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We propose a new, data-driven approach for efficient pricing of - fixed- and floating-strike - discrete arithmetic Asian and Lookback options when the underlying process is driven by the Heston model dynamics. The method proposed in this article constitutes an extension of Perotti and Grzelak [Fast sampling from time-integrated bridges using deep learning, J. Comput. Math. Data Sci. 5 (2022)], where the problem of sampling from time-integrated stochastic bridges was addressed. The model relies on the Seven-League scheme [S. Liu et al. The seven-league scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations, Risks 10 (2022), p. 47], where artificial neural networks are employed to 'learn' the distribution of the random variable of interest utilizing stochastic collocation points [L.A. Grzelak et al. The stochastic collocation Monte Carlo sampler: Highly efficient sampling from expensive distributions, Quant. Finance 19 (2019), pp. 339-356]. The method results in a robust procedure for Monte Carlo pricing. Furthermore, semi-analytic formulae for option pricing are provided in a simplified, yet general, framework. The model guarantees high accuracy and a reduction of the computational time up to thousands of times compared to classical Monte Carlo pricing schemes.

Machine vision as an essential component of artificial intelligence poses a significant influence on dimension measurement, quality control, autonomous driving, and so on. In this study, a high-performance ultraviolet (UV) imaging and detection system enabled by Gallium Nitride (GaN) nanowire (NW) n-i-n photodetector (PD) is presented. Based on supreme optoelectronic properties of the NW, including high responsivity of 5098 A/W, a low dark current of 4.88 pA and a photo-to-dark current ratio of 1223, machine vision system composed of a GaN NW array could achieve an accuracy of 96.21%. Furthermore, feasibility of artificial neural network (ANN) and convolutional neural network (CNN) in such a machine vision system is discussed, featuring dim and noisy environment. The visualization process shows that the superiority of CNN over ANN in image recognition is attributed to the capability of extracting spatial information and characteristics. The research results provide important insight into the development of both sensors and algorithms for machine vision systems based on GaN NW PD, inspiring further investigation into UV image detection and other areas of artificial intelligence.

On the 29th of August, the vessel Stena Scandica experienced a blackout. Before the blackout, 294 alarms were issued in 4 minutes. With the number of alarms, the operators could not prevent the blackout. The amount of information and the way it was presented became a hindrance to operators. They could not interpret their surroundings' information without fault from them. This interpretation is called situational awareness. This thesis will solve how information can be provided to operators without hindrance to situational awareness. The focus will be on the Swedish Navy's operators and their needs. The aim is to solve the problem by creating a system that provides situational awareness. The system will use the information on air- and seaborne targets from a radar and a camera display. Three research questions were proposed: how will the radar data structure be, how will it be ranked, and how will it be presented? The structure was expected to tell the targets' location, size, and movement. The ranking of the targets would tell if the targets were a threat to the naval operators. Lastly, the targets were expected to be presented with some of their information on a camera display. For the first question, the structure for both kinds of targets was constructed to meet the expectations. Two models were used to solve the second question. An artificial neural network and fuzzy c-means. The artificial neural network was chosen as it is one of the best classification algorithms. Fuzzy c-means were chosen since it can cluster similar behaviors together, therefore clustering high-threat targets together. Of these two models, the result showed that the artificial neural network was a better ranking method, with a higher accuracy of 92.9% for airborne targets and 80.6% for seaborne targets. A simulation was made to answer the third question and was built according to the expectations. The simulation only displayed the highest threat targets in the camera display. By presenti, Den 29 augusti drabbades fartyget Stena Scandica av ett strömavbrott. Innan strömavbrottet utlöste 294 larm inom 4 minuter, vilket gjorde det omöjligt för operatörerna att förhindra avbrottet. Mängden information och sättet den presenterades på blev ett hinder för operatörerna, vilket påverkade deras lägesbild. Arbete syftar till att lösa hur information kan tillhandahållas till operatörer utan att hindra deras situationsmedvetenhet, med fokus på den svenska marinens operatörer och deras behov. Detta arbete föreslår ett system som använder radardata och kameradisplayer för att tillhandahålla lägesbilden. Tre forskningsfrågor ställs: hur ska radarns datastruktur vara, hur ska den rankas, och hur ska den presenteras? Strukturen förväntas visa målens plats, storlek och rörelse. Rankningen ska indikera om målen utgör ett hot, och hög-hotmål ska presenteras på kameradisplayen. För att svara på den första frågan konstruerades strukturen för båda typerna av mål. För den andra frågan användes två modeller: ett artificiellt neuralt nätverk och fuzzy c-means. Det artificiella neurala nätverket visade sig vara den bästa metoden med en noggrannhet på 92,9% för luftmål och 80,6% för sjömål. En simulering gjordes för att svara på den tredje frågan, där endast de mest hotfulla målen visades på kameradisplayen. Detta gav operatörerna en bättre förståelse för var målen befann sig. Framtida studier bör undersöka systemets implementering på svenska marinens fartyg. Exempelvis om tillräcklig beräkningskraft finns för ett artificiellt neuralt nätverk.

