Search results for: slice thickness accuracy

Authors: Po-Hui Yang, Jing-Min Chen, Po-Yu Kuo, Chia-Chun Wu

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This paper proposed a high resolution Vernier Delay Line (VDL) measurement circuit with coarse and fine detection mechanism, which improved the trade-off problem between high resolution and less delay cells in traditional VDL circuits. And the measuring time of proposed measurement circuit is also under the high resolution requests. At first, the testing range of input signal which proposed high resolution delay line is detected by coarse detection VDL. Moreover, the delayed input signal is transmitted to fine detection VDL for measuring value with better accuracy. This paper is implemented at 0.18μm process, operating frequency is 100 MHz, and the resolution achieved 2.0 ps with only 16-stage delay cells. The test range is 170ps wide, and 17% stages saved compare with traditional single delay line circuit.

Keywords: vernier delay line, D-type flip-flop, DFF, metastable phenomenon

Procedia PDF Downloads 599

Authors: Perumalla Naveen Kumar

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Global Positioning System (GPS) is used for civilian and military user positioning applications. The accuracy of GPS is degrading mainly because of ionospheric error. It is very important to analyze the effects of ionosphere on the performance of satellite systems especially in the low latitude regions. These variations depend on the Total Electron Content (TEC) in the ionosphere. To investigate the variations in the atmosphere, a mini satellite known as YOUTHSAT is launched by India. This is the outcome of the collaboration between India and USSR. One of the YOUTHSAT Indian payload is RaBIT (Radio Beacon for Ionospheric Tomography). In this paper, YOUTHSAT RaBIT payload data for the three typical days of 2011 are considered. The analysis is carried out for four Indian stations. The variations of Slant TEC, elevation angle and azimuth angles are analyzed with respect to local time. The obtained results are encouraging.

Keywords: Global Positioning System (GPS), Total Electron Content (TEC), YOUTHSAT, Radio Beacon for Ionospheric Tomography (RaBIT)

Procedia PDF Downloads 390

Authors: M. S. Meon, M. N. Rao, K-U. Schröder

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Mechanical fasteners especially bolting is commonly used in joining carbon-fiber reinforced polymer (CFRP) composite structures due to their good joinability and easy for maintenance characteristics. Since this approach involves with notching, a proper progressive damage model (PDM) need to be implemented and verified to capture existence of damages in the structure. A three dimensional (3D) failure criteria of Puck is established to predict the ultimate bearing failure of such joint. The failure criteria incorporated with degradation scheme are coded based on user subroutine executed in Abaqus. Single lap joint (SLJ) of composite bolted joint is used as target configuration. The results revealed that the PDM adopted here could sufficiently predict the behaviour of composite bolted joint up to ultimate bearing failure. In addition, mesh refinement near holes increased the accuracy of predicted strength as well as computational effort.

Keywords: bearing strength, bolted joint, degradation scheme, progressive damage model

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Authors: Xiaohui Wang, Zhichuan Guan, Roman Shor, Chuanbin Xu

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Early monitoring and real-time quantitative description of gas intrusion under the premise of ensuring the integrity of the drilling fluid circulation system will greatly improve the accuracy and effectiveness of deepwater gas-kick monitoring. Therefore, in order to study the propagation characteristics of ultrasonic waves in the gas-liquid two-phase flow within the marine riser, in this paper, a numerical simulation method of ultrasonic propagation in the annulus of the riser was established, and the credibility of the numerical analysis was verified by the experimental results of the established gas intrusion monitoring simulation experimental device. The numerical simulation can solve the sound field in the gas-liquid two-phase flow according to different physical models, and it is easier to realize the single factor control. The influence of each parameter on the received signal can be quantitatively investigated, and the law with practical guiding significance can be obtained.

