Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agent Path Finding with Priority for Cooperative Automated Valet Parking

Shortage of parking lots has been a serious problem due to the rapid population growth in urban areas. Cooperative Automated Valet Parking (Co-AVP) is a promising approach to mitigate the problem. Co-AVP system would realize high density and efficient parking by automatically navigating vehicles to vacant parking spaces. Cooperative path planning has been extensively studied as Multi-Agent Path Finding (MAPF). In Co-AVP, vehicles would be often prioritized. For instance, vehicles would be able to spend more time to park in vacant spaces than moving from parking spaces to waiting drivers. Previous MAPF settings, however, cannot treat such priorities. In this paper, we formulate MAPF considering each agent’s priority. We also develop the optimal method based on Conflict-Based Search (named CBS-Pri) and the heuristic method based on Cooperative A* (named CA*-Pri). We verified that higher prioritized vehicles tended to preferentially arrive at waiting drivers by our methods in numerical experiments. We also found that CBS-Pri always searched better solutions than or equal to CA*-Pri but requires more computational time.
Analysis of the Relationship Between Physiological Signals and Vehicle Maneuvers During a Naturalistic Driving Study

As a driver prepares to complete a maneuver, his/her internal cognitive state triggers physiological responses that are manifested, for example, in changes in heart rate (HR), breath rate (BR), and electrodermal activity (EDA). This process opens opportunities to understand driving events by observing the physiological data of the driver. In particular, this work studies the relation between driver maneuvers and physiological signals during naturalistic driving recordings. It presents both feature and discriminant analysis to investigate how physiological data can signal driver’s responses for planning, preparation, and execution of driving maneuvers. We study recordings with extreme values in the physiological data (high and low values in HR, BR, and EDA). The analysis indicates that most of these events are associated with driving events. We evaluate the values obtained from physiological signals as the driver complete specific maneuvers. We observe deviations from typical physiological responses during normal driving recordings that are statistically significant. These results are validated with binary classification problems, where the task is to recognize between a driving maneuver and a normal driving condition (e.g., left turn versus normal). The average F1-score of these classifiers is 72.8%, demonstrating the discriminative power of features extracted from physiological signals.
Hybrid Phase Converter with Enhanced Efficiency and dc Fault Tolerant Capability for HVDC Application

The parallel hybrid modular multilevel converter (PHMMC) is gaining more popularity for HVDC applications due to lower losses and requirement of small sub-module (SM) capacitor. However, PHMMC suffers from fixed modulation index (MI), large dc side voltage ripples and inability to act against dc faults. A novel hybrid phase converter (HPC) topology is presented in this paper to eliminate the aforementioned limitations of PHMMC. The proposed HPC not only has wide MI range but is also tolerant to dc faults. The dc side voltage ripples of the HPC can be minimized by using the proposed modified control method, which excludes the requirement of dc inductor. A new control method is also proposed to maintain the SMs capacitor voltage. DC fault analysis is also carried out to substantiate the fault blocking capability of HPC. Component level models are developed in PSCAD to validate the working of the proposed HPC and its controller. SM capacitor size is determined analytically, which proves that the HPC requires lower capacitive energy storage per unit apparent power and consequently has lower footprint.
Enhancing GNSS Performance and Detection of Road Crossing in Urban Area Using Deep Learning

In urban areas, signals from Global Navigation Satellite System (GNSS) satellites often arrive at ground receivers with distortion due to non-line-of-sight (NLOS) propagation, and measurements from these distorted signals can cause large positioning errors. When signals arriving at the receiver through an NLOS path are excluded from the position calculation process, the receiver can achieve significantly improved positioning performance. In this paper, we propose a recurrent neural network (RNN)-based NLOS classifier that discriminates LOS and NLOS satellites in urban environments. In addition, as an important application of the proposed classifier, we utilize the proposed technique to detect pedestrian road crossing. Using measurements collected in urban environments, the proposed classifier shows about 90% accuracy in NLOS classification and about 20% better discrimination performance in comparison to the conventional SVM-based NLOS classifier. The proposed positioning technique for road crossing detection using the proposed classifier was demonstrated to achieve positioning accuracy about 45% higher better than that of conventional techniques.
Dynamic Analysis of the Negative OFF-State Current in Thin-film Transistors

