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Journal articles on the topic 'Stacker Selection'
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ULUTAŞ, Alptekin. "Stacker Selection with PSI and WEDBA Methods." International Journal of Contemporary Economics and Administrative Sciences 10, no. 2 (2020): 493–504. https://doi.org/10.5281/zenodo.4430021.
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<strong>Abstract </strong> Material handling equipment selection is of great importance for companies as it will greatly increase the efficiency and productivity of the companies and impose a large cost on the companies in case of any wrong choice. In this study, by utilizing an integrated MCDM model consisting of PSI and WEDBA, the selection of stacker, which is one of the material handling equipment, is made. In this study, PSI and WEDBA methods are used together for the first time. In addition, the use of the PSI method to obtain the weights of the criteria rarely appears in the literature. Besides, there is no study on the selection of manual stacker in the literature. Thus, this study aims to fill above-mentioned gaps in the literature. According to results of WEDBA method, the best stacker was determined as "ST5". This stacker was followed by "ST3", "ST4", "ST1" and "ST2" respectively
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Han, Hui Xian, Xiong Hua Guo, and Xian Li Cao. "Design and Analysis for Hydraulic Travel System of Container Stacker." Advanced Materials Research 168-170 (December 2010): 230–34. http://dx.doi.org/10.4028/www.scientific.net/amr.168-170.230.
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Work conditions and requirements of a container stacker are analyzed. Aiming at the disadvantages of hydrodynamic travel system of stacker, a design method for hydraulic chassis of a stacker is presented. With a practical example, its operation performances under the work conditions of stable travel in low-speed are analyzed. New method to design its travel drive system is presented, it contains the hydraulic system design and component selection. It is considered that the manufacture cost for special hydraulic chassis of a stacker is equal to or lower than that of a hydrodynamics chassis, so there is wide application prospect of it.
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Setiawan, Ari, and Teguh Ersada Natail Sitepu. "Scheduling Flexible Manufacturing System with Stacker Crane Using Coloured Petri Nets." Jurnal Teknik Industri 20, no. 2 (2019): 113. http://dx.doi.org/10.9744/jti.20.2.113-126.
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Scheduling Flexible Manufacturing System (FMS) can increase production speed and accuracy. It because FMS has an ability to process various variety of product at same work station. However, FMS need efficient allocation of resources, for example: allocation of material handling equipment. This paper presents production scheduling FMS modelling to 24 jobs and 4 machines considering stacker crane. Coloured Petri Nets (CP-Nets) is the programming language which used to simulate model because it’s simplicity. This model consists two main model activities. The first one is Physical Activity (PA) which related to every activity that involve physical movement, including stacker crane processes. PA consist five CP-Nets models: loading/unloading station, stacker crane, machine, picking mechanism, and pallet stocker. The second activity is Logical Expression (LE) which related to the rules on how FMS should operate. LE consist three CP-Nets models: machine selection, pick-up request, and stage two procedure. A simulation and numerical report show utilization level of all machines around 80-84% and stacker crane 8,74%.
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Ding, Hai Dong, Ming Li, and Zhu Lei Feng. "Reconfigurable Modular Design of Glass Stacker." Advanced Materials Research 1039 (October 2014): 603–8. http://dx.doi.org/10.4028/www.scientific.net/amr.1039.603.
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With the vigorous development of the market of building glass, flat glass products’ deep processing is more and more diversified, enterprises become highly request for the production lines, especially the glass piling machinery. As one of the important equipment in glass production line, glass piling machinery has a vital role to ensure production efficiency, safety and quality. This article is based on modular design technology, combining the theory of reconfigurable, completed the modular design, the series planning and interface design, realized the reconfigurable modular function of the glass stacker. Make glass stacker can through the selection and combination of modules, constitute the different products quickly, to meet the different needs of the market.
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Song, Yubo, and Haibo Mu. "Integrated Optimization of Input/Output Point Assignment and Twin Stackers Scheduling in Multi-Input/Output Points Automated Storage and Retrieval System by Ant Colony Algorithm." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–18. http://dx.doi.org/10.1155/2022/5997095.
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This paper researches the problem of operational optimization in the multiple-input/output (multi-I/O) points automated storage and retrieval system (AS/RS), where the destination addresses of the retrieval requests are unknown, twin stackers work simultaneously in the same aisle and cannot cross each other, and each stacker can carry two cargo units at the same time. The problem is formalized as an integrated optimization problem with noncrossing constraint for the I/O point assignment and the storage/retrieval (S/R) requests grouping and scheduling. The complexity of the problem is analyzed without considering the I/O point assignment and double units transport (DUT). In order to adapt to the integrated optimization characteristics of the problem, a node structure with multi-I/O points is considered, and the probability selection formula of ant colony algorithm is improved for multiple pheromones. Various numerical experiments show that the average deviation of the objective function value between the proposed method and CPLEX is no more than 0.4%, and the maximum deviation is only 1.22%. Although the phased algorithm avoids the waiting of stacker in operation, the average value of its objective function is 12.14% higher than that of the proposed method, and the maximum deviation is more than 30%.
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Shishlov, Sergey A., Aleksandr N. Shishlov, Aleksandr A. Fadeev, Dmitry S. Shishlov, and Ekaterina I. Reshetnik. "To substantiation of the shape of the side surface of a pocket-type cell for a vertical disk precision sowing apparatus." Far Eastern Agrarian Herald 17, no. 3 (2023): 157–62. https://doi.org/10.22450/19996837_2023_3_157.
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Soybean sowing is one of the main agrotechnical operations in the technology of its cultivation, which determines the receipt of a high yield. The conditions for providing each plant with an equally individual feeding area and illumination are formed at the initial stage of sowing by a single selection of seeds by the cells of the sowing apparatus. In most existing de- signs of mechanical precision sowing apparatus, single selection and seeding of seeds is carried out using a rotating disk with cylindrical radially made cells, which makes it difficult to load the cells with seeds, and an ejector is required to unload the cells. As a result, the speed of rotation of the disk decreases and, as a consequence, the productivity of the sowing unit, the seeds are injured by the impact of the ejector and the stacker roller. In the proposed design of the sowing apparatus containing a sowing disc with pocket-type cells located at an angle to the radius of the sowing disc (patent of the Russian Federation No. 164890), a single selection of soybean seeds is made by scooping them with cells, which allows filling the cells in a shorter period of time compared to existing similar designs. Unloading of the cells is carried out from the impact of centrifugal force on the seeds. Such a constructive design of the sowing apparatus will increase the productivity of the sowing unit by increasing the peripheral speed of the disk, and the absence of a stacker roller and an ejector will reduce seed injury. This constructive design of the sowing apparatus will in- crease the productivity of the sowing unit by increasing the peripheral speed of the disk, and the absence of a stacker roller and ejector will reduce seed injury. The article presents some results of theoretical studies to substantiate the shape of the lateral surface of the cell and its effect on the process of loading and unloading a pocket-type cell in a vertical disk precision seeding machine.