Keywords: gas-kick detection, ultrasonic, void fraction, coda wave velocity

Procedia PDF Downloads 160

Authors: Meza-Quintero Myriam, Lobo Torrado Katty Andrea, Sanchez Picon Yesenia, Hurtado-Lugo Naudin

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The objective of the study was to evaluate the internal and external quality of the egg in the three production housing systems: floor, cage, and grazing of laying birds of the Isa Brown line, in the laying period between weeks 35 to 41; 135 hens distributed in 3 treatments of 45 birds per repetition were used (the replicas were the seven weeks of the trial). The feeding treatment supplied in the floor and cage systems contained 114 g/bird/day; for the grazing system, 14 grams less concentrate was provided. Nine eggs were collected to be studied and analyzed in the animal nutrition laboratory (3 eggs per housing system). The random statistical model was implemented: for the statistical analysis of the data, the statistical software of IBM® Statistical Products and Services Solution (SPSS) version 2.3 was used. The evaluation and follow-up instruments were the vernier caliper for the measurement in millimeters, a YolkFan™16 from Roche DSM for the evaluation of the egg yolk pigmentation, a digital scale for the measurement in grams, a micrometer for the measurement in millimeters and evaluation in the laboratory using dry matter, ashes, and ethereal extract. The results suggested that equivalent to the size of the egg (0.04 ± 3.55) and the thickness of the shell (0.46 ± 3.55), where P-Value> 0.05 was obtained, weight albumen (0.18 ± 3.55), albumen height (0.38 ± 3.55), yolk weight (0.64 ± 3.55), yolk height (0.54 ± 3.55) and for yolk pigmentation (1.23 ± 3.55). It was concluded that the hens in the three production systems, floor, cage, and grazing, did not show significant statistical differences in the internal and external quality of the chicken in the parameters studied egg for the production system.

Keywords: biological, territories, genetic resource, egg

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Authors: Areeg Shermaddo, Abedulgader Baktheer

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Solar updraft power plants (SUPPs) made from reinforced concrete (RC) are an innovative technology to generate solar electricity. An up to 1000 m high chimney represents the major part of each SUPP ensuring the updraft of the warmed air from the ground. Numerical simulation of nonlinear behavior of such large mega shell concrete structures is a challenging task, and computationally expensive. A general finite element approach to simulate reinforced concrete bearing behavior is presented and verified on a simply supported beam, as well as the technique of submodeling. The verified numerical approach is extended and consecutively transferred to a more complex chimney structure of a SUPP. The obtained results proved the reliability of submodeling technique in analyzing critical regions of simple and complex mega concrete structures with high accuracy and dramatic decrease in the computation time.

Keywords: ABAQUS, nonlinear analysis, submodeling, SUPP

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Authors: Buse T. Aydın, Enver Ozdemir

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In this study, a double layer authentication scheme between the aircraft and the Air Traffic Control (ATC) tower is designed to prevent any unauthorized aircraft from introducing themselves as friends. The method is a combination of classical cryptographic methods and new generation physical layers. The first layer has employed the embedded key of the aircraft. The embedded key is assumed to installed during the construction of the utility. The other layer is a physical attribute (flight path, distance, etc.) between the aircraft and the ATC tower. We create a mathematical model so that two layers’ information is employed and an aircraft is authenticated as a friend or foe according to the accuracy of the results of the model. The results of the aircraft are compared with the results of the ATC tower and if the values found by the aircraft and ATC tower match within a certain error margin, we mark the aircraft as a friend. In this method, even if embedded key is captured by the enemy aircraft, without the information of the second layer, the enemy can easily be determined. Overall, in this work, we present a more reliable system by adding a physical layer in the authentication process.

Keywords: ADS-B, communication with physical layer security, cryptography, identification friend or foe

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Authors: Chaoyi Sun, Congxin Chen, Yun Zheng, Kaizong Xia, Wei Zhang

