Search results for: accuracy of payment time

Authors: Yun Chen, Qihang Liu, Catherine Ticehurst, Chandrama Sarker, Fazlul Karim, Dave Penton, Ashmita Sengupta

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The application of ICESAT-2 altimetry data in river hydrology critically depends on the accuracy of the mean water surface elevation (WSE) at a virtual station (VS) where satellite observations intersect with water. The ICESAT-2 track generates multiple VSs as it crosses the different water bodies. The difficulties are particularly pronounced in large river basins where there are many tributaries and meanders often adjacent to each other. One challenge is to split photon segments along a beam to accurately partition them to extract only the true representative water height for individual elements. As far as we can establish, there is no automated procedure to make this distinction. Earlier studies have relied on human intervention or river masks. Both approaches are unsatisfactory solutions where the number of intersections is large, and river width/extent changes over time. We describe here an automated approach called “auto-segmentation”. The accuracy of our method was assessed by comparison with river water level observations at 10 different stations on 37 different dates along the Lower Murray River, Australia. The congruence is very high and without detectable bias. In addition, we compared different outlier removal methods on the mean WSE calculation at VSs post the auto-segmentation process. All four outlier removal methods perform almost equally well with the same R2 value (0.998) and only subtle variations in RMSE (0.181–0.189m) and MAE (0.130–0.142m). Overall, the auto-segmentation method developed here is an effective and efficient approach to deriving accurate mean WSE at river VSs. It provides a much better way of facilitating the application of ICESAT-2 ATL13 altimetry to rivers compared to previously reported studies. Therefore, the findings of our study will make a significant contribution towards the retrieval of hydraulic parameters, such as water surface slope along the river, water depth at cross sections, and river channel bathymetry for calculating flow velocity and discharge from remotely sensed imagery at large spatial scales.

Keywords: lidar sensor, virtual station, cross section, mean water surface elevation, beam/track segmentation

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Authors: Hui-Ling Huang, Tamara Vasylenko, Phasit Charoenkwan, Shih-Hsiang Chiu, Shinn-Ying Ho

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The knowledge of the human transporters is still limited due to technically demanding procedure of crystallization for the structural characterization of transporters by spectroscopic methods. It is desirable to develop bioinformatics tools for effective analysis of available sequences in order to identify human transmembrane transporter proteins (HMTPs). This study proposes a scoring card method (SCM) based method for predicting HMTPs. We estimated a set of propensity scores of dipeptides to be HMTPs using SCM from the training dataset (HTS732) consisting of 366 HMTPs and 366 non-HMTPs. SCM using the estimated propensity scores of 20 amino acids and 400 dipeptides -as HMTPs, has a training accuracy of 87.63% and a test accuracy of 66.46%. The five top-ranked dipeptides include LD, NV, LI, KY, and MN with scores 996, 992, 989, 987, and 985, respectively. Five amino acids with the highest propensity scores are Ile, Phe, Met, Gly, and Leu, that hydrophobic residues are mostly highly-scored. Furthermore, obtained propensity scores were used to analyze physicochemical properties of human transporters.

Keywords: dipeptide composition, physicochemical property, human transmembrane transporter proteins, human transmembrane transporters binding propensity, scoring card method

Procedia PDF Downloads 353

Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

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Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Hung-Yuan Chang, Wen-Lung Chiang, Kuo-Liang Wu, Chen-Tsung Lin