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Chatterjee, Saikat, and Shankar Chakraborty. "Application of the R method in solving material handling equipment selection problems." Decision Making: Applications in Management and Engineering 6, no. 2 (2023): 74–94. http://dx.doi.org/10.31181/dmame622023391.
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In manufacturing industries, material handling equipment plays a vital role and is considered as one of the important pillars to increase production efficiency. Hence, the selection of appropriate material handling equipment for a specific task is well acknowledged, but the complexity of this selection process drastically increases with the rise in the number of alternative equipment available in the market and a set of conflicting evaluation criteria. To resolve this problem, several multi-criteria decision-making (MCDM) techniques have been proposed by past researchers. In this paper, the application potentiality of a newly developed MCDM technique, i.e. R method is explored while solving five material handling equipment selection problems, i.e. conveyor, automated guided vehicle (AGV), stacker, wheel loader and excavator. The derived ranking results are contrasted with other popular MCDM techniques to validate its potentiality in shortlisting the candidate alternatives from the best to the worst, which would ultimately help in improving the overall efficiency of the manufacturing processes.
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Krstić, Mladen Dragan, Snežana Radoman Tadić, Nikolina Brnjac, and Slobodan Zečević. "Intermodal Terminal Handling Equipment Selection Using a Fuzzy Multi-criteria Decision-making Model." PROMET - Traffic&Transportation 31, no. 1 (2019): 89–100. http://dx.doi.org/10.7307/ptt.v31i1.2949.
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Intermodal transport enables energy, costs and time savings, improves the service quality and supports sustainable development. The basic element of the intermodal transport system is an intermodal terminal, whose efficiency largely depends on the subsystems’ technologies. Accordingly, the topic of this paper is the evaluation and the selection of the appropriate handling equipment within the intermodal terminal. As the decision-making on the handling equipment is influenced by different economic, technical, technological and other criteria, the appropriate multi-criteria decision-making (MCDM) methods have to be applied in order to solve the problem. In this paper, a novel hybrid model which combines the fuzzy step-wise weight assessment ratio analysis (FSWARA) and the fuzzy best-worst method (FBWM) is developed. The defined model is applied for solving the case study of selecting adequate handling equipment for the planned intermodal terminal in Belgrade. The reach stacker is selected as the most adequate handling equipment since it suits best the characteristics of the planned terminal in the given conditions and in relation to the defined criteria. Solving the case study demonstrated the justification for using the MCDM methods to solve these kinds of problems as well as the applicability of the proposed MCDM model.
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Krstić, Mladen, Snežana Tadić, Valerio Elia, Stefania Massari, and Muhammad Umar Farooq. "Intermodal Terminal Subsystem Technology Selection Using Integrated Fuzzy MCDM Model." Sustainability 15, no. 4 (2023): 3427. http://dx.doi.org/10.3390/su15043427.
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Intermodal transportation is the use of multiple modes of transportation, which can lead to greater sustainability by reducing environmental impact and traffic congestion and increasing the efficiency of supply chains. One of the preconditions for efficient intermodal transport is the efficient intermodal terminal (IT). ITs allow for the smooth and efficient handling of cargo, thus reducing the time, cost, and environmental impact of transportation. Adequate selection of subsystem technologies can significantly improve the efficiency and productivity of an IT, ultimately leading to cost savings for businesses and a more efficient and sustainable transportation system. Accordingly, this paper aims to establish a framework for the evaluation and selection of appropriate technologies for IT subsystems. To solve the defined problem, an innovative hybrid multi-criteria decision making (MCDM) model, which combines the fuzzy factor relationship (FFARE) and the fuzzy combinative distance-based assessment (FCODAS) methods, is developed in this paper. The FFARE method is used for obtaining criteria weights, while the FCODAS method is used for evaluation and a final ranking of the alternatives. The established framework and the model are tested on a real-life case study, evaluating and selecting the handling technology for a planned IT. The study defines 12 potential variants of handling equipment based on their techno-operational characteristics and evaluates them using 16 criteria. The results indicate that the best handling technology variant is the one that uses a rail-mounted gantry crane for trans-shipment and a reach stacker for horizontal transport and storage. The results also point to the conclusion that instead of choosing equipment for each process separately, it is important to think about the combination of different handling technologies that can work together to complete a series of handling cycle processes. The main contributions of this paper are the development of a new hybrid model and the establishment of a framework for the selection of appropriate IT subsystem technologies along with a set of unique criteria for their evaluation and selection.
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Ulutaş, Alptekin, Darjan Karabasevic, Gabrijela Popovic, Dragisa Stanujkic, Phong Thanh Nguyen, and Çağatay Karaköy. "Development of a Novel Integrated CCSD-ITARA-MARCOS Decision-Making Approach for Stackers Selection in a Logistics System." Mathematics 8, no. 10 (2020): 1672. http://dx.doi.org/10.3390/math8101672.
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The main goal of this paper is to propose a Multiple-Criteria Decision-Making (MCDM) approach that will facilitate decision-making in the field of logistics—i.e., in the selection of the optimal equipment for performing a logistics activity. For defining the objective weights of the criteria, the correlation coefficient and the standard deviation (CCSD method) are applied. Furthermore, for determining the semi-objective weights of the considered criteria, the indifference threshold-based attribute ratio analysis method (ITARA) is used. In this way, by combining these two methods, the weights of the criteria are determined with a higher degree of reliability. For the final ranking of the alternatives, the measurement of alternatives and ranking according to the compromise solution method (MARCOS) is utilized. For demonstrating the applicability of the proposed approach, an illustrative case study pointing to the selection of the best manual stacker for a small warehouse is performed. The final results are compared with the ones obtained using the other proved MCDM methods that confirmed the reliability and stability of the proposed approach. The proposed integrated approach shows itself as a suitable technique for applying in the process of logistics equipment selection, because it defines the most influential criteria and the optimal choice with regard to all of them in a relatively easy and comprehensive way. Additionally, conceiving the determination of the criteria with the combination of objective and semi-objective methods enables defining the objective weights concerning the attitudes of the involved decision-makers, which finally leads to more reliable results.
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Bai, Dongqiao, Qi Yang, Jian Zhang, and Shouzhi Li. "Process Plant Upgradation Using Reliability, Availability, and Maintainability (RAM) Criteria." International Journal of Optics 2022 (January 13, 2022): 1–17. http://dx.doi.org/10.1155/2022/4287346.
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The objective of this study is to propose a solution for process plant upgradation becoming extinct due to obsoleteness of spares. The study will help in reliability, availability, and maintainability (RAM) based upgradation of control system of process plants in developing countries. Available options for plant upgradation are compact control, modular, and semiautomatic. RAM based upgradation provides solution which is high in reliability and availability (usually all parts are replaced with upgraded and compatible technology) and is easy to maintain throughout the service life of process plant. Case study for stacker and reclaimer of cement plant upgradation is considered to both implement and evaluate the idea. Upgradation methodology is finalized by expert’s feedback regarding selection of hardware with respect to availability, market survey to validate the opinion, and economical availability viability of selected hardware. Pre- and postupgradation scenarios are analyzed to validate the implementation of study and conclude the expected outcomes. The process plant upgradation yielded a cost-effective solution to the problem with automation increasing by 17%, plant maintainability increasing by 80%, and downtime of plant decreasing by 17%. Among all available options, modular design Op1 is considered the best choice that can satisfy RAM criteria.
