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Relevant bibliographies by topics / Antelope Algorithm

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Academic literature on the topic 'Antelope Algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Antelope Algorithm"

1

Lenin, K. "ANTELOPE ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM." International Journal of Research -GRANTHAALAYAH 5, no. 8 (2017): 191–201. http://dx.doi.org/10.29121/granthaalayah.v5.i8.2017.2212.

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In this paper, Antelope Algorithm (AA) is proposed for solving optimal reactive power dispatch problem. A population of candidate solution move toward as a herd of Antelope out a sequence of jumps through the exploration space in order to find the most outstanding solution. The main idea of this algorithm is fairly different from the population based algorithms, as the individual solutions are stirred collectively in a herd-like approach. Projected Antelope Algorithm (AA) algorithm has been tested in standard IEEE 30 bus test system and simulation results show clearly about the superior perfor

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2

Dr.K.Lenin. "ANTELOPE ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM." International Journal of Research - Granthaalayah 5, no. 8 (2017): 191–201. https://doi.org/10.5281/zenodo.886268.

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In this paper, Antelope Algorithm (AA) is proposed for solving optimal reactive power dispatch problem. A population of candidate solution move toward as a herd of Antelope out a sequence of jumps through the exploration space in order to find the most outstanding solution. The main idea of this algorithm is fairly different from the population based algorithms, as the individual solutions are stirred collectively in a herd-like approach. Projected Antelope Algorithm (AA) algorithm has been tested in standard IEEE 30 bus test system and simulation results show clearly about the superior perfor

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3

Hu, Jing, Junchao Niu, Bangcheng Zhang, Xiang Gao, Xinming Zhang, and Sa Huang. "Obstacle Avoidance Strategy and Path Planning of Medical Automated Guided Vehicles Based on the Bionic Characteristics of Antelope Migration." Biomimetics 10, no. 3 (2025): 142. https://doi.org/10.3390/biomimetics10030142.

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Automated Guided Vehicles (AGVs) face dynamic and static obstacles in the process of transporting patients in medical environments, and they need to avoid these obstacles in real time. This paper proposes a bionic obstacle avoidance strategy based on the adaptive behavior of antelopes, aiming to address this problem. Firstly, the traditional artificial potential field and dynamic window algorithm are improved by using the bionic characteristics of antelope migration. Secondly, the success rate and prediction range of AGV navigation are improved by adding new potential field force points and in

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4

Millicent Anyango Owiti, Maurice Ongong’a Ogoma, and Grace Wanjiru Kibue. "Land use land cover change and its potential implications on conservation of Sitatunga antelope in Saiwa wetland, western Kenya." Open Access Research Journal of Science and Technology 11, no. 2 (2024): 001–2. http://dx.doi.org/10.53022/oarjst.2024.11.2.0084.

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Land use and land cover (LULC) changes are significant drivers of global environmental change, impacting ecosystem processes, biological cycles, and biodiversity. This study aimed to describe the LULC changes in Saiwa Wetland from 1993 to 2023 and discuss their potential impacts on the rare Sitatunga antelope. We utilized remote sensing and GIS technologies, downloading cloud-free Landsat images from the USGS website, including Landsat TM, ETM+, OLI, and Sentinel images. These images underwent supervised and unsupervised classification to identify spectral signatures of various LULC classes. P

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Huang, Rui, Han Zhou, Tong Liu, and Hanlin Sheng. "Multi-UAV Collaboration to Survey Tibetan Antelopes in Hoh Xil." Drones 6, no. 8 (2022): 196. http://dx.doi.org/10.3390/drones6080196.

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Reducing the total mission time is essential in wildlife surveys owing to the dynamic movement of animals throughout their migrating environment and potentially extreme changes in weather. This paper proposed a multi-UAV path planning method for counting various flora and fauna populations, which can fully use the UAVs’ limited flight time to cover large areas. Unlike the current complete coverage path planning methods, based on sweep and polygon, our work encoded the path planning problem as the satisfiability modulo theory using a one-hot encoding scheme. Each instance generated a set of fea

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Luo, Wei, Xiaofang Li, Guoqing Zhang, et al. "High-Accuracy and Low-Latency Tracker for UAVs Monitoring Tibetan Antelopes." Remote Sensing 15, no. 2 (2023): 417. http://dx.doi.org/10.3390/rs15020417.

