Search results for: deep profile control
14106 Modeling of the Cavitation by Bubble around a NACA0009 Profile
Authors: L. Hammadi, D. Boukhaloua
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In this study, a numerical model was developed to predict cavitation phenomena around a NACA0009 profile. The equations of the Rayleigh-Plesset and modified Rayleigh-Plesset are used to modeling the cavitation by bubble around a NACA0009 profile. The study shows that the distributions of pressures around extrados and intrados of profile for angle of incidence equal zero are the same. The study also shows that the increase in the angle of incidence makes it possible to differentiate the pressures on the intrados and the extrados.Keywords: cavitation, NACA0009 profile, flow, pressure coefficient
Procedia PDF Downloads 17314105 Development and Control of Deep Seated Gravitational Slope Deformation: The Case of Colzate-Vertova Landslide, Bergamo, Northern Italy
Authors: Paola Comella, Vincenzo Francani, Paola Gattinoni
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This paper presents the Colzate-Vertova landslide, a Deep Seated Gravitational Slope Deformation (DSGSD) located in the Seriana Valley, Northern Italy. The paper aims at describing the development as well as evaluating the factors that influence the evolution of the landslide. After defining the conceptual model of the landslide, numerical simulations were developed using a finite element numerical model, first with a two-dimensional domain, and later with a three-dimensional one. The results of the 2-D model showed a displacement field typical of a sackung, as a consequence of the erosion along the Seriana Valley. The analysis also showed that the groundwater flow could locally affect the slope stability, bringing about a reduction in the safety factor, but without reaching failure conditions. The sensitivity analysis carried out on the strength parameters pointed out that slope failures could be reached only for relevant reduction of the geotechnical characteristics. Such a result does not fit the real conditions observed on site, where a number of small failures often develop all along the hillslope. The 3-D model gave a more comprehensive analysis of the evolution of the DSGSD, also considering the border effects. The results showed that the convex profile of the slope favors the development of displacements along the lateral valley, with a relevant reduction in the safety factor, justifying the existing landslides.Keywords: deep seated gravitational slope deformation, Italy, landslide, numerical modeling
Procedia PDF Downloads 36114104 Effects of Resistance Exercise Training on Blood Profile and CRP in Men with Type 2 Diabetes Mellitus
Authors: Mohsen Salesi, Seyyed Zoheir Rabei
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Exercise has been considered a cornerstone of diabetes prevention and treatment for decades, but the benefits of resistance training are less clear. The purpose of this study was to determine the impact of resistance training on blood profile and inflammatory marker (CRP) of type 2 diabetes mellitus people. Thirty diabetic male were recruited (age: 50.34±10.28 years) and randomly assigned to 8 weeks resistance exercise training (n=15) and control groups (n=15). Before and after training blood pressure, weight, lipid profile (TC, TG, LDL-c, and HDL-c) and hs-CRP were measured. The resistance exercise training group took part in supervised 50–80 minutes resistance training sessions, three days a week on non-consecutive days for 8 weeks. Each exercise session included approximately 10 min of warm-up and cool-down periods. Results showed that TG significantly decreased (pre 210.19±9.31 vs. 101.12±7.25, p=0.03) and HDL-c significantly increased (pre 42.37±3.15 vs. 47.50±2.19, p=0.01) after exercise training. However, there was no difference between groups in TC, LDL-c, BMI and weight. In addition, a decrease in fasting blood glucose levels showed significant difference between groups (pre 144.65±5.73 vs. 124.21±6.48 p=0.04). Regular resistance exercise training can improve the lipid profile and reducing the cardiovascular risk factors in T2DM patients.Keywords: lipid profile, resistance exercise, type 2 diabetes mellitus, men
Procedia PDF Downloads 40514103 Effects of in Ovo Injection of Royal Jelly on Hatchability, One-Day Old Chickens Quality, Total Antioxidant Status and Blood Lipoproteins
Authors: Amin Adeli, Maryam Zarei
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Background and purpose: Royal jelly (RJ) is a natural product with anti-hyperlipidemic and antioxidant properties. In ovo administration of RJ may improve lipid profile and antioxidant properties. This study was conducted to evaluate, for first time, the effects of in ovo injection of the RJ on hatchability, one-day old chick quality, total antioxidant status and blood lipoproteins. Methods: 400 incubating eggs produced by Ross 308 strain (52 weeks of age in first stage of production) were prepared and assigned into 4 groups (n=100) and 4 replications per group (n=25). These 4 groups were injected by the following pattern: 1) 0.1 ml normal saline (control), 2) 0.1 mg RJ+0.1 ml normal saline, 3) 0.2 mg RJ+0.1 ml normal saline, and 4) 0.3 mg RJ+0.1 ml normal saline. Injections were performed using a laminar flow system Lipid profile, antioxidant properties, hatchability, and one-day old chicken quality were assessed. Results: The administration of RJ at concentration of 0.1increased the percentage of hatchability compared to concentration of 0.2 and control, significant