Search results for: slice thickness accuracy

Authors: Veronica Sorgato, Jeremy Belhassen, Philippe Chartier, Roddy Sihanath, Nicolas Docquiere, Jean-Yves Giraud

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We analyzed the image-guided radiotherapy method used by the TomoTherapy® System (Accuray Corp.) for patient repositioning in clinical routine. The TomoTherapy® System computes X, Y, Z and roll displacements to match the reference CT, on which the dosimetry has been performed, with the pre-treatment MV CT. The accuracy of the repositioning method has been studied according to the treatment localization. For this, a database of 18774 treatment sessions, performed during 2 consecutive years (2016-2017 period) has been used. The database includes the X, Y, Z and roll displacements proposed by TomoTherapy® System as well as the manual correction of these proposals applied by the radiation therapist. This manual correction aims to further improve the repositioning based on the clinical situation and depends on the structures surrounding the target tumor tissue. The statistical analysis performed on the database aims to define repositioning limits to be used as security and guiding tool for the manual adjustment implemented by the radiation therapist. This tool will participate not only to notify potential repositioning errors but also to further improve patient positioning for optimal treatment.

Keywords: accuracy, IGRT MVCT, image-guided radiotherapy megavoltage computed tomography, statistical analysis, tomotherapy, localization

Procedia PDF Downloads 226

Authors: R. Bennai, H. Ait Atmane, Jr., A. Tounsi

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In this work, a new analytical approach using a refined theory of hyperbolic shear deformation of a beam was developed to study the free vibration of graduated sandwiches beams under different boundary conditions. The effects of transverse shear strains and the transverse normal deformation are considered. The constituent materials of the beam are supposed gradually variable depending the height direction based on a simple power distribution law in terms of the volume fractions of the constituents; the two materials with which we worked are metals and ceramics. The core layer is taken homogeneous and made of an isotropic material; while the banks layers consist of FGM materials with a homogeneous fraction compared to the middle layer. Movement equations are obtained by the energy minimization principle. Analytical solutions of free vibration and buckling are obtained for sandwich beams under different support conditions; these conditions are taken into account by incorporating new form functions. In the end, illustrative examples are presented to show the effects of changes in different parameters such as (material graduation, the stretching effect of the thickness, boundary conditions and thickness ratio - length) on the vibration free and buckling of an FGM sandwich beams.

Keywords: functionally graded sandwich beam, refined shear deformation theory, stretching effect, free vibration

Procedia PDF Downloads 247

Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

Procedia PDF Downloads 246

Authors: Sun-Jin Han, Deuck Hang Lee, Hyo-Eun Joo, Hyun Kang, Kang Su Kim

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In pretensioned concrete members, the transfer length region is existed, in which the stress in prestressing strand is developed due to the bond mechanism with surrounding concrete. The stress of strands in the transfer length zone is smaller than that in the strain plateau zone, so-called effective prestress, therefore the web-shear strength in transfer length region is smaller than that in the strain plateau zone. Although the transfer length is main key factor in the shear design, a few analytical researches have been conducted to investigate the transfer length. Therefore, in this study, a theoretical approach was used to estimate the transfer length. The bond stress developed between the strands and the surrounding concrete was quantitatively calculated by using the Thick-Walled Cylinder Model (TWCM), based on this, the transfer length of strands was calculated. To verify the proposed model, a total of 209 test results were collected from the previous studies. Consequently, the analysis results showed that the main influencing factors on the transfer length are the compressive strength of concrete, the cover thickness of concrete, the diameter of prestressing strand, and the magnitude of initial prestress. In addition, the proposed model predicted the transfer length of collected test specimens with high accuracy. Acknowledgement: This research was supported by a grant(17TBIP-C125047-01) from Technology Business Innovation Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Keywords: bond, Hoyer effect, prestressed concrete, prestressing strand, transfer length

