Relevant bibliographies by topics / VARION algorithm

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Academic literature on the topic 'VARION algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "VARION algorithm"

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Savastano, Giorgio, Attila Komjathy, Esayas Shume, et al. "Advantages of Geostationary Satellites for Ionospheric Anomaly Studies: Ionospheric Plasma Depletion Following a Rocket Launch." Remote Sensing 11, no. 14 (2019): 1734. http://dx.doi.org/10.3390/rs11141734.

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In this study, we analyzed signals transmitted by the U.S. Wide Area Augmentation System (WAAS) geostationary (GEO) satellites using the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm in a simulated real-time scenario, to characterize the ionospheric response to the 24 August 2017 Falcon 9 rocket launch from Vandenberg Air Force Base in California. VARION is a real-time Global Navigation Satellites Systems (GNSS)-based algorithm that can be used to detect various ionospheric disturbances associated with natural hazards, such as tsunamis and earthquakes. A noise reduction algorithm was applied to the VARION-GEO solutions to remove the satellite-dependent noise term. Our analysis showed that the interactions of the exhaust plume with the ionospheric plasma depleted the total electron content (TEC) to a level comparable with nighttime TEC values. During this event, the geometry of the satellite-receiver link is such that GEO satellites measured the depleted plasma hole before any GPS satellites. We estimated that the ionosphere relaxed back to a pre-perturbed state after about 3 h, and the hole propagated with a mean speed of about 600 m/s over a region of 700 km in radius. We conclude that the VARION-GEO approach can provide important ionospheric TEC real-time measurements, which are not affected by the motion of the ionospheric pierce points (IPPs). Furthermore, the VARION-GEO measurements experience a steady noise level throughout the entire observation period, making this technique particularly useful to augment and enhance the capabilities of well-established GNSS-based ionosphere remote sensing techniques and future ionospheric-based early warning systems.
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Bobby, Barua, Kishore Pandit Dhrubo, Golam-Mostafa Md., Ahmed Turjo Tawheed, Rahman Ishmam, and Aktheruzzaman K.M. "Feasibility Study of Bangabandhu Satellite – 2." Recent Trends in Information Technology and its Application 4, no. 3 (2022): 1–18. https://doi.org/10.5281/zenodo.5844195.

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<em>Bangabandhu Satellite-1, a powerful emblem of the country&#39;s ambitions, is aiming to boost economic development and create employment. The tiniest changes in this software sparked a frenzy on social media and sparked unparalleled excitement. Defense, remote patient monitoring, literacy, science, web conferencing, and other business sectors in the area have all benefited from the satellite. With the advent of the Bangabandhu Satellite-1, Bangladesh&#39;s satellite communication exploration has taken a new direction. We must first research and evaluate satellite operation principles before we can make a significant contribution to this exciting wake of globalization. The overall goal of the research is to recognize a feasibility review for the Bangabandhu Satellite-2. Only certain case study on Bangabandhu Satellite-1 is included in the research. It is possible that it would not include all aspects of the country&#39;s first satellite because doing so will be too time intensive and unclear. Furthermore, the data collection process was halted due to the Covid-19 epidemic, which made conducting a printed survey method impractical. We have fixated on the Bangabandhu Satellite-1 in depth. The motivating forces for us are technical, socioeconomic, and resource availability. The principle of satellites was first adopted. Afterwards, we included an outline of satellite communication systems along with various satellite types. We largely focused on its various facets, benefits, and drawbacks. The standing of the Bangabandhu Satellite-2 was also distinguished. The thesis winds down with rational conclusions and debate. It clearly highlights the potential of the Bangabandhu Satellite-2.</em>
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Sunandar, Endang, and Indrianto Indrianto. "Implementasi Algoritma Bubble Sort Terhadap 2 Buah Model Varian Pengurutan Data Menggunakan Bahasa Program Java." PETIR 13, no. 2 (2020): 255–65. http://dx.doi.org/10.33322/petir.v13i2.1008.

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In the field of computer science the efforts made in solving a problem are to use systematic, logical, and strategic flow and steps. These steps are known as algorithms. So many algorithms exist for solving a particular problem. In connection with this paper the algorithm discussed is about the data sorting algorithm, among the few known are: Merge, Selection, Insertion, Bubble, Quick, Heap, Shell. All of these algorithms have advantages and disadvantages of each. One interesting algorithm to be implemented on 2 models of data sorting variants is the Bubble Sort algorithm, the reason is that this algorithm has a fairly long and detailed process flow to produce sequential data sequences from previously unsordered data sequences. And the purpose of implementing them using the language of the program is to be able to provide a translation picture of a complicated and long Bubble Sort algorithm that becomes easier and concise to solve using the program language, which in this case uses the Java program language. For ascending sequencing moving from left to right the method is by shifting the larger values ​​to the right position in sequence, so that finally the smaller values ​​are on the left, whereas for descending sorting moving from left to right the method is by shifting the smaller values ​​to the right position in sequence, so that finally the larger values ​​are on the left. And the programming technique differs only in relation operators that are used against the data being compared.
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Rana, Muhammad Masud, S. M. Azharul Islam, M. Moinul Islam, Md Shakilur Rahman, Sarwar Alam, and M. A. Bari. "Comparison of 3DCRT Dose Distribution in Radiotherapy for Lung Cancer Patient by Using AAA and PBC Algorithms." International Letters of Chemistry, Physics and Astronomy 68 (July 2016): 54–60. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.68.54.

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The Pencil Beam Convolution (PBC) algorithm in radiation treatment planning system is widely used to calculate the radiation dose distribution in radiotherapy planning. A new photon dose calculation algorithm known as Anisotropic Analytical Algorithm (AAA) by Varian Medical Systems is applied to investigate the difference of dose distribution by using AAA and PBC algorithms for the lung cancer with an inhomogeneity of its low density. In the present work, radiotherapy treatment planning of 10 lung cancer patients are designed with 6 MV photon beam using three-dimensional conformal radiation therapy (3DCRT) and dose distribution was calculated by the AAA and the PBC Algorithms. The dose distribution performance is evaluated by dose profile curve along transversal slice of PTV and Dose Volume Histogram (DVH) covered by the 95% isodose of PTV. The mean dose of organ at risks did not changed significantly but the volume of the PTV covered by the 95% isodose curve was decreased by 6% with inhomogeneity due to the algorithms. The dose distribution and the accuracy in calculating the absorbed dose of the AAA algorithm of the Varian Eclipse treatment planning system is analyzed and discussed.
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Rana, Muhammad Masud, S. M. Azharul Islam, M. Moinul Islam, Md Shakilur Rahman, Sarwar Alam, and M. A. Bari. "Comparison of 3DCRT Dose Distribution in Radiotherapy for Lung Cancer Patient by Using AAA and PBC Algorithms." International Letters of Chemistry, Physics and Astronomy 68 (July 19, 2016): 54–60. http://dx.doi.org/10.56431/p-38s75r.