Negative drain currents under positive drain-to-gate voltage were found in the OFF-state transfer curves of thin-film transistors (TFTs). A dynamic model was developed, by accounting for capacitances, to interpret the phenomenon. The capacitor discharging during gate voltage sweeping leads to the negative currents. Test conditions and intrinsic device parameters affect the existence of this phenomenon, which is verified by our experimental results of InGaZnO TFTs.
Finite Element Modeling of IGBT Modules to Explore the Correlation between Electric Parameters and Damage in Bond Wires

This paper proposes a method to identify damage-sensitive electrical parameters (DESPs) for insulated gate bipolar transistor (IGBT) bond wires. Multiphysics simulations are performed to emulate the realistic electrical, thermal and mechanical behaviors of IGBT module. Temperature-dependent material properties are considered in silicon chips and aluminum bond wires. Steady-state electro-thermo-mechanical simulation results of IGBT modules in a given collector current are presented. The degradation mechanisms of wire-bonding crack propagation and complete lift-off are explored. Simulations are validated through the power cycling tests. The trend of measured forward voltage VCE(on) is similar to the simulation result. The modeling method and results can be used to evaluate the health status of IGBT module bond wires.
7% hypertonic saline and hyaluronic acid and in the treatment of infants mild-moderate bronchiolitis

The treatment of bronchiolitis is only supportive. The aim of our study was to evaluate the efficacy of hypertonic saline and hyaluronic acid (HS-HA) given by inhalation, in infants hospitalized for mild-moderate bronchiolitis. In a double-blind placebo-controlled study, 42 infants (26 males) less than 6 months of age (median age 2 months) were enrolled and randomly assigned to receive either HS-HA (7% NaCl + 0.1% HA) (n:21) or normal saline (n:20) at a dose of 2.5 ml twice a day for three days. To all infants a clinical severity score was assigned at the admission and four times daily during the hospitalization. Main outcome measures of our study were: number of days of hospitalization and reduction of the severity score. No differences were observed between the two groups for sex, age and clinical severity score at the admission. One child interrupted the protocol in the study group and two in the placebo group. 21% of children in the study group and 11% in the placebo group had mild cough after the aerosol. No difference was observed between the two groups with regard to the number of days of hospitalization (placebo group: 3.5 days vs study group: 3.1 days, p=0.4) and to the clinical score reduction during the first three days of hospitalization (placebo group: 3.3 vs study group: 3.7, p=0.6). HS-HA is a safe but not effective therapy in treating infants hospitalized for mild-moderate bronchiolitis.
Loss Prediction of Medium Voltage Power Modules: Trade-offs between Accuracy and Complexity

Advances in high breakdown voltage SiC MOSFETs allows design of medium voltage power modules with simpler topologies, however, high dv/dt in addition to high di/dt makes parasitics of power modules more important to consider in loss estimations. Correct estimations can help prevent costly and time-consuming problems in a design process. Data sheet values can be used directly for loss estimation, but more detailed approaches in using device-level SPICE model fitting and inclusion of power module parasitics will give a more detailed estimation and may be used to predict transient behavior at a cost of increased complexity. This paper will quantitatively compare the switching loss predictive capability of different approaches in the case study of a custom-built medium voltage power module for varying operating conditions. It is found that by including parasitics loss prediction error is reduced from 15-25% to a few percent at high DC-link voltage for the case-study chosen.
Thermal Stability Enhancement of NiGe Metal Source/Drain and Ge pMOSFETs by Dopant Segregation