Procedia PDF Downloads 298

Authors: Anas El Amraoui, Abdelkhalek Cheddadi, Mohammed Touhami Ouazzani

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Laminar natural convection in a cylindrical annular cavity filled with air and provided with two fins is studied numerically using the discretization of the governing equations with the Centered Finite Difference method based on the Alternating Direction Implicit (ADI) scheme. The fins are attached to the inner cylinder of radius r_i (hot wall of temperature T_i). The outer cylinder of radius r_o is maintained at a temperature T_o (T_o < T_i). Two values of the dimensionless thickness of the fins are considered: 0.015 and 0.203. We consider a low fin height equal to 0.078 and medium fin heights equal to 0.093 and 0.203. The position of the fin is 0.82π and the radius ratio is equal to 2. The effect of Rayleigh number, Ra, on the flow structure and heat transfer is analyzed for a range of Ra from 10³ to 10⁴. The results for established flow structures and heat transfer at low height indicate that the flow regime that occurs is unicellular for all Ra and fin thickness; in addition, the heat transfer rate increases with increasing Rayleigh number and is the same for both thicknesses. At median fin heights 0.093 and 0.203, the increase of Rayleigh number leads to transitions of flow structure which correspond to significant variations of the heat transfer. The critical Rayleigh numbers, Ra_c.app and Ra_c.disp corresponding to the appearance of the bicellular flow regime and its disappearance, are determined and their influence on the change of heat transfer rate is analyzed.

Keywords: natural convection, fins, critical Rayleigh number, heat transfer, fluid flow regime, horizontal annulus

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Authors: Pechpailin Kortnoi, Tanitnun Paprad

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Background: Myasthenia gravis (MG) is an autoimmune disorder characterized by impaired neuromuscular transmission due to antibodies against nicotinic receptors, leading to muscle weakness, ptosis, and respiratory issues. The incidence of MG has risen globally, emphasizing the need for effective diagnostics. Objective: This study evaluates the validity of a combined bedside test (the ice pack test and fatigability test) for diagnosing juvenile myasthenia gravis (JMG) in pediatric patients with ptosis. Methods: This cross-sectional study, conducted from January 2012 to May 2024 at King Chulalongkorn Memorial Hospital, Thailand, included pediatric patients (1 month to 18 years) with ptosis undergoing ice pack and fatigability tests. Data included demographics, clinical findings, and test results. Diagnostic efficacy was assessed using sensitivity, specificity, accuracy, PPV, NPV, Fagan Nomogram, Kappa Statistics, and McNemar’s Chi-Square. Results: Of 43 identified patients, 32 were included, with 47% male and a mean age of 7 years. The combined bedside test had high sensitivity (92.8%) and accuracy (87.5%) but moderate specificity (50%). It significantly outperformed the ice pack test (P = 0.0005), which showed low sensitivity (42.8%) and accuracy (43.8%). The fatigability test had 82% sensitivity and 92% PPV. Confirmatory tests (AChR-Ab, MuSK-Ab, neostigmine, repetitive nerve stimulation) supported most diagnoses. Conclusions: The combined bedside test, with high sensitivity (92.8%) and accuracy (87.5%), is an effective screening tool for juvenile myasthenia gravis, outperforming the ice pack test. Integrating it into clinical practice may improve diagnosis and enable timely treatment. The fatigability test (82% sensitivity) is also useful as an adjunct screening tool.

Keywords: myasthenia gravis, the fatigability test, the ice pack test, the combined bedside test

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Authors: Youtao Gao, Bingyu Jin, Tanran Zhao, Bo Xu

