Academic literature on the topic 'Transformeur robuste'
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Journal articles on the topic "Transformeur robuste"
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Yang, Mingze, Hai Zhu, Runzhe Zhu, Fei Wu, Ling Yin, and Yuncheng Yang. "WiTransformer: A Novel Robust Gesture Recognition Sensing Model with WiFi." Sensors 23, no. 5 (February 27, 2023): 2612. http://dx.doi.org/10.3390/s23052612.
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The past decade has demonstrated the potential of human activity recognition (HAR) with WiFi signals owing to non-invasiveness and ubiquity. Previous research has largely concentrated on enhancing precision through sophisticated models. However, the complexity of recognition tasks has been largely neglected. Thus, the performance of the HAR system is markedly diminished when tasked with increasing complexities, such as a larger classification number, the confusion of similar actions, and signal distortion To address this issue, we eliminated conventional convolutional and recurrent backbones and proposed WiTransformer, a novel tactic based on pure Transformers. Nevertheless, Transformer-like models are typically suited to large-scale datasets as pretraining models, according to the experience of the Vision Transformer. Therefore, we adopted the Body-coordinate Velocity Profile, a cross-domain WiFi signal feature derived from the channel state information, to reduce the threshold of the Transformers. Based on this, we propose two modified transformer architectures, united spatiotemporal Transformer (UST) and separated spatiotemporal Transformer (SST) to realize WiFi-based human gesture recognition models with task robustness. SST intuitively extracts spatial and temporal data features using two encoders, respectively. By contrast, UST can extract the same three-dimensional features with only a one-dimensional encoder, owing to its well-designed structure. We evaluated SST and UST on four designed task datasets (TDSs) with varying task complexities. The experimental results demonstrate that UST has achieved recognition accuracy of 86.16% on the most complex task dataset TDSs-22, outperforming the other popular backbones. Simultaneously, the accuracy decreases by at most 3.18% when the task complexity increases from TDSs-6 to TDSs-22, which is 0.14–0.2 times that of others. However, as predicted and analyzed, SST fails because of excessive lack of inductive bias and the limited scale of the training data.
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Santamaria-Bonfil, Guillermo, Gustavo Arroyo-Figueroa, Miguel A. Zuniga-Garcia, Carlos Gustavo Azcarraga Ramos, and Ali Bassam. "Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches." Energies 17, no. 1 (December 22, 2023): 77. http://dx.doi.org/10.3390/en17010077.
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A key component for the performance, availability, and reliability of power grids is the power transformer. Although power transformers are very reliable assets, the early detection of incipient degradation mechanisms is very important to preventing failures that may shorten their residual life. In this work, a comparative analysis of standard machine learning (ML) algorithms (such as single and ensemble classification algorithms) and automatic machine learning (autoML) classifiers is presented for the fault diagnosis of power transformers. The goal of this research is to determine whether fully automated ML approaches are better or worse than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from dissolved gas analysis results. The methodology uses a transformer fault database (TDB) gathered from specialized databases and technical literature. Fault data were processed using the Duval pentagon diagnosis approach and user–expert knowledge. Parameters from both single and ensemble classifiers were optimized through standard machine learning procedures. The results showed that the best-suited algorithm to tackle the problem is a robust, automatic machine learning classifier model, followed by standard algorithms, such as neural networks and stacking ensembles. These results highlight the ability of a robust, automatic machine learning model to handle unbalanced power transformer fault datasets with high accuracy, requiring minimum tuning effort by electrical experts. We also emphasize that identifying the most probable transformer fault condition will reduce the time required to find and solve a fault.
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Wei, Jiangshu, Jinrong Chen, Yuchao Wang, Hao Luo, and Wujie Li. "Improved deep learning image classification algorithm based on Swin Transformer V2." PeerJ Computer Science 9 (October 30, 2023): e1665. http://dx.doi.org/10.7717/peerj-cs.1665.
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While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependencies. However, the self-attention mechanism used in Transformers lacks a local mechanism for information exchange within specific regions. This article attempts to leverage the strengths of both Transformers and convolutional neural networks (CNNs) to enhance the Swin Transformer V2 model. By incorporating both convolutional operation and self-attention mechanism, the enhanced model combines the local information-capturing capability of CNNs and the long-range dependency-capturing ability of Transformers. The improved model enhances the extraction of local information through the introduction of the Swin Transformer Stem, inverted residual feed-forward network, and Dual-Branch Downsampling structure. Subsequently, it models global dependencies using the improved self-attention mechanism. Additionally, downsampling is applied to the attention mechanism’s Q and K to reduce computational and memory overhead. Under identical training conditions, the proposed method significantly improves classification accuracy on multiple image classification datasets, showcasing more robust generalization capabilities.
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Ottele, Andy, and Rahmat Shoureshi. "Neural Network-Based Adaptive Monitoring System for Power Transformer." Journal of Dynamic Systems, Measurement, and Control 123, no. 3 (February 11, 1999): 512–17. http://dx.doi.org/10.1115/1.1387248.
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Power transformers are major elements of the electric power transmission and distribution infrastructure. Transformer failure has severe economical impacts from the utility industry and customers. This paper presents analysis, design, development, and experimental evaluation of a robust failure diagnostic technique. Hopfield neural networks are used to identify variations in physical parameters of the system in a systematic way, and adapt the transformer model based on the state of the system. In addition, the Hopfield network is used to design an observer which provides accurate estimates of the internal states of the transformer that can not be accessed or measured during operation. Analytical and experimental results of this adaptive observer for power transformer diagnostics are presented.
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Sai, K. N., A. Galodha, P. Jain, and D. Sharma. "DEEP AND MACHINE LEARNING FOR MONITORING GROUNDWATER STORAGE BASINS AND HYDROLOGICAL CHANGES USING THE GRAVITY RECOVERY AND CLIMATE EXPERIMENT (GRACE) SATELLITE MISSION AND SENTINEL-1 DATA FOR THE GANGA RIVER BASIN IN THE INDIAN REGION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 1265–70. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1265-2023.
