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2022 ◽  
Vol 26 (1) ◽  
pp. 197-220
Author(s):  
Emixi Sthefany Valdez ◽  
François Anctil ◽  
Maria-Helena Ramos

Abstract. This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: system A, forcing, system B, forcing and initial conditions, system C, forcing and model structure, and system D, forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to 7 d of forecast horizon. The evaluation is carried out across 30 catchments in the province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast attribute, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable, and in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation of how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems.


2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Naoyuki Haba ◽  
Toshifumi Yamada

Abstract We study colored Higgsino-mediated proton decay (dimension-five proton decay) in a model based on the flipped SU(5) GUT. In the model, the GUT-breaking 10, $$ \overline{\mathbf{10}} $$ 10 ¯ fields have a GUT-scale mass term and gain VEVs through higher-dimensional operators, which induces an effective mass term between the color triplets in the 5, $$ \overline{\mathbf{5}} $$ 5 ¯ Higgs fields that is not much smaller than the GUT scale. This model structure gives rise to observable dimension-five proton decay, and at the same time achieves moderate suppression on dimension-five proton decay that softens the tension with the current bound on Γ(p → K+$$ \overline{\nu} $$ ν ¯ ). We investigate the flavor dependence of the Wilson coefficients of the operators relevant to dimension-five proton decay, by relating them with diagonalized Yukawa couplings and CKM matrix components in MSSM, utilizing the fact that the GUT Yukawa couplings are in one-to-one correspondence with the MSSM Yukawa couplings in flipped models. Then we numerically evaluate the Wilson coefficients, and predict the distributions of the ratios of the partial widths of various proton decay modes.


2022 ◽  
Vol 14 (1) ◽  
pp. 180
Author(s):  
Fang Zhou ◽  
Fengjie He ◽  
Changchun Gui ◽  
Zhangyu Dong ◽  
Mengdao Xing

A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods.


Buildings ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Stefanus Adi Kristiawan ◽  
Isyana Ratna Hapsari ◽  
Edy Purwanto ◽  
Marwahyudi Marwahyudi

Many damage limit states have been defined to characterize the extent of damages occurred in RC frame. Some of the damage limit states are defined by models that relate the limit states with the control points. Both control points and the limit state are expressed in term of response quantities. This research aims to evaluate the validity of such models by identifying the defined damage limit state with the corresponding damage based on FE modeling. The FE modeling provides a direct link between the damage and the response quantities. This link can be exploited as a basis for the evaluation. Based on the evaluation, this study proposed modified damage limit states. The response quantities with its corresponding progressive damage from FE simulation will also be used to inspect whether damage that can be expected to occur in the model structure is within the range estimated by the code based approach (CBA) damage limit state. Finally, fragility curves are constructed to assess the probability of the expected damage in the model structure under Design Basis Earthquake (DBE) and Maximum Considered Earthquake (MCE) scenarios. Utilizing the proposed damage limit states, the most probable damage in the structure falls in the category of slight if an earthquake at a level of DBE or MCE strikes the structure. However, at MCE level the probability of moderate damage attains 35%, or an increase by 23% compared to the DBE level.


2021 ◽  
Vol 7 (4) ◽  
pp. 3-13
Author(s):  
О. V. Kutova ◽  
O. A. Ruban ◽  
R. V. Sahaidak-Nikitiuk ◽  
I. V. Kovalevska ◽  
N. V. Demchenko

Scientific research in pharmacy, due to their multifactorial nature, is closely related to modeling of complex static systems. For this purpose, the so-called “input-output” mathematical models, which are built based on the results of the experiment, are widely used. Modeling of static systems based on the experimental data requires the solution of three interrelated tasks: planning of the experiment and its implementation; identification of the model structure and its parameters; approximation, if necessary, of a complex model to a simpler mathematical description.


2021 ◽  
Author(s):  
Eva Sebok ◽  
Hans Jørgen Henriksen ◽  
Ernesto Pastén-Zapata ◽  
Peter Berg ◽  
Guillume Thirel ◽  
...  

Abstract. Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.


2021 ◽  
Author(s):  
Nutchanart Sriwongsitanon ◽  
Wasana Jandang ◽  
Thienchart Suwawong ◽  
Hubert H. G. Savenije

Abstract. A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 32 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at the internal stations; 2) the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better or similar performance both during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values, and the annual average NDII values to construct 2 alternative models: FLEX-SD-NDIIMax-Min and FLEX-SD-NDIIAvg, respectively. The additional constraint on the moisture holding capacity by the NDII improved both model performance and the realism of the distribution. Distribution of Sumax using annual average NDII values was found to be well correlated with the percentage of evergreen forest in 31 sub-catchments. Spatial average NDII values were proved to be highly corresponded with the root zone soil moisture of the river basin, not only in the dry season but also in the water limited ecosystem. To check how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for validation.


Data ◽  
2021 ◽  
Vol 6 (12) ◽  
pp. 134
Author(s):  
Kangying Li ◽  
Biligsaikhan Batjargal ◽  
Akira Maeda

This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.


Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8369
Author(s):  
Yizhi Luo ◽  
Zhixiong Zeng ◽  
Huazhong Lu ◽  
Enli Lv

In this paper, a lightweight channel-wise attention model is proposed for the real-time detection of five representative pig postures: standing, lying on the belly, lying on the side, sitting, and mounting. An optimized compressed block with symmetrical structure is proposed based on model structure and parameter statistics, and the efficient channel attention modules are considered as a channel-wise mechanism to improve the model architecture.The results show that the algorithm’s average precision in detecting standing, lying on the belly, lying on the side, sitting, and mounting is 97.7%, 95.2%, 95.7%, 87.5%, and 84.1%, respectively, and the speed of inference is around 63 ms (CPU = i7, RAM = 8G) per postures image. Compared with state-of-the-art models (ResNet50, Darknet53, CSPDarknet53, MobileNetV3-Large, and MobileNetV3-Small), the proposed model has fewer model parameters and lower computation complexity. The statistical results of the postures (with continuous 24 h monitoring) show that some pigs will eat in the early morning, and the peak of the pig’s feeding appears after the input of new feed, which reflects the health of the pig herd for farmers.


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