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The Pencil Beam Convolution (PBC) algorithm in radiation treatment planning system is widely used to calculate the radiation dose distribution in radiotherapy planning. A new photon dose calculation algorithm known as Anisotropic Analytical Algorithm (AAA) by Varian Medical Systems is applied to investigate the difference of dose distribution by using AAA and PBC algorithms for the lung cancer with an inhomogeneity of its low density. In the present work, radiotherapy treatment planning of 10 lung cancer patients are designed with 6 MV photon beam using three-dimensional conformal radiation therapy (3DCRT) and dose distribution was calculated by the AAA and the PBC Algorithms. The dose distribution performance is evaluated by dose profile curve along transversal slice of PTV and Dose Volume Histogram (DVH) covered by the 95% isodose of PTV. The mean dose of organ at risks did not changed significantly but the volume of the PTV covered by the 95% isodose curve was decreased by 6% with inhomogeneity due to the algorithms. The dose distribution and the accuracy in calculating the absorbed dose of the AAA algorithm of the Varian Eclipse treatment planning system is analyzed and discussed.
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Çelen, Yonca Yahşi, and Atilla Evcin. "Comparison of Different Algorithms in the Radiotherapy Plans of Breast Cancer." ITM Web of Conferences 22 (2018): 01048. http://dx.doi.org/10.1051/itmconf/20182201048.

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It is aimed to evaluate portal dosimetry results of planned breast cancer patients with intensity-modulated radiotherapy (YART) of Anisotropic Analytical Algorithm (AAA) and Pencil Beam Convolution (PBC) dose calculation algorithms. The plans of 10 treated patients will receive 6 MV photon energy and a total of 25 fractions of 50 Gray dose using the inverse YART technique, which is reverse planned in the Eclipse (ver.13.6) treatment planning system with Varian Trilogy Linear Accelerator prescribing. For each plan, dose was calculated after optimization using PBC and then AAA algorithms. The quality controls of the plans were made using the Electronic Portal Imaging Device (EPID) by creating individual verification plans for each algorithm. In addition, the maximum and average dose values in the target volume were compared in inverse YART plans calculated using PBC and AAA. When treatment plans generated by AAA and PBC dose calculation algorithms are analyzed using EPID, for the PBC algorithm, the mean values of VArea and VAvg are 98.15 ± 1.07, 0.40 ± 0.048 and 98.72 ± 1.13, 0.37 ± 0.051, respectively, for the AAA algorithm. The PTV Dmax value for the PBC algorithm is 109.3 ± 1.09 and the DAvg value is 101.7 ± 0.51. For the AAA algorithm, the PTV Dmax value is 110.6 ± 1.12 and the DAvg value is 102.9 ± 0.62. When the mean values of portal dosimetry VArea and VAvg evaluated using PBC and AAA algorithms were compared, the differences between the algorithms were not statistically significant (p&gt; 0.05). Differences between the algorithms for PTV Dmax and DAvg values are not statistically significant (p&gt; 0.05).
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Malhotra Aman Singh, Mini. "Study of Various Cryptographic Algorithms." International Journal of Scientific Engineering and Research 1, no. 3 (2013): 77–88. https://doi.org/10.70729/j201341.

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Khan, Beenish, Ms Poonam, and Mr Mohammad Talib. "Matlab Based Image Compression Using Various Algorithm." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 1638–42. http://dx.doi.org/10.31142/ijtsrd14394.

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Dr., Saeed Q. Al-Khalidi Al-Maliki, Prakash Kuppuswamy Dr., Rajan John Dr., and Nithya Rekha Sivakumar Dr. "COVID-19: An Efficient Big Data Analytics for SARS-CoV-2 Mutations Prediction: A Machine Learning Approach." Indian Journal of Science and Technology 15, no. 21 (2022): 1022–31. https://doi.org/10.17485/IJST/v15i21.374.

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Abstract <strong>Objectives:</strong>&nbsp;This research article aims to identify the level of further mutations of COVID-19 and its vulnerability. Healthcare workers are urged to take precautionary measures to prevent further damage to their community and to avoid further human deaths. At this stage, it is crucial to study, analyze, and understand the COVID-19 variant mutation.&nbsp;<strong>Methods:</strong>&nbsp;The mutation of COVID- 19, however, has not yet been validated for the way in which it affects the risk of underlying comorbidities. It may be possible to tackle this challenge by using artificial intelligence, machine learning, and deep learning algorithms. We have used the J48 algorithm and the Linear Regression algorithm. These algorithms were then analyzed based on the accuracy they provided after running them in the output window of WEKA data mining.&nbsp;<strong>Findings:</strong>&nbsp;In this paper, machine learning is used to help clinicians and medical researchers understand COVID- 19 variant mutations at various stages. Artificial intelligence-based algorithms provide a better understanding of COVID-19 stages and vulnerability levels.&nbsp;<strong>Novelty:</strong>&nbsp;Artificial intelligence can be a valuable tool for healthcare operations in this pandemic situation. Moreover, healthcare professionals and a data analysis algorithm will probably arrive at the same conclusion based on the data set. However, the use of machine learning-based techniques described in this article will allow for quicker and earlier diagnosis of any type of pandemic situation in the future. <strong>Keywords:</strong> COVID19; Machine Learning; Mutation; Variant; Coronavirus; SARSCoV2; Prediction algorithm etc
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Hsien-Wen Cheng and Lan-Rong Dung. "A Vario-power ME architecture using content-based subsample algorithm." IEEE Transactions on Consumer Electronics 50, no. 1 (2004): 349–54. http://dx.doi.org/10.1109/tce.2004.1277884.

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Dissertations / Theses on the topic "VARION algorithm"

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Chowuraya, Tawanda. "Online content clustering using variant K-Means Algorithms." Thesis, Cape Peninsula University of Technology, 2019. http://hdl.handle.net/20.500.11838/3089.