In forensic autopsies, accurately estimating the postmortem interval (PMI) is crucial. Traditional methods, relying on physical parameters and police data, often lack precision, particularly after approximately two days have passed since the person's death. New methods are increasingly focusing on analyzing postmortem metabolomics in biological systems, acting as a 'fingerprint' of ongoing processes influenced by internal and external molecules. By carefully analyzing these metabolomic profiles, which span a diverse range of information from events preceding death to postmortem changes, there is potential to provide more accurate estimates of the PMI. The limitation of available real human data has hindered comprehensive investigation until recently. Large-scale metabolomic data collected by the National Board of Forensic Medicine (RMV, Rättsmedicinalverket) presents a unique opportunity for predictive analysis in forensic science, enabling innovative approaches for improving PMI estimation. However, the metabolomic data appears to be large, complex, and potentially nonlinear, making it difficult to interpret. This underscores the importance of effectively employing machine learning algorithms to manage metabolomic data for the purpose of PMI predictions, the primary focus of this project. In this study, a dataset consisting of 4,866 human samples and 2,304 metabolites from the RMV was utilized to train a model capable of predicting the PMI. Random Forest (RF) and Artificial Neural Network (ANN) models were then employed for PMI prediction. Furthermore, feature selection and incorporating sex and age into the model were explored to improve the neural network's performance. This master's thesis shows that ANN consistently outperforms RF in PMI estimation, achieving an R2 of 0.68 and an MAE of 1.51 days compared to RF's R2 of 0.43 and MAE of 2.0 days across the entire PMI-interval. Additionally, feature selection indicates that only 35% of total metabolites are nec

Prestressed concrete bridges experience complex load scenarios throughout their lifespan, which impacts their load capacity and overall performance. To ensure opyimal functionality and safety it is crucial to understand the diverse responses from the structure under varying load. By deploying sensors in strategically placed locations, a numerical representation of varying responses can be attained during operation. Variation in temperature can affect the reading of a sensor. This can lead to false positive structural damage responses. Thus, it is important to differentiate between variations in structural properties induced by temperature fluctuations, and variations from structural damage. This paper presents a novel approach for mapping the temperature effect in structural responses. By employing an artificial neural network (ANN) and measurement data from a prestressed concrete bridge located in northern Sweden, a correlation between temperature and the sensor values is achieved., Full text license: CC BY-NC-ND;ISBN for host publication: 978-1-032-77040-6, 978-1-032-77560-9, 978-1-003-48375-5, Sustainable Construction and Build Environment in Arctic Climate project

This thesis investigated the possibility of using machine learning algorithms for classifying items in a queuing system to optimize a production line. The evaluated algorithms are Artificial Neural Network (ANN) and Support Vector Machine (SVM), selected based on other research projects. A quasi-experiment evaluates the two machine learning algorithms trained on the same data. The dataset used in the experiment was complex and contained 47,212 rows of samples with features of items from a production setting. Both models performed better than the current system, with ANN reaching 97,5% and SVM 98% on all measurements. The ANN and SVM models differed in training time where ANN took almost 205 seconds and SVM took 1.97 seconds, ANN was however 20 times faster to classify. We conclude that ANN and SVM are feasible options for using Artificial Intelligence (AI) to classify items in an industrial environment with similar scenarios.

Background: High-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs). Methods: Deep Learning (DL) NLP models were trained on data from 389,865 surgical cases across all 11 hospitals in the Capital Region of Denmark. Surgical cases in the training dataset were performed between January 01st, 2017, and October 30th, 2021. We trained a forward reading and a backward reading universal language model on unlabeled postoperative chart notes recorded within 30 days of a surgical procedure. The two language models were subsequently finetuned on labeled data for the classification of SSSIs. Validation and testing were performed on surgical cases performed during the month of November 2021. We propose two different use cases: a stand-alone machine learning (SAM) pipeline and a human-in-the-loop (HITL) pipeline. Performances of both pipelines were compared to administrative data and to manual curation. Results: The models were trained on 3,983,864 unlabeled chart notes and finetuned on 1,231,656 labeled notes. Models had a test area under the receiver operating characteristic curves (ROC AUC) of 0.989 on individual chart notes and 0.980 on an aggregated case level. The SAM pipeline had a sensitivity of 0.604, a specificity of 0.996, a positive predictive value (PPV) of 0.763, and a negative predictive value (NPV) of 0.991. Prior to human review, the HITL pipeline had a sensitivity of 0.854, a specificity of 0.987, a PPV of 0.603, and a NPV of 0.997. Conclusion: The performance of the SAM pipeline was superior to administrative data, and significantly outperformed previously published results. The performance of the HITL pipeline

