Miracle-dz/UniFAR-FTUs huggingface dataset:
Latency Optimization for Mobile Edge Computing Based Proximity Detection in Road Networks

In recent years, continuous breakthroughs in 5G and artificial intelligence technology have promoted the development of autonomous driving technology. In the actual application scenario of autonomous driving, the mobile user needs to find which mobile users are in proximity with him/her in the road network in real time, referred to as the problem of proximity detection in road networks. However, in the case of limited storage resources and computing resources at the mobile devices, how to calculate the proximity relationship between users in a short time at the mobile devices becomes a tough issue. Therefore, in this paper, we propose a computation offloading scheme in the scenario of proximity detection based on mobile edge computing (MEC), and formulate a communication latency optimization problem. To the formulated latency minimization problem in the computational offloading scheme, we use a convex optimization solution, namely, SLSQP, which significantly optimizes the communication latency. Simulation results demonstrate that our solution can solve the latency optimization problem effectively and efficiently.
Safety-Critical Control of Autonomous Surface Vehicles in the Presence of Ocean Currents

Autonomous surface vehicles (ASVs) are safety-critical systems that must provide strict safety guarantees such as collision avoidance to enable fully autonomous operations. This paper presents a unified framework for safety-critical control of ASVs for maneuvering, dynamic positioning, and control allocation with safety guarantees in the presence of unknown ocean currents. The framework utilizes control Lyapunov function (CLF)- and control barrier function (CBF)-based quadratic programs (QPs), and is applicable to a general class of nonlinear affine control systems. The stabilization objective is formulated as a maneuvering problem and integral action is introduced in the CLFs to counteract the effect of unknown irrotational ocean currents. Furthermore, ocean current estimates are constructed for robust CBF design, and analytic conditions under which the estimates guarantee safety are derived. Subsequently, robust CBFs are designed to achieve collision avoidance of static obstacles. The paper concludes by verifying the framework in simulation for a double-ended passenger ferry.
Reducing Fault-tolerant Overhead for Distributed Stream Processing with Approximate Backup

The stream processing model continuously processes online data in an on-pass fashion that can be more vulnerable to failures than other offline-data processing schemes. Checkpoint-based fault-tolerant methods have been widely used to enhance the reliability of stream processing systems. To ensure exact data recoveries upon failures, full-backup mechanisms are used to store a complete copy of data, which introduces substantial runtime overhead and increases output latency. In the meantime, a wide range of online processing applications prefer quick-and-dirty results with a slight degradation inaccuracy to delayed exact results. This paper introduces a novel approximate fault-tolerant problem (OAFP) with the objective of reducing the failure-free fault-tolerant overhead and ensuring user-defiled output accuracy requirement upon failure at the same time. We present an approximate fault-tolerant scheme based on sampling backup mechanism and study the trade-off between fault-tolerant overhead and output accuracy in stream processing systems. We proposed two algorithms to compute backup plans for both single-node failure and correlated failure scenarios. Extensive experiments with different types of stream topologies are conducted on our simulator to verify the correctness and effectiveness of our approach. We prove our solution guarantees the output accuracy requirement with minimum FT latency for directed acyclic graph (DAG) stream topologies with single-node failures.
THE ESTIMATION OF THE DISPERSAL RATE OF THE ADULT CABBAGE ROOT FLY (ERIOISCHIA BRASSICAE (BOUCHE)) IN THE PRESENCE OF A BRASSICA CROP

