Search results for: computational error

Authors: Elham Bagheri, Yalda Mohsenzadeh

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Image memorability refers to the phenomenon where certain images are more likely to be remembered by humans than others. It is a quantifiable and intrinsic attribute of an image. Understanding how visual perception and memory interact is important in both cognitive science and artificial intelligence. It reveals the complex processes that support human cognition and helps to improve machine learning algorithms by mimicking the brain's efficient data processing and storage mechanisms. To explore the computational underpinnings of image memorability, this study examines the relationship between an image's reconstruction error, distinctiveness in latent space, and its memorability score. A trained autoencoder is used to replicate human-like memorability assessment inspired by the visual memory game employed in memorability estimations. This study leverages a VGG-based autoencoder that is pre-trained on the vast ImageNet dataset, enabling it to recognize patterns and features that are common to a wide and diverse range of images. An empirical analysis is conducted using the MemCat dataset, which includes 10,000 images from five broad categories: animals, sports, food, landscapes, and vehicles, along with their corresponding memorability scores. The memorability score assigned to each image represents the probability of that image being remembered by participants after a single exposure. The autoencoder is finetuned for one epoch with a batch size of one, attempting to create a scenario similar to human memorability experiments where memorability is quantified by the likelihood of an image being remembered after being seen only once. The reconstruction error, which is quantified as the difference between the original and reconstructed images, serves as a measure of how well the autoencoder has learned to represent the data. The reconstruction error of each image, the error reduction, and its distinctiveness in latent space are calculated and correlated with the memorability score. Distinctiveness is measured as the Euclidean distance between each image's latent representation and its nearest neighbor within the autoencoder's latent space. Different structural and perceptual loss functions are considered to quantify the reconstruction error. The results indicate that there is a strong correlation between the reconstruction error and the distinctiveness of images and their memorability scores. This suggests that images with more unique distinct features that challenge the autoencoder's compressive capacities are inherently more memorable. There is also a negative correlation between the reduction in reconstruction error compared to the autoencoder pre-trained on ImageNet, which suggests that highly memorable images are harder to reconstruct, probably due to having features that are more difficult to learn by the autoencoder. These insights suggest a new pathway for evaluating image memorability, which could potentially impact industries reliant on visual content and mark a step forward in merging the fields of artificial intelligence and cognitive science. The current research opens avenues for utilizing neural representations as instruments for understanding and predicting visual memory.

Keywords: autoencoder, computational vision, image memorability, image reconstruction, memory retention, reconstruction error, visual perception

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Authors: Neetu Manocha

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Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR).

Keywords: satellite image, image segmentation, edge detection, error rate, MSE, PSNR, HIST-SI, linking, filtering, imp_HIST-SI

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Authors: Sie Long Kek, Wah June Leong, Kok Lay Teo

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Linear quadratic Gaussian model is a standard mathematical model for the stochastic optimal control problem. The combination of the linear quadratic estimation and the linear quadratic regulator allows the state estimation and the optimal control policy to be designed separately. This is known as the separation principle. In this paper, an efficient computational method is proposed to solve the linear quadratic Gaussian problem. In our approach, the Hamiltonian function is defined, and the necessary conditions are derived. In addition to this, the output error is defined and the least-square optimization problem is introduced. By determining the first-order necessary condition, the gradient of the sum squares of output error is established. On this point of view, the stochastic approximation approach is employed such that the optimal control policy is updated. Within a given tolerance, the iteration procedure would be stopped and the optimal solution of the linear-quadratic Gaussian problem is obtained. For illustration, an example of the linear-quadratic Gaussian problem is studied. The result shows the efficiency of the approach proposed. In conclusion, the applicability of the approach proposed for solving the linear quadratic Gaussian problem is highly demonstrated.

Keywords: iteration procedure, least squares solution, linear quadratic Gaussian, output error, stochastic approximation

Procedia PDF Downloads 138

Authors: Cinna Soltanpur, Mohammad Ghamari, Behzad Momahed Heravi, Fatemeh Zare

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Low-density parity-check (LDPC) codes have been shown to deliver capacity approaching performance; however, problematic graphical structures (e.g. trapping sets) in the Tanner graph of some LDPC codes can cause high error floors in bit-error-ratio (BER) performance under conventional sum-product algorithm (SPA). This paper presents a serial concatenation scheme to avoid the trapping sets and to lower the error floors of LDPC code. The outer code in the proposed concatenation is the LDPC, and the inner code is a high rate array code. This approach applies an interactive hybrid process between the BCJR decoding for the array code and the SPA for the LDPC code together with bit-pinning and bit-flipping techniques. Margulis code of size (2640, 1320) has been used for the simulation and it has been shown that the proposed concatenation and decoding scheme can considerably improve the error floor performance with minimal rate loss.