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Thesis (MTech)--Cape Peninsula University of Technology, 2019<br>We live at a time when so much information is created. Unfortunately, much of the information is redundant. There is a huge amount of online information in the form of news articles that discuss similar stories. The number of articles is projected to grow. The growth makes it difficult for a person to process all that information in order to update themselves on a subject matter. There is an overwhelming amount of similar information on the internet. There is need for a solution that can organize this similar information into specific themes. The solution is a branch of Artificial intelligence (AI) called machine learning (ML) using clustering algorithms. This refers to clustering groups of information that is similar into containers. When the information is clustered people can be presented with information on their subject of interest, grouped together. The information in a group can be further processed into a summary. This research focuses on unsupervised learning. Literature has it that K-Means is one of the most widely used unsupervised clustering algorithm. K-Means is easy to learn, easy to implement and is also efficient. However, there is a horde of variations of K-Means. The research seeks to find a variant of K-Means that can be used with an acceptable performance, to cluster duplicate or similar news articles into correct semantic groups. The research is an experiment. News articles were collected from the internet using gocrawler. gocrawler is a program that takes Universal Resource Locators (URLs) as an argument and collects a story from a website pointed to by the URL. The URLs are read from a repository. The stories come riddled with adverts and images from the web page. This is referred to as a dirty text. The dirty text is sanitized. Sanitization is basically cleaning the collected news articles. This includes removing adverts and images from the web page. The clean text is stored in a repository, it is the input for the algorithm. The other input is the K value. All K-Means based variants take K value that defines the number of clusters to be produced. The stories are manually classified and labelled. The labelling is done to check the accuracy of machine clustering. Each story is labelled with a class to which it belongs. The data collection process itself was not unsupervised but the algorithms used to cluster are totally unsupervised. A total of 45 stories were collected and 9 manual clusters were identified. Under each manual cluster there are sub clusters of stories talking about one specific event. The performance of all the variants is compared to see the one with the best clustering results. Performance was checked by comparing the manual classification and the clustering results from the algorithm. Each K-Means variant is run on the same set of settings and same data set, that is 45 stories. The settings used are, • Dimensionality of the feature vectors, • Window size, • Maximum distance between the current and predicted word in a sentence, • Minimum word frequency, • Specified range of words to ignore, • Number of threads to train the model. • The training algorithm either distributed memory (PV-DM) or distributed bag of words (PV-DBOW), • The initial learning rate. The learning rate decreases to minimum alpha as training progresses, • Number of iterations per cycle, • Final learning rate, • Number of clusters to form, • The number of times the algorithm will be run, • The method used for initialization. The results obtained show that K-Means can perform better than K-Modes. The results are tabulated and presented in graphs in chapter six. Clustering can be improved by incorporating Named Entity (NER) recognition into the K-Means algorithms. Results can also be improved by implementing multi-stage clustering technique. Where initial clustering is done then you take the cluster group and further cluster it to achieve finer clustering results.
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Lattarulo, Valerio. "Development of a multi-objective variant of the alliance algorithm." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270076.

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Optimization methodologies are particularly relevant nowadays due to the ever-increasing power of computers and the enhancement of mathematical models to better capture reality. These computational methods are used in many different fields and some of them, such as metaheuristics, have often been found helpful and efficient for the resolution of practical applications where finding optimal solutions is not straightforward. Many practical applications are multi-objective optimization problems: there is more than one objective to optimize and the solutions found represent trade-offs between the competing objectives. In the last couple of decades, several metaheuristics approaches have been developed and applied to practical problems and multi-objective versions of the main single-objective approaches were created. The Alliance Algorithm (AA) is a recently developed single-objective optimization algorithm based on the metaphorical idea that several tribes, with certain skills and resource needs, try to conquer an environment for their survival and try to ally together to improve the likelihood of conquest. The AA method has yielded reasonable results in several fields to which it has been applied, thus the development in this thesis of a multi-objective variant to handle a wider range of problems is a natural extension. The first challenge in the development of the Multi-objective Alliance Algorithm (MOAA) was acquiring an understanding of the modifications needed for this generalization. The initial version was followed by other versions with the aim of improving MOAA performance to enable its use in solving real-world problems: the most relevant variations, which led to the final version of the approach, have been presented. The second major contribution in this research was the development and combination of features or the appropriate modification of methodologies from the literature to fit within the MOAA and enhance its potential and performance. An analysis of the features in the final version of the algorithm was performed to better understand and verify their behavior and relevance within the algorithm. The third contribution was the testing of the algorithm on a test-bed of problems. The results were compared with those obtained using well-known baseline algorithms. Moreover, the last version of the MOAA was also applied to a number of real-world problems and the results, compared against those given by baseline approaches, are discussed. Overall, the results have shown that the MOAA is a competitive approach which can be used `out-of-the-box' on problems with different mathematical characteristics and in a wide range of applications. Finally, a summary of the objectives achieved, the current status of the research and the work that can be done in future to further improve the performance of the algorithm is provided.
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Andersson, Mathias. "Image processing algorithms for compensation of spatially variant blur." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2947.

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<p>This report adresses the problem of software correction of spatially variant blur in digital images. The problem arises when the camera optics contains flaws, when the scene contains multiple moving objects with different relative motion or the camera itself is i.e. rotated. Compensation through deconvolving is impossible due to the shift-variance in the PSF hence alternative methods are required. There are a number of suggested methods published. This report evaluates two methods</p>
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Belmonte, Mula Irene. "Actualización del algoritmo de diagnóstico de laboratorio del déficit de alfa-1-antitripsina: incorporación de la detección genotípica de la variante deficitaria Mmalton y utilización de muestras alternativas a la sangre total." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/457763.