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The history of satellite navigation can be dated back to the 1960s. From the U.S. Transit system and the Russian Tsikada system to the modern Global Positioning System (GPS) and the Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS), performance of satellite navigation has been greatly improved. Nowadays, the navigation accuracy and coverage of these existing systems have already fully fulfilled the requirement of near-Earth users, but these systems are still beyond the reach of deep space targets. Due to the renewed interest in space exploration, a novel high-precision satellite navigation system is becoming even more important. The increasing demand for such a deep space navigation system has contributed to the emergence of a variety of new constellation architectures, such as the Lunar Global Positioning System. Apart from a Walker constellation which is similar to the one adopted by GPS on Earth, a novel constellation architecture which consists of libration point satellites in the Earth-Moon system is also available to construct the lunar navigation system, which can be called accordingly, the libration point satellite navigation system. The concept of using Earth-Moon libration point satellites for lunar navigation was first proposed by Farquhar and then followed by many other researchers. Moreover, due to the special characteristics of Libration point orbits, an autonomous orbit determination technique, which is called ‘Liaison navigation’, can be adopted by the libration point satellites. Using only scalar satellite-to-satellite tracking data, both the orbits of the user and libration point satellites can be determined autonomously. In this way, the extensive Earth-based tracking measurement can be eliminated, and an autonomous satellite navigation system can be developed for future space exploration missions. The method of state estimate is an unnegligible factor which impacts on the orbit determination accuracy besides type of orbit, initial state accuracy and measurement accuracy. We apply the extended Kalman filter(EKF) and the unscented Kalman filter(UKF) to determinate the orbits of Lagrangian navigation satellites. The autonomous orbit determination errors are compared. The simulation results illustrate that UKF can improve the accuracy and z-axis convergence to some extent.

Keywords: extended Kalman filter, autonomous orbit determination, unscented Kalman filter, navigation constellation

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

Procedia PDF Downloads 97

Authors: Norhashidah Hj Mohd Ali, Teng Wai Ping

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In this paper, the formulation of a new group explicit method with a fourth order accuracy is described in solving the two-dimensional Helmholtz equation. The formulation is based on the nine-point fourth-order compact finite difference approximation formula. The complexity analysis of the developed scheme is also presented. Several numerical experiments were conducted to test the feasibility of the developed scheme. Comparisons with other existing schemes will be reported and discussed. Preliminary results indicate that this method is a viable alternative high accuracy solver to the Helmholtz equation.

Keywords: explicit group method, finite difference, Helmholtz equation, five-point formula, nine-point formula

Procedia PDF Downloads 501

Authors: Korosh Khorshidi, Mohammad Khodadadi

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In this paper, exact close form solution for out of plate free flexural vibration of moderately thick rectangular nanoplates are presented based on nonlocal modified trigonometric shear deformation theory, with assumptions of the Levy's type boundary conditions, for the first time. The aim of this study is to evaluate the effect of small-scale parameters on the frequency parameters of the moderately thick rectangular nano-plates. To describe the effects of small-scale parameters on vibrations of rectangular nanoplates, the Eringen theory is used. The Levy's type boundary conditions are combination of six different boundary conditions; specifically, two opposite edges are simply supported and any of the other two edges can be simply supported, clamped or free. Governing equations of motion and boundary conditions of the plate are derived by using the Hamilton’s principle. The present analytical solution can be obtained with any required accuracy and can be used as benchmark. Numerical results are presented to illustrate the effectiveness of the proposed method compared to other methods reported in the literature. Finally, the effect of boundary conditions, aspect ratios, small scale parameter and thickness ratios on nondimensional natural frequency parameters and frequency ratios are examined and discussed in detail.

Keywords: exact solution, nonlocal modified sinusoidal shear deformation theory, out of plane vibration, moderately thick rectangular plate

