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Academic literature on the topic 'MITIGATION ALGORITHM'

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Published: 11 September 2023

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Journal articles on the topic "MITIGATION ALGORITHM"

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Ding, Yi, Weiwei Fan, Zijing Zhang, Feng Zhou, and Bingbing Lu. "Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties." Remote Sensing 14, no. 3 (February 7, 2022): 775. http://dx.doi.org/10.3390/rs14030775.

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Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.
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Dhineshkumar, K., and C. Subramani. "Kalman Filter Algorithm for Mitigation of Power System Harmonics." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 771. http://dx.doi.org/10.11591/ijece.v8i2.pp771-779.

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<p>The maiden application of a variant of Kalman Filter (KF) algorithms known as Local Ensemble Transform Kalman Filter (LET-KF) are used for mitigation and estimation power system harmonics are proposed in this paper. The proposed algorithm is applied for estimating the harmonic parameters of power signal containing harmonics, sub-harmonics and inter-harmonics in presence of random noise. The KF group of algorithms are tested and applied for both stationary as well as dynamic signal containing harmonics. The proposed LET-KF algorithm is compared with conventional KF based algorithms like KF, Ensemble Kalman Filter (En-KF) algorithms for harmonic estimation with the random noise values 0.001, 0.05 and 0.1. Among these three noises, 0.01 random noise results will give better than other two noises. Because the phase deviation and amplitude deviation less in 0.01 random noise. The proposed algorithm gives the better results to improve the efficiency and accuracy in terms of simplicity and computational features. Hence there are less multiplicative operations, which reduce the rounding errors. It is also less expensive as it reduces the requirement of storing large matrices, such as the Kalman gain matrix used in other KF based methods.</p>
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Majumder, Swarnadeep, Christopher G. Yale, Titus D. Morris, Daniel S. Lobser, Ashlyn D. Burch, Matthew N. H. Chow, Melissa C. Revelle, Susan M. Clark, and Raphael C. Pooser. "Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses." Quantum 7 (May 15, 2023): 1006. http://dx.doi.org/10.22331/q-2023-05-15-1006.

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Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware codesign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.
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Yousif, Tasneem, and Paul Blunt. "Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA." Eng 3, no. 4 (October 31, 2022): 439–66. http://dx.doi.org/10.3390/eng3040032.

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GNSS receivers process signals with very low received power levels (<−160 dBW) and, therefore GNSS signals are susceptible to interference. Interference mitigation algorithms have become common in GNSS receiver designs in both professional and mass-market applications to combat both unintentional and intentional (jamming) interference. Interference excision filters using fast Fourier transforms (FFTs) have been proposed in the past as a powerful method of interference mitigation. However, the hardware implementations of this algorithm mostly limited their use to military GNSS receivers where greater power and resources were available. Novel implementation of existing FPGA technology should make interference mitigation feasible with limited hardware resources. This paper details the practicalities of implementing excision filters on currently available FPGAs trading off the achievable performance against the required hardware resources. The hardware implementation of the FFT excision mitigation algorithm is validated with the GNSS software receiver. The results indicate that the desired performance of the developed algorithm has achieved the expectations and can provide significant improvement on mitigation techniques in current GNSS receiver hardware. Two hardware implementation designs (fixed-point and float-point data type format) are developed and compared to achieve the optimal design that can provide the best performance with the possible minimum hardware resources.
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Sánchez-Cartas, Juan Manuel, Alberto Tejero, and Gonzalo León. "Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events." Sustainability 13, no. 5 (February 26, 2021): 2542. http://dx.doi.org/10.3390/su13052542.

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Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.
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Ahmed, Mohamed, and Ezz Eldin Abdelkawy. "GPS correlated jammer mitigation utilizing MUSIC algorithm." International Conference on Electrical Engineering 7, no. 7 (May 1, 2010): 1–10. http://dx.doi.org/10.21608/iceeng.2010.33043.

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VU, Trung Kien, Sungoh KWON, and Sangchul OH. "Cooperative Interference Mitigation Algorithm in Heterogeneous Networks." IEICE Transactions on Communications E98.B, no. 11 (2015): 2238–47. http://dx.doi.org/10.1587/transcom.e98.b.2238.

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Akanji, Oluwatobi Shadrach, Opeyemi Aderiike Abisoye, and Mohammed Awwal Iliyasu. "MITIGATING SLOW HYPERTEXT TRANSFER PROTOCOL DISTRIBUTED DENIAL OF SERVICE ATTACKS IN SOFTWARE DEFINED NETWORKS." Journal of Information and Communication Technology 20, Number 3 (June 11, 2021): 277–304. http://dx.doi.org/10.32890/jict2021.20.3.1.

