Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Numerical Analysis on Communication Delay Boundary for CACC String Stability

Co-operative Adaptive Cruise Control (CACC) uses wireless communication between vehicles to form a vehicle platoon. It has been proven that CACC aids in the stability and efficiency of traffic flow, and also reduces the energy consumption of the vehicles by lowering air resistance of the following vehicles. However, communication delay is a natural uncertainty for a platoon under CACC mode. When the delay gets worse to a particular level, the string stability of the platoon will be destroyed, meaning spacing or speed errors will be amplified upstream from vehicle to vehicle in the platoon. Therefore, in this study, a numerical method is proposed to identify the communication delay boundary according to the vehicle dynamics and the parameter settings of the CACC controller. First, in the Laplace domain, a series of string stable bodies under different disturbance frequencies are created based on the string stability criteria. Considering the frequency characteristics of traffic flows, the string stable bodies are then combined into a uniform one. Lastly, with the increase of communication delay, its upper bound will be obtained until the area of string stability totally disappears. Two simulations were conducted to verify the correctness and accuracy of the proposed method. The results indicate that the string stability cannot be guaranteed once the communication delay just exceeds the upper bound. This study can provide an objective boundary for practitioners or engineers, thereby allowing them to determine whether wireless inter-vehicle communication is capable of maintaining a string-stable platoon.
An Implicit Brain Computer Interface Supported by Gaze Monitoring for Virtual Therapy

Advanced Brain-Computer Interface (BCI) paradigms aim to solve some problems as BCI illiteracy and unfamiliarity of the subjects to be able to control their elicited motor imagery (MI) successfully, hence improving training time and performance of BCI systems. This work evaluates the effect and performance of an Implicit BCI supported by the Gaze Monitoring (IBCI-GM) paradigm for virtual rehabilitation therapy of patients suffering from partial or total paralysis of their upper limbs; this paradigm also was compared with alternative forms of advanced BCI methods such as Virtual Reality-based BCI (VR-BCI) with a head-mounted display (HMD) and a computer screen (CS). Eight subjects participated in the experiments; four subjects tested the VR-BCI with a CS, and the rest of them tested both BCI advanced methods (IBCI-GM and VR-BCI with an HMD). The subjects were asked to control a virtual arm through MI of flexion and extension movements. The VR-BCI HMD was the approached best method; however, IBCI-GM had significant results and was more practical for users, but it depends on the ability to perform eye movements to be applied by patients. Therefore, these methods should be tested with more subjects to have definitive results.
Comparative Analysis of Novel Fractional Slot Non-overlapping Winding Hybrid Excited Machines Having Different Consequent Pole Rotor Topologies

This paper investigates novel fractional slot nonoverlapping winding hybrid excited (HE) machines with different consequent pole permanent magnet rotors. The HE machines with different rotor configurations are developed to retain the PMs and utilize the flux concentrating effect. They are comprehensively analyzed and compared by finite element (FE) method. The optimal current ratio between the field and armature windings is identified theoretically and the torque improvement ratio by hybrid excitation is demonstrated to show the benefits of the proposed topology. Finally, the prototypes with different rotor configurations are fabricated and tested to validate the analyses.
A medical information system for monitoring respiratory function and related nonlinear dynamics*

In this paper the nonlinear effects in the respiratory systems at low frequencies are measured and evaluated in healthy children and healthy adults. To this aim forced oscillations technique (FOT) has been used to non-invasively measure the lung tissue mechanics. FOT does not require any special effort from the patient in contrast with standardized tests where maneuvers are necessary. Hence, FOT is an ideal lung function test for extreme ages, more specifically children and elderly, given the simpleness of measurement technique. Hitherto, measurements at low frequencies (i.e. close to the breathing frequency $\approx$0.3 Hz) have been invasively performed in sacrificed animals and on anesthetized humans. Here we measure in the frequency interval 0.1-2 Hz a total number of 94 volunteers (37 adults with ages between 25-35 years and 57 children with ages between 8-11 years). To evaluate the non-linear contributions of the respiratory tissue, a novel T-index has been introduced. We have tested the hypothesis whether the nonlinear distortions are changing with growth/development of the respiratory tree and aim to quantity its dependence to biometric values. The results obtained indicate that the proposed index can differentiate between the two analyzed groups and that there is a dependence to age, height and weight. A medical information system may use this information to update predictions of respiratory function and provide aid in decision-making process of drug therapy.
Task Offloading for Social Sensing Applications in Mobile Edge Computing