differences have not been observed among groups for quality scores (P>0.05). The results showed that in ovo injection of the RJ did not have any significant effects on lipid profile; but administration of the RJ only decreased High-density lipoprotein (HDL cholesterol, HDL-C) (P<0.05). The results showed that injection of the RJ at concentration of 0.3 increased total antioxidant capacity (TAC) compared to control group (p<0.05). Injection of the RJ progressively increased gluthation peroxidase (GPx) activity (p<0.05). The results showed that injection of the RJ decreased superoxide dismutase (SOD) compared to control group (p<0.05). Conclusion: In ovo injection of the RJ at the highest concentration increased TAC and GPx, but it did not have significant effects on lipid profile. Future studies are needed to investigate the effects of the RJ on the above-mentioned mechanisms.Keywords: antioxidant enzymes, chicken quality, hatchability, royal jelly
Procedia PDF Downloads 8714102 Leveraging Automated and Connected Vehicles with Deep Learning for Smart Transportation Network Optimization
Authors: Taha Benarbia
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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metricsKeywords: automated vehicles, connected vehicles, deep learning, smart transportation network
Procedia PDF Downloads 7014101 Design of Open Framework Based Smart ESS Profile for PV-ESS and UPS-ESS
Authors: Young-Su Ryu, Won-Gi Jeon, Byoung-Chul Song, Jae-Hong Park, Ki-Won Kwon
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In this paper, an open framework based smart energy storage system (ESS) profile for photovoltaic (PV)-ESS and uninterruptible power supply (UPS)-ESS is proposed and designed. An open framework based smart ESS is designed and developed for unifying the different interfaces among manufacturers. The smart ESS operates under the profile which provides the specifications of peripheral devices such as different interfaces and to the open framework. The profile requires well systemicity and expandability for addible peripheral devices. Especially, the smart ESS should provide the expansion with existing systems such as UPS and the linkage with new renewable energy technology such as PV. This paper proposes and designs an open framework based smart ESS profile for PV-ESS and UPS-ESS. The designed profile provides the existing smart ESS and also the expandability of additional peripheral devices on smart ESS such as PV and UPS.Keywords: energy storage system (ESS), open framework, profile, photovoltaic (PV), uninterruptible power supply (UPS)
Procedia PDF Downloads 46614100 Effect of Dietary Waste Date Meal (Phoneix dactylifera) on Chemical Body Composition, Nutrition Value and Fatty Acids Profile of Fingerling Common Carp (Cyprinus carpio)
Authors: Mehrdad Kamali-Sanzighi, Maziar Kamali-sanzighi
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Effect of waste date meal (WDM) addition to the diet on body chemical composition and fatty acids profile of fingerling cyprinus carpio were evaluated. Four treatments with 3 replication such as control treatment (no additional WDM; T1), 5% WDM (50 gr/kg; T2), 10% WDM (100 gr/kg; T3) and 15% WDM (150 gr/kg; T4) were done. 168 fish with initial weight of 2.48±0.06 gr were fed 3 times per day according to 5 % of fish body weight for 12 weeks. The body composition results showed that there is no significant differences between treatments (P>0.05). All of Fatty acids profile parameters show significant differences between different treatments (P<0.05). Although, the highest value of MUFA+PUFA, PUFA/SFA, MUFA+PUFA/SFA, W3, EPA+DHA parameters belong to control treatment (T1) and 5% WDM treatment (T2) had lowest value of MUFA, PUFA, MUFA+PUFA, PUFA/SFA, MUFA+PUFA/SFA, W3, W3/W6, DHA/EPA and EPA+DHA parameters except of SFA and W6/W3 that show highest value than other treatments. Atherogenic index (AI) had no significant differences between different treatments (P>0.05) but Thrombogenic index (TI) had significant differences between different experimental treatments (P<0.05). The 5% WDM and control treatment show highest and lowest values. Generally, treatments of 10 and 15% WDM (T3-T4) had moderate performance than the other experimental treatments. Finally, addition of WDM to common carp fingerlings diets help to insignificant improvement of chemical body composition and the saturated and unsaturated fatty acids profile of them were significant.Keywords: waste, date, common carp, nutrition value
Procedia PDF Downloads 8414099 Control Flow around NACA 4415 Airfoil Using Slot and Injection
Authors: Imine Zakaria, Meftah Sidi Mohamed El Amine
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One of the most vital aerodynamic organs of a flying machine is the wing, which allows it to fly in the air efficiently. The flow around the wing is very sensitive to changes in the angle of attack. Beyond a value, there is a phenomenon of the boundary layer separation on the upper surface, which causes instability and total degradation of aerodynamic performance called a stall. However, controlling flow around an airfoil has become a researcher concern in the aeronautics field. There are two techniques for controlling flow around a wing to improve its aerodynamic performance: passive and active controls. Blowing and suction are among the active techniques that control the boundary layer separation around an airfoil. Their objective is to give energy to the air particles in the boundary layer separation zones and to create vortex structures that will homogenize the velocity near the wall and allow control. Blowing and suction have long been used as flow control actuators around obstacles. In 1904 Prandtl applied a permanent blowing to a cylinder to delay the boundary layer separation. In the present study, several numerical investigations have been developed to predict a turbulent flow around an aerodynamic profile. CFD code was used for several angles of attack in order to validate the present work with that of the literature in the case of a clean profile. The variation of the lift coefficient CL with the momentum coefficientKeywords: CFD, control flow, lift, slot
