Relevant bibliographies by topics / CNN ALGORITHM

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'CNN ALGORITHM'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "CNN ALGORITHM"

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Reddy, Y. Venkat Sai, G. Chandana, G. Chetan Redddy, Ayush Kumar, Suvarna Kumar, and Dr Syed Siraj Ahmed. "Lung Cancer Detection using YOLO CNN Algorithm." International Journal of Research Publication and Reviews 4, no. 5 (June 2023): 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.

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Diqi, Mohammad. "Waste Classification using CNN Algorithm." International Conference on Information Science and Technology Innovation (ICoSTEC) 1, no. 1 (February 26, 2022): 130–35. http://dx.doi.org/10.35842/icostec.v1i1.17.

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One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste efficiently. This paper uses the CNN algorithm to provide a problem-solving strategy to waste classification. The model achieves an accuracy of 0.9969 and a loss of 0.0205. As a result, we argue that employing CNN algorithms to categorize waste yields better results and reduces losses efficiently.
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Tiancheng, Li, Ren Qing-dao-er-ji, and Qiu Ying. "Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia." Advances in Meteorology 2019 (December 6, 2019): 1–13. http://dx.doi.org/10.1155/2019/5176576.

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Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.
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Bahaa, Ahmed, Abdalla Sayed, Laila Elfangary, and Hanan Fahmy. "A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach." PLOS ONE 17, no. 12 (December 1, 2022): e0278493. http://dx.doi.org/10.1371/journal.pone.0278493.

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Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms.
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Ramekar, Aditya Dhanraj, Pooja Rajendra Sanas, Akshay Rajendra Ghodekar, Shailesh Ramesh, and Prof S. S. Bhagat. "Crop Prediction Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2714–19. http://dx.doi.org/10.22214/ijraset.2022.41873.

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Abstract: In general, agriculture is the backbone of India and also plays an important role in Indian economy by providing a certain percentage of domestic product to ensure the food security. But now-a-days, food production and prediction is getting depleted due to unnatural climatic changes, which will adversely affect the economy of farmers by getting a poor yield and also help the farmers to remain less familiar in forecasting the future crops. This research work helps the beginner farmer in such a way to guide them for sowing the reasonable crops by deploying machine learning, one of the advanced technologies in crop prediction. Convolution Neural Network one of the most popular deep neural networks puts forth in the way to achieve it. The soil type image is taken here like alluvial soil, black soil, red soil, sandy soil. Etc. to start the prediction process. Keywords: Crop prediction, Machine Learning, Convolution Neural Network, Supervised Machine Learning.
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N, Krishnamoorthy. "TV Shows Popularity and Performance Prediction Using CNN Algorithm." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1541–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202257.

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Qin, Jiangping, Yan Zhang, Huan Zhou, Feng Yu, Bo Sun, and Qisheng Wang. "Protein Crystal Instance Segmentation Based on Mask R-CNN." Crystals 11, no. 2 (February 4, 2021): 157. http://dx.doi.org/10.3390/cryst11020157.

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Protein crystallization is the bottleneck in macromolecular crystallography, and crystal recognition is a very important step in the experiment. To improve the recognition accuracy by image classification algorithms further, the Mask R-CNN model is introduced for the detection of protein crystals in this paper. Because the protein crystal image is greatly affected by backlight and precipitate, the contrast limit adaptive histogram equalization (CLAHE) is applied with Mask R-CNN. Meanwhile, the Transfer Learning method is used to optimize the parameters in Mask R-CNN. Through the comparison experiments between this combined algorithm and the original algorithm, it shows that the improved algorithm can effectively improve the accuracy of segmentation.
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HUANG, Jiawei, Caixia BI, Jiayue LIU, and Shaohua DONG. "Research on CNN-based intelligent recognition method for negative images of weld defects." Journal of Physics: Conference Series 2093, no. 1 (November 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/2093/1/012020.

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Abstract The existing technology of automatic classification and recognition of welding negative images by computer is difficult to achieve a multiple classification defect recognition while maintaining a high recognition accuracy, and the developed automatic recognition model of negative image defect cannot meet the actual needs of the field. Therefore, the convolutional neural network (CNN)-based intelligent recognition algorithm for negative image of weld defects is proposed, and a B/S (Browser/Server) architecture of weld defect feature image database combined with CNN is established subsequently, which converted from the existing CNN by the migration learning method. It makes full use of the negative big data and simplifies the algorithm development process, so that the recognition algorithm has a better generalization ability and the training algorithm accuracy of 97.18% achieved after training. The results of the comparison experiments with traditional recognition algorithms show that the CNN-based intelligent recognition algorithm for defective weld negatives has an accuracy of 92.31% for dichotomous defects, which is significantly better than the traditional recognition algorithm, the established recognition algorithm effectively improving the recognition accuracy and achieving multi-category defect recognition. At the same time, the CNN-based defect recognition method was established by combining the image segmentation algorithm and the defect intelligent recognition algorithm, which was applied to the actual negative images in the field with good results, further verifying the feasibility of CNN-based intelligent recognition algorithm in the field of defect recognition of welding negative images.
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Vitale, S., G. Ferraioli, and V. Pascazio. "EDGE PRESERVING CNN SAR DESPECKLING ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 97–100. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-97-2020.