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Tkachov, Artem, and Serhii Makhnovsky. "Models of blended learning and approaches to their selection in the educational process of higher education." Bulletin of Luhansk Taras Shevchenko National University 2, no. 8 (346) (2021): 321–32. http://dx.doi.org/10.12958/2227-2844-2021-8(346)-2-321-332.
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The article states that one of the strategic directions of modernization of higher education today is to provide a scientifically sound combination of online and offline learning formats. In light of this, the purpose of the article is to identify the main models of blended learning and disclose approaches to their selection in the educational process of higher education. It has been established that blended learning means all-in-one educational system that comprehensively combines different methods and forms of educational activities of its subjects online and offline. Based on the study of the scientific literature, it was established that one of the most well-known classifications of blended learning models is the classification of H. Stacker and M. Horn that includes the following models: Rotation (provides a sequential alternation of online and offline learning); Flex (online learning mode prevails); Self-blend (students study full-time and additionally study online courses); Enriched-Virtual Model (students’ study time is divided between educational activities in classrooms and remotely). As it was identified in the study, some scientists offer various recommendations for the teacher to choose a specific model of learning from possible options or create your own model. It was concretized that L. D. Fink recommends to choose a model by taking specific steps, including the following: to identify and evaluate various situational factors; to determine the learning objectives of the course; to consider the ways of ensuring the implementation of feedback and evaluation; to consider the course and a content of teaching and learning activities; to ensure the system integrity of all components of the designed educational process; to develop a learning strategy; to make the general scheme of educational activity of students; to specify how the system of assessment of student learning outcomes will be implemented; to predict what difficulties may arise in the educational process; to bring to the notice of applicants the necessary information about the learning process; to identify the necessary current feedback and the ways to obtain it.
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Журавлев, А. Г., та Е. Д. Кардашин. "МОДЕЛИРОВАНИЕ ГРУЗОПОТОКОВ ПРИ ФУНКЦИОНИРОВАНИИ ДРОБИЛЬНО-КОНВЕЙЕРНОГО КОМПЛЕКСА КАРЬЕРА". Проблемы недропользования, № 4(43) (27 грудня 2024): 6–15. https://doi.org/10.25635/2313-1586.2024.04.006.
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Практика применения автомобильно-конвейерного транспорта в карьерах показывает, что его параметры нередко не соответствуют фактическим параметрам и динамике развития горных работ в той части, которая обеспечивает его рациональное и производительное использование с максимальным экономическим эффектом. Проблема наиболее характерна для конвейерного звена этого вида транспорта, основой которого является дробильно-конвейерный комплекс. Причины в том числе заключаются в отсутствии применимых на практике инструментов оптимизации параметров потоков горной массы в ДКК, в том числе обосновании рациональной вместимости бункеров, структурной схемы дробильно-перегрузочной установки, обосновании рациональной производительности оборудования и его количества по видам, необходимости организации распараллеливания рудопотоков в пределах ДКК, а также резервирования оборудования по мощности и/или надежности. В связи с этим в ИГД УрО РАН ведется работа по созданию имитационной модели дробильно-конвейерного комплекса, обеспечивающей решение вышеуказанных задач. В статье представлены обоснованные в ходе исследований структура и алгоритм модели, а также результаты апробации имитационной модели, реализованной на примере однолинейного ДКК с ДПУ на базе одной конусной дробилки. Подтверждена работоспособность модели, возможность ее применения для подбора рациональных параметров ДКК путем перебора вариантов, обеспечивающих стабильность работы ДКК по грузопотоку на выходе штабелеукладчика. Ключевые слова: компьютерное имитационное моделирование, дробильно-конвейерный комплекс (ДКК), дробильно-перегрузочная установка (ДПУ), рациональные параметры, циклично-поточная технология (ЦПТ). The application praxis of automobile and conveyor transport in open pits shows that their parameters often do not correspond to the actual parameters and dynamics of development of mining operations in the part that provides their rational and productive use with maximum economic effect. The problem is most typical for the conveyor section of this transportation type, the basis of which is the crushing and conveyor complex (СCC). The reasons for are the lack in practical tools for optimizing the parameters of rock mass flows in the СCC, including justification of the rational capacity of hoppers, in structural scheme of the crushing and conveying plant, in justification of the rational productivity of equipment and its number by type, and in the need to organize the paralleling of ore flows within the СCC, as well as the redundancy of equipment by capacity and/or reliability. In this regard, the work on creation of a simulation model of crushing and conveyor complex, providing the solution of the above mentioned problems, is being carried out in the Institute of Mining of the Ural Branch of the Russian Academy of Sciences. The article presents the structure and algorithm of the model substantiated in the course of research, as well as the results of approbation of the simulation model realized on the example of a single-line CCC with CRP on the basis of one cone crusher. The performance of the model was confirmed, the possibility of its application for selection of rational parameters of CCC by means of selection of variants ensuring the stability of CCC operation in terms of load flow at the stacker output was confirmed.
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Panchenko, O. "Integrated approach to analyzing the stress-strain state of the tubbing erector manipulator mechanism based on the SolidWorks program." Collection of Research Papers of the National Mining University 74 (September 2023): 230–40. http://dx.doi.org/10.33271/crpnmu/74.230.
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Purpose. The research purpose is todevelop and substantiate a method for computer analysis of the stress-strain state that occurs in the tubbing erector manipulator mechanism with several degrees of freedom, using modern computing complexes based on the finite-element method. Methods. The research uses software complexes, with the help of which a calculation-theoretical apparatus has been created, which is intended for calculating dynamic and static loads that occur in the elements of the tubbing erector manipulator mechanism. The results of modeling the stress-strain state in the parts and nodes of this mechanism, obtained using the SolidWorks Motion and SolidWorks Simulation programs, have been compared. The errors in these results have been estimated. Findings. The effectiveness of the proposed analysis method, based on the combination of SolidWorks applications, in particular, SolidWorks Motion and SolidWorks Simulation, has been proven on the example of modeling the most complex design of the UT62 tubbing erector mechanism.The essence of the method is that initially, using the SolidWorks Motion application, the laws of motion of the motors are set. The tubbing erecting process itself is divided into discrete moments of time with the selection of parts in which the stress fields are calculated and with the determination of the maximum equivalent stress, using the Simulation Setup tool. The change in these stresses during the erecting cycle is analyzed, and if it is necessary to refine the data, the discrete time step of the mechanism motion is reduced. Specific information about the stress-strain state of the part is obtained in the SolidWorks Simulation application, using data imported from the SolidWorks Motion program about gravitational and inertial forces that occur in the tubbing erector mechanism. Originality. The scientific novelty of the developed method of computer analysis for the mechanism of the manipulator of the UT62 type tubing stacker is manifested in a comprehensive approach to modeling the stress-strain state, which includes the integration of data from SolidWorks Motion and SolidWorks Simulation programs for accurate determination of stresses, taking into account both gravitational forces and dynamic loads, which ensures high accuracy and complexity of calculations in real operating conditions of the mechanism. Practical implications. The proposed method can be used for modeling the motion of mechanisms of different complexity, in particular, in the design of domestic tubbing erectors.