Dispersal has been defined as any movement away from an aggregation or population (Southwood 1966) and it can result from migration or the 'trivial' movements associated with appetitive behaviour. Migration is usually considered to be a long distance, 'straightened-out' movement which, if successful, results in the arrival of the animal at a new habitat, and the term 'trivial' is applied to short meandering movements limited to one habitat. Greater dispersal is therefore likely to occur as a result of migration than from 'trivial' movements. A behavioural difference distinguishes migrants from non-migrants (Kennedy 1961); the temporary inhibition of the responsiveness of migrants to the 'vegetative' stimuli which finally arrest them separates migratory from 'trivial' movements which are readily interrupted by responses to such stimuli. Only some species of insects show migratory behaviour (see Southwood 1962) and thus there is variation in the innate tendencies of species to disperse. The dispersal of an insecticide-resistant strain of the cabbage root fly (Erioischia brassicae (Bouche)), was studied by Mowat & Coaker (1968). Their first observations indicated that considerable dispersal had occurred and this was attributed to an abnormally dense population and the destruction of the host-crop at the site of origin of resistance. In two subsequent years, the distribution of resistant flies changed only slightly and it was concluded that E. brassicae had little innate tendency to disperse. The assessment of the dispersal rate of a gene for resistance is an indirect method of measuring the dispersal of a wild population of flies and such estimates may be influenced by interfering factors such as the densities of populations in the area under study and the relative fitness of the strains. Experiments were, therefore, undertaken to measure the dispersal of E. brassicae more directly by the release and recapture of marked flies. The results of experiments to estimate its dispersal rate in the presence of a brassica crop are described in this paper.
QoS Aware Task Management Strategies for Mobile Crowdsensing Applications

Mobile Crowdsensing (MCS) systems are under rapid development due to the popularization of smart mobile devices with various sensing abilities. MCS systems are useful in applications that require large scale spatial data collecting. To provide satisfactory service, the MCS system is expected to contain effective and efficient task management strategies, considering worker qualification, and possible worker moving. Most existing works design moving trajectory for each worker, in order to improve task accomplishment ratio (TAR) for QoS consideration. Other than great real time operation cost, it is also not practical to control workers’ moving due to privacy issue. In this work, we propose a MCS model with comprehensive integrated parameters to support the design of effective QoS aware task assignment strategies. We design strategies to perform task assignment based on worker qualification, using gradient to represent worker moving probability. We develop the accumulated gradient based reward allocation (AGRA) that improves QoS by motivating worker moving. Our experiments show that the proposed QoS MCS management strategies improve task accomplishment ratio significantly.
Extended Operation Range of Photovoltaic Inverters by Current Waveform Shaping

The grid connection of photovoltaic voltage source inverters depends on the dc-link voltage level that can be supplied by the maximum power tracking of the photovoltaic system. The inverter disconnects from the grid, if the minimum required dc-link voltage level is violated, which leads to unwanted energy curtailments implying losses to the system owner. This article proposes to apply current waveform shaping to the inverter current in order to reduce the peak value of the voltage waveform at the point of common coupling by which the minimum required dc-link voltage level for power injection is reduced. This extended operation range of photovoltaic inverters is achieved through third harmonic current injection and can be applied to single-phase and three-phase, four-wire inverters without additional converter stages. A control structure for harmonic current injection and harmonic phase determination is presented and validated by simulations and the analysis is verified by experiments.
pcIRM: Complex Ideal Ratio Masking for Speaker-Independent Monaural Source Separation with Utterance Permutation Invariant Training

Typical speech separation systems usually operate in the time-frequency (T-F) domain by enhancing the magnitude response and leaving the phase response unaltered. Recent studies, however, suggest that phase is important for perceptual quality, leading some researchers to consider magnitude and phase spectrum enhancements. The merging of the complex ideal ratio masking (cIRM) estimation and training with deep neural network (DNN) has been proved to be an effective way to improve speech separation. Furthermore, the label ambiguity (or permutation) problem has become a major barrier for speaker-independent multi-talker source separation, which prompts us to come up with new solutions. In this paper, to solve the problem of speaker-independent monaural source separation, we propose a novel method called pcIRM, which creatively achieves the cIRM estimation with the utterance-level permutation invariant training (uPIT). Specifically, pcIRM is implemented with the deep bidirectional LSTM (Bi-LSTM) RNN network, and evaluated with the WSJ0-2mix datasets. We report separation results for the proposed method and compare them to that of the existing state-of-the-art methods. Extensive experimental results demonstrate the advantages of our proposed pcIRM method in terms of the signal-to-distortion ratio (SDR) metric.
Virtual rotating speed meter: extracting machinery rotating speed from vibration signals based on deep learning and transfer learning

