Effects of Individual Differences on Measurements’ Drowsiness-Detection Performance

Individual differences (IDs) may reduce the detection-accuracy of drowsiness-driving by influencing measurements’ drowsiness-detection performance (MDDP). The purpose of this paper is to propose a model that can quantify the effects of IDs on MDDP and find measurements with less impact by IDs to build drowsiness-detection models. Through field experiments, drivers’ naturalistic driving data and subjective-drowsiness levels were collected, and drowsiness-related measurements were calculated using the double-layer sliding time window. In the model, MDDP was represented by |Z-statistics| of the Wilcoxon-test. First, the individual driver’s measurements were analysed by Wilcoxon-test. Next, drivers were combined in pairs, measurements of paired-driver combinations were analysed by Wilcoxon-test, and measurement’s IDs of paired-driver combinations were calculated. Finally, linear regression was used to fit the measurements’ IDs and changes of MDDP that equalled the individual driver’s |Z-statistics| minus the paired-driver combination’s |Z-statistics|, and the slope’s absolute value (|k|) indicated the effects of ID on the MDDP. As a result, |k| of the mean of the percentage of eyelid closure (MPECL) is the lowest (4.95), which illustrates MPECL is the least affected by IDs. The results contribute to the measurement selection of drowsiness-detection models considering IDs

LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation

The accurate segmentation of breast tumors is an important prerequisite for lesion detection, which has significant clinical value for breast tumor research. The mainstream deep learning-based methods have achieved a breakthrough. However, these high-performance segmentation methods are formidable to implement in clinical scenarios since they always embrace high computation complexity, massive parameters, slow inference speed, and huge memory consumption. To tackle this problem, we propose LightBTSeg, a dual-path joint knowledge distillation framework, for lightweight breast tumor segmentation. Concretely, we design a double-teacher model to represent the fine-grained feature of breast ultrasound according to different semantic feature realignments of benign and malignant breast tumors. Specifically, we leverage the bottleneck architecture to reconstruct the original Attention U-Net. It is regarded as a lightweight student model named Simplified U-Net. Then, the prior knowledge of benign and malignant categories is utilized to design the teacher network combined dual-path joint knowledge distillation, which distills the knowledge from cumbersome benign and malignant teachers to a lightweight student model. Extensive experiments conducted on breast ultrasound images (Dataset BUSI) and Breast Ultrasound Dataset B (Dataset B) datasets demonstrate that LightBTSeg outperforms various counterparts.Comment: 7 pages, 7 figures, conferenc

Integrated motor drives: state of the art and future trends

With increased need for high power density, high efficiency and high temperature capabilities in Aerospace and Automotive applications, Integrated Motor Drives (IMD) offers a potential solution. However, close physical integration of the converter and the machine may also lead to an increase in components temperature. This requires careful mechanical, structural and thermal analysis; and design of the IMD system. This paper reviews existing IMD technologies and their thermal effects on the IMD system. The effects of the power electronics (PE) position on the IMD system and its respective thermal management concepts are also investigated. The challenges faced in designing and manufacturing of an IMD along with the mechanical and structural impacts of close physical integration is also discussed and potential solutions are provided. Potential converter topologies for an IMD like the Matrix converter, 2-level Bridge, 3-level NPC and Multiphase full bridge converters are also reviewed. Wide band gap devices like SiC and GaN and their packaging in power modules for IMDs are also discussed. Power modules components and packaging technologies are also presented

A Combined Method of Two-model based on Forecasting Meteorological Data for Photovoltaic Power Generation Forecasting

Under the background of the continuous development of photovoltaic power generation technology, accurate prediction of photovoltaic output power has become an important subject. In this paper, a combined method of two-model based on forecasting meteorological data for photovoltaic power generation forecasting is proposed. To solve the problem of the adaptability of a single model, two different models are used according to the different types of output power characteristics. The K-means clustering algorithm is used to classify different weather types according to the historical meteorological data. After predicting the irradiance and temperature of the period to be predicted and classifying the period into different types, the photovoltaic output power is predicted by a suitable model. The two prediction models are the Wavelet- Decomposition-ARIMA model and EDM-SA-DBN model, which are suitable for periods with larger and smaller fluctuation amplitude of photovoltaic output, respectively. Wavelet decomposition can refine the data with large fluctuations on multiple scales, make the data smooth, and improve the prediction accuracy of the Autoregressive Integrated Moving Average model (ARIMA). The Deep Belief Network (DBN) can effectively process a large number of complex data and deep mining the data features. While the empirical mode decomposition (EMD) can decompose the more stable data and amplify the details in the signal as much as possible. Meanwhile, the simulated annealing algorithm (SA) can avoid the network falling into a local optimal solution and improve the prediction accuracy. This paper uses a large number of photovoltaic power station data for experimental verification. The results show that this combined model has high accuracy and generalization ability