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Abstract. SAR despeckling is a key tool for Earth Observation. Interpretation of SAR images are impaired by speckle, a multiplicative noise related to interference of backscattering from the illuminated scene towards the sensor. Reducing the noise is a crucial task for the understanding of the scene. Based on the results of our previous solution KL-DNN, in this work we define a new cost function for training a convolutional neural network for despeckling. The aim is to control the edge preservation and to better filter man-made structures and urban areas that are very challenging for KL-DNN. The results show a very good improvement on the not homogeneous areas keeping the good results in the homogeneous ones. Result on both simulated and real data are shown in the paper.
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Mehta, Jahangir Jepee, Furqaan Ahmad Wani, Aamir Ashraf Ahangar, Kanwaljeet Kaur, and Najmusher H. "Leaf Disease Remedy Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1148–51. http://dx.doi.org/10.22214/ijraset.2022.41468.

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Abstract: The proposed method aids in the diagnosis of plant diseases as well as the provision of medicines that may be employed as a defensive machine against them. The file collected from the web is correctly separated, and the various plant types are recognized and named again to produce a suitable record. A test file including several plant ailments is then obtained, which is used to assess the project's accuracy and confidence level. We'll next train our classifier with training data, and the result will be expected with maximum accuracy. We employ a Deep Convolutional Neuronic network (CNN), which consists of many layers for an estimate. A newly designed drone prototypical is also being developed that can be used to provide live updates of huge farming lands. The drone will be equipped with a highresolution photographic camera that will capture the image of the plants, which will be used as a contribution to the software, which will determine whether the plant is healthy or not. We reached a 78 percent accuracy level with our programming and training model. Our programmer provides us with the identity of the plant species, as well as the confidence level of the species and the medicine that may be used to treat it. Keywords: Machine Learning, Leaf Disease, Remedy, CNN
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Dissertations / Theses on the topic "CNN ALGORITHM"

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Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of the response time. This thesis proposes a detection algorithm that utilizes RNN in predicting sensor drifts from time-series data streams. The detection algorithm was later named; Predictive Sliding Detection Window (PSDW) and consisted of both forecasting and classification models. Three different RNN algorithms, i.e., LSTM, CNN-LSTM, and GRU, were designed to predict sensor drifts using forecasting and classification techniques. The algorithms were compared against each other in terms of relevant accuracy metrics for forecasting and classification. The operability of the solution was investigated by developing a web server that hosted the PSDW algorithm on an AWS computing instance. The resulting forecasting and classification algorithms were able to make reasonably accurate predictions for this particular scenario. More specifically, the forecasting algorithms acquired relatively low RMSE values as ~0.6, while the classification algorithms obtained an average F1-score and accuracy of ~80% but with a high standard deviation. However, the response time was ~5700% slower during the simulation of the HTTP requests. The obtained results suggest the need for future investigations to improve the accuracy of the models and experiment with other computing paradigms for more reliable deployments.
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Reiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.

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Brandt, Carl-Simon, Jonathan Kleivard, and Andreas Turesson. "Convolutional, adversarial and random forest-based DGA detection : Comparative study for DGA detection with different machine learning algorithms." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20103.

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Malware is becoming more intelligent as static methods for blocking communication with Command and Control (C&C) server are becoming obsolete. Domain Generation Algorithms (DGAs) are a common evasion technique that generates pseudo-random domain names to communicate with C&C servers in a difficult way to detect using handcrafted methods. Trying to detect DGAs by looking at the domain name is a broad and efficient approach to detect malware-infected hosts. This gives us the possibility of detecting a wider assortment of malware compared to other techniques, even without knowledge of the malware’s existence. Our study compared the effectiveness of three different machine learning classifiers: Convolutional Neural Network (CNN), Generative Adversarial Network (GAN) and Random Forest (RF) when recognizing patterns and identifying these pseudo-random domains. The result indicates that CNN differed significantly from GAN and RF. It achieved 97.46% accuracy in the final evaluation, while RF achieved 93.89% and GAN achieved 60.39%. In the future, network traffic (efficiency) could be a key component to examine, as productivity may be harmed if the networkis over burdened by domain identification using machine learning algorithms.
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El-Shafei, Ahmed. "Time multiplexing of cellular neural networks." Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365221.

