Готові списки джерел за темами / Car model classification

Добірка наукової літератури з теми "Car model classification"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Car model classification"

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Liang, Jun, Xu Chen, Mei-ling He, Long Chen, Tao Cai, and Ning Zhu. "Car detection and classification using cascade model." IET Intelligent Transport Systems 12, no. 10 (December 1, 2018): 1201–9. http://dx.doi.org/10.1049/iet-its.2018.5270.

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2

Shah, Dhairya. "Car Image Classification and Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 2096–101. http://dx.doi.org/10.22214/ijraset.2021.38336.

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Анотація:
Abstract: Vehicle positioning and classification is a vital technology in intelligent transportation and self-driving cars. This paper describes the experimentation for the classification of vehicle images by artificial vision using Keras and TensorFlow to construct a deep neural network model, Python modules, as well as a machine learning algorithm. Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex, and less accurate. The outcomes are used to assess the best camera location for filming, the vehicular traffic to determine the highway occupancy. An accurate, simple, and hardware-efficient architecture is required to be developed for image classification. Keywords: Convolutional Neural Networks, Image Classification, deep neural network, Keras, Tensorflow, Python, machine learning, dataset
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Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.
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4

Kette, Efraim Kurniawan Dairo. "MODIFIED CORRELATION WEIGHT K-NEAREST NEIGHBOR CLASSIFIER USING TRAINING DATASET CLEANING METHOD." Indonesian Journal of Physics 32, no. 2 (December 28, 2021): 20–25. http://dx.doi.org/10.5614/itb.ijp.2021.32.2.5.

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In pattern recognition, the k-Nearest Neighbor (kNN) algorithm is the simplest non-parametric algorithm. Due to its simplicity, the model cases and the quality of the training data itself usually influence kNN algorithm classification performance. Therefore, this article proposes a sparse correlation weight model, combined with the Training Data Set Cleaning (TDC) method by Classification Ability Ranking (CAR) called the CAR classification method based on Coefficient-Weighted kNN (CAR-CWKNN) to improve kNN classifier performance. Correlation weight in Sparse Representation (SR) has been proven can increase classification accuracy. The SR can show the 'neighborhood' structure of the data, which is why it is very suitable for classification based on the Nearest Neighbor. The Classification Ability (CA) function is applied to classify the best training sample data based on rank in the cleaning stage. The Leave One Out (LV1) concept in the CA works by cleaning data that is considered likely to have the wrong classification results from the original training data, thereby reducing the influence of the training sample data quality on the kNN classification performance. The results of experiments with four public UCI data sets related to classification problems show that the CAR-CWKNN method provides better performance in terms of accuracy.
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Qu, Ying, Na Yang, and Zhuangzhi Sun. "Research on classification management of car dealers in used car platform based on sd simulation model." Journal of Physics: Conference Series 1774, no. 1 (January 1, 2021): 012057. http://dx.doi.org/10.1088/1742-6596/1774/1/012057.

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Buzzelli, Marco, and Luca Segantin. "Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results." Sensors 21, no. 2 (January 15, 2021): 596. http://dx.doi.org/10.3390/s21020596.

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Анотація:
We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.
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Weidner, Wiltrud, Fabian W. G. Transchel, and Robert Weidner. "Telematic driving profile classification in car insurance pricing." Annals of Actuarial Science 11, no. 2 (September 13, 2016): 213–36. http://dx.doi.org/10.1017/s1748499516000130.

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AbstractThis paper presents pricing innovations to German car insurance. The purpose is to provide an effective approach to adapting actuarial pricing decision to incorporate telematic data, which differs substantially from established tariff criteria in complexity and volume. A vehicle mobility model and a real-world sample of driving profiles form the input into the analysis. We propose an allocation of the driving profiles based on velocity and acceleration parameters to specific driving styles for evaluating the driving behaviour to subsequently enable discounts or surcharges on the premiums to obtain usage-based insurance premiums. The result is highly relevant for actuaries, who calculate the tariffs, but also for managers, as they have to make a pricing decision.
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8

Darney, P. Ebby. "Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification." December 2021 3, no. 4 (December 10, 2021): 341–52. http://dx.doi.org/10.36548/jaicn.2021.4.006.

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Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.
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9

Hosameldeen, Osama. "Deep learning-based car seatbelt classifier resilient to weather conditions." International Journal of Engineering & Technology 9, no. 1 (February 25, 2020): 229. http://dx.doi.org/10.14419/ijet.v9i1.30050.

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Deep Learning is a very promising field in image classification. It leads to the automation of many real-world problems. Currently, Car seatbelt violation detection is done manually or partial manual. In this paper, an approach is proposed to make the seat belt detection process fully automated. To make the detection more accurate, sensors are set to detect the weather condition. When spe-cific weather condition is detected, the corresponding pre-trained model is assigned the detection task. In other words, a research is conducted to check the possibility of dividing the big-sized deep-learning model - that can classify car seatbelt, into sub-models each one can detect specific weather condition. Accordingly, a single specialized model is used for each weather condition, Deep convolutional neural network (CNN) model AlexNet is used in the detection/classification process. The proposed system is sensor based AlexNet (S-AlexNet). Results support our hypothesis that “Using single model for each weather condition is better than gen-eral model that support all weather conditions”. On average, previous approaches that trained single model for all weather condi-tions have accuracy less than 90%. The proposed S-AlexNet approach successfully reaches 90+% accuracy.
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Bernardi, Mario Luca, Marta Cimitile, Fabio Martinelli, and Francesco Mercaldo. "Driver and Path Detection through Time-Series Classification." Journal of Advanced Transportation 2018 (2018): 1–20. http://dx.doi.org/10.1155/2018/1758731.