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National Space Organization (NSPO) has completed in 2014 the development of a space-borne GPS receiver, including design, manufacture, comprehensive functional test, environmental qualification test and so on. The main performance of this receiver include 8-meter positioning accuracy, 0.05 m/sec speed-accuracy, the longest 90 seconds of cold start time, and up to 15g high dynamic scenario. The receiver will be integrated in the autonomous FORMOSAT-7 NSPO-Built satellite scheduled to be launched in 2019 to execute pre-defined scientific missions. The flight model of this receiver manufactured in early 2015 will pass comprehensive functional tests and environmental acceptance tests, etc., which are expected to be completed by the end of 2015. The space-borne GPS receiver is a pure software design in which all GPS baseband signal processing are executed by a digital signal processor (DSP), currently only 50% of its throughput being used. In response to the booming global navigation satellite systems, NSPO will gradually expand this receiver to become a multi-mode, multi-band, high-precision navigation receiver, and even a science payload, such as the reflectometry receiver of a global navigation satellite system. The fundamental purpose of this extension study is to port some software algorithms such as signal acquisition and correlation, reused code and large amount of computation load to the FPGA whose processor is responsible for operational control, navigation solution, and orbit propagation and so on. Due to the development and evolution of the FPGA is pretty fast, the new system architecture upgraded via an FPGA should be able to achieve the goal of being a multi-mode, multi-band high-precision navigation receiver, or scientific receiver. Finally, the results of tests show that the new system architecture not only retains the original overall performance, but also sets aside more resources available for future expansion possibility. This paper will explain the detailed DSP/FPGA architecture, development, test results, and the goals of next development stage of this receiver.

Keywords: space-borne, GPS receiver, DSP, FPGA, multi-mode multi-band

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Authors: Tarek Abdel Azim Ahmed, Eman Sarhan Shaker

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This research aims to explore how well the extended model of internet commerce adoption (eMICA) model is often used to determine the extent of internet commerce adoption in the travel agencies sector in both Egypt and Kingdom of Saudi Arabia (KSA). The web content analysis method was used to analyze the level of adoption of Egyptian travel agencies and Saudi travel agencies according to data immensely available on their websites. Therefore, each site was categorized according to the phases and levels proposed. In order to achieve this, 120 websites were evaluated by the two authors over a three-month period, from August to October 2020, and then categorized according to the phases and levels of (eMICA). The results show that there are deficiencies in the application of the eMICA model by both KSA and Egyptian travel agencies, generally, updating their websites, the absence of quality certification, offering secure online payment, virtual tours, and videos using Flash animation. In general, the Egyptian companies slightly outperformed the KSA ones in applying eMICA model.

Keywords: e-commerce, internet marketing, eMICA, travel agencies, websites

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Ismaila Olabisi Ogundiji, Hakeem Mayowa Olujide, Qasim Usamot

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The main reasons for site-specific management for agricultural inputs are to increase the profitability of crop production, to protect the environment and to improve products’ quality. Information about the variability of different soil attributes within a field is highly essential for the decision-making process. Lack of fast and accurate acquisition of soil characteristics remains one of the biggest limitations of precision agriculture due to being expensive and time-consuming. Adaptive sampling has been proven as an accurate and affordable sampling technique for planning within a field for site-specific management of agricultural inputs. This study employed spatial uncertainty of soil apparent electrical conductivity (ECa) estimates to identify adaptive re-survey areas in the field. The original dataset was grouped into validation and calibration groups where the calibration group was sub-grouped into three sets of different measurements pass intervals. A conditional simulation was performed on the field ECa to evaluate the ECa spatial uncertainty estimates by the use of the geostatistical technique. The grouping of high-uncertainty areas for each set was done using image segmentation in MATLAB, then, high and low area value-separate was identified. Finally, an adaptive re-survey was carried out on those areas of high-uncertainty. Adding adaptive re-surveying significantly minimized the time required for resampling whole field and resulted in ECa with minimal error. For the most spacious transect, the root mean square error (RMSE) yielded from an initial crude sampling survey was minimized after an adaptive re-survey, which was close to that value of the ECa yielded with an all-field re-survey. The estimated sampling time for the adaptive re-survey was found to be 45% lesser than that of all-field re-survey. The results indicate that designing adaptive sampling through spatial uncertainty models significantly mitigates sampling cost, and there was still conformity in the accuracy of the observations.