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MOREIRA, André Luis Cavalcanti. "An adaptable storage slicing algorithm for content delivery networks." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17331.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-12T12:20:38Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Thesis - André Luis Cavalcanti Moreira.pdf: 3666881 bytes, checksum: 956e0e6be2bd9f076c0d30eea9d3ea25 (MD5)
Made available in DSpace on 2016-07-12T12:20:38Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Thesis - André Luis Cavalcanti Moreira.pdf: 3666881 bytes, checksum: 956e0e6be2bd9f076c0d30eea9d3ea25 (MD5) Previous issue date: 2015-08-28
Several works study the performance of Content Delivery Networks (CDNs) under various network infrastructure and demand conditions. Many strategies have been proposed to deal with aspects inherent to the CDN distribution model. Though mostly very effective, a traditional CDN approach of statically positioned elements often fails to meet quality of experience (QoE) requirements when network conditions suddenly change. CDN adaptation is a key feature in this process and some studies go even further and try to also deal with demand elasticity by providing an elastic infrastructure (cloud computing) to such CDNs. Each Content Provider (CP) gets served only the amount of storage space and network throughput that it needs and pays only for what has been used. Some IaaS providers offer simple CDN services on top of their infrastructure. However, in general, there is a lack of PaaS tools to create rapidly a CDN. There is no standard or open source software able to deliver CDN as a service for each tenant through well-known managers. A PaaS CDN should be able to implement content delivery service in a cloud environment, provision and orchestrate each tenant, monitor usage and make decisions on planning and dimensioning of resources. This work introduces a framework for the allocation of resources of a CDN in a multi-tenant environment. The framework is able to provision and orchestrate multi-tenant virtual CDNs and can be seen as a step towards a PaaS CDN. A simple dot product based module for network change detection is presented and a more elaborate multi-tenant resource manager model is defined. We solve the resulting ILP problem using both branch and bound as well as an efficient cache slicing algorithm that employs a three phase heuristic for orchestration of multi-tenant virtual CDNs. We finally show that a distributed algorithm with limited local information may be also offer reasonable resource allocation while using limited coordination among the different nodes. A self-organization behavior emerges when some of the nodes reach consensus.
Vários trabalhos estudam o desempenho de Redes de Distribuição de Conteúdo (CDN) em diferentes condições e demanda e de infraestrutura. Muitas estratégias têm sido propostas para lidar com aspectos inerentes ao modelo de distribuição de CDN. Embora essas técnicas sejam bastante eficazes, uma abordagem tradicional de elementos estaticamente posicionados numa CDN muitas vezes não consegue atender os requisitos de qualidade de experiência (QoE) quando as condições da rede mudam repentinamente. Adaptação CDN é uma característica fundamental neste processo e alguns estudos vão ainda mais longe e tentam lidar com a elasticidade da demanda, proporcionando uma infraestrutura elástica (computação em nuvem) para a CDN. Cada provedor de conteúdo obtém apenas a quantidade de armazenamento e de rede necessários, pagando apenas pelo efetivo uso. Alguns provedores IaaS oferecem serviços de CDN sobre suas estruturas. No entanto, em geral, não existe padrão ou softwares de código aberto capazes de entregar serviços de CDN por meio de gerenciadores. Uma CDN PaaS deve ser capaz de fornecer um serviço de entrega de conteúdo em um ambiente de nuvem, provisionar e orquestrar cada tenant, monitorar uso e tomar decisões de planejamento e dimensionamento de recursos. Este trabalho apresenta um framework para alocação de recursos de uma CDN em ambiente multi-tenant. O framework é capaz de provisionar e orquestrar CDNs virtuais e pode ser visto como um passo em direção a uma PaaS CDN. Um módulo baseado em simples produto escalar para detecção de mudanças na rede é apresentado, bem como um modelo mais elaborado de gerenciamento de recursos. Resolvemos o problema ILP resultante dessa abordagem por meio de um algoritmo de divisão de cache que emprega uma heurística em três fases para a orquestração de CDN virtuais. Por fim, mostramos uma outra abordagem com algoritmo distribuído que usa informação local e que também oferece uma alocação razoável usando coordenação limitada entre os diferentes nós. Um comportamento de auto-organização surge quando alguns desses nós chegam a um consenso.
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Yavaş, Gökhan. "Algorithms for Characterizing Structural Variation in Human Genome." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1279345476.

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Tamaki, Suguru. "Improved Algorithms for CNF Satisfiability Problems." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/68895.

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Wathugala, Wathugala Gamage Dulan Manujinda. "Formal Modeling Can Improve Smart Transportation Algorithm Development." Thesis, University of Oregon, 2017. http://hdl.handle.net/1794/22608.