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Driver identification and path kind identification are becoming very critical topics given the increasing interest of automobile industry to improve driver experience and safety and given the necessity to reduce the global environmental problems. Since in the last years a high number of always more sophisticated and accurate car sensors and monitoring systems are produced, several proposed approaches are based on the analysis of a huge amount of real-time data describing driving experience. In this work, a set of behavioral features extracted by a car monitoring system is proposed to realize driver identification and path kind identification and to evaluate driver’s familiarity with a given vehicle. The proposed feature model is exploited using a time-series classification approach based on a multilayer perceptron (MLP) network to evaluate their effectiveness for the goals listed above. The experiment is done on a real dataset composed of totally 292 observations (each observation consists of a given person driving a given car on a predefined path) and shows that the proposed features have a very good driver and path identification and profiling ability.
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Дисертації з теми "Car model classification"

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Castellucci, Davide. "Model for car brand classification and estimation of price tag." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Анотація:
Nowadays Machine Learning and Deep Learning are becoming more and more important for several problems in many fields. For this reason, we decide to use them for improving the results and the efficiency of different tasks as view understanding on cars, car’s brand and model classification, and price estimation. Behind defining and modifying the correct model for each problem we obtained very good results for each of them. This thesis will present them in detail and will point out possible future improvements.
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2

Dodsworth, Joel Andrew. "The application of vehicle classification, vehicle-to-infrastructure communication and a car-following model to single intersection traffic signal control." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22741/.

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On-line responsive traffic signal optimization strategies most commonly use data received from loop detectors to feed information into an underlying traffic model. The limited data available from conventional detection systems has dictated the way that current 'state-of-the-art' traffic signal control systems have been developed. Such systems tend to consider traffic as having homogenous properties to avoid the requirement for more detailed knowledge of individual vehicle properties. However, a consequence of this simplification is to limit an optimizer in achieving its objectives. The first element of this study investigates whether additional data regarding vehicle type can be reliably extracted from conventional detection to improve optimizer performance using existing infrastructure. A single detector classification algorithm is developed and it is shown that, using a modification of an existing state-of-the-art optimization method, a modest improvement in performance can be achieved. The emergence of connected vehicle technology and, in particular, Vehicle-to-Infrastructure (V2I) communications promises more comprehensive data. V2I-based optimization methods proposed in literature require a minimum penetration rate of V2I equipped vehicles before performance matches existing systems. To address this problem, the second part of the study focuses on the development of a hybrid detection model that is capable of simultaneously using information from conventional and V2I detection. It is demonstrated that the hybrid detection model can begin to realise benefits as soon as V2I data becomes available. V2I-based vehicle classification is then applied to the developed hybrid model and significant benefits are demonstrated for HGVs. The final section of the thesis introduces the use of a more sophisticated internal traffic model and a new optimization method is developed to implement it. The car-following model based optimization method addresses the lack of modelled interaction between vehicles and is shown to be capable of reducing vehicle stops over and above the developed (vertical queue based) hybrid model.
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Kato, Jien, Toyohide Watanabe, Sebastien Joga, Rittscher Jens, Blake Andrew, ジェーン 加藤, and 豊英 渡邉. "An HMM-based segmentation method for traffic monitoring movies." IEEE, 2002. http://hdl.handle.net/2237/6744.

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Lindespång, Victor. "Bildklassificering av bilar med hjälp av deep learning." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-58361.

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Den här rapporten beskriver hur en bildklassificerare skapades med förmågan att via en given bild på en bil avgöra vilken bilmodell bilen är av. Klassificeringsmodellen utvecklades med hjälp av bilder som företaget CAB sparat i samband med försäkringsärenden som behandlats via deras nuvarande produkter. Inledningsvis i rapporten så beskrivs teori för maskininlärning och djupinlärning på engrundläggande nivå för att leda in läsaren på ämnesområdet som rör rapporten, och fortsätter sedan med problemspecifika metoder som var till nytta för det aktuella problemet. Rapporten tar upp metoder för hur datan bearbetats i förväg, hur träningsprocessen gick  till med de valda verktygen samt diskussion kring resultatet och vad som påverkade det – med kommentarer om vad som kan göras i framtiden för att förbättra slutprodukten.
This report describes how an image classifier was created with the ability to identify car makeand model from a given picture of a car. The classifier was developed using pictures that the company CAB had saved from insurance errands that was managed through their current products. First of all the report begins with a brief theoretical introduction to machine learning and deep learning to guide the reader in to the subject of the report, and then continues with problemspecific methods that were of good use for the project. The report brings up methods for how the data was processed before training took place, how the training process went with the chosen tools for this project and also discussion about the result and what effected it – with comments about what can be done in the future to improve the end product.
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Ziegenfuss, Katharina. "Bewertung innovativer Geschäftsmodelle: Entwicklung eines Simulationsmodells und Anwendung auf die bedarfsabhängige Funktionserweiterung im vernetzten Fahrzeug: Development of a simulation model and application to the ‘Function on Demand’ concept of the connected car." Universitätsverlag Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A73123.