Rotating machinery like wind turbines often operates under varying speed conditions. Measuring speed signals is critical for vibration-based condition monitoring of rotating machines. However, in real applications, sometimes it is difficult to install speed sensors to collect speed signals due to physical space and/or cost restrictions. Considering vibration signals are widely collected for condition monitoring, this paper proposes a virtual rotating speed meter which can extract the rotating speed of a rotating machine from its vibration signals. The proposed virtual speed meter is a framework of deep neural network in nature, which takes the vibration signal as the input, and directly outputs the corresponding speed signal. The training of this neural network is based on transfer learning across two machines: a source machine and a target machine. The source machine has massive historically measured vibration and speed signals available, while the target machine only has vibration available. The virtual speed meter is firstly trained with historical vibration signals and speed signals of the source machine, and then fine-tuned to fit a target machine to extract its speed from its vibration signals. Limited speed labels of the target machine for transfer learning are estimated using a reported signal processing method named Path Optimization. Case studies on two experimental datasets validate the effectiveness of the proposed virtual rotating speed meter.
Performance Analysis of Rotary Magnetorheological Brake With Multiple Fluid Flow Channels

In order to solve the problems of low magnetic field utilization rate and low volume-torque ratio of the traditional magnetorheological (MR) brake under a volume constraint, a rotary MR brake with multiple fluid flow channels was proposed. The magnetic flux was guided into the external axial fluid flow channel by inserting a non-magnetic ring in the middle of the magnetic conduction sleeves which could improve the magnetic circuit structure greatly, the working area where the MR brake producing rheological effect was increased, and the effective damping gaps were also increased from two sections to four sections. The working principle of rotary MR brake was expounded and torque mathematical model was also deduced. The electromagnetic field was modeled and the distribution of magnetic flux density in multiple fluid flow channels was analyzed using finite element method. The prototypes of initial and optimal design were fabricated by using the obtained optimal geometric parameters. An experimental test system was setup to investigate the dynamic performance of the proposed rotary MR brake. The experimental results show that the maximum braking torque and torque ratio of the optimal MR brake are increased by 13.5% and 2.3% compared with the initial MR brake at the applied current of 1.8 A, respectively. At the same time, the variation trend of experimental and simulation results is basically consistent, and the rotational speed has almost no effect on the torque performance, which is conducive to the application of MR brakes under different working conditions.
Decision-Making Model Based on Ensemble Method in Auxiliary Medical System for Non-Small Cell Lung Cancer

In many countries, lung cancer is the chief cancer type, and the overall survival rate in 5 years remains at a low rate of 16.8%. Among lung cancer patients, the proportion of patients with non-small cell lung cancer (NSCLC) can reach 85%. Especially in developing countries, it has become a great challenge for doctors to diagnose efficiently because of the complex diagnostic process of NSCLC, a large number of patients, and limited medical resources. Based on the above situation, our main objective in this study is to construct an auxiliary Artificial Intelligence medical system for doctors to diagnose the NSCLC patients efficiently. In this research, we select the combination of Support Vector Machine (SVM) and Artificial Neural Network (ANN) to complete the classification and training tasks of the medical system. In this study, we trained and tested our decision model based on the data information of 12,186 patients in three hospitals in China. The results of this experiment show that the accuracy of our system has reached 88% when the amount of data reaches 4000, which is close to that of doctors.
Research on Single-Phase High Impedance Fault Identification of Neutral Ungrounded System