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201 pages
Ensuring algorithms work accurately is crucial, especially when they drive safety critical systems like self-driving cars. We formally model a published distributed algorithm for autonomous vehicles to collaborate and pass thorough an intersection. Models are built and validated using the “Labelled Transition System Analyser” (LTSA). Our models reveal situations leading to deadlocks and crashes in the algorithm. We demonstrate two approaches to gain insight about a large and complex system without modeling the entire system: Modeling a sub system - If the sub system has issues, the super system too. Modeling a fast-forwarded state - Reveals problems that can arise later in a process. Some productivity tools developed for distributed system development are also presented. Manulator, our distributed system simulator, enables quick prototyping and debugging on a single workstation. LTSA-O, extension to LTSA, listens to messages exchanged in an execution of a distributed system and validates it against a model.
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Pallotti, Davide. "Integrazione di dati di disparità sparsi in algoritmi per la visione stereo basati su deep-learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16633/.

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La visione stereo consiste nell’estrarre informazioni di profondità da una scena a partire da una vista sinistra e una vista destra. Il problema si riduce a determinare punti corrispondenti nelle due immagini, che nel caso di immagini rettificate risultano traslati solo orizzontalmente, di una distanza detta disparità. Tra gli algoritmi stereo tradizionali spiccano SGM e la sua implementazione rSGM. SGM minimizza una funzione di costo definita su un volume dei costi, che misura la somiglianza degli intorni di potenziali punti omologhi per numerosi valori di disparità. L’abilità delle reti neurali convoluzionali (CNN) nello svolgere attività di percezione ha rivoluzionato l’approccio alla visione stereo. Un esempio di CNN stereo è GC-Net, adatta alla sperimentazione dato il numero contenuto di parametri. Anche GC-Net produce la mappa di disparità a partire da un volume dei costi, ottenuto combinando feature estratte dalle due viste. Obiettivo di questa tesi è integrare in un algoritmo stereo dati di disparità sparsi suggeriti dall’esterno, con l’intento di migliorare l’accuratezza. L’idea proposta è di utilizzare i dati noti associati a punti sparsi per modulare i valori corrispondenti a quegli stessi punti nel volume dei costi. Inizialmente sperimenteremo questo approccio su GC-Net. Dapprima faremo uso di disparità estratte casualmente dalla ground truth: ciò permetterà di verificare la bontà del metodo e simulerà l’impiego di un sensore di profondità a bassa risoluzione. Dopodiché impiegheremo gli output di SGM e rSGM, ancora campionati casualmente, chiedendoci se ciò risulti già in un primo miglioramento rispetto alla sola GC-Net. In seguito saggeremo l’applicabilità di questo stesso metodo a un algoritmo tradizionale, rSGM, utilizzando soltanto la ground truth come fonte di disparità. Infine riprenderemo l’idea di fornire a GC-Net l’aiuto di rSGM, ma sceglieremo solo i punti più promettenti rispetto a una misura di confidenza calcolata con la rete neurale CCNN.
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Essink, Wesley. "CNC milling toolpath generation using genetic algorithms." Thesis, University of Bath, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715245.

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The prevalence of digital manufacturing in creating increasingly complex products with small batch sizes, requires effective methods for production process planning. Toolpath generation is one of the challenges for manufacturing technologies that function based on the controlled movement of an end effector against a workpiece. The current approaches for determining suitable tool paths are highly dependent on machine structure, manufacturing technology and product geometry. This dependence can be very expensive in a volatile production environment where the products and the resources change quickly. In this research, a novel approach for the flexible generation of toolpaths using a mathematical formulation of the desired objective is proposed. The approach, based on optimisation techniques, is developed by discretising the product space into a number of grid points and determining the optimal sequence of the tool tip visiting these points. To demonstrate the effectiveness of the approach, the context of milling machining has been chosen and a genetic algorithm has been developed to solve the optimisation problem. The results show that with meta-heuristic methods, flexible tool paths can indeed be generated for industrially relevant parts using existing computational power. Future computing platforms, including quantum computers, could extend the applicability of the proposed approach to much more complex domains for instantaneous optimisation of the detailed manufacturing process plan.
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Books on the topic "CNN ALGORITHM"

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

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Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power.
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Munerman, Viktor, Vadim Borisov, and Aleksandra Kononova. Mass data processing. Algebraic models and methods. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1906037.

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The monograph is devoted to mathematical and algorithmic support of mass data processing based on algebraic models. One of the most common classes of mass processing is considered - processing of highly active structured data. The construction of algebraic models of data and calculations and methods of proving their correspondence are analyzed. Three algebraic systems are studied, which can be used both as data models and as models of calculations. The algebraic and axiomatic methods of proving the correspondence of these models are investigated. A proof of their correspondence is given: homomorphism and isomorphism. The problem of optimizing the processes of mass processing of data presented in the form of algebraic expressions in the proposed algebra models is raised. The algorithms of synthesis and optimization of calculation of these expressions, the method of symmetric horizontal data distribution providing parallel implementation of calculation of algebraic expressions and generalization of the block algorithm of parallel matrix multiplication for the case of multiplication of multidimensional matrices are described in detail. Architectures of software and hardware complexes for effective parallel implementation of operations in the considered algebra models are proposed. A number of real-world examples illustrating the application of the proposed methods are given. For students, postgraduates and teachers of technical and physical-mathematical universities and faculties.
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1944-, Webb William, ed. Cake-cutting algorithms: Be fair if you can. Natick, Mass: A.K. Peters, 1998.

