Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters

Monolingual Recognizers Fusion for Code-switching Speech Recognition

The bi-encoder structure has been intensively investigated in code-switching (CS) automatic speech recognition (ASR). However, most existing methods require the structures of two monolingual ASR models (MAMs) should be the same and only use the encoder of MAMs. This leads to the problem that pre-trained MAMs cannot be timely and fully used for CS ASR. In this paper, we propose a monolingual recognizers fusion method for CS ASR. It has two stages: the speech awareness (SA) stage and the language fusion (LF) stage. In the SA stage, acoustic features are mapped to two language-specific predictions by two independent MAMs. To keep the MAMs focused on their own language, we further extend the language-aware training strategy for the MAMs. In the LF stage, the BELM fuses two language-specific predictions to get the final prediction. Moreover, we propose a text simulation strategy to simplify the training process of the BELM and reduce reliance on CS data. Experiments on a Mandarin-English corpus show the efficiency of the proposed method. The mix error rate is significantly reduced on the test set after using open-source pre-trained MAMs.Comment: Submitted to ICASSP202

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Huntzinger, D. N., Schaefer, K., Schwalm, C., Fisher, J. B., Hayes, D., Stofferahn, E., Carey, J., Michalak, A. M., Wei, Y., Jain, A. K., Kolus, H., Mao, J., Poulter, B., Shi, X., Tang, J., & Tian, H. Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems. Environmental Research Letters, 15(2), (2020): 025005, doi:10.1088/1748-9326/ab6784.Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.This work was supported by NASA'S Arctic Boreal Vulnerability Experiment (ABoVE; https://above.nasa.gov); NNN13D504T. Funding for the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP; https://nacp.ornl.gov/MsTMIP.shtml) activity was provided through NASA ROSES Grant #NNX10AG01A. Data management support for preparing, documenting, and distributing model driver and output data was performed by the Modeling and Synthesis Thematic Data Center at Oak Ridge National Laboratory (MAST-DC; https://nacp.ornl.gov), with funding through NASA ROSES Grant #NNH10AN681. Finalized MsTMIP data products are archived at the ORNL DAAC (https://daac.ornl.gov). We also acknowledge the modeling groups that provided results to MsTMIP. The synthesis of site-level soil respiration, temperature, and moisture data reported in Carey et al 2016a, 2016b) was funded by the US Geological Survey (USGS) John Wesley Powell Center for Analysis and Synthesis Award G13AC00193. Additional support for that work was also provided by the USGS Land Carbon Program. JBF carried out the research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged

Dietary supplementation of <em>Astragalus</em> fermentation products improves the growth performance, immunological characteristics, and disease resistance of crucian carp (<em>Carassius auratus</em>)

The fermentation products of Astragalus have been acknowledged for their ability to enhance immune functions. This study assessed the impact of incorporating Astragalus, fermented with Lactobacillus plantarum and Bacillus coagulans, on crucian carp's growth, disease resistance, and immunological characteristics. The experimental groups were fed with common feed (C), C + Astragalus (A), A + Lactobacillus plantarum (AL), A + Bacillus coagulans (AB), and AL + Bacillus coagulans (ALB). The fermented products were mixed with common feed at a 1:99 ratio, and crucian carp were fed 2% of their body weight for four weeks, with sampling conducted on days 3, 7, 14, 21, and 28. Disease resistance was evaluated using Aeromonas hydrophila (A. hydrophila) at a concentration of 0.2 mL (1.0×10^7 CFU/mL). The final weights in the AL, AB, and ALB groups significantly increased compared to the C group. The ALB group exhibited elevated serum albumin levels, alkaline phosphatase, intestinal lipase, protease enzyme, C3, and IgM gene expression compared to the C group. At the same time, alanine aminotransferase, aspartate aminotransferase, and glucose contents were significantly reduced. The survival rate significantly increased in all experimental groups after treatment with A. hydrophila. In conclusion, Astragalus products fermented with Lactobacillus plantarum and Bacillus coagulans could effectively improve crucian carp's growth, disease resistance, and immune response

Facile Fabrication of Sandwich Structural Membrane With a Hydrogel Nanofibrous Mat as Inner Layer for Wound Dressing Application

A common problem existing in wound dressing is to integrate the properties of against water erosion while maintaining a high water-uptake capacity. To tackle this issue, we imbedded one layer of hydrogel nanofibrous mat into two hydrophobic nanofibrous mats, thereafter, the sandwich structural membrane (SSM) was obtained. Particularly, SSM is composed of three individual nanofibrous layers which were fabricated through sequential electrospinning technology, including two polyurethane/antibacterial agent layers, and one middle gelatin/rutin layer. The obtained SSM is characterized in terms of morphology, component, mechanical, and functional performance. In addition to the satisfactory antibacterial activity against Staphylococcus aureus and Escherichia coli, and antioxidant property upon scavenging DPPH free radicals, the obtained SSM also shows a desirable thermally regulated water vapor transmission rate. More importantly, such SSM can be mechanically stable and keep its intact morphology without appearance damage while showing a high water-absorption ratio. Therefore, the prepared sandwich structural membrane with hydrogel nanofibrous mat as inner layer can be expected as a novel wound dressing

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment

Mass Concentration, Source and Health Risk Assessment of Volatile Organic Compounds in Nine Cities of Northeast China