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Abstract. Accurate estimation of groundwater levels in river basins is paramount for hydro-geological research and sustainable water resource management. In this paper, we introduce a deep learning framework explicitly developed for precise groundwater level estimation in the Ganga River Basin. Leveraging the combined band information of Sentinel-1 synthetic aperture radar (SAR) and GRACE satellite data, our approach capitalizes on the trans-formative capabilities of Vision Transformers (ViT) and their variants, with a particular focus on Swin-Transformer variant enriched with Normalization Attention Modules (NAMs).To address the unique challenges of the Ganga River Basin, we curated a comprehensive dataset, forming a robust foundation for training computer vision models tailored to this distinct geographical region. Through rigorous experiments, our state-of-the-art Vision Transformers demonstrated significant potential in groundwater level estimation, with the Swin-Transformer NAM-based model achieving an outstanding Mean Absolute Error (MAE) of 1.2. These remarkable results surpass conventional methodologies and underscore the substantial advancements achieved through advanced transformer-based architectures in this domain. Moreover, this research contributes a robust dataset for future endeavours, fostering further advancements in groundwater estimation and related fields. This study represents a substantial step towards advancing sustainable groundwater utilization practices in the Ganga River Basin and beyond.
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Remigius Obinna Okeke, Akan Ime Ibokette, Onuh Matthew Ijiga, Enyejo, Lawrence Anebi, Godslove Isenyo Ebiega, and Odeyemi Michael Olumubo. "THE RELIABILITY ASSESSMENT OF POWER TRANSFORMERS." Engineering Science & Technology Journal 5, no. 4 (April 3, 2024): 1149–72. http://dx.doi.org/10.51594/estj.v5i4.981.
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This research investigated the assessment of power transformer reliability with emphasis on the transmission network within Rivers State in Nigeria, focusing on the perspectives of electricity consumers, organizational personnel, and business operators using descriptive survey. The study encompassed the entire population of 725,372 electricity consumers in the Port Harcourt Electricity Distribution Company (PEDC) in Rivers State, which included both households and business owners. To select a representative sample, the Convenience Sampling Technique was employed, resulting in the inclusion of 390 electricity consumers in Rivers State. Data collection utilized the Consumers Perception of Electricity Power Transformer Reliability (COPEPT) questionnaire and Structured Interview, with the instrument's reliability established through the test-retest technique, yielding a reliability coefficient for each investigation. Research questions were addressed through the weighted mean score (WMS) analysis. Key findings indicated dissatisfaction among electricity consumers in Rivers State, primarily attributed to factors such as transformer age, overall condition, uncontrolled overloading, adverse weather conditions, and inadequate transformer capacity to meet increasing demand. Addressing these issues, different approaches including upgrading or replacing outdated transformers, implementing limits on transformer loading, introducing robust earthing systems, and increasing transformer capacity, were recommended to enhance consumer satisfaction and overall reliability. The study further revealed that persistent power transformer failures resulted in power outages, adversely impacting businesses, households, communication, and contributing to reduced production and national income. Based on these findings, recommendations in the form of strategies were provided, emphasizing a comprehensive analysis of power surge control during adverse weather conditions, a plan for upgrading or replacing outdated transformers, an assessment of power transformer capacity needs, and collaboration with relevant stakeholders to develop strategies mitigating the negative economic impact and enhancing communication with consumers.
Keywords: Reliability Assessment, Power Transformers, Rivers State and Transmission Network.
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Paul, Sayak, and Pin-Yu Chen. "Vision Transformers Are Robust Learners." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2071–81. http://dx.doi.org/10.1609/aaai.v36i2.20103.
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Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy. What remains largely unexplored is their robustness evaluation and attribution. In this work, we study the robustness of the Vision Transformer (ViT) (Dosovitskiy et al. 2021) against common corruptions and perturbations, distribution shifts, and natural adversarial examples. We use six different diverse ImageNet datasets concerning robust classification to conduct a comprehensive performance comparison of ViT(Dosovitskiy et al. 2021) models and SOTA convolutional neural networks (CNNs), Big-Transfer (Kolesnikov et al. 2020). Through a series of six systematically designed experiments, we then present analyses that provide both quantitative andqualitative indications to explain why ViTs are indeed more robust learners. For example, with fewer parameters and similar dataset and pre-training combinations, ViT gives a top-1accuracy of 28.10% on ImageNet-A which is 4.3x higher than a comparable variant of BiT. Our analyses on image masking, Fourier spectrum sensitivity, and spread on discrete cosine energy spectrum reveal intriguing properties of ViT attributing to improved robustness. Code for reproducing our experiments is available at https://git.io/J3VO0.
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Jancarczyk, Daniel, Marcin Bernaś, and Tomasz Boczar. "Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods." Sensors 19, no. 22 (November 10, 2019): 4909. http://dx.doi.org/10.3390/s19224909.
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This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification.
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Cortés-Caicedo, Brandon, Oscar Danilo Montoya, and Andrés Arias-Londoño. "Application of the Hurricane Optimization Algorithm to Estimate Parameters in Single-Phase Transformers Considering Voltage and Current Measures." Computers 11, no. 4 (April 11, 2022): 55. http://dx.doi.org/10.3390/computers11040055.
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In this research paper, a combinatorial optimization approach is proposed for parameter estimation in single-phase transformers considering voltage and current measurements at the transformer terminals. This problem is represented through a nonlinear programming model (NLP), whose objective is to minimize the root mean square error between the measured voltage and current values and the calculated values from the equivalent model of the single-phase transformer. These values of voltage and current can be determined by applying Kirchhoff’s Laws to the model T of the transformer, where its parameters, series resistance and reactance as well as the magnetization resistance and reactance, i.e., R1, R2′, X1, X2′, Rc y Xm, are provided by the Hurricane Optimization Algorithm (HOA). The numerical results in the 4 kVA, 10 kVA and 15 kVA single-phase test transformers demonstrate the applicability of the proposed method since it allows the reduction of the average error between the measured and calculated electrical variables by 1000% compared to the methods reported in the specialized literature. This ensures that the parameters estimated by the proposed methodology, in each test transformer, are close to the real value with an accuracy error of less than 6%. Additionally, the computation times required by the algorithm to find the optimal solution are less than 1 second, which makes the proposed HOA robust, reliable, and efficient. All simulations were performed in the MATLAB programming environment.
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Xie, Fei, Dalong Zhang, and Chengming Liu. "Global–Local Self-Attention Based Transformer for Speaker Verification." Applied Sciences 12, no. 19 (October 10, 2022): 10154. http://dx.doi.org/10.3390/app121910154.