Keywords: soil electrical conductivity, adaptive sampling, conditional simulation, spatial uncertainty, site-specific management

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Authors: Nathan Elvidge

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This paper offers a philosophical argument against the possibility of so-called “ethical porn,” that is, pornographic material produced in a way attempting to remain consistent with feminist principles and female empowerment. One key feature of such material is the requirement for the material to be consensual on the part of the actors or those involved in the material. However, in the contemporary pornography industry, this typically amounts to a single historic act of consent given in exchange for a lump-sum payment which grants the producer lifetime property rights over the explicit material. This paper argues that, by the lights of feminist principles, this situation is inherently unjust and that, as a consequence, the pornography industry requires a radical systematic upheaval before any material produced within it can be considered genuinely ethical. These feminist principles require that for the consumption of pornography to be genuinely ethical, the actors must consent not only to the acts recorded in the material but also to the consumption of that material. This paper argues that this consent to consumption should be treated as on par with other matters of sexual consent and, therefore, that actors should have the right to withdraw consent to the consumption of their material. From this, it is argued to follow that the system of third-party ownership of property rights over someone else’s sexually explicit material legally nullifies this right and therefore is inherently unjust.

Keywords: consent, feminism, pornography, sex work

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Authors: Yu Guan, Song Lu, Wei Yuan, Heping Zhang

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Gas fire extinguishing system is widely used due to its cleanliness and efficiency, and since its spray will be affected by many factors such as convection and obstacles in jetting region, so in order to evaluate its effectiveness, detecting concentration distribution in the jetting area is indispensable, which is commonly achieved by aspirating concentration detection technique. During the concentration measurement, the response time of detector is a very important parameter, especially for those fire-extinguishing systems with rapid gas dispersion. Long response time will not only underestimate its concentration but also prolong the change of concentration with time. Therefore it is necessary to analyze the factors influencing the response time. In the paper, an aspirating concentration detection method was introduced, which is achieved by using a small critical nozzle and a laminar flowmeter, and because of the response time is mainly related to the gas transport process from sampling site to the sensor, the effects of exhaust pipe size, gas flow rate, and gas concentration on its response time were analyzed. During the research, Bromotrifluoromethane (CBrF₃) was used. The effect of the sampling tube was investigated with different length of 1, 2, 3, 4 and 5 m (5mm in pipe diameter) and different pipe diameter of 3, 4, 5, 6 and 8 mm (3m in length). The effect of gas flow rate was analyzed by changing the throat diameter of the critical nozzle with 0.5, 0.682, 0.75, 0.8, 0.84 and 0.88 mm. The effect of gas concentration on response time was studied with the concentration range of 0-25%. The result showed that the response time increased with the increase of both the length and diameter of the sampling pipe, and the effect of length on response time was linear, but for the effect of diameter, it was exponential. It was also found that as the throat diameter of critical nozzle increased, the response time reduced a lot, in other words, gas flow rate has a great influence on response time. For the effect of gas concentration, the response time increased with the increase of the CBrF₃ concentration, and the slope of the curve was reduced.

Keywords: aspirating concentration detection, fire extinguishing, gaseous agent, response time

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Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

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Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

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Authors: S. Thongchai

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Result of the study on knowledge management systems in businesses was shown that the most of these businesses provide internet accessibility for their employees in order to study new knowledge via internet, corporate website, electronic mail, and electronic learning system. These business organizations use information technology application for knowledge management because of convenience, time saving, ease of use, accuracy of information and knowledge usefulness. The result indicated prominent improvements for corporate knowledge management systems as the following; 1) administrations must support corporate knowledge management system 2) the goal of corporate knowledge management must be clear 3) corporate culture should facilitate the exchange and sharing of knowledge within the organization 4) cooperation of personnel of all levels must be obtained 5) information technology infrastructure must be provided 6) they must develop the system regularly and constantly.