Most of 6-10kv distribution networks in China are neutral ungrounded systems. High impedance fault (HIF) is a common complex fault in distribution network. With the current is small and unstable, it is difficult to detect. In this paper, a method based on wavelet analysis and support vector machines(SVM) optimized by particle swarm optimization(PSO) is proposed to identify HIF in ungrounded systems. Firstly, a simulation model was established to simulate the high impedance fault. Then, discrete wavelet transform (DWT) was used as the feature extractor to decompose the HIF current signal by three-layer wavelet, and the signal energy of each frequency band was extracted as the characteristic quantity. After normalization, the feature vectors were input into PSO-SVM for training. Finally, the field high impedance fault signal is used to verify. Compared with the traditional method of SVM recognition after Fourier transform extraction, the recognition accuracy and speed are improved.
Evaluating the Reliability Value of Distributed PV+Storage against Household Blackout

Distributed energy resource (DER) has been proven as an effective and promising solution to enhance power system resilience and improve household-level reliability. In this paper, we propose a method for evaluating the reliability value of photovoltaic (PV) + battery storage system (BSS) when considering the probability of grid outages leading to household blackouts. Considering such reliability value, economic profit and capital cost of PV+BSS, a simple formula is derived to calculate the optimal planning strategy. This strategy can provide household-level customers with a simple and straightforward expression of invested PV+BSS capacity. Based on 600 households in 8 zones of the U.S. from 2006 to 2015, case studies demonstrate that by adding reliability value on top of economic profit, households can invest in larger PV+BSS to avoid loss of load caused by blackouts. Due to the difference in blackout hours, the households from 8 zones show distinct willingness to install PV+BSS. In the northwest, for example, PV+BSS can offer households with the maximum annual reliability value and total revenue up to $ 251.13 and $ 115.48, respectively.
Coordinated Voltage Control of Renewable Energy Power Plants in Weak Sending-End Power Grid

The renewable energy connected to the sending-end power grid is increasing rapidly. This paper proposes a coordinated voltage control strategy based on Model Predictive Control (MPC) for renewable power plants of wind and solar power connected to the weak sending-end power grid. Wind Turbine Generators (WTGs), Photovoltaic Arrays (PVAs) and Static Synchronous Compensator (STATCOM) are coordinated to keep voltages within the feasible range during operation, which makes full use of the reactive power capability of WTGs and PVAs. Besides, the impact of active power outputs of WTGs and PVAs on voltage control are taken into consideration because of the high R/X ratio of the collector system. To improve the computation efficiency, an analytical method is used for the calculation of sensitivity coefficients. A renewable energy power plant with 80 WTGs and 20 PVAs connected to a weak sending-end power grid was used to verify the proposed voltage control strategy.
Roofline-Model-Based Design Space Exploration for Dataflow Techniques of CNN Accelerators

To effectively compute convolutional layers, a complex design space must exist (e.g., the dataflow techniques associated with the layer parameters, loop transformation techniques, and hardware parameters). For efficient design space exploration (DSE) of various dataflow techniques, namely, the weight-stationary (<italic>WS</italic>), output-stationary (<italic>OS</italic>), row-stationary (<italic>RS</italic>), and no local reuse (<italic>NLR</italic>) techniques, the processing element (PE) structure and computational pattern of each dataflow technique are analyzed. Various performance metrics are calculated, namely, the throughput (in giga-operations per second, GOPS), computation-to-communication ratio (<italic>CCR</italic>), on-chip memory usage, and off-chip memory bandwidth, as closed-form expressions of the layer and hardware parameters. In addition, loop interchange and loop unrolling techniques with a double-buffer architecture are assumed. Many roofline model-based simulations are performed to explore relevant dataflow techniques for a wide variety of convolutional layers of typical neural networks. Through simulation, this paper provides insights into the trends in accelerator performance as the layer parameters change. For convolutional layers with large input and output feature map (<italic>ifmap</italic> and <italic>ofmap</italic>) widths and heights, the GOPS of the <italic>NLR</italic> dataflow technique tends to be higher than that of the techniques. For convolutional layers with low <italic>weight</italic> and <italic>ofmap</italic> widths and heights, the <italic>RS</italic> dataflow technique achieves optimal GOPS and on-chip memory usage. In the case of convolutional layers with small <italic>weight</italic> widths and heights, the GOPS of the <italic>WS</italic> dataflow technique tends to be high. In the case of convolutional layers with small <italic>ofmap</italic> widths and heights, the <italic>OS</italic> dataflow technique achieves optimal GOPS and on-chip memory usage.
An Active Bypass Pulse Injection-Based Low Switching Frequency PWM Approach for Harmonic Compensation of Current-Source Converters

