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NASA Technical Reports Server (Ntrs) 20120013259: 3d drop Size distribution Extrapolationd* algorithm Using a Single disdrometer

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2016年

NASA Technical Reports Server (Ntrs) 20120013259: 3d drop Size distribution Extrapolationd* algorithm Using a Single disdrometer by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 20120013259: algorithm algorithms cloud physics disdrometer distribution drop size extrapolation interpolation lane, john meteorological radar nasa technical reports server (ntrs) particle size distribution precipitation particle measurement radio meteorology raindrops size distribution terminal velocity

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NASA Technical Reports Server (Ntrs) 20010099429: Integration of Libration Point Orbit dynamics Into a Universal 3-d Autonomous Formation Flyingd* algorithm

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2016年

NASA Technical Reports Server (Ntrs) 20010099429: Integration of Libration Point Orbit dynamics Into a Universal 3-d Autonomous Formation Flyingd* algorithm by NASA Technical Reports Server (Ntrs); NASA Technical Report... 详细信息

关键词： (ntrs) 20010099429: algorithm algorithms automatic control autonomous autonomy dynamics earth observing system (eos) feedback control folta, david gravitational effects linear quadratic regulator linearization matrices (mathematics) nasa technical reports server (ntrs) transfer orbits

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NASA Technical Reports Server (Ntrs) 20130009435: 3d Hail Size distribution Interpolation/Extrapolationd* algorithm

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2016年

NASA Technical Reports Server (Ntrs) 20130009435: 3d Hail Size distribution Interpolation/Extrapolationd* algorithm by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 20130009435: algorithm algorithms distribution hail hailstorms ice formation interpolation interpolation extrapolation lane, john nasa technical reports server (ntrs) particle size distribution precipitation particle measurement radar data size distribution spatial distribution

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Stability analysis of generalized second-order nonlinear control systems

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第6期362卷

作者： Wang, Cong Li, Li Yao, Minghui Wu, Qiliang Niu, Yan Tiangong Univ Sch Aeronaut & Astronaut Tianjin 300387 Peoples R China Harbin Inst Technol Coll New Energy Weihai 264209 Shandong Peoples R China

To overcome the constant boundedness and slow time-varying constraints of disturbances, this paper presents a generalized second-order nonlinear controld* algorithm (GSONCA) and resulting a generalized second-order nonlinear control system (GSONCS), and further studies the stability and disturbance rejection of GSONCS. Unlike existing similar works, the GSONCS is a universal second-order system framework including nonlinear, time-varying, and switching terms, which is able to deal with time-dependent and state-dependent disturbances. All possible equilibrium points are discussed for the GSONCS, and the existence condition of a unique equilibrium point is constructed. Several practical stability inequalities of coefficients are established for the GSONCS where the coefficients can be almost arbitrary functions of state variable and time, which unify the stability criterion of second-order linear and nonlinear systems. Based on the proposed stability results, the disturbance rejection conditions of GSONCS are derived, and the good robustness of state-dependent-type second-order nonlinear systems is confirmed. As the applications of GSONCS, the parameter tuning methods of popular second-orderd* algorithms are provided, and simulations on dC-dC converters are presented to validate the proposed GSONCA.

关键词： Generalized second-order nonlinear control algorithm Stability analysis disturbance rejection Parameter tuning

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Groundwater storage anomalies projection by optimized deep learning refines groundwater management in typical arid basins

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JOURNAL OF HYdROLOGY 2025年 649卷