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Dubanov, Aleksandr. Simulation of pursuit and parallel approach methods in pursuit problems. ru: Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02071-5.

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This monograph publishes a description of methods and algorithms for pursuit problems on surfaces. Simulation of tasks in the Mathcad programming environment was made. The development of digital technologies makes it possible to simulate a variety of problems from the theory of differential games. As a result of computer modeling, a lot of animation videos were obtained, which allow you to see the algorithmic solutions proposed by the author in pursuit problems. The monograph can be useful for students of technical universities, graduate students and developers of robotic systems with elements of artificial intelligence.
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Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed for undergraduate and graduate programs, studying information technology, for in-depth training in analysis and design of systems of complex structure. Also the guide can be useful to specialists of the IT sphere in the study of algorithmic aspects of graph theory.
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P, Banks Stephen. Can Perceptrons find Lyapunov functions?: An algorithmic approach to systems stability. Sheffield: University of Sheffield, Dept. of Control Engineering, 1989.

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Dapporto, Paolo, Paola Paoli, Patrizia Rossi, and Annalisa Guerri. The UTN program. Florence: Firenze University Press, 2001. http://dx.doi.org/10.36253/88-8453-032-6.

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We give an algorithm which goal is to find the energy barrier between a given pair of points in a graph which represents the conformational space of a molecule. If the conformational space is homeomorphic to an -dimensional torus, then the graph can be chosen of a particular form. The UTN software, which implements the algorithm in this case, is described in detail. Finally we focus on applications: to show how UTN works, some examples are carried on in detail, with the additional support of graphical animation1 in the twodimensional case. The source code of the program and some data of the examples are available to the reader.
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van Es, Karin, and Nanna Verhoeff. Situating Data. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463722971.

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Taking up the challenges of the datafication of culture, as well as of the scholarship of cultural inquiry itself, this collection contributes to the critical debate about data and algorithms. How can we understand the quality and significance of current socio-technical transformations that result from datafication and algorithmization? How can we explore the changing conditions and contours for living within such new and changing frameworks? How can, or should we, think and act within, but also in response to these conditions? This collection brings together various perspectives on the datafication and algorithmization of culture from debates and disciplines within the field of cultural inquiry, specifically (new) media studies, game studies, urban studies, screen studies, and gender and postcolonial studies. It proposes conceptual and methodological directions for exploring where, when, and how data and algorithms (re)shape cultural practices, create (in)justice, and (co)produce knowledge.
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Ulyanina, Olga, Azalia Zinatullina, and Elena Lyubka. Countering terrorism: psychological assistance to students and the formation of a safe type of personality. ru: Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02048-7.

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The manual describes a program of psychological support for students exposed to the ideology of terrorism or falling under the influence of this ideology. In this regard, the content of educational, psychodiagnostic, correctional and developmental stages of its implementation is revealed. The paper presents an algorithm for conducting psychological counseling with students and recommendations for parents on psychological support for children exposed to the ideology of terrorism. The practical tools described in the manual can be used in the framework of preventive and corrective work with participants in the educational process. The developed materials are addressed to education administrators, teachers, educational psychologists of educational organizations and parents.
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Cevelev, Aleksandr. Strategic development of railway transport logistics. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1194747.

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The monograph is devoted to the methodology of material and technical support of railway transport. According to the types of activities, the nature of the material and technical resources used, technologies, means and management systems, Russian railways belong to the category of high-tech industries that must have high quality and technical level, reliability and technological efficiency in operation. For this reason, the logistics system itself, both in structure and in the algorithm of the functions performed as a whole, needs a serious improvement in the quality of its work. The economic situation in Russia requires a revision of the principles and mechanisms of management based on the corporate model of supply chain management, focused on logistics knowledge. In the difficult economic conditions of the current decade, it is necessary to improve the quality of the supply organization of enterprises and structural divisions of railway transport, directly related to the implementation of the process approach, the advantage of which is a more detailed regulation of management actions and their mutual coordination. In order to increase the efficiency of its activities and develop the management system, Russian Railways is developing a lean production system aimed at further expanding the implementation of the principles of customer orientation, ideology and corporate culture. At the present time, the solution of many issues is impossible without a cybernetic approach to the formulation of problems of material and technical support and logistics analysis of information technologies, to the implementation of the developed algorithms and models of development strategies and concepts for improving the business processes of the production system. The management strategy, or the general plan for the implementation of activities for the management of material resources, is based on a fundamental assessment of the alignment and correlation of forces and factors operating in the economic and political field, taking into account the impact on the specific form of the management strategy. The materials will be useful to the heads and specialists of the directorates of the MTO, CDZs and can be used in the scientific research of bachelors, masters and postgraduates interested in the economics of railway transport and supply logistics.
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Book chapters on the topic "CNN ALGORITHM"

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Su, Te-Jen, Yi Hui, Chiao-Yu Chuang, and Wen-Pin Tsai. "MCSA-CNN Algorithm for Image Noise Cancellation." In Lecture Notes in Electrical Engineering, 209–20. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74935-8_15.

