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Optimizing the Vegetation Health Index for Agricultural Drought Monitoring: Evaluation and Application in the Yellow River Basin

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REMOTE SENSING 2024年第23期16卷 4507-4507页

作者： Hang, Qinghou Guo, Hao Meng, Xiangchen Wang, Wei Cao, Ying Liu, Rui De Maeyer, Philippe Wang, Yunqian Qufu Normal Univ Sch Geog & Tourism Rizhao 276826 Peoples R China Sino Belgian Joint Lab Geoinformat Rizhao 276826 Peoples R China Sino Belgian Joint Lab Geoinformat B-9000 Ghent Belgium Univ Ghent Dept Geog B-9000 Ghent Belgium

The ecological environment of the Yellow River Basin in China is characterized by drought, which has been exacerbated by global warming. It is critical to keep accurate track of the region's agricultural drought conditions. To enhance the vegetation health index (VHI), the optimal time scale for the standardized precipitation evapotranspiration index (SPEI) was determined by using the maximum correlation coefficient method, and the calculation method for VHI was optimized. The contributions of the vegetation condition index (VCI) and the temperature condition index (TCI) to the VHI were scientifically optimized, leading to the development of the optimal VHI (VHIopt). Soil moisture anomaly (SMA) and the SPEI were employed for assessing the performance of VHIopt. Furthermore, the temporal and spatial evolution of agricultural drought in the Yellow River Basin (YRB) was analyzed using VHIopt. The results indicate the following: (1) In the YRB, the optimal contribution of the VCI to the VHI is lower than that of the TCI. (2) The drought monitoring accuracy of VHIopt in forests, grasslands, croplands, and other vegetation types exceeds that of the original VHI (VHIori). Additionally, it demonstrates a high level of consistency with the SMA and the SPEI03 regarding spatial and temporal characteristics. (3) Agricultural drought in the YRB is gradually diminishing;however, significant regional differences remain. Generally, the findings of this study highlight that VHIopt is better suited to the specific climate and vegetation conditions of the Yellow River Basin, enhancing its effectiveness for agricultural drought monitoring in this region.

关键词： vegetation health index yellow river basin algorithm optimization drought monitoring

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A novel measurement strategy for explosion temperature field towards enhancing the fire process safety

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FIRE SAFETY JOURNAL 2024年 145卷

作者： Yu, Hao Sun, Yaru Liu, Yan Wang, Xiaofeng Huang, Fenglei Liu, Haipeng Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China

Chemical explosives such as TNT are widely used in special scenarios, such as civil explosives, due to their simple preparation and high energy density. However, the temperature of the fireball produced by the explosion of these explosives is extremely high. Moreover, this approach may cause many casualties and economic losses if not prevented. To accurately measure the temperature of a transient explosion field and study its space-time distribution, a two-dimensional high-speed temperature measurement system was built based on blackbody radiation theory, high-speed photography, image Bayer array and improved interpolation algorithm. The explosive fireball produced in a static explosion test of 10 kg of TNT was measured within the shooting range. TNT was added with an auxiliary blackbody (tungsten). Compared to traditional explosion temperature measurement methods, the experimental results showed that the colorimetric temperature measurement method based on the improved interpolation algorithm and the addition of tungsten powder could more accurately measure the temporal and spatial distributions of the temperature field. This work can aid in the prevention in the accidents during the transportation, storage, and manufacturing of chemicals relevant to process industries.

关键词： Process safety Space-time distribution algorithm optimization Explosion temperature field

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Efficient federated learning algorithm using sparse ternary compression based on layer variation classification

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COMPUTER NETWORKS 2024年 247卷

作者： Liu, Yuanyuan Chen, Kexin Zhu, Lu East China Jiaotong Univ Inst Informat Engn Nanchang Peoples R China

Federated learning is a distributed machine learning technique that ensures user privacy and enables multiple clients to jointly train a shared global model without transmitting local data. However, the frequent exchange of model parameters between numerous clients and the server results in heavy network delay and bandwidth limitation in federated learning. In view of that, we propose an efficient algorithm for federated learning using sparse ternary compression based on layer variation classification(LVC). First, use layer variation as a metric to assess the significance of each layer of the model parameters, and after client training, categorize the model parameters into different levels by the layer variation and sensitivity analysis. Then, during the upstream and downstream transmission of model parameters, we assign corresponding sparse and ternary quantization ratios for different levels to maximize compression efficiency while preserving crucial parameters. Finally, on the server side, a majority-layer aggregation strategy is adopted to further reduce the communication cost. Experimental results from image classification tasks conducted on MNIST and Fashion-MNIST datasets demonstrate that proposed LVC algorithm achieves high accuracy with minimal communication cost, thereby striking an optimal balance between communication efficiency and accuracy.

