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Tomato SlPti5 plays a regulative role in the plant immune response against Botrytis cinerea through modulation of ROS system and hormone pathways

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Journal of Integrative Agriculture 2022年第3期21卷 697-709页

作者： TANG Qiong ZHENG Xiao-dong GUO Jun YU Ting College of Biosystems Engineering and Food Science National Engineering Laboratory of Intelligent Food Technology and Equipment/Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture/Zhejiang Key Laboratory for AgroFood ProcessingZhejiang UniversityHangzhou 310058P.R.China State Key Laboratory of Food Nutrition and Safety/Key Laboratory of Food Nutrition and Safety Ministry of Education/Key Laboratory of Food Quality and Health of TianjinTianjin University of Science and TechnologyTianjin 300457P.R.China

While SlPti5 has been shown to play a crucial role in the regulation of antagonistic genes in Solanum lycopersicum and Arabidopsis against pathogen infection,there have been no comprehensive studies on the effects of SlPti5 on the regulatory response mechanism of reactive oxygen species(ROS) system and hormone pathways during growth and disease resistance of tomato ***,we investigated the function of SlPti5 in the defense response of tomato against Botrytis cinerea utilizing a virus-induced gene silencing(VIGS)-based *** profile analysis showed that SlPti5 was significantly induced upon *** infection,with high expression levels in the leaves and fruit of ***-based silencing of SlPti5 inhibited early vegetative growth,increased the plant’s susceptibility to infection,promoted the development of ROS,affected the expression of genes involved in the ROS scavenging system,and attenuated the expression of genes associated with pathogenesis and the ethylene/jasmonic acid signaling *** sum,our data demonstrated that SlPti5 stimulates the immune response of tomato plant to Botrytis cinerea infection by involving the ethylene(ET)-and jasmonic acid(JA)-mediated pathways and modulating the expression of some key pathogenesis-related(PR) genes.

关键词： tomato(Solanum lycopersicum) Botrytis cinerea Sl Pti5 immune response ET-and JA-mediated signaling pathways pathogenesis-related proteins

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Online Identification of Boost Converter Components with Multiple Updating RLS Algorithm 42

Online Identification of Boost Converter Components with Mul...

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42nd Chinese Control Conference, CCC 2023

作者： Zhou, Tong Zhang, Zhicheng Zuo, Zhiqiang Wang, Yijing Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789887581543

In this paper, component parameters of the boost converter are identified online using a multiple updating recursive least squares (MURLS) algorithm. The component parameters, such as resistance, inductor inductance and capacitor capacitance, are obtained directly through the identification procedure rather than transfer function coefficients. The MURLS algorithm is applied to improve the rapidity of system identification compared to the traditional recursive least squares (RLS) algorithm, which is verified by a comparative simulation between MURLS and RLS and the simulation of a load-switching scenario. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Parameter estimation

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Robust Video Object Segmentation with Restricted Attention 48

Robust Video Object Segmentation with Restricted Attention

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Huaizheng Guo, Pinxue Le, Zhongwen Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing China Fudan University School of Computer Science Shanghai China Fudan University Academy for Engineering and Technology Shanghai China

ISBN: (纸本)9781728163277

This paper focuses on the two problems of the similar objects distraction and the lack of robustness for unseen object categories in semi-supervised video object segmentation task. Existing methods have achieved great results on the benchmark dataset, but these two problems still have not been completely solved. We propose the Robust Video Object Segmentation With Restricted Attention (RVOSR), which can suppress the effects caused by similar objects and filter out noise confusion from other irrelevant regions. Meanwhile augmenting the semantic information of features, which makes the features more suitable for video object segmentation task. Extensive experiments demonstrate the effectiveness of our approach and achieve the state-of-the-art performance on the widely-used VOS benchmarks including DAVIS-2016 (92.1% J& F), DAVIS-2017 (86.8% J& F) and YouTubeVOS-2019 (84.8%). © 2023 IEEE.