Keywords: business organizations, information technology application, knowledge management systems, prominent improvements

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Authors: Pouya Kaafi, Gholamreza Ghodrati Amiri

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Abnormal loads due to natural events, implementation errors and some other issues can lead to occurrence of progressive collapse in structures. Most of the past researches consist of 2- Dimensional (2D) models of steel frames without consideration of the floor system effects, which reduces the accuracy of the modeling. While employing a 3-Dimensional (3D) model and modeling the concrete slab system for the floors have a crucial role in the progressive collapse evaluation. In this research, a 3D finite element model of a 5-story steel building is modeled by the ABAQUS software once with modeling the slabs, and the next time without considering them. Then, the progressive collapse potential is evaluated. The results of the analyses indicate that the lack of the consideration of the slabs during the analyses, can lead to inaccuracy in assessing the progressive failure potential of the structure.

Keywords: abnormal loads, composite floor system, intermediate steel moment resisting frame system, progressive collapse

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Authors: D. Signore, M. Ferraiuolo, P. Caramuta, O. Petrella, C. Toscano

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The Digital Image Correlation (DIC) is a newly developed optical technique that is spreading in all engineering sectors because it allows the non-destructive estimation of the entire surface deformation without any contact with the component under analysis. These characteristics make the DIC very appealing in all the cases the global deformation state is to be known without using strain gages, which are the most used measuring device. The DIC is applicable to any material subjected to distortion caused by either thermal or mechanical load, allowing to obtain high-definition mapping of displacements and deformations. That is why in the civil and the transportation industry, DIC is very useful for studying the behavior of metallic materials as well as of composite materials. DIC is also used in the medical field for the characterization of the local strain field of the vascular tissues surface subjected to uniaxial tensile loading. DIC can be carried out in the two dimension mode (2D DIC) if a single camera is used or in a three dimension mode (3D DIC) if two cameras are involved. Each point of the test surface framed by the cameras can be associated with a specific pixel of the image, and the coordinates of each point are calculated knowing the relative distance between the two cameras together with their orientation. In both arrangements, when a component is subjected to a load, several images related to different deformation states can be are acquired through the cameras. A specific software analyzes the images via the mutual correlation between the reference image (obtained without any applied load) and those acquired during the deformation giving the relative displacements. In this paper, a metrological characterization of the digital image correlation is performed on aluminum and composite targets both in static and dynamic loading conditions by comparison between DIC and strain gauges measures. In the static test, interesting results have been obtained thanks to an excellent agreement between the two measuring techniques. In addition, the deformation detected by the DIC is compliant with the result of a FEM simulation. In the dynamic test, the DIC was able to follow with a good accuracy the periodic deformation of the specimen giving results coherent with the ones given by FEM simulation. In both situations, it was seen that the DIC measurement accuracy depends on several parameters such as the optical focusing, the parameters chosen to perform the mutual correlation between the images and, finally, the reference points on image to be analyzed. In the future, the influence of these parameters will be studied, and a method to increase the accuracy of the measurements will be developed in accordance with the requirements of the industries especially of the aerospace one.

Keywords: accuracy, deformation, image correlation, mechanical analysis

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Authors: Pouneh Soleimaninejadian, Chengyu Yang