In this article, a bypass pulse injection-based pulsewidth modulation approach is proposed for mitigating line current harmonics of grid-tied current-source converters (CSCs) operating at a low switching frequency. First, the relationship between the CSC output harmonic current and the format of the bypass pulse is analyzed. Then, through online adjustment of both bypass pulse location and width, the seventh harmonic component of the output current is actively regulated to mitigate the corresponding seventh line harmonic current. Meanwhile, this action may be associated with additional amplification of the fifth output current. To deal with this issue, the unsymmetrical selective harmonic elimination (SHE) modulation with online adjustable switching angles is applied to further mitigate the fifth harmonic line current. It has been experimentally validated that with the application of both the bypass pulse injection and the unsymmetrical SHE-based modulation approaches, both the fifth and the seventh harmonic line currents can be simultaneously suppressed with ultralow switching frequency below 1 kHz.
Reactive collision avoidance for ASVs based on control barrier functions

A reactive collision avoidance method for autonomous surface vehicles based on control barrier functions (CBFs) is proposed. An encounter between the ownship (the vessel that we control) and a target ship is classified, in accordance with the International Regulations for Preventing Collisions at Sea (COLREGs), to be either a head-on, overtake, give-way, stand-on or a safe situation with respect to the ownship. Subsequently, a spatial region is assigned to the target ship based on the classification, and this region is used to define a collision-free set. Based on this, a CBF is formulated to ensure forward invariance of the collision-free set. This CBF can then be applied as an inequality constraint to any guidance, navigation and control system with an optimization-based trajectory tracking or thrust allocation system. The method is verified through simulations and is seen to handle head-on, overtaking and crossing situations with both give-way and stand-on duty in compliance with COLREGs rules 13-15 and 17.
Battery Analysis using Kinetic Battery Model with Voltage Response

Chemical storage batteries have particularities, the capacity is not constant and it varies with different loads, also the voltage can rise without charging the battery soon after the load is disconnected. This paper discusses the behavior of batteries throughout the Kinetic Battery Model. The kinetic battery model was used in this case to model a Lithium-ion battery but can be applied also in Lead-Acid Batteries or Ni-Mh technology. These energy storage systems have a strong growth trend especially in distribution systems and electric vehicle applications.
Analyzing the Efficacy of a Restorative Virtual Reality Environment using HRV Biofeedback for Pain and Anxiety Management

Approximately 50 million surgeries are performed in the US annually, and about 70% of patients are middle age and older adults. Pain and anxiety are common accompaniments of surgery, and opioids have been the mainstay of pain management for decades. However, opioid abuse and addiction now place the US in an “opioid epidemic,” with one in 16 surgical patients prescribed opioids becoming a long-term user. To mitigate surgical pain and anxiety, we developed a virtual reality experience based on Attention Restoration Theory and affective computing techniques to deliver adaptive content based on the user’s heart rate variability biofeedback. A randomized control study among 12 patients undergoing Total Knee Arthroplasty at Pate wood Memorial Hospital, Greenville, SC demonstrated that patients in the VR group reported a 29.6% and 31.2% decrease in surgical pain and anxiety, respectively, compared to a control group.