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Distributed Denial of Service (DDoS) attacks has been one of the persistent forms of attacks on information technology infrastructure connected to public networks due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS which overwhelms the target with a large number of request packets. However, compared to slow DDoS, limited number of research has been executed on mitigating slow DDoS. Attackers have resorted to slow DDoS because it mimics the behaviour of a slow legitimate client thereby causing service unavailability. This paper provides the scholarly community with an approach to boosting service availability in web servers under slow Hypertext Transfer Protocol (HTTP) DDoS attacks through attack detection using Genetic Algorithm and Support Vector Machine which facilitates attack mitigation in a Software-Defined Networking (SDN) environment simulated in GNS3. Genetic algorithm was used to select the Netflow features which indicates the presence of an attack and also determine the appropriate regularization parameter, C, and gamma parameter for the Support Vector Machine classifier. Results obtained showed that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and a false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. Also, the algorithm for subsequent implementation of the selective adaptive bubble burst mitigation mechanism was presented. This study contributes to the ongoing research in detecting and mitigating slow HTTP DDoS attacks with emphasis on the use of machine learning classification and meta-heuristic algorithms.
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SHIN, J. H., J. HEO, S. YOON, and S. Y. KIM. "Interference Cancellation and Multipath Mitigation Algorithm for GPS Using Subspace Projection Algorithms." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 3 (March 1, 2008): 905–8. http://dx.doi.org/10.1093/ietfec/e91-a.3.905.

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Garzia, Fabio, Johannes Rossouw van der Merwe, Alexander Rügamer, Santiago Urquijo, and Wolfgang Felber. "HDDM Hardware Evaluation for Robust Interference Mitigation." Sensors 20, no. 22 (November 13, 2020): 6492. http://dx.doi.org/10.3390/s20226492.

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Interference can significantly degrade the performance of global navigation satellite system (GNSS) receivers. Therefore, mitigation methods are required to ensure reliable operations. However, as there are different types of interference, robust, multi-purpose mitigation algorithms are needed. This paper describes the most popular state-of-the-art interference mitigation techniques. The high-rate DFT-based data manipulator (HDDM) is proposed as a possible solution to overcome their limitations. This paper presents a hardware implementation of the HDDM algorithm. The hardware HDDM module is integrated in three different receivers equipped with analog radio-frequency (RF) front-ends supporting signals with different dynamic range. The resource utilization and power consumption is evaluated for the three cases. The algorithm is compared to a low-end mass-market receiver and a high-end professional receiver with basic and sophisticated interference mitigation capabilities, respectively. Different type of interference are used to compare the mitigation capabilities of the receivers under test. Results of the HDDM hardware implementation achieve the similar or improved performance to the state of the art. With more complex interferences, like frequency hopping or pulsed, the HDDM shows even better performance.
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Dissertations / Theses on the topic "MITIGATION ALGORITHM"

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Zubi, Hazem M. "A genetic algorithm approach for three-phase harmonic mitigation filter design." Thesis, University of Bath, 2013. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604881.

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In industry, adjustable speed drives (ASDs) are widely employed in driving AC motors for variable speed applications due to the high performance and high energy efficiency obtained in such systems. However, ASDs have an impact on the power quality and utilisation of AC power feeds by injecting current harmonics and causing resonances, additional losses, and voltage distortion at the point of common coupling. Due to these problems, electric power utilities have established stringent rules and regulations to limit the effects of this distortion. As a result, efficient, reliable, and economical harmonic mitigation techniques must now be implemented in practical systems to achieve compliance at reasonable cost. A variety of techniques exist to control the harmonic current injected by ASDs, and allow three-phase AC-line-connected medium-power systems to meet stringent power quality standards. Of these, the broadband harmonic passive filter deserves special attention because of its good harmonic mitigation and reactive power compensation abilities, and low cost. It is also relatively free from harmonic resonance problems, has relatively simple structural complexity and involves considerably less engineering effort when compared to systems of single tuned shunt passive filters or active filters and active rectifier solutions. In this thesis, passive broadband harmonic filters are investigated. In particular, the improved broadband filter (IBF) which has superior overall performance and examples of its application are increasing rapidly. During this research project, the IBF operating principle is reviewed and its design principles are established. As the main disadvantage of most passive harmonic filters is the large-sized components, the first proposed design attempts to optimize the size of the filter components (L and C) utilized in the existing IBF topology. The second proposed design attempts to optimize the number and then the size of filter components resulting in an Advanced Broadband passive Filter (ABF) novel structure. The proposed design methods are based on frequency domain modelling of the system and then using a genetic algorithm optimization technique to search for optimal filter component values. The results obtained are compared with the results of a linear searching approach. The measured performance of the optimal filter designs (IBF and ABF) is evaluated under different loading conditions with typical levels of background voltage distortion. This involves assessing input current total harmonic distortion, input power factor, rectifier voltage regulation, efficiency, size and cost. The potential resonance problem is addressed and the influence of voltage imbalance on performance is investigated. The assessment is based on analysis, computer simulations and experimental results. The measured performance is compared to various typical passive harmonic filters for three-phase diode rectifier front-end type adjustable speed drives. Finally, the broadband filter design’s effectiveness and performance are evaluated by involving them in a standard IEEE distribution network operating under different penetration levels of connected nonlinear total loads (ASD system). The study is conducted via detailed modelling of the distribution network and the linked nonlinear loads using computer simulations.
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Ikuma, Takeshi. "Non-Wiener Effects in Narrowband Interference Mitigation Using Adaptive Transversal Equalizers." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26772.