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In this paper, we use data on Household Income and Expenditure survey of Statistics Centre of Iran, conducted from 2005-2014, to calculate several inequality measures and to estimate the effects of Iran’s targeted subsidy reform act on consumption and income inequality. We first calculate Gini coefficients for income and consumption in order to study the relation between the two and also the effects of subsidy reform. Results show that consumption inequality has not been always mirroring changes in income inequality. However, both Gini coefficients indicate that subsidy reform caused improvement in inequality. Then we calculate Generalized Entropy Index based on consumption and income for years before and after the Subsidy Reform Act of 2010 in order to have a closer look into the changes in internal structure of inequality after subsidy reforms. We find that the improvement in income inequality is mostly caused by the decrease in inequality of lower income individuals. At the same time consumption inequality has been decreased as a result of more equal consumption in both lower and higher income groups. Moreover, the increase in Engle coefficient after the subsidy reform shows that a bigger portion of income is allocated to consumption on food which is a sign of lower living standard in general. This increase in Engle coefficient is due to rise in inflation rate and relative increase in price of food which partially is another consequence of subsidy reform. We have conducted some experiments on effect of subsidy payments and possible effects of change on distribution pattern and amount of cash subsidy payments on income inequality. Result of the effect of cash payments on income inequality shows that it leads to a definite decrease in income inequality and had a bigger share in improvement of rural areas compared to those of urban households. We also examine the possible effect of constant payments on the increasing income inequality for years after 2011. We conclude that reduction in value of payments as a result of inflation plays an important role regardless of the fact that there may be other reasons. We finally experiment with alternative allocations of transfers while keeping the total amount of cash transfers constant or make it smaller through eliminating three higher deciles from the cash payment program, the result shows that income equality would be improved significantly.

Keywords: consumption inequality, generalized entropy index, income inequality, Irans subsidy reform

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Miljan B. Petrović, Dušan B. Petrović, Goran S. Nikolić

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This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful.

Keywords: noise, signal-to-noise ratio, stochastic signals, variance estimation

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Authors: R. Bouhennache, T. Bouden, A. A. Taleb, A. Chaddad

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In this paper we propose a method to map the urban areas. The method uses an arithmetic calculation processed from the land features indexes and Tasseled cap transformation TC of multi spectral Thematic Mapper Landsat TM image. For this purpose the derived indexes image from the original image such SAVI the soil adjusted vegetation index, UI the urban Index, and EBBI the enhanced built up and bareness index were staked to form a new image and the bands were uncorrelated, also the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification approaches were first applied on the new image TM data using the reference spectra of the spectral library and subsequently the four urban, vegetation, water and soil land cover categories were extracted with their accuracy assessment.The urban features were represented using a logic calculation applied to the brightness, UI-SAVI, NDBI-greenness and EBBI- brightness data sets. The study applied to Blida and mentioned that the urban features can be mapped with an accuracy ranging from 92 % to 95%.

Keywords: EBBI, SAVI, Tasseled Cap Transformation, UI

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Authors: Dong-Ho Kim, Zheng-Xun Jin, Yong-Woon Cha, Su-Sang Lim, Sang-Won Han, Chang-Taek Hyun

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As construction is a labor-intensive industry, it is important to identify and manage labor quantities for accurate progress management of the construction project. However, the progress management that focuses on construction cost calculated based on materials rather than labor quantities has led to a difference in the implementation of cost and progress of the actual construction. In addition, since it is not easy to predict accurate labor quantities in the estimation of labor quantity-based progress rate, there have been limited researches into the progress rate estimation based on labor quantity. Accordingly, this study proposed a process for labor quantity-based progress rate estimation using a standard of estimate to predict accurate progress rate of the construction project in Republic Korea. It is expected that the utilization of the proposed process will help to identify the progress rate closer to that of the actual site management and adjust the workforce in each construction type, thereby contributing to improving construction efficiency.

Keywords: labor based, labor cost, progress management, progress rate, progress payment

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Authors: Israa Sh. Tawfic, Sema Koc Kayhan

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Given a large sparse signal, great wishes are to reconstruct the signal precisely and accurately from lease number of measurements as possible as it could. Although this seems possible by theory, the difficulty is in built an algorithm to perform the accuracy and efficiency of reconstructing. This paper proposes a new proved method to reconstruct sparse signal depend on using new method called Least Support Matching Pursuit (LS-OMP) merge it with the theory of Partial Knowing Support (PSK) given new method called Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP). The new methods depend on the greedy algorithm to compute the support which depends on the number of iterations. So to make it faster, the PKLS-OMP adds the idea of partial knowing support of its algorithm. It shows the efficiency, simplicity, and accuracy to get back the original signal if the sampling matrix satisfies the Restricted Isometry Property (RIP). Simulation results also show that it outperforms many algorithms especially for compressible signals.