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Die Bedeutung innovativer Geschäftsmodelle als Bestimmungsfaktor für den Unternehmenserfolg steht weitestgehend außer Frage. Aufgrund der hohen Komplexität von Geschäftsmodellen hat sich jedoch bislang kein praktisch anwendbares Bewertungskonzept etablieren können, welches Geschäftsmodellinnovationen in Hinblick auf deren Erfolgsentwicklung untersucht. Zur Adressierung dieser Problemstellung wird unter Anwendung des systemdynamischen Ansatzes ein Simulationsmodell entwickelt, welches den Wertbeitrag einer Geschäftsmodellinnovation ausweist. Neben dem Kapitalwert als finanzielle Wertgröße des Geschäftsmodells werden ferner der Kundenwert sowie der Wert der unternehmerischen Fähigkeiten als wichtige Wertgrößen explizit gemacht, da sie die zukünftige Leistungs- und Wettbewerbsfähigkeit des Geschäftsmodells determinieren. Damit liefert das Bewertungsmodell einen Ansatz zur ganzheitlichen Geschäftsmodellbewertung, die die Anwendung finanzieller Standardkalkulationen mit der Messbarmachung nicht-finanzieller Erfolgsgrößen kombiniert.:1 Einführung 2 Geschäftsmodelle und Geschäftsmodellbewertung 3 Entwicklungsprozess des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 4 Aufbau des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 5 Simulation des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 6 Schlussbetrachtung
Business model innovations provide powerful levers for creating sustainable competitive advantage and thus have a positive impact on the value of an enterprise. However, due to the complexity of business models, no practically applicable framework, evaluating an innovative business model with regard to its effect on a company’s success, has been established. Hence, a simulation model assessing the value contribution of a business model innovation is developed. Using the mathematical modeling technique ‘System Dynamics’ to frame the value drivers of a business allows for simulation experiments that reveal the effect of the business model’s design on its profitability, therewith guiding policymakers towards better decisions. As a result, the simulation model reports the net present value of a business model. In addition, the success indicators customer lifetime value and the value of the enterprises’ capabilities are identified as important assets that have to be monitored closely as they determine the company’s prospective performance. In combining financial standard calculations with the operationalization of non-financial measures, the simulation model represents a comprehensive approach for business model evaluation.:1 Einführung 2 Geschäftsmodelle und Geschäftsmodellbewertung 3 Entwicklungsprozess des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 4 Aufbau des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 5 Simulation des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 6 Schlussbetrachtung
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Padgett, Curtis. "A neural network model for facial affect classification /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campusesd, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9907599.

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Hansen, Vedal Amund. "Comparing performance of convolutional neural network models on a novel car classification task." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213468.

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Recent neural network advances have lead to models that can be used for a variety of image classification tasks, useful for many of today’s media technology applications. In this paper, I train hallmark neural network architectures on a newly collected vehicle image dataset to do both coarse- and fine-grained classification of vehicle type. The results show that the neural networks can learn to distinguish both between many very different and between a few very similar classes, reaching accuracies of 50.8% accuracy on 28 classes and 61.5% in the most challenging 5, despite noisy images and labeling of the dataset.
Nya neurala nätverksframsteg har lett till modeller som kan användas för en mängd olika bildklasseringsuppgifter, och är därför användbara många av dagens medietekniska applikationer. I detta projektet tränar jag moderna neurala nätverksarkitekturer på en nyuppsamlad bilbild-datasats för att göra både grov- och finkornad klassificering av fordonstyp. Resultaten visar att neurala nätverk kan lära sig att skilja mellan många mycket olika bilklasser,  och även mellan några mycket liknande klasser. Mina bästa modeller nådde 50,8% träffsäkerhet vid 28 klasser och 61,5% på de mest utmanande 5, trots brusiga bilder och manuell klassificering av datasetet.
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Pretty, Christopher Grant. "Analysis, classification and management of insulin sensitivity variability in a glucose-insulin system model for critical illness." Thesis, University of Canterbury. Mechanical Engineering, 2012. http://hdl.handle.net/10092/6580.