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Transformer models are now widely used for speech processing tasks due to their powerful sequence modeling capabilities. Previous work determined an efficient way to model speaker embeddings using the Transformer model by combining transformers with convolutional networks. However, traditional global self-attention mechanisms lack the ability to capture local information. To alleviate these problems, we proposed a novel global–local self-attention mechanism. Instead of using local or global multi-head attention alone, this method performs local and global attention in parallel in two parallel groups to enhance local modeling and reduce computational cost. To better handle local location information, we introduced locally enhanced location encoding in the speaker verification task. The experimental results of the VoxCeleb1 test set and the VoxCeleb2 dev set demonstrated the improved effect of our proposed global–local self-attention mechanism. Compared with the Transformer-based Robust Embedding Extractor Baseline System, the proposed speaker Transformer network exhibited better performance in the speaker verification task.
Dissertations / Theses on the topic "Transformeur robuste"
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Douzon, Thibault. "Language models for document understanding." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0075.
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Chaque jour, les entreprises du monde entier reçoivent et traitent d'énormes volumes de documents, entraînant des coûts considérables. Pour réduire ces coûts, de grandes entreprises automatisent le traitement documentaire, visant une automatisation complète. Cette thèse se concentre sur l'utilisation de modèles d'apprentissage machine pour extraire des informations de documents. Les progrès récents en matière d'architecture de modèle, en particulier les transformeurs, ont révolutionné le domaine grâce à leur utilisation généralisée de l'attention et à l'amélioration des pré-entraînements auto-supervisés. Nous montrons que les transformeurs, pré-entraînés sur des documents, effectuent des tâches de compréhension de documents avec précision et surpassent les modèles à base de réseaux récurrents pour l'extraction d'informations par classification de mots. Les transformeurs nécessitent également moins de données d'entraînement pour atteindre des performances élevées, soulignant l'importance du pré-entraînement auto-supervisé. Dans la suite, nous introduisons des tâches de pré-entraînement spécifiquement adaptées aux documents d'entreprise, améliorant les performances même avec des modèles plus petits. Cela permet d'atteindre des niveaux de performance similaires à ceux de modèles plus gros, ouvrant la voie à des modèles plus petits et plus économiques. Enfin, nous abordons le défi du coût d'évaluation des transformeurs sur de longues séquences. Nous montrons que des architectures plus efficaces dérivées des transformeurs nécessitent moins de ressources et donnent de meilleurs résultats sur de longues séquences. Cependant, elles peuvent perdre légèrement en performance sur de courtes séquences par rapport aux transformeurs classiques. Cela suggère l'avantage d'utiliser plusieurs modèles en fonction de la longueur des séquences à traiter, ouvrant la possibilité de concaténer des séquences de différentes modalitésEvery day, an uncountable amount of documents are received and processed by companies worldwide. In an effort to reduce the cost of processing each document, the largest companies have resorted to document automation technologies. In an ideal world, a document can be automatically processed without any human intervention: its content is read, and information is extracted and forwarded to the relevant service. The state-of-the-art techniques have quickly evolved in the last decades, from rule-based algorithms to statistical models. This thesis focuses on machine learning models for document information extraction. Recent advances in model architecture for natural language processing have shown the importance of the attention mechanism. Transformers have revolutionized the field by generalizing the use of attention and by pushing self-supervised pre-training to the next level. In the first part, we confirm that transformers with appropriate pre-training were able to perform document understanding tasks with high performance. We show that, when used as a token classifier for information extraction, transformers are able to exceptionally efficiently learn the task compared to recurrent networks. Transformers only need a small proportion of the training data to reach close to maximum performance. This highlights the importance of self-supervised pre-training for future fine-tuning. In the following part, we design specialized pre-training tasks, to better prepare the model for specific data distributions such as business documents. By acknowledging the specificities of business documents such as their table structure and their over-representation of numeric figures, we are able to target specific skills useful for the model in its future tasks. We show that those new tasks improve the model's downstream performances, even with small models. Using this pre-training approach, we are able to reach the performances of significantly bigger models without any additional cost during finetuning or inference. Finally, in the last part, we address one drawback of the transformer architecture which is its computational cost when used on long sequences. We show that efficient architectures derived from the classic transformer require fewer resources and perform better on long sequences. However, due to how they approximate the attention computation, efficient models suffer from a small but significant performance drop on short sequences compared to classical architectures. This incentivizes the use of different models depending on the input length and enables concatenating multimodal inputs into a single sequence
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Sanchez, Sébastien. "Contribution à la conception de coupleurs magnétiques robustes pour convertisseurs multicellulaires parallèles." Phd thesis, Toulouse, INPT, 2015. http://oatao.univ-toulouse.fr/14499/1/sanchez_partie_1_sur_2_2.pdf.
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Face aux enjeux énergétiques actuels, l’électronique de puissance est un domaine de recherche de premier plan. La relative fragilité des composants présents dans les chaînes de conversion implique néanmoins de devoir prendre en compte la gestion des défaillances dès la phase de conception. La défaillance d'un composant est une situation hautement critique tant sur le plan de la sécurité environnante que sur le plan de l'indisponibilité du système qui en découle. Cette problématique de sûreté de fonctionnement constitue la ligne directive de ce travail de thèse, visant à imaginer, et concevoir des solutions permettant de sécuriser ces structures en présence de défauts. Dans cette thèse, nous étudions de nouveaux composants magnétiques offrant un excellent compromis entre la densité de puissance traitée et rendement de conversion, mais très sensibles à toutes perturbations électriques. Une méthode de pré-dimensionnement des composants magnétiques a été développée et des solutions ont été apportés pour sécuriser et maintenir le fonctionnement de la chaîne de conversion suite à l’apparition de plusieurs défauts.
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Athanasius, Germane Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "Robust decentralised output feedback control of interconnected grid system." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/39591.
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The novel contribution of the thesis is the design and implementation of decentralised output feedback power system controllers for power oscillation damping (POD) over the entire operating regime of the power system. The POD controllers are designed for the linearised models of the nonlinear power system dynamics. The linearised models are combined and treated as parameter varying switched systems. The thesis contains novel results for the controller design, bumpless switching and stability analysis of such switched systems. Use of switched controllers against the present trend of having single controller helps to reduce the conservatism and to increase the uncertainty handling capability of the power system controller design. Minimax-LQG control design method is used for the controller design. Minimax-LQG control combines the advantages of both LQG and H control methods with respect to robustness and the inclusion of uncertainty and noise in the controller design. Also, minimax-LQG control allows the use of multiple integral quadratic constraints to bound the different types of uncertainties in the power system application. During switching between controllers, switching stability of the system is guaranteed by constraining the minimum time between two consecutive switchings. An expression is developed to compute the minimum time required between switchings including the effect of jumps in the states. Bumpless switching scheme is used to minimise the switching transients which occur when the controllers are switched. Another contribution of the thesis is to include the effect of on load tap changing transformers in the power system controller design. A simplified power system model linking generator and tap changing transformer dynamics is developed for this purpose and included in the controller design. The performance of the proposed linear controllers are validated by nonlinear computer simulations and through real time digital simulations. The designed controllers improve power system damping and provide uniform performance over the entire operating regime of the generator.