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Crest loads are often encountered in hydropower, highway, open-pit and other engineering rock slopes. Toppling failure is one of the most common deformation failure types of anti-dip bedding rock slopes. Analysis on such failure of anti-dip bedding rock slopes subjected to crest loads has an important influence on engineering practice. Based on the step-by-step analysis approach proposed by Goodman and Bray, a geo-mechanical model was developed, and the related analysis approach was proposed for the toppling failure of anti-dip bedding rock slopes subjected to crest loads. Using the transfer coefficient method, a formulation was derived for calculating the residual thrust of slope toe and the support force required to meet the requirements of the slope stability under crest loads, which provided a scientific reference to design and support for such slopes. Through slope examples, the influence of crest loads on the residual thrust and sliding ratio coefficient was investigated for cases of different block widths and slope cut angles. The results show that there exists a critical block width for such slope. The influence of crest loads on the residual thrust is non-negligible when the block thickness is smaller than the critical value. Moreover, the influence of crest loads on the slope stability increases with the slope cut angle and the sliding ratio coefficient of anti-dip bedding rock slopes increases with the crest loads. Finally, the theoretical solutions and numerical simulations using Universal Distinct Element Code (UDEC) were compared, in which the consistent results show the applicability of both approaches.

Keywords: anti-dip bedding rock slope, crest loads, stability analysis, toppling failure

Procedia PDF Downloads 183

Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

Procedia PDF Downloads 190

Authors: Manisha Mukherjee, Diptarka Saha

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Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature.

Keywords: time series, ARIMA, auto.arima, ARIMA parameters, forecast, R function

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Authors: Samir El-Gendy, Doaa Zaghloul

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The current study dealt with the gross and microscopic anatomy of the integument of male ostrich in addition to the histological features of different areas of skin by light and SEM. The ostrich skin is characterized by prominent feather follicles and bristles. The number of feather follicles was determined per cm2 in different regions. The integument of ostrich had many modifications which appeared as callosities and scales, nail and toe pads. They were sternal, pubic and Achilles tendon callosities. The vacuolated epidermal cells were seen mainly in the skin of legs and to a lesser extent in the skin of back and Achilles areas. Higher lipogenic potential was expressed by epidermis from glabrous areas of ostrich skin. The dermal papillae were found in the skin of feathered area of neck and back and this was not a common finding in bird's skin which may give resistance against shearing forces in these regions of ostrich skin. The thickness of the keratin layer of ostrich varied, being thick and characteristically loose in the skin at legs, very thin and wavy at neck, while at Achilles skin area, scale and toe pad were thick and more compact, with the thickest very dense and wavy keratin layer at the nail. The dermis consisted of superficial layer of dense irregular connective tissue characterized by presence of many vacuoles of different sizes just under the basal lamina of the epithelium of epidermis and deep layer of dense regular connective tissue. This result suggested presence of fat droplets in this layer which may be to overcome the lack of good barrier of cutaneous water loss in epidermis.

Keywords: ostrich, light microscopy, scanning electron microscopy, integument, skin modifications

Procedia PDF Downloads 249

Authors: M. Fakharian, M. I. Khodakarami

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In this paper, a new trend for improvement in semi-analytical method based on scale boundaries in order to solve the 2D elastodynamic problems is provided. In this regard, only the boundaries of the problem domain discretization are by specific sub-parametric elements. Mapping functions are uses as a class of higher-order Lagrange polynomials, special shape functions, Gauss-Lobatto -Legendre numerical integration, and the integral form of the weighted residual method, the matrix is diagonal coefficients in the equations of elastodynamic issues. Differences between study conducted and prior research in this paper is in geometry production procedure of the interpolation function and integration of the different is selected. Validity and accuracy of the present method are fully demonstrated through two benchmark problems which are successfully modeled using a few numbers of DOFs. The numerical results agree very well with the analytical solutions and the results from other numerical methods.

Keywords: 2D elastodynamic problems, lagrange polynomials, G-L-Lquadrature, decoupled SBFEM

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Liu Lin Xin

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With the increasing complexity of systems, log parsing and anomaly detection have become crucial for maintaining system stability. However, traditional methods often struggle with adaptability and accuracy, especially when dealing with rapidly evolving log content and unfamiliar domains. To address these challenges, this paper proposes approach that integrates Retrieval Augmented Generation (RAG) technology with Prompt Engineering for Large Language Models, applied specifically in LogPrompt. This approach enables dynamic log parsing and intelligent anomaly detection by combining real-time information retrieval with prompt optimization. The proposed method significantly enhances the adaptability of log analysis and improves the interpretability of results. Experimental results on several public datasets demonstrate the method's superior performance, particularly in scenarios lacking training data, where it significantly outperforms traditional methods. This paper introduces a novel technical pathway for log parsing and anomaly detection, showcasing the substantial theoretical value and practical potential.