From April 2008 to July 2009, ambient measurements of 58 volatile organic compounds (VOCs), including alkanes, alkenes, and aromatics, were conducted in nine industrial cities (Shenyang, Fushun, Changchun, Jilin, Harbin, Daqing, Huludao, Anshan and Tianjin) of the Northeast Region, China (NRC). Daqing had the highest concentration of VOCs (519.68 &plusmn; 309.88 &mu;g/m3), followed by Changchun (345.01 &plusmn; 170.52 &mu;g/m3), Harbin (231.14 &plusmn; 46.69 &mu;g/m3), Jilin (221.63 &plusmn; 34.32 &mu;g/m3), Huludao (195.92 &plusmn; 103.26 &mu;g/m3), Fushun (135.43 &plusmn; 46.01 &mu;g/m3), Anshan (109.68 &plusmn; 23.27 &mu;g/m3), Tianjin (104.31 &plusmn; 46.04 &mu;g/m3), Shenyang (75.2 &plusmn; 40.09 &mu;g/m3). Alkanes constituted the largest percentage (&gt;40%) in concentrations of the quantified VOCs in NRC, and the exception was Tianjin dominated by aromatics (about 52.34%). Although alkanes were the most abundant VOCs at the cities, the most important VOCs contributing to ozone formation potential (OFP) were alkenes and aromatics. Changchun had the highest OFP (537.3 &mu;g/m3), Tianjin had the lowest OFP (111.7 &mu;g/m3). The main active species contributing to OFP in the nine cities were C2~C6 alkanes, C7~C8 aromatic hydrocarbons, individual cities (Daqing) contained n-hexane, propane and other alkane species. Correlation between individual hydrocarbons, B/T ratio and principal component analysis model (PCA) were deployed to explore the source contributions. The results showed that the source of vehicle exhausts was one of the primary sources of VOCs in all nine cities. Additionally, individual cities, such as Daqing, petrochemical industry was founded to be an important source of VOCs. The results gained from this study provided a large of useful information for better understanding the characteristics and sources of ambient VOCs incities of NRC. The non-carcinogenic risk values of the nine cities were within the safe range recognized by the U.S. Environmental Protection Agency (HQ &lt; 1), and the lifetime carcinogenic risk values of benzene were 3.82 &times; 10&minus;5~1.28 &times; 10&minus;4, which were higher than the safety range specified by the US Environmental Protection Agency (R &lt; 1.00 &times; 10&minus;6). The results of risk values indicated that there was a risk of cancer in these cities

The efficient synthesis of dibenzo[d,d′]benzo[1,2-b:4,3-b′]dithiophene and cyclopenta[1,2-b:4,3-b′]bis(benzo[d]thiophen)-6-one

With 3,3′-bi[benzo[b]thiophenyl] as starting material, dibenzo[d,d′]benzo[1,2-b:4,3-b′]dithiophene, a [5]heterohelicene, was synthesized efficiently in 60% yield via formylation and McMurry reaction. Cyclopenta[1,2-b:4,3-b′]bis(benzo[d]thiophen)-6-one, another interesting helical ketone, was also prepared in 79% yield via deprotonation and ketonization of 3,3′-bi[benzo[b]thiophenyl]. In addition, the single-crystal structure of dibenzo[d,d′]benzo[1,2-b:4,3-b′]dithiophene and UV–vis spectra of both title compounds are described

Variation Characteristics and Source Analysis of Pollutants in Jinghong before and after the COVID-19 Pandemic

With the outbreak of COVID-19 in early 2020, China&rsquo;s urban epidemic prevention and control policies have caused significant changes in air pollution sources. In order to clarify the change characteristics of urban air pollution in Yunnan Province before and after the epidemic, using statistics and correlation analysis methods, Jinghong city was selected as the research object, and based on the ambient air quality monitoring data (SO2, NO2, CO, O3, PM2.5, and PM10) and meteorological data from 2017 to 2021, the concentration characteristics of air pollutants in Jinghong in the past five years were analyzed, and the sources of air pollutants were analyzed using the local emission source inventory and HYSPLIT model. The results show that: &#9312; The air quality in Jinghong was the worst in 2019 before the outbreak of the epidemic, and then gradually improved, with an average 5-year excellent and good rate of 91.8%. The pollutants are mainly particulate matter and O3. &#9313; Except for SO2, the concentrations of other pollutants have similar seasonal changes, with the highest in spring and the lowest in summer. &#9314; The air quality in Jinghong is mainly affected by the combined effects of local emissions and external transportation. According to the local emission inventory, biomass combustion sources have the largest contribution to CO, PM2.5, and PM10, mobile sources have the highest share rate of NOx, and industrial enterprises are the largest emission sources of SO2. Air mass backward trajectory research shows that the westward and southerly airflow are the main transport direction of pollutants entering Jinghong, especially in spring, which significantly affects the local pollutant concentration level. In addition, meteorological conditions such as temperature, precipitation, and wind speed also have a great impact on the dilution, diffusion, and transfer of air pollutants in Jinghong. The results of this study further improve the characteristics of the spatial and temporal distribution of air pollutants and pollutant sources in the border areas of China and before and after the epidemic, and also provide a theoretical basis for air environment management in the border areas