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Di, Lei, Hongzhong Ma, Yi Luo, and Zhiru Li. "A CNN-Based Information Network Attack Detection Algorithm." In Advances in Wireless Communications and Applications, 155–61. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3486-5_19.

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Wu, Jin, Lei Wang, and Yu Wang. "An Improved CNN-LSTM Model Compression Pruning Algorithm." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 727–36. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_75.

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Wan, Yanchen, Yu Liu, Yuan Li, and Puhong Zhang. "p-Faster R-CNN Algorithm for Food Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 132–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00916-8_13.

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Nandhini, S., R. Suganya, K. Nandhana, S. Varsha, S. Deivalakshmi, and Senthil Kumar Thangavel. "Automatic Detection of Leaf Disease Using CNN Algorithm." In Machine Learning for Predictive Analysis, 237–44. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_24.

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Vardhani, P. Ragha, Y. Indira Priyadarshini, and Y. Narasimhulu. "CNN Data Mining Algorithm for Detecting Credit Card Fraud." In Soft Computing and Medical Bioinformatics, 85–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0059-2_10.

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Wang, Ying, Aili Wang, and Changyu Hu. "A Novel Airplane Detection Algorithm Based on Deep CNN." In Communications in Computer and Information Science, 721–28. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2203-7_60.

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Yu, Wenjun, Sumi Kim, Fei Chen, and Jaeho Choi. "Pedestrian Detection Based on Improved Mask R-CNN Algorithm." In Advances in Intelligent Systems and Computing, 1515–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51156-2_176.

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Guo, Yuwen, Xin Zhang, Qi Yang, and Hong Guo. "A Novel Image Recognition Method Based on CNN Algorithm." In Lecture Notes in Computer Science, 281–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74717-6_29.

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Yan, XinQing, YuHan Yang, and GuiMing Lu. "A Target Detection Algorithm Based on Faster R-CNN." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 502–9. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69066-3_44.

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Conference papers on the topic "CNN ALGORITHM"

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Mody, Mihir, Chaitanya Ghone, Manu Mathew, and Jason Jones. "Efficient frequency domain CNN algorithm." In 2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2017. http://dx.doi.org/10.1109/icce-asia.2017.8307846.

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Velmurugadass, P., Ancha Rohith, Vamshi Krishna B, Harish M, and Bharath Reddy G. "Dentalcariesdetectionsystem Using R-CNN Algorithm." In 2023 4th International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2023. http://dx.doi.org/10.1109/iciem59379.2023.10165754.

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Wang, Yifeng, Yang Wang, Hongyi Li, Zhuoxi Cai, Xiaohan Tang, and Yin Yang. "CNN Hyperparameter Optimization Based on CNN Visualization and Perception Hash Algorithm." In 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2020. http://dx.doi.org/10.1109/dcabes50732.2020.00029.

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Gahiwad, Prasad, Nilesh Deshmane, Sachet Karnakar, Sujit Mali, and Rohini Pise. "Brain Stroke Detection Using CNN Algorithm." In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126125.

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Reiling, Anthony, William Mitchell, Stefan Westberg, Eric Balster, and Tarek Taha. "CNN Optimization with a Genetic Algorithm." In NAECON 2019 - IEEE National Aerospace and Electronics Conference. IEEE, 2019. http://dx.doi.org/10.1109/naecon46414.2019.9058307.

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Vitale, S., G. Ferraioli, and V. Pascazio. "Edge Preserving Cnn Sar Despeckling Algorithm." In 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). IEEE, 2020. http://dx.doi.org/10.1109/lagirs48042.2020.9165559.

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M, Nivethaa, Pavithra N, Priyanka, and Palaniappan Sambandam. "Speech Emotional Recognition Using CNN Algorithm." In 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021. http://dx.doi.org/10.1109/conecct52877.2021.9622714.

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Jagadeesh, Mandala, P. Chitra, K. Srilatha, M. Sumathi, and I. Rexiline Sheeba. "Brain Tumour Classification using CNN Algorithm." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9752096.

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Pesaru, Swetha, K. Sucharitha, R. Lahari, and P. Prakash. "Music Recommedation System Using CNN Algorithm." In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917811.