Keywords: log parsing, anomaly detection, RAG, prompt engineering, LLMs

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Authors: Samaneh Nadali, Masrah Azrifah Azmi Murad

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As a result of the growth of the web, people are able to express their views and opinions. They can now post reviews of products at merchant sites and express their views on almost anything in internet forums, discussion groups, and blogs. Therefore, the number of product reviews has grown rapidly. The large numbers of reviews make it difficult for manufacturers or businesses to automatically classify them into different semantic orientations (positive, negative, and neutral). For sentiment classification, most existing methods utilize a list of opinion words whereas this paper proposes a fuzzy approach for evaluating sentiments expressed in customer product reviews, to predict the strength levels (e.g. very weak, weak, moderate, strong and very strong) of customer product reviews by combinations of adjective, adverb and verb. The proposed fuzzy approach has been tested on eight benchmark datasets and obtained 74% accuracy, which leads to help the organization with a more clear understanding of customer's behavior in support of business planning process.

Keywords: fuzzy logic, customer product review, sentiment analysis

Procedia PDF Downloads 368

Authors: Maydariana Ayuningtyas, Bambang Riyanto, Akhiruddin Maddu

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Industrial waste of crustacean shells, such as shrimp and crab, has been considered as one of the major issues contributing to environmental pollution. The waste processing mechanisms to form new, practical substances with added value have been developed. Chitosan, a derived matter from chitin, which is obtained from crab and shrimp shells, performs prodigiously in broad range applications. A chitosan composite-based diaphragm is a new inspiration in fiber optic acoustic sensor advancement. Elastic modulus, dynamic response, and sensitivity to acoustic wave of chitosan-based composite film contribute great potentials of organic-based sound-detecting material. The objective of this research was to develop chitosan diaphragm application in fiber optic microphone system. The formulation was conducted by blending 5% polyvinyl alcohol (PVA) solution with dissolved chitosan at 0%, 1% and 2% in 1:1 ratio, respectively. Composite diaphragms were characterized for the morphological and mechanical properties to predict the desired acoustic sensor sensitivity. The composite with 2% chitosan indicated optimum performance with 242.55 µm thickness, 67.9% relative humidity, and 29-76% light transmittance. The Young’s modulus of 2%-chitosan composite material was 4.89×104 N/m2, which generated the voltage amplitude of 0.013V and performed sensitivity of 3.28 mV/Pa at 1 kHz. Based on the results above, chitosan from crustacean shell waste can be considered as a viable alternative material for fiber optic acoustic sensor sensing pad development. Further, the research in chitosan utilisation is proposed as novel optical microphone development in anthropogenic noise controlling effort for environmental and biodiversity conservation.

Keywords: acoustic sensor, chitosan, composite, crab shell, diaphragm, waste utilisation

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Authors: Louis Denis Kevin Catherine, Raja Aziz Raja Ma’arof, Azrina Arshad, Sangeeth Suresh

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Machining parameters are very important in determining the surface quality of any material. In the past decade, some new engineering materials were developed for the manufacturing industry which created a need to conduct an investigation on the impact of the said parameters on their surface roughness. The polyurethane (PU) block is widely used in the automotive industry to manufacture parts such as checking fixtures that are used to verify the dimensional accuracy of automotive parts. In this paper, the design of experiment (DOE) was used to investigate the effect of the milling parameters on the PU block. Furthermore, an analysis of the machined surface chemical composition was done using scanning electron microscope (SEM). It was found that the surface roughness of the PU block is severely affected when PU undergoes a flood machining process instead of a dry condition. In addition, the step over and the silicon content were found to be the most significant parameters that influence the surface quality of the PU block.