Denna uppsats presenterar resultaten av en kvalitativ undersökning som syftar till att ge en djupare förståelse för användningen av AI inom radiologi, dess framtida påverkan på yrket och hur det används idag. Genom att genomföra tre intervjuer med personer som arbetar inom radiologi, har datainsamlingen fokuserat på att identifiera de positiva och negativa aspekterna av AI i radiologi, samt dess potentiella konsekvenser på yrket. Resultaten visar på en allmän acceptans för AI inom radiologi och dess förmåga att förbättra diagnostiska processer och effektivisera arbetet. Samtidigt finns det en viss oro för att AI kan ersätta människor och minska behovet av mänskliga bedömningar. Denna uppsats ger en grundläggande förståelse för hur AI används inom radiologi och dess möjliga framtida konsekvenser., This essay presents the results of a qualitative study aimed at gaining a deeper understanding of the use of artificial intelligence (AI) in radiology, its potential impact on the profession and how it’s used today. By conducting three interviews with individuals working in radiology, data collection focused on identifying the positive and negative aspects of AI in radiology, as well as its potential consequences on the profession. The results show a general acceptance of AI in radiology and its ability to improve diagnostic processes and streamline work. At the same time, there is a certain concern that AI may replace humans and reduce the need for human judgments. This report provides a basic understanding of how AI is used in radiology and its possible future consequences.

An innovative Dynamic Voltage Restorer (DVR) system based on Artificial Neural Network (ANN) technology, implemented in MATLAB Simulink, accurately detects, and dynamically restores voltage sags, significantly improving power quality and ensuring a reliable supply to critical loads, contributing to the advancement of power quality enhancement techniques. Voltage sags are a prevalent power quality concern that can have a significant impact on sensitive electrical equipment. An innovative approach to address voltage sags through the operation of a Dynamic Voltage Restorer (DVR) based on Artificial Neural Network (ANN) technology. The proposed system, developed using MATLAB Simulink, leverages the ANN's capabilities to accurately detect voltage sags and dynamically restore the voltage to the affected load. The ANN is trained using a comprehensive dataset comprising voltage sag events, enabling it to learn the intricate relationships between sag characteristics and optimal compensation techniques. By integrating the trained ANN into the DVR control scheme, real-time compensation for voltage sags is achieved. The effectiveness of the proposed system is rigorously evaluated through extensive simulations and performance analysis. The results demonstrate the superior performance of the ANN-based DVR in terms of voltage sag detection accuracy and restoration precision. Consequently, the proposed system presents an intelligent and adaptive solution for voltage sag compensation, ensuring a reliable and high-quality power supply to critical loads. This research contributes to the advancement of power quality enhancement techniques, facilitating the implementation of intelligent power system.

The paper aims to solve the distortion problem of the inverter output voltages that cause harmonic deformation of the phase currents and ripple of dq- currents of the three-phase permanent magnet synchronous motor (PMSM). The inverter non-linearities adversely affect the effectiveness of the PMSM control algorithm. The compensation strategy is based on the neural network and knowledge of the three-phase PMSM model structure and its parameters. The input data for the neural network consist of the normed values and detected polarities of the phase currents and rotor position information. As a result, the proposed artificial neural network (ANN) can extract non-linear functions from the measured data to compensate for the VSI output voltages. The ANN is designed to learn online while the PMSM is running. The back-propagation algorithm is used for neural network learning. The proposed stratégy was implemented in an AURIX TC397 microcontroller and validated by experiments on a real PMSM. The presented results demonstrate the effectiveness of the proposed solution.

While embracing the metaverse within Information Technology (IT) companies could present unique opportunities, it also brings about challenges in adoption behavior. However, research on the factors influencing intentional behavior to use the metaverse in IT companies is scarce. To bridge this gap, this study develops a research model that integrates elements from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), Task-Technology Fit (TTF), and awareness studies, and hypothesizes key variables such as performance expectancy, effort expectancy, and social influence. Through a comprehensive survey of 234 participants, the research model is evaluated employing a unique combination of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN), which serve as advanced modeling techniques. The SEM and ANN analyses elucidate intricate relationships and make predictions about adoption behavior, while uncovering patterns and insights into metaverse adoption in IT companies. Although the primary focus is on SEM and ANN, this study also utilizes Partial Least Squares (PLS) in the research design. It identifies and discusses key findings from descriptive analysis, measurement model assessments, and structural model assessments. Furthermore, the ANN results and sensitivity analysis paint a more nuanced picture of metaverse adoption behavior in the IT sector, providing valuable predictions and insights. In addition to the theoretical contributions, the findings offer practical implications for IT companies and suggest future research directions to help them make informed decisions related to the implementation and use of the metaverse. Overall, this study contributes to the growing body of literature on the metaverse and its application in the business landscape, with specific emphasis on IT companies.

Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water lev

The purpose of our research is to model the monthly price of the KSE 100 index based on Pakistan's macroeconomic indicators using a Machine Learning (ML) approach. The novelty of the study is forecasting the future value of the stock market using ML. Monthly data was collected for the period from Feb 2004 to Dec 2020. The output layer of our study is the closing price of the KSE 100 index, and the input layer consists of 16 macroeconomic variables of the Pakistan economy, which are the industrial production index (IPI), the exchange rate (EX-RATE), money supply (M2), consumer price index (CPI), foreign direct investment (FDI), Treasury bill on 3-months treasury, interest rate as KIBOR – Month Average (1 Month), Foreign Exchange reserves (FXR), Consumer Financing for house building (house financing), Balance of Trade (BOT), crude oil, Gold, Labor force participation rate, GDP growth (annual %), Households and NPISHs Final consumption expenditure (household consumption) (current US$), and Domestic savings. The prediction uses the Artificial Neural Network (ANN) Backpropagation algorithm. The model developed in this research achieved 99% accuracy using macroeconomic indicators. The accuracy level indicates that the model of the KSE 100 index can predict future trends. This study also forecasts the future monthly values of the KSE 100 index from Jan 21 to Jun 23 and daily future values from Oct 1, 2022, to Dec 31, 2020.

Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in milliseconds. The technique is proposed is validated on a 4-element planar non-uniform sub-array structure. Excellent accuracy on the EEP prediction while providing great efficiency in computational time and load in comparison to the full-wave simulations is demonstrated., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Microwave Sensing, Signals & Systems

The primary purpose of the current study was to investigate the performance of artificial neural networks to predict the time series of the water surface level (WSL), base shear, and overturning moment using two types of ANN models: Nonlinear Autoregressive models with exogenous inputs (NARX) and Nonlinear Autoregressive models (NAR). After determining the suitable model, NARX, the possibility of predicting the time series of the base shear and the overturning moment data was investigated by considering the water surface level and time as the multivariable model inputs. A jacket model with a height of 4.55m was fabricated and tested in the 402m-long wave flume of the NIMALA marine laboratory. The jacket was tested at the water depth of 4m and subjected to random waves with a JONSWAP energy spectrum. Three input wave heights were chosen for the tests: 20cm, 23cm, and 28cm. The findings showed that using the NARX neural network is a convenient method to predict the base shear and overturning moment values based on the water surface level data as input values. Finally, after suitable neural network determination, using the NARX neural network, the correlation value (R) for calculating water surface level (WSL), Base Shear, and Overturning Moment were obtained as 0.994, 0.97, and 0.94, respectively.

Renewable energy induced by wind and wave sources is playing an indispensable role in electricity production. The innovative hybrid renewable offshore platform concept, which combines Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs), has proven to be a promising solution to harvest clean energy. The hybrid platform can increase the total energy absorption, reduce the unwanted dynamic response of the platform, mitigate the load in critical situations, and improve the system's cost efficiency. However, the nonlinear dynamical behavior of the hybrid offshore wind system presents an opportunity for stabilization via challenging control applications. Wind and wave loads lead to stress on the FOWT tower structure, increasing the risk of damage and failure, and raising maintenance costs while lowering its performance and lifespan. Moreover, the dynamics of the tower and the platform are extremely sensitive to wind speed and wave elevation, which causes substantial destabilization in extreme conditions, particularly to the tower top displacement and the platform pitch angle. Therefore, this article focuses on two main novel targets: (i) regressive modeling of the hybrid aero-hydro-servo-elastic-mooring coupled numerical system and (ii) an ad-hoc fuzzy-based control implementation for the stabilization of the platform. In order to analyze the performance of the hybrid FOWT-OWCs, this article first employs computational Machine Learning (ML) techniques, i.e., Artificial Neural Networks (ANNs), to match the behavior of the detailed FOWT-OWCs numerical model. Then, a Fuzzy Logic Control (FLC) is developed and applied to establish a structural controller mitigating the undesired structural vibrations. Both modeling and control schemes are successfully implemented, showing a superior performance compared to the FOWT system without OWCs. Experimental results demonstrate that the proposed ANN-based modeling is a promising alternative to other intricate

Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously., Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously., Deutsche Forschungsgemeinschaft (DFG), Bundesministerium für Digitales und Verkehr, Bundesministerium für Bildung und Forschung (BMBF)

The major goal of this work is to give a survey and comparison of several plant disease detection strategies in the field of image processing. As we all know, India is a predominantly agricultural country, with agriculture serving as the primary source of income for the vast majority of the population. It is critical to focus on the domain of farming with modern technology to make their lives more comfortable and simpler. Crop productivity can be increased by introducing modern technologies. An autonomous plant disease detection technology using image processing and a neural network approach can be utilized to solve problems with plant and agricultural diseases. Detecting maize leaf disease is an important endeavor during the maize planting stage. The detection of these disorders necessitates the use of several patterns. There are various classification techniques available, including Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), Back Propagation (BP) Network, Support Vector Machine (SVM), and Convolution Neural Network (CNN).

The paper shows and includes targeted supervision of optimizing machining parameters. Material Removal Rate and surface roughness being integral to machining efficacy of any workpiece, simulation-based modeling helps in failure mitigation. The potential of ANN-GA mathematical approach for prediction and optimization of MRR and surface roughness of AL 6061, an analysis based statistical study has been discussed. The computational model between the desired output and the inputs have been configured using Multiple Regression- Genetic Algorithm and Artificial Neural Network (ANN) methods. The closeness in predicted and optimized data sets were mapped using integrational ANN with GA to interpolate efficacy in optimality.

