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Predicting the small strain shear modulus of sands and sand-fines binary mixtures using machine learning algorithms

TRANSPORTATION GEOTECHNICS 2024年 44卷

作者： Khodkari, Naser Hamidian, Pouria Khodkari, Homayoun Payan, Meghdad Behnood, Ali Univ Tehran Coll Engn Sch Civil Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Turbulence Lab Tehran Iran Univ Guilan Fac Engn Dept Civil Engn Rasht Iran Univ Mississippi Dept Civil Engn Oxford MS USA

This study aims to develop several novel machine learning (ML) evolutionary algorithms for the prediction of small strain shear modulus (Gmax) of clean sands and sand-fines binary mixtures. To this end, five key features of isotropic confining pressure (p), void ratio (e), uniformity coefficient (Cu), particle shape descriptor (rho), and non plastic fines content (FC) are adopted as the inputs to artificial neural network (ANN) models as well as genetic programming (GP) algorithm so as to render the maximum shear modulus of granular soils as the output. Accordingly, a comprehensive dataset containing 1055 Gmax data points is exploited to develop ML simulations. The validity of ML-based models in estimating the Gmax of clean sands and sand-silt mixtures is rigorously examined through various statistical indices and measurement criteria. The results show that a novel ML model utilizing ANN-Levenberg Marquardt (LM) in conjunction with an evolutionary optimization method named Success History-based Adaptive Differential Evolution with Linear population size reduction (LSHADE) is capable of predicting Gmax data with a very high precision rendering R2 values of 0.9833, 0.9841, 0.9802, and 0.9835 for the whole, training, validation, and test datasets, respectively. Meanwhile, using the well-established GP algorithm, a new practical model is proposed to predict the Gmax of clean sands and sand-fines mixtures containing non-cohesive silt inclusion with R2 values of 0.9323, 0.9351, and 0.9312 for the whole, training, and test datasets, respectively. Finally, the proposed models of ANN-LSHADE-LM and GP are shown to be appreciably superior, in terms of accuracy, to all commonly used empirical correlations in the literature for Gmax estimation.

关键词： Small strain shear modulus Artificial neural network (ANN) optimization algorithm Differential evolution (DE) Genetic programming (GP) Sand-silt mixtures

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When to sleep and consume caffeine to boost alertness

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SLEEP 2024年第10期47卷 zsae133页

作者： Vital-Lopez, Francisco G. Doty, Tracy J. Reifman, Jaques US Army Biotechnol High Performance Comp Software Applicat Telemed & Adv Technol Res Ctr Dept DefMed Res & Dev Command ATTN FCMR TT504 Scott St Ft Detrick MD 21702 USA Henry M Jackson Fdn Advancement Mil Med Inc Bethesda MD USA Walter Reed Army Inst Res Behav Biol Branch Silver Spring MD USA

Study Objectives Sleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to suboptimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the *** We combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm's recommendations against that achieved by following the U.S. Army caffeine *** Compared to the alertness-impairment levels in the original studies, the algorithm's recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine *** We provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times. Graphical Abstract

关键词： alertness caffeine fatigue management optimization algorithm sleep schedules

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Repeater Coordination Using Optimism algorithm

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Procedia Engineering 2011年 15卷 2814-2818页

作者： Chungen Xu Yuhang Dou Hanlin Yang Xing Sun Department of Applied Mathematics Nanjing University of Science & Technology Nanjing 210094 China School of Electronic and Optical Engineering Nanjing University of Science & Technology Nanjing 210094 China

An optimization algorithm for repeater coordination is developed. Using a repeater with the system of CTCSS, much more low-power users can communicate with one another in situations where direct user-to-user contact would not be possible. Since the locations of these repeaters are arbitrary, the problem of repeater coordination in a circular flat area with minimum number of repeaters is difficult. But by using the completely seamless coverage theory and the hexagonal grid partition method, we can determine 14 optional repeater locations in two topological structures. Considering the necessary constraint conditions, we transfer this problem into the optimization planning problem.

关键词： repeater coordination optimization algorithm communication hexagonal

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Priority-aware task assignment and path planning for efficient and load-balanced multi-UAV operation

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VEHICULAR COMMUNICATIONS 2023年 42卷

作者： Poudel, Sabitri Moh, Sangman Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

Unmanned aerial vehicles (UAVs) have emerged as a viable option in numerous real-world applications. In large mission planning systems, demanding the effective completion of a wide variety of tasks, multi-UAV systems are significantly preferred. Collaborative task assignment and path planning have a significant impact on autonomous operation of multi-UAV systems. In this study, we propose a priority-aware task assignment and path planning (AMTP) algorithm based on actor-critic multi-agent reinforcement learning for multi-UAV operations. We utilize an actor-critic multi-agent framework based on centralized training and decentralized execution for task assignment and path planning of heterogeneous UAVs. A multi-agent deep deterministic policy gradient framework is used to train the system to deal with both the elements of the dynamic environment and the heterogeneity of UAVs and tasks. The critic network evaluates the policy by maintaining the global network state information and combines the actions of agents to simplify the training state. On the other hand, agents decide which actions to comply with their local observations to improve the policy. The performance evaluation results obtained through simulation show that the proposed AMTP outperforms existing methods in terms of task completion rate, load balancing, energy efficiency, and convergence time. & COPY;2023 Elsevier Inc. All rights reserved.

