88 research outputs found
TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices
Classification of rice disease is one significant research topics in rice phenotyping. Recognition of rice diseases such as Bacterialblight, Blast, Brownspot, Leaf smut, and Tungro are a critical research field in rice phenotyping. However, accurately identifying these diseases is a challenging issue due to their high phenotypic similarity. To address this challenge, we propose a rice disease phenotype identification framework which utilizing the transfer learning and SENet with attention mechanism on the cloud platform. The pre-trained parameters are transferred to the SENet network for parameters optimization. To capture distinctive features of rice diseases, the attention mechanism is applied for feature extracting. Experiment test and comparative analysis are conducted on the real rice disease datasets. The experimental results show that the accuracy of our method reaches 0.9573. Furthermore, we implemented a rice disease phenotype recognition platform based microservices architecture and deployed it on the cloud, which can provide rice disease phenotype recognition task as a service for easy usage
Enhancing Traffic Prediction with Learnable Filter Module
Modeling future traffic conditions often relies heavily on complex
spatial-temporal neural networks to capture spatial and temporal correlations,
which can overlook the inherent noise in the data. This noise, often
manifesting as unexpected short-term peaks or drops in traffic observation, is
typically caused by traffic accidents or inherent sensor vibration. In
practice, such noise can be challenging to model due to its stochastic nature
and can lead to overfitting risks if a neural network is designed to learn this
behavior. To address this issue, we propose a learnable filter module to filter
out noise in traffic data adaptively. This module leverages the Fourier
transform to convert the data to the frequency domain, where noise is filtered
based on its pattern. The denoised data is then recovered to the time domain
using the inverse Fourier transform. Our approach focuses on enhancing the
quality of the input data for traffic prediction models, which is a critical
yet often overlooked aspect in the field. We demonstrate that the proposed
module is lightweight, easy to integrate with existing models, and can
significantly improve traffic prediction performance. Furthermore, we validate
our approach with extensive experimental results on real-world datasets,
showing that it effectively mitigates noise and enhances prediction accuracy
Accurate simulation of ice and snow runoff for the mountainous terrain of the Kunlun Mountains, China
While mountain runoff provides great potential for the development and life quality of downstream populations, it also frequently causes seasonal disasters. The accurate modeling of hydrological processes in mountainous areas, as well as the amount of meltwater from ice and snow, is of great significance for the local sustainable development, hydropower regulations, and disaster prevention. In this study, an improved model, the Soil Water Assessment Tool with added ice-melt module (SWATAI) was developed based on the Soil Water Assessment Tool (SWAT), a semi-distributed hydrological model, to simulate ice and snow runoff. A temperature condition used to determine precipitation types has been added in the SWATAI model, along with an elevation threshold and an accumulative daily temperature threshold for ice melt, making it more consistent with the runoff process of ice and snow. As a supplementary reference, the comparison between the normalized difference vegetation index (NDVI) and the quantity of meltwater were conducted to verify the simulation results and assess the impact of meltwater on the ecology. Through these modifications, the accuracy of the daily flow simulation results has been considerably improved, and the contribution rate of ice and snow melt to the river discharge calculated by the model increased by 18.73%. The simulation comparison of the flooding process revealed that the accuracy of the simulated peak flood value by the SWATAI was 77.65% higher than that of the SWAT, and the temporal accuracy was 82.93% higher. The correlation between the meltwater calculated by the SWATAI and the NDVI has also improved significantly. This improved model could simulate the flooding processes with high temporal resolution in alpine regions. The simulation results could provide technical support for economic benefits and reasonable reference for flood prevention
Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term
Deep Neural Networks (DNNs) generalization is known to be closely related to
the flatness of minima, leading to the development of Sharpness-Aware
Minimization (SAM) for seeking flatter minima and better generalization. In
this paper, we revisit the loss of SAM and propose a more general method,
called WSAM, by incorporating sharpness as a regularization term. We prove its
generalization bound through the combination of PAC and Bayes-PAC techniques,
and evaluate its performance on various public datasets. The results
demonstrate that WSAM achieves improved generalization, or is at least highly
competitive, compared to the vanilla optimizer, SAM and its variants. The code
is available at
https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers.Comment: 10 pages. Accepted as a conference paper at KDD '2
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
Pervasive integration of GPS-enabled devices and data acquisition
technologies has led to an exponential increase in GPS trajectory data,
fostering advancements in spatial-temporal data mining research. Nonetheless,
GPS trajectories contain personal geolocation information, rendering serious
privacy concerns when working with raw data. A promising approach to address
this issue is trajectory generation, which involves replacing original data
with generated, privacy-free alternatives. Despite the potential of trajectory