African agriculture is adversely impacted by arable soil compaction, the degree of which is affected by the speed at which the tractor is maneuvered on the fields, which affects the degree of soil compaction. However, there is no reliable, existing mathematical correlation between the extent of compaction on the one hand, and the tractor speed/s and soil moisture levels on the other. This paper bridges this gap in knowledge by resorting to the artificial neural networks (ANNs) method to predict the effects of tractor speed and soil moisture on the state of soil compaction. The models were ‘trained’ with penetration resistance (CPR) and bulk density test data obtained from field measurements. The resulting correlation coefficient (R = 0.9) showed good compliance of the prediction made with the ANN models with on-field data. It follows, thereby, that the model developed by the authors in this study can be effectively used for predicting the effects of speed, soil density, and moisture content on compaction of alluvial, poorly developed soil with much greater precision, thereby providing guidance to farmers around the world.

Purpose: This study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study. Methods: The structure of the ANN model was designed considering various input parameters, namely patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. The aforementioned examination details of 551 abdominal CT scans were used as retrospective data. Different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of the accuracy of the training data. Results: The R-value representing the correlation coefficient for the real system and system output is given as 0.925, 0.785, and 0.854 for the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, respectively. The findings showed that the Levenberg-Marquardt algorithm comprehensively detects DLP values for abdominal CT examinations. It can be a helpful approach to simplify CT quality assurance. Conclusion: It can be concluded that outcomes of this novel artificial intelligence method can be used for high accuracy DLP estimations before the abdominal CT examinations, where the radiation-related risk factors are high or risk evaluation of multiple CT scans is needed for patients in terms of ALARA. Likewise, it can be concluded that artificial learning methods are powerful tools and can be used for different types of radiation-related risk assessments for quality assurance in diagnostic radiology. Copyright © 2022 Tekin, Almisned, Erguzel, Abuzaid, Elshami, Ene, Issa and Zakaly.

Development of portable devices, that allows real-time detection of atrial fibrillation, requires the implementation of automatic pattern recognition algorithms and an appropriate methodology for their execution in embedded systems. In the present article, the performances of an artificial neural network, a machine vector support, a k-nearest neighbors algorithm and a hybrid classifier implemented on a single-board computer, were compared in terms of detection capacity of arrhythmia and time response associated with real-time execution. The MIT-BIH AFIB database was used to train and validate the algorithms. In advance, the extraction of parameters associated with the stationary wavelet transform was developed. Results between 92 % and 97 % for sensitivity and specificity, and time responses between 6 s and 7.1 s were found in this research., El desarrollo de dispositivos portables, que permita la detección en tiempo real de fibrilación auricular, requiere la implementación de algoritmos de reconocimiento automático de patrones con la metodología adecuada para su ejecución en sistemas embebidos. En el presente artículo se expone la implementación de una red neuronal artificial (ANN), una máquina de soporte vectorial (SVM) y un algoritmo de K vecinos más cercanos (KNN) en un computador de placa reducida para así comparar su desempeño en cuanto a la capacidad de detección de esta arritmia y el tiempo de respuesta asociado en su ejecución en tiempo real. La base de datos MIT-BIH AFIB es usada para el entrenamiento y validación de los algoritmos previa extracción de parámetros asociados a la transformada wavelet estacionaria. Se encontraron resultados entre el 92% y 97% para la sensibilidad y especificidad de los algoritmos mencionados y tiempos de respuesta variados entre 6 s y 7,1 s.

The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models., Journal of Civil Engineering and Management, 28(4), pp261-277; 2022

To cope with the weak grid stability issue of grid-tied Voltage Source Converters (VSCs), this letter proposes an Artificial Neural Network (ANN)-based approach for online stabilization control of the grid-tied VSC with the pole-tracking feature. First, an ANN is adopted to establish the mapping between the control parameters and the closed-loop poles of the grid-VSC system, serving as a computationally light model surrogate that is favorable for real-time control applications. Then, an online parameter search algorithm enabling simultaneous tuning of multiple controllers and parameters is developed, by which the systems poles under a new grid condition can be pulled to the reference ones, i.e., achieving the pole-tracking-based stabilization control of this work. Finally, the efficacy of the proposed method along with its stabilization effect is verified by MATLAB simulations and experimental results.

DC microgrids can be operated under a hierarchical control strategy, and it needs a communication-based layer. The implementation of digital controllers and the communication infrastructure can make a dc microgrid vulnerable to cyber-attacks. This article introduces an approach based on Artificial Intelligence (AI) to detect and mitigate cyber-attacks in a dc microgrid. The proposed method is based on the artificial neural network (ANN), which can be categorized as an AI-based method. The proposed application implements an ANN to detect and mitigate false data injection attacks (FDIAs). FDIAs try to inject false data into the system to affect the control application of the dc microgrid, and it can shut down the dc microgrid. The proposed method can calculate the value of the false data, and it can detect and remove the attack simultaneously. The proposed method is tested in a MATLAB/Simulink environment. Also, to have more accurate results, the introduced approach is examined under different conditions and cyber/physical disturbances (e.g., communication delay, noise, plug-and-play of additional units, and time-varying FDIAs). Besides, a comparison is considered to evaluate the effectiveness of the proposed strategy. The obtained results can conclusively prove the effectiveness, accuracy, and authenticity of the proposed method to successfully detect the FDIAs and remove the cyber-attack.