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Swain, Sushree Diptimayee. "Design and Experimental Realization of Robust and Adaptive Control Schemes for Hybrid Series Active Power Filter." Thesis, 2017. http://ethesis.nitrkl.ac.in/9369/1/2017_PhD_SDSwain_512EE109.pdf.
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The degradation in power quality causes adverse economical impact on the utilities and customers. Harmonics in current and voltage are one of the most commonly known power quality issues and are solved by employing passive power filter. But these filters are designed to provide a low impedance path for harmonics and maintaining good power quality with an simplest design and low cost. However PPFs have some drawbacks such as its performance is not better for transient condition of load. So for high-quality power requirements, numerous topologies of active filters i.e. APF connected in series or in parallel (series active filters and shunt active filters) to the non-linear loads with the aim of improving voltage or current distortion. These filters are the most widely used solution, as they efficiently eliminate current distortion and the reactive power produced by non-linear loads. But they are generally expensive and have high operating losses. Henceforth to overcome these drawbacks and to improve the compensation performance with reduced cost of the APFs, a novel HSAPF is introduced by Peng et al., in which APF is connected in series with the source as well as non-linear load and PPF connected in parallel with the load, which behave as power factor correction capacitor. This topology attracted much more attention to endure high load currents and work as a harmonic isolator between source and non-linear load.
Selection of an appropriate reference voltage extraction approach plays an impor- tant role in HSAPF performance. This thesis analyzes three time-domain approaches namely p-q, dual-p-q and HSRF schemes along with one switching pattern technique based on CBPWM method. The objective is to bring down the THD of source current below 5%, to fulfill the condition of IEEE-519 standard recommendations on harmonic limits. The comparative investigation proves that, HSRF is the best reference gener- ation approach in HSAPF irrespective of various grid perturbation conditions. The efficacy of the proposed HSRF-CBPWM based HSAPF are validated through simula-tion as well as experimentation.
Subsequently, Two voltage control techniques namely, CSVPWM and DSVPWM are proposed in HSAPF system along with a fast reference generation approach based on the proposed HSRF method. This CSVPWM and DSVPWM based HSAPF system is employed for compensation of both harmonic voltage as well as current simultane- ously. The efficacy of the proposed HSRF-CSVPWM and HSRF-DSVPWM based HSAPF system has been verified through MATLAB/Simulink power system block en- vironment and also validated by experimentation through dSPACE 1103 controller board. Moreover, to study the performance of DSVPWM over CSVPWM, a com- parative analysis has been performed using both steady state as well as transient conditions. It is observed from the result that the HSRF-DSVPWM based HSAPF is more efficient than HSRF-CBPWM and HSRF-CSVPWM based HSAPF in terms of harmonic compensation and relative power compensation under different operating condition of load.
Subsequently, one robust control approach namely sliding mode control-2 (SMC2) is proposed in HSAPF along with one adaptive DC-link voltage control (ADCV) scheme and one improved reference generation approach based on HSRF. The con- trol scheme is designed with the purpose to achieve gain stability, perfect tracking and distortion free current and load voltage. From the simulation results, it is observed that they are not only adaptive and robust against parametric variation condition of load, but also gives satisfactory THD as well as HCR factor in HSAPF. An experimen-tal laboratory prototype has been developed with dSPACE 1103 controlling platform to verify their adaptivity and robustness. It is observed from the simulation and ex- perimentation that the proposed HSRF-SMC2-ADCV based HSAPF system is found to be more adaptive, robust as compared to the HSRF-SMC2 based HSAPF system. Therefore, the superiority of proposed HSRF-SMC2-ADCV is proved among all the proposed control approaches of HSAPF system.
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Pradhan, Prangya Parimita. "Robust Control Schemes for a Doubly Fed Induction Generator based Wind Energy Conversion System." Thesis, 2022. http://ethesis.nitrkl.ac.in/10344/1/2022_PhD_PPPradhan_514EE1009_Robust.pdf.
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Due to many advantages, such as variable speed operation, low noise, high torque, and ease of maintenance, the Doubly Fed Induction Generator (DFIG) is extensively employed in a Wind Energy Conversion System (WECS). To control a DFIG-based WECS, active power extraction from the wind must be regulated while reactive power must be kept at zero in order to ensure unity power factor operation. A number of control algorithms for controlling the active and reactive power of WECS have been proposed in the past. A WECS is encountered with several parametric uncertainties and external disturbances. Thus, it is essential to tackle the impact of parametric uncertainties and disturbances in the WECS characteristics by suitable design and implementation of appropriate robust control algorithms to achieve good performance. Controlling the active and reactive power of WECS has been the subject of a lot of research. Parametric uncertainties, on the other hand, have a substantial impact on the performance of active and reactive power regulation in a WECS. As a result, developing appropriate controllers for managing the active and reactive power of the WECS in the presence of parametric uncertainties and disturbances is regarded as a challenging control problem. The purpose of this dissertation is to develop robust control algorithms for a DFIG-based grid-connected WECS that can control both active and reactive power in the presence of parametric uncertainties and disturbances. As the active and reactive power of a DFIG are dependent, it becomes necessary to design suitable controller such as that the active and reactive power can be regulated separately by decoupling the active and reactive power control loops. The thesis starts with the development of a Proportional-Integral (PI) controller and Sliding Mode Controller (SMC) to control the active and reactive power of DFIG-based WECS that are delivered to the grid. The performance of PI and SMC controllers are evaluated under nominal conditions. An Autoregressive Moving Average with Exogenous (ARMAX) model is developed for DFIG-based WECS. This ARMAX model of the WECS is used to design a Model Predictive Control (MPC). By using the input-output values from previous sampling instants over a time horizon, the MPC predicts the system’s future output. The computing time, on the other hand, is a substantial barrier to MPC implementation. As a result, a variety of approaches have been used to lessen MPC’s computational load. To lessen the time complexity of the MPC problem, usually optimal solutions are adopted. A Linear Matrix Inequality (LMI) approach is used to reduce the computation time caused by the MPC. The optimization problem is solved using the LMI. PI is an excellent choice in the majority of industrial applications among all classical and current control methods. Because WECS has uncertainties due to intermittency in the wind speed various suitable feedback control mechanisms are necessary to address these concerns. An Extended State Observer (ESO) successfully estimates the unknown dynamics and disturbance. The rotor resistance and mutual inductance of the DFIG are modified to evaluate the robustness of the proposed MADRC. Peak overshoot and settling duration of the active power response are studied as a function of the aforementioned parameters. In the face of parametric uncertainty, the developed controller is found effective in managing the active and reactive power of DFIG, as well as rejecting external disturbance in desired value tracking. The proposed MADRC successfully handles parametric variation for set point tracking of active and reactive power of WECS, according to simulation and experimentation results. To improve the tracking accuracy of a DFIG-based WECS an online optimization approach based on wavelet neural networks for parameter adjustment of Active Disturbance Rejection Control (ADRC).