Procedia PDF Downloads 390

Authors: Wenhao Lan, Ning Li, Qiang Tong

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To improve the registration accuracy of a source point cloud and template point cloud when the initial relative deflection angle is too large, a PointNetLK algorithm combined with an oriented bounding box (PointNetLK-OBB) is proposed. In this algorithm, the OBB of a 3D point cloud is used to represent the macro feature of source and template point clouds. Under the guidance of the iterative closest point algorithm, the OBB of the source and template point clouds is aligned, and a mirror symmetry effect is produced between them. According to the fitting degree of the source and template point clouds, the mirror symmetry plane is detected, and the optimal rotation and translation of the source point cloud is obtained to complete the 3D point cloud registration task. To verify the effectiveness of the proposed algorithm, a comparative experiment was performed using the publicly available ModelNet40 dataset. The experimental results demonstrate that, compared with PointNetLK, PointNetLK-OBB improves the registration accuracy of the source and template point clouds when the initial relative deflection angle is too large, and the sensitivity of the initial relative position between the source point cloud and template point cloud is reduced. The primary contribution of this paper is the use of PointNetLK to avoid the non-convex problem of traditional point cloud registration and leveraging the regularity of the OBB to avoid the local optimization problem in the PointNetLK context.

Keywords: mirror symmetry, oriented bounding box, point cloud registration, PointNetLK-OBB

Procedia PDF Downloads 152

Authors: Masoud Mohammadpour, Nima Yazdian, Radovan Kovacevic

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The Oil and Gas industries are vigorously looking for new ways to increase the efficiency of their pipeline constructions. Besides the other approaches, implementing of new welding methods for joining pipes can be the best candidate on this regard. Hybrid Laser Arc Welding (HLAW) with the capabilities of high welding speed, deep penetration, and excellent gap bridging ability can be a possible alternative method in pipeline girth welding. This paper investigates the feasibility of applying the HLAW to join ASTM A106-B as the mostly used piping material for transporting high-temperature and high-pressure fluids and gases. The experiments were carried out on six-inch diameter pipes with the wall thickness of 10mm. AWS ER 70 S6 filler wire with diameter of 1.2mm was employed. Relating to this welding procedure, characterization of welded samples such as hardness, tensile testing and Charpy V-notch testing were performed and the results will be reported in this paper. In order to have better understanding about the thermal history and the microstructural alterations caused by the welding heat cycle, a comprehensive Finite Element (FE) model was also conducted. The obtained results have shown that the Gas Metal Arc Welding (GMAW) procedure with the minimum number of 5 passes to complete the wall thickness, was reduced to only single pass by using the HLAW process with the welding time less than 15s.

Keywords: finite element modeling, high-temperature service, hybrid laser/arc welding, welding pipes

Procedia PDF Downloads 209

Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

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Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: B. Paygozar, S.A. Dizaji

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Adhesive bonding is a highly valued type of fastening mechanical parts in complex structures, where joining some simple components is always needed. This method is of several merits, such as uniform stress distribution, appropriate bonding strength, and fatigue performance, and lightness, thereby outweighing other sorts of bonding methods. This study is to investigate the failure load of adhesive single-strap joints, including adherends of different sizes and materials. This kind of adhesive joint is very practical in different industries, especially when repairing the existing joints or attaching substrates of dissimilar materials. In this research, experimentally validated numerical analyses carried out in a commercial finite element package, ABAQUS, are utilized to extract the failure loads of the joints, based on the cohesive zone model. In addition, the stress analyses of the substrates are performed in order to acquire the effects of lowering the thickness of the substrates on the stress distribution inside them to avoid designs suffering from the necking or failure of the adherends. It was found out that this method of bonding is really feasible in joining dissimilar materials which can be utilized in a variety of applications. Moreover, the stress analyses indicated the minimum thickness for the adherends so as to avoid the failure of them.

Keywords: cohesive zone model, dissimilar materials, failure load, single strap joint

Procedia PDF Downloads 124

Authors: Miroslav H. Hristov, Velizar A. Vassilev, Georgi K. Dukendjiev

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Due to the constant development of measurement systems and the aim for computerization, unavoidable improvements are made for the main disadvantages of air gauges. With the appearance of the air-electronic measuring devices, some of their disadvantages are solved. The output electrical signal allows them to be included in the modern systems for measuring information processing and process management. Producer efforts are aimed at reducing the influence of supply pressure and measurement system setup errors. Increased accuracy requirements and preventive error measures are due to the main uses of air electronic systems - measurement of geometric dimensions in the automotive industry where they are applied as modules in measuring systems to measure geometric parameters, form, orientation and location of the elements.