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.

Direct current (DC) microgrids can be considered as cyber-physical systems due to implementation of measurement devices, communication network, and control layers. Consequently, dc microgrids are also vulnerable to cyber-attacks. False-data injection attacks (FDIAs) are a common type of cyber-attacks, which try to inject false data into the system in order to cause the defective behavior. This article proposes a method based on model predictive control (MPC) and artificial neural networks (ANNs) to detect and mitigate the FDIA in dc microgrids that are formed by parallel dc–dc converters. The proposed MPC/ANN-based strategy shows how MPC and ANNs can be coordinated to provide a secure control layer to detect and remove the FDIAs in the dc microgrid. In the proposed strategy, an ANN plays the role of the estimator to implement in the cyber-attack detection and mitigation strategy. The proposed method is examined under different conditions, physical events and cyber disturbances (i.e. load changing and communication delay, and time-varying attack), and the results of the MPC-based scheme is compared with conventional proportional-integral controllers. The obtained results show the effectiveness of the proposed strategy to detect and mitigate the attack in dc microgrids.

Purpose This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al2O3) nanoparticles. Design/methodology/approach Metal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al2O3 particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model. Findings The combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al2O3, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites. Originality/value The specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research.

It is well understood that with  a large number of data, an excellent interpretation of the subsurface condition can be produced, and also our understandings of the subsurface conditions can be improved significantly. However, having abundant subsurface geological and petrophysical data sometimes may not be possible, mainly due to budget issues. This situation can generate issues during hydrocarbon exploration and/or development activities. In this paper, the authors tried to apply artificial intelligence (AI) techniques to estimate outcomes values of particular wireline log data, using available petrophysic data. Two types of AI were selected and these are artificial neural network (ANN), and multiple linear regression (MLR). This research aims to advance our understanding of AI and its application in geology. There are three objectives of this study: (1) to estimate sonic log (DT) and density log (RhoB) using different types of AI (ANN and MLR); (2) to assess the best AI technique that can be used to estimate certain wireline log data; and (3) to compare the estimated wireline log values with the real, recorded values from the subsurface. Findings from this study show that ANN consistently provided a better accuracy percentage compared to MLR when estimating density log (RhoB). While using different set of data and technique, estimation of sonic log (DT) produced different accuracy level. Moreover, crossplot validation of the results show that the results from ANN analysis produced higher trendline reliability (R2) and correlation coefficient (R) than the results from MLR analysis. Comparison of the estimated RhoB and DT log data with the original recorded data shows minor mismatch. This is evident that AI technique can be a reliable solution to estimate particular outcomes of wireline log data, due to limited availability of the original recorded subsurface petrophysic data. It is expected that these findings would provide new insights into the application o

This paper proposes a novel application method, Inverse Application of Artificial Intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of Artificial Neural Network (ANN) is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multi-level converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility, and practicality of IAAI.

Of the two gate electrodes sandwiching the channel region of a parallel, dual-gate (DG) thin-film transistor (TFT), one can be used for modulating the threshold voltage associated with the other. This unique capability is used in sensing to set the operating point of the sensing electrode and in neuromorphic computing to adjust the "weight"of the input. Made of units each containing a DG TFT with an extended top sensing gate, a tactile sensor array is demonstrated for distributed force sensing. Capable of emulating both the excitative and inhibitive transmission of a signal across a synaptic gap, a unit containing such a DG TFT combined with a memory capacitor is replicated and organized to construct an artificial neural network (ANN). Lastly, the monolithic merging of a sensing array and an ANN is proposed. © 2022 IEEE.

Implementing in-memory computation, an artificial neural network (ANN) consisting of thin-film transistors (TFTs) monolithically integrated in each unit of an array of capacitors is constructed. Both single-gate and parallel, dual-gate (DG) TFTs are deployed. The capacitors and the DG TFTs serve as the respective memory and computational elements. The DG TFT offers the capability of amplifying a weak but relevant input signal and suppressing a strong but irrelevant input signal across a synaptic gap, and the storage of charge on the capacitor is pseudostatic because of the exceptionally low oFr-state leakage current of the accompanying address TFT built on a metal-oxide semiconductor. The feasibility of such an ANN is demonstrated using a 4 x 6 array for classifying a specific set of Tetris patterns.

Image category is a complicated system that can be stricken by many factors. This paper examines modern-day practices, problems, and potentialities of image category. The emphasis is positioned at the summarization of primary advanced category procedures and the strategies used for enhancing category accuracy. In addition, a few essential problems affecting category performance are discussed. This literature evaluate indicates that designing an appropriate image processing system is a prerequisite for a a hit category of remotely sensed records right into a thematic map. Effective use of a couple of functions of remotely sensed records and the choice of a appropriate category approach are especially extensive for enhancing category accuracy. Non-parametric classifiers such as neural network, choice tree classifier, and knowledge-primarily based totally category have an increasing number of turn out to be essential procedures for multi-source records classification. Integration of faraway sensing, geographical data systems (GIS), and professional machine emerges as a brand-new studies frontier. More studies, however, is had to discover and decrease uncertainties with inside the image-processing chain to enhance category accuracy.