Social sensing effectively solves the problem of large-scale data collection especially for most data-driven applications such as traffic flow monitoring, disaster situation detection, etc. But the current cloud-based social sensing system can only provide limited support for latency-sensitive applications. Fortunately, mobile edge computing (MEC) pushes the frontier of data storage and computation from cloud to the edge of networks. In this paper, we focus on the task offloading problem in a MEC-enabled social sensing service system. Several challenges are unique to this problem, i.e., user devices are non-dedicated for executing sensing tasks, task offloading on devices and servers are highly coupled, and the hardness of achieving a long-term objective. To address these challenges, we first present a system model where sensing task execution logic is characterized, and the latency and cost are analyzed accordingly. Lyapunov optimization technique is leveraged to covert long-term optimization problem to multi-period optimizations. An efficient algorithm is developed to minimize the cost given a deadline on latency. Experimental results show that our algorithm helps providers to efficiently reduce cost and meet latency constraint.
A Hybrid BCI Approach to Detect Brain Switch in Action Observation by Utilizing Convolution Neural Network

Action observation (AO) is a promising methodology for promoting cortical activations of the sensorimotor area via the mirror neuron systems. Integration of AO with brain-computer interfaces (BCIs) is appealing for neural rehabilitation applications. One of the important issues in BCI-based AO is discriminating when a user is actively engaged with the BCI,i.e. intentional control (IC) state versus when a user is not engaged with the BCI, i.e. non-intentional control (NC) state. In this study, we designed a visual gaiting stimulus, which was in the form gaiting sequence of a human, to elicit the steady-state motion visual evoked potential (SSMVEP) response in visual area. And we proposed a convolutional neural network (CNN) to discriminate IC and NC states. The results illustrated the proposed CNN can discriminate the IC and NC states. Furthermore, combining attention feature from the frontal area and SSMVEP features, i.e. a hybrid approach, showed significant performance improvement over only using SSMVEP features when the time window length was longer than 2s. And the accuracy achieved 91.94±6.20% when utilizing the hybrid approach in IC vs. NC discrimination.
Moving Horizon Parameter Estimation of Permanent Magnet Synchronous Machines

In this paper a general moving horizon estimation (MHE) scheme for online parameter estimation of permanent magnet synchronous machines (PMSMs) is investigated. The optimization problem is solved with the generalized Gauss-Newton method and the real-time iteration approach, which enables the real time implementation of the moving horizon estimation. Furthermore, the proposed estimator is compared with state-of-the-art observers, i.e. extended Kalman filter (EKF) and unscented Kalman filter (UKF), under two test cases. The proposed estimation scheme shows superior performance at the steady state and during the transient both in simulation as well as in experiment.
Finite Element Simulation based method for Design and Optimization of Flux Switching Motor for EV/HEV Traction Application

This paper presents a high power density design and optimization method of a flux switching permanent magnet (FSPM) motor for traction application based on a drive cycle. Using an advance simulation tool for design is time consuming. There are simplification methods that will save time at the cost of fidelity. Motor design process is an iterative process and optimization of that design based on a drive cycle multiply the amount of computation and the time consumption. The goal is to develop a method which has the accuracy of finite element method but with much lesser time consumption. The specifications of FreedomCar 2020 is considered to demonstrate the design and optimization work flow and a WLTP drive cycle is used for optimization. Flux Switching Permanent Magnet motor is chosen, owing to the morphology because this type of machine contains the most loss inducing components on the stator and also has a quadrature magnet structure. This motor has better scope for thermal management and is almost immune to demagnetization. The performance of the designed machine shows that the machine can deliver continuous and peak power over all operating range with smaller volume and high efficiency.
Applicability of IoT Technologies for 5G Use Cases in Brazil

This work investigates the applicability of available Internet of Things (IoT) technologies to 5G use cases in Brazil. We are particularly interested in two priority verticals: smart cities and agribusiness. It has been selected one use case for each of these two verticals, considering the particularities of the Brazilian scenario. For the smart cities vertical, the application of surveillance cameras in public lighting was chosen due to its high impact in terms of cost savings and improvement in public safety. In the agribusiness vertical, the application of cattle monitoring was chosen since Brazil is one of the largest beef exporter in the world, representing more than 6% of its GDP (Gross Domestic Product). For the chosen use cases an analysis of the limitations of the available IoT technologies such as LPWA (Low Power Wide Area) has been presented to guide future deployments.
Objective and Subjective Analysis to Quantify Influence Factors of Driving Risk*

This paper proposes a method to quantify the driving risks of different traffic elements. We incorporate the objective analysis using naturalistic driving study (NDS) and the subjective analysis with driver attitude questionnaire (DAQ). The objective driving risks are investigated by a large-scale NDS experiment with multiple sources. Meanwhile, typical driver behavior parameters, such as velocity, time headway, and acceleration, are selected and analyzed. A self-reported survey of 364 drivers is conducted to subjectively evaluate the potential risks which drivers may suffer in various situations. NDS and DAQ are then combined together using the multinomial logit model to obtain the relative risks. Results demonstrate that the proposed method can provide an effective measure to quantify the influence factors of driving risks in dynamic environment. It is interesting to note that the risk value from subjective evaluation tends to be higher, implying that the subjective evaluation might be emotional and multi-factor coupling.
A High-Gain, Soft-switched PV Micro-Converter Using a Single Switch with A Low Switch-Voltage-to-Output-Bus-Voltage Ratio