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Beruete, M., M. Navarro-Cía, M. Sorolla, and I. Campillo. "Polarization selection with stacked hole array metamaterial." Journal of Applied Physics 103, no. 5 (2008): 053102. http://dx.doi.org/10.1063/1.2841471.
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SenGupta, Breeta, Dimitar Nikolov, Assmitra Dash, and Erik Larsson. "Test Flow Selection for Stacked Integrated Circuits." Journal of Electronic Testing 35, no. 4 (2019): 425–40. http://dx.doi.org/10.1007/s10836-019-05813-z.
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WAKAMATSU, Kenta, and Nobuyuki MORONUKI. "2303 Fabrication of Stacked Layers of Fine Particle Assembly Aiming at Wavelength-Selective Reflection." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _2303–1_—_2303–4_. http://dx.doi.org/10.1299/jsmelem.2015.8._2303-1_.
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Yu, Jianbo. "Manifold regularized stacked denoising autoencoders with feature selection." Neurocomputing 358 (September 2019): 235–45. http://dx.doi.org/10.1016/j.neucom.2019.05.050.
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He, Huan, Xinyun Guo, Jialin Yu, Chen Ai, and Shaoping Shi. "Overcoming the inadaptability of sparse group lasso for data with various group structures by stacking." Bioinformatics 38, no. 6 (2021): 1542–49. http://dx.doi.org/10.1093/bioinformatics/btab848.
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Abstract Motivation Efficiently identifying genes based on gene expression level have been studied to help to classify different cancer types and improve the prediction performance. Logistic regression model based on regularization technique is often one of the effective approaches for simultaneously realizing prediction and feature (gene) selection in genomic data of high dimensionality. However, standard methods ignore biological group structure and generally result in poorer predictive models. Results In this article, we develop a classifier named Stacked SGL that satisfies the criteria of prediction, stability and selection based on sparse group lasso penalty by stacking. Sparse group lasso has a mixing parameter representing the ratio of lasso to group lasso, thus providing a compromise between selecting a subset of sparse feature groups and introducing sparsity within each group. We propose to use stacked generalization to combine different ratios rather than choosing one ratio, which could help to overcome the inadaptability of sparse group lasso for some data. Considering that stacking weakens feature selection, we perform a post hoc feature selection which might slightly reduce predictive performance, but it shows superior in feature selection. Experimental results on simulation demonstrate that our approach enjoys competitive and stable classification performance and lower false discovery rate in feature selection for varying sets of data compared with other regularization methods. In addition, our method presents better accuracy in three public cancer datasets and identifies more powerful discriminatory and potential mutation genes for thyroid carcinoma. Availability and implementation The real data underlying this article are available from https://github.com/huanheaha/Stacked_SGL; https://zenodo.org/record/5761577#.YbAUyciEwk2. Supplementary information Supplementary data are available at Bioinformatics online.
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Li, Hao Ping, Zi Fan Fang, and Ying Wang. "Research on Path Optimization of Automated Warehouse Based on Ant Colony Algorithm." Advanced Materials Research 201-203 (February 2011): 1112–15. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.1112.
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Based on analysis of the cargo selecting strategy of fixed shelf automated warehouse, the idea of path optimization is put forward and the stacker path optimization method is studied. A mathematical model of stacker operation path optimization is built to minimize the length of operation path and the operation time. The model is solved by using the ant colony optimization method. Simulation shows that chosen stacker operation path by using the optimization model and optimization algorithm, can not only reduce energy consumption and warehouse operating costs, but also improve the efficiency of goods storage.
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ENGLISH, LYN D. "LINKING PROBABILITY AND STATISTICS IN YOUNG STUDENTS’ REASONING WITH CHANCE." STATISTICS EDUCATION RESEARCH JOURNAL 22, no. 2 (2023): 7. http://dx.doi.org/10.52041/serj.v22i2.418.
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This article reports on a study in which third-grade students (8–9 years) were given a degree of agency in conducting chance experiments and representing the outcomes. Students chose their own samples of 12 coloured counters, ensuring all colours were represented. They predicted the outcomes of item selection, tested their predictions, explained the outcomes, quantified their chances of colour selections, and created two representations displaying the probabilities. Children displayed awareness of randomness and variation, together with proportional reasoning, as evident in their identification of one or more colours as having a greater chance of being selected, or equal chances when proportions of colours were equal. Evidence of children’s metarepresentational competence appeared in their creation of two representations to display their probabilistic outcomes, with bar and circle graphs, as well as stacked bars, created. The inclusion of their own forms of inscription revealed a range of probability and statistics understandings. In selecting and justifying their preferred representations for conveying their outcomes, students favoured both bar and circle graphs, with a focus on how accurately, effectively, and efficiently their representation displayed the data, with the importance of the inscriptions highlighted.
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Zhou, Dong Xiang, Huan Liu, Shu Ping Gong, and Dao Li Zhang. "Preparation of Multilayer Barium Titanate PTC Thermistor with Low Room Temperature Resistance." Key Engineering Materials 280-283 (February 2007): 1921–24. http://dx.doi.org/10.4028/www.scientific.net/kem.280-283.1921.
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Chip-type PTC thermistors with multilayer stacked structure have been fabricated by bonding sintered ceramic chips with internal electrodes to offer low resistance at room temperature and correspondence to surface mounted technology. The resistance-temperature characteristics of multiplayer stacked PTC thermistors made up of different numbers (N = 1, 3, 5) of layers were experimentally investigated (the typical size of each layer was 10 mm × 7.0 mm × 0.38 mm). The selection and extraction of additives in roll-forming process were also discussed. This resulted in a crack-free multiplayer stacked PTC thermistor.
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Zhou, Jianmin, Lulu Liu, and Xiwen Shen. "SSDStacked-BLS with Extended Depth and Width: Infrared Fault Diagnosis of Rolling Bearings under Dual Feature Selection." Mathematics 11, no. 17 (2023): 3677. http://dx.doi.org/10.3390/math11173677.