Keywords: concatenated coding, low–density parity–check codes, array code, error floors

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Authors: Houcine Naim, Abdelatif Hassini, Noureddine Benabadji, Alex Van Den Bossche

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In this paper, two models to estimate the overall monthly average daily radiation on a horizontal surface were applied to the site of Oran (35.38 ° N, 0.37 °W). We present a comparison between the first one is a regression equation of the Angstrom type and the second model is developed by the present authors some modifications were suggested using as input parameters: the astronomical parameters as (latitude, longitude, and altitude) and meteorological parameters as (relative humidity). The comparisons are made using the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), and mean absolute bias error (MABE). This comparison shows that the second model is closer to the experimental values that the model of Angstrom.

Keywords: meteorology, global radiation, Angstrom model, Oran

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Authors: Filippo Portera

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Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

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Authors: Watchareeporn Chaimongkol

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In this paper, we compare the statistical measures accuracy of composite forecasting model to estimate automobile customer demand in Thailand. A modified simple exponential smoothing and autoregressive integrate moving average (ARIMA) forecasting model is built to estimate customer demand of passenger cars, instead of using information of historical sales data. Our model takes into account special characteristic of the Thai automobile market such as sales promotion, advertising and publicity, petrol price, and interest rate for loan. We evaluate our forecasting model by comparing forecasts with actual data using six accuracy measurements, mean absolute percentage error (MAPE), geometric mean absolute error (GMAE), symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), median relative absolute error (MdRAE), and geometric mean relative absolute error (GMRAE).

Keywords: composite forecasting, simple exponential smoothing model, autoregressive integrate moving average model selection, accuracy measurements

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: R. Sabre, W. Horrigue, J. C. Simon

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This paper is interested in two difficulties encountered in practice when observing a continuous time process. The first is that we cannot observe a process over a time interval; we only take discrete observations. The second is the process frequently observed with a constant additive error. It is important to give an estimator of the spectral density of such a process taking into account the additive observation error and the choice of the discrete observation times. In this work, we propose an estimator based on the spectral smoothing of the periodogram by the polynomial Jackson kernel reducing the additive error. In order to solve the aliasing phenomenon, this estimator is constructed from observations taken at well-chosen times so as to reduce the estimator to the field where the spectral density is not zero. We show that the proposed estimator is asymptotically unbiased and consistent. Thus we obtain an estimate solving the two difficulties concerning the choice of the instants of observations of a continuous time process and the observations affected by a constant error.

Keywords: spectral density, stable processes, aliasing, periodogram

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Authors: Kunal Das, Priya Sengupta, Abhishek K. Singh

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Pin assignment, scheduling, routing and error detection for arbitrary biochemical protocols in Digital Microfluidic Biochip have been reported in this paper. The research work is concentrating on pin assignment for 2 or 3 droplets routing in the arbitrary biochemical protocol, scheduling and routing in m × n biochip. The volumetric error arises due to droplet split in the biochip. The volumetric error detection is also addressed using biochip AND logic gate which is known as microfluidic AND or mAND gate. The algorithm for pin assignment for m × n biochip required m+n-1 numbers of pins. The basic principle of this algorithm is that no same pin will be allowed to be placed in the same column, same row and diagonal and adjacent cells. The same pin should be placed a distance apart such that interference becomes less. A case study also reported in this paper.

Keywords: digital microfludic biochip, cross-contamination, pin assignment, microfluidic AND gate

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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In this paper, we briefly introduce the concept of Poisson attacks in the case of defense/attack strategies where attacks are assumed to be continuous. We suggest a game model in which the attacker will combine both criteria of a sufficient confidence level of a successful attack and a reasonably small size of the estimation error in order to launch an attack. Here, estimation error arises from assessing the system failure upon attack using aggregate data at the system level. The corresponding error is referred to as aggregation error. On the other hand, the defender will attempt to deter attack by making one or both criteria inapplicable. The defender will build his/her strategy by both strengthening the targeted system and increasing the size of error. We will formulate the defender problem based on appropriate optimization models. The attacker will opt for a Bayesian updating in assessing the impact on the improvement made by the defender. Then, the attacker will evaluate the feasibility of the attack before making the decision of whether or not to launch it. We will provide illustrations to better explain the process.