Keywords: polyurethane (PU), design of experiment (DOE), scanning electron microscope (SEM), surface roughness

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Authors: Cheng Yang Kwa, Yoke Rung Wong

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Laminated composite materials are widely employed in automotive, aerospace, and other industries. These materials provide distinct benefits due to their high specific strength, high specific modulus, and ability to be customized for a specific function. However, delamination of laminated composite materials is one of the main defects which can occur during manufacturing, regular operations, or maintenance. Delamination can bring about considerable internal damage, unobservable by visual check, that causes significant loss in strength and stability, leading to composite structure catastrophic failure. Structural health monitoring (SHM) is known to be the automated method for monitoring and evaluating the condition of a monitored object. There are several ways to conduct SHM in aerospace. One of the effective methods is to monitor the natural frequency shift of structure due to the presence of defect. This study investigated the mechanical resonance frequency shift of a multi-layer composite cantilever beam due to interlaminar delamination. ANSYS Workbench® was used to create a 4-plies laminated composite cantilever finite element model with [90/0]s fiber setting. Epoxy Carbon UD (230GPA) Prepreg was chosen, and the thickness was 2.5mm for each ply. The natural frequencies of the finite element model with various degree of delamination were simulated based on modal analysis and then validated by using literature. It was shown that the model without delamination had natural frequency of 40.412 Hz, which was 1.55% different from the calculated result (41.050 Hz). Thereafter, the various degree of delamination was mimicked by changing the frictional conditions at the middle ply-to-ply interface. The results suggested that delamination in the laminated composite cantilever induced a change in its stiffness which alters its mechanical resonance frequency.

Keywords: structural health monitoring, NDT, cantilever, laminate

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

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Authors: Adnan Baig, Ishteeaq Naeem, Saad Mansoor

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The Internet of Things (IoT) gadgets have advanced quickly in recent years, and their use is steadily rising daily. However, cyber-attackers can target these gadgets due to their distributed nature. Additionally, many IoT devices have significant security flaws in their implementation and design, making them vulnerable to security threats. Hence, these threats can cause important data security and privacy loss from a single attack on network devices or systems. Botnets are a significant security risk that can harm the IoT network; hence, sophisticated techniques are required to mitigate the risk. This work uses a machine learning-based method to identify IoT orchestrated by botnets. The proposed technique identifies the net attack by distinguishing between legitimate and malicious traffic. This article proposes a hyperparameter tuning model to improvise the method to improve the accuracy of existing processes. The results demonstrated an improved and more accurate indication of botnet-based cyber-attacks.

Keywords: Internet of Things, Botnet, BoT-IoT dataset, ML techniques

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Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi

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Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.

Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction

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Authors: Pavel Chmelar, Martin Dobrovolny

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Laser projection or laser footprint detection is today widely used in many fields of robotics, measurement, or electronics. The system accuracy strictly depends on precise laser footprint detection on target objects. This article deals with the laser line detection based on the RGB segmentation and the component labeling. As a measurement device was used the developed optical rangefinder. The optical rangefinder is equipped with vertical sweeping of the laser beam and high quality camera. This system was developed mainly for automatic exploration and mapping of unknown spaces. In the first section is presented a new detection algorithm. In the second section are presented measurements results. The measurements were performed in variable light conditions in interiors. The last part of the article present achieved results and their differences between day and night measurements.

Keywords: color segmentation, component labelling, laser line detection, automatic mapping, distance measurement, vector map

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Authors: B. G. Patel, A. K. Desai, S. G. Shah

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The main of objective of this paper is to develop new measurement technique without touching the object. DIC is advance measurement technique use to measure displacement of particle with very high accuracy. This powerful innovative technique which is used to correlate two image segments to determine the similarity between them. For this study, nine geometrically similar beam specimens of different sizes with (steel fibers and glass fibers) and without fibers were tested under three-point bending in a closed loop servo-controlled machine with crack mouth opening displacement control with a rate of opening of 0.0005 mm/sec. Digital images were captured before loading (unreformed state) and at different instances of loading and were analyzed using correlation techniques to compute the surface displacements, crack opening and sliding displacements, load-point displacement, crack length and crack tip location. It was seen that the CMOD and vertical load-point displacement computed using DIC analysis matches well with those measured experimentally.