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Hyperglycaemia in critical care is common and has been linked to increased mortality and morbidity. Tight control of blood glucose concentrations to more normal levels can significantly reduce the negative outcomes associated with hyperglycaemia. However, hypoglycaemia and glycaemic variability have also been independently shown to increase mortality in critically ill patients. Further complicating the matter, critically ill patients exhibit high inter- and intra patient metabolic variability and thus consistent, safe control of glycaemia has proved very difficult. Model-based and model-derived tight glycaemic control methods have shown significant ability to provide very tight control with little or no hypoglycaemia in the intensive care unit (ICU). The model-based control practised in the Christchurch Hospital ICU uses a physiological model that relies on a single, time-varying parameter, SI, to capture the patient-specific glycaemic response to insulin. As an identified parameter, SI is prone to also capturing other, unintended, dynamics that add variability on multiple timescales. The objective of this research was to enable enhanced glycaemic control by addressing this variability of the SI parameter through better modelling and implementation. An improved model of insulin secretion as a function of blood glucose concentration was developed using data collected from a recent study at the Christchurch Hospital ICU. Separate models were identified for non-diabetic patients and diagnosed, or suspected type II diabetic patients, with R2 = 0.61 and 0.69, respectively. The gradients of the functions identified were comparable to data published in a number of other studies on healthy and diabetic subjects. The transcapilliary diffusion (nI) and cellular clearance (nC) rate parameters were optimised using data from published microdialysis studies. Interactions between these key parameters determine maximum interstitial insulin concentrations available for glucose disposal, and thus directly influence SI. The optimal values of these parameters were determined to be nI = nC = 0.0060 1/min. Models of endogenous glucose production (EGP), as functions of blood glucose concentration and time, were assessed. These models proved unsatisfactory due to difficulties in identifying reliable functions with the available data set. Thus, it was determined that EGP should continue to be treated as a population constant, except during real-time glycaemic control, where the value may be adjusted temporarily to ensure valid SI values. The first 24 hours of ICU stay proved to be a period of significantly increased SI variability, both in terms of hour-to-hour changes and longer-term evolution of level. This behaviour was evident for the entire study cohort as a whole and was particularly pronounced during the first 12-18 hours. The subgroup of cardiovascular surgery patients, in which there was sufficient data for analysis, mirrored the results of the whole cohort, but was found to have even lower and more variable SI. Glucocorticoid steroids were also found to be associated with clinically significant reductions in overall level and increases in hour-to-hour variability of SI. To manage variability caused by factors external to the physiological model, the use of several stochastic models was proposed. Using different models for the early part of ICU stay and for different diagnostic subgroups as well as when patients were receiving certain drug therapies would permit control algorithms to reduce the impact of the SI variability on outcome glycaemia. The impact of measurement timing and BG concentration errors on the variability of SI was assessed. Results indicated that the impact of both sources of errors on SI level was unlikely to be clinically significant. The impact of BG sensor errors on hour-to-hour SI variability was more pronounced. Understanding the effect of sensor and timing errors on SI allows their impact to be reduced by using the 5-95 percentile forecast range of stochastic models during glycaemic control. The performance of the model incorporating the proposed insulin kinetic parameters and secretion enhancements was validated for clinical glycaemic control and virtual trial purposes. This validation was conducted by self- and cross validation on a cohort independent to that with which the model was developed. The use of multiple stochastic models to reduce the impact of this extrinsic variability during glycaemic control was validated using virtual trials.
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Grundel, Martin, and Jutta Abulawi. "SkiPo – Ein skizzen- und portbasiertes Modell für die Entwicklung von mechanischen Systemen." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214760.

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Dieser Beitrag stellt ein neues, hybrides Modell für die Entwicklung mechanischer und mechatronischer Systeme vor. Ziel ist es, die derzeitig bestehende Lücke zwischen abstrakten Funktionsmodellen und sehr konkreten, geometrieorientierten 3D-CAD-Modellen zu überbrücken. Das hier vorgestellte SkiPo-Modell beschreibt die Interaktionen zwischen den Komponenten eines Systems basierend auf den zugehörigen Material-, Energie- und Signalflüssen. Ergänzt wird diese abstrakte Darstellung mit Skizzen, die wichtige Konstruktionsentscheidungen in einer strukturierten, semistandardisierten Weise dokumentieren. Das Ziel dieser hybriden Modellierung ist es, die unvermeidbaren Iterationen zwischen abstrakten und sehr detaillierten Betrachtungen von mechanischen und mechatronischen Systemen in der frühen Phase der Produktentstehung zu unterstützen. In Erprobungen mit Studentengruppen zeigte sich, dass dieser Modellierungsansatz das Verständnis und die Kommunikation im Team fördern kann.
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McDaniel, Cleve. "Retention classification models for an historically black university with an open admission policy /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841172.

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Книги з теми "Car model classification"

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Naumov, Vladimir. Consumer behavior. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014653.

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The book describes the basic issues concerning consumer behavior on the basis of the simulation of the decision-making process on buying behavior of customers in the sales area of the store and shopping Internet sites. The classification of models of consumer behavior, based on research in the area of economic, social and psychological theories and empirical evidence regarding decision-making by consumers when purchasing the goods, including online stores. Methods of qualitative and quantitative research of consumer behavior, fundamentals of statistical processing of empirical data. Attention is paid to the processes of consumers ' perception of brands (brands) and advertising messages, the basic rules for the display of goods (merchandising) and its impact on consumer decision, recommendations on the use of psychology of consumer behavior in personal sales. Presents an integrated model of consumer behavior in the Internet environment, the process of perception of the visitor of the company, the factors influencing consumer choice of goods online. Is intended for preparation of bachelors in directions of preparation 38.03.02 "Management", 38.03.06 "trading business" and can be used for training of bachelors in direction of training 43.03.01 "Service", and will also be useful for professionals working in the field of marketing, distribution and sales.
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2

Popadyuk, Tat'yana, Irina Smirnova, Nataliya Linder, Arkadiy Trachuk, Gayk Nalbandyan, Anastasiya Karikova, Aleksandra Pogosyan, et al. Modern electrical substations. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1861116.