Keywords: attacker, defender, game theory, information

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Authors: Chaiwat Tantarangsee

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The purposes of this study are: 1) to study the frequent English writing errors of students registering the course: Reading and Writing English for Academic Purposes II, and 2) to find out the results of writing error correction by using coded indirect corrective feedback and writing error treatments. Samples include 28 2nd year English Major students, Faculty of Education, Suan Sunandha Rajabhat University. Tool for experimental study includes the lesson plan of the course; Reading and Writing English for Academic Purposes II, and tool for data collection includes 4 writing tests of short texts. The research findings disclose that frequent English writing errors found in this course comprise 7 types of grammatical errors, namely Fragment sentence, Subject-verb agreement, Wrong form of verb tense, Singular or plural noun endings, Run-ons sentence, Wrong form of verb pattern and Lack of parallel structure. Moreover, it is found that the results of writing error correction by using coded indirect corrective feedback and error treatment reveal the overall reduction of the frequent English writing errors and the increase of students’ achievement in the writing of short texts with the significance at .05.

Keywords: coded indirect corrective feedback, error correction, error treatment, frequent English writing errors

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Authors: Safiullah Abdullahi

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This paper describes the design of a digital control system which controls the speed and direction of a robot. The robot is expected to follow a black thick line with the highest possible speed and lowest error around the line. The control system of the robot will correct for the angle error that is made between the frame axis of the robot and the line. The cause for error is the difference in speed of the two driving wheels of the robot which are driven by two separate DC motors, whereas the speed difference in wheels is due to the un-modeled fraction that is available in the wheels with different magnitudes in each. The control scheme is that a number of photo sensors are mounted in the front of the robot and report their position in reference to the black line to the digital controller. The controller then, evaluates the position error and generates the needed duty cycle for the related wheel motor to drive it faster or slower.

Keywords: digital control, robot, controller, control system

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Authors: Jun Sung Go, Jong Kang Park, Jong Tae Kim

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As semi-conductor manufacturing technology evolves; the single event transient problem becomes more significant issue. Single event transient has a critical impact on both combinational and sequential logic circuits, so it is important to evaluate the soft error tolerance of the circuits at the design stage. In this paper, we present a soft error detecting simulation using scan chain. The simulation model generates a single event transient randomly in the circuit, and detects the soft error during the execution of the test patterns. We verified this model by inserting a scan chain in an 8051 microprocessor using 65 nm CMOS technology. While the test patterns generated by ATPG program are passing through the scan chain, we insert a single event transient and detect the number of soft errors per sub-module. The experiments show that the soft error rates per cell area of the SFR module is 277% larger than other modules.

Keywords: scan chain, single event transient, soft error, 8051 processor

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Authors: Daniel Fulus Fom, Gau Patrick Damulak

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In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN.

Keywords: auto regressive, mean absolute error, neural network, root square mean error

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Authors: Gabriela Estrada, Joaquim Lloveras

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Design for error-proofing assembly is a new DFX approach to prevent assembly issues since early design stages. Assembly issues that can happen during the life phases of a system such as: production, installation, operation, and replacement phases. This prevention is possible by designing the product with poka-yoke or error-proofing characteristics. This approach guide designers to make decisions based on poka-yoke assembly design requirements. As a result of applying these requirements designers are able to create solutions to prevent assembly issues for the product in development stage. This paper integrates the needs to design products in an error proofing way into the systematic approach of design process by Pahl and Beitz. A case study is presented applying this approach.

Keywords: poka-yoke, error-proofing, assembly issues, design process, life phases of a system

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Authors: Gabriela Estrada, Joaquim Lloveras

Abstract:

Design for error-proofing assembly is a new DFX approach to prevent assembly issues since early design stages. Assembly issues that can happen during the life phases of a system such as: production, installation, operation and replacement phases. This prevention is possible by designing the product with poka-yoke or error-proofing characteristics. This approach guide designers to make decisions based on poka-yoke assembly design requirements. As a result of applying these requirements designers are able to create solutions to prevent assembly issues for the product in development stage. This paper integrates the needs to design products in an error proofing way into the systematic approach of design process by Pahl and Beitz. A case study is presented applying this approach.

Keywords: poka-yoke, error-proofing, assembly issues, design process, life phases of a system

Procedia PDF Downloads 292

Authors: Chaiwat Tantarangsee

Abstract:

The purposes of this study are 1) to study the frequent English writing errors of students registering the course: Reading and Writing English for Academic Purposes II, and 2) to find out the results of writing error correction by using coded indirect corrective feedback and writing error treatments. Samples include 28 2nd year English Major students, Faculty of Education, Suan Sunandha Rajabhat University. Tool for experimental study includes the lesson plan of the course; Reading and Writing English for Academic Purposes II, and tool for data collection includes 4 writing tests of short texts. The research findings disclose that frequent English writing errors found in this course comprise 7 types of grammatical errors, namely Fragment sentence, Subject-verb agreement, Wrong form of verb tense, Singular or plural noun endings, Run-ons sentence, Wrong form of verb pattern and Lack of parallel structure. Moreover, it is found that the results of writing error correction by using coded indirect corrective feedback and error treatment reveal the overall reduction of the frequent English writing errors and the increase of students’ achievement in the writing of short texts with the significance at .05.