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Fauzi, Fauzi, Adhistya Erna Permanasari, and Noor Akhmad Setiawan. "Butterfly Image Classification Using Convolutional Neural Network (CNN)." In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA). IEEE, 2021. http://dx.doi.org/10.1109/icera53111.2021.9538686.

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Reports on the topic "CNN ALGORITHM"

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Baader, Franz, Jan Hladik, and Rafael Peñaloza. PSpace Automata with Blocking for Description Logics. Aachen University of Technology, 2006. http://dx.doi.org/10.25368/2022.157.

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In Description Logics (DLs), both tableau-based and automatabased algorithms are frequently used to show decidability and complexity results for basic inference problems such as satisfiability of concepts. Whereas tableau-based algorithms usually yield worst-case optimal algorithms in the case of PSpace-complete logics, it is often very hard to design optimal tableau-based algorithms for ExpTime-complete DLs. In contrast, the automata-based approach is usually well-suited to prove ExpTime upper-bounds, but its direct application will usually also yield an ExpTime-algorithm for a PSpace-complete logic since the (tree) automaton constructed for a given concept is usually exponentially large. In the present paper, we formulate conditions under which an on-the-fly construction of such an exponentially large automaton can be used to obtain a PSpace-algorithm. We illustrate the usefulness of this approach by proving a new PSpace upper-bound for satisfiability of concepts w.r.t. acyclic terminologies in the DL SI, which extends the basic DL ALC with transitive and inverse roles.
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Lewis, Dustin, ed. A Compilation of Materials Apparently Reflective of States’ Views on International Legal Issues pertaining to the Use of Algorithmic and Data-reliant Socio-technical Systems in Armed Conflict. Harvard Law School Program on International Law and Armed Conflict, December 2020. http://dx.doi.org/10.54813/cawz3627.

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This document is a compilation of materials that at least appear to be reflective of one or more states’ views on international legal issues pertaining to the actual or possible use of algorithmic and data-reliant socio-technical systems in armed conflict. In September of 2018, the Harvard Law School Program on International Law and Armed Conflict (HLS PILAC) commenced a project titled “International Legal and Policy Dimensions of War Algorithms: Enduring and Emerging Concerns.”[1] The project builds on the program’s earlier research and policy initiative on war-algorithm accountability. A goal of the current project is to help strengthen international debate and inform policymaking on the ways that artificial intelligence and complex computer algorithms are transforming war, as well as how international legal and policy frameworks already govern, and might further regulate, the design, development, and use of those technologies. The project is financially supported by the Ethics and Governance of Artificial Intelligence Fund. In creating this compilation, HLS PILAC seeks in part to provide a resource through which the positions of states with divergent positions on certain matters potentially of international public concern can be identified. Legal aspects of war technologies are more complex than some governments, scholars, and advocates allow. In the view of HLS PILAC, knowledge of the legal issues requires awareness of the multiple standpoints from which these arguments are fashioned. An assumption underlying how we approach these inquiries is that an assessment concerning international law in this area ought to take into account the perspectives of as many states (in addition to other relevant actors) as possible.
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Baader, Franz, Oliver Fernández Gil, and Barbara Morawska. Hybrid Unification in the Description Logic EL. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.197.

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Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. For the DL EL, which is used to define several large biomedical ontologies, unification is NP-complete. However, the unification algorithms for EL developed until recently could not deal with ontologies containing general concept inclusions (GCIs). In a series of recent papers we have made some progress towards addressing this problem, but the ontologies the developed unification algorithms can deal with need to satisfy a certain cycle restriction. In the present paper, we follow a different approach. Instead of restricting the input ontologies, we generalize the notion of unifiers to so-called hybrid unifiers. Whereas classical unifiers can be viewed as acyclic TBoxes, hybrid unifiers are cyclic TBoxes, which are interpreted together with the ontology of the input using a hybrid semantics that combines fixpoint and descriptive semantics. We show that hybrid unification in EL is NP-complete and introduce a goal-oriented algorithm for computing hybrid unifiers.
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Allende López, Marcos, Diego López, Sergio Cerón, Antonio Leal, Adrián Pareja, Marcelo Da Silva, Alejandro Pardo, et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003313.

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This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Cambridge Quantum Computing (CQC), and Tecnológico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH, will be no longer secure. Quantum computers will be able to break them within a short period of time. Similarly, Grover's algorithm concedes a quadratic advantage for mining blocks in certain consensus protocols such as proof of work. Today, there are hundreds of billions of dollars denominated in cryptocurrencies that rely on blockchain ledgers as well as the thousands of blockchain-based applications storing value in blockchain networks. Cryptocurrencies and blockchain-based applications require solutions that guarantee quantum resistance in order to preserve the integrity of data and assets in their public and immutable ledgers. We have designed and developed a layer-two solution to secure the exchange of information between blockchain nodes over the internet and introduced a second signature in transactions using post-quantum keys. Our versatile solution can be applied to any blockchain network. In our implementation, quantum entropy was provided via the IronBridge Platform from CQC and we used LACChain Besu as the blockchain network.
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Baader, Franz, Stefan Borgwardt, and Barbara Morawska. Unification in the Description Logic EL w.r.t. Cycle-Restricted TBoxes. Technische Universität Dresden, 2011. http://dx.doi.org/10.25368/2022.183.