关键词： Semantics

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Microgrid energy management strategy considering microgrid economy and rSOC stability: A model-based study 43

Microgrid energy management strategy considering microgrid e...

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43rd Chinese Control Conference, CCC 2024

作者： Huang, Haizhou Xia, Zhiping Shan, Xinkai Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

ISBN: (纸本)9789887581581

Microgrids possess the ability to use renewable energy efficiently and play an increasingly significant role in environmental protection and sustainable development. Meanwhile, reversible solid oxide fuel cell (rSOC) is currently a new type of energy conversion and storage. Combining with microgrid systems is a great way to benefit from its high capacity, high efficiency, and cleanliness. However, rSOC's complex system of coupled heat, electricity, and gas presents a significant challenge in finding energy management strategies for microgrid applications containing rSOC. In this study, for a microgrid system combining rSOC, battery energy storage system (BESS), solar photovoltaic (PV), and wind turbine generator (WTG), we propose a model-based energy management strategy that simultaneously takes into account the economy of the microgrid system and the stability of the rSOC system. First of all, the microgrid is modeled with great detail, including the wind turbine, solar panels, battery storage system, and rSOC. In addition, the microgrid energy management problem is framed as a mathematical programming problem, with the attention focusing on the costs of BESS aging and operational energy loss of rSOC. In particular, this study also proposes a penalty strategy to reduce the power fluctuation amplitude of the rSOC system and reduce the mode switching frequency of the rSOC system. Using the solver, solving for each device's target power in a microgrid. The actual power operating curve is output by tracking the target power under slope constraints using the Simulink model. We then conduct a case study using data from Wuhan, China. Our research shows that compared with the control strategy based on rule control, the control strategy proposed in this study can reduce the power variation amplitude of the rSOC system by 79%, reduce the mode switching frequency by 85%, reduce the system operating cost by 4.5% and increase the energy self-sufficiency rate by 9%. © 2024 Techn

关键词： Wind turbines

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Continual Learning with a Memory of Non-similar Samples 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Min, Qilang He, Juanjuan Yang, Liuyan Fu, Yue College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

The replay method is an effective strategy to address the problem of catastrophic forgetting, a key challenge for continuous learning. However, the sample sets obtained by replay-based methods generally suffer from local data information deficiencies. This problem leads to an imbalance in the plasticity-stability of the model on older tasks. This paper proposes a novel method, called non-similar sample storage (NSS). Non-similar refers to the Euclidean distance for feature vectors of different samples being far. NSS extracts the feature vectors of the samples and then calculates the similarity of the feature vectors for each sample after the current task training. Samples that contribute less to the model classification effect among similar samples are iteratively deleted, and the subset of low-similarity samples is retained. Moreover, NSS reserves 30% of the storage space for saving samples near the center of the sample set. Low-similarity samples stored by NSS get larger losses during the replay process, leading to lower training effectiveness of the current task. This paper introduces a knowledge distillation strategy to solve this problem. A variable parameter was used to balance the classification loss of the new task with the distillation loss of the old task (NSS-D). Experimental results in CIFAR10 and imbalanced CIFAR10 show that NSS maximizes the data’s global information and can retain the model’s ability to recognize old tasks better. Compared with classical algorithms, NSS-D performs better on CIFAR100 (48.8%) and ImageNet-200 (36.7%). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Distillation

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Large depth range dithered binary focusing fringe projection technique 1

Large depth range dithered binary focusing fringe projection...