Keywords: coded indirect corrective feedback, error correction, error treatment, English writing

Procedia PDF Downloads 277

Authors: Anood Al-Shibli

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Designing a learner corpus is not an easy task to accomplish because dealing with learners’ language has many variables which might affect the results of any study based on learners’ language production (spoken and written). Also, it is very essential to systematically design a learner corpus especially when it is aimed to be a reference to language research. Therefore, designing the Omani Learner Corpus (OLEC) has undergone many explicit and systematic considerations. These criteria can be regarded as the foundation to design any learner corpus to be exploited effectively in language use and language learning studies. Added to that, OLEC is manually error-annotated corpus. Error-annotation in learner corpora is very essential; however, it is time-consuming and prone to errors. Consequently, a navigating tool is designed to help the annotators to insert errors’ codes in order to make the error-annotation process more efficient and consistent. To assure accuracy, error annotation procedure is followed to annotate OLEC and some preliminary findings are noted. One of the main results of this procedure is creating an error-annotation system based on the Omani learners of English language production. Because OLEC is still in the first stages, the primary findings are related to only one level of proficiency and one error type which is verb related errors. It is found that Omani learners in OLEC has the tendency to have more errors in forming the verb and followed by problems in agreement of verb. Comparing the results to other error-based studies indicate that the Omani learners tend to have basic verb errors which can found in lower-level of proficiency. To this end, it is essential to admit that examining learners’ errors can give insights to language acquisition and language learning and most errors do not happen randomly but they occur systematically among language learners.

Keywords: error-annotation system, error-annotation manual, learner corpora, verbs related errors

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Authors: Jae-Hyun Ro, Yong-Jun Kim, Chang-Bin Ha, Hyoung-Kyu Song

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In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection.

Keywords: detection, DFE, MIMO-OFDM, ML

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Authors: Zijian Zhang, Yangyang Dong

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Synchronization is of great importance to ensure the multi-arm robot to complete the task. Therefore, a synchronous controller is designed to coordinate task space motion of the multi-arm in the paper. The position error, the synchronous position error, and the coupling position error are all considered in the controller. Besides, an adaptive control method is used to adjust parameters of the controller to improve the effectiveness of coordinated control performance. Simulation in the Matlab shows the effectiveness of the method. At last, a robot experiment platform with two 7-DOF (Degree of Freedom) robot arms has been established and the synchronous controller simplified to control dual-arm robot has been validated on the experimental set-up. Experiment results show the position error decreased 10% and the corresponding frequency is also greatly improved.

Keywords: synchronous control, space robot, task space control, multi-arm robot

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Authors: Dalvinder Kaur Mangal

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For the past decade, noise corrupted output measurements have been a fundamental research problem to be investigated. On the other hand, the estimation of the parameters for linear dynamic systems when also the input is affected by noise is recognized as more difficult problem which only recently has received increasing attention. Representations where errors or measurement noises/disturbances are present on both the inputs and outputs are usually called errors-in-variables (EIV) models. These disturbances may also have additive effects which are also considered in this paper. Parameter estimation of false EIV problem using equation error, output error and iterative prefiltering identification schemes with and without additive uncertainty, when only the output observation is corrupted by noise has been dealt in this paper. The comparative study of these three schemes has also been carried out.

Keywords: errors-in-variable (EIV), false EIV, equation error, output error, iterative prefiltering, Gaussian noise

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Maria Torres

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After the acknowledgment of contrastive analysis, Pit Coder’s establishment of error analysis revolutionized the way instructors analyze and examine students’ writing errors. One question that relates to error analysis with speakers of a first language, in this case, Spanish, who are learning a second language (English), is the type of errors that these learners make along with the causes of these errors. Many studies have looked at the way the native tongue influences second language acquisition, but this method does not take into account other possible sources of students’ errors. This paper examines writing samples from an advanced ESL class whose first language is Spanish at non-profit organization, Learning Quest Stanislaus Literacy Center. Through error analysis, errors in the students’ writing were identified, described, and classified. The purpose of this paper was to discover the type and origin of their errors which generated appropriate treatments. The results in this paper show that the most frequent errors in the advanced ESL students’ writing pertain to interlanguage and a small percentage from an intralanguage source. Lastly, the least type of errors were ones that originate from negative transfer. The results further solidify the idea that there are other errors and sources of errors to account for rather than solely focusing on the difference between the students’ mother and target language. This presentation will bring to light some strategies and techniques that address the issues found in this research. Taking into account the amount of error pertaining to interlanguage, an ESL teacher should provide metalinguistic awareness of the students’ errors.