Procedia PDF Downloads 18614098 Effect of Different Oils on Quality of Deep-fried Dough Stick
Authors: Nuntaporn Aukkanit
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The aim of this study was to determine the effect of oils on chemical, physical, and sensory properties of deep-fried dough stick. Five kinds of vegetable oil which were used for addition and frying consist of: palm oil, soybean oil, sunflower oil, rice bran oil, and canola oil. The results of this study showed that using different kinds of oil made significant difference in the quality of deep-fried dough stick. Deep-fried dough stick fried with the rice bran oil had the lowest moisture loss and oil absorption (p≤0.05), but it had some unsatisfactory physical properties (color, specific volume, density, and texture) and sensory characteristics. Nonetheless, deep-fried dough stick fried with the sunflower oil had moisture loss and oil absorption slightly more than the rice bran oil, but it had almost higher physical and sensory properties. Deep-fried dough sticks together with the sunflower oil did not have different sensory score from the palm oil, commonly used for production of deep-fried dough stick. These results indicated that addition and frying with the sunflower oil are appropriate for the production of deep-fried dough stick.Keywords: deep-fried dough stick, palm oil, sunflower oil, rice bran oil
Procedia PDF Downloads 27714097 A Detailed Experimental Study and Evaluation of Springback under Stretch Bending Process
Authors: A. Soualem
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The design of multi stage deep drawing processes requires the evaluation of many process parameters such as the intermediate die geometry, the blank shape, the sheet thickness, the blank holder force, friction, lubrication etc..These process parameters have to be determined for the optimum forming conditions before the process design. In general sheet metal forming may involve stretching drawing or various combinations of these basic modes of deformation. It is important to determine the influence of the process variables in the design of sheet metal working process. Especially, the punch and die corner for deep drawing will affect the formability. At the same time the prediction of sheet metals springback after deep drawing is an important issue to solve for the control of manufacturing processes. Nowadays, the importance of this problem increases because of the use of steel sheeting with high stress and also aluminum alloys. The aim of this paper is to give a better understanding of the springback and its effect in various sheet metals forming process such as expansion and restraint deep drawing in the cup drawing process, by varying radius die, lubricant for two commercially available materials e.g. galvanized steel and Aluminum sheet. To achieve these goals experiments were carried out and compared with other results. The original of our purpose consist on tests which are ensured by adapting a U-type stretching-bending device on a tensile testing machine, where we studied and quantified the variation of the springback.Keywords: springback, deep drawing, expansion, restricted deep drawing
Procedia PDF Downloads 45014096 Static Simulation of Pressure and Velocity Behaviour for NACA 0006 Blade Profile of Well’s Turbine
Authors: Chetan Apurav
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In this journal the behavioural analysis of pressure and velocity has been done over the blade profile of Well’s turbine. The blade profile that has been taken into consideration is NACA 0006. The analysis has been done in Ansys Workbench under CFX module. The CAD model of the blade profile with certain dimensions has been made in CREO, and then is imported to Ansys for further analysis. The turbine model has been enclosed under a cylindrical body and has been analysed under a constant velocity of air at 5 m/s and zero relative pressure in static condition of the turbine. Further the results are represented in tabular as well as graphical form. It has been observed that the relative pressure of the blade profile has been stable throughout the radial length and hence will be suitable for practical usage.Keywords: Well's turbine, oscillating water column, ocean engineering, wave energy, NACA 0006
Procedia PDF Downloads 19514095 Speed Breaker/Pothole Detection Using Hidden Markov Models: A Deep Learning Approach
Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya
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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.Keywords: deep learning, hidden Markov model, pothole, speed breaker
Procedia PDF Downloads 13814094 Carbohydrate Intake Estimation in Type I Diabetic Patients Described by UVA/Padova Model
Authors: David A. Padilla, Rodolfo Villamizar