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The least mean square (LMS) algorithm is widely expected to operate near the corresponding Wiener filter solution. An exception to this popular perception occurs when the algorithm is used to adapt a transversal equalizer in the presence of additive narrowband interference. The steady-state LMS equalizer behavior does not correspond to that of the fixed Wiener equalizer: the mean of its weights is different from the Wiener weights, and its mean squared error (MSE) performance may be significantly better than the Wiener performance. The contributions of this study serve to better understand this so-called non-Wiener phenomenon of the LMS and normalized LMS adaptive transversal equalizers. The first contribution is the analysis of the mean of the LMS weights in steady state, assuming a large interference-to-signal ratio (ISR). The analysis is based on the Butterweck expansion of the weight update equation. The equalization problem is transformed to an equivalent interference estimation problem to make the analysis of the Butterweck expansion tractable. The analytical results are valid for all step-sizes. Simulation results are included to support the analytical results and show that the analytical results predict the simulation results very well, over a wide range of ISR. The second contribution is the new MSE estimator based on the expression for the mean of the LMS equalizer weight vector. The new estimator shows vast improvement over the Reuter-Zeidler MSE estimator. For the development of the new MSE estimator, the transfer function approximation of the LMS algorithm is generalized for the steady-state analysis of the LMS algorithm. This generalization also revealed the cause of the breakdown of the MSE estimators when the interference is not strong, as the assumption that the variation of the weight vector around its mean is small relative to the mean of the weight vector itself. Both the expression for the mean of the weight vector and for the MSE estimator are analyzed for the LMS algorithm at first. The results are then extended to the normalized LMS algorithm by the simple means of adaptation step-size redefinition.
Ph. D.
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Manmek, Thip Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Real-time power system disturbance identification and its mitigation using an enhanced least squares algorithm." Awarded by:University of New South Wales. Electrical Engineering and Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/26233.

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This thesis proposes, analyses and implements a fast and accurate real-time power system disturbances identification method based on an enhanced linear least squares algorithm for mitigation and monitoring of various power quality problems such as current harmonics, grid unbalances and voltage dips. The enhanced algorithm imposes less real-time computational burden on processing the system and is thus called ???efficient least squares algorithm???. The proposed efficient least squares algorithm does not require matrix inversion operation and contains only real numbers. The number of required real-time matrix multiplications is also reduced in the proposed method by pre-performing some of the matrix multiplications to form a constant matrix. The proposed efficient least squares algorithm extracts instantaneous sine and cosine terms of the fundamental and harmonic components by simply multiplying a set of sampled input data by the pre-calculated constant matrix. A power signal processing system based on the proposed efficient least squares algorithm is presented in this thesis. This power signal processing system derives various power system quantities that are used for real-time monitoring and disturbance mitigation. These power system quantities include constituent components, symmetrical components and various power measurements. The properties of the proposed power signal processing system was studied using modelling and practical implementation in a digital signal processor. These studies demonstrated that the proposed method is capable of extracting time varying power system quantities quickly and accurately. The dynamic response time of the proposed method was less than half that of a fundamental cycle. Moreover, the proposed method showed less sensitivity to noise pollution and small variations in fundamental frequency. The performance of the proposed power signal processing system was compared to that of the popular DFT/FFT methods using computer simulations. The simulation results confirmed the superior performance of the proposed method under both transient and steady-state conditions. In order to investigate the practicability of the method, the proposed power signal processing system was applied to two real-life disturbance mitigation applications namely, an active power filter (APF) and a distribution synchronous static compensator (D-STATCOM). The validity and performance of the proposed signal processing system in both disturbance mitigations applications were investigated by simulation and experimental studies. The extensive modelling and experimental studies confirmed that the proposed signal processing system can be used for practical real-time applications which require fast disturbance identification such as mitigation control and power quality monitoring of power systems
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Gandhi, Nikhil Tej. "Automatic Dependent Surveillance - Broadcast Enabled, Wake Vortex Mitigation Using Cockpit Display." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1354313600.