generation, the complex nature of human behavior and its inherent stochastic
characteristics pose challenges in generating high-quality trajectories. In
this work, we propose a spatial-temporal diffusion probabilistic model for
trajectory generation (DiffTraj). This model effectively combines the
generative abilities of diffusion models with the spatial-temporal features
derived from real trajectories. The core idea is to reconstruct and synthesize
geographic trajectories from white noise through a reverse trajectory denoising
process. Furthermore, we propose a Trajectory UNet (Traj-UNet) deep neural
network to embed conditional information and accurately estimate noise levels
during the reverse process. Experiments on two real-world datasets show that
DiffTraj can be intuitively applied to generate high-fidelity trajectories
while retaining the original distributions. Moreover, the generated results can
support downstream trajectory analysis tasks and significantly outperform other
methods in terms of geo-distribution evaluations
Silicon acquisition and accumulation in plant and its significance for agriculture
Although silicon (Si) is ubiquitous in soil and plant, evidence is still lacking that Si is essential for higher plants. However, it has been well documented that Si is beneficial for healthy growth of many plant species. Si can promote plant mechanical strength, light interception, as well as resistance to various forms of abiotic and biotic stress, thus improving both yield and quality. Indeed, application of Si fertilizer is a rather common agricultural practice in many countries and regions. As the beneficial effects provided by Si are closely correlated with Si accumulation level in plant, elucidating the possible mechanisms of Si uptake and transport in plants is extremely important to utilize the Si-induced beneficial effects in plants. Recently, rapid progress has been made in unveiling molecular mechanisms of Si uptake and transport in plants. Based on the cooperation of Si influx channels and efflux transporters, a model to decipher Si uptake, transport and distribution system in higher plants has been developed, which involves uptake and radial transport in root, xylem and inter-vascular transport and xylem unloading and deposition in leaf. In this paper, we overviewed the updated knowledge concerning Si uptake, transport and accumulation and its significance for the major crops of agricultural importance and highlighted the further research needs as well
TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China
TiEV is an autonomous driving platform implemented by Tongji University of
China. The vehicle is drive-by-wire and is fully powered by electricity. We
devised the software system of TiEV from scratch, which is capable of driving
the vehicle autonomously in urban paths as well as on fast express roads. We
describe our whole system, especially novel modules of probabilistic perception
fusion, incremental mapping, the 1st and the 2nd planning and the overall
safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future
Challenge of China held at Changshu. We show our experiences on the development
of autonomous vehicles and future trends
Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator
This article proposes a new incremental model predictive control (IMPC) strategy, which allows for constrained control of a robot manipulator, while the resulting incremental model is derived without a concrete mathematical system model. First, to reduce dependence on the nominal model of robot manipulators, the continuous-time nonlinear system model is approximated by an incremental system using the time-delay estimation (TDE). Then, based on the incremental system, the tracking IMPC is designed in the framework of MPC without terminal ingredients. Thus, compared with existing MPC methods, the nominal mathematical model is not required. Moreover, we investigate reachable reference trajectories and confirm the local input-to-state stability (ISS) of IMPC, considering the bounded TDE error as the disturbance of the incremental system. For reachable reference trajectories, the local ISS of IMPC is analyzed using the continuity of the value function, and the cumulative error bound is not overconservative. Finally, several real-time experiments are conducted to verify the effectiveness of IMPC. Experimental results show that the system can achieve optimal control performance while guaranteeing that input and state constraints are not violated
Multistage Strike-Slip Fault in the Narrowest Portion of the Qinling Orogen, Central China: Deformation Mechanism and Tectonic Significance
The North Huicheng Basin strike-slip fault system is on the northeastern frontier of the Tibetan Plateau and separates the West and East Qinling differential orogeny. However, the deformation mechanism of this strike-slip fault system and its exact tectonic significance are unclear. Here, we carried out systematic field structural analysis, physical analog modeling, and multiproxy geochronological dating to address these issues. The field structural analysis indicates that the North Huicheng Basin strike-slip fault system was induced from the plate-like movement of the West and East Qinling Orogens, which underwent multiple left-lateral strike-slip faulting and controlled salient and recessed structures. The scaled physical analog experiment results confirm this hypothesis and reveal the primary spatial-temporal deformational kinematic process. Combined with published works, multiproxy geochronological dating (zircon U‒Pb age of 213 Ma, biotite 40Ar/39Ar age of 203 Ma, and apatite fission-track age of 56 Ma) outlines the main thermal history of the hanging wall. Based on the above facts, the integrated research suggests that multistage strike-slip faulting played a significant role in the main tectonic events, that is, late Triassic magmatic emplacement, Jurassic/Cretaceous local pull-apart, and Cenozoic rapid exhumation driven by Tibetan Plateau growth
- …