The thermal stability has been investigated for the NiGe–n-Ge junctions with and without the dopant segregation (DS). It is found that the B DS sufficiently improves the thermal tolerance of the NiGe–n-Ge junctions, and the DS NiGe–n-Ge junctions can be stabilized up to 550 °C. This phenomenon suggests that the DS NiGe metal source/drain (S/D) is a feasible structure for broadening the processing window of future high-performance Ge MOSFETs.
Ultra-fast voltage clamping for fast power-semiconductor modules

The fast switching of 1200/1700- V semiconductors, like silicon carbide MOSFET or optimized IGBTs, is often limited by the parasitic inductance due to overvoltages during switching. To utilize the full switching speed a very low-inductive package or a very fast overvoltage limitation is necessary. Within this paper, an ultra-fast voltage clamping is presented which is placed as close as possible to the switching devices.
Cyclist Intent Prediction using 3D LIDAR Sensors for Fully Automated Vehicles

One of the main barriers against the full deployment of autonomous vehicles in urban traffic environments is the understanding of the intentions and behaviours of the human around them. Moreover, understanding and predicting intentions of vulnerable road users such as cyclists is still one of the most challenging tasks. In this work, we are proposing a novel framework for the task of intent prediction of cyclists via hand signalling from point cloud scans. We utilised our developed data generation pipeline for generating synthetic point cloud scans of cyclists doing a set of hand signals in urban traffic environments. Then, we feed a sequence of the generated point cloud scans to our framework which jointly segments all cyclists instances and predicts their most probable intended actions in an end-to-end fashion. Our proposed framework has achieved superior results with 83% in F1-Measure score over the testing split of our generated dataset. Additionally, the proposed framework outperformed other compared baseline approaches with more than 39% improvement in F1-Measure score.
Field-Dependent Mobility Enhancement and Contact Resistance in a-IGZO TFTs

In this article, we performed gated four-probe measurements on amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) to extract their intrinsic mobility and contact resistance as functions of gate voltage and temperature. The abnormal degradation of field-effect mobility was observed in a-IGZO TFTs both at high gate voltage and at high temperature. Results showed that contact resistance played a major role in mobility degradation at high gate bias, whereas band-like transport (phonon scattering) accounts for mobility degeneration at high temperature. We proposed a novel method, which exposed the contact regions to ultraviolet (UV) ozone. The mobility was boosted from 23 to 30 cm2/Vs, nearly 40% increment at high gate bias.
Energy-Efficient Mobile Edge Hosts for Mobile Edge Computing System

Mobile edge computing is emerging as the new solution for next generation mobile applications, which require very low latency and high computing resources. By offloading the computation intensive tasks to mobile edge (ME) hosts located in the radio access network, users could gain energy saving benefits at the user equipment. However, two key factors that determine the total computing resource requirement of a ME host are the user mobility and offloading tasks request patterns. As a large number of ME hosts with physical servers are going to be deployed within the radio access network, energy efficiency is a key challenge for mobile edge service providers in terms of environmental impacts and operational costs. Our objective is to minimize the total energy consumption of the ME host system without compromising the users' quality of service based on correlated mobility pattern. We proposed a method to select the suitable server class for ME host deployment based on offloading task request profile considering energy efficiency. Then, we developed three processes to minimize the total power consumption of the ME host based on single threshold utilization: (i) Virtual Machine (VM) migration process that selects the suitable VMs for migration in order to save energy without compromising the quality of service; (ii) VM placement process that finds the best server, which minimizes the energy consumption; (iii) Physical server activation process that is responsible for activating servers from sleep state based on the network traffic demand. We propose a queueing-triggered based approach to initiate processes (ii) and (iii) to minimize both queueing delay and energy consumption. We evaluated our proposed methodologies using correlated mobility pattern and the uniform workload offloading pattern. Our results show that up to 34.32% of the energy can be saved by carefully selecting the physical servers in a ME host and an average of 16.15% energy savings can be achieved in the ME system using our proposed method during peak hours.
Optimal Design of High-speed Solid Rotor Cage Induction Motors Considering Ferromagnetic Materials Behavior and Manufacturing Process