作者： deng, Xiaoya Wang, Guangyan Han, Feifei Gong, Yanming Hao, Xingming Zhang, Guangpeng Zhang, Pei Shan, Qianjuan China Inst Water Resources & Hydropower Res Dept Water Resources Beijing 100038 Peoples R China Xinjiang Tarim River Basin Management Bur Korla 841000 Xinjiang Peoples R China Xian Univ Technol Sch Water Resources & Hydropower Xian 710048 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The GRACE satellite provides tools for accurately characterizing the spatiotemporal variations of regional groundwater storage anomalies (GWSA) under the background of climate change and anthropogenic disturbances. However, its low spatial resolution restricts the refined management of groundwater. Multi-scale geographically weighted regression (MGWR) residuals are innovatively introduced for bias correction, which improves the GRACE-based GWSA downscaling accuracy (average R2 = 0.98). Further application of the Kmeans identifies four spatial distribution patterns of GWSA in the Tarim River mainstream (TRM), which showed a downward trend from 2003 to 2020. However, under effective groundwater management (such as ecological water transfer, ecological gate water diversion, etc.), the decline rate is gradually decreasing. Feature contribution analysis demonstrates that soil moisture storage (SMS), land surface temperature (LST), and normalized difference vegetation index (NdVI) are the primary driving factors of GWSA changes. Using the long short-term memory (LSTM) deep learning model optimized by multi-strategy gray wolf optimizationd* algorithm (MSGWO), the GWSA of four spatial patterns is predicted under two shared socioeconomic pathways (SSPs, including SSP245 and SSP585). The model achieved a maximum R/NSE of 0.95/0.91 on the train dataset and 0.88/0.71 on the test dataset, outperforming similar models. The future groundwater reserves of TRM will show an improving trend, indicating that groundwater management has achieved significant benefits. Notably, high emissions without government intervention (SSP585) have exacerbated the risk of groundwater resource shortages, and refined groundwater management needs to be further strengthened in the future. Overall, the proposed GRACEbased GWSA downscaling framework and MSGWO-LSTM predictive model provide tools for the refined scientific management of groundwater in arid basins.

关键词： GRACE-based groundwater storage anomalies Multi-scale geographically weighted regression Multi-strategy gray wolf optimization algorithm deep learning Arid basins Case study

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dynamic task offloading in edge computing for computer access point selection based on adaptive deep reinforcement learning with meta-heuristic optimization

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APPLIEd SOFT COMPUTING 2025年 176卷

作者： Vidya, S. Gopalakrishnan, R. KS Rangasamy Coll Technol Dept Elect & Elect Engn Namakkal 637215 Tamil Nadu India

The computationally intensive tasks are processed by mobile devices which include data processing, virtual reality, and artificial intelligence. The computational resources of the mobile devices are very low so they are suited to perform all tasks with low latency. Mobile Edge Computing (MEC) is a cutting-edge computing model that offloads computation-intensive tasks to MEC servers to increase the capability of computing in Mobile devices (Mds). due to the extensive use of Wireless Local Area Networks (WLAN), each Md can use numerous Wireless Access Points (WAPs) to offload tasks to a server. In this research work, the task offloading problem is determined by considering the delay-sensitive task along with edge load dynamics to reduce the long-term cost. The distributed* algorithm based on Adaptive deep Reinforcement Learning (AdRL) is introduced, where every device is analyzed for offloading decisions without knowing the task model of other devices. The parameters in the model are optimized using the Fitness-based Piranha Foraging Optimizationd* algorithm (F-PFOA) to enhance the performance of the model. Finally, the evaluation is done by using the various metrics to showcase the effectiveness of the proposed model, and it gives the throughput is 93.5, which is enhanced than other existing models. Thus, the simulation outcome with a greater number of mobile devices and corresponding edge nodes showed that the developed optimization minimizes the dropped task's ratio and average task delay respectively. The result of the designed model outperformed better than other available models.

关键词： dynamic Task Offloading Mobile Edge Computing Computer Access Point Selection Fitness-Based Piranha Foraging Optimization algorithm Adaptive deep Reinforcement Learning

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Advancing healthcare systems: A tri-tier architecture by using data communication, AI data generative and regulation and compliance standards

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EXPERT SYSTEMS 2025年第2期42卷

作者： Qureshi, Kashif Naseer Nafea, Hanaa Kim, Pyoungwon Univ Limerick Dept Elect & Comp Engn Limerick Ireland Taibah Univ Coll Comp Sci & Engn Dept Comp Sci Al Madinah Al Munawwarah Saudi Arabia Incheon Natl Univ Coll Educ Incheon South Korea Lero Sci Fdn Ireland Res Ctr Software Limerick Ireland

Traditional healthcare systems have suffered from different data communication, security, data processing, and compliance issues. The traditional systems are also not well equipped to handle the new technologies like Artificial Intelligence (AI) by enabling more accurate diagnostics, personalized treatment plans, and improved patient outcomes. The existing data communication and security protocols and compliance are also not fully implemented to tackle the system's challenges. This article proposes a Tri-Tier architecture by using data communication, AI data generative, and regulation and compliance tiers. The data communication tier is based on advanced sensing and monitoring technologies like cloud and edge-based systems integrated with security detection mechanisms. The edge and cloud layer provides the all functions of the perception layer like smart sensing, visual sensing, and monitoring services, and can control the device's perception and behaviour. The second tier provides the AI data generative functionalities to handle real-time synthetic medical images for predictive analytics to enhance patient care. This tier also automates routine tasks, such as administrative work and data analysis, which can free up healthcare professionals to focus on more complex tasks. The last regulation and compliance tier is responsible for handling the standards and compliance for healthcare systems. Experiments are conducted to test the data communication and security level of the proposed architecture. The results showed the suitability of existing solutions and synchronization with the proposed architecture.