关键词： Unmanned aerial vehicle Task assignment Path planning optimization algorithm Reinforcement learning Multi-UAV network

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Deep learning-based inversion of soil and rock compaction density utilizing Falling Weight Deflectometer (FWD) and Surface Waves

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TRANSPORTATION GEOTECHNICS 2024年 49卷

作者： Li, Chen Gao, Guozhong Shen, Pengtian Wang, Yanling Wang, Yunsheng Wuhan Business Univ Coll Informat Engn Wuhan 430100 Hubei Peoples R China VGP Serv LLC Houston TX 77406 USA Wuhan 99 Future Intelligent Tech Co Ltd Intelligent Geophys Res Wuhan 430000 Hubei Peoples R China Yunnan Jiaohang Engn Technol Co Ltd Anning 650300 Yunnan Peoples R China

Methods relying on a single dynamic parameter to estimate the material density for the compaction quality testing of earth-rock dams and roadbed projects, present certain limitations in terms of applicability, testing accuracy, and work efficiency. The theory of elastic wave propagation reveals that the compaction density of the medium results from the combined interaction of multiple material properties. This study proposes a method that utilizes the transient surface wave method and a vehicle-mounted Falling Weight Deflectometer (FWD) to jointly enhance the accuracy of soil and rock material compaction detection. The transient surface wave method is employed to extract surface wave velocity and compressional wave velocity. The analysis of the FWD signal involves extracting dynamic parameters closely related to material density, including stiffness coefficients, central frequencies, and stiffness impedances, from time-domain, frequency-domain, and mechanical impedance analyses. A fully connected deep neural network is introduced to intelligently estimate the compaction density. The deep learning model is optimized by selecting the optimal activation function and optimization algorithm, as well as additional tuning. Extensive testing shows that deep learning model produce results closely approximating the true compaction density values. The average relative errors for the wet density and the dry density are 2.9% and 2.85%, respectively, meeting the quality control requirements for density testing. The proposed approach enables rapid detection and control of the compaction quality of soil and rock materials, providing a new method for the in-situ rapid detection of compaction quality.

关键词： Transient Surface Wave FWD Compaction density Characteristic parameters Deep learning optimization algorithm

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Mathematical Method of Scheduling Lead-Acid Battery for Cycling Use

Mathematical Method of Scheduling Lead-Acid Battery for Cycl...

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29th Annual IEEE Applied Power Electronics Conference and Exposition (APEC)

作者： Liu, Baoquan Zhuo, Fang Zhu, Yixin Zhong, Shaohui Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaan Xi Peoples R China

ISBN: (纸本)9781479923250

A mathematical method is proposed for optimal scheduling lead-acid batteries for cycling use in this work. Typical battery characteristic curves and experiential data, including the discharge rate vs. discharge time, discharge depth vs. cycling times, are first converted into mathematical expressions with consideration of the circumstance temperature. And then through the proposed optimization algorithm aiming at the minimum capital cost, the recommended battery capacity, installation plan, discharge depth and discharge rate are derived, benefiting the designers and customers. A case study is carried out finally to verify the design procedure and optimization algorithm.

关键词： lead acid batteries optimisation scheduling circumstance temperature cycling times cycling use discharge depth discharge rate discharge time installation plan lead-acid battery optimal scheduling mathematical method minimum capital cost optimization algorithm recommended battery capacity typical battery characteristic curves Batteries Capacity planning Curve fitting Discharges (electric) Lead optimization

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Efficiency analysis of dual motor powertrain with planetary gear set

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2023年第5期24卷 601-608页

作者： Mishra, Shivam Dominic, Arun Mudliyar, Karuna Divakar, Manjula Natl Inst Technol Surathkal Dept Elect & Elect Engn Surathkal Karnataka India Daimler Truck Innovat Ctr Bengaluru India

This paper focuses on electric vehicles with dual motors combined with a planetary gear train. The combination of sun and ring motor allows for achieving higher efficiencies and satisfying power demands as compared to single motor powertrains. Primarily dual motor drive with planetary gear train is proposed and simulated with different drive cycles. Secondarily by extracting the motor efficiency maps working area of different modes, speed and efficiency are determined. Finally, an optimization algorithm was developed to automatically change speed and torque as per load demand. In this way, our work could represent a framework for improving efficiency performances and load demands of an electric vehicle.