Keywords: compressed sensing, lest support orthogonal matching pursuit, partial knowing support, restricted isometry property, signal reconstruction

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Authors: Lai Wenfang

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Tthis study will describe how to use artificial intelligence (AI) technology to build the user-oriented platform for integrated archival service. The platform will be launched in 2020 by the National Archives Administration (NAA) in Taiwan. With the progression of information communication technology (ICT) the NAA has built many systems to provide archival service. In order to cope with new challenges, such as new ICT, artificial intelligence or blockchain etc. the NAA will try to use the natural language processing (NLP) and machine learning (ML) skill to build a training model and propose suggestions based on the data sent to the platform. NAA expects the platform not only can automatically inform the sending agencies’ staffs which records catalogues are against the transfer or destroy rules, but also can use the model to find the details hidden in the catalogues and suggest NAA’s staff whether the records should be or not to be, to shorten the auditing time. The platform keeps all the users’ browse trails; so that the platform can predict what kinds of archives user could be interested and recommend the search terms by visualization, moreover, inform them the new coming archives. In addition, according to the Archives Act, the NAA’s staff must spend a lot of time to mark or remove the personal data, classified data, etc. before archives provided. To upgrade the archives access service process, the platform will use some text recognition pattern to black out automatically, the staff only need to adjust the error and upload the correct one, when the platform has learned the accuracy will be getting higher. In short, the purpose of the platform is to deduct the government digital transformation and implement the vision of a service-oriented smart government.

Keywords: artificial intelligence, natural language processing, machine learning, visualization

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Authors: Mojtaba Farzaneh, Majid Baghaei Nejad

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This article proposes a new Flexible Capacitive Tactile Sensors based on paper sheets. This method combines the parameters of sensor's material and dielectric, and forms a new model of flexible capacitive sensors. The present article tries to present a practical explanation of this method's application and advantages. With the use of this new method, it is possible to make a more flexibility and accurate sensor in comparison with the current models. To assess the performance of this model, the common capacitive sensor is simulated and the proposed model of this article and one of the existing models are assessed. The results of this article indicate that the proposed model of this article can enhance the speed and accuracy of tactile sensor and has less error in comparison with the current models. Based on the results of this study, it can be claimed that in comparison with the current models, the proposed model of this article is capable of representing more flexibility and more accurate output parameters for touching the sensor, especially in abnormal situations and uneven surfaces, and increases accuracy and practicality.

Keywords: capacitive sensor, paper sheets, flexible, tactile, uneven

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Authors: Ziyu Diao, Yanyan Zhang, Zhentao Liu, Ruidong Yan

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The update frequency of wear profile in main bearing wear prognosis of internal combustion engine plays an important role in the calculation efficiency and accuracy. In order to investigate the appropriate cycle index of the simplified working condition of wear simulation, the main bearing-crankshaft journal friction pair of a diesel engine in service was studied in this paper. The method of multi-body dynamics simulation was used, and the wear prognosis model of the main bearing was established. Several groups of cycle indexes were set up for the wear calculation, and the maximum wear depth and wear profile were compared and analyzed. The results showed that when the cycle index reaches 3, the maximum deviation rate of the maximum wear depth is about 2.8%, and the maximum deviation rate comes to 1.6% when the cycle index reaches 5. This study provides guidance and suggestions for the optimization of wear prognosis by selecting appropriate value of cycle index according to the requirement of calculation cost and accuracy of the simulation work.

Keywords: cycle index, deviation rate, wear calculation, wear profile

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Authors: Firas M. Tuaimah, Huda M. Abdul Abbas

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Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity.