关键词： Federated learning Communication efficiency Model sparsity Parameter quantization algorithm optimization

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Stator Curvature optimization and Analysis of Axial Hydraulic Vane Pumps

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ENERGIES 2022年第17期15卷 6229页

作者： Sun, Yongguo Xue, Dong Liu, Shisheng Wu, Jinghang Bai, Xingyu Harbin Univ Sci & Technol Sch Mech Power Engn Harbin 150008 Peoples R China

Aiming at the problem of large vibration of a high-subside stator inner cavity curve vane pump, the force analysis of the vane at the transition curve is carried out, and the functional relationship between the vane turning angle theta, the large arc radius R, the small arc radius r and the sliding friction is established. The particle swarm algorithm is used to optimize the solution of the objective function, and the optimized parameter values are brought into the MATLAB simulation program to obtain the optimized stator curve profile diagram. The dynamic performance of the vane pump before and after optimization is simulated using ADAMS. The results show that: the acceleration of the vane pump slide is significantly reduced;the friction between the slide and the slide groove is significantly reduced;the contact force between the slide and the stator is significantly reduced;the impact vibration of the optimized vane pump is significantly reduced;and the dynamic performance of the vane pump is improved.

关键词： stator blade force analysis algorithm optimization dynamic performance

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A closed-form solution for evaluating the Findley critical plane factor

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2024年 105卷

作者： Chiocca, A. Sgamma, M. Frendo, F. Univ Pisa Dept Civil & Ind Engn Pisa Italy

The fatigue assessment of structural components is a significant topic investigated both in the academia and industry. Despite the significant progress in comprehension over the past few decades, fatigue damage remains a significant challenge, often leading to unexpected component failures. One commonly used approach for fatigue assessment is the critical plane analysis, which aids in identifying the critical location and early crack propagation direction in a component. However, the conventional method for calculating critical plane factors is computationally demanding and is typically utilized only when the critical regions of the component are already known. In situations where the critical areas are difficult to be identified due to complex geometry, loads, or constraints, a more efficient method is required for evaluating critical plane factors. This research paper introduces an analytical algorithm to efficiently evaluates the widely used Findley critical plane factor. The algorithm operates within the framework of linear-elastic material behavior and proportional loading conditions, relying on tensor invariants and coordinate transformation laws. The algorithm has been tested on different component geometries, including a box-welded joint and a tubular specimen, subjected to proportional loading conditions such as tension, torsion, and a combination of them. The analytical method allowed a significant reduction in computation time while providing the exact solution of critical plane factor and critical plane orientations.

关键词： Critical plane Findley Material fatigue Computational efficiency algorithm optimization

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Research on image classification based on residual group multi-scale enhanced attention network

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期118卷

作者： Wang, Chunzhi Deng, Xizhi Sun, Yun Yan, Lingyu Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

To address the challenges associated with insufficient feature extraction and gradient degradation encountered when dealing with deepening network structures in image classification tasks, this paper presents a ResGMEANet(Residual Group Multi-scale Enhanced Attention Network). The model introduces a multi-scale attention enhancement module. This design draws inspiration from the original model's capability to independently capture feature correlations in channels and spaces. By implementing shuffle operations and feature transformations within the group, our method expands the receptive field through the utilization of multiple convolution kernels. Additionally, we incorporate an improved tensor synthesis attention, building upon the traditional convolution attention, to derive attention feature maps after feature enhancement. Evaluation on the CIFAR-10 and CIFAR-100 datasets shows that ResGMEANet outperforms both the original backbone model and several existing mainstream methods in classification accuracy. This work aims to provide a new perspective for the future by combining residual neural networks with different attention mechanisms.