关键词： dual motor drive e-CVT motor efficiency optimization algorithm planetary gear train

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Classification of Liver Disease Using Conventional Tree-Based Machine Learning Approaches with Feature Prioritization Using a Heuristic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第11期15卷 357-363页

作者： Mondal, Proloy Kumar Byeon, Haewon Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna Bangladesh Inje Univ Dept Digital Antiaging Healthcare BK21 Gimhae 50834 South Korea

Liver disease ranks as one of the leading causes of mortality globally, often going undetected until advanced stages. This study aims to enhance early detection of liver disease by employing machine learning models that utilize key health indicators. Utilizing the Indian Liver Patient Dataset (ILPD) from the UCI repository, we developed a predictive model using the CatBoost algorithm, achieving an initial accuracy of 74%. To improve this, feature selection was performed using the Whale optimization algorithm (WOA) and Harris Hawk optimization (HHO), which increased accuracy to 82% and 85% respectively. The methodology involved preprocessing to correct data imbalances and outlier removal through univariate and bivariate analyses. These optimizations highlight the critical features enhancing the model's predictive capability. The results indicate that integrating metaheuristic algorithms in feature selection significantly improves the accuracy of liver disease prediction models. Future research could explore the integration of additional datasets and machine learning models to further refine predictive capabilities and understand the underlying pathophysiology of liver diseases.

关键词： Liver disease classification prediction CatBoost algorithm machine learning optimization algorithm

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DNA algorithmS BASED ON EXON SHUFFLING

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IFAC Proceedings Volumes 2005年第1期38卷 17-21页

作者： Tsuyoshi Okayama Haruhiko Murase Osaka Prefecture University 1-1 Gakuen Sakai 599-8531 Japan

An understanding of a natural system's information handling can lead to more effective artificial optimization techniques. There are successful optimization algorithms represented in biosystems that have proven useful in engineering applications (artificial neural networks, immune system algorithms, etc). The goal of our study is to develop a new biosystem derived an optimization algorithm which is called a DNA algorithm (DNAA) based on optimization procedures in DNA. We have focused on an analogy between optimizing procedures for protein functions using exon shuffling and those for an optimization problem in the engineering field. We used a traveling salesman problem (TSP) for evaluation of the performance of the DNAA. The DNAA could estimate approximately optimal tour routes in the 25-city TSP.

关键词： intron exon exon shuffling optimization algorithm traveling salesman problem

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Implementation of a steganography system based on hybrid square quaternion moment compression in IoMT

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第7期35卷

作者： Tahiri, Mohamed Amine Bencherqui, Ahmed Karmouni, Hicham Amakdouf, Hicham Mirjalili, Seyedali Motahhir, Saad Abouhawwash, Mohamed Askar, S. S. Sayyouri, Mhamed Qjidaa, Hassan Sidi Mohamed Ben Abdellah Fez Univ Dhar El Mahrez Fac Sci Lab Elect Signals & Syst Informat LESSI STICCED ST Fes 30000 Morocco Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes 30000 Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech 40000 Morocco Sidi Mohamed Ben Abdellah Univ Inst Sports Sci Fes 30000 Morocco Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Michigan State Univ Coll Engn Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt King Saud Univ Coll Sci Dept Stat & Operat Res POB 2455 Riyadh 11451 Saudi Arabia

Internet of Medical Things (IoMT) systems generate medical data transmissions between patients, med-ical experts, and medical centers over public networks, which require high levels of security to protect the content of medical images and the personal information they contain. In this paper, we propose a new stego image encryption scheme based on a new secret image compression method, wavelet trans-formation, QR decomposition of the cover image, and a new chaotic map. The secret image is compressed by the Hahn-Krawtchouk hybrid quaternion square moments (HK-HQSM), which are optimized by a new hybrid metaheuristic algorithm based on the Salp Swarm algorithm (SSA) and the Arithmetic optimization algorithm (AOA). To increase the security level when transmitting the proposed stego im-ages over public networks, we introduce a new chaotic map based on the 2D fractional Henon map to encrypt the stego image. To demonstrate the effectiveness of the proposed steganography scheme for IoMT, we implemented this scheme on a low-cost Raspberry Pi 4 hardware board. The results of the per-formed numerical experiments show that our method is secure and provides exceptional robustness against common standard image processing attacks (steganalysis attacks). The results also demonstrate that our strategy is able to work efficiently and quickly when implemented on a Raspberry Pi board.& COPY;2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Index Terms Image steganography Internet of Medical Things Hybrid square moments optimization algorithm Raspberry Pi

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