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El déficit de alfa-1-antitripsina (DAAT) es una enfermedad genética que se caracteriza por la presencia de niveles bajos de la proteína alfa-1-antitripsina (AAT) en suero y causa principalmente desarrollo temprano de enfisema pulmonar y hepatopatía. Se han descrito alrededor de 125 variantes alélicas del gen de la AAT. El alelo normal más común en la población es el M, y las variantes deficitarias más frecuentes son la S y la Z, siendo esta última causante de un déficit severo. Sin embargo, otra variante deficitaria rara, llamada Mmalton, causante de un déficit similar al alelo Z, es considerada la segunda causa de DAAT en España. Esta variante es difícil de caracterizar mediante las técnicas de diagnóstico habituales (cuantificación de AAT en suero y fenotipado). Este hecho, ha contribuido a la clasificación errónea de esta variante y con ello, a la subestimación de su prevalencia real en la población. Por ello, en esta tesis se diseñó una técnica de genotipado alelo-específico para la detección de la variante Mmalton y se testó su aplicabilidad utilizando muestras de sangre total, DBS (gota de sangre seca) y de suero. Los resultados mostraron la utilidad de esta técnica para la caracterización rápida y coste-efectiva de una variante deficitaria de difícil detección. Así mismo, este método puede ser adaptado para la detección de las variantes raras más prevalentes de cada región. Cuando se ha de realizar el genotipado, es necesario ADN procedente de sangre total o DBS. En ocasiones estas muestras no están disponibles en el laboratorio, por lo que se debe realizar una nueva extracción, generando un retraso en el diagnóstico del DAAT. Para evitar esto, se desarrollaron protocolos de genotipado alelo-específico y secuenciación exónica del gen de la AAT utilizando muestra de suero (muestra utilizada en los primeros pasos del diagnóstico). Estas técnicas fueron incorporadas al algoritmo de diagnóstico del laboratorio, permitiendo realizar un diagnóstico completo utilizando un solo tipo de muestra y por tanto, evitando el retraso generado cuando el genotipado era necesario. Por otro lado, con el fin de incrementar los programas de cribado para la identificación de individuos con DAAT, se presentó la muestra de frotis bucal como una alternativa al DBS. Esta muestra es fácil de obtener, almacenar y enviar y permite disponer de gran cantidad de ADN para la realización del genotipado. Los resultados mostraron que las metodologías descritas ayudan a promover la expansión de los programas de detección del DAAT y mejorar la rapidez de su diagnóstico. El algoritmo propuesto en el presente trabajo, permite realizar un diagnóstico completo del DAAT evitando dos problemas principales del diagnóstico de esta enfermedad, como son el significativo retraso producido una vez el clínico realiza la petición de diagnóstico y el infradiagnóstico de las variantes raras de la AAT.<br>Alpha-1-antitrypsin deficiency (AATD) is a genetic disorder characterized by low serum levels of alpha-1-antitrypsin protein (AAT) and a high risk of developing early onset emphysema and liver disease. About 125 allelic variants of the AAT gene have been described. The most common normal AAT allele is the M variant, and the most frequent deficient variants are Z and S. However, another rare deficient variant, called Mmalton, which causes a deficiency similar to variant Z, is considered to be the second cause of severe AATD in Spain. This variant is difficult to detect with the common diagnostic techniques (serum AAT measurement and phenotype characterization). This fact has contributed to a misclassification of this variant and the subsequent underestimation of its real prevalence in the population. Thus, we designed an allele-specific genotyping technique for the detection of the Mmalton variant and we tested its applicability using whole blood, DBS (dried blood spot) and serum samples. Results showed the utility of this technique for the rapid and cost-effective characterization of a deficiency variant with a difficult detection. Moreover, this method could be adapted for the study of the most prevalent rare variants in each region. When genotyping is required, DNA from whole blood or DBS sample is necessary. Occasionally these kinds of samples are not available in the laboratory and additional new extraction is required, causing a significant delay in AATD diagnosis. To avoid this, we developed allele-specific genotyping and exonic sequencing of AAT gene protocols using DNA present in serum samples (sample used at the first steps of the diagnosis). These techniques were incorporated to the laboratory diagnostic algorithm, allowing the complete diagnosis using an only kind of sample and thus, avoiding the delay generated when genotyping is necessary. With the purpose of promoting the expansion of screening programs for the identification of AATD individuals, buccal swab samples were assessed as an alternative to DBS samples. This kind of sample is easy to collect, store and deliver and it recovers a large amount of DNA, resulting in an easy genotyping. Results showed these methodologies may be useful to expand AATD screening programs and complete the diagnostic without delay. The algorithm proposed in this study allows a complete AATD diagnosis avoiding two main problems: significant delay when the doctor performs the diagnostic request and the underdiagnosis of the rare AAT variants.
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Farver, Jennifer M. (Jennifer Margaret) 1976. "Continuous time algorithms for a variant of the dynamic traffic assignment problem." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/84247.

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Emde, Anne-Katrin [Verfasser]. "Next-generation sequencing algorithms : from read mapping to variant detection / Anne-Katrin Emde." Berlin : Freie Universität Berlin, 2013. http://d-nb.info/1045194964/34.

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Dowling, John F. "Algorithmic techniques for the acoustical analysis of exhaust systems." Thesis, Loughborough University, 2005. https://dspace.lboro.ac.uk/2134/12936.

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One dimensional, linear, plane-wave modelling of silencer systems in the frequency domain provides an efficient means to analyse their acoustic performance. Software packages are available to analyse silencers within these modelling parameters; however, they are heavily restricted. The thesis develops an algorithm that increases the computational efficiency of the silencer analysis. The thesis concentrates on how data, within a software package, is stored, retrieved and analysed. The computational efficiency is increased as a result of the predictable patterns caused by the repetitive nature of exhaust system analysis. The work uses the knowledge gained from the construction of two previous algorithms of similar parameters; it isolates and maximises their advantages whilst minimising their associated disadvantages. The new algorithm is dependent on identifying consecutively sequenced exhaust components and sub-systems of such components within the whole exhaust system. The algorithm is further generalised to include multiple time-variant sources, multiple radiation points and exhaust systems that have a balance pipe. Another feature of the improved algorithm encompasses the option of modelling secondary noise sources such as might arise from flow generated noise or be included for active noise cancellation systems. The validation of these algorithmic techniques is demonstrated by comparison of the theoretical noise predictions with experimental or known results. These predictions are achieved by writing computational code using object orientated programming techniques in the language of c++ to implement the algorithms.
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Saleh, Sherine. "A novel dynamic feature selection and prediction algorithm for clinical decision involving high-dimensional and varied patient data." Thesis, Aston University, 2016. http://publications.aston.ac.uk/30072/.

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Predicting suicide risk for mental health patients is a challenging task performed by practitioners on a daily basis. Failure to perform proper evaluation of this risk could have a direct effect on the patient's quality of life and possibly even lead to fatal outcomes. Risk predictions are based on data that are difficult to analyse because they involve a heterogeneous set of patients’ records from a high-dimensional set of potential variables. Patient heterogeneity forces the need for various types and numbers of questions to be asked regarding the individual profile and perceived level of risk. It also results in records having different combinations of present variables and a large percentage of missing ones. Another problem is that the data collected consist of risk judgements given by several thousand assessors for a large number of patients. The problem is how to use the associations between patient profiles and clinical judgements to generate a model that reflects the agreement across all practitioners. In this thesis, a novel dynamic feature selection algorithm is proposed which can predict the risk level based only on the most influential answers provided by the patient. The feature selection optimises the vector for predictions by selecting variables that maximise correlation with the assessors’ risk judgement and minimise mutual information within the ones already selected. The final vector is then classified using a linear regression equation learned for all patients with a matching set of variables. The overall approach has been named the Dynamic Feature Selection and Prediction algorithm, DFSP. The results show that the DFSP is at least as accurate or more accurate than alternative gold-standard approaches such as random forest classification trees. The comparison was based on accuracy and error measures applied to each risk level separately ensuring no preference to one risk over the other.
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Yavaş, Gökhan. "Algorithms for Characterizing Structural Variation in Human Genome." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1279345476.

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Rohr, Andreas. "Ein Algorithmus zur Bestimmung zweifacher ASN-optimaler Variablenprüfpläne für normalverteilte Merkmale mit unbekannter Varianz /." Berlin : Mensch- & -Buch-Verl, 2009. http://d-nb.info/998778443/04.

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Books on the topic "VARION algorithm"

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Quadflieg, Sven, Klaus Neuburg, and Simon Nestler, eds. (Dis)Obedience in Digital Societies. transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839457634.