Several high performance industries (i.e. natural gas compression) require high-speed motors. Solid rotor cage induction motors (SRCIMs) are perfect nominees to such applications, owing to their high reliability. However, the restrictions over ferromagnetic materials and manufacturing process overwhelm the SRCIM design. The motor is required to have low power losses in stator core and the solid rotor. Furthermore, low number of rotor slots is demanded in order to facilitate welding process in rotor side. Those two objectives are contradicting in design stage. This study investigates the losses characteristics of high-speed SRCIM considering the behavior of different ferromagnetic materials, and the complexity of welding process of solid rotors. With a 160-kW and 3000-kW SRCIMs, the influences of stator ferromagnetic material, rotor conductivity, slot combination and air-gap length on power loss characteristics are comprehensively analyzed. Based on the aforementioned analysis, an optimal design criterion for SRCIM is proposed to decline the eddy current losses and decrease the complexity of welding process in the SRCIM's rotor. To verify this criterion, a 160-kW SRCIM prototype is designed and manufactured. Experimental validation is performed and the effectiveness of the proposed design procedure is verified.
High-Performance Frequency Converter Controlled Variable-Speed Wind Generator Using Linear-Quadratic Regulator Controller

This article proposes an optimal control strategy with a view of achieving the best performance of a wind energy conversion system (WECS). The optimal control strategy depends on the linear-quadratic regulator (LQR) algorithm, which provides a fast convergence and less mathematical intricacy. The machine- and the grid-side converter/inverter are adjusted using the LQR controller. In this study, the system model and its control strategies are illustrated. Practical wind speed data are considered in this study for achieving realistic responses. The system performance is evaluated by comparing the results obtained using the LQR controller with that realized when the grey wolf optimizer algorithm-based optimized proportional-integral controllers are used, taken into account severe network disturbances. The simulation studies are extensively performed through the MATLAB/Simulink environment that prove the validity of the LQR controller for improving the performance of the WECS.
Radiation Pattern Measurement in the Planar Coordinate

A planar measurement approach is presented to obtain the radiation pattern, by which rotation operations can be removed or reduced for a source with narrow and divergent viewing angle, respectively. The Étendue conservation is applied to derive the intensity distribution relationship between planar and spherical surfaces, which is further used for the radiation feature measurement on planar systems. As an indirect approach, sampling position distribution and measurement sensitivity is then discussed. Four application scenarios of the method are proposed and experimentally studied, including: 1D, 3D wireframe, pattern reconstruction for Monte Carlo ray-tracing planar data and 3D pattern estimation for luminaires. 1D radiation pattern obtained by classical goniophotometer method is employed as an evaluation standard, and experiments indicate that the results agree well between the two approaches. Therefore, the planar measurement can be applied as an alternative or supplementary method of the goniophotometer to test angle-dependent attributes of sources.
Constant Common-Mode Voltage Transformerless Inverter for Grid-Tied Photovoltaic Application

This paper proposes a new H-bridge clamped transformerless inverter for the grid-connected Photovoltaic (PV) application. The clamping circuit consists of two switches and a full bridge diodes, which can clamp the AC terminal voltage to the DC midpoint during the freewheeling period. As a result, the common mode voltage (CMV) is held constant, which makes it a suitable candidate for the grid-connected PV system. Furthermore, the efficiency of the proposed topology is relatively higher than other topologies. The operating principle of the proposed topology is analyzed in brief. Thermal modeling for the proposed topology has presented for loss calculation. Finally, the theoretical analysis is validated with simulation and experimental results. Validation is carried out using PLECS simulation and experimental results.
Visual fingerprinting for lobsters using deep learning