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In fault diagnosis, broad learning systems (BLS) have been applied in recent years. However, the best fault diagnosis cannot be guaranteed by width node extension alone, so a stacked broad learning system (stacked BLS) was proposed. Most of the methods for choosing the number of depth layers used optimization algorithms that tend to increase computation time. In addition, the data under single feature selection are not sufficiently representative, and effective features are easily lost. To solve these problems, this article proposes an infrared fault diagnosis model for rolling bearings based on integration of principal component analysis and singular value decomposition (IPS) and the stacked BLS with self-selected depth model (SSDStacked-BLS). First, 72 second-order statistical features are extracted from the pre-processed infrared images of rolling bearings. Next, feature selection is performed using IPS. he IPS feature selection module consists of principal component analysis (PCA) and singular value decomposition (SVD). The feature selection is performed by PCA and SVD separately, which are then stitched together to form a new feature. This ensures a comprehensive coverage of infrared image features. Finally, the acquired features are input into SSDStacked-BLS. This model establishes a data storage group for the residual training characteristics of stacked BLS, adding one block at a time. The accuracy rate of each newly added block is output and saved to the data storage group. If the diagnostic rate fails to increase three consecutive times, the block stacking is stopped and the results are output. IPS-SSDStacked-BLS achieved an accuracy of 0.9667 in 0.1775 s. This is almost five times faster than stacked BLS optimized using the grid search method. Compared with the original BLS, its accuracy was 0.0445 higher and the time was approximated. Compared with IPS-SVM, IPS-RF, IPS-1DCNN and 2DCNN, IPS-SSDStacked-BLS was more advantageous in terms of accuracy and time consumption.
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Gokhale, Madhuri, Sraban Kumar Mohanty, and Aparajita Ojha. "A stacked autoencoder based gene selection and cancer classification framework." Biomedical Signal Processing and Control 78 (September 2022): 103999. http://dx.doi.org/10.1016/j.bspc.2022.103999.
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Ahmed Hamza, Manar, Siwar Ben Haj Hassine, Ibrahim Abunadi, et al. "Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining." Computers, Materials & Continua 72, no. 2 (2022): 2581–96. http://dx.doi.org/10.32604/cmc.2022.024764.
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Ekbal, Asif, and Sriparna Saha. "Stacked ensemble coupled with feature selection for biomedical entity extraction." Knowledge-Based Systems 46 (July 2013): 22–32. http://dx.doi.org/10.1016/j.knosys.2013.02.008.
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Li, Ya, Zhanglin Peng, Depeng Liang, Huiyou Chang, and Zhaoquan Cai. "Facial age estimation by using stacked feature composition and selection." Visual Computer 32, no. 12 (2015): 1525–36. http://dx.doi.org/10.1007/s00371-015-1137-4.
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M. Elshewey, Ahmed, Mahmoud Y. Shams, Zahraa Tarek, Mohamed Megahed, El-Sayed M. El-kenawy, and Mohamed A. El-dosuky. "Weight Prediction Using the Hybrid Stacked-LSTM Food Selection Model." Computer Systems Science and Engineering 46, no. 1 (2023): 765–81. http://dx.doi.org/10.32604/csse.2023.034324.
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Polilli, Walter, Angelica Galieni, and Fabio Stagnari. "Nitrate Content in Open Field Spinach, Applicative Case for Hyperspectral Reflectance Data." Remote Sensing 17, no. 11 (2025): 1873. https://doi.org/10.3390/rs17111873.
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Spinach, leafy vegetables with growing demand and high nutritional value, has a heightened focus on nitrate content. An open-field experiment evaluated the potential of vis-NIR-SWIR hyperspectral data for classifying spinach nitrate content. Shallow artificial neural networks (ANN) and ensemble techniques—majority voting (MV) and stacked generalization (stacked)—were applied. The competitive adaptive reweighted sampling (CARS), its stability version (SCARS), Elastic Net, and modified boosted versions of each (CARSplus, SCARSplus, and ENplus) were used as feature selection methods. ANNs were optimized for hidden layer size. The resulting models were further used in ensemble techniques by grouping them into two sets: one with all models and another with models trained using the three boosted feature selection subsets (fifty-three wavelengths). The best-performing ANNs were based on the SCARS, SCARSplus, and full datasets, achieving an accuracy (Acc) of 0.83. While the majority voting approach did not improve performance (Acc 0.82), the stacked ensemble models reached Acc 0.88. Notably, stacked performed well also with models trained on 53 wavelengths, demonstrating strong potential for transferability as the required sensors would be less complex than those used in this study. Furthermore, a simulation of the practical application was conducted using Italian Ministry of Health official data with the scope of showing a potential use case in improving nitrate management and for advancing efficient farming practices in agriculture. The stacked models demonstrated their utility in doubling the monitoring capacity for internal quality assurance in spinach farming within a regulated framework.
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Panea, Ionelia, Deyan Draganov, Carlos Almagro Vidal, and Victor Mocanu. "Retrieval of reflections from ambient noise recorded in the Mizil area, Romania." GEOPHYSICS 79, no. 3 (2014): Q31—Q42. http://dx.doi.org/10.1190/geo2013-0292.1.
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We applied seismic interferometry (SI) by crosscorrelation to ambient-noise panels recorded in the Mizil area, Romania, aiming to retrieve body-wave reflections. To achieve this goal, surface waves in the noise panels input to SI should be suppressed. We did this by selecting for input to SI-only noise panels that are not dominated by surface waves; the selection was either after visual inspection in the time domain or after automatic slowness evaluation on crosscorrelated panels. The latter used the slowness of arrivals passing through the virtual-source position at time 0 s. We discovered that the automatic slowness-evaluation method allows better retrieval of reflections. From the retrieved reflection gathers, we obtained stacked images of the subsurface. Comparing the SI images to a stacked image from active-source data, we concluded that some retrieved events correspond to reflectors in the active seismic section, including known geologic markers. In a previous application of SI to ambient noise, the retrieved reflections exhibited frequency content lower than that of active data. In our results, the frequency content of the SI retrieved data was comparable to the one of the active data.
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Parth, Duddoo Shantanu Dudhal Ghanashyam Dudhe Sujeetkumar warkade Prof. S. S. Borade. "Design, Analysis &Development of Double Stacked Heat Exchanger." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 139–44. https://doi.org/10.5281/zenodo.12516240.
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The double stacked heat exchanger represents a significant advancement in thermal management systems, catering to the increasing demand for enhanced energy efficiency and performance in various industrial applications. This study focuses on optimizing heat transfer processes through the innovative integration of two heat exchanger units in a stacked configuration. Optimal Configuration: The investigation begins with a thorough analysis of different stacking configurations to identify the most efficient arrangement of the double stacked heat exchanger. Parameters such as flow rates, material properties, and geometric considerations are taken into account to achieve optimal heat transfer performance. Thermal Analysis: A comprehensive thermal analysis is conducted to evaluate the temperature distribution, heat transfer rates, and thermal gradients within the double stacked heat exchanger. Material Selection: The choice of materials plays a crucial role in the overall efficiency and durability of the heat exchanger. The theoretical findings are validated through experimental testing of a prototype double stacked heat exchanger.
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Chukin, D. M., D. N. Gushchin, V. Е. Kotyshev, and Yu A. Frolov. "Scientific and technical support for the devel-opment of sinter plant no. 5 of PJSC MMK." Ferrous Metallurgy. Bulletin of Scientific , Technical and Economic Information 78, no. 12 (2023): 1038–48. http://dx.doi.org/10.32339/0135-5910-2022-12-1038-1048.