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Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power.
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Yurchuk, Ol'ga, Ol'ga Ul'yanina, Yuliya Tukfeeva, et al. Professional activity of a teacher-psychologist: organizational basis and work protocols: methodological recommendations. Publishing Center RIOR, 2023. http://dx.doi.org/10.29039/02123-1.

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The methodological recommendations consider a wide range of aspects concerning the organizing of psychological and pedagogical support for participants in educational relations. The list of normative legal documents regulating the activity of an educational psychologist (psychologist in the field of education) is presented. Goals and objectives, main types and directions of teacher-psychologist’s activity are given. Forms of reporting documentation based on the results of the professional activity of an educational psychologist (psychologist in the field of education) are presented. The algorithm of interaction between an educational organization and a psychological-medical-pedagogical commission, the algorithm of interdepartmental interaction in case of an emergency involving minors are described.&#x0D; The recommendations make it possible to develop unified approaches to psychological and pedagogical support for participants in educational relations and to optimize the activities of an educational psychologist (psychologist in the field of education) by enriching and standardizing methodological tools. Interaction algorithms can be used to organize joint activities of various experts in order to guarantee the legitimate rights and interests of students and other participants in educational relations.&#x0D; The publication is intended for educational psychologists, employees and heads of centers for psychological, pedagogical, medical and social assistance. It can be useful for teaching staff, researchers, and administrative and managerial staff in the field of education.
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Kirsanov, Mihail. Flat farms. Deflections and oscillation frequencies. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2173835.

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For various schemes of flat regular statically definable farms, the handbook provides formulas for the dependence of the first natural oscillation frequency on the number of panels. An algorithm for deriving the desired analytical dependencies in the Maple computer mathematics system is given. Formulas for deflection and forces in the rods of trusses under the influence of gravity of the masses located at the nodes are given. It is intended for engineers, researchers, students and postgraduates of technical universities.
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van Es, Karin, and Nanna Verhoeff. Situating Data. Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463722971.

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Taking up the challenges of the datafication of culture, as well as of the scholarship of cultural inquiry itself, this collection contributes to the critical debate about data and algorithms. How can we understand the quality and significance of current socio-technical transformations that result from datafication and algorithmization? How can we explore the changing conditions and contours for living within such new and changing frameworks? How can, or should we, think and act within, but also in response to these conditions? This collection brings together various perspectives on the datafication and algorithmization of culture from debates and disciplines within the field of cultural inquiry, specifically (new) media studies, game studies, urban studies, screen studies, and gender and postcolonial studies. It proposes conceptual and methodological directions for exploring where, when, and how data and algorithms (re)shape cultural practices, create (in)justice, and (co)produce knowledge.
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Biryulin, Vladimir, Aleksey None, Dar'ya Kudelina, Oleg Larin, and Anton Tancyura. Relay protection and automation systems elektroenergeticheskim. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058880.

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The manual includes basic information about the element base of relay protection and automation, principles of operation and circuit implementation of various types of relay protection and automation systems, algorithms of calculation of parameters of the action of relay protection and automation.&#x0D; Designed for students in specialty "power and electrical engineering", as well as professionals involved in the design and operation of relay protection and automation of electric power systems.
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Pokamestov, Il'ya, Anna Gamilovskaya, Mihal Lednev, Viktoriya Frolova, and Grigoriy Chvanov. Refinancing of trade loans - technologies and financial models. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2144525.

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The monographs outline the theoretical and methodological foundations of refinancing trade loans, researching technologies and financial models of such transactions in the innovative economy of various sectors. The features of trade loans, methods of assessing and managing risks in operations of this kind and combating fraud in the trade finance system are highlighted. Rating models and risk management algorithms are used in this area, in particular in factor transactions and functionality.
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Mandra, Yuliya, Elena Semencova, Sergey Griroriev, et al. MODERN METHODS OF COMPLEX TREATMENT OF PATIENTS WITH HERPES SIMPLEX LIPS. TIRAZH Publishing House, 2019. http://dx.doi.org/10.18481/textbook_5dfa340500ebf6.85792235.

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The training manual is devoted to the problem of herpetic infection in dentistry and was developed taking into account world scientific and clinical practice, experience working on clinical recommendations of the Ministry of Health of the Russian Federation, as well as experimental, laboratory and clinical data obtained by the authors. This manual presents materials related to modern ideas about the etiology and pathogenesis of herpetic infection, modern diagnostic methods are highlighted, and current complex treatment algorithms are proposed, and clinical cases are presented. Recommended as a guide for practitioners of various specialties, clinical residents, senior students.
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Peschanskiy, Aleksey. Semi-Markov models of prevention of unreliable single-channel service system with losses. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1870597.

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The monograph examines various maintenance strategies of a single-channel system with losses and unreliable recoverable service device under the assumption of a general type of random variables describing random processes occurring in the system. The apparatus for constructing models of the functioning of the system are semi-Markov processes with a measurable phase space of states and phase enlargement algorithms. Stationary probabilistic and economic indicators of the system are explicitly determined and the tasks of optimal frequency of maintenance of the device are solved.&#x0D; For researchers, engineers and specialists in the field of metamathematic theory of reliability, system analysis. It can be useful for graduate students and students of relevant specialties of technical universities and universities.
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Pitelinskiy, Kirill, and Aleksandr Borodin. Simulation modeling of sociotechnical systems: fundamentals of queuing theory. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1856786.

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The textbook forms an idea of various principles and technologies of management of economic and technological systems and processes. A comparative analysis of the principles of construction and synergetic management of various organizational structures is given; the theoretical foundations of the theory of queuing and statistical methods used to build economic and mathematical models. The theoretical material is accompanied by the solution of examples and questions for self-examination. The practical application of algorithmic and software useful for the analysis of statistical characteristics of data arrays studied by means of simulation modeling is considered.&#x0D; Meets the requirements of the federal state educational standards of higher education of the latest generation.&#x0D; For students studying in the fields of "Economics" and "Management".
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Nazarov, Dmitriy, and Anton Kopnin. Information technologies in professional activity: data mining and business analytics. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2110964.