Keywords: short term load forecasting, prediction interval, type 2 fuzzy logic systems, electric, computer systems engineering

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Authors: Paul Han, Jun-Geol Baek

Abstract:

In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model

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Authors: Laheeb M. Ibrahim, Ibrahim A. Salih

Abstract:

Biometrics is a technology that has been widely used in many official and commercial identification applications. The increased concerns in security during recent years (especially during the last decades) have essentially resulted in more attention being given to biometric-based verification techniques. Here, a novel fusion approach of palmprint, dental traits has been suggested. These traits which are authentication techniques have been employed in a range of biometric applications that can identify any postmortem PM person and antemortem AM. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing, deterring spoofing activities and reducing enrolment failure. In this paper, a first unimodel biometric system has been made by using (palmprint and dental) traits, for each one classification applying an artificial neural network and a hybrid technique that combines swarm intelligence and neural network together, then attempt has been made to combine palmprint and dental biometrics. Principally, the fusion of palmprint and dental biometrics and their potential application has been explored as biometric identifiers. To address this issue, investigations have been carried out about the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. Also the results of the multimodal approach have been compared with each one of these two traits authentication approaches. This paper studies the features and decision fusion levels in multimodal biometrics. To determine the accuracy of GAR to parallel system decision-fusion including (AND, OR, Majority fating) has been used. The backpropagation method has been used for classification and has come out with result (92%, 99%, 97%) respectively for GAR, while the GAR) for this algorithm using hybrid technique for classification (95%, 99%, 98%) respectively. To determine the accuracy of the multibiometric system for feature level fusion has been used, while the same preceding methods have been used for classification. The results have been (98%, 99%) respectively while to determine the GAR of feature level different methods have been used and have come out with (98%).

Keywords: back propagation neural network BP ANN, multibiometric system, parallel system decision-fusion, practical swarm intelligent PSO

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

Abstract:

While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Aghbalou Nihad, Charki Abderafi, Saida Rahali, Reklaoui Kamal

Abstract:

Maximize the benefit from the wind energy potential is the most interest of the wind power stakeholders. As a result, the wind tower size is radically increasing. Nevertheless, choosing an appropriate wind turbine for a selected site require an accurate estimate of vertical wind profile. It is also imperative from cost and maintenance strategy point of view. Then, installing tall towers or even more expensive devices such as LIDAR or SODAR raises the costs of a wind power project. Various models were developed coming within this framework. However, they suffer from complexity, generalization and lacks accuracy. In this work, we aim to investigate the ability of neural network trained using the Bayesian Regularization technique to estimate wind speed profile up to height of 100 m based on knowledge of wind speed lower heights. Results show that the proposed approach can achieve satisfactory predictions and proof the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights.

Keywords: bayesian regularization, neural network, wind shear, accuracy

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Authors: Guang Zou, Kian Banisoleiman, Arturo González

Abstract:

Crack initiation and propagation threatens structural integrity of welded joints and normally inspections are assigned based on crack propagation models. However, the approach based on crack propagation models may not be applicable for some high-quality welded joints, because the initial flaws in them may be so small that it may take long time for the flaws to develop into a detectable size. This raises a concern regarding the inspection planning of high-quality welded joins, as there is no generally acceptable approach for modeling the whole fatigue process that includes the crack initiation period. In order to address the issue, this paper reviews treatment methods for crack initiation period and initial crack size in crack propagation models applied to inspection planning. Generally, there are four approaches, by: 1) Neglecting the crack initiation period and fitting a probabilistic distribution for initial crack size based on statistical data; 2) Extrapolating the crack propagation stage to a very small fictitious initial crack size, so that the whole fatigue process can be modeled by crack propagation models; 3) Assuming a fixed detectable initial crack size and fitting a probabilistic distribution for crack initiation time based on specimen tests; and, 4) Modeling the crack initiation and propagation stage separately using small crack growth theories and Paris law or similar models. The conclusion is that in view of trade-off between accuracy and computation efforts, calibration of a small fictitious initial crack size to S-N curves is the most efficient approach.

Keywords: crack initiation, fatigue reliability, inspection planning, welded joints

Procedia PDF Downloads 343