Analyzing the Influencing Factors and Workload Variation of Takeover Behavior in Semi-Autonomous Vehicles

There are many factors that will influence the workload of drivers during autonomous driving. To examine the correlation between different factors and the workload of drivers, the influence of different factors on the workload variations is investigated from subjective and objective viewpoints. Thirty-seven drivers were recruited to participant the semi-autonomous driving experiments, and the drivers were required to complete different NDRTs (Non-Driving-Related Tasks): mistake finding, chatting, texting, and monitoring when the vehicle is in autonomous mode. Then, we introduced collision warning to signal there is risk ahead, and the warning signal was triggered at different TB (Time Budget)s before the risk, at which time the driver had to take over the driving task. During driving, the NASA-TLX-scale data were obtained to analyze the variation of the driver’s subjective workload. The driver’s pupil-diameter data acquired by the eye tracker from 100 s before the TOR (Take-Over Request) to 19 s after the takeover were obtained as well. The sliding time window was set to process the pupil-diameter data, and the 119-s normalized average pupil-diameter data under different NDRTs were fitted and modeled to analyze the variation of the driver’s objective workload. The results show that the total subjective workload score under the influence of different factors is as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s and TB = 3 s have no significant difference; and mistake finding > chatting > texting > monitoring. The results of pupil-diameter data under different factors are as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s > TB = 3 s; and monitoring type (chatting and monitoring) > texting type (mistake finding and texting). The research results can provide a reference for takeover safety prediction modeling based on workload

Leaching of silica from vanadium-bearing steel slag in sodium hydroxide solution

The work aims to selectively extract silica from vanadium-bearing steel slag by a leaching process. The effects of the particle size, the ratio of solid to liquid, the concentration of sodium hydroxide solution and the leaching temperature on the leaching behavior of silica from vanadium-bearing steel slag were investigated. The leaching kinetics of silica from vanadium-bearing steel slag in 30-50% w/w NaOH solutions was studied at 240 degrees C and the shrinking-core model was established to express the leaching kinetics of silica. The data showed that the leaching rate was controlled by the chemical reaction on the system interface and the activation energy for the process was found to be 36.4 kJ mol(-1). By the leaching process, the majority of silica could be removed effectively from the vanadium-bearing steel slag and a residue with a low SiO(2) content of 4.28% and a high V(2)O(5) content of 11.15% was obtained. Under these conditions there was partial dissolution of Al and slight dissolution of Cr, Mn and Ti. (C) 2010 Elsevier B.V. All rights reserved

Electrosynthesis of nitriles from primary alcohols and ammonia on Ni catalyst

Despite the increasing interest on electrocatalytic refinery of renewable feedstocks to produce value-added chemicals, nitrile electrosynthesis from alcohols is rarely studied. Here, we report the direct electrosynthesis of nitriles from primary alcohols and ammonia, with a simple nickel catalyst under benign conditions in aqueous electrolytes. The highest benzonitrile faradaic efficiency of 63.0% was achieved at 1.375 V vs. RHE. The reaction proceeds via a dehydrogenation-imination-dehydrogenation sequence, with the rate-determining step likely involving the cleavage of α-carbon C-H bond of the alcohol. Based on the electrochemical and in-situ Raman analyses, we propose that the in-situ formed Ni2+/Ni3+ redox species serves as the active site for converting alcohol to nitrile, while Ni2+ also exhibits capability for the oxidation of imine. Various aromatic, aliphatic and heterocyclic primary alcohols were transformed to the corresponding nitriles, exhibiting broad feasibility. This study offers a promising electrocatalytic system for the sustainable synthesis of high-value nitriles

Reversible Dendrite-Free Potassium Plating and Stripping Electrochemistry for Potassium Secondary Batteries

Rechargeable potassium metal batteries have recently emerged as alternative energy storage devices beyond lithium-ion batteries. However, potassium metal anodes suffer from poor reversibility during plating and stripping processes due to their high reactivity and unstable solid electrolyte interphase (SEI). Herein, it is reported for the first time that a potassium bis­(fluoro­slufonyl)­imide (KFSI)-dimethoxy­ethane (DME) electrolyte forms a uniform SEI on the surface of potassium enabling reversible potassium plating/stripping electrochemistry with high efficiency (∼99%) at ambient temperature. Furthermore, the superconcentrated KFSI-DME electrolyte shows excellent electrochemical stability up to 5 V (vs K/K⁺) which enables good compatibility with high-voltage cathodes. Full cells with potassium Prussian blue cathodes are demonstrated. Our work contributes toward the understanding of potassium plating/stripping electrochemistry and paves the way for the development of potassium metal battery technologies