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Loh, Nolan. "Buildings as urban climate infrastructure: A framework for designing building forms and facades that mitigate urban heat." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553513750865168.

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Al-Odat, Zeyad Abdel-Hameed. "Analyses, Mitigation and Applications of Secure Hash Algorithms." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/32058.

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Cryptographic hash functions are one of the widely used cryptographic primitives with a purpose to ensure the integrity of the system or data. Hash functions are also utilized in conjunction with digital signatures to provide authentication and non-repudiation services. Secure Hash Algorithms are developed over time by the National Institute of Standards and Technology (NIST) for security, optimal performance, and robustness. The most known hash standards are SHA-1, SHA-2, and SHA-3. The secure hash algorithms are considered weak if security requirements have been broken. The main security attacks that threaten the secure hash standards are collision and length extension attacks. The collision attack works by finding two different messages that lead to the same hash. The length extension attack extends the message payload to produce an eligible hash digest. Both attacks already broke some hash standards that follow the Merkle-Damgrard construction. This dissertation proposes methodologies to improve and strengthen weak hash standards against collision and length extension attacks. We propose collision-detection approaches that help to detect the collision attack before it takes place. Besides, a proper replacement, which is supported by a proper construction, is proposed. The collision detection methodology helps to protect weak primitives from any possible collision attack using two approaches. The first approach employs a near-collision detection mechanism that was proposed by Marc Stevens. The second approach is our proposal. Moreover, this dissertation proposes a model that protects the secure hash functions from collision and length extension attacks. The model employs the sponge structure to construct a hash function. The resulting function is strong against collision and length extension attacks. Furthermore, to keep the general structure of the Merkle-Damgrard functions, we propose a model that replaces the SHA-1 and SHA-2 hash standards using the Merkle-Damgrard construction. This model employs the compression function of the SHA-1, the function manipulators of the SHA-2, and the $10*1$ padding method. In the case of big data over the cloud, this dissertation presents several schemes to ensure data security and authenticity. The schemes include secure storage, anonymous privacy-preserving, and auditing of the big data over the cloud.
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Kivrikis, Andreas, and Johan Tjernström. "Development and Evaluation of Multiple Objects Collision Mitigation by Braking Algorithms." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2676.

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A CMbB system is a system that with the help of sensors in the front of a car detects when a collision in unavoidable. When a situation like that is detected, the brakes are activated. The decision of whether to activate the brakes or not is taken by a piece of software called a decision maker. This software continuously checks for routes that would avoid an object in front of the car and as long as a path is found nothing is done. Volvo has been investigating several different CMbB-systems, and the research done by Volvo has previously focused on decision makers that only consider one object in front of the car. By instead taking all present objects in consideration, it should be possible to detect an imminent collision earlier. Volvo has developed some prototypes but needed help evaluating their performance.

As part of this thesis a testing method was developed. The idea was to test as many cases as possible but as the objects’ possible states increase, the number of test cases quickly becomes huge. Different ways of removing irrelevant test cases were developed and when these ideas were realized in a test bench, it showed that about 98 % of the test cases could be removed.

The test results showed that there is clearly an advantage to consider many objects if the cost of increased complexity in the decision maker is not too big. However, the risk of false alarms is high with the current decision makers and several possible improvements have therefore been suggested.

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Santos, Fernando Fernandes dos. "Reliability evaluation and error mitigation in pedestrian detection algorithms for embedded GPUs." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/159210.