[EN] Metal containers (both food and beverage cans) are made from huge steel or aluminum coils that are transformed into two- or three-piece products. During the manufacturing process, the metal is sprayed on both sides and the aerosol acts as insulation, but unfortunately produces volatile organic compounds (VOCs). The present work presents a different way to manufacture these containers using a novel prelaminated two-layer polymer steel. It was experimentally possible to verify that the material survives all the involved manufacturing processes. Thus tests were carried out in an ironing simulator to measure roughness, friction coefficient and surface quality. In addition, two theoretical ironing models were developed: upper bound model and artificial neural network. These models are useful for packaging designers and manufacturers.

Biodiesel will provide a significant renewable energy source for transportation in the near future. In the present study, principal component analysis (PCA) has been used to understand the relationship between important properties of biodiesel and its chemical composition. Finally, several artificial intelligence-based models were developed to predict specific biodiesel properties based on their chemical composition. The experimental study was conducted in order to generate training data for the artificial neural network (ANN). Available (experimental) data from the literature was also employed for this modeling strategy. The analytical part of this study found a complex multi-dimensional correlation between chemical composition and biodiesel properties. Average numbers of double bonds in the chemical structure (representing the unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel had a great impact on biodiesel properties. The simulation result in this study demonstrated that ANN is a useful tool for investigating the fuel properties from its chemical composition which eventually can replace the time consuming and costly experimental test.

AbstractAdditive manufacturing (AM) technologies such as fused deposition modeling (FDM) rely on the quality of manufactured products and the process capability. Currently, the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard, and variations are controlled without significantly affecting the functioning of processes, machines, and product structures. This study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical parts. In this study, a new class of experimental design techniques for integrated second-order definitive screening design (DSD) and an artificial neural network (ANN) are proposed for designing experiments to evaluate and predict the effects of six important operating variables. By determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts, the time consumption and number of complex experiments are reduced considerably in this study. The optimum fabrication conditions generated through a second-order DSD are verified with experimental measurements. The results indicate that the slice thickness, part print direction, and number of perimeters significantly affect the percentage of length difference, whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap, bead width, number of perimeters, and part print direction. Furthermore, the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

Acoustic emission (AE) is often used for structural health monitoring (SHM) in the wide field of engineering structures and one of its most beneficial attributes is the ability to localize the damage/crack based on the AE events. The vast majority of ongoing work on AE monitoring focues on geometrically simple structures or a confined area, but the AE source location strategies are rather complicated for real engineering structures. In this paper, an effective method for source localization in realistic structures is presented based on the application of artificial neural networks (ANN), using finite element (FE) simulation results of Lamb waves as the modelling basis. Pencil lead break experiments and related FE simulations on a steel-concrete composite girder are conducted to evaluate the performance of the method. The identification of different wave modes is carried by comparing alternative onset time detection methods. Numerical results are found to be matching closely with the experimental results. To get a reliable ANN model, the validated FE model is used to create a comprehensive database with five different sensor arrangements. It is found that the proposed method is superior to the classical Time of Arrival (TOA) method with the same input data. The results indicate that using trained neural networks based on numerical data is a viable option for AE source location in the case of the I-shaped girder, increasing the likelihood of design and optimization of the AE technique in monitoring realistic structures., Steel & Composite Structures, Structural Integrity & Composites

The computing industry is rapidly moving from a programming to a learning area, with the reign of the von Neumann architecture starting to fade, after many years of dominance. The new computing paradigms of non-von Neumann architectures have started leading to the development of emerging artificial neural network (ANN)-based analog electronic artificial intelligence (AI) chipsets with remarkable energy efficiency. However, the size and energy advantages of electronic processing elements are naturally counteracted by the speed and power limits of the electronic interconnects inside the circuits due to resistor-capacitor (RC) parasitic effects. Neuromorphic photonics has come forward as a new research field, which aims to transfer the well-known high-bandwidth and low-energy interconnect credentials of photonic circuitry in the area of neuromorphic platforms. The high potential of neuromorphic photonics and their well-established promise for fJ/Multiply-ACcumulate energy efficiencies at orders of magnitudes higher neuron densities require a number of breakthroughs along the entire technology stack, being confronted with a major advancement in the selection of the best-in-class photonic material platforms for weighting and activation functions and their transformation into co-integrated photonic computational engines. With this paper, we analyze the current status in neuromorphic computing and in available photonic integrated technologies and propose a novel three-dimensional computational unit which, with its compactness, ultrahigh efficiency, and lossless interconnectivity, is foreseen to allow scalable computation AI chipsets that outperform electronics in computational speed and energy efficiency to shape the future of neuromorphic computing.

