This paper presents a new gate driver concept that allows an online, open-loop adjustment of the switching behavior of power semiconductors during operation. An inductive impedance instead of an ohmic impedance enables the required adjustable gate current and thus the desired gate voltage curve. The driver can individually adapt the switching behavior of the transistor to each individual switching edge. Thus for example the dv/dt and di/dt can be influenced, whereby the EMI behavior, the reverse recovery rate, the occurring overvoltage or the switching losses can also be influenced. The new gate driver was compared to a conventional resistive gate driver and the measurement results clearly show the advantages of the proposed driver concept.
Atomistic Simulation of a New Label-Free DNA Nanosensor Based on Ballistic Carbon Nanotube Field-Effect Transistor

In this paper, a new label-free nanobiosensor called dielectric-modulated carbon nanotube field-effect transistor (DM CNTFET) is proposed. The nanobiosensor is simulated using an atomistic simulation based on the non-equilibrium Green's function (NEGF) formalism. The main sensing mechanism is based on the dielectric modulation technique. The threshold voltage shift is considered as a biosensing metric. The investigation includes the potential and charge distributions, the transfer and output characteristics, and the sensitivity behavior. The performance assessment reveals that the proposed nanoscale DM CNTFET-based biosensor is effective and can be considered as a promising candidate for high-performance biosensing applications. The proposed label-free DM CNTFET-based nanosensor can be used to detect other bio-measurands such as the antigens and antibodies, which also are adaptable with the dielectric modulation sensing approach.
A Novel Hierarchical Edge Computing Solution Based on Deep Learning for Distributed Image Recognition in IoT Systems

Traditionally, IoT systems utilize cloud computing platforms for ease of deployment. However, such platforms are resource-intensive, relatively expensive, and lead to longer response time due to network latency. Hence, cloud computing may not always be suitable for unusually large scale deployment. Various edge computing solutions have been proposed to overcome the above issues. However, most of such solutions rely upon expensive edge servers, which make them unsuitable for distributed applications like image recognition in agricultural fields. In this paper, we propose a hierarchical edge computing-based image recognition system in which the major processing is carried out at low-cost gateway devices like Raspberry Pi. As an example case, we address the issue of recognition of animals intruding in agricultural fields. We implement a dynamic learning method, in which a convolutional neural network is dynamically trained to recognize potential target classes based on a specific deployment environment. The AI detection module is then loaded on to the lowest level of edge servers on gateway devices for detection of animals and providing feedback. Experiments show that our proposed recognition system can perform offline image classification tasks with up to seven times higher accuracy and more than two times faster evaluation time in comparison with general-purpose cloud recognition systems. Besides, it consumes less than 6% of the network bandwidth and only a fraction of the energy as well as other computational resources compared with existing approaches.
LCL Filter Design for Grid-tied N-Level Cascaded Inverters Used in Renewable Energy Systems

Renewable energy resources are usually needed to be connected to the distribution grids through a power electronics converter with an appropriate filter in the output. Therefore, harmonics injected to the grid by converters must be handled to maintain power quality indices within standard limits. LCL filters with passive damping resistors are the most reliable and efficient devices to fulfill the standards. The main objectives in the design of an efficient LCL filter are to reduce the cost and weight of the filter, as well as to increase robustness and stability. Due to the outstanding ability of multilevel inverters in diminishing the output voltage harmonics, conventional 2-level inverters are being replaced with them. However, the output filter design for inverters based on multilevel Pulse Width Modulation algorithms (PWM) has not yet been considered in the literature. Hence, exact formulas should be derived to design each element in the filter considering N-levels of the output voltage. In this paper, a comprehensive and mathematical approach is proposed to calculate the maximum current ripple of a PWM-based N-level cascaded inverter, which helps to precisely calculate the size of the inverter-side inductor. Finally, an algorithm is proposed to design an LCL or LCCL filter step-by-step by modification of previous algorithms.