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A confiabilidade de algoritmos para detecção de pedestres é um problema fundamental para carros auto dirigíveis ou com auxílio de direção. Métodos que utilizam algoritmos de detecção de objetos como Histograma de Gradientes Orientados (HOG - Histogram of Oriented Gradients) ou Redes Neurais de Convolução (CNN – Convolutional Neural Network) são muito populares em aplicações automotivas. Unidades de Processamento Gráfico (GPU – Graphics Processing Unit) são exploradas para executar detecção de objetos de uma maneira eficiente. Infelizmente, as arquiteturas das atuais GPUs tem se mostrado particularmente vulneráveis a erros induzidos por radiação. Este trabalho apresenta uma validação e um estudo analítico sobre a confiabilidade de duas classes de algoritmos de detecção de objetos, HOG e CNN. Esta pesquisa almeja não somente quantificar, mas também qualificar os erros produzidos por radiação em aplicações de detecção de objetos em GPUs embarcadas. Os resultados experimentais com HOG foram obtidos usando duas arquiteturas de GPU embarcadas diferentes (Tegra e AMD APU), cada uma foi exposta por aproximadamente 100 horas em um feixe de nêutrons em Los Alamos National Lab (LANL). As métricas Precision e Recall foram usadas para validar a criticalidade do erro. Uma análise final mostrou que por um lado HOG é intrinsecamente resiliente a falhas (65% a 85% dos erros na saída tiveram um pequeno impacto na detecção), do outro lado alguns erros críticos aconteceram, tais que poderiam resultar em pedestres não detectados ou paradas desnecessárias do veículo. Este trabalho também avaliou a confiabilidade de duas Redes Neurais de Convolução para detecção de Objetos:Darknet e Faster RCNN. Três arquiteturas diferentes de GPUs foram expostas em um feixe de nêutrons controlado (Kepler, Maxwell, e Pascal), com as redes detectando objetos em dois data sets, Caltech e Visual Object Classes. Através da análise das saídas corrompidas das redes neurais, foi possível distinguir entre erros toleráveis e erros críticos, ou seja, erros que poderiam impactar na detecção de objetos. Adicionalmente, extensivas injeções de falhas no nível da aplicação (GDB) e em nível arquitetural (SASSIFI) foram feitas, para identificar partes críticas do código para o HOG e as CNNs. Os resultados mostraram que não são todos os estágios da detecção de objetos que são críticos para a confiabilidade da detecção final. Graças a injeção de falhas foi possível identificar partes do HOG e da Darknet, que se protegidas, irão com uma maior probabilidade aumentar a sua confiabilidade, sem adicionar um overhead desnecessário. A estratégia de tolerância a falhas proposta para o HOG foi capaz de detectar até 70% dos erros com 12% de overhead de tempo.
Pedestrian detection reliability is a fundamental problem for autonomous or aided driving. Methods that use object detection algorithms such as Histogram of Oriented Gradients (HOG) or Convolutional Neural Networks (CNN) are today very popular in automotive applications. Embedded Graphics Processing Units (GPUs) are exploited to make object detection in a very efficient manner. Unfortunately, GPUs architecture has been shown to be particularly vulnerable to radiation-induced failures. This work presents an experimental evaluation and analytical study of the reliability of two types of object detection algorithms: HOG and CNNs. This research aim is not just to quantify but also to qualify the radiation-induced errors on object detection applications executed in embedded GPUs. HOG experimental results were obtained using two different architectures of embedded GPUs (Tegra and AMD APU), each exposed for about 100 hours to a controlled neutron beam at Los Alamos National Lab (LANL). Precision and Recall metrics are considered to evaluate the error criticality. The reported analysis shows that, while being intrinsically resilient (65% to 85% of output errors only slightly impact detection), HOG experienced some particularly critical errors that could result in undetected pedestrians or unnecessary vehicle stops. This works also evaluates the reliability of two Convolutional Neural Networks for object detection: You Only Look Once (YOLO) and Faster RCNN. Three different GPU architectures were exposed to controlled neutron beams (Kepler, Maxwell, and Pascal) detecting objects in both Caltech and Visual Object Classes data sets. By analyzing the neural network corrupted output, it is possible to distinguish between tolerable errors and critical errors, i.e., errors that could impact detection. Additionally, extensive GDB-level and architectural-level fault-injection campaigns were performed to identify HOG and YOLO critical procedures. Results show that not all stages of object detection algorithms are critical to the final classification reliability. Thanks to the fault injection analysis it is possible to identify HOG and Darknet portions that, if hardened, are more likely to increase reliability without introducing unnecessary overhead. The proposed HOG hardening strategy is able to detect up to 70% of errors with a 12% execution time overhead.
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Salomon, Sophie. "Bias Mitigation Techniques and a Cost-Aware Framework for Boosted Ranking Algorithms." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586450345426827.

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Bhattacharya, Koustav. "Architectures and algorithms for mitigation of soft errors in nanoscale VLSI circuits." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003280.

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Books on the topic "MITIGATION ALGORITHM"

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Casola, Linda, and Dionna Ali, eds. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies. Washington, D.C.: National Academies Press, 2019. http://dx.doi.org/10.17226/25534.

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Wu, Jinsong, Sundeep Rangan, and Honggang Zhang. Green Communications: Theoretical Fundamentals, Algorithms and Applications. Taylor & Francis Group, 2016.