This paper presents a new coupled-inductor based quasi-resonant step-up converter for solar energy applications. The proposed converter requires only one switch and is able to achieve high voltage gain without suffering high voltage stress across the switch. The improved coupled inductor structure employed in the proposed circuit is able to significantly reduce the ratio between the switch voltage and the output voltage allowing for a lower rated switch to be used. The input inductor of the proposed circuit operates in continuous conduction mode (CCM), allowing for the use of small-sized film capacitor at the input side. Maximum power operation is achieved in the proposed converter through the use of variable switching control. The frequency range of the circuit at which ZVS operation functions is lengthened through the use of simultaneous duty cycle control. The description and operating principles of the proposed circuit are provided. Results from simulation on a 100W solar energy system as well as preliminary experimental results on a proof-of-concept prototype are given to highlight the merits of the proposed circuit.
A Novel Self-adaptive Wireless Power Transfer System to Cancel the Reactance of the Series Resonant Tank and Deliver More Power

Wireless power transfer (WPT) is a promising technology to supply power in applications where physical contact is not available. Conventionally, both the primary side and the secondary side need resonant capacitor to increase transmission ability. However, the detuning of WPT systems caused by the component tolerance of inductors and capacitors and aging of the capacitors will decrease the system performance especially in very low coupling (k=0.05) applications. The delivered power is very sensitive to the reactance under very low coupling conditions, accordingly, a self-adaptive WPT system to cancel the reactance caused by the tolerance of inductors and capacitors in the primary and secondary is proposed. The novel concept uses an auxiliary circuit in series with the secondary, which can automatically compensate the reactance in the circuit, hence increases the system performance in terms of delivered power. This concept is applicable to both single- and multiple-pickup WPT systems. A prototype has been built and tested experimentally to validate the feasibility of the proposed concept.
Fault Localization Algorithm in Computer Networks Based on the Boolean Particle Swarm Optimization

The components of a computer network are vulnerable to a variety of faults such as a link break or node failure. In order to prevent faulty components from impeding the execution of network applications, it is very important to detect, locate and repair faulty components. Existing approaches to fault localization in communication systems use active or passive measures. Active measures involve additional traffic for network monitoring. On the other hand, the passive measures use the existing end-to-end data in the network in order to extract the necessary information and thus introduces no additional traffic in the network. In this paper, we propose an end-to-end approach that uses passive measures for fault isolation in communication networks and formulate the issue of fault isolation as an optimization problem. In fact, we used the Boolean Particle Swarm Optimization algorithm inferring the best node(s) to be tested, with the objective of minimizing the expected cost for all the faulty elements in the network. The performance of the proposed schemes is evaluated through an intensive simulation of different network scenarios.
Improved Current Profile for Noise Reduction of Switched Reluctance Motor at Middle Speed

An improved current profile for noise reduction of switched reluctance motor at middle speed operation is presented with the principle of flattening the three-phase radial force sum. The current profile is derived from the proposed algorithm which includes the consideration of limited input voltage and back electromotive force influence at middle speed operation. The improved current profile is compared with the single pulse current, which is typical current for switched reluctance motor at middle speed operation. The analysis result shows the variation of radial force sum by improved current profile is less than the single pulse current. The result of Finite Element Method shows that the peak noise of switched reluctance motor can be reduced by improved current profile with the significant peak reduction up to 17 decibels.
Electrical characterizations of Schottky diode with zinc oxide nanowires

This paper presents an in-depth analysis of the I (V) and I(V-T) characteristics of the Schottky Pt / ZnO nanowire diode. Detailed analyzes were performed to extract the various electrical parameters and information on the interface states. The characteristic parameters of the structure such as barrier height, ideality factor, saturation current and series resistance were determined from the current-voltage measurement. The ideality factor (3.58) value at room temperature is higher than unity probably due to the presence of the n-ZnO / p-Si heterojunction in series with the Pt / ZnO nanowire. The value of the computed barrier height of the diode (0.65 eV) using the thermionic emission model is in good agreement with the theoretically predicted value. Indeed, these two values obtained by the current-voltage I(V) characteristics suggested the presence of interface states between the Pt and the nanowire ZnO. To confirm this observation, we carried out the measurement C (V).
Optimal Charging Scheduling for Electric Vehicle in Parking Lot with Renewable Energy System