关键词： algorithm artificial intelligence compliance data communication data processing education healthcare security systems

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Scalable Methods for Multiple Time Series Comparison in Second Order dynamics

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TECHNOMETRICS 2025年第1期67卷 82-96页

作者： Jin, Lei Li, Bo Texas A&M Univ Corpus Christi Dept Math & Stat Corpus Christi TX 78412 USA Univ Illinois Dept Stat Champaign IL USA

Statistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually independent time series, and empirical results for dependent time series are only limited to comparing two time series. We propose scalable methods based on a newd* algorithm that enables us to compare the spectral density of a large number of time series. The newd* algorithm helps us efficiently obtain all pairwise feature differences in frequency patterns between M time series, which plays an essential role in our methods. When all M time series are independent of each other, we derive the joint asymptotic distribution of their pairwise feature differences. The asymptotic dependence structure between the feature differences motivates our proposed test for multiple mutually independent time series. We then adapt this test to the case of multiple dependent time series by partially accounting for the underlying dependence structure. Additionally, we introduce a global test to further enhance the approach. To examine the finite sample performance of our proposed methods, we conduct simulation studies. The new approaches demonstrate the ability to compare a large number of time series, whether independent or dependent, while exhibiting competitive power. Finally, we apply our methods to compare multiple mechanical vibrational time series.

关键词： algorithm dynamics Pairwise differences Periodogram Spectral method Vibration data

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development of a Software and Hardware Complex for Monitoring Processes in Production Systems

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SENSORS 2025年第5期25卷 1527-1527页

作者： Pechenin, Vadim Paringer, Rustam Ruzanov, Nikolay Khaimovich, Aleksandr Samara Natl Res Univ Res Lab Artificial Intelligence Prod Syst Moskovskoye Shosse 34 Samara 443086 Russia Samara Natl Res Univ Dept Tech Cybernet Moskovskoye Shosse 34 Samara 443086 Russia Samara Natl Res Univ Dept Engine Prod Technol Moskovskoye Shosse 34 Samara 443086 Russia

The article presents a detailed exposition of a hardware-software complex that has been developed for the purpose of enhancing the productivity of accounting for the state of the production process. This complex facilitates the automation of the identification of parts in production containers and the utilisation of supplementary markers. The complex comprises a mini computer (system unit in industrial version) with connected cameras (IP or WEB), a communication module with LEd and signal lamps, and developed software. The cascaded* algorithm developed for the detection of labels and objects in containers employs trained convolutional neural networks (YOLO and VGG19), thereby enhancing the recognition accuracy while concurrently reducing the size of the training sample for neural networks. The efficacy of the developed system was assessed through laboratory experimentation, which yielded experimental results demonstrating 93% accuracy in detail detection using the developed* algorithm, in comparison to the 72% accuracy achieved through the utilisation of the traditional approach employing a single neural network.

关键词： camera hardware-software complex container detection neural network algorithm

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A REVIEW OFd* algorithmS FOR dISTRIBUTIONALLY ROBUST OPTIMIZATION USING STATISTICAL dISTANCES

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JOURNAL OF INdUSTRIAL ANd MANAGEMENT OPTIMIZATION 2025年第3期21卷 1749-1770页

作者： Wang, Zude Jiang, Suhong Lin, Yue Tao, Yuan Chen, Caihua Nanjing Univ Sch Management & Engn Nanjing Peoples R China Nanjing Univ Finance & Econ Sch Accounting Nanjing Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

distributionally robust optimization (dRO) utilizing statistical distances has witnessed significant advancements, driven by its appealing properties and promising applications. However, its computational complexity poses a challenge, especially in large-scale scenarios. To address this challenge, extensive research efforts have been directed towards developingd* algorithms tailored for dRO problems utilizing statistical distances. This paper focuses on exploring solution methodologies for dRO problems mainly incorporating both phi-divergences and Wasserstein metric, providing a comprehensive survey of existingd* algorithmic frameworks.

关键词： distributionally robust optimization statistical distance phi-divergence Wasserstein metric algorithm

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