Keywords: air-electronic, geometrical parameters, improvement, measurement systems

Procedia PDF Downloads 228

Authors: Seyed Sobhan Alvani, Mohammad Gohari

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By developing cities and increasing the population, traffic congestion has become a vital problem. Due to this crisis, urban designers try to present solutions to decrease this difficulty. On the other hand, predicting the model with perfect accuracy is essential for solution-providing. The current study presents a model based on artificial intelligence which can predict traffic index based on city population, growth rate, and area. The accuracy of the model was evaluated, which is acceptable and it is around 90%. Thus, urban designers and planners can employ it for predicting traffic index in the future to provide strategies.

Keywords: traffic index, population growth rate, cities wideness, artificial neural network

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Authors: Miao Sabrina Wang

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This paper examines the impact of grammatical differences on simultaneous interpreting from English into Mandarin Chinese by drawing upon an empirical study of professional and student interpreters. The research focuses on the effects of three grammatical categories including passives, adverbial components and noun phrases on simultaneous interpreting. For each category, interpretations of instances in which the grammatical structures are the same across the two languages are compared with interpretations of instances in which the grammatical structures differ across the two languages in terms of content accuracy and delivery appropriateness. The results indicate that grammatical differences have a significant impact on the interpreting performance of both professionals and students.

Keywords: content accuracy, delivery appropriateness, grammatical differences, simultaneous interpreting

Procedia PDF Downloads 542

Authors: Ashwin Bala, K. Panthalaraja Kumaran, S. Prithviraj, R. Pradeep, J. Udhayakumar, S. Ajith

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In this paper, studies have been carried out for an in-house design of a waste heat recovery system for effectively utilizing the domestic air conditioner heat energy for producing hot water. Theoretical studies have been carried to optimizing the flow rate for getting maximum output with a minimum size of the heater. Critical diameter, wall thickness, and total length of the water pipeline have been estimated from the conventional heat transfer model. Several combinations of pipeline shapes viz., spiral, coil, zigzag wound through the radiator has been attempted and accordingly shape has been optimized using heat transfer analyses. The initial condition is declared based on the water flow rate and temperature. Through the parametric analytical studies we have conjectured that water flow rate, temperature difference between incoming water and radiator skin temperature, pipe material, radiator material, geometry of the water pipe viz., length, diameter, and wall thickness are having bearing on the lucrative design of a waste heat recovery system for air conditioners. Results generated through the numerical studies have been validated using an in-house waste heat recovery system for air conditioners.

Keywords: air conditioner design, energy conversion system, radiator design for energy recovery systems, waste heat recovery system

Procedia PDF Downloads 357

Authors: Kwa-Sur Tam, Byung O. Kang

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By using different ranges of daily sky clearness index defined in this paper, any day can be classified as a clear sky day, a partly cloudy day or a cloudy day. This paper demonstrates how short-term forecasting of solar irradiation can be improved by taking into consideration the type of day so defined. The source of day type dependency has been identified. Forecasting methods that take into consideration of day type have been developed and their efficacy have been established. While all methods that implement some form of adjustment to the cloud cover forecast provided by the U.S. National Weather Service provide accuracy improvement, methods that incorporate day type dependency provides even further improvement in forecast accuracy.

Keywords: day types, forecast methods, National Weather Service, sky cover, solar energy

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Authors: Lin Cheng, Zijiang Yang

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Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

Procedia PDF Downloads 120

Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

Procedia PDF Downloads 151

Authors: Yeon-Jong Jeong, Jun-Hong Park, Hyuk Choi

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In this study, we have studied the design of intermediate die in multipass drawing. Research has been continuously studied because of the advantage of better dimensional accuracy, smooth surface and improved mechanical properties in the case of drawing. Among them, multipass drawing, which is a method to realize complicated shape by drawing, was discussed in this study. The most important factor in the multipass drawing is the dimensional accuracy and simplify the process. To accomplish this, a multistage shape drawing was performed using various intermediate die shape designs, and finite element analysis was performed.