Keywords: Digital Image Correlation, fibres, self compacting concrete, size effect

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Authors: Asimina Paparrigopoulou, John Pavlopoulos, Maria Konstantinidou

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Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. By experimenting with data obtained from the Collaborative Database of Dateable Greek Bookhands and the PapPal online collections of objectively dated Greek papyri, this study shows that deep learning dating models, pre-trained on generic images, can achieve accurate chronological estimates for a test subset (67,97% accuracy for book hands and 55,25% for documents). To compare the estimates of these models with those of humans, experts were asked to complete a questionnaire with samples of literary and documentary hands that had to be sorted chronologically by century. The same samples were dated by the models in question. The results are presented and analysed.

Keywords: image classification, papyri images, dating

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Authors: Nesrine T. Lamie

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Four, accurate, precise, and sensitive spectrophotometric methods are developed for the simultaneous determination of a binary mixture containing amlodipine besylate (AM) and atenolol (AT) where AM is determined at its λmax 360 nm (0D), while atenolol can be determined by different methods. Method (A) is absorpotion factor (AFM). Method (B) is the new Ratio Difference method(RD) which measures the difference in amplitudes between 210 and 226 nm of ratio spectrum., Method (C) is novel constant center spectrophotometric method (CC) Method (D) is mean centering of the ratio spectra (MCR) at 284 nm. The calibration curve is linear over the concentration range of 10–80 and 4–40 μg/ml for AM and AT, respectively. These methods are tested by analyzing synthetic mixtures of the cited drugs and they are applied to their commercial pharmaceutical preparation. The validity of results was assessed by applying standard addition technique. The results obtained were found to agree statistically with those obtained by a reported method, showing no significant difference with respect to accuracy and precision.

Keywords: amlodipine, atenolol, absorption factor, constant center, mean centering, ratio difference

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Iman Iraei, Mina Sharifi

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A method for object tracking in motion blurred images is proposed in this article. This paper shows that object tracking could be improved with this approach. We use mean shift algorithm to track different objects as a main tracker. But, the problem is that mean shift could not track the selected object accurately in blurred scenes. So, for better tracking result, and increasing the accuracy of tracking, wavelet transform is used. We use a feature named as blur extent, which could help us to get better results in tracking. For calculating of this feature, we should use Harr wavelet. We can look at this matter from two different angles which lead to determine whether an image is blurred or not and to what extent an image is blur. In fact, this feature left an impact on the covariance matrix of mean shift algorithm and cause to better performance of tracking. This method has been concentrated mostly on motion blur parameter. transform. The results reveal the ability of our method in order to reach more accurately tracking.

Keywords: mean shift, object tracking, blur extent, wavelet transform, motion blur

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Authors: Javad Jamali Khouei, Mohammadreza Khoshravan

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Considering that application of functionally graded material is increasing in most industries, it seems necessary to present a methodology for designing optimal profile of structures such as plate under mechanical loading which is highly consumed in industries. Therefore, volume fraction variation profile of functionally graded circular plate which has been considered two-directional is optimized so that stress of structure is minimized. For this purpose, equilibrium equations of two-directional functionally graded circular plate are solved by applying semi analytical-numerical method under mechanical loading and support conditions. By solving equilibrium equations, deflections and stresses are obtained in terms of control variables of volume fraction variation profile. As a result, the problem formula can be defined as an optimization problem by aiming at minimization of critical von-mises stress under constraints of deflections, stress and a physical constraint relating to structure of material. Then, the related problem can be solved with help of one of the metaheuristic algorithms such as genetic algorithm. Results of optimization for the applied model under constraints and loadings and boundary conditions show that functionally graded plate should be graded only in radial direction and there is no need for volume fraction variation of the constituent particles in thickness direction. For validating results, optimal values of the obtained design variables are graphically evaluated.

Keywords: two-directional functionally graded material, single objective optimization, semi analytical-numerical solution, genetic algorithm, graphical solution with contour

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Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

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Authors: Salima Smiti, Ines Gasmi, Makram Soui

Abstract:

Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

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