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Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. The inexpressive Description Logic EL is of particular interest in this context since, on the one hand, several large biomedical ontologies are defined using EL. On the other hand, unification in EL has recently been shown to be NP-complete, and thus of significantly lower complexity than unification in other DLs of similarly restricted expressive power. However, the unification algorithms for EL developed so far cannot deal with general concept inclusion axioms (GCIs). This paper makes a considerable step towards addressing this problem, but the GCIs our new unification algorithm can deal with still need to satisfy a certain cycle restriction.
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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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Goldberg, L. A., P. D. MacKenzie, and D. S. Greenberg. Network congestion can be controlled: Routing algorithms in optical networks and Ethernets. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/565650.

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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Baader, Franz, Stefan Borgwardt, and Barbara Morawska. A Goal-Oriented Algorithm for Unification in ELHR+ w.r.t. Cycle-Restricted Ontologies. Technische Universität Dresden, 2012. http://dx.doi.org/10.25368/2022.189.

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Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. For the DL EL, which is used to define several large biomedical ontologies, unification is NP-complete. A goal-oriented NP unification algorithm for EL that uses nondeterministic rules to transform a given unification problem into solved form has recently been presented. In this report, we extend this goal-oriented algorithm in two directions: on the one hand, we add general concept inclusion axioms (GCIs), and on the other hand, we add role hierarchies (H) and transitive roles (R+). For the algorithm to be complete, however, the ontology consisting of the GCIs and role axioms needs to satisfy a certain cycle restriction.
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Horrocks, Ian, Ulrike Sattler, and Stephan Tobies. A Description Logic with Transitive and Converse Roles, Role Hierarchies and Qualifying Number Restrictions. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.94.

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As widely argued [HG97; Sat96], transitive roles play an important role in the adequate representation of aggregated objects: they allow these objects to be described by referring to their parts without specifying a level of decomposition. In [HG97], the Description Logic (DL) ALCHR+ is presented, which extends ALC with transitive roles and a role hierarchy. It is argued in [Sat98] that ALCHR+ is well-suited to the representation of aggregated objects in applications that require various part-whole relations to be distinguished, some of which are transitive. However, ALCHR+ allows neither the description of parts by means of the whole to which they belong, or vice versa. To overcome this limitation, we present the DL SHI which allows the use of, for example, has part as well as is part of. To achieve this, ALCHR+ was extended with inverse roles. It could be argued that, instead of defining yet another DL, one could make use of the results presented in [DL96] and use ALC extended with role expressions which include transitive closure and inverse operators. The reason for not proceeding like this is the fact that transitive roles can be implemented more efficiently than the transitive closure of roles (see [HG97]), although they lead to the same complexity class (ExpTime-hard) when added, together with role hierarchies, to ALC. Furthermore, it is still an open question whether the transitive closure of roles together with inverse roles necessitates the use of the cut rule [DM98], and this rule leads to an algorithm with very bad behaviour. We will present an algorithm for SHI without such a rule. Furthermore, we enrich the language with functional restrictions and, finally, with qualifying number restrictions. We give sound and complete decision proceduresfor the resulting logics that are derived from the initial algorithm for SHI. The structure of this report is as follows: In Section 2, we introduce the DL SI and present a tableaux algorithm for satisfiability (and subsumption) of SI-concepts—in another report [HST98] we prove that this algorithm can be refined to run in polynomial space. In Section 3 we add role hierarchies to SI and show how the algorithm can be modified to handle this extension appropriately. Please note that this logic, namely SHI, allows for the internalisation of general concept inclusion axioms, one of the most general form of terminological axioms. In Section 4 we augment SHI with functional restrictions and, using the so-called pairwise-blocking technique, the algorithm can be adapted to this extension as well. Finally, in Section 5, we show that standard techniques for handling qualifying number restrictions [HB91;BBH96] together with the techniques described in previous sections can be used to decide satisfiability and subsumption for SHIQ, namely ALC extended with transitive and inverse roles, role hierarchies, and qualifying number restrictions. Although Section 5 heavily depends on the previous sections, we have made it self-contained, i.e. it contains all necessary definitions and proofs from scratch, for a better readability. Building on the previous sections, Section 6 presents an algorithm that decides the satisfiability of SHIQ-ABoxes.
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