Keywords: FEM (Finite Element Method), multipass drawing, intermediate die, hollow pipe

Procedia PDF Downloads 316

Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: P. Nafisi Poor, P. Javid

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Over the past few years, the idea of adaptive buildings and specifically, adaptive building energy management systems (ABEMS) has become popular. Well-performed management in terms of energy is to create a balance between energy consumption and user comfort; therefore, in new energy management models, efficient energy consumption is not the sole factor and the user's comfortability is also considered in the calculations. One of the main ways of measuring this factor is by analyzing user feedback on the conditions to understand whether they are satisfied with conditions or not. This paper provides a comprehensive review of recent approaches towards energy management systems based on users' feedbacks and subsequently performs a comparison between them premised upon their efficiency and accuracy to understand which approaches were more accurate and which ones resulted in a more efficient way of minimizing energy consumption while maintaining users' comfortability. It was concluded that the highest accuracy rate among the presented works was 95% accuracy in determining satisfaction and up to 51.08% energy savings can be achieved without disturbing user’s comfort. Considering the growing interest in designing and developing adaptive buildings, these studies can support diverse inquiries about this subject and can be used as a resource to support studies and researches towards efficient energy consumption while maintaining the comfortability of users.

Keywords: adaptive buildings, energy efficiency, intelligent buildings, user comfortability

Procedia PDF Downloads 134

Authors: A. Ramachandra Reddy, V. Murugan, Prema Kumari

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Residual solvents in Pharmaceutical samples are monitored using gas chromatography with headspace (GC-HS). Based on current regulatory and compendial requirements, measuring the residual solvents are mandatory for all release testing of active pharmaceutical ingredients (API). Generally, isopropyl alcohol is used as the residual solvent in proline and tryptophan; methanol in cysteine monohydrate hydrochloride, glycine, methionine and serine; ethanol in glycine and lysine monohydrate; acetic acid in methionine. In order to have a single method for determining these residual solvents (isopropyl alcohol, ethanol, methanol and acetic acid) in all these 7 amino acids a sensitive and simple method was developed by using gas chromatography headspace technique with flame ionization detection. During development, no reproducibility, retention time variation and bad peak shape of acetic acid peaks were identified due to the reaction of acetic acid with the stationary phase (cyanopropyl dimethyl polysiloxane phase) of column and dissociation of acetic acid with water (if diluent) while applying temperature gradient. Therefore, dimethyl sulfoxide was used as diluent to avoid these issues. But most the methods published for acetic acid quantification by GC-HS uses derivatisation technique to protect acetic acid. As per compendia, risk-based approach was selected as appropriate to determine the degree and extent of the validation process to assure the fitness of the procedure. Therefore, Total error concept was selected to validate the analytical procedure. An accuracy profile of ±40% was selected for lower level (quantitation limit level) and for other levels ±30% with 95% confidence interval (risk profile 5%). The method was developed using DB-Waxetr column manufactured by Agilent contains 530 µm internal diameter, thickness: 2.0 µm, and length: 30 m. A constant flow of 6.0 mL/min. with constant make up mode of Helium gas was selected as a carrier gas. The present method is simple, rapid, and accurate, which is suitable for rapid analysis of isopropyl alcohol, ethanol, methanol and acetic acid in amino acids. The range of the method for isopropyl alcohol is 50ppm to 200ppm, ethanol is 50ppm to 3000ppm, methanol is 50ppm to 400ppm and acetic acid 100ppm to 400ppm, which covers the specification limits provided in European pharmacopeia. The accuracy profile and risk profile generated as part of validation were found to be satisfactory. Therefore, this method can be used for testing of residual solvents in amino acids drug substances.