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As the habitat areas of Tibetan antelopes usually exhibit poaching and unpredictable risks, combining target recognition and tracking with intelligent Unmanned Aerial Vehicle (UAV) technology is necessary to obtain the real-time location of injured Tibetan antelopes to better protect and rescue them. (1) Background: The most common way to track an object is to detect each frame of it, and it is not necessary to run the object tracker and classifier at the same rate, because the speed for them to change class is slower than objects move. Especially in the edge reasoning scene, UAV real-time mon

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7

Kramsky, Joely A., Elizabeth J. B. Manning, and Michael T. Collins. "Protein G Binding to Enriched Serum Immunoglobulin from Nondomestic Hoofstock Species." Journal of Veterinary Diagnostic Investigation 15, no. 3 (2003): 253–61. http://dx.doi.org/10.1177/104063870301500306.

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Quick and cost-effective serologic assays, such as those based on enzyme-linked immunosorbent assay (ELISA) technology, are useful for screening animal populations for infectious diseases. Recombinant protein G is described as an almost universal ELISA conjugate for the detection of antibodies from a wide range of animal species. However, there is limited data documenting the ability of protein G to bind immunoglobulin (Ig) from many captive and free-ranging nondomestic hoofstock (Order Artiodactyla, e.g., elk, antelope, bison). Protein G binding to Ig from 11 species within this taxonomic ord

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Liang, Xiaolong, Derun Pan, and Jiayi Yu. "Intelligent identification system of wild animals image based on deep learning in biodiversity conservation law." Journal of Computational Methods in Sciences and Engineering 24, no. 3 (2024): 1523–38. http://dx.doi.org/10.3233/jcm-247185.

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This study aims to overcome the impact of complex environmental backgrounds on the recognition of wildlife in monitoring images, thereby exploring the role of a deep learning-based intelligent wildlife recognition system in biodiversity conservation. The automatic identification of wildlife images is conducted based on convolutional neural networks (CNNs). Target detection technology, based on regression algorithms, is initially employed to extract Regions of Interest (ROI) containing wildlife from images. The wildlife regions in monitoring images are detected, segmented, and converted into RO

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9

Guo, Meng-wei, Jie-sheng Wang, Wei Xie, Sha-sha Guo, and Ling-feng Zhu. "Pseudo-parallel chaotic self-learning antelope migration algorithm based on mobility models." Applied Intelligence, June 10, 2021. http://dx.doi.org/10.1007/s10489-021-02510-3.

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Lazagabaster, Ignacio A., Chris D. Thomas, Juliet V. Spedding, et al. "Evaluating species distribution model predictions through time against paleozoological records." Ecology and Evolution 14, no. 10 (2024). http://dx.doi.org/10.1002/ece3.70288.

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AbstractSpecies distribution models (SDMs) are widely used to project how species distributions may vary over time, particularly in response climate change. Although the fit of such models to current distributions is regularly enumerated, SDMs are rarely tested across longer time spans to gauge their actual performance under environmental change. Here, we utilise paleozoological presence/absence records to independently assess the predictive accuracy of SDMs through time. To illustrate the approach, we focused on modelling the Holocene distribution of the hartebeest, Alcelaphus buselaphus, a w

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Conference papers on the topic "Antelope Algorithm"

1

Zhou, Jianer, Xinyi Qiu, Zhenyu Li, et al. "Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms." In 2021 IEEE 29th International Conference on Network Protocols (ICNP). IEEE, 2021. http://dx.doi.org/10.1109/icnp52444.2021.9651912.

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Lin, Na, Luwei Fu, Liang Zhao, Lu Liu, and Geyong Min. "A High-Performance Self-Learning Antelopes Migration Algorithm (SAMA) for Global Optimization." In 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2019. http://dx.doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00147.

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Li, Jiaxuan, Weiqiang Zhu, Ettore Biondi, and Zhongwen Zhan. "Seismic Source Characterization with Distributed Acoustic Sensing." In 58th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2024. http://dx.doi.org/10.56952/arma-2024-1111.

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ABSTRACT: The emerging distributed acoustic sensing (DAS) technology can convert a pre-existing telecommunication fiber cable of tens of kilometers in length into a dense seismic array of thousands of recording channels. By measuring the laser phase change of the Rayleigh backscattering from intrinsic impurities in the fiber, a DAS system can infer the longitudinal strain or strain rate every few meters along the fiber at a frequency of hundreds of Hertz. DAS unprecedented temporal and spatial resolution makes it a promising technology for monitoring and characterizing earthquake source proper

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