关键词： Multi -scale convolution Feature enhancement Image classification algorithm optimization

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Study on Dense Phase Separation of Associated Gas with High Carbon Dioxide Content

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PROCESSES 2024年第12期12卷 2924页

作者： Jing, Yuxiao Zhang, Ming Wang, Qihang Zhu, Jianlu Xie, Naiya Li, Yuxing State Key Lab Offshore Oil & Gas Exploitat Beijing 100027 Peoples R China China Univ Petr East China Coll Pipeline & Civil Engn Natl Virtual Simulat Qingdao 266580 Peoples R China

With the continuous exploitation of offshore natural gas, the content of CO2 produced gradually increases. It is not economical to separate more CO2 from natural gas after transportation, and more CO2 will aggravate the corrosion of pipelines. The commonly used decarburization process is not suitable for offshore platforms, and there are problems of high energy consumption and large space occupation. Therefore, dense phase separation of associated gas with high carbon dioxide content is a better separation method. In this paper, the equation of state is optimized by comparing the experimental and CO2 system phase characteristics simulation. Based on the selected equation of state (EOS), a three-level separation model of phase equilibrium characteristics is established. The separation efficiency is simulated to complete the separation of CO2 and methane. The separation process is optimized by a genetic algorithm, and the temperature and pressure under the best separation efficiency are determined. The PR-EOS was selected as the equation with the highest calculation accuracy. Through process simulation and algorithm optimization, the best separation efficiency was 72.23%.

关键词： high carbon dioxide-associated gas equation of state dense phase separation process simulation algorithm optimization

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Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2021年第4期14卷 2511-2523页

作者： Tian, Zhun Zhang, Shengrui Changan Univ Coll Transportat Engn Xian 710064 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China Changan Univ Key Lab Transport Ind Management Control & Cycle Xian 710064 Shaanxi Peoples R China

As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper.

关键词： Cloud computing Big data Clustering algorithm Traffic accident prediction algorithm optimization

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Sys-TM: A Fast and General Topic Modeling System

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第6期33卷 2790-2802页

作者： Shao, Yingxia Li, Xupeng Chen, Yiru Yu, Lele Cui, Bin Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Peking Univ Sch EECS Key Lab High Confidence Software Technol MOE Haidian 100871 Peoples R China Tencent Inc Shenzhen 518000 Guangdong Peoples R China

Topic models, such as LDA and its variants, are popular probabilistic models for discovering the abstract "topics" that occur in a collection of documents. However, the performance of topic models may vary a lot for different workloads, and it is not a trivial task to achieve a well-optimized implementation. In this paper, we systematically study all recently proposed samplers over LDA: AliasLDA, F+LDA, LightLDA, and WarpLDA, and discover a novel system tradeoff by considering the diversity and skewness of workloads. Then, we propose a hybrid sampler which can cleverly choose an efficient sampler with the tradeoff, and apply the hybrid sampler to LDA and its variants, including STM, TOT and CTM. Finally, we build a fast and general topic modeling system Sys-TM, which provides a unified topic modeling framework by integrating the hybrid sampler. Based on our empirical studies, the hybrid sampler outperforms the state-of-the-art samplers by up to 2x over various topic models, and with carefully engineered implementation, Sys-TM is able to outperform the existing systems by up to 10x.

关键词： Topic model sampling method algorithm optimization hybrid

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Prime Number Sieving-A Systematic Review with Performance Analysis

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algorithmS 2024年第4期17卷 157页

作者： Ghidarcea, Mircea Popescu, Decebal Univ Politehn Bucuresti Comp Sci Splaiul Independentei 313 Bucharest 060042 Romania

The systematic generation of prime numbers has been almost ignored since the 1990s, when most of the IT research resources related to prime numbers migrated to studies on the use of very large primes for cryptography, and little effort was made to further the knowledge regarding techniques like sieving. At present, sieving techniques are mostly used for didactic purposes, and no real advances seem to be made in this domain. This systematic review analyzes the theoretical advances in sieving that have occurred up to the present. The research followed the PRISMA 2020 guidelines and was conducted using three established databases: Web of Science, IEEE Xplore and Scopus. Our methodical review aims to provide an extensive overview of the progress in prime sieving-unfortunately, no significant advancements in this field were identified in the last 20 years.

关键词： prime numbers prime number sieving prime number generation algorithms algorithm optimization parallel algorithms

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