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Computer Science and Telecommunications Board, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Intelligence Community Studies Board, and Board on Mathematical Sciences and Analytics. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. National Academies Press, 2019.

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National Academies of Sciences, Engineering, and Medicine. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. National Academies Press, 2019.

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Computer Science and Telecommunications Board, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Intelligence Community Studies Board, and Board on Mathematical Sciences and Analytics. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. National Academies Press, 2019.

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Wu, Jinsong, Sundeep Rangan, and Honggang Zhang. Green Communications: Theoretical Fundamentals, Algorithms, and Applications. Taylor & Francis Group, 2016.

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Wu, Jinsong, Sundeep Rangan, and Honggang Zhang. Green Communications: Theoretical Fundamentals, Algorithms, and Applications. Taylor & Francis Group, 2016.

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Wu, Jinsong, Sundeep Rangan, and Honggang Zhang. Green Communications: Theoretical Fundamentals, Algorithms, and Applications. Taylor & Francis Group, 2016.

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Wu, Jinsong, Sundeep Rangan, and Honggang Zhang. Green Communications: Theoretical Fundamentals, Algorithms, and Applications. Taylor & Francis Group, 2016.

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Green Communications: Theoretical Fundamentals, Algorithms and Applications. Taylor & Francis Group, 2012.

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Book chapters on the topic "MITIGATION ALGORITHM"

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Zhu, Xuanzhang. "A Novel NLOS Mitigation Algorithm Using VBS." In Lecture Notes in Electrical Engineering, 209–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40633-1_27.

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Ádám, Zsófia, Levente Bajczi, Mihály Dobos-Kovács, Ákos Hajdu, and Vince Molnár. "Theta: portfolio of CEGAR-based analyses with dynamic algorithm selection (Competition Contribution)." In Tools and Algorithms for the Construction and Analysis of Systems, 474–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_34.

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AbstractTheta is a model checking framework based on abstraction refinement algorithms. In SV-COMP 2022, we introduce: 1) reasoning at the source-level via a direct translation from C programs; 2) support for concurrent programs with interleaving semantics; 3) mitigation for non-progressing refinement loops; 4) support for SMT-LIB-compliant solvers. We combine all of the aforementioned techniques into a portfolio with dynamic algorithm selection.
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Hu, Yang, Zhongliang Deng, Jichao Jiao, Jie Xia, Yuezhou Hu, and Zhuang Yuan. "Cross Correlation Mitigation Algorithm for Indoor Positioning Receiver." In Lecture Notes in Electrical Engineering, 485–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46632-2_42.

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Ray, Papia, and Surender Reddy Salkuti. "Short-Term Load Forecasting Using Jaya Algorithm." In Power Quality in Microgrids: Issues, Challenges and Mitigation Techniques, 315–42. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2066-2_15.

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Shi, Sheng, Shanshan Wei, Zhongchao Shi, Yangzhou Du, Wei Fan, Jianping Fan, Yolanda Conyers, and Feiyu Xu. "Algorithm Bias Detection and Mitigation in Lenovo Face Recognition Engine." In Natural Language Processing and Chinese Computing, 442–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60457-8_36.

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Li, Jie, Yuliang Li, and Yingwu Zhou. "GPS Multipath Mitigation Algorithm Using C/A Code Correlation Character." In The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems, 1047–58. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00536-2_121.

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Namrata, Kumari, Nishant Kumar, Ch Sekhar, Ramjee Prasad Gupta, and Surender Reddy Salkuti. "Modeling and Sizing of the Hybrid Renewable System Opting Genetic Algorithm." In Power Quality in Microgrids: Issues, Challenges and Mitigation Techniques, 535–63. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2066-2_25.

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Chen, Lei, Shuai Han, Weixiao Meng, and Zijun Gong. "A Spoofing Mitigation Algorithm Based on Subspace Projection for GNSS Receiver." In China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume I, 727–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46638-4_63.

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Mallala, Balasubbareddy, P. Venkata Prasad, and Kowstubha Palle. "Analysis of Power Quality Issues and Mitigation Techniques Using HACO Algorithm." In Intelligent Sustainable Systems, 841–51. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1726-6_65.

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Minango, Juan, Marcelo Zambrano, and Jorge Caraguay. "Co-channel Interference Mitigation Using Convolutional Enconder via Joint Decoding Viterbi Algorithm." In Innovation and Research - A Driving Force for Socio-Econo-Technological Development, 11–21. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11438-0_2.

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Conference papers on the topic "MITIGATION ALGORITHM"

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Meng, Dawei, Zhenming Feng, and Mingquan Lu. "HOS-Based Interference Mitigation Algorithm." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281616.