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The textbook offers a comprehensive look covering a wide range of topics related to the role of information technology in the modern world. Starting with an overview of the history of information technology and its evolution, the book introduces the reader to professions in the field of information and digital technologies, emphasizing them in various professional fields. The content of the textbook includes a detailed analysis of key concepts such as Data Science, Data Mining and Machine Learning, and their role in healthcare, law, education, science and business. The textbook provides a detailed overview of Data Science methods and algorithms, including teaching methods with and without a teacher, as well as specific methods such as Dematel. In the section on business intelligence tools, special attention is paid to Yandex Cloud DataLens, its data analysis functionality, and practical recommendations on registration, the use of intelligent detection of patterns in data, visualization and the development of analytical panels are provided. The section of the textbook devoted to the R and Python programming languages contains recommendations on the use of the R and Python programming languages for statistical data analysis, a description of the main data types, operations, and specific algorithms used for analytical purposes. This section provides a quick guide on how to use the RStudio and PyCharm tools. The final section is devoted to the application of data analysis tools in real projects, providing the reader with the opportunity to immerse themselves in the world of data processing using advanced technologies and techniques. The presentation of the material of each chapter is accompanied by control questions and tests to consolidate the theoretical material, located at the end of the chapter. This textbook will be an indispensable resource for students, professionals and researchers who want to deeply understand and apply information and digital technologies in their professional activities.
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Book chapters on the topic "VARION algorithm"

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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Conclusion and Further Research Directions." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_6.

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AbstractThe increasing complexity of real-world optimization problems demands fast, robust, and efficient meta-heuristic algorithms. The popularity of these intelligent techniques is gaining popularity day by day among researchers from various disciplines of science and engineering. The sine cosine algorithm is a simple population-based stochastic approach for handling different optimization problems. In this work, we have discussed the basic sine cosine algorithm for continuous optimization problems, the multi-objective sine cosine algorithm for handling multi-objective optimization problems, and the discrete (or binary) versions of sine cosine algorithm for discrete optimization problems. Sine cosine algorithm (SCA) has reportedly shown competitive results when compared to other meta-heuristic algorithms. The easy implementation and less number of parameters make the SCA algorithm, a recommended choice for performing various optimization tasks. In this present chapter, we have studied different modifications and strategies for the advancement of the sine cosine algorithm. The incorporation of concepts like opposition-based learning, quantum simulation, and hybridization with other meta-heuristic algorithms have increased the efficiency and robustness of the SCA algorithm, and meanwhile, these techniques have also increased the application spectrum of the sine cosine algorithm.
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Shukla, Richa, Bramah Hazela, Shashwat Shukla, Ravi Prakash, and Krishn K. Mishra. "Variant of Differential Evolution Algorithm." In Advances in Computer and Computational Sciences. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3770-2_56.

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Tekriwal, Mohit, Avi Tachna-Fram, Jean-Baptiste Jeannin, Manos Kapritsos, and Dimitra Panagou. "Formally verified asymptotic consensus in robust networks." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57246-3_14.

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AbstractDistributed architectures are used to improve performance and reliability of various systems. Examples include drone swarms and load-balancing servers. An important capability of a distributed architecture is the ability to reach consensus among all its nodes. Several consensus algorithms have been proposed, and many of these algorithms come with intricate proofs of correctness, that are not mechanically checked. In the controls community, algorithms often achieve consensus asymptotically, e.g., for problems such as the design of human control systems, or the analysis of natural systems like bird flocking. This is in contrast to exact consensus algorithm such as Paxos, which have received much more recent attention in the formal methods community.This paper presents the first formal proof of an asymptotic consensus algorithm, and addresses various challenges in its formalization. Using the Coq proof assistant, we verify the correctness of a widely used consensus algorithm in the distributed controls community, the Weighted-Mean Subsequence Reduced (W-MSR) algorithm. We formalize the necessary and sufficient conditions required to achieve resilient asymptotic consensus under the assumed attacker model. During the formalization, we clarify several imprecisions in the paper proof, including an imprecision on quantifiers in the main theorem.
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Rafiei, Majid, Frederik Wangelik, and Wil M. P. van der Aalst. "TraVaS: Differentially Private Trace Variant Selection for Process Mining." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_9.

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AbstractIn the area of industrial process mining, privacy-preserving event data publication is becoming increasingly relevant. Consequently, the trade-off between high data utility and quantifiable privacy poses new challenges. State-of-the-art research mainly focuses on differentially private trace variant construction based on prefix expansion methods. However, these algorithms face several practical limitations such as high computational complexity, introducing fake variants, removing frequent variants, and a bounded variant length. In this paper, we introduce a new approach for direct differentially private trace variant release which uses anonymized partition selection strategies to overcome the aforementioned restraints. Experimental results on real-life event data show that our algorithm outperforms state-of-the-art methods in terms of both plain data utility and result utility preservation.
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Fei, Jiangang. "Optimal Arrangement of Pressure Monitoring Points in Water Supply Network Based on Intelligent Optimization Algorithm." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7251-3_39.

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AbstractAiming at the optimal arrangement of pressure monitoring points in urban water supply network, with the goal of maximizing the monitoring range, an optimal arrangement model of monitoring points is constructed, considering the pressure correlation, node flow sensitivity and the effectiveness of the water flow path in the pipe network. Taking an urban water supply pipe network in East China as an example, the model is solved by three kinds of population intelligent algorithms: Krill Harvesting Algorithm (KHA), Bat Algorithm (BA), and Particle Swarm Algorithm (PSO). The optimization results of the three algorithms are compared in various aspects, and it is found that the KHA algorithm shows the most excellent search accuracy and efficiency in the problem of optimal arrangement of monitoring points, because of the strong global optimization ability and not easy to fall into local optimal solutions.
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Zhang, Han, Qing Li, and Xin Yao. "Knowledge-Guided Optimization for Complex Vehicle Routing with 3D Loading Constraints." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70055-2_9.

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AbstractThe split delivery vehicle routing problem with three-dimensional loading constraints (3L-SDVRP) intertwines complex routing and packing challenges. The current study addresses 3L-SDVRP using intelligent optimization algorithms, which iteratively evolve towards optimal solutions. A pivotal aspect of these algorithms is search operators that determine the search direction and the search step size. Effective operators significantly improve algorithmic performance. Traditional operators like swap, shift, and 2-opt fall short in complex scenarios like 3L-SDVRP, mainly due to their limited capacity to leverage domain knowledge. Additionally, the search step size is crucial: smaller steps enhance fine-grained search (exploitation), while larger steps facilitate exploring new areas (exploration). However, optimally balancing these step sizes remains an unresolved issue in 3L-SDVRP. To address this, we introduce an adaptive knowledge-guided insertion (AKI) operator. This innovative operator uses node distribution characteristics for adaptive node insertion, enhancing search abilities through domain knowledge integration and larger step sizes. Integrating AKI with the local search framework, we develop an adaptive knowledge-guided search (AKS) algorithm, which effectively balances exploitation and exploration by combining traditional neighbourhood operators for detailed searches with the AKI operator for broader exploration. Our experiments demonstrate that the AKS algorithm significantly outperforms the state-of-the-art method in solving various 3L-SDVRP instances.
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Yang, Lixing. "Meerkat: An Algorithm to Reliably Identify Structural Variations and Predict Their Forming Mechanisms." In Variant Calling. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2293-3_8.