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Deng, Yu-Shiu, and 鄧宇修. "A Fast and Robust Local Descriptor Using Features in The Intensity and Transformed Domains." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/38514468834376849455.
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碩士國立中興大學
資訊科學與工程學系
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Feature point matching is to find out the point correspondences between two images of the same scene or object, and this task is a vital part in many images processing technique, such as image matching, object recognition, and other vision-based application. However, there often exist some different kinds of transformation between two images, and will cause bad matching result. To solve the problem, the best way is to construct local descriptor by extracting robust and invariant local feature from interest region, and that will bring out better matching result. SIFT is the most robust local descriptor and has been widely used in many application, but since it is a high-dimensional local descriptor and is complex on feature extracting, the main disadvantage of SIFT is very time-consuming. In order to construct a local descriptor with efficient computation and good matching performance, we refer to Contrast Context Histogram(CCH) which has good matching performance with fast computation and low-frequency DCT coefficients, as well as it keeps important information of an image, and then we proposed a fast and robust local descriptor using features in combining intensity and transformed domains in our study. In experimental results, we can observe that proposed local descriptor has good matching performance under different kinds of transformation. Compared with other local descriptors with good matching performance, proposed local descriptor is much faster on features extracting and has lower dimension, so it has more potential to be used in real-time applications.
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"Robust Control of Wide Bandgap Power Electronics Device Enabled Smart Grid." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.46215.
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abstract: In recent years, wide bandgap (WBG) devices enable power converters with higher power density and higher efficiency. On the other hand, smart grid technologies are getting mature due to new battery technology and computer technology. In the near future, the two technologies will form the next generation of smart grid enabled by WBG devices. This dissertation deals with two applications: silicon carbide (SiC) device used for medium voltage level interface (7.2 kV to 240 V) and gallium nitride (GaN) device used for low voltage level interface (240 V/120 V). A 20 kW solid state transformer (SST) is designed with 6 kHz switching frequency SiC rectifier. Then three robust control design methods are proposed for each of its smart grid operation modes. In grid connected mode, a new LCL filter design method is proposed considering grid voltage THD, grid current THD and current regulation loop robust stability with respect to the grid impedance change. In grid islanded mode, µ synthesis method combined with variable structure control is used to design a robust controller for grid voltage regulation. For grid emergency mode, multivariable controller designed using H infinity synthesis method is proposed for accurate power sharing. Controller-hardware-in-the-loop (CHIL) testbed considering 7-SST system is setup with Real Time Digital Simulator (RTDS). The real TMS320F28335 DSP and Spartan 6 FPGA control board is used to interface a switching model SST in RTDS. And the proposed control methods are tested. For low voltage level application, a 3.3 kW smart grid hardware is built with 3 GaN inverters. The inverters are designed with the GaN device characterized using the proposed multi-function double pulse tester. The inverter is controlled by onboard TMS320F28379D dual core DSP with 200 kHz sampling frequency. Each inverter is tested to process 2.2 kW power with overall efficiency of 96.5 % at room temperature. The smart grid monitor system and fault interrupt devices (FID) based on Arduino Mega2560 are built and tested. The smart grid cooperates with GaN inverters through CAN bus communication. At last, the three GaN inverters smart grid achieved the function of grid connected to islanded mode smooth transitionDissertation/Thesis
Doctoral Dissertation Electrical Engineering 2017
Books on the topic "Transformeur robuste"
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Helfont, Samuel. A Transformed Religious Landscape. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190843311.003.0009.
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This chapter begins by re-evaluating state–society relations in Iraq during the 1990s and early 2000s. It argues, along with other archival-based works, that the regime was much more robust than the literature on Iraq had suggested during this period. Then the chapter discusses a similar dynamic with regard to the relationship between religion and state. The regime had much more control over the religious landscape in the 1990s than has been stated in previous works on Iraq, but creating the regime’s system was a long, arduous process, carried out by countless officials, to co-opt, coerce, and create a religious landscape that would be capable of contributing to the Ba‘thists’ political goals. When it politically instrumentalized its view on Islam during a Faith Campaign that Saddam launched in 1993, it did so from a position of strength rather than weakness.
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Rid, Thomas, and Marc Hecker. War 2.0. Praeger Security International, 2009. http://dx.doi.org/10.5040/9798216033455.
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War 2.0: Irregular Warfare in the Information Age argues that two intimately connected grassroots trends—the rise of insurgencies and the rise of the web—are putting modern armies under huge pressure to adapt new forms of counterinsurgency to new forms of social war. After the U.S. military—transformed into a lean, lethal, computerized force—faltered in Iraq after 2003, a robust insurgency arose. Counterinsurgency became a social form of war—indeed, the U.S. Army calls it "armed social work"—in which the local population was the center of gravity and public opinion at home the critical vulnerability. War 2.0 traces the contrasting ways in which insurgents and counterinsurgents have adapted irregular conflict to novel media platforms. It examines the public affairs policies of the U.S. land forces, the British Army, and the Israel Defense Forces. Then, it compares the media-related counterinsurgency methods of these conventional armies with the methods devised by their irregular adversaries, showing how such organizations as al-Qaeda, the Taliban, and Hezbollah use the web, not merely to advertise their political agenda and influence public opinion, but to mobilize a following and put violent ideas into action.
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Pearson, David. Rebel Music in the Triumphant Empire. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197534885.001.0001.