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Leonid, T. Thomas, Harish Kanna, Claudia Christy V J, Hamritha A S, and Chebolu Lokesh. "Human Wildlife Conflict Mitigation Using YOLO Algorithm." In 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). IEEE, 2023. http://dx.doi.org/10.1109/iconstem56934.2023.10142629.

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Ho, M. T., H. A. Krichene, G. F. Ricciardi, and W. J. Geckle. "Multipath mitigation in calibration range estimation algorithm." In 2017 IEEE Radar Conference (RadarConf17). IEEE, 2017. http://dx.doi.org/10.1109/radar.2017.7944397.

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Mostefa, Mohammed Belhadj, Abdelfettah Boussaid, and Abdelmalek Khezzar. "FPGA-based Algorithm for Harmonic Current Mitigation." In 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE). IEEE, 2022. http://dx.doi.org/10.1109/icaee53772.2022.9962021.

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Concatto, Fernando, Wellington Zunino, Luigi A. Giancoli, Rafael Santiago, and Luís C. Lamb. "Genetic algorithm for epidemic mitigation by removing relationships." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3071218.

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Peng, Xiaohui, Fei Hu, Feng He, Liang Wu, Jun Li, Dong Zhu, Zhiqiang Liao, and Cuifang Qian. "RFI mitigation of SMOS image based on CLEAN algorithm." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729206.

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Su, Y. Q., Y. L. Yang, Y. T. Mi, and X. F. Yin. "Multipath mitigation method of GPS signals utilizing SAGE algorithm." In 2012 International Conference on System Simulation (ICUSS 2012). IET, 2012. http://dx.doi.org/10.1049/cp.2012.0561.

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Hoshida, Takeshi, Liang Dou, Weizhen Yan, Lei Li, Zhenning Tao, Shoichiro Oda, Hisao Nakashima, Chihiro Ohshima, Tomofumi Oyama, and Jens C. Rasmussen. "Advanced and Feasible Signal Processing Algorithm for Nonlinear Mitigation." In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/ofc.2013.oth3c.3.

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Bhagwat, N., K. Liu, and B. Jabbari. "Robust Bias Mitigation Algorithm for Localization in Wireless Networks." In ICC 2010 - 2010 IEEE International Conference on Communications. IEEE, 2010. http://dx.doi.org/10.1109/icc.2010.5501752.

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Pardhasaradhi, Bethi, Gunnery Srinath, Ashoka Chakravarthi Mahipathi, Pathipati Srihari, and Linga Reddy Cenkeramaddi. "A GNSS Position Spoofing Mitigation Algorithm using Sparse Estimation." In 2022 IEEE 19th India Council International Conference (INDICON). IEEE, 2022. http://dx.doi.org/10.1109/indicon56171.2022.10039936.

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Reports on the topic "MITIGATION ALGORITHM"

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Visser, R., H. Kao, R. M. H. Dokht, A. B. Mahani, and S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia: the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.

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To increase our understanding of induced seismicity, we develop and implement methods to enhance seismic monitoring capabilities in northeastern British Columbia (NE BC). We deploy two different machine learning models to identify earthquake phases using waveform data from regional seismic stations and utilize an earthquake database management system to streamline the construction and maintenance of an up-to-date earthquake catalogue. The completion of this study allows for a comprehensive catalogue in NE BC from 2014 to 2020 by building upon our previous 2014-2016 and 2017-2018 catalogues. The bounds of the area where earthquakes were located were between 55.5°N-60.0°N and 119.8°W-123.5°W. The earthquakes in the catalogue were initially detected by machine learning models, then reviewed by an analyst to confirm correct identification, and finally located using the Non-Linear Location (NonLinLoc) algorithm. Two distinct sub-areas within the bounds consider different periods to supplement what was not covered in previously published reports - the Northern Montney Trend (NMT) is covered from 2017 to 2020 while the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA) is covered from 2019 to 2020. The two sub-areas are distinguished by the BC Oil &amp; Gas Commission (BCOGC) due to differences in their geographic location and geology. The catalogue was produced by picking arrival phases on continuous seismic waveforms from 51 stations operated by various organizations in the region. A total of 17,908 events passed our quality control criteria and are included in the final catalogue. Comparably, the routine Canadian National Seismograph Network (CNSN) catalogue reports 207 seismic events - all events in the CNSN catalogue are present in our catalogue. Our catalogue benefits from the use of enhanced station coverage and improved methodology. The total number of events in our catalogue in 2017, 2018, 2019, and 2020 were 62, 47, 9579 and 8220, respectively. The first two years correspond to seismicity in the NMT where poor station coverage makes it difficult to detect small magnitude events. The magnitude of completeness within the KSMMA (ML = ~0.7) is significantly smaller than that obtained for the NMT (ML = ~1.4). The new catalogue is released with separate files for origins, arrivals, and magnitudes which can be joined using the unique ID assigned to each event.
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Pound, B. G. GRI-99-0000 Gap Analysis of the GRI Research Program on Internal Corrosion. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 1999. http://dx.doi.org/10.55274/r0010720.