Visual recognition of marine animal species, like lobsters, is used in research to monitor their growth. This is usually achieved by physically tagging individual animals with uniquely coded tags and tracking them through their tags. However, tagging can sometimes cause physical harm or restriction in the mobility of a lobster, or other animal, with a hard exoskeleton and thus defeats the purpose of tagging. Lobsters periodically shed their exoskeletons which makes tracking them through their tags all the more difficult. This paper aims to identify individual lobsters based on the patterns on their exoskeleton. Convolutional neural network architectures have been identified as an effective tool towards that, specifically siamese networks which were used to identify unique lobsters with a top-3 accuracy of about 84%.
Informative Feature Clustering and Selection for Gene Expression Data

Feature selection aims to remove irrelevant and redundant features from input data. For gene expression, selecting important genes from gene expression data is essential since the gene expression data often consists of a large number of genes. However, the commonly-used feature selection methods are usually biased toward the highest rank features, and the correlation of these selected features may be high. To overcome these problems, we propose an informative feature clustering and selection method to select informative and diverse genes from the gene expression data. The method consists of two steps. In the first step, a feature clustering (FC) method is designed to cluster total genes into several gene clusters. In FC, a set of feature weights are computed to respect the importance of each gene, and we sort the genes in different gene clusters based on the feature weights. In the second step, we propose a stratified feature selection (SFS) method to select genes from different gene clusters and combine them to form the final feature set. Experiments on several gene expression data demonstrate the superiority of the proposed method over six popular feature selection methods.
Decision on energy efficiency from the perspective of the managers: its barriers

The Energy efficiency is nowadays a priority on the political agendas and, consequently in the expected priority actions. However, the effective implementation of energy efficiency projects by companies is still far short of the necessary and desirable aim. Many investments in energy efficiency are not successful despite their apparent viability and necessity.This paper intends to contribute in this area by investigating the nature of the barriers that are affecting the decisions for energy efficiency projects implementation in Portuguese companies. A quantitative methodology was used through application of an original survey. All companies participating in this study are located in Portugal and belong to the industrial sector.The main results suggest the existence of barriers at the level of projects costs, the lack of time and the lack of understanding what the implementation of an energy efficiency project may require. These factors are strongly highlighted by medium-sized firms.Based on these findings, this paper argues that governments may simultaneously improve overall energy efficiency and increase private sector productivity by providing informational and organizational services that must go beyond the traditional regulatory framework, namely boosting literacy in energy efficiency. Finally, our study reveals that smaller firms as most critical and therefore the ones to deserve greater attention from policy-makers.
iRBP-Motif-PSSM: Identification of RNA-Binding Proteins Based on Collaborative Learning

RNAs and RNA-binding proteins (RBPs) in cells can bind with each other to form a nuclear ribonucleoprotein (RNP) complex, playing important roles in life processes, and gene regulation. How to accurately predict the RNA-binding proteins is a big challenge and hot research task. Here, we proposed a new computational predictor called iRBP-Motif-PSSM for identifying RNA-binding proteins by combining the motif information and the evolutionary information extracted from the Position Specific Scoring Matrixes. Collaborative Learning was employed to address the instability problem of the predictor. The experimental results showed that iRBP-Motif-PSSM showed better performance than other existing methods for identifying NA-binding proteins, indicating that iRBP-Motif-PSSM is a useful tool for biological analysis.
A Fund Selection Robo-Advisor with Deep-learning Driven Market Prediction

This paper proposes a new investment strategy with deep-learning market prediction for mutual fund portfolio optimization. Our strategy uses the capital asset pricing model (CAPM) that applies macroeconomic factors to predict whether the market is bull or bear. Then, we develop a robo-advisor (RA) to predict future market, optimize portfolio and automate investment. Experiments use 22 years’ data of S&P500 and mutual funds of U.S. to validate our strategy. Results show that the accuracy of our market prediction method can reach 84.3% and the rate-of-return of our RA is 13.S7%. Our model is more accurate and profitable than other algorithms.