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At the dawn of the 1990s, as the United States celebrated its victory in the Cold War and sole superpower status by waging war on Iraq and proclaiming democratic capitalism as the best possible society, the 1990s underground punk renaissance transformed the punk scene into a site of radical opposition to American empire. Nazi skinheads were ejected from the punk scene; apathetic attitudes were challenged; women, Latino, and LGBTQ participants asserted their identities and perspectives within punk; the scene debated the virtues of maintaining DIY purity versus venturing into the musical mainstream; and punks participated in protest movements from animal rights to stopping the execution of Mumia Abu-Jamal to shutting down the 1999 World Trade Organization meeting. Punk lyrics offered strident critiques of American empire, from its exploitation of the Third World to its warped social relations. Numerous subgenres of punk proliferated to deliver this critique, such as the blazing hardcore punk of bands like Los Crudos, propagandistic crust-punk/dis-core; grindcore and power violence with tempos over 800 BPM, and So-Cal punk with its combination of melody and hardcore. Musical analysis of each of these styles and the expressive efficacy of numerous bands reveals that punk is not merely simplistic three-chord rock music, but a genre that is constantly revolutionizing itself in which nuances of guitar riffs, vocal timbres, drum beats, and song structures are deeply meaningful to its audience, as corroborated by the robust discourse in punk zines.
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Woźniak, Monika, and Maria Wyke, eds. The Novel of Neronian Rome and its Multimedial Transformations. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198867531.001.0001.
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When in 1905 the Polish writer Henryk Sienkiewicz was awarded the Nobel Prize for Literature ‘for outstanding services as an epic writer’, it was his novel Quo vadis. A Narrative of the Time of Nero that motivated the committee to bestow this notable honour. The extraordinary international success of Quo vadis catapulted the author into literary stardom, placing him at the top of international league tables for the sheer quantity of his readers. But, before long, the historical novel began to detach itself from the person of its author and to become a multimedial, mass–culture phenomenon. In the West and East, Quo vadis was adapted for the stage and screen, provided the inspiration for works of music and other genres of literature, was transformed into comic strips and illustrated children’s books, and was cited in advertising and referenced in everyday objects of material culture. No work in English to date has explored in depth the mechanisms that released Quo vadis into mass circulation and the influence that its diverse spin-off forms exercised on other areas of culture—even on the reception and interpretation of the literary text itself. In the context of a robust scholarly interest in the processes of literary adaptation and classical reception, and set alongside the recent emergence of interest in the ‘Ben-Hur tradition’, this volume provides a coherent forum for a much-needed exploration, from various disciplinary and national perspectives, of the multimedial transformations of Quo vadis. Uniquely, also, for its English-speaking readers this collection of essays renders more visible the cultural conquests achieved by Poland on the world map of classical reception.
Book chapters on the topic "Transformeur robuste"
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Barnes, John R. "Inductors and Transformers." In Robust Electronic Design Reference Book, 126–55. New York, NY: Springer US, 2004. http://dx.doi.org/10.1007/1-4020-7830-7_9.
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Liao, Brian Hsuan-Cheng, Chih-Hong Cheng, Hasan Esen, and Alois Knoll. "Are Transformers More Robust? Towards Exact Robustness Verification for Transformers." In Lecture Notes in Computer Science, 89–103. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40923-3_8.
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Gu, Jindong, Volker Tresp, and Yao Qin. "Are Vision Transformers Robust to Patch Perturbations?" In Lecture Notes in Computer Science, 404–21. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_24.
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Wang, Libo, Si Chen, Zhen Wang, Da-Han Wang, and Shunzhi Zhu. "Graph Attention Transformer Network for Robust Visual Tracking." In Communications in Computer and Information Science, 165–76. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1639-9_14.
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Zhang, Yaning, Tianyi Wang, Minglei Shu, and Yinglong Wang. "A Robust Lightweight Deepfake Detection Network Using Transformers." In Lecture Notes in Computer Science, 275–88. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20862-1_20.
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Mvula, Paul K., Paula Branco, Guy-Vincent Jourdan, and Herna L. Viktor. "HEART: Heterogeneous Log Anomaly Detection Using Robust Transformers." In Discovery Science, 673–87. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_45.
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Peiris, Himashi, Munawar Hayat, Zhaolin Chen, Gary Egan, and Mehrtash Harandi. "A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation." In Lecture Notes in Computer Science, 162–72. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16443-9_16.
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Najari, Naji, Samuel Berlemont, Grégoire Lefebvre, Stefan Duffner, and Christophe Garcia. "RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection." In Advanced Analytics and Learning on Temporal Data, 66–82. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24378-3_5.
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Pande, Jay, Wookhee Min, Randall D. Spain, Jason D. Saville, and James Lester. "Robust Team Communication Analytics with Transformer-Based Dialogue Modeling." In Lecture Notes in Computer Science, 639–50. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36272-9_52.
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Almalik, Faris, Mohammad Yaqub, and Karthik Nandakumar. "Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification." In Lecture Notes in Computer Science, 376–86. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16437-8_36.
Full textConference papers on the topic "Transformeur robuste"
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Ottele, Andy, Rahmat Shoureshi, Duane Torgerson, and John Work. "Neural Network-Based Adaptive Monitoring System for Power Transformer." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0069.
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Abstract Power transformers are major elements of the electric power transmission and distribution infrastructure. Transformer failure has severe economical impacts from the utility industry and customers. This paper presents analysis, design, development and experimental results of a robust failure diagnostic technique. Hopfield neural networks are used to identify variations in physical parameters of the system in a systematic way, and adapt the transformer model based on the state of the system. In addition, the Hopfield network is used to design an observer which provides accurate estimates of the internal states of the transformer that can not be accessed or measured during operation. The estimated physical parameters are then passed to a neural network to be classified into regions representing various parameter patterns. Analytical and experimental results of this adaptive observer for power transformer diagnostics are presented.
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Wen, Qingsong, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. "Transformers in Time Series: A Survey." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/759.
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Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance.
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Li, Jinpeng, Haibo Jin, Shengcai Liao, Ling Shao, and Pheng-Ann Heng. "RePFormer: Refinement Pyramid Transformer for Robust Facial Landmark Detection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/152.
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This paper presents a Refinement Pyramid Transformer (RePFormer) for robust facial landmark detection. Most facial landmark detectors focus on learning representative image features. However, these CNN-based feature representations are not robust enough to handle complex real-world scenarios due to ignoring the internal structure of landmarks, as well as the relations between landmarks and context. In this work, we formulate the facial landmark detection task as refining landmark queries along pyramid memories. Specifically, a pyramid transformer head (PTH) is introduced to build both homologous relations among landmarks and heterologous relations between landmarks and cross-scale contexts. Besides, a dynamic landmark refinement (DLR) module is designed to decompose the landmark regression into an end-to-end refinement procedure, where the dynamically aggregated queries are transformed to residual coordinates predictions. Extensive experimental results on four facial landmark detection benchmarks and their various subsets demonstrate the superior performance and high robustness of our framework.
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Roncancio, José. "Uso del Toolbox Simulink de Matlab en la enseñanza del cálculo de la disponibilidad programada en un sistema de producción." In Ingeniería para transformar territorios. Asociación Colombiana de Facultades de Ingeniería - ACOFI, 2023. http://dx.doi.org/10.26507/paper.3151.
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Al momento de evaluar las condiciones de operación de un sistema de producción, se debe establecer el factor tiempo, lo cual implica por parte del ingeniero industrial constituir las Disponibilidades Teórica (DT), Efectiva (DE) y Real (DR). Su cálculo involucra una apropiación integral del conocimiento por parte de los ingenieros en formación, para posteriormente usar el Toolbox Simulink de Matlab, que proporciona funciones analíticas, fundadas en una programación robusta, acompañado de una diagramación realizada a través de bloques, que permiten simular los procesos de producción. Lo significativo en este tipo de actividades, radica substancialmente en el sentido que el estudiante debió previamente interiorizar los contextos más esenciales, tener una visión empresarial lógica, real y luego generar las sentencias de programación propias en Matlab, para después realizar un proceso de interpretación adecuado a las lógicas de programación, y del sistema productivo. Las condiciones de programación obtenidas, fueron acordes a los planteamientos matemáticos, generando un modelo potencialmente eficaz a las necesidades del programador.
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Liu, Qinqing, Fei Dou, Meijian Yang, Ezana Amdework, Guiling Wang, and Jinbo Bi. "Customized Positional Encoding to Combine Static and Time-varying Data in Robust Representation Learning for Crop Yield Prediction." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/676.
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Accurate prediction of crop yield under the conditions of climate change is crucial to ensure food security. Transformers have shown remarkable success in modeling sequential data and hold the potential for improving crop yield prediction. To understand how weather and meteorological sequence variables affect crop yield, the positional encoding used in Transformers is typically shared across different sample sequences. We argue that it is necessary and beneficial to differentiate the positional encoding for distinct samples based on time-invariant properties of the sequences. Particularly, the sequence variables influencing crop yield vary according to static variables such as geographical locations. Sample data from southern areas may benefit from more tailored positional encoding different from that for northern areas. We propose a novel transformer based architecture for accurate and robust crop yield prediction, by introducing a Customized Positional Encoding (CPE) that encodes a sequence adaptively according to static information associated with the sequence. Empirical studies demonstrate the effectiveness of the proposed novel architecture and show that partially lin- earized attention better captures the bias introduced by side information than softmax re-weighting. The resultant crop yield prediction model is robust to climate change, with mean-absolute-error reduced by up to 26% compared to the best baseline model in extreme drought years.
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León Montañez, Johan Sebastián, Jennifer Gabriela Ortiz Mora, Camilo Hernández Acevedo, Camilo Ayala García, Édgar Alejandro Marañón León, Andrés González Barrios, Óscar Alberto Álvarez Solano, and Niyireth Porras Holguín. "Metodologías de extracción de aceite de coraza de marañón: evaluación de impacto ambiental por emisiones de CO2." In Ingeniería para transformar territorios. Asociación Colombiana de Facultades de Ingeniería - ACOFI, 2023. http://dx.doi.org/10.26507/paper.3159.
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El aceite de la coraza de marañón (ACM) es un subproducto de alto interés en la industria química por sus aplicaciones en el diseño de materiales poliméricos, surfactantes y componentes bioactivos. Sin embargo, dentro de la cadena productiva del marañón en el Departamento del Vichada este subproducto ha sido poco explorado. Para recuperar el ACM se han reportado metodologías como extracciones térmicas, mecánicas y químicas. Dichas técnicas presentan variaciones en rendimientos, capacidad tecnológica, costo y capacidad de procesamiento; por lo cual, es necesario establecer parámetros de decisión robustos para la selección de un método. Así, se realiza un análisis comparativo de los impactos ambientales relacionados a la extracción del ACM por medio de la metodología de Soxhlet y prensado mecánico. Esto se realiza cuantificando el impacto relacionado al uso de solventes (emisiones por manufactura, reutilización y disposición) y al consumo de energía de las operaciones unitarias involucradas en cada metodología en términos de las emisiones de CO2 equivalente. De este modo, es posible establecer parámetros de decisión para la implementación de dichas técnicas de extracción en la región del Vichada en función de su sostenibilidad ambiental. Adicionalmente, se brinda una aproximación inicial para realizar un análisis de ciclo de vida de potenciales productos de valor agregado derivados del aprovechamiento del aceite de coraza del marañón.
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Fernández, Javier Ernesto, Javier Leyton, María Cristina Ledezma, Patricia Acosta, and Andrés Quiroga. "Experiencias en el control del patógeno emergente Helicobacter pylori en sistemas de abastecimiento de agua rural por medio de la tecnología de Filtración en Múltiples Etapas." In Ingeniería para transformar territorios. Asociacion Colombiana de Facultades de Ingeniería - ACOFI, 2023. http://dx.doi.org/10.26507/paper.2989.
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Helicobacter pylori (H. pylori) es un microorganismo que afecta al 60% de la población mundial y por lo menos al 80% de la población colombiana. Es el principal agente etiológico de gastritis crónica, úlceras pépticas y cáncer gástrico; en Colombia, este cáncer representa la primera causa de muerte por cáncer en hombres y la tercera en mujeres. Tres rutas de transmisión han sido planteadas: oral-oral, gastro-oral y fecal-oral; en esta última, el agua es considerada como un vehículo intermediario actuando como reservorio ambiental. Se ha establecido relación entre la presencia de H. pylori y el consumo de agua sin tratamiento o parcialmente tratada, siendo los sistemas de abasto de agua rurales donde acentúa este fenómeno por las limitaciones de funcionamiento que ellos presentan. Según el SIASAR el 55% de los sistemas instalados no funcionan y de ellos, el 25% requiere su reconstrucción total. Estas limitaciones están asociadas la construcción de infraestructura robusta y sistemas complejos en zonas apartadas se generan altos costos de operación, que rebasan la capacidad de pago de los usuarios y la capacidad técnicas de la organización comunitaria para hacer mantenimiento de la infraestructura y los equipos. La Filtración en múltiples etapas (FiME) es considerada como una alternativa fácil de operar y mantener localmente, muy efectiva en la remoción de la contaminación microbiológica, que facilita la sostenibilidad por parte de las comunidades. Con el fin de verificar si FiME es eficaz en el control de H. pylori en agua, se evaluó un sistema de tratamiento rural compuesto por un filtro dinámico y un filtro en gravas y además se validó, a nivel de planta piloto, la remoción del patógeno en filtros lentos en arena. Los resultados mostraron que una inadecuada operación y mantenimiento de los filtros en gravas limita la remoción de H. pylori y potencializa su presencia en el agua tratada, mientras que los resultados del estudio piloto con filtros lentos mostraron que esta tecnología es capaz de remover hasta 3 unidades logarítmicas (99.9%), garantizando una calidad de agua con una baja o nula concentración del patógeno. Considerando que un plan de monitoreo de H. pylori resulta costoso para los acueductos rurales, la aplicación de la tecnología FiME permite satisfacer las necesidades de la calidad microbiológica en zona rural.