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The design characteristics of the sinter plant No. 5 of PJSC MMK, commissioned in 2019, are presented. The results of studies of the processes of homogenization, mixing, moistening and pelletizing of the burden, its burdening on the sintering machine, ignition, sintering and cooling of the sinter cake are presented. The erroneousness of the design solutions for installing the pelletizing drum at the zero level and the charge bunker with a volume of 60 m3 in the sintering body without an intermediate bunker is shown. Technical solutions are presented to prevent a discrete charge descent of the charge from the drum feeder of the loading unit and to form the profile of the charge layer on the sintering machine, which increases the uniformity of the process of its sintering along the width of the pallets. The operation of a multi-roll charge stacker, first used at sinter plants in the CIS, was studied. Industrial experiments were carried out on sintering a conditionally “dense” and loosened by a special device batch layer, the advantage of the latter in terms of the speed of the sintering process was established. A technical solution has been developed and accepted for implementation to reduce the length of the hearth by 40%, which will increase the sintering area of the sintering machine by 3%. The operation of the energy-consuming exhaust gas recirculation system based on their selection from eight vacuum chambers of the sinter machine out of twenty with the installation of a smoke exhauster with a drive capacity of 3.1 MW and gas cleaning from dust was stopped. Vacuum chambers of the recirculation system are connected to two parallel collectors of the sintering machine; an analysis of the impact of these measures on the operation of the sintering machines has been carried out. The possibility of using gas recirculation with the installation of a smoke exhauster with a drive power of 400 kW without additional purification of gases from dust is shown. In the future, it is proposed to increase the open section of the collectors by making them from three sections of different sections. It was found that the area of linear coolers was overestimated due to the use of “direct” loading of sinter on them, in which the cooling of the material is limited by its large lumps located in the upper part of the layer. A sinter back loading system was proposed and implemented in the project, which allows to reduce the cooling area by 30% (turn off two of the six blowers), it was decided to implement it. As a result of development of AF-5, for the first time in the practice of sintering plants in the CIS, the height of sintered charge layer of 700 mm was established. The specific productivity of sintering machines during the sintering of the charge based on the Sokolovsko-Sarbaisky concentrate was achieved, equal to 1.10 t/(m2•h); the cold strength of the sinter according to GOST 15137–77 for impact is on average 82.5%, for abrasion ‒ 4.5%.
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G. Mohamed, Heba, Saud S. Alotaibi, Majdy M. Eltahir, et al. "Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment." Computers, Materials & Continua 73, no. 3 (2022): 5441–58. http://dx.doi.org/10.32604/cmc.2022.031887.
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Kushwaha, Jai Prakash, Saumya Bhadauria, and Shashikala Tapaswi. "Multi-Method Stacked Feature Selection Approach based IDS for IoT Networks." Procedia Computer Science 230 (2023): 564–73. http://dx.doi.org/10.1016/j.procs.2023.12.112.
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Miller, Richard D. "Normal moveout stretch mute on shallow‐reflection data." GEOPHYSICS 57, no. 11 (1992): 1502–7. http://dx.doi.org/10.1190/1.1443217.
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This paper demonstrates the potential consequences of overlooking the significance of allowable stretch ratio when performing normal moveout corrections on shallow‐reflection data. Two shallow reflection data sets with drastically different near‐surface geologic settings conclusively show the significance of subtle changes in allowable stretch mute. An improper stretch mute can reduce the dominant frequency and bandwidth of a stacked reflection by as much as 50 Hz. The sensitivity of shallow reflections to offset may require allowable stretch selection accuracy to be within ± 1 percent. It may be necessary to reduce the fold of an individual stacked shallow reflection by as much as 60 percent to avoid excessive degradation of wavelet properties and consequent loss of resolution. A proper normal moveout stretch mute can reduce distortion of reflection wavelet spectra caused by nonvertical incidence recording to less than 10 percent. Stretched reflection wavelets improperly muted can be misinterpreted on CDP stacked sections as stacked refractions or subtle stratigraphic features.
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Sudhan Reddy, D. Madhu, and N. Usha Rani. "Crop prediction using an enhanced stacked ensemble machine learning model." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1840. https://doi.org/10.11591/ijeecs.v38.i3.pp1840-1850.
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In India, agriculture is a major sector that fulfils the population's food requirements and significantly contributes to the gross domestic product (GDP). The careful selection of crops is fundamental to maximizing agricultural yield, thereby elevating the economic vitality of the farming community. Precision agriculture (PA) leverages weather and soil data to inform crop selection strategies. Conventional machine learning (ML) models such as decision trees (DT), support vector classifier, K-nearest neighbors (KNN), and extreme gradient boost (XGBoost) have been deployed to predict the best crop. However, these model's efficiency is suboptimal in the current circumstances. The enhanced stacked ensemble ML model is a sophisticated meta-model that addresses these limitations. It harnesses the predictive power of individual ML models, stratified in a layered architecture to improve the prediction accuracy. This advanced model has demonstrated a commendable accuracy rate of 93.1% prediction by taking input of 12 soil parameters such as Nitrogen, Phosphorus, Potassium, and weather parameters such as temperature and rainfall, substantially outperforming the accuracies achieved by the individual contributing models. The efficacy of the proposed meta-model in crop selection based on agronomic parameters signifies a substantial advancement, fortifying the economic resilience of India's agriculture.
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Tuli, Tadele Belay, and Andrea Cesarini. "Automated Unsupervised 3D Tool-Path Generation Using Stacked 2D Image Processing Technique." Journal of Manufacturing and Materials Processing 3, no. 4 (2019): 84. http://dx.doi.org/10.3390/jmmp3040084.
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Tool-path, feed-rate, and depth-of-cut of a tool determine the machining time, tool wear, power consumption, and realization costs. Before the commissioning and production, a preliminary phase of failure-mode identification and effect analysis allows for selecting the optimal machining parameters for cutting, which, in turn, reduces machinery faults, production errors and, ultimately, decreases costs. For this, scalable high-precision path generation algorithms requiring a low amount of computation might be advisable. The present work provides such a simplified scalable computationally low-intensive technique for tool-path generation. From a three dimensional (3D) digital model, the presented algorithm extracts multiple two dimensional (2D) layers. Depending on the required resolution, each layer is converted to a spatial image, and an algebraic analytic closed-form solution provides a geometrical tool path in Cartesian coordinates. The produced tool paths are stacked after processing all object layers. Finally, the generated tool path is translated into a machine code using a G-code generator algorithm. The introduced technique was implemented and simulated using MATLAB® pseudocode with a G-code interpreter and a simulator. The results showed that the proposed technique produced an automated unsupervised reliable tool-path-generator algorithm and reduced tool wear and costs, by allowing the selection of the tool depth-of-cut as an input.
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Ghazi, Mohd Rehan, and N. S. Raghava. "A Scalable and Stacked Ensemble Approach to Improve Intrusion Detection in Clouds." Information Technology and Control 52, no. 4 (2023): 898–914. http://dx.doi.org/10.5755/j01.itc.52.4.32042.
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The availability of automated data collection techniques and the growth in the amount of data collected from cloud network traffic and cloud resource activities has transformed into a big data challenge, compelling the engagement of big data tools to handle, manage, and interpret it. A single classification method may fail to execute successfully for the amount of acquired data. Despite being more complex and consuming more computational resources, the research shows that stacking-based ensemble Machine Learning (ML) methodologies perform better in data classification approaches than single classifiers. This research proposes Intrusion Detection Systems (IDS), both based on the ensemble of ML algorithms built on the Stacked Generalization Approach (SGA) and big data technology. The suggested approaches are tested and assessed on NSL-KDD and UNSW-NB15 datasets, utilizing a Gain Ration (GR) based Feature Selection (FS) approach, J48, OneR, Support Vector Machine (SVM), Random Forest (RF), Multi- layer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost) classifiers and Apache Spark, a prominent big data processing platform. The first technique involves storing data on HDFS, while the second involves selecting the most suitable subset of base classifiers for stacking. A thorough performance investigation reveals that our proposed model outperforms other current IDS models either in terms of accuracy or FPR or other performance metrics, in discovering intrusions for the Cloud.
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Brahmane, Anilkumar V., and Chaitanya B. Krishna. "Rider Chaotic Biography Optimization-driven Deep Stacked Auto-encoder for Big Data Classification Using Spark Architecture." International Journal of Web Services Research 18, no. 3 (2021): 42–62. http://dx.doi.org/10.4018/ijwsr.2021070103.
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The novelty in big data is rising day-by-day in such a way that the existing software tools face difficulty in supervision of big data. Furthermore, the rate of the imbalanced data in the huge datasets is a key constraint to the research industry. Thus, this paper proposes a novel technique for handling the big data using Spark framework. The proposed technique undergoes two steps for classifying the big data, which involves feature selection and classification, which is performed in the initial nodes of Spark architecture. The proposed optimization algorithm is named rider chaotic biography optimization (RCBO) algorithm, which is the integration of the rider optimization algorithm (ROA) and the standard chaotic biogeography-based optimisation (CBBO). The proposed RCBO deep-stacked auto-encoder using Spark framework effectively handles the big data for attaining effective big data classification. Here, the proposed RCBO is employed for selecting suitable features from the massive dataset.
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Chaudhuri, Avijit Kumar, Dilip K. Banerjee, and Anirban Das. "A Dataset Centric Feature Selection and Stacked Model to Detect Breast Cancer." International Journal of Intelligent Systems and Applications 13, no. 4 (2021): 24–37. http://dx.doi.org/10.5815/ijisa.2021.04.03.
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World Health Organisation declared breast cancer (BC) as the most frequent suffering among women and accounted for 15 percent of all cancer deaths. Its accurate prediction is of utmost significance as it not only prevents deaths but also stops mistreatments. The conventional way of diagnosis includes the estimation of the tumor size as a sign of plausible cancer. Machine learning (ML) techniques have shown the effectiveness of predicting disease. However, the ML methods have been method centric rather than being dataset centric. In this paper, the authors introduce a dataset centric approach(DCA) deploying a genetic algorithm (GA) method to identify the features and a learning ensemble classifier algorithm to predict using the right features. Adaboost is such an approach that trains the model assigning weights to individual records rather than experimenting on the splitting of datasets alone and perform hyper-parameter optimization. The authors simulate the results by varying base classifiers i.e, using logistic regression (LR), decision tree (DT), support vector machine (SVM), naive bayes (NB), random forest (RF), and 10-fold cross-validations with a different split of the dataset as training and testing. The proposed DCA model with RF and 10-fold cross-validations demonstrated its potential with almost 100% performance in the classification results that no research could suggest so far. The DCA satisfies the underlying principles of data mining: the principle of parsimony, the principle of inclusion, the principle of discrimination, and the principle of optimality. This DCA is a democratic and unbiased ensemble approach as it allows all features and methods in the start to compete, but filters out the most reliable chain (of steps and combinations) that give the highest accuracy. With fewer characteristics and splits of 50-50, 66-34, and 10 fold cross-validations, the Stacked model achieves 97 % accuracy. These values and the reduction of features improve upon prior research works. Further, the proposed classifier is compared with some state-of-the-art machine-learning classifiers, namely random forest, naive Bayes, support-vector machine with radial basis function kernel, and decision tree. For testing the classifiers, different performance metrics have been employed – accuracy, detection rate, sensitivity, specificity, receiver operating characteristic, area under the curve, and some statistical tests such as the Wilcoxon signed-rank test and kappa statistics – to check the strength of the proposed DCA classifier. Various splits of training and testing data – namely, 50–50%, 66–34%, 80–20% and 10-fold cross-validation – have been incorporated in this research to test the credibility of the classification models in handling the unbalanced data. Finally, the proposed DCA model demonstrated its potential with almost 100% performance in the classification results. The output results have also been compared with other research on the same dataset where the proposed classifiers were found to be best across all the performance dimensions.
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Wang, Xinan, Yishen Wang, Jianhui Wang, and Di Shi. "Residential Customer Baseline Load Estimation Using Stacked Autoencoder With Pseudo-Load Selection." IEEE Journal on Selected Areas in Communications 38, no. 1 (2020): 61–70. http://dx.doi.org/10.1109/jsac.2019.2951932.
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Yan, Xiaoan, Ying Liu, and Minping Jia. "Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder." Structural Health Monitoring 19, no. 5 (2019): 1602–26. http://dx.doi.org/10.1177/1475921719893594.
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Stacked denoising autoencoder is one of the most classic models of deep learning. However, there are two problems in the traditional stacked denoising autoencoder: (1) the parameter selection of stacked denoising autoencoder mainly depends on expert experience and (2) stacked denoising autoencoder is mainly restricted to learn automatically single-domain features from raw vibration signals while identifying the fault type, which implies that no linear mapping relationship located in other domains of vibration data is neglected, which may lead to the imperfect diagnostic results. Consequently, to address these issues, learn the well-rounded feature representation, and improve recognition accuracy, this article presents a novel approach called multi-domain indicator-based optimized stacked denoising autoencoder for automatic fault identification of rolling bearing. First, multi-domain indicator of the original vibration signal is constructed through calculating the expression of different domains (e.g. time frequency domain, and time frequency domain). Second, the constructed multi-domain indicator is regarded as the input dataset to train stacked denoising autoencoder architecture containing three hidden layers, and a recently reported nature-inspired algorithm named grasshopper optimization algorithm is employed to synchronously determine the model parameters of stacked denoising autoencoder, which is aimed at learning more robust and reliable feature representation. Finally, the feature representation learned in the testing set is fed into the trained stacked denoising autoencoder model containing softmax classifier for identifying bearing health conditions. The presented method is evaluated using two bearing vibration datasets. Experimental results indicate that our approach can provide high-accuracy recognition over 99% for bearing health condition, and it achieves more decent and precise classification results compared with some shallow learning model and standard deep learning architecture.
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王薇婷, 王薇婷, Xiao-Yan Jiang Xiao-Yan Jiang, Jun-Xian Han Xiao-Yan Jiang, Mei Han Jun-Xian Han, and Jun-Kai Zhang Mei Han. "Method and System Design for Industrial Robots to Grip Stacked and Disorder Parts Under 3D Visual Guidance." 電腦學刊 35, no. 4 (2024): 207–24. http://dx.doi.org/10.53106/199115992024083504015.
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<p>With the production of bulk parts and a large number of assembly scenarios, most parts are stacked in an unordered manner. This article focuses on the accurate grasping of parts in an assembly line under the guidance of binocular vision. Firstly, based on production needs, this article designs a robot grasping system based on binocular vision, and describes the selection of key equipment such as cameras and robotic arms in the system. Then, in order to achieve the recognition of stacked parts, analysis and research were conducted on point cloud data extraction, point cloud feature recognition, point cloud registration, and part pose estimation in the recognition process. Finally, through the construction of a simulation system, the recognition of stacked parts was achieved, and the recognition accuracy was improved.</p> <p>&nbsp;</p>
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Imangaliyev, Sultan, Jörg Schlötterer, Folker Meyer, and Christin Seifert. "Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data." Diagnostics 12, no. 10 (2022): 2514. http://dx.doi.org/10.3390/diagnostics12102514.
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Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure.
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Wang, Shitong, Erhao Zhou, and Yuchen Li. "An Interpretable Deep-Stacked TSK Fuzzy Classifier for High-Dimensional Problems." International Journal of Information Technologies and Systems Approach 17, no. 1 (2024): 1–20. http://dx.doi.org/10.4018/ijitsa.353902.
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This study proposes a novel deep-stacked fully interpretable and more generalization Takagi-Sugeno-Kang fuzzy system (D-FIMG-TSK) to obtain short fuzzy rules with high interpretability in each layer and simultaneously earn more generalization capability for high-dimensional data classification tasks. With the help of both discriminative and residual information, D-FIMG-TSK combines several fully interpretable TSK classifier FIMG-TSK in a stacked manner to conveniently move apart the manifolds existing in the original data space to achieve better linear separability. Besides, a feature selection method is designed to make feature selection from original feature sets on all layers to guarantee the classification performances. On the training process of D-FIMG-TSK, the importance of all original features and the antecedent and consequent parts of all fuzzy rules in each FIMG-TSK can be determined at the same time. The effectiveness of D-FIMG-TSK is manifested by the experimental results on eight binary high-dimensional classification datasets.
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JIJÓN-PALMA, Mario Ernesto, Caisse AMISSE, Jaime Carlos MACUÁCUA, and Jorge Antonio Silva CENTENO. "Noisy band selection based on the integration of the Stacked-Autoencoder and Convolutional Neural Network approaches for hyperspectral data." Geosciences = Geociências 42, no. 2 (2023): 269–80. http://dx.doi.org/10.5016/geociencias.v42i2.16976.
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The presence of noise on hyperspectral images causes degradation and hinders efficiency of processing for land cover classification. In this sense, removing noise or detecting noisy bands automatically on hyperspectral images becomes a challenge for research in remote sensing. To cope this problem, an integrated model (SAE-1DCNN) is presented in this study, based on Stacked-Autoencoders (SAE) and Convolutional Neural Networks (CNN) algorithms for the selection and exclusion of noisy bands. The proposed model employs convolutional layers to improve the performance of autoencoders focused on discriminating the training data by analyzing the hyperspectral signature of the pixel. Thus, in the SAE-1DCNN model, information can be compressed, and then redundant information can be detected and extracted by taking advantage of the efficiency of the deep architecture based on the convolutional and pooling layers. Hyperspectral data from the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor were used to evaluate the performance of the proposed automatic method based on feature selection. The results showed effectiveness to identify noisy bands automatically, suggesting that the proposed methodology was found to be promising and can be an alternative to identify noisy bands within the scope of hyperspectral data pre-processing.
 Keywords: noisy bands; feature selection; convolutional neural network; stacked-autoencoders; hyperspectral data
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Sun, Xun, Xiao Yan, Huijuan Su та ін. "Non-Stacked γ-Fe2O3/C@TiO2 Double-Layer Hollow Nanoparticles for Enhanced Photocatalytic Applications under Visible Light". Nanomaterials 12, № 2 (2022): 201. http://dx.doi.org/10.3390/nano12020201.
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Herein, a non-stacked γ-Fe2O3/C@TiO2 double-layer hollow nano photocatalyst has been developed with ultrathin nanosheets-assembled double shells for photodegradation phenol. High catalytic performance was found that the phenol could be completely degraded in 135 min under visible light, due to the moderate band edge position (VB at 0.59 eV and CB at −0.66 eV) of the non-stacked γ-Fe2O3/C@TiO2, which can expand the excitation wavelength range into the visible light region and produce a high concentration of free radicals (such as ·OH, ·O2−, holes). Furthermore, the interior of the hollow composite γ-Fe2O3 is responsible for charge generation, and the carbon matrix facilitates charge transfer to the external TiO2 shell. This overlap improved the selection/utilization efficiency, while the unique non-stacked double-layered structure inhibited initial charge recombination over the photocatalysts. This work provides new approaches for photocatalytic applications with γ-Fe2O3/C-based materials.
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Huang, Ruiying. "Research progress on Sb-rich nanostructured films for phase-change memory." MATEC Web of Conferences 404 (2024): 03008. http://dx.doi.org/10.1051/matecconf/202440403008.
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In the era of big data, the demand for data storage is increasing. Researchers are currently seeking to develop a non-volatile, high-speed, highly reliable, and low-energy storage medium. Phase change memory is a effective memory of them. This paper focuses on exploring different characteristics of thin films such as Ge2Sb2Te5, Sb2Te, Ge2Sb2Te5/ZnSb (GST/ZS) stacked thin films, Ru-doped Sb2Te, and MnTe/GeTe stacked thin films to analyze which stacked thin film for phase change memory can meet the requirements for high speed and thermal stability. Single-matter materials can improve stability to data and material loss by combining with Sb groups or stacking with their related materials to form new phase change materials. By controlling the temperature and resistance of Ge2Sb2Te5 film can have a better selection of optimization; Sb2Te film doped with C to get a higher improvement in the nature of the film, but the lack of stability; after stacking the two to better solve this problem.
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Urmi, Wahida Ferdose, Mohammed Nasir Uddin, Md Ashraf Uddin, et al. "A stacked ensemble approach to detect cyber attacks based on feature selection techniques." International Journal of Cognitive Computing in Engineering 5 (2024): 316–31. http://dx.doi.org/10.1016/j.ijcce.2024.07.005.
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Lee, Sang-Ho, Wandong Kim, Dae Woong Kwon, et al. "Layer Selection by Multi-Level Permutation in 3-D Stacked NAND Flash Memory." IEEE Electron Device Letters 37, no. 7 (2016): 866–69. http://dx.doi.org/10.1109/led.2016.2568171.
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