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Skeirik, Stephen, and José Meseguer. "Metalevel Algorithms for Variant Satisfiability." In Rewriting Logic and Its Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44802-2_10.

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Joux, Antoine, and Vanessa Vitse. "A Variant of the F4 Algorithm." In Topics in Cryptology – CT-RSA 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19074-2_23.

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van Heerdt, Gerco, Clemens Kupke, Jurriaan Rot, and Alexandra Silva. "Learning Weighted Automata over Principal Ideal Domains." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_31.

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AbstractIn this paper, we study active learning algorithms for weighted automata over a semiring. We show that a variant of Angluin’s seminal $$\mathtt {L}^{\!\star }$$ L ⋆ algorithm works when the semiring is a principal ideal domain, but not for general semirings such as the natural numbers.
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Conference papers on the topic "VARION algorithm"

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Kwaśny, A., M. Krakowski, and G. Soboń. "Optimization of femtosecond pulses by AI-assisted spectral phase modulation: influence of spectral resolution." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jw2a.49.

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We investigate the possibility of optimization of ultrashort laser pulses by spectral phase modulation using machine learning algorithms. The Grey Wolf Algorithm is applied, and the efficiency of various spectral resolution patterns is examined.
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Brończyk, Karolina, Michał Adamski, Agata Dąbrowska, Adam Konieczka, and Adam Dąbrowski. "Electrochemical determination of pollutants from various passenger car engines." In 2024 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2024. http://dx.doi.org/10.23919/spa61993.2024.10715606.

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Dasgupta, Anubhab, Vedant Zope, and Asrul Harun Ismail. "Implementation of the Bees Algorithm for UAV Mission Plan." In 14th International Seminar on Industrial Engineering and Management. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-nad6qa.

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The increasing use of Unmanned Aerial Vehicles (UAVs) in various applications, such as military operations and civilian tasks, has created a demand for efficient algorithms to plan their mission. This study explores the use of the Bees Algorithm (BA), a nature-inspired optimisation method, to solve two main problems in UAV mission planning: the UAV collision avoidance problem (UCAP) and the Asymmetric TSP (ATSP). Results of comparison between the proposed algorithm and other metaheuristic algorithms indicate that BA can produce competitive solutions. However, the study also found that the BA had a deviation of 10% on ATSP instances, whereas it was able to achieve similar deviation when solving symmetrical TSP of 200 dimensions. This highlights the need for further improvement of the neighbour search mechanism in the BA for better accuracy on the problems.
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Kunz, Philipp, Ilche Georgievski, and Marco Aiello. "Towards a Framework for Learning of Algorithms: The Case of Learned Comparison Sorts." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/481.

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Designing algorithms is cumbersome and error-prone. This, among other things, has increasingly led to efforts to extend or even replace designing algorithms with machine learning models. While previous research has demonstrated that some machine learning models possess Turing-completeness, the findings are largely theoretical, and solutions for specific algorithmic tasks remain unclear. With this in mind, we investigate the feasibility of learning representations of classical algorithms from data on their execution, enabling their application to different inputs. We propose a novel and general framework for algorithm learning consisting of a model of computation that facilitates algorithm analysis across various levels of abstraction. We formalize the problem of learning an algorithm using an algebraic approach for graph traversal. We apply this framework to comparison sorts and evaluate the inferred machine learning models' performance, demonstrating the applicability of the approach in terms of accuracy and sensitivity.
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Efimov, Aleksey Igorevich, and Dmitry Igorevich Ustukov. "Comparative Analysis of Stereo Vision Algorithms Implementation on Various Architectures." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-484-489.

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A comparative analysis of the functionality of stereo vision algorithms on various hardware architectures has been carried out. The quantitative results of stereo vision algorithms implementation are presented, taking into account the specifics of the applied hardware base. The description of the original algorithm for calculating the depth map using the summed-area table is given. The complexity of the algorithm does not depend on the size of the search window. The article presents the content and results of the implementation of the stereo vision method on standard architecture computers, including multi-threaded implementation, a single-board computer and FPGA. The proposed results may be of interest in the design of vision systems for applied applications.
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Murukesh, Aadhithya, and R. Logeshwari. "Rock, Paper and Scissor Using AI- Random Forest Algorithm." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-udj0cq.

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Rock, paper, scissors (RPS) is a game of crucial decision-making that can be used to test various learning algorithms due to its simplicity. Random Forest is a Rock Paper Scissors Agent that makes predictions using the Random Forest Classification with a bit of random. The randomness on this agent has been limited only while losing (and partially due to window length), displaying the algorithm's true potential. A simulation of various AI agents is developed to compare and analyze against the Random forest algorithm.
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Hulicki, Maciej. "ALGORITHM TRANSPARENCY AS A SINE QUA NON PREREQUISITE FOR A SUSTAINABLE COMPETITION IN A DIGITAL MARKET?" In International Jean Monnet Module Conference of EU and Comparative Competition Law Issues "Competition Law (in Pandemic Times): Challenges and Reforms. Faculty of Law, Josip Juraj Strossmayer University of Osijek, 2021. http://dx.doi.org/10.25234/eclic/18823.

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Algorithms play a fundamental role in the digital economy. Their impact on the situation of market participants is significant. Hence, ensuring transparency of algorithms, through access to them, is crucial for the proper functioning of the market. Several models of algorithmic transparency are analyzed in the paper: from lack of transparency to complete regulation of algorithms. In particular, transparency through explanation, and “on-demand transparency” were proposed. The goal of the paper is to determine the optimal form and scope of regulation of this area, in order to ensure sustainable competition in the digital market. Hence, the paper focuses on the concept of algorithmic transparency, the nature of the competition in the digital market, the role of algorithms within the digital trade, and problems related to the regulation of algorithms. This allows to answering the question of whether algorithmic transparency is an indispensable condition for sustainable competition in the digital market, and what are the legal challenges, which may arise with respect to various models of algorithm transparency. The paper is embedded within the EU legal framework, discusses new legislative developments in the EU law, such as the proposal for the Digital Markets Act, and includes analysis of EU antitrust case-law and market practices.
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Guo, Lei, Lijian Zhou, Shaohui Jia, Li Yi, Haichong Yu, and Xiaoming Han. "An Automatic Segmentation Algorithm Used in Pipeline Integrity Alignment Sheet Design." In 2010 8th International Pipeline Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ipc2010-31036.