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1st Advanced Imaging and Information processing Conference, AIIP 2023

作者： Tan, Ji Wang, Xu Su, Wenqing He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9781510671904

The binary defocusing technique is sensitive to the defocusing degree. The defocusing projection mechanism will introduce high-frequency harmonics at the inappropriate defocused level, leading to limitations in measurement accuracy and depth range. In this paper, a binary-focusing projection technique combining generative adversarial networks is proposed. First, the focusing binary patterns based on error diffusion are projected on the measured surface, and then the captured fringe patterns are input to generative adversarial networks, which achieves sinusoidal correction and optimization for both the focused region and the low-quality defocused region due to its strong image translation ability. Finally, 3D measurement is realized by a phase-shifting algorithm. Compared with the traditional binary defocusing technique, the proposed method is not limited by the defocusing degree and maintains the advantages of high-speed projection, so it can achieve a larger measured depth range and improve measurement accuracy. Simulation and experiments verify the performance of the proposed method. © 2023 SPIE.

关键词： Focusing

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A Preliminary Study on University Computer Basic Curriculum Reform Aimed at Cultivating Computational Thinking from the Perspective of Smart Education 18th

A Preliminary Study on University Computer Basic Curriculum ...

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18th International Conference on Computer Science and Education, ICCSE 2023

作者： Yu, Niefang Liu, Yiwen Peng, Xiaoning Li, Xiaomei Lu, Youmin School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819707904

With the rapid advancement of information technology such as artificial intelligence, cloud computing, big data, virtual reality, etc., in the context of the deep integration of information technology and education and teaching, smart classroom has set off a new round of revolution. Based on this background, this paper discuss how to cultivate students’ computational thinking in a smart education environment, and explore the cultivation of computational thinking in basic university computer courses from three aspects: before class, during class, and after class in smart classrooms. In the background of smart education, only by comprehensively cultivating students’ computational thinking that we can achieve more results in the future. Thereby improving students’ ability to comprehensively apply computer technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Real-time arbitrarily-shaped scene text spotting with weakly supervised points

Real-time arbitrarily-shaped scene text spotting with weakly...

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2023 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Zhao, Longyang Zhang, Shuhao Xu, Zhi School of Computer Science and Information Security Guilin University of Electronics Technology No.1 Jinji road Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9781510677685

Recently, there has been widespread interest in arbitrary-shaped scene text spotting, showing notable progress in both speed and accuracy. However, common issues such as model complexity, slow inference speed, and challenges in deployment persist. For this reason, this paper proposes Weakly Supervised Point-collection Network (WSPNet) for arbitrarily shaped scene text, which adopts deformable convolutional single-shot network architecture to realize multi-branch parallel reading of text, and combines weakly supervised text line point sampling to realize sampling recognition and transcription of arbitrarily shaped text instances. To enhance the robustness of multi-scale fusion, we utilize the Text Feature Fusion (TFF) module to dynamically acquire key features at different scales during the fusion process. This approach aims to reduce discrepancies in scale fusion. Extensive experiments on arbitrarily shaped benchmarks show that WSPNet achieves competitive accuracy and speed. For example, the proposed method achieves an end-to-end text recognition F-measure of 63.4 on the Total-Text dataset at 35.5 FPS, which is competitive with the best results in terms of speed and accuracy balance. © 2024 SPIE.

关键词： Network architecture

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A super resolution target separation and reconstruction approach for single channel sar against deceptive jamming

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Defence technology（防务技术） 2023年第3期21卷 164-175页

作者： Shi-qi Liu Bing Li Bo Zhao Lei Huang Yue-zhou Wu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information EngineeringShenzhen UniversityShenzhen518060China School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China School of Computer Science Civil Aviation Flight University of ChinaGuanghan618307China China Aerospace Science and Technology Corporation Beijing100048China

The excellent remote sensing ability of synthetic aperture radar(SAR)will be misled seriously when it encounters deceptive jamming which possesses high fidelity and *** this paper,the dynamic synthetic aperture(DSA)scheme is used to extract the difference between the true and false targets.A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing *** system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be *** iterative reweighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework,where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its *** dictionary parameter learning,the grid mismatching problem in a fixed grid based method is ***,the proposed method can reap a super resolution ***,a simple yet effective DSA section scheme is developed for the SAR data excerpting,in which only two DSAs are *** results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

关键词： channel resolution separation

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