Keywords: amino acid, head space, gas chromatography, total error

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Authors: Carolina Senabre, Sergio Valero, Miguel Lopez, Antonio Gabaldon

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This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research.

Keywords: short-term load forecasting, power demand, neural networks, load forecasting

Procedia PDF Downloads 191

Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

Abstract:

Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

Procedia PDF Downloads 125

Authors: Engy S. El-Kayal, Mohamed M. S. Arafa

Abstract:

There are various causes of ankle pain including traumatic and non-traumatic causes. Various imaging techniques are available for assessment of AP. MRI is considered to be the imaging modality of choice for ankle joint evaluation with an advantage of its high spatial resolution, multiplanar capability, hence its ability to visualize small complex anatomical structures around the ankle. However, the high costs and the relatively limited availability of MRI systems, as well as the relatively long duration of the examination all are considered disadvantages of MRI examination. Therefore there is a need for a more rapid and less expensive examination modality with good diagnostic accuracy to fulfill this gap. HRU has become increasingly important in the assessment of ankle disorders, with advantages of being fast, reliable, of low cost and readily available. US can visualize detailed anatomical structures and assess tendinous and ligamentous integrity. The aim of this study was to compare the diagnostic accuracy of HRU with MRI in the assessment of patients with AP. We included forty patients complaining of AP. All patients were subjected to real-time HRU and MRI of the affected ankle. Results of both techniques were compared to surgical and arthroscopic findings. All patients were examined according to a defined protocol that includes imaging the tendon tears or tendinitis, muscle tears, masses, or fluid collection, ligament sprain or tears, inflammation or fluid effusion within the joint or bursa, bone and cartilage lesions, erosions and osteophytes. Analysis of the results showed that the mean age of patients was 38 years. The study comprised of 24 women (60%) and 16 men (40%). The accuracy of HRU in detecting causes of AP was 85%, while the accuracy of MRI in the detection of causes of AP was 87.5%. In conclusions: HRU and MRI are two complementary tools of investigation with the former will be used as a primary tool of investigation and the latter will be used to confirm the diagnosis and the extent of the lesion especially when surgical interference is planned.

Keywords: ankle pain (AP), high-resolution ultrasound (HRU), magnetic resonance imaging (MRI) ultrasonography (US)

Procedia PDF Downloads 193

Authors: Irtaza M. Syed, Kaamran Raahemifar

Abstract:

This paper presents modeling of a Alternating Current (AC) Photovoltaic (PV) module using Matlab/Simulink. The proposed AC-PV module model is simple, realistic, and application oriented. The model is derived on module level as compared to cell level directly from the information provided by the manufacturer data sheet. DC-PV module, MPPT control, BC, VSI and LC filter, all were treated as a single unit. The model accounts for changes in variations of both irradiance and temperature. The AC-PV module proposed model is simulated and the results are compared with the datasheet projected numbers to validate model’s accuracy and effectiveness. Implementation and results demonstrate simplicity and accuracy, as well as reliability of the model.

Keywords: PV modeling, AC PV Module, datasheet, VI curves irradiance, temperature, MPPT, Matlab/Simulink

Procedia PDF Downloads 575

Authors: Xiaoqi Jia, Qing Zhu, Hao Zhang, Su Yang

Abstract:

A gender recognition system is able to tell the gender of the given person through a few of frontal facial images. An effective gender recognition approach enables to improve the performance of many other applications, including security monitoring, human-computer interaction, image or video retrieval and so on. In this paper, we present an effective method for gender classification task in frontal facial images based on deep belief networks (DBNs), which can pre-train model and improve accuracy a little bit. Our experiments have shown that the pre-training method with DBNs for gender classification task is feasible and achieves a little improvement of accuracy on FERET and CAS-PEAL-R1 facial datasets.

Keywords: gender recognition, beep belief net-works, semi-supervised learning, greedy-layer wise RBMs

Procedia PDF Downloads 455