As the popularity of electric vehicles grows, it is an important issue to effectively schedule the charging plan of electric vehicles in order to supply a large number of electric vehicles while charging and reducing the impact on the grid. The charging station managers obtain the time of use pricing provided by the power company and minimize the charging cost. Owing to electric vehicle charging schedule belongs to combinatorial optimization problems, the popular intelligent control algorithm may have been generated too more infeasible solutions. In this paper, the elitism simulated annealing is proposed for the electric vehicle charging schedule, and the storage batteries and photovoltaic system are included in the case study, so that the charging demand of each electric vehicle can be satisfied and the minimum electricity cost can be attained.
Nanoscale pearl powder exhibits non-peroxidase activity, fluorescence and inhibits cell spreading of prostate cancer

Pearl powder is an ingredient in complex of traditional Chinese medicine in treatment on skin. However, how pearl powder affects cells especially cancer cells is unclear. Here we investigated the pearl powder electrochemical characters and biological functions. We found the major component of carbon and calcium with metals of Mn and Fe in pearl powder. The possible magnetic and non-peroxidase nanozyme activity suggest the complex functions of pearl powder in biological response. Moreover, the pearl powder at mixed nanoscale can inhibit cancer cells spreading. Our data suggest that pearl powder at mixed nanoscale can exert more efficient function in cells. Nano Chinese medicine may play complicated roles in therapy.
Characterization of Time-Variant Wireless Channels in Railway Communication Scenarios

We present results from a train-to-infrastructure (T2I) and train-to-train (T2T) measurement campaign where we perform single-input-single-output channel sounding with a 150 MHz bandwidth, allowing us to resolve multi-path components with high precision. We analyze the measurements done for both T2I and T2T communication links, with trains going at speeds of maximum 20 km/h~~ 5.56 m/s. Using low velocities allows us to run two trains behind each other on the same rail track with very short distances (0 < d < 200 m). The analyzed data combined with GPS and video recordings serves for describing the geometry of the environment and developing a physical geometry-based stochastic channel model that enables virtual channel emulation for scenarios with trains moving at high velocities (0 < v < 300 km/h~~ 83.33 m/s), but short distances. Characterizing the channel through empirically obtained data in such scenarios is still impossible in practice due to safety issues. Nevertheless, it presents a key step towards ultra-reliable low-latency link research for 5G use cases in the high-speed railway domain. We show that neighboring traffic and surrounding infrastructure has a big impact on the delay spread and that at extremely close distances, path loss variation depends on used antenna polarization.
Prognostics for Rotating Machinery Using Variational Mode Decomposition and Long Short-Term Memory Network

Rotating machinery prognostics plays an important role in promoting reliability and efficiency in the operation of machinery and reducing maintenance costs. This paper proposes a novel bearing remaining useful life prediction approach which puts emphasis on deriving effective features from raw vibration data. To enhance the degradation feature extraction, a non-recursive method named variational mode decomposition (VMD) is adopted to decompose the raw vibration data into several principal modes, then feature smoothing with a local regression filter is performed and some suitable features are selected by evaluating feature fitness using monotonicity and correlation analysis. Based on the selected features, long short-term memory (LSTM) network is introduced for bearings RUL prediction. Numerical experiments with real bearing dataset exhibit the effectiveness and superiority of the proposed approach in comparison to other data-driven approaches.
High Precision, Intuitive Teleoperation of Multiple Micro Aerial Vehicles Using Virtual Reality

In this work, we present a method for the individual, simultaneous teleoperation of multiple multirotors. Instead of using a traditional four axis remote controller to steer the small sized quadrotors used in this work, all degrees of freedom of the device are intuitively controlled by a human operator in virtual reality, allowing high precision maneuvering of the vehicles through obstacles in three dimensional space. The proposed method enables the human operator to precisely control multiple micro aerial vehicles simultaneously, without the use of formation flight or other grouping methods. The teleoperation control performance of this approach is compared to a traditional four axis control method in several experiments.
Genetic Algorithm for Dynamic Economic Dispatch with Short-Term Load Forecasting

The shift from conventional energy sources to renewable sources leads to high penetration of Distributed Generators (DGs) in Active Distribution Networks (ADNs). This trend along with other factors causes major problems in ADNs such as energy management, voltage imbalances, poor efficiency, and reliability. To overcome these challenges the article suggests a novel approach by optimally scheduling energy using advanced methods for Dynamic Economic Dispatch (DED) algorithms with machine learning based load forecasts. Initially, the proposed system deploys Short-Term Load Forecasting (STLF) on the load buses based on the previously observed patterns using Multilayer Artificial Neural Networks (MANN). Then, the system implements a Genetic Algorithm based DED with major operational and network constraints. The proposed methods are integrated and tested on the IEEE 30-bus network using Kazakhstan's power plant and electricity demand parameters. The proposed system has shown improvement in the reliability and efficiency of the distributed system. Since the proposed methodology is generic and was verified on the standard radial network it can be further implemented in other systems.