Rainfall is a result of several complex atmospheric processes making it challenging to predict. For countries whose economy is dominated by agricultural sector, accurate rainfall prediction is highly essential. A huge network of weather stations is spread across the globe for the observation of meteorological parameters. These generate vast amounts of data which can be used to accurately predict the weather. This necessitates the use better tools such as various artificially intelligent algorithms. This study aims to explore global research trends in monsoon rainfall prediction techniques using Artificial Intelligence (AI) and Artificial Neural Networks (ANN). Scopus database has been used for carrying out bibliometric analysis for the period 1979 to 2021. The Scopus database has been analyzed for a number of publications, sources, languages, countries, affiliations etc. The analysis revealed that monsoon rainfall is sensitive to various factors such as sea surface temperature, El Nino, Southern Oscillation and many more. Statistical and dynamic models were used for monsoon rainfall forecasting and AI tools have been used for monsoon rainfall forecasting since 2000. Publications are mainly in the form of research articles and 99.7% of the literature is in the English language. Of the total publications, contributions from India are 55% while the United States and China contributed 18.67% and 14.3%, respectively.

As the internet grows and diversity, attackers use various attacks to crash the servers and to stop specific sites. Multiple computers and multiple Internet connections are targeted by using distributed denial of service (DDoS) attacks. The aim of this paper is to identify the best algorithm among the selected algorithms (i.e., gradient descent with momentum algorithm, scaled conjugate gradient, and variable learning rate gradient descent algorithm.  In this study, the recurrent neural network was trained to check the accuracy and detection of DDoS attacks. The intention of this training was to allow the system to learn and classify the input traffic into the category. The proposed system's training was composed of three separate algorithms utilizing recurrent neural networks. The MATLAB 2018a simulator was used for training purpose. Moreover, clean the Knowledge Discovery Dataset (KDD) during design and include the values of protocols, attacks, and flags. The neural network model was subsequently developed, and the KDD was trained using Artificial Neural Network (ANN). The results of DDoS attacks’ detection were analyzed using MATLAB's ANN toolbox. The success rate of the variable learning rate gradient descent algorithm was 99.9% accuracy and the short timing was 2 minutes and 29 seconds. The variable learning rate gradient descent algorithm gives better results than gradient descent with momentum and scaled conjugate gradient algorithms.  In the state of the art, different algorithms have been trained in different neural networks and different KDD datasets by using selective DDoS attacks but in this research recurrent neural network was used for three different algorithms. In this research, we have used total of 22 attacks for detection of DDoS attacks’ accuracy.

Vegetation canopy height (CH) is one of the important remote-sensing parameters related to forests’ structure, and it can be related to the biomass and the carbon stock. Global navigation satellite system-reflectometry (GNSS-R) has proved capable to retrieve vegetation information at a moderate resolution from space (20–65 km) using L1 C/A signals. In this study, data retrieved by the airborne microwave interferometric reflectometer (MIR) GNSS-R instrument at L1 and L5 are compared to the Global Forest CH product, with a spatial resolution of 30 m. This work analyzes the waveforms (WFs) measured at both bands, and the correlation of the waveform width and the reflectivity values to the CH product. A neural network algorithm is used for the retrieval, showing that the combination of the reflectivity and the waveform width allows to estimate the CH information at a very high resolution, with a root-mean-square error (RMSE) of 4.25 and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual CH., Postprint (updated version)

As humans are intelligent, to mimic or models of human certain intelligent behavior to a computer or a machine is called Artificial Intelligence (AI). Learning is one of the activities by a human that helps to gain knowledge or skills by studying, practising, being taught, or experiencing something. Machine Learning (ML) is a field of AI that mimics human learning behavior by constructing a set of algorithms that can learn from data, i.e. it is a field of study that gives computers the ability to learn without being explicitly programmed. The reasoning is a set of processes that enable humans to provide a basis for judgment, making decisions, and prediction. Machine Reasoning (MR), is a part of AI evolution towards human-level intelligence or the ability to apply prior knowledge to new situations with adaptation and changes. This book chapter presents some AI, ML and MR techniques and approached those are widely used in health informatics domains. Here, the overview of each technique is discussed to show how they can be applied in the development of a decision support system.

Python is one of the most widely adopted programming languages, having replaced a number of those in the field. Python is popular with developers for a variety of reasons, one of which is because it has an incredibly diverse collection of libraries in which users can run. This paper provides the most current survey on Keras, which is a Python-based deep learning Application Programming Interface (API) that runs on top of the machine learning framework TensorFlow. The mentions library is used in conjunction with TensorFlow, PyTorch, CODEEPNEATM, and Pygame to allow integration of deep learning models such as cardiovascular disease diagnostics, graph neural networks, identify health issues, COVID-19 recognition, skin tumors, image detection, and so on, in the applied area. Furthermore, the author used Keras details, goals, challenges, and significant outcomes, as well as the findings, obtained using this method.   Keywords

This work develops an Artificial Neural Network (ANN) model for performing Breast Cancer (BC) classification tasks. The design of the model considers studying different ANN architectures from the literature and chooses the one with the best performance. This ANN model aims to classify BC cases more systematically and more quickly. It provides facilities in the field of medicine to detect breast cancer among women. The ANN classification model is able to achieve an average accuracy of 98.88 % with an average run time of 0.182 seconds. Using this model, the classification of BC can be carried out much more faster than manual diagnosis and with good enough accuracy.