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Pipeline segmentation design is the first step to design alignment sheet. In this step, several rectangular boxes are used to cover pipeline and each box will become the basic unit of alignment sheet design. After studying various pipeline alignment sheet mapping technologies, the author found that traditional manual design method, which can take advantage of designers’ subjectivity, causes low work efficiency. By reviewing and studying existing works at home and abroad, the author believed that it is possible and feasible to develop an automatic segmentation algorithm based on existing curve simplification algorithms to improve to improve the efficiency of pipeline section design and alignment sheet mapping. Based on several classical curve simplification algorithms, the author proposed the automatic segmentation algorithm, which automatically adjusts the location of rectangular boxes according to the number of pipeline/circle intersection points and pipeline/ rectangular box intersection points. Finally, through comparing time and result with the traditional manual method, the author proved the algorithm’s effectiveness and feasibility.
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Xia, Yifan, and Xiangyi Zhang. "A Neural Column Generation Approach to the Vehicle Routing Problem with Two-Dimensional Loading and Last-In-First-Out Constraints." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/218.

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The vehicle routing problem with two-dimensional loading constraints (2L-CVRP) and the last-in-first-out (LIFO) rule presents significant practical and algorithmic challenges. While numerous heuristic approaches have been proposed to address its complexity, stemming from two NP-hard problems: the vehicle routing problem (VRP) and the two-dimensional bin packing problem (2D-BPP), less attention has been paid to developing exact algorithms. Bridging this gap, this article presents an exact algorithm that integrates advanced machine learning techniques, specifically a novel combination of attention and recurrence mechanisms. This integration accelerates the state-of-the-art exact algorithm by a median of 29.79% across various problem instances. Moreover, the proposed algorithm successfully resolves an open instance in the standard test-bed, demonstrating significant improvements brought about by the incorporation of machine learning models. Code is available at https://github.com/xyfffff/NCG-for-2L-CVRP.
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Hashmi, Muhammad Baqir, Amare Desalegn Fentaye, Mohammad Mansouri, and Konstantinos G. Kyprianidis. "A Comparative Analysis of Various Machine Learning Approaches for Fault Diagnostics of Hydrogen Fueled Gas Turbines." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-129279.

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Abstract Global energy transition efforts towards decarbonization requires significant advances within the energy sector. In this regard, hydrogen is envisioned as a long-term alternative fuel for gas turbines. Accordingly, the gas turbine industry has expedited their efforts in developing 100% hydrogen compliant burners and associated auxiliary components for retrofitting the existing gas turbines. The utilization of hydrogen in gas turbines has some underlying challenges such as corrosion mainly originating from increased steam content in the hot gas path. In addition to corrosion, the gas turbine compressor is vulnerable to fouling which is the most commonly occurring fault in gas turbine operating over certain time window. Both faults are susceptible to performance and health degradation. To avoid expensive asset loss caused by unexpected downtimes and shutdowns, timely maintenance decision making is required. Therefore, simple, accurate and computationally efficient fault detection and diagnostics models become crucial for timely assessment of health status of the gas turbines. The present study encompassed development of a physics-based performance model of a 100-kWe micro gas turbine running on 100% hydrogen fuel. The model is validated with experimental data acquired from test campaigns at the University of Stavanger. Data synthesized from experimentally validated performance model are utilized further for training machine learning algorithms. To identify an accurate algorithm, various algorithms such as support vector machine, decision tree, random forest algorithm, k-nearest neighbors, and artificial neural network were tested. The findings from fault diagnostics process (classification) revealed that ANN outperformed its counterpart algorithm by giving accuracy of 94.55%. Similarly, ANN also showed higher accuracy in performance degradation estimation process (regression) by showing the MSE of training loss as low as ∼0.14. The comparative analysis of all the chosen algorithms in the present study revealed ANN as the most accurate algorithm for fault diagnostics of hydrogen fueled gas turbines. However, there is need to further implement the ensemble machine learning models or deep learning model to explore and expedite the real time fault diagnostic accuracy to avoid false alarms and missed detections in context of hydrogen fuel.
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Reports on the topic "VARION algorithm"

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Pirohov, Vladyslav M., Anna M. Horlo, and Iryna S. Mintii. Software development of the algorithm of adaptating of the website design for people with color-blindness. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2888.

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The purpose of the study is the program implementation of the basic algorithms of the website design adaptation for people with color perception. This article examines the implementation of the following algorithms: the conversion algorithm from the rgb color model to the lms model (there is a special algorithm precisely in the color model lms for modeling various types of color perception violations), simulation of different color blindness types in the lms-model (this is the transformation of normal values in the color model lms to values with different types of color perception violations), convert data from lms color model to rgb model, conversion from color rgb to hsl model, color filtering in hsl-model and conversion from hsl model to rgb color model.
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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means
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Holtman, K., and A. Mutz. HTTP Remote Variant Selection Algorithm -- RVSA/1.0. RFC Editor, 1998. http://dx.doi.org/10.17487/rfc2296.

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Peñaloza, Rafael, and Anni-Yasmin Turhan. Completion-based computation of least common subsumers with limited role-depth for EL and Prob-EL⁰¹. Technische Universität Dresden, 2010. http://dx.doi.org/10.25368/2022.175.

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The least common subsumer (lcs) w.r.t general EL-TBoxes does not need to exists in general due to cyclic axioms. In this report we present an algorithm for computing role-depth bounded EL-lcs based on the completion algorithm for EL. We extend this computation algorithm to a recently introduced probabilistic variant of EL: Prob-EL⁰¹.
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Dennis, Jr, Morshedi J. E., Turner A. M., and Kathryn. A Variable-Metric Variant of the Karmarkar Algorithm for Linear Programming. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada453840.

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Chowdhary, M., Y. C. Chao, and S. Witherspoon. Comparison between various beam steering algorithms for the CEBAF lattice. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/563275.

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Andersen, Kamilla Heimar, Markus Schaffer, Hicham Johra, Anna Marszal-Pomianowska, William O'Brien, and Per Kvols Heiselberg. Evaluation of Various Algorithms' Performance in Supervised Binary Classification for Occupant Detection Using a Dataset from a Residential Building. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau645740910.

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This technical report describes the evaluation process of various machine learning algorithms' performance used for supervised binary classification for occupant detection, using a dataset from a residential building in the North of Denmark.
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Hirsch, Colin, and Stephan Tobies. A Tableau Algorithm for the Clique Guarded Fragment. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.106.

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Aus der Einleitung: The Guarded Fragment of first-order logic, introduced by Andréka, van Benthem, and Németi, has been a succesful attempt to transfer many good properties of modal, temporal, and description logics to a larger fragment of predicate logic. Among these are decidability, the finite modal property, invariance under an appropriate variant of bisimulation, and other nice modal theoretic properties.
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Hodgdon, Taylor, Anthony Fuentes, Jason Olivier, Brian Quinn, and Sally Shoop. Automated terrain classification for vehicle mobility in off-road conditions. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40219.

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.
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Alwall, J., S. Hoche, F. Krauss, et al. Comparative Study of Various Algorithms for the Merging of Parton Showers and Matrix Elements in Hadronic Collisions. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/909301.

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