18 research outputs found

    Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection

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    Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicated to establishing specialized modules to identify camouflaged objects with complete and fine details, while the boundary can not be well located for the lack of object-related semantics. In this paper, we propose a novel ``pre-train, adapt and detect" paradigm to detect camouflaged objects. By introducing a large pre-trained model, abundant knowledge learned from massive multi-modal data can be directly transferred to COD. A lightweight parallel adapter is inserted to adjust the features suitable for the downstream COD task. Extensive experiments on four challenging benchmark datasets demonstrate that our method outperforms existing state-of-the-art COD models by large margins. Moreover, we design a multi-task learning scheme for tuning the adapter to exploit the shareable knowledge across different semantic classes. Comprehensive experimental results showed that the generalization ability of our model can be substantially improved with multi-task adapter initialization on source tasks and multi-task adaptation on target tasks

    Method of high timing resolution pulse synthesis based on virtual sampling

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    Adjustable-width pulse signals are widely used in systems such as test equipment for hold time, response time and radar testing. In this study, we proposed a pulse generation method based on virtual sampling with ultra-high pulse width resolution. In the proposed method, the sampling rate of a digital-to-analogue converter (DAC) was adjusted to considerably improve pulse width resolution. First, the sampling rate was matched with the target pulse width resolution to digitally sample the ideal signal and generate digital waveform sampling points. Next, the signal bandwidth of the DAC was matched using a low-pass digital filter. Finally, the waveform sampling points were downsampled using an integer factor and output after digital-to-analogue conversion. The waveform pulse width information generated by high-frequency digital sampling was passed step by step and retained in the final output analogue signal. A DAC with a sampling rate of 1.25 GSa/s was used, and the pulse width resolution of the pulse signal was 0.1 ns. Theoretically, a sampling rate of 10 GSa/s is required to achieve 0.1 ns resolution. This method is simple, has a low cost, and exhibits excellent performance

    Modeling and Simulation of Departure Passenger’s Behavior Based on an Improved Social Force Approach: A Case Study on an Airport Terminal in China

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    The unprecedented growth of passenger throughput in large airport terminals highlights the importance of analyzing passengers’ movement to achieve airport terminal’s elaborate management. Based on the theory of original social force model, video data from a departure hall of a large airport terminal in China were analyzed to summarize passengers’ path planning characteristics. Then, a double-level model was established to describe passengers’ path planning behaviors. At the decision level of the proposed model, the avoiding force model including common avoiding force and additional horizontal avoiding force was established on the basis of setting time and space limitations for taking avoiding action and was used to describe passengers’ path planning in close-range space. At the tactical level of the proposed model, the route and node choice models were established to describe passengers’ path planning in long-range space. In the route choice model, a distribution of intermediate destination areas was proposed, with detouring distance, pedestrian density, speed difference, and pedestrian distribution considered in choosing an intermediate destination area. In the node choice model, the walking distance, the quantity of people waiting, and luggage were considered in choosing a check-in counter or security check channel. The main parameters of the proposed model were confirmed according to video data. Simulation results show that the proposed model can simulate departure passengers’ path planning behaviors at an acceptable accuracy level

    “Coffee Ring” Fabrication and Its Application in Aflatoxin Detection Based on SERS

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    The fabrication of a coffee ring was studied in this work to improve its sensitivity in detecting trace analytes based on surface-enhanced Raman scattering (SERS). Gold nanoparticles were synthesized with diameters of ~40 nm through the sodium citrate reduction method, and rhodamine 6G (R6G) was employed as a probe to evaluate the performance of the fabricated coffee rings. The results showed that the coffee ring formed from the water-washed gold nanoparticles presented more orderly and regular morphology as well as better SERS properties than the unwashed ones. Furthermore, both the concentration and the amount of gold nanoparticles were found to affect its SERS performance. Using the optimized coffee ring as a SERS substrate, trace R6G with a concentration of 5 × 10−8 M was detected. This sensing platform could realize aflatoxin B1 (AFB1) detection down to 5 × 10−7 M and was demonstrated to function well in real-sample testing

    Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China

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    In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches

    Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China

    No full text
    In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches

    An Investigation on Glucuronidation Metabolite Identification, Isozyme Contribution, and Species Differences of GL-V9 In Vitro and In Vivo

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    GL-V9 is a prominent derivative of wogonin with a wide therapeutic spectrum and potent anti-tumor activity. The metabolism characteristics of GL-V9 remain unclear. This study aimed to clarify the metabolic pathway of GL-V9 and investigate the generation of its glucuronidation metabolites in vitro and in vivo. HPLC-UV-TripleTOF was used to identify metabolites. The main metabolite that we found was chemically synthesized and the synthetic metabolite was utilized as standard substance for the subsequent metabolism studies of GL-V9, including enzyme kinetics in liver microsomes of five different species and reaction phenotyping metabolism using 12 recombinant human UDP-glucuronosyltransferase (UGT) isoforms. Results indicated that the glucuronidation reaction occurred at C5-OH group, and 5-O-glucuronide GL-V9 is the only glucuronide metabolite and major phase II metabolite of GL-V9. Among 12 recombinant human UGTs, rUGT1A9 showed the strongest catalytic capacity for the glucuronidation reaction of GL-V9. rUGT1A7 and rUGT1A8 were also involved in the glucuronidation metabolism. Km of rUGT1A7-1A9 was 3.25 ± 0.29, 13.92 ± 1.05, and 4.72 ± 0.28 μM, respectively. In conclusion, 5-O-glucuronide GL-V9 is the dominant phase II metabolite of GL-V9 in vivo and in vitro, whose formation rate and efficiency are closely related to isoform-specific metabolism profiles and the distribution of UGTs in different tissues of different species

    Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China

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    Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model’s construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events
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