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In recent years, closed loop control strategies have been developed in order to establish a healthy glucose profile in type 1 diabetic mellitus (T1DM) patients. However, the controller itself is unable to define a suitable reference trajectory for glucose. In this paper, a control strategy Is proposed where the shape of the reference trajectory is generated bases in the amount of carbohydrates present during the digestive process, due to the effect of carbohydrate intake. Since there no exists a sensor to measure the amount of carbohydrates consumed, an estimator is proposed. Thus this paper presents the entire process of designing a carbohydrate estimator, which allows estimate disturbance for a predictive controller (MPC) in a T1MD patient, the estimation will be used to establish a profile of reference and improve the response of the controller by providing the estimated information of ingested carbohydrates. The dynamics of the diabetic model used are due to the equations described by the UVA/Padova model of the T1DMS simulator, the system was developed and simulated in Simulink, taking into account the noise and limitations of the glucose control system actuators.Keywords: estimation, glucose control, predictive controller, MPC, UVA/Padova
Procedia PDF Downloads 25814093 Epidemiological Profile of Healthcare Associated Infections in Intensive Care Unit
Authors: Abdessamad Dali-Ali, Houaria Beldjillali, Fouzia Agag, Asmaa Oukebdane, Ramzi Tidjani, Arslane Bettayeb, Khadidja Meddeber, Radia Dali-Yahia, Nori Midoun
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Healthcare-associated infections are a real public health problem, especially in intensive care units. The aim of our study was to describe the epidemiological profile and to estimate the incidence of these infections at the intensive care unit of our teaching hospital. A prospective study was conducted, from June 2012 to December 2013. During this period, 305 patients having a duration of hospitalization equal or more than 48 hours were included in the study. In terms of the incidence of healthcare associated infections, nosocomial pneumonia occupied the first position with a cumulative incidence rate of 20.0%, followed by bacteremia (5.6%), central venous catheter infections (4%), and urinary tract infections (3%). In the case of isolated microorganisms, Gram-negative bacilli not enterobacteriaceae occupied the first place with 48.5%, followed by enterobacteria (32.1%). Acinetobacter baumannii was the most common germ (27.6%). Our study showed that the rate of health-care-associated infections was relatively high in the intensive care unit. A control program to reduce all infections is a priority for the Infection Control Associated Committee.Keywords: epidemiological profile, healthcare associated infections, intensive care units, teaching hospital of Oran, Algeria
Procedia PDF Downloads 29414092 Facial Emotion Recognition Using Deep Learning
Authors: Ashutosh Mishra, Nikhil Goyal
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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.Keywords: facial recognition, computational intelligence, convolutional neural network, depth map
Procedia PDF Downloads 21914091 Advanced Approach to Analysis the Thin Strip Profile in Cold Rolling of Pair Roll Crossing and Shifting Mill Using an Arbitrary Lagrangian-Eulerian Technique
Authors: Abdulrahman Aljabri, Essam R. I. Mahmoud, Hamad Almohamedi, Zhengyi Jiang
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Cold rolled thin strip has received intensive attention through technological and theoretical progress in the rolling process, as well as researchers have focused on its control during rolling as an essential parameter for producing thinner strip with good shape and profile. An advanced approach has been proposed to analysis the thin strip profile in cold rolling of pair roll crossing and shifting mill using Finite Element Analysis (FEA) with an ALE technique. The ALE (Arbitrary Lagrangian-Eulerian) techniques to enable more flexibility of the ALE technique in the adjustment of the finite element mesh, which provides a significant tool for simulating the thin strip under realistic rolling process constraint and provide accurate model results. The FEA can provide theoretical basis for the 3D model of controlling the strip shape and profile in thin strip rolling, and deliver an optimal rolling process parameter, and suggest corrective changes during cold rolling of thin strip.Keywords: pair roll crossing, work roll shifting, strip shape and profile, finite element modeling
Procedia PDF Downloads 9014090 Forecasting the Temperature at a Weather Station Using Deep Neural Networks
Authors: Debneil Saha Roy
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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast horizon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron
Procedia PDF Downloads 16614089 Gaits Stability Analysis for a Pneumatic Quadruped Robot Using Reinforcement Learning
Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam
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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped
Procedia PDF Downloads 30614088 Clinical Profile of Oral Sensory Abilities in Developmental Dysarthria
Authors: Swapna N., Deepthy Ann Joy
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One of the major causes of communication disorders in pediatric population is Motor speech disorders. These disorders which affect the motor aspects of speech articulators can have an adverse effect on the communication abilities of children in their developmental period. The motor aspects are dependent on the sