Academic literature on the topic 'Two way classification'
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Journal articles on the topic "Two way classification"
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Balakrishnan, N., M. L. Tiku, and A. H. El Shaarawi. "Robust Univariate Two-Way Classification." Biometrical Journal 27, no. 2 (1985): 123–38. http://dx.doi.org/10.1002/bimj.4710270202.
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Singh, Vaishali, and Sanjay K. Dwivedi. "Two Way Question Classification in Higher Education Domain." International Journal of Modern Education and Computer Science 7, no. 9 (September 8, 2015): 59–65. http://dx.doi.org/10.5815/ijmecs.2015.09.08.
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Celardo, Livia, and Martin G. Everett. "Network text analysis: A two-way classification approach." International Journal of Information Management 51 (April 2020): 102009. http://dx.doi.org/10.1016/j.ijinfomgt.2019.09.005.
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Lin, C. S., and G. Butler. "Cluster Analyses for Analyzing Two‐Way Classification Data." Agronomy Journal 82, no. 2 (March 1990): 344–48. http://dx.doi.org/10.2134/agronj1990.00021962008200020034x.
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Piepho, Hans-Peter. "Distribution-Free Tests for One-Way Homoscedasticity in a Two-Way Classification." Biometrics 53, no. 1 (March 1997): 340. http://dx.doi.org/10.2307/2533119.
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Qiao, Mu, and Jia Li. "Two-way Gaussian mixture models for high dimensional classification." Statistical Analysis and Data Mining: The ASA Data Science Journal 3, no. 4 (July 13, 2010): 259–71. http://dx.doi.org/10.1002/sam.10082.
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Zhang, Qinghua, Deyou Xia, and Guoyin Wang. "Three-way decision model with two types of classification errors." Information Sciences 420 (December 2017): 431–53. http://dx.doi.org/10.1016/j.ins.2017.08.066.
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Wei, Laisheng, and Jiahua Chen. "Empirical Bayes estimation and its superiority for two-way classification model." Statistics & Probability Letters 63, no. 2 (June 2003): 165–75. http://dx.doi.org/10.1016/s0167-7152(03)00079-8.
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Brzeskwiniewicz, Henryk, and Wiesław Wagner. "Error Normality Testing in a Model of Two-Way Nested Classification." Biometrical Journal 36, no. 1 (1994): 87–94. http://dx.doi.org/10.1002/bimj.4710360112.
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Samadi, Hossein, Iman Aghayan, Amir Saman Abdollahzadeh Nasiri, Omid Rahmani, and Mehrzad Hasanvand. "Platoon-Based Assessment of Two-Way Two-Lane Roads Performance Measure: A Classification Method." Journal of Advanced Transportation 2023 (July 24, 2023): 1–13. http://dx.doi.org/10.1155/2023/5054427.
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Two-way two-lane roads have a significant impact on road transportation infrastructure. Platoon formation on two-lane roads is one of the factors that affect the quality of traffic flow on two-lane roads. More specifically, the creation of a platoon increases the density of vehicles and the number of overtaking maneuvers and decreases the traffic performance of two-lane roads. The present study made an effort to investigate the effect of the platoon characteristics on the traffic flow of vehicles on two-lane roads. Moreover, it strived to develop the nonlinear regression model with a new approach to capacity calculation. Finally, a method for estimating the level of service (LOS) based on the number of followers per capacity (NFPC) and LOS classification using the KNN method was presented. Considering these aims, first, the relevant variables (which were related to platoon) including time headway (Ht), average travel speed (ATS), platoon size (PS), average platoon speed (APS), percentage of heavy vehicles (HV), percent time spent following (PTSF), number of overtaking (NO), density (ρ), and traffic flow were investigated on the examined roads. The results showed that the speed and the Ht were the most effective and the least effective platoon characteristics, respectively. Moreover, it was accompanied by the increase in PTSF which resulted in the increase in overtaking maneuver. Finally, the results regarding the developed model showed that the NFPC measure was able to predict the traffic flow of two-lane roads in a more satisfactory way compared to the two criteria, namely, PTSF and ATS in the Highway Capacity Manual (2016) since it increased ATS by 65% and decreased the capacity of two-lane roads by 21%.
Dissertations / Theses on the topic "Two way classification"
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Kreikemeier, Udo, and Jens Wrona. "Two-Way Migration between Similiar Countries." Technische Universität Dresden, 2015. https://tud.qucosa.de/id/qucosa%3A29256.
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We develop a model to explain two-way migration of high-skilled individuals between countries that are similar in their economic characteristics. High-skilled migration results from the combination of workers whose abilities are private knowledge, and a production technology that gives incentives to firms for hiring workers of similar ability. In the presence of migration cost, high-skilled workers self-select into the group of migrants. The laissez-faire equilibrium features too much migration, explained by a negative migration externality. We also show that for sufficiently low levels of migration cost the optimal level of migration, while smaller than in the laissez-faire equilibrium, is strictly positive. Finally, we extend our model into different directions to capture stylized facts in the data and show that our baseline results also hold in these more complex modelling environments.
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Papanicolaou, Catherine, Thanasis Triantafillou, Ioannis Papantoniou, and Christos Balioukos. "Strengthening of two-way reinforced concrete slabs with Textile Reinforced Mortars (TRM)." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1244048746186-75760.
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An innovative strengthening technique is applied for the first time in this study to provide flexural strengthening in two-way reinforced concrete (RC) slabs supported on edge beams. The technique comprises external bonding of textiles on the tension face of RC slabs through the use of polymer-modified cement- based mortars. The textiles used in the experimental campaign comprised fabric meshes made of long stitch-bonded fibre rovings in two orthogonal directions. The specimens measured 2 x 2 m in plan and were supported on hinges at the corners. Three RC slabs strengthened by textile reinforced mortar (TRM) overlays and one control specimen were tested to failure. One specimen received one layer of carbon fibre textile, another one received two, whereas the third specimen was strengthened with three layers of glass fibre textile having the same axial rigidity (in both directions) with the single-layered carbon fibre textile. All specimens failed due to flexural punching. The load-carrying capacity of the strengthened slabs was increased by 26%, 53%, and 20% over that of the control specimen for slabs with one (carbon), two (carbon) and three (glass) textile layers, respectively. The strengthened slabs showed an increase in stiffness and energy absorption. The experimental results are compared with theoretical predictions based on existing models specifically developed for two-way slabs and the performance of the latter is evaluated. Based on the findings of this work the authors conclude that TRM overlays comprise a very promising solution for the strengthening of two-way RC slabs.
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Pachler, Klaus, Thomas Frank, and Klaus Bernert. "Simulation of Unsteady Gas-Particle Flows including Two-way and Four-way Coupling on a MIMD Computer Architectur." Universitätsbibliothek Chemnitz, 2002. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200200352.
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The transport or the separation of solid particles or droplets suspended in a fluid flow is a common task in mechanical and process engineering. To improve machinery and physical processes (e.g. for coal combustion, reduction of NO_x and soot) an optimization of complex phenomena by simulation applying the fundamental conservation equations is required. Fluid-particle flows are characterized by the ratio of density of the two phases gamma=rho_P/rho_F, by the Stokes number St=tau_P/tau_F and by the loading in terms of void and mass fraction.
Those numbers (Stokes number, gamma) define the flow regime and which relevant forces are acting on the particle. Dependent on the geometrical configuration the particle-wall interaction might have a heavy impact on the mean flow structure. The occurrence of particle-particle collisions becomes also more and more important with the increase of the local void fraction of the particulate phase. With increase of the particle loading the interaction with the fluid phase can not been neglected and 2-way or even 4-way coupling between the continous and disperse phases has to be taken into account.
For dilute to moderate dense particle flows the Euler-Lagrange method is capable to resolve the main flow mechanism. An accurate computation needs unfortunately a high number of numerical particles (1,...,10^7) to get the reliable statistics for the underlying modelling correlations. Due to the fact that a Lagrangian algorithm cannot be vectorized for complex meshes the only way to finish those simulations in a reasonable time is the parallization applying the message passing paradigma.
Frank et al. describes the basic ideas for a parallel Eulererian-Lagrangian solver, which uses multigrid for acceleration of the flow equations. The performance figures are quite good, though only steady problems are tackled. The presented paper is aimed to the numerical prediction of time-dependend fluid-particle flows using the simultanous particle tracking approach based on the Eulerian-Lagrangian and the particle-source-in-cell (PSI-Cell) approach. It is shown in the paper that for the unsteady flow prediction efficiency and load balancing of the parallel numerical simulation is an even more pronounced problem in comparison with the steady flow calculations, because the time steps for the time integration along one particle trajectory are very small per one time step of fluid flow integration and so the floating point workload on a single processor node is usualy rather low.
Much time is spent for communication and waiting time of the processors, because for cold flow particle convection not very extensive calculations are necessary. One remedy might be a highspeed switch like Myrinet or Dolphin PCI/SCI (500 MByte/s), which could balance the relative high floating point performance of INTEL PIII processors and the weak capacity of the Fast-Ethernet communication network (100 Mbit/s) of the Chemnitz Linux Cluster (CLIC) used for the presented calculations. Corresponding to the discussed examples calculation times and parallel performance will be presented. Another point is the communication of many small packages, which should be summed up to bigger messages, because each message requires a startup time independently of its size. Summarising the potential of such a parallel algorithm, it will be shown that a Beowulf-type cluster computer is a highly competitve alternative to the classical main frame computer for the investigated Eulerian-Lagrangian simultanous particle tracking approach.
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Richter, Johannes, Elke Franz, Sabrina Engelmann, Stefan Pfennig, and Eduard A. Jorswieck. "Physical Layer Security vs. Network Layer Secrecy: Who Wins on the Untrusted Two-Way Relay Channel?" Technische Universität Dresden, 2013. https://tud.qucosa.de/id/qucosa%3A28133.
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We consider the problem of secure communications in a Gaussian two-way relay network where two nodes exchange confidential messages only via an untrusted relay. The relay is assumed to be honest but curious, i.e., an eavesdropper that conforms to the system rules and applies the intended relaying scheme. We analyze the achievable secrecy rates by applying network coding on the physical layer or the network layer and compare the results in terms of complexity, overhead, and efficiency. Further, we discuss the advantages and disadvantages of the respective approaches.
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Holmes, Marilyn. "Community Engagement: Home School Partnership." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-80198.
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Five year old children starting their formal education in primary schools bring with them a range of informal mathematical understandings. Transitioning from an early childhood setting to the reception class at school can have a profound impact on their developing mathematical concepts. Traditionally
their first teachers (parents, caregivers and whanau) gradually remove the support and encouragement and some of the familiar surroundings of their early childhood centres are no longer there. As children from 5 – 13 years of age spend approximately 85% of their time out of school it is important that their first teachers are encouraged to continue that support. This paper outlines a New Zealand project ‘Home School Partnership: Numeracy’ that gives one approach to enhancing children’s mathematical learning through shared understandings between home and school.
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Lyon, Robert James. "Why are pulsars hard to find?" Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/why-are-pulsars-hard-to-find(f15226ec-355d-4794-b2b8-e0a8e793948e).html.
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Searches for pulsars during the past fifty years, have been characterised by two problems making their discovery difficult: i) an increasing volume of data to be searched, and ii) an increasing number of `candidate' pulsar detections arising from that data, requiring analysis. Whilst almost all are caused by noise or interference, these are often indistinguishable from real pulsar detections. Deciding which candidates should be studied is therefore difficult. Indeed it has become known as the `candidate selection problem'. This thesis presents an interdisciplinary study of the selection problem, with the aim of developing a new method able to mitigate it. Specifically for future pulsar surveys undertaken with the Square kilometre Array (SKA). Through a combination of critical literature evaluations, theoretical modelling exercises, and empirical investigations, the selection problem is described in-depth here for the first time. It is shown to be characterised by the dominance of Gaussian distributed noise signals, a factor that no existing selection method accounts for. It also reveals the presence of a significant trend in survey data rates, which suggest that candidate selection is transitioning from an off-line processing procedure, to an on-line, and real-time, decision making process. In response, a new real-time machine learning based method, the GH-VFDT, is introduced in this thesis. The results presented here show that a significant improvement in selection performance can be achieved using the GH-VFDT, which utilises a learning procedure optimised for data characterised by skewed class distributions. Whilst the principled development of new numerical features that maximise the separation between pulsars and Gaussian noise, have also greatly improved GH-VFDT pulsar recall. It is therefore concluded that the sub-optimal performance of existing selection systems, is due to a combination of poor feature design, insensitivity to noise, and an inability to deal with skewed class distributions.
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Reumann, Andreas, and Friedrich Thießen. "Why are employment figures in airport studies too high?" Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-153384.
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Luftverkehrsprognosen stellen ein wichtiges Instrument dar, die Luftverkehrsinfrastruktur zu beeinflussen. Hinter vielen der Projekte, die von Luftverkehrsprognosen begleitet werden, stehen Interessen. Dies gilt insbesondere für Ausbauvorhaben von Flughäfen, die von eindeutigen Zielen und Wünschen getragen werden. Die Gutachter, die im Rahmen solcher Ausbauvorhaben tätig werden, sind der Gefahr ausgesetzt, beeinflusste Prognosen zu erstellen. Die Art und Intensität dieser Beeinflussung sowie die Methodik dieser Analysen sind Gegenstand dieses Aufsatzes. Zunächst wurden neuere Ergebnisse der OECD und EU erarbeitet und denen der Flughafenstudien gegenübergestellt. Nachfolgend wurde die Intensität der Beeinflussung auf Grundlage einer eigenen Untersuchung analysiert. Dabei wurden politische Entscheidungsträger im Rhein-Main-Gebiet nach Ihrer Verwendung von Studien und deren Ergebnisse befragt. Die Impact Study, als häufigste Forschungsmethodik, wurde in ihre Bestandteile zerlegt und mit der Full Cost-Benefit-Analyse, welche die durch die FAA empfohlene Methode ist, verglichen. Grundsätzlich sind solche Gutachten meist externe Analysen aus privatwirtschaftlicher Hand, die einerseits im Auftrag von regionalen Entscheidungsträgern, aber andererseits auch im Auftrag von Flughafen- und Fluggesellschaften angefertigt wurden. Die Unabhängigkeit der Prognosen ist damit gefährdet. Auf Grundlage der (neutralen) OECD- und EU-Ergebnisse zeigte sich, dass etwaige regionale Wirtschaftsimpulse und damit einhergehendes Beschäftigungswachstum durch erhöhten Flugverkehr nicht zu finden sind. Die kritische Analyse von „bezahlten Studien“ ist demnach von Bedeutung. Die Mehrzahl der politischen Amts- und Entscheidungsträger in Deutschland trifft allerdings Entscheidungen auf Basis keiner oder ausschließlich nicht-neutraler, bezahlter Auftragsstudien. Historisch betrachtet ist dies kein Novum. Bereits in den 60er Jahren kann man solche Studien und Entscheidungen nachweisen. Bezüglich des Forschungsdesign zeigte sich, dass Impact-Studien kaum für zur Beurteilung des Luftverkehrs geeignet sind. Sie beziehen die relevanten Effekte nur teilweise in ihre Berechnungen ein. Es wurde eine Reihe von systematischen Fehlern identifiziert. Die FAA-Richtlinien in den USA für methodisch korrekte Studien greifen nur wenig. Für weitere Forschung sehen wir die wichtige Aufgabe, die Methodik der Full Cost-Benefit-Analyse für die Prognose möglicher Effekte zur Anwendung zu bringenAviation traffic forecasts and airport analyses are important instruments which influence decisions on aviation related infrastructure. Behind many of such infrastructure projects, which are supported by forecast analyses, one finds political interests. This is especially the case for aviation projects, such as infrastructure enlargement projects of airports, which are motivated by distinct goals and desires. Referees who act within this framework are exposed to the risk of producing biased results. The form and degree of intensity of such influence and manipulation, as well as the methodology of such forecast analyses, are the subject of this working paper. To begin with, newer research results by the OECD and the EU have been formulated and further compared to results of studies commissioned by airport operators. Subsequently, the degree of intensity of such influence has been analysed on the basis of our own research. A survey was thereby produced, investigating the application of neutral and non-neutral studies in the decision-making processes of the public administration in the Rhein-Main-area. Impact studies, which are currently the most used method, have been segmented and compared with studies using full cost-benefit-analysis, the recommended method by the FAA. With regard to these results, it can be argued that most forecast analyses are produced by private consultancies, by order of public entities but also by order of airport operators. The independence of such research and its results is therefore endangered. Recent OECD and EU research results have shown that eventual effects, such as regional economic stimulus and employment growth, are absent. Thus, care in the application of study results in this field is necessary. However, the majority of policy makers (in Germany) unfortunately base their decisions mostly on non-neutral studies. Historically seen, this is not a novelty. Regarding the research design, impact studies are hardly suitable for airport studies. Further, it can be shown that impact studies incorporate mostly only non-negative items in their cash flow calculations, compared to full cost-benefit-analyses which incorporate all relevant items. A number of systematic flaws are further identified. The regulation-guidelines by the FAA, which demand to incorporate all items, have had little impact on the research design of airport studies in the US so far. The promotion and fostering of full cost-benefit-analyses is necessary to lift the quality of airport studies
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Müller, Philipp, Ronny Grünker, Volodymyr Bon, Martin Pfeffermann, Irena Senkovska, Manfred S. Weiss, Xinliang Feng, and Stefan Kaskel. "Topological control of 3,4-connected frameworks based on the Cu2-paddle-wheel node: tbo or pto, and why?" Royal Society of Chemistry, 2016. https://tud.qucosa.de/id/qucosa%3A30235.
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Two trigonal tritopic ligands with different conformational degree of freedom: conformationally labile H3tcbpa (tris((4-carboxyl)phenylduryl)amine) and conformationally obstructed H3hmbqa (4,4′,4′′-(4,4,8,8,12,12-hexamethyl-8,12-dihydro-4H-benzo[9,1]quino-lizino[3,4,5,6,7-defg]acridine-2,6,10-triyl)tribenzoic acid) are assembled with square-planar paddle-wheel nodes with the aim of selective engineering of the frameworks with tbo and pto underlying net topologies. In the case of H3tcbpa, both topological types were obtained forming non-interpenetrated MOFs namely DUT-63 (tbo) and DUT-64 (pto). Whereas synthesis of DUT-63 proceeds under typical conditions, formation of DUT-64 requires an additional topology directing reagent (topological modifier). Solvothermal treatment of the conformationally hindered H3hmbqa ligand with the Cu-salt results exclusively in DUT-77 material, based on the single pto net. The possibility to insert the salen based metallated pillar ligand into networks with pto topology post-synthetically results in DUT-78 and DUT-79 materials (both ith-d) and opens new horizons for post-synthetic insertion of catalytically active metals within the above-mentioned topological type of frameworks.
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Sattler, Tatjana, Eveline Wodak, and Friedrich Schmoll. "Evaluation of the specificity of a commercial ELISA for detection of antibodies against porcine respiratory and reproductive syndrome virus in individual oral fluid of pigs collected in two different ways." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-167323.
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Background: The monitoring of infectious diseases like the porcine reproductive and respiratory syndrome (PRRS) using pen-wise oral fluid samples becomes more and more established. The collection of individual oral fluid, which would be useful in the monitoring of PRRSV negative boar studs, is rather difficult. The aim of the study was to test
two methods for individual oral fluid collection from pigs and to evaluate the specificity of a commercial ELISA for detection of PRRSV antibodies in these sample matrices. For this reason, 334 serum samples from PRRSV negative pigs (group 1) and 71 serum samples from PRRSV positive pigs (group 2) were tested for PRRSV antibodies with a
commercial ELISA. Individual oral fluid was collected with a cotton gauze swab from 311 pigs from group 1 and 39 pigs from group 2. Furthermore, 312 oral fluid samples from group 1 and 67 oral fluid samples from group 2 were taken with a self-drying foam swab (GenoTube). The recollected oral fluid was then analysed twice with a commercial ELISA for detection of PRRSV antibodies in oral fluid.
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Prokop-Janiec, Eugenia. "A woman assimilationist and the Great War: The Case of Aniela Kallas." HATiKVA e.V. – Die Hoffnung Bildungs- und Begegnungsstätte für Jüdische Geschichte und Kultur Sachsen, 2016. https://slub.qucosa.de/id/qucosa%3A34824.
Full textBooks on the topic "Two way classification"
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Len'kov, Roman. Social forecasting and planning. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058988.
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The tutorial describes the preconditions of sociopragmatics research in Russia on the background of evolutionary processes of social prognostics of the twentieth century. Considered the essential characteristics of social forecasting, its subject and range of issues. Based on analysis of classification schemes methods of scientific forecasting offers the author's approach to classification of methods of social forecasting. Special attention is paid to the description of the characteristics, the specific application and selection procedure of the ways of making social predictions.
Theoretical and applied analysis of the foundations of social design, the direction of its implementation and research methods used for it. The conceptual basis of design in education on the example of the educational process in the University. Given the model structure, rationale and testing of design solutions.
The third edition of the book is dedicated to the 100th anniversary of the State University of management.
Meets the current requirements of the Federal state educational standard of higher education.
For students of higher educational institutions, students of humanitarian directions and specialities.
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Volkovitckaia, Galina. Management of labor incentives: Monograph. au: AUS PUBLISHERS, 2021. http://dx.doi.org/10.26526/monography_61c306c32b0054.44427921.
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The monograph is devoted to the problem of effective incentives for personnel
as one of the most acute in the theory and practice of modern management. The
paper considers the main stages of labor incentives , the specifics of personnel
management from the standpoint of a motivational approach, suggests ways
to assess the quality of incentive systems, analyzes the motivational structure
of the employee's personality. The definitions, classifications, typologies and
models proposed in the monograph can be used not only for educational and
methodological purposes, but also in the direct practice of personnel management.
The monograph is addressed to specialists in personnel management, students,
graduate students, teachers, as well as everyone who is interested in the field of
HR management.
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Bagdasaryan, Vardan. Leadership. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1086964.
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The study of the course "Leadership" is associated with an increasing demand for the formation and disclosure of human leadership potentials. The presented textbook allows you to carry out this work in a targeted way. It summarizes and systematizes the world experience in the development of the theory and practice of leadership, discusses the classification of types of leaders, issues of practical use in leadership scenarios of personal qualities of a person, and offers a methodology for developing team strategies and team building. The distinctive features of the proposed program are its adaptability to the socio-cultural context of Russian society and its strong connection with the task of training the future generation of leaders in the interests of the Russian state. Each of the sections of the textbook is accompanied by practical tasks, the solution of which develops the skills of self-knowledge of a person in the perspective of developing leadership potentials and understanding the variability of leadership strategies. It is focused primarily on the preparation of bachelors studying in pedagogical areas of training, but it can also serve as a basic source for training in the course "Leadership" within the framework of a bachelor's degree in the humanities and the direction of social sciences.
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Arbustini, Eloisa, Valentina Favalli, Alessandro Di Toro, Alessandra Serio, and Jagat Narula. Classification of cardiomyopathies. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0348.
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For over 50 years, the definition and classification of cardiomyopathies have remained anchored in the concept of ventricular dysfunction and myocardial structural remodelling due to unknown cause. The concept of idiopathic was first challenged in 2006, when the American Heart Association classification subordinated the phenotype to the aetiology. Cardiomyopathies were classified as genetic, acquired, and mixed. In 2008, the European Society of Cardiology proposed a phenotype-driven classification that separated familial (genetic) from non-familial (non-genetic) forms of cardiomyopathy. Both classifications led the way to a precise phenotypic and aetiological description of the disease and moved away from the previously held notion of idiopathic disease. In 2013, the World Heart Federation introduced a descriptive and flexible nosology—the MOGE(S) classification—describing the morphofunctional (M) phenotype of cardiomyopathy, the involvement of additional organs (O), the familial/genetic (G) origin, and the precise description of the (a)etiology including genetic mutation, if applicable (E); reporting of functional status such as American College of Cardiology/American Heart Association stage and New York Heart Association classification (S) was left optional. MOGE(S) is a bridge between the past and the future. It allows description of comprehensive phenotypic data, all genetic and non-genetic causes of cardiomyopathy, and incorporates description of familial clustering in a genetic disease. MOGE(S) is the instrument of precision diagnosis for cardiomyopathies. The addition of the early and unaffected phenotypes to the (M) descriptor outlines the clinical profile of an early affected family member; the examples include non-dilated hypokinetic cardiomyopathy in dilated cardiomyopathy and septal thickness (13–14 mm) in hypertrophic cardiomyopathy classes.
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van Huyssteen, CW. Relating the South African soil taxonomy to the World Reference Base for soil resources. SunBonani Scholar, 2020. http://dx.doi.org/10.18820/9781928424666.
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The South African Taxonomic soil classification system (SAT) is well established and utilised in South Africa. However, it is not internationally well known and therefore the need arose to provide a tool by which South African soil taxonomists can convert South African soil classifications and profile descriptions to the international classifications of the World Reference Base (WRB) for soil resources. The diagnostics and tacit knowledge presented in this publication are therefore based on the SAT and the WRB. When necessary, further substantiation was derived from the Land Type Survey of South Africa. The adopted procedure is effective in providing a reasonable classification based on the South African soil forms and families, while excluding certain WRB soil groups and qualifiers, because these are irrelevant to South African taxonomy. Lastly, this publication also highlights some peculiarities, omissions and inconsistencies observed between the SAT and WRB.
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Giesen, Bernhard. Inbetweenness and Ambivalence. Edited by Jeffrey C. Alexander, Ronald N. Jacobs, and Philip Smith. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780195377767.013.30.
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This article focuses on the extraordinary space in between the opposites based on a paradigm in cultural sociology that conceives of ambivalence and inbetweenness as a fundamental and indissoluble given of classification and interpretation. More specifically, it considers something that transcends the sucessful ordering and splitting of the world into neat binaries, arguing that this inbetweenness is essential for the construction of culture. Reality does not provide any firm ground for neat classification. Therefore, in applying classifications to raw reality, there will always be an unclassifiable remainder. Furthermore, in specifying meaning, there is no way to achieve absolute clarity and to avoid a rest of fuzziness. This article first provides an overview of some theoretical concepts that point to the essence of inbetweenness before discussing various phenomena of inbetweenness such as garbage and monsters, heroes and victims, and seduction.
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Button, Tim, and Sean Walsh. Classification and uncountable categoricity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198790396.003.0017.
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The topic of this chapter is classification. We start by formulating a wholly general philosophical framework for understanding classification programs within mathematics, in which calculable invariants play an important role. We then consider the most famous classification program in contemporary model theory, due to Shelah, who has suggested that classification concerns identifying which theories do not have too many models. We critically compare these two different perspectives on classification— calculable mechanisms vs. not too many models. We close the chapter by discussing Zilber's ambitious proposal for the classification of uncountably categorical theories (i.e. theories which have only one model up to isomorphism in a given uncountable cardinality). Whereas categoricity was seen as a potential philosophical salve in chapters 6-8, Zilber's program regards uncountable categoricity as a kind of extreme classification.
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Catanzaro, Michael P., and Rachel J. Kwon. Hinchey Classification of Acute Diverticulitis. Edited by Rachel J. Kwon. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199384075.003.0048.
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This chapter provides a summary of a landmark historical study in surgery: the Hinchey classification of acute diverticulitis. It describes the history of the disease, gives a summary of the study including study design and results, and relates the study to a modern-day principle of evidence-based medicine: validation of scoring systems. Hinchey’s classification of diverticulitis has become the most widespread system and while the Hinchey score may currently have less clinical relevance as it did in his time, its publication and eventual adoption marked a practice-changing paradigm shift in the way diverticulitis is viewed and managed today.
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Muris, Peter. Classification and Diagnosis of Psychopathology. Edited by Thomas H. Ollendick, Susan W. White, and Bradley A. White. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190634841.013.4.
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This chapter deals with the classification and diagnosis of psychopathology in children and adolescents. An overview is given of the most prevalent mental health problems in youth that can be classified according to the Diagnostic and Statistical Manual of Mental Disorders (DSM). Methods are then described that can be employed to classify psychopathology in youth in terms of DSM nomenclature. Next, the pros and cons of the DSM classification system are discussed, after which a number of alternative ways that can be employed to classify psychopathology are addressed. These include the Research Domain Criteria framework and the complex network approach.
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Simmons, K. Merinda. Identifying Race and Religion. Edited by Paul Harvey and Kathryn Gin Lum. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190221171.013.31.
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The common usage of “race” and “religion” in popular discourse makes overlooking the complicated histories of both terms easy to do. As a corrective to this simple oversight, this chapter examines the scholarly transitions that began to reintroduce race and religion as modern classifications rather than as universal phenomena that transcend specific societal contexts. Both terms underwent massive overhauls as recently as the eighteenth and nineteenth centuries, at the height of European imperial projects. Even more recently were the mid-twentieth century transitions that saw Religious Studies and Black Studies formalized as academic fields grounded in social scientific approaches to identity. Poststructuralism paved the way for analyses that deviated from the essentialist logics of theology and biological determinism, respectively. The scholarly discourse on “slave religion” is one productive site for thinking about race and religion as organizing and legitimizing tools of classification.
Book chapters on the topic "Two way classification"
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Sahai, Hardeo, and Mario Miguel Ojeda. "Two-Way Nested Classification." In Analysis of Variance for Random Models, 277–331. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8168-5_6.
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Sahai, Hardeo, and Mohammed I. Ageel. "Two-Way Nested (Hierarchical) Classification." In The Analysis of Variance, 347–94. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1344-4_6.
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Sahai, Hardeo, and Mario Miguel Ojeda. "Two-Way Crossed Classification without Interaction." In Analysis of Variance for Random Models, 115–69. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8168-5_3.
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Sahai, Hardeo, and Mario Miguel Ojeda. "Two-Way Crossed Classification with Interaction." In Analysis of Variance for Random Models, 171–233. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8168-5_4.
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Sahai, Hardeo, and Mohammed I. Ageel. "Two-Way Crossed Classification Without Interaction." In The Analysis of Variance, 125–75. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1344-4_3.
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Sahai, Hardeo, and Mohammed I. Ageel. "Two-Way Crossed Classification with Interaction." In The Analysis of Variance, 177–280. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1344-4_4.
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Chrobak, Marek, and Neal E. Young. "Classification via Two-Way Comparisons (Extended Abstract)." In Lecture Notes in Computer Science, 275–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38906-1_19.
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Cutzu, Florin. "How to Do Multi-way Classification with Two-Way Classifiers." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 375–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_45.
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Chepoi, Victor, and Bernard Fichet. "A Note on Three-Way Dissimilarities and Their Relationship with Two-Way Dissimilarities." In Selected Contributions in Data Analysis and Classification, 465–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73560-1_43.
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Vichi, Maurizio. "Discrete and Continuous Models for Two-way Data." In Studies in Classification, Data Analysis, and Knowledge Organization, 139–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55991-4_15.
Full textConference papers on the topic "Two way classification"
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Ahmed, Houida, U. Bekbaev, and I. Rakhimov. "Complete classification of two-dimensional algebras." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES: Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society. Author(s), 2017. http://dx.doi.org/10.1063/1.4980965.
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Guendel, Ronny G. "Further Investigation of Two-Way Classification for Activities of Daily Living." In 2020 17th European Radar Conference (EuRAD). IEEE, 2021. http://dx.doi.org/10.1109/eurad48048.2021.00061.
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Moctezuma, Daniela, Víctor Muníz, and Jorge García. "Multimodal Data Evaluation for Classification Problems." In 7th International Conference on VLSI and Applications (VLSIA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112105.
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Social media data is currently the main input to a wide variety of research works in many knowledge fields. This kind of data is generally multimodal, i.e., it contains different modalities of information such as text, images, video or audio, mainly. To deal with multimodal data to tackle a specific task could be very difficult. One of the main challenges is to find useful representations of the data, capable of capturing the subtle information that the users who generate that information provided, or even the way they use it. In this paper, we analysed the usage of two modalities of data, images, and text, both in a separate way and by combining them to address two classification problems: meme's classification and user profiling. For images, we use a textual semantic representation by using a pre-trained model of image captioning. Later, a text classifier based on optimal lexical representations was used to build a classification model. Interesting findings were found in the usage of these two modalities of data, and the pros and cons of using them to solve the two classification problems are also discussed.
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Listik, Clarice, Eduardo Listik, Jorge Dornellys Lapa, Graziele Costa Santos, Fabricio Vianna do Vale, Rubens Gisbert Cury, Manoel Jacobsen Teixeira, et al. "Classifying pain in dystonia: a way to improve pain outcome measure in dystonia." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.250.
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Background: Pain is a frequent and incapacitating non-motor symptom in dystonia. Evidence indicates that pain in dystonia is not only of muscular origin, but pain’s descending modulatory systems are impaired in dystonia. There is much to be learned about this topic. Still, we do not have a simple and straightforward way to classify, evaluate pain, and assess its improvement after pharmacological, surgical, and non-invasive treatments. Objective: To improve the classification system for pain in dystonia. Design and setting: This are the preliminary results of a multicentric study that at this moment selects patients in the Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo and in the Hospital Sao Paulo da Universidade Federal de São Paulo. Methods: We evaluated 36 patients with hereditary/idiopathic dystonia of any distribution. These results are preliminary finds of this multicentric assessment. We applied the Burke-Fahn-Marsden (BFM) dystonia scale, two pain scales (Douleur Neuropathique - DN4 and the short-form Brief Pain Inventory - BPI), and our developed classification system. Results: Patients (54.9 ± 14.6 years, 41.7% male) had a BFM motor and disability subscores of 17.6 ± 6.8 and 4.5 ± 5.7, respectively, and 15 patients (41.7%) had chronic pain. Four patients had chronic pain non-related to dystonia, and 11 patients had chronic pain directly related to dystonia. Six patients had a second chronic pain, one of which was aggravated by dystonia, and five were directly associated with dystonia. DN4 was 2.1 ± 1.9, and BPI pain severity 5.2 ± 2.0 interference 5.0 ± 3.2. Conclusions: Chronic pain is prevalent in dystonic patients and is frequently directly related to dystonia.
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Yao, Yu-Ta, Yu-Wei Wu, and Po Ting Lin. "A Two-Stage Multi-Fidelity Design Optimization for K-mer-Based Pattern Recognition (KPR) in Image Processing." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22263.
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Abstract Pattern recognition has been widely used in various applications of image processing. It is used to extract meaningful image features from the given image samples and to build classification systems with the intelligence of human recognition. Convolutional Neural Network (CNN) [1] has been one of the most popular and widely used methods for image pattern recognition applications. However, CNN was known not to be rotation-invariant to image patterns. It usually required a larger amount of training image dataset with greater variations in positions and orientations, or additional numerical treatments of spatial transformations [2]. On the other hand, K-mer-based Pattern Recognition (KPR) [3] has been developed to apply an unique way of rotation-invariant sampling to the inspected image pattern and analyze the frequency of the captured pattern features. A classification system was then built based on the K-mer frequency for the desired pattern recognition. In this paper, a series of tests and verifications of the KPR was done. It was found that finding the appropriate design parameters of the KPR for a specific application of image pattern recognition could be costly. A two-stage multi-fidelity design optimization was utilized to improve the efficiency of finding the parameters of KPR. In each iteration of the multi-fidelity design optimization procedure, the first stage was to evaluate the accuracy and efficiency of the design parameters in the K-mer-based pattern classification based on a full set of the given images. The second stage was to find a newer set of design parameters that performed the best based on a smaller set of images, which provided a classification set with lower fidelity than the original one. As a result, the proposed strategy of the multi-fidelity design optimization was more efficient than finding the optimal design parameters based on the full set of the given images.
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Xirouchakis, Paul C. "Production and Classification-Based Active Structural Design Databases." In ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium collocated with the ASME 1995 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/edm1995-0844.
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Abstract We consider the beam section design problem domain and its representation using the LOOM knowledge representation and reasoning database system. We use LOOM’s concept classification capabilities to built a taxonomy of terms describing the beam section design process. This allows LOOM to automatically classify concepts and instances of objects in the proper location in the taxonomy of terms. This classification-based reasoning is what distinguishes LOOM from other knowledge or object-oriented database systems. We also use LOOM’s two behavioral programming features: object-oriented and rule-based in our beam problem domain. This way we define the beam concepts of flat bar, exactly one flange, exactly two flanges and box type. We also define three versions of a section area calculation method that fires depending on the type of the beam section. Finally, we define a production that fires when the type of an object changes to tank boundary. As a result the applicable beam design pressure is changed accordingly.
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Li, Zheng, Yu Zhang, Ying Wei, Yuxiang Wu, and Qiang Yang. "End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/311.
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Domain adaptation tasks such as cross-domain sentiment classification have raised much attention in recent years. Due to the domain discrepancy, a sentiment classifier trained in a source domain may not work well when directly applied to a target domain. Traditional methods need to manually select pivots, which behave in the same way for discriminative learning in both domains. Recently, deep learning methods have been proposed to learn a representation shared by domains. However, they lack the interpretability to directly identify the pivots. To address the problem, we introduce an end-to-end Adversarial Memory Network (AMN) for cross-domain sentiment classification. Unlike existing methods, our approach can automatically capture the pivots using an attention mechanism. Our framework consists of two parameter-shared memory networks: one is for sentiment classification and the other is for domain classification. The two networks are jointly trained so that the selected features minimize the sentiment classification error and at the same time make the domain classifier indiscriminative between the representations from the source or target domains. Moreover, unlike deep learning methods that cannot tell us which words are the pivots, our approach can offer a direct visualization of them. Experiments on the Amazon review dataset demonstrate that our approach can significantly outperform state-of-the-art methods.
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Zhang, Qian-Wen, Ximing Zhang, Zhao Yan, Ruifang Liu, Yunbo Cao, and Min-Ling Zhang. "Correlation-Guided Representation for Multi-Label Text Classification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/463.
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Multi-label text classification is an essential task in natural language processing. Existing multi-label classification models generally consider labels as categorical variables and ignore the exploitation of label semantics. In this paper, we view the task as a correlation-guided text representation problem: an attention-based two-step framework is proposed to integrate text information and label semantics by jointly learning words and labels in the same space. In this way, we aim to capture high-order label-label correlations as well as context-label correlations. Specifically, the proposed approach works by learning token-level representations of words and labels globally through a multi-layer Transformer and constructing an attention vector through word-label correlation matrix to generate the text representation. It ensures that relevant words receive higher weights than irrelevant words and thus directly optimizes the classification performance. Extensive experiments over benchmark multi-label datasets clearly validate the effectiveness of the proposed approach, and further analysis demonstrates that it is competitive in both predicting low-frequency labels and convergence speed.
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Wang, Nan, Xibin Zhao, Yu Jiang, and Yue Gao. "Iterative Metric Learning for Imbalance Data Classification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/389.
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In many classification applications, the amount of data from different categories usually vary significantly, such as software defect predication and medical diagnosis. Under such circumstances, it is essential to propose a proper method to solve the imbalance issue among the data. However, most of the existing methods mainly focus on improving the performance of classifiers rather than searching for an appropriate way to find an effective data space for classification. In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and construct an effective data space for classification. Given the imbalance training data, it is important to select a subset of training samples for each testing data. Thus, we aim to find a more stable neighborhood for testing data using the iterative metric learning strategy. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.
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Zhou, Qianrong, Xiaojie Wang, and Xuan Dong. "Differentiated Attentive Representation Learning for Sentence Classification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/644.
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Attention-based models have shown to be effective in learning representations for sentence classification. They are typically equipped with multi-hop attention mechanism. However, existing multi-hop models still suffer from the problem of paying much attention to the most frequently noticed words, which might not be important to classify the current sentence. And there is a lack of explicitly effective way that helps the attention to be shifted out of a wrong part in the sentence. In this paper, we alleviate this problem by proposing a differentiated attentive learning model. It is composed of two branches of attention subnets and an example discriminator. An explicit signal with the loss information of the first attention subnet is passed on to the second one to drive them to learn different attentive preference. The example discriminator then selects the suitable attention subnet for sentence classification. Experimental results on real and synthetic datasets demonstrate the effectiveness of our model.
Reports on the topic "Two way classification"
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.
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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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Oliver, Peter, and Gillian Robert. PR-420-183903-R01 Pipeline Right-of-Way River Crossing Monitoring With Satellites. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2022. http://dx.doi.org/10.55274/r0012247.
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The goal of the work described herein is to provide PRCI and the pipeline industry further understanding of the current capabilities and limitations of combined SAR and high resolution optical satellite imagery for the monitoring of pipeline ROWs which span river crossings. Four Areas of Interests (AOIs) with pipeline ROWs that span river crossings were selected for analysis: South Saskatchewan River, Saskatchewan, Canada operated by SaskEnergy Incorporated; Thompson Creek, Louisiana, USA operated by Colonial Pipeline Company; Gila River, Arizona, USA operated by Kinder Morgan Incorporated; and Humber Estuary, UK, operated by National Grid. For each AOI, monitoring requirements were defined by the operators. Amplitude Change Detection (ACD) and Interferometric Synthetic Aperture Radar (InSAR) were performed for all AOIs; results correlated to the defined monitoring requirements are discussed. A high level summary of the role of combined SAR and optical satellite operational monitoring of pipeline river crossings is listed below: - InSAR "Phase" used for (a) Subsidence (b) Slope Movement - SAR "Amplitude" used to both detect and classify (a) large scale Land Cover/Land Use Change (e.g. bridge construction), (b) flooding, (c) river channel changes, (d) river bed exposure, and (e) vessel traffic. - SAR "Amplitude" used to detect changes resulting from (a) small scale Land Cover/ Land Use (e.g. construction of individual buildings), and possibly (b) bank erosion and (c) pipeline exposure. Optical Satellite imagery is required for classification of these changes.
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Olivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42425.
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Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Ley, Matt, Tom Baldvins, David Jones, Hanna Pilkington, and Kelly Anderson. Vegetation classification and mapping: Gulf Islands National Seashore. National Park Service, May 2023. http://dx.doi.org/10.36967/2299028.
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The Gulf Islands National Seashore (GUIS) vegetation inventory project classified and mapped vegetation on park-owned lands within the administrative boundary and estimated thematic map accuracy quantitatively. The project began in June 2016. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2016 in Ocean Springs, Mississippi where representatives gathered from GUIS, the NPS Gulf Coast Inventory and Monitoring Network, and Colorado State University. Primary imagery used for interpretation was 4-band (RGB and CIR) orthoimages from 2014 and 2016 with resolutions of 15 centimeters (cm) (Florida only) and 30 cm. Supplemental imagery with varying coverage across the study area included National Aerial Imagery Program 50 cm imagery for Mississippi (2016) and Florida (2017), 15 and 30 cm true color Digital Earth Model imagery for Mississippi (2016 and 2017), and current and historical true-color Google Earth and Bing Map imagery. National Oceanic Atmospheric Administration National Geodetic Survey 30 cm true color imagery from 2017 (post Hurricane Nate) supported remapping the Mississippi barrier islands after Hurricane Nate. The preliminary vegetation classification included 59 United States National Vegetation Classification (USNVC) associations. Existing vegetation and mapping data combined with vegetation plot data contributed to the final vegetation classification. Quantitative classification using hierarchical clustering and professional expertise was supported by vegetation data collected from 250 plots in 2016 and 29 plots in 2017 and 2018, as well as other observational data. The final vegetation classification includes 39 USNVC associations and 5 park special types; 18 forest and woodland, 7 shrubland, 17 herbaceous, and 2 sparse vegetation types were identified. The final GUIS map consists of 38 map classes. Land cover classes include four types: non-vegetated barren land / borrow pit, developed open space, developed low – high intensity, and water/ocean. Of the 34 vegetation map classes, 26 represent a single USNVC association/park special, six map classes contain two USNVC associations/park specials, and two map classes contain three USNVC associations/park specials. Forest and woodland associations had an abundance of sand pine (Pinus clausa), slash pine (Pinus elliottii), sand live oak (Quercus geminata), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), and saw palmetto (Serenoa repens). Shrubland associations supported dominant species such as eastern baccharis (Baccharis halimifolia), yaupon (Ilex vomitoria), wax myrtle (Morella cerifera), saw palmetto (Serenoa repens), and sand live oak (Quercus geminata). Herbaceous associations commonly included camphorweed (Heterotheca subaxillaris), needlegrass rush (Juncus roemerianus), bitter seabeach grass (Panicum amarum var. amarum), gulf bluestem (Schizachyrium maritimum), saltmeadow cordgrass (Spartina patens), and sea oats (Uniola paniculata). The final GUIS vegetation map consists of 1,268 polygons totaling 35,769.0 hectares (ha) or 88,387.2 acres (ac). Mean polygon size excluding water is 3.6 ha (8.9 ac). The most abundant land cover class is open water/ocean which accounts for approximately 31,437.7 ha (77,684.2 ac) or 87.9% of the total mapped area. Natural and ruderal vegetation consists of 4,176.8 ha (10,321.1 ac) or 11.6% of the total area. Within the natural and ruderal vegetation types, herbaceous types are the most extensive with 1945.1 ha (4,806.4 ac) or 46.5%, followed by forest and woodland types with 804.9 ha (1,989.0 ac) or 19.3%, sparse vegetation types with 726.9 ha (1,796.1 ac) or 17.4%, and shrubland types with 699.9 ha (1,729.5 ac) or 16.8%. Developed open space, which can include a matrix of roads, parking lots, park-like areas and campgrounds account for 153.8 ha (380.0 ac) or 0.43% of the total mapped area. Artificially non-vegetated barren land is rare and only accounts for 0.74 ha (1.82 ac) or 0.002% of the total area. We collected 701 AA samples to evaluate the thematic accuracy of the vegetation map. Final thematic accuracy, as a simple proportion of correct versus incorrect field calls, is 93.0%. Overall weighted map class accuracy is 93.6%, where the area of each map class was weighted in proportion to the percentage of total park area. This method provides more weight to larger map classes in the park. Each map class had an individual thematic accuracy goal of at least 80%. The hurricane impact area map class was the only class that fell below this target with an accuracy of 73.5%. The vegetation communities impacted by the hurricane are highly dynamic and regenerated quickly following the disturbance event, contributing to map class disagreement during the accuracy assessment phase. No other map classes fell below the 80% accuracy threshold. In addition to the vegetation polygon database and map, several products to support park resource management are provided including the vegetation classification, field key to the associations, local association descriptions, photographic database, project geodatabase, ArcGIS .mxd files for map posters, and aerial imagery acquired for the project. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and the PLOTS database. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 16 N. The final report includes methods and results, contingency tables showing AA results, field forms, species list, and a guide to imagery interpretation. These products provide useful information to assist with management of park resources and inform future management decisions. Use of standard national vegetation classification and mapping protocols facilitates effective resource stewardship by ensuring the compatibility and widespread use throughout the NPS as well as other federal and state agencies. Products support a wide variety of resource assessments, park management and planning needs. Associated information provides a structure for framing and answering critical scientific questions about vegetation communities and their relationship to environmental processes across the landscape.
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Glushko, E. Ya, and A. N. Stepanyuk. The multimode island kind photonic crystal resonator: states classification. SME Burlaka, 2017. http://dx.doi.org/10.31812/0564/1561.
Full textAbstract:
In this work, we consider a new calculation method to solve the eigenvalue problem for electromagnetic field in finite 2D structures including the modes distribution through the system. The field amplitude distribution is valuable if the signal energy inside the system should be transformed in most effective way. The method proposed for finite resonators operates with open boundary conditions that are important to account the electromagnetic field non-periodicity in a finite system.
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.
Full textAbstract:
This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.
Full textAbstract:
This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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Brown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.
Full textAbstract:
30 Day Hospital Readmission Rates, Frequencies, and Heart Failure Classification for Patients with Heart Failure Background Congestive heart failure (CHF) is the leading cause of mortality, morbidity, and disability worldwide among patients. Both the incidence and the prevalence of heart failure are age dependent and are relatively common in individuals 40 years of age and older. CHF is one of the leading causes of inpatient hospitalization readmission in the United States, with readmission rates remaining above the 20% goal within 30 days. The Center for Medicare and Medicaid Services imposes a 3% reimbursement penalty for excessive readmissions including those who are readmitted within 30 days from prior hospitalization for heart failure. Hospitals risk losing millions of dollars due to poor performance. A reduction in CHF readmission rates not only improves healthcare system expenditures, but also patients’ mortality, morbidity, and quality of life. Purpose The purpose of this DNP project is to determine the 30-day hospital readmission rates, frequencies, and heart failure classification for patients with heart failure. Specific aims include comparing computed annual re-admission rates with national average, determine the number of multiple 30-day re-admissions, provide descriptive data for demographic variables, and correlate age and heart failure classification with the number of multiple re-admissions. Methods A retrospective chart review was used to collect hospital admission and study data. The setting occurred in an urban hospital in Memphis, TN. The study was reviewed by the UTHSC Internal Review Board and deemed exempt. The electronic medical records were queried from July 1, 2019 through December 31, 2019 for heart failure ICD-10 codes beginning with the prefix 150 and a report was generated. Data was cleaned such that each patient admitted had only one heart failure ICD-10 code. The total number of heart failure admissions was computed and compared to national average. Using age ranges 40-80, the number of patients re-admitted withing 30 days was computed and descriptive and inferential statistics were computed using Microsoft Excel and R. Results A total of 3524 patients were admitted for heart failure within the six-month time frame. Of those, 297 were re-admitted within 30 days for heart failure exacerbation (8.39%). An annual estimate was computed (16.86%), well below the national average (21%). Of those re-admitted within 30 days, 50 were re-admitted on multiple occasions sequentially, ranging from 2-8 re-admissions. The median age was 60 and 60% male. Due to the skewed distribution (most re-admitted twice), nonparametric statistics were used for correlation. While graphic display of charts suggested a trend for most multiple re-admissions due to diastolic dysfunction and least number due to systolic heart failure, there was no statistically significant correlation between age and number or multiple re-admissions (Spearman rank, p = 0.6208) or number of multiple re-admissions and heart failure classification (Kruskal Wallis, p =0.2553).
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.
Full textAbstract:
We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.