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Extensive information on the corrosion of steel in aqueous CO2 systems is now available from this program and numerous other sources. This information was reviewed to identify what research has been performed and what research remains to be undertaken to develop a expert system/risk management program. Four areas were examined: corrosion mechanisms, mitigation strategies, monitoring techniques, and models/risk assessment. There were five gaps in mechanisms (two for bacteria and one each for flow rate/chloride concentration, hydrocarbons, and organic acids) and four gaps in mitigation (antibiofilm additives, antibacterial corrosion inhibitors, antibiofilm coatings, and UV light). Monitoring techniques have one principal gap, which is the lack of a single sensor that can indicate the type of corrosion and whether bacteria are involved. Various gaps were found in the approaches used for modeling and risk assessment: semiempirical and mechanistic models (deficient in their ability to treat films, H2S, and bacteria); thermodynamic models (incomplete range of key chemical species); statistical models (limited testing); probabilistic risk assessment (lack of usable data); and ranking risk assessment (lack of appropriate algorithms for internal corrosion).
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Lillard, Scott. DTPH56-15-H-CAP02 Understanding and Mitigating the Threat of AC Induced Corrosion on Buried Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2017. http://dx.doi.org/10.55274/r0011875.

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Explores new methods for assessing the threat of AC corrosion on buried pipelines. The results from this project will improve indirect inspection methods for assessing the impact of induced AC currents on pipeline corrosion rates and could be used for national and international standards. To accomplish this goal the project has three thrust areas: laboratory studies, industrial test facility benchmarking, and in-service pipeline validation. Previous work in our lab has shown that the magnitude of interfacial capacitance of the corroding metal is a key parameter in determining the AC corrosion rate. As such we will investigate the interfacial capacitance that develops on pipeline steel as a function of corrosion product build-up (scaling) and soil properties such as, resistivity, mineral content, and pH. In addition, we will conduct exploratory studies to determine the susceptibility of pipeline steel to environmental fracture during exposure to AC. Results from these tests will be benchmarked in experiments conducted in industrial pipeline testing facilities at Mears Integrity and Marathon Petroleum. Finally, we will validate the project by collecting indirect inspection data on an in-service pipeline in a transmission line right-of-way owned by Marathon. These data will be used as input to an AC Risk Algorithm to prioritize direct inspection of the pipeline. If permissible, the section of the pipeline identified as being at the greatest risk will be assessed using direct inspection.
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Li, Baisong, and Bo Xu. PR-469-19604-Z01 Auto Diagnostic Method Development for Ultrasonic Flow Meter. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2022. http://dx.doi.org/10.55274/r0012204.

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The objectives of this research are to develop methods for performing ultrasonic flow meter (USM) diagnostic evaluation automatically and a software tool with all necessary attachments. USM-based diagnostics have been established and thirteen categories of knowledge rules of existing cases have been learned and integrated. A search engine for relevant standards, specifications, and other documents of the measurement system has been developed, which enables the free search of text content. Further, with the assistance of modern reasoning techniques, the authorized user only needs to configure an EXCEL file or scripts to activate the rules of the knowledge base by using Drools technology. Therefore, the integration of any potential knowledge rules is convenient and requires no professional skills or changing of the internal source code of the software. Secondly, a new flow meter diagnostic method is proposed based on multiple information methodologies and it is based on the real-time measurement data, operation data, and video data if applicable. The method is intended to identify abnormal states of the measurement system on a real-time basis with the assistance of the knowledge rules and to provide a strategy for mitigating the meter error of components within the measurement system. Thirdly, the applications of Gaussian quadrature diagnostics in daily acquisition nomination change situations and compressor-induced pulsating flow scenarios have been investigated and results are shown in the document. Based on the results, it can be concluded that the measurement uncertainty caused by compressor-induced pulsating flows is obvious, while the measurement uncertainty caused by daily acquisition nomination change is relatively smaller. The software is then developed based on the knowledge, the idea of multiple information methods, and applications of the Gaussian quadrature diagnostics method with all necessary attachments. The architecture, the algorithm, and a few examples are introduced.
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