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Lin, Yuzhang, and Ali Abur. "Robust transformer tap estimation." In 2017 IEEE Manchester PowerTech. IEEE, 2017. http://dx.doi.org/10.1109/ptc.2017.7980919.
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Mao, Xiaofeng, Gege Qi, Yuefeng Chen, Xiaodan Li, Ranjie Duan, Shaokai Ye, Yuan He, and Hui Xue. "Towards Robust Vision Transformer." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01173.
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Prato García, Dorian. "Hidrógeno en Colombia: evaluando el potencial de la agroindustria para una transición energética sostenible." In Ingeniería para transformar territorios. Asociación Colombiana de Facultades de Ingeniería - ACOFI, 2023. http://dx.doi.org/10.26507/paper.2780.
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La idea de usar hidrógeno (H2) como portador energético se remonta a los setenta; sin embargo, factores como el costo de las materias primas, su huella ambiental y limitantes tecnológicas, han frenado su introducción. Actualmente, más del 96% del H2 proviene de fuentes no renovables como el gas natural (49%), hidrocarburos líquidos (29%) y carbón (18%). Aunque el uso o aprovechamiento del H2 no genera GEI, durante su síntesis se pueden emitir entre 10 y 20 kgCO2-eqv/kg H2. La síntesis de H2 por vía electrolítica, usando fuentes renovables de electricidad, conduce a emisiones próximas a 1 kgCO2-eqv/kg H2. Su desempeño ambiental y los bajos costos pronosticados para la electricidad y los sistemas de electrólisis, la convertirán en la alternativa dominante para el 2050; no obstante, se debe señalar que su rentabilidad técnica y sostenibilidad ambiental está ligada al grado de renovabilidad de la electricidad usada. Los informes presentados por la IEA y el IRENA, destacan que la transición energética será clave para reducir el impacto del cambio climático; no obstante, esta no resultará sencilla ni económica incluso para los países más ricos. Por tal motivo, resulta necesario estudiar rutas alternas y potencialmente aplicables en economías emergentes y con recursos renovables abundantes como la nuestra. El H2 también puede ser producido a partir de residuos orgánicos, aguas residuales urbanas e industriales y subproductos agroindustriales por vía fermentativa y a temperatura y presión ambiente. Tomando como referencia azúcares simples (glucosa, xilosa, lactosa) presentes en la mayoría de industrias y recursos naturales del país, es posible generar entre 2.5-3.5 kmol H2/kg azúcar; sin embargo, los resultados de laboratorio señalan la necesidad de emplear un proceso anaerobio complementario, el cual puede operar en serie o en paralelo, con el fin de mejorar la eficiencia energética del proceso y su robustez. Los modelos y los estudios de caso desarrollados sugieren que el metano (CH4) generado puede ser usado para la generación de energía eléctrica requerida por el proceso o para su uso como materia prima para la producción de H2 por reformado catalítico. La integración de la energía residual de otros procesos sugiere la posibilidad de obtener H2 con una huella de carbono negativa, es decir, un producto que consume el CO2 atmosférico. De igual forma, el uso de sustratos complejos como la vinaza de caña (2-3 kmol H2/kg azúcar) y de residuos sólidos urbanos ponen de manifiesto el potencial de diversos subproductos agroindustriales para la síntesis de H2 con huellas de carbono próximas o inferiores (-10 hasta 7 kgCO2-eqv/kg H2) a las reportadas por procesos convencionales sin comprometer la producción de alimentos o su costo. Finalmente, se debe señalar que en nuestro país se ha evidenciado la penetración a escala industrial de los procesos de producción de biogás (CH4-CO2) esto permitiría, además de producir H2 y CH4, dar lugar a mezclas combustibles potencialmente adaptables al mercado nacional como el hitano (H2: 10%, CH4: 50% y CO2: 40%) que conducen a una combustion más limpia y eficiente a menores temperaturas.
Reports on the topic "Transformeur robuste"
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Jones, Emily, Beatriz Kira, Anna Sands, and Danilo B. Garrido Alves. The UK and Digital Trade: Which way forward? Blavatnik School of Government, February 2021. http://dx.doi.org/10.35489/bsg-wp-2021/038.
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The internet and digital technologies are upending global trade. Industries and supply chains are being transformed, and the movement of data across borders is now central to the operation of the global economy. Provisions in trade agreements address many aspects of the digital economy – from cross-border data flows, to the protection of citizens’ personal data, and the regulation of the internet and new technologies like artificial intelligence and algorithmic decision-making. The UK government has identified digital trade as a priority in its Global Britain strategy and one of the main sources of economic growth to recover from the pandemic. It wants the UK to play a leading role in setting the international standards and regulations that govern the global digital economy. The regulation of digital trade is a fast-evolving and contentious issue, and the US, European Union (EU), and China have adopted different approaches. Now that the UK has left the EU, it will need to navigate across multiple and often conflicting digital realms. The UK needs to decide which policy objectives it will prioritise, how to regulate the digital economy domestically, and how best to achieve its priorities when negotiating international trade agreements. There is an urgent need to develop a robust, evidence-based approach to the UK’s digital trade strategy that takes into account the perspectives of businesses, workers, and citizens, as well as the approaches of other countries in the global economy. This working paper aims to inform UK policy debates by assessing the state of play in digital trade globally. The authors present a detailed analysis of five policy areas that are central to discussions on digital trade for the UK: cross-border data flows and privacy; internet access and content regulation; intellectual property and innovation; e-commerce (including trade facilitation and consumer protection); and taxation (customs duties on e-commerce and digital services taxes). In each of these areas the authors compare and contrast the approaches taken by the US, EU and China, discuss the public policy implications, and examine the choices facing the UK.