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The textbook provides general information about the operating modes of electrical systems and substations, methods for calculating short-circuit currents and selecting electrical equipment for substations and power grids, classification of substations is given, designs of manual control transformers, high-voltage and low-voltage substations are considered, issues of their relay protection, requirements of electrical installation rules and technical operation rules for the device and operation of substations. It is intended for students of the training direction 13.03.02 "Electric power and electrical engineering", and can also be useful for students of secondary vocational education, masters and electrical engineers who improve their technical level on the job.
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3

Yudaev, Vasiliy. Hydraulics. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/996354.

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The textbook corresponds to the general education programs of the general courses "Hydraulics" and "Fluid Mechanics". The basic physical properties of liquids, gases, and their mixtures, including the quantum nature of viscosity in a liquid, are described; the laws of hydrostatics, their observation in natural phenomena, and their application in engineering are described. The fundamentals of the kinematics and dynamics of an incompressible fluid are given; original examples of the application of the Bernoulli equation are given. The modes of fluid motion are supplemented by the features of the transient flow mode at high local resistances. The basics of flow similarity are shown. Laminar and turbulent modes of motion in pipes are described, and the classification of flows from a creeping current to four types of hypersonic flow around the body is given. The coefficients of nonuniformity of momentum and kinetic energy for several flows of Newtonian and non-Newtonian fluids are calculated. Examples of solving problems of transient flows by hydraulic methods are given. Local hydraulic resistances, their use in measuring equipment and industry, hydraulic shock, polytropic flow of gas in the pipe and its outflow from the tank are considered. The characteristics of different types of pumps, their advantages and disadvantages, and ways of adjustment are described. A brief biography of the scientists mentioned in the textbook is given, and their contribution to the development of the theory of hydroaeromechanics is shown. The four appendices can be used as a reference to the main text, as well as a subject index. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions who study full-time, part-time, evening, distance learning forms of technological and mechanical specialties belonging to the group "Food Technology".
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4

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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5

Ferraty, Frédéric, and Philippe Vieu. A Unifying Classification for Functional Regression Modeling. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.1.

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This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.
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6

Harmeyer, Gary R. A prototype model for automating nursing diagnosis, nurse care planning and patient classification. 1986.

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7

Bag-of-Words Algorithms Can Supplement Transformer Sequence Classification & Improve Model Interpretability. RAND Corporation, 2022. http://dx.doi.org/10.7249/wra1719-1.

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8

Herridge, Margaret S., and Jill I. Cameron. Models of Rehabilitative Care after Critical Illness. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199653461.003.0050.

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Critical illness is transformative. Patients and caregivers are traumatized and acquire new mood disorders and disability. These are costly and consequential. Knowledge of current rehabilitation theory may help to inform emerging models of care for our critically ill patients and families. The International Classification of Functioning, Disability, and Health (ICF) model is presented as a candidate construct for patients and families after critical illness. It highlights the complexity and interdependence of factors that determine outcome and incorporates multiple facets of the individual experience. ICF may facilitate the development of a novel framework of aetiologically neutral clinical phenotypes with distinct recovery trajectories after critical illness. This informs tailored interventions for distinct patient and family groupings, independent of initial diagnostic groups, and acknowledges the similar themes of ICUAW, cognitive dysfunction, and mood disorders following complex critical illness.
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9

Glennan, Stuart. Mechanisms, Models, and Kinds. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198779711.003.0004.

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This chapter explores how mechanisms and their constituents can be classified into kinds. It argues for a weakly realist account of natural kinds—one which suggests that classification into kinds is based upon real similarities between instances of those kinds, but which denies that kinds have essences or have some reality apart from their instances. I introduce a models-first account of kinds, which suggests that two things are of the same kind to the extent that they can be represented by the same model. Because target entities can be represented by multiple models, they will belong to multiple kinds. I illustrate the approach by showing how the entities and activities that make up mechanisms can be classified into kinds.
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10

Abdoo, Yvonne Marie. A MODEL FOR NURSE STAFFING AND THE IMPACT OF INTER-RATER RELIABILITY OF PATIENT CLASSIFICATION ON NURSE STAFFING REQUIREMENTS. 1987.

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Частини книг з теми "Car model classification"

1

Corrales, H., D. F. Llorca, I. Parra, S. Vigre, A. Quintanar, J. Lorenzo, and N. Hernández. "CNNs for Fine-Grained Car Model Classification." In Computer Aided Systems Theory – EUROCAST 2019, 104–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45096-0_13.

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2

Nanthakumar, A. J. D., Karan Jariwala, Kumawat Harshit, S. Yokeshwaran, and S. Madhankumar. "Classification of Road Profile Using Golden Car Parameters for Quarter Car Model." In Advances in Design and Thermal Systems, 341–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6428-8_27.

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3

Abele, Stephan, and Matthias von Davier. "CDMs in Vocational Education: Assessment and Usage of Diagnostic Problem-Solving Strategies in Car Mechatronics." In Handbook of Diagnostic Classification Models, 461–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05584-4_22.

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4

Montanari, Giorgio E., M. Giovanna Ranalli, and Paolo Eusebi. "Multilevel Latent Class Models for Evaluation of Long-term Care Facilities." In Data Analysis and Classification, 249–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_29.

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5

Chakravarthy, S. R. Sannasi, and Harikumar Rajaguru. "Voting Based CAD Model for Breast Cancer Classification." In IFMBE Proceedings, 151–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93564-1_17.