Keywords: error analysis, ESL, interlanguage, intralangauge

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Authors: Man Li, Wanting Zhou, Lei Li

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Communication stability is the primary concern of communication satellites. Communication satellites are easily affected by particle radiation to generate single event effects (SEE), which leads to soft errors (SE) of the combinational logic circuit. The existing research on soft error rates (SER) of the combined logic circuit is mostly based on the assumption that the logic gates being bombarded have the same pulse width. However, in the actual radiation environment, the pulse widths of the logic gates being bombarded are different due to different linear energy transfers (LET). In order to improve the accuracy of SER evaluation model, this paper proposes a soft error rate evaluation method based on LET. In this paper, the authors analyze the influence of LET on the pulse width of combinational logic and establish the pulse width model based on the LET. Based on this model, the error rate of test circuit ISCAS'85 is calculated. The effectiveness of the model is proved by comparing it with previous experiments.

Keywords: communication satellite, pulse width, soft error rates, LET

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Authors: Mouna Hemdi, Jamel bel Hadj Tahar

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The increasing development of multimedia applications requiring the simultaneous transport of several different services contributes to the evolution of the need for very high-speed network. In this paper, we propose an effective solution to achieve the very high speed while retaining elements of the optical transmission channel. So our study focuses on error correcting codes that aim for quality improvement on duty. We present a comparison of the quality of service for single channels and integrating the code BCH, RS and LDPC in order to find the best code in the different conditions of the transmission.

Keywords: code error correction, high speed broadband, optical transmission, information systems security

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Authors: Tijing Cai, Qimeng Xu, Daijin Zhou

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This paper puts forward a short-baseline dual-antenna BDS/MEMS-IMU integrated navigation, constructs the carrier phase double difference model of BDS (BeiDou Navigation Satellite System), and presents a 2-position initial orientation method on BDS. The Extended Kalman-filter has been introduced for the integrated navigation system. The differences between MEMS-IMU and BDS position, velocity and carrier phase indications are used as measurements. To show the performance of the short-baseline dual-antenna BDS/MEMS-IMU integrated navigation system, the experiment results show that the position error is less than 1m, the pitch angle error and roll angle error are less than 0.1°, and the heading angle error is about 1°.

Keywords: MEMS-IMU (Micro-Electro-Mechanical System Inertial Measurement Unit), BDS (BeiDou Navigation Satellite System), dual-antenna, integrated navigation

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Authors: Salisu ibrahim, Abdalla Rababah

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In this research, we use Weighted G2-Multi-degree reduction of Bezier curve of degree n to a Bezier curve of degree m, m < n. The degree reduction of Bezier curves is used to represent a given Bezier curve of n by a Bezier curve of degree m, m < n. Exact degree reduction is not possible, and degree reduction is approximate process in nature. We derive a weighted degree reducing method that is geometrically continuous at the end points. Different norms will be considered, several error minimizations will be given. The proposed methods produce error function that are less than the errors of existing methods.

Keywords: Bezier curves, multiple degree reduction, geometric continuity, error function

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Authors: Albert Acheampong, Tamer Elshandidy

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We investigate the extent to which sustainability disclosure from the narrative section of European banks’ annual reports improves analyst forecast accuracy. We capture sustainability disclosure using a machine learning approach and use forecast error to proxy analyst forecast accuracy. Our results suggest that sustainability disclosure significantly improves analyst forecast accuracy by reducing the forecast error. In a further analysis, we also find that the induction of Directive 2014/95/European Union (EU) is associated with increased disclosure content, which then reduces forecast error. Collectively, our results suggest that sustainability disclosure improves forecast accuracy, and the induction of the new EU directive strengthens this improvement. These results hold after several further and robustness analyses. Our findings have implications for market participants and policymakers.

Keywords: sustainability disclosure, machine learning, analyst forecast accuracy, forecast error, European banks, EU directive

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Authors: Young Hee Geum

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In this paper, we present the iterative method and determine the control parameters to converge cubically for solving nonlinear equations. In addition, we derive the asymptotic error constant.

Keywords: asymptotic error constant, iterative method, multiple root, root-finding, order of convergent

Procedia PDF Downloads 265