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6

Jaya Bhavani, D., K. Jhansi Naga Indu Sri, D. Padma, and M. Ashok Kumar. "Hybrid Feature Selection Model for Credit Card Data Classification." In Advances in Intelligent Systems and Computing, 175–92. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7330-6_14.

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7

Nong, Yingxiong, Zhibin Chen, Cong Huang, Jian Pan, Dong Liang, and Ying Lu. "Recognition Model Based on BP Neural Network and Its Application." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 292–302. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_31.

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AbstractThe BP neural network model used in data classification can change the traditional manual classification, which has the disadvantages of low efficiency and subjective interference. According to the principle of BP, this paper determines the relevant parameters of network structure, and establishes an optimized BP. The BP model is used to analyze the chemical composition data of tobacco leaves to determine the grade of tobacco leaves. Experiments show that this model has better recognition accuracy than KNN and random forest model. It effectively improves the efficiency of classification and reduces the interference of subjective factors in classification.
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8

Xu, Yuemei, Zuwei Fan, and Han Cao. "A Multi-task Text Classification Model Based on Label Embedding Learning." In Communications in Computer and Information Science, 211–25. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9229-1_13.

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AbstractDifferent text classification tasks have specific task features and the performance of text classification algorithm is highly affected by these task-specific features. It is crucial for text classification algorithms to extract task-specific features and thus improve the performance of text classification in different text classification tasks. The existing text classification algorithms use the attention-based neural network models to capture contextualized semantic features while ignores the task-specific features. In this paper, a text classification algorithm based on label-improved attention mechanism is proposed by integrating both contextualized semantic and task-specific features. Through label embedding to learn both word vector and modified-TF-IDF matrix, the task-specific features can be extracted and then attention weights are assigned to different words according to the extracted features, so as to improve the effectiveness of the attention-based neural network models on text classification. Experiments are carried on three text classification task data sets to verify the performance of the proposed method, including a six-category question classification data set, a two-category user comment data set, and a five-category sentiment data set. Results show that the proposed method has an average increase of 3.02% and 5.85% in F1 value compared with the existing LSTMAtt and SelfAtt models.
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9

Tran, Quan M., Linh V. Nguyen, Tai Huynh, Hai H. Vo, and Vuong T. Pham. "Efficient CNN Models for Beer Bottle Cap Classification Problem." In Future Data and Security Engineering, 713–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35653-8_51.

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10

Kralj Novak, Petra, Teresa Scantamburlo, Andraž Pelicon, Matteo Cinelli, Igor Mozetič, and Fabiana Zollo. "Handling Disagreement in Hate Speech Modelling." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 681–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08974-9_54.

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AbstractHate speech annotation for training machine learning models is an inherently ambiguous and subjective task. In this paper, we adopt a perspectivist approach to data annotation, model training and evaluation for hate speech classification. We first focus on the annotation process and argue that it drastically influences the final data quality. We then present three large hate speech datasets that incorporate annotator disagreement and use them to train and evaluate machine learning models. As the main point, we propose to evaluate machine learning models through the lens of disagreement by applying proper performance measures to evaluate both annotators’ agreement and models’ quality. We further argue that annotator agreement poses intrinsic limits to the performance achievable by models. When comparing models and annotators, we observed that they achieve consistent levels of agreement across datasets. We reflect upon our results and propose some methodological and ethical considerations that can stimulate the ongoing discussion on hate speech modelling and classification with disagreement.
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Тези доповідей конференцій з теми "Car model classification"

1

Varga, M., and J. Radford. "Automatic car model classification." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150254.

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2

Simoni, Alessandro, Andrea D’Eusanio, Stefano Pini, Guido Borghi, and Roberto Vezzani. "Improving Car Model Classification through Vehicle Keypoint Localization." In 16th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010207803540361.

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3

Gunadi, Farhan, Muhammad Fauzi, Bagas Firdaus, and Afrida Helen. "Preprocessing Application for Car Insurance Claim Classification Model." In 2021 International Conference on Artificial Intelligence and Big Data Analytics (ICAIBDA). IEEE, 2021. http://dx.doi.org/10.1109/icaibda53487.2021.9689717.

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4

Albini, Lucas, Matheus Gutoski, and Heitor Silvério Lopes. "Car Make and Model Classification with Deep Learning Methods." In Congresso Brasileiro de Inteligência Computacional. ABRICOM, 2020. http://dx.doi.org/10.21528/cbic2019-103.

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5

Koç, Aykut, Erhan Gundogdu, Berkan Solmaz, Enes Sinan Parildi, and Veysel Yucesoy. "Deep learning-based fine-grained car make/model classification for visual surveillance." In Counterterrorism, Crime Fighting, Forensics, and Surveillance, edited by Henri Bouma, Felicity Carlysle-Davies, Robert J. Stokes, and Yitzhak Yitzhaky. SPIE, 2017. http://dx.doi.org/10.1117/12.2278862.

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6

Yu, Ye, Qiang Jin, and Chang Wen Chen. "FF-CMnet: A CNN-Based Model for Fine-Grained Classification of Car Models Based on Feature Fusion." In 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018. http://dx.doi.org/10.1109/icme.2018.8486443.

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7

Santana, Paula. "Exploring Transfer Learning for Self-driving Car Dataset." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai2018120312.

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A core subject in the self-driving cars domain is how to use images taken in real time to best steer a vehicle in a road. Mapping visual inputs to steering commands can be seen from a machine learning perspective as a regression or classification problem, depending mainly if the control outputs will be discrete e.g. ’turn left’, ’go forward’ or continuous e.g. steering angle: +0.014◦. In this experiment, the road following task will be treated as a classification problem, so given an input image the learning model will predict a control command (‘up’, ‘left’, ‘right’).
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8

Ben Ahmed, Walid, Michel Bigand, Mounib Mekhilef, and Yves Page. "Development of Knowledge Based System to Facilitate Design of On-Board Car Safety Systems." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48743.

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The development of on-board car safety systems requires an accidentology knowledge base for the development of new functionalities as well as their improvement and evaluation. The Knowledge Discovery in accident Database (KDD) is one of the approaches allowing the construction of this knowledge base. However, considering the complexity of the accident data and the variety of their sources (biomechanics, psychology, mechanics, ergonomics, etc.), the analytical methods of the KDD (clustering, classification, association rules etc.) should be combined with expert approaches. Indeed, there is background knowledge in accidentology which exists in the minds of accidentologist experts and which is not formalized in the accident database. The aim of this paper is to develop a Knowledge Representation Model (KRM) intended to incorporate this knowledge in the KDD process. The KRM is implemented in a knowledge-based system, which provides an expert classification of the attributes characterizing an accident. This expert classification provides an efficient tool for data preparation in a KDD process. Our method consists of combining the modeling systemic approach of complex systems and the modeling cognitive approach KOD (Knowledge Oriented Design) in knowledge engineering.
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9

Chen, Hao, Zhenguo Nie, Qingfeng Xu, Jianghua Fei, Kang Yang, Yaguan Li, Wenhui Fan, and Xin-Jun Liu. "Classification of Surface Defects on Galvanized Cold-Rolled Steel Sheets Using Data-Driven Fault Model With Attention Mechanism." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-91218.

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Abstract In the production of the galvanized cold-rolled steel sheets used for stamping car body parts, in-situ and real-time defective detecting is crucial for quality control, in which various types of defects will inevitably occur. It is challenging to improve the accuracy of defect image classification by appropriate means to assist the manual screening process better. Defects under actual production conditions are often not prominent enough in defect characteristics, and there may be a significant similarity between different defect categories. To eliminate this weakness, we propose a data-driven faulty detection model named Steel Faulty Detection Attention Net (SFDANet) that uses images of the galvanized steel surface as input to identify whether the product is qualified and automatic classification of defect types instantaneously. This method can shorten product inspection time and improve production line efficiency automatically. In addition, the attention mechanism is utilized, enhancing the performance of SFDANet. Compared with the baseline that applied the ResNet method, SFDANet achieves a noticeable improvement in the classification accuracy of the test data. The well-trained model can successfully show an improved performance than the baseline models on the multiple types of faulty. Enhanced by SFDANet with high classification accuracy, the defect rate of products is significantly reduced, and the production speed of the production line is significantly improved.
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10

Borges, Fernando Elias Melo, Danton Diego Ferreira, and Antônio Carlos de Sousa Couto Júnior. "Classificação e Interpretação de dados do Cadastro Ambiental Rural utilizando técnicas de Aprendizagem de Máquina." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-108.

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The Rural Environmental Registry (CAR) consists of a mandatory public electronic registry for all rural properties in the Brazilian territory, integrates environmental information of the properties, assists the monitoring of them and the fight against deforestation. However, a large number of registrations are carried out erroneously generating inconsistent data, leading these to be canceled and/or to be requested to correct the registration. Performing automatic verification of these records is important to improve the processing of records. This paper proposes an automatic classification method to approve or cancel the CAR registers with interpretation of the classifications performed. For this, four machine learning-based classifiers were tested and the results were evaluated. The model with the best performance was used to interpret the classification using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The results showed the potential of the method in future real applications.
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Звіти організацій з теми "Car model classification"

1

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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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.

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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.
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Downard, Alicia, Stephen Semmens, and Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40439.

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The orientation of constructed levee embankments relative to alluvial swales is a useful measure for identifying regions susceptible to backward erosion piping (BEP). This research was conducted to create an automated, efficient process to classify patterns and orientations of swales within the Lower Mississippi Valley (LMV) to support levee risk assessments. Two machine learning algorithms are used to train the classification models: a convolutional neural network and a U-net. The resulting workflow can identify linear topographic features but is unable to reliably differentiate swales from other features, such as the levee structure and riverbanks. Further tuning of training data or manual identification of regions of interest could yield significantly better results. The workflow also provides an orientation to each linear feature to support subsequent analyses of position relative to levee alignments. While the individual models fall short of immediate applicability, the procedure provides a feasible, automated scheme to assist in swale classification and characterization within mature alluvial valley systems similar to LMV.
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Ross, Andrew, David Johnson, Hai Le, Danny Griffin, Carl Mudd, and David Dawson. USACE Advanced Modeling Object Standard : Release 1.0. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42152.

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The U.S. Army Corps of Engineers (USACE) Advanced Modeling Object Standard (AMOS) has been developed by the CAD/BIM Technology Center for Facilities, Infrastructure, and Environment to establish standards for support of the Advanced Modeling process within the Department of Defense (DoD) and the Federal Government. The critical component of Advanced Modeling is the objects themselves- and either make the modeling process more difficult or more successful. This manual is part of an initiative to develop a nonproprietary Advanced Modeling standard that incorporates both vertical construction and horizontal construction objects that will address the entire life cycle of facilities within the DoD. The material addressed in this USACE Advanced Modeling Object Standard includes a classification organization that is needed to identify models for specific use cases. Compliance with this standard will allow users to know whether the object model they are getting is graphically well developed but data poor or if it does have the data needed for creating contract documents. This capability will greatly reduce the designers’ efforts to either build an object or search/find/edit an object necessary for the development of their project. Considering that an advanced model may contain hundreds of objects this would represent a huge time savings and improve the modeling process.
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5

Hoinkes, Ulrich. Indexicality and Enregisterment as Theoretical Approaches to the Sociolinguistic Analysis of Romance Languages. Universitatsbibliothek Kiel, November 2019. http://dx.doi.org/10.21941/hoinkesindexenregromlang.

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Social indexicality and enregisterment are basic notions of a theoretical model elaborated in the United States, the aim of which is to describe the relationship between the use of language variation and patterns of social behavior at the level of formal classification. This analytical approach is characterized by focusing on the interrelation of social performance and language awareness. In my contribution, I want to show how this modern methodology can give new impetus to the study of today’s problem areas in Europe, such as migration and language or urban life and language use. In particular, I am interested in the case of Catalan, which has been studied for some time by proponents of the North American enregisterment theory. This leads me to indicate that explicit forms of social conduct, such as language shift or the emblematic use of linguistic forms, can be interpreted with regard to the social indexicality of Catalan. I thus analyze them in a way which shows that authenticity and integration in Catalan society can be achieved to a considerable extent by practicing forms of linguistic enregisterment.
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6

Gutierrez-Arias, Ruvistay, Ximena Neculhueque-Zapata, Raul Valenzuela-Suazo, and Pamela Seron. Assessing people's functioning through rehabilitation registries systems. A rapid scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0006.

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Review question / Objective: 1.- To systematize the available scientific evidence on rehabilitation models and rehabilitation registries systems, which allow for the assessment of people's functioning; 2.- To describe rehabilitation data registries systems used internationally and the "minimum data set" that relate to the functioning of persons. Eligibility criteria: - Population: Studies that have enrolled adult or paediatric patients, with any condition or pathology that could potentially result in low functioning or disability, related to impairments, activity limitation or restriction in participation, according to the International Classification of Functioning, Disability and Health (ICF) framework will be included. - Concept: Studies that submitted data from a rehabilitation registry, bank, or database containing a minimum data set will be included. These registries may include clinical and administrative information that can be used to improve the quality of care, monitor or answer research questions. - Context: Studies that have been conducted in a context of rehabilitation programs and assessment of function or disability, at any level of care, and that have directly or indirectly addressed aspects or variables that can account for functioning, capacity, or participation according to the ICF framework will be included. The inclusion of studies will not be limited by their methodological design, since they will be used to identify rehabilitation registries or databases, so primary studies (cohort studies, case-control studies, among others) and secondary studies (systematic reviews, exploratory reviews, among others) will be considered.
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7

Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber, and Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, August 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.
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8

Savosko, V., I. Komarova, Yu Lykholat, E. Yevtushenko, and T. Lykholat. Predictive model of heavy metals inputs to soil at Kryvyi Rih District and its use in the training for specialists in the field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4511.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values (mg ⋅ m2/year): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline "Computer modelling in biology").
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9

Савосько, Василь Миколайович, Ірина Олександрівна Комарова, Юрій Васильович Лихолат, Едуард Олексійович Євтушенко,, and Тетяна Юріївна Лихолат. Predictive Model of Heavy Metals Inputs to Soil at Kryvyi Rih District and its Use in the Training for Specialists in the Field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4266.

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Анотація:
The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values ( mg ∙ m ଶ year ⁄ ): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline “Computer modelling in biology”).
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10

Sinclair, Samantha, and Sally Shoop. Automated detection of austere entry landing zones : a “GRAIL Tools” validation assessment. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45265.

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Анотація:
The Geospatial Remote Assessment for Ingress Locations (GRAIL) Tools software is a geospatial product developed to locate austere entry landing zones (LZs) for military aircraft. Using spatial datasets like land classification and slope, along with predefined LZ geometry specifications, GRAIL Tools generates binary suitability filters that distinguish between suitable and unsuitable terrain. GRAIL Tools combines input suitability filters, searches for LZs at user‐defined orientations, and plots results. To refine GRAIL Tools, we: (a) verified software output; (b) conducted validation assessments using five unpaved LZ sites; and (c) assessed input dataset resolution on outcomes using 30 and 1‐m datasets. The software was verified and validated in California and the Baltics, and all five LZs were correctly identified in either the 30 or the 1‐m data. The 30‐m data provided numerous LZs for consideration, while the 1‐m data highlighted hazardous conditions undetected in the 30‐m data. Digital elevation model grid size affected results, as 1‐m data produced overestimated slope values. Resampling the data to 5 m resulted in more realistic slopes. Results indicate GRAIL Tools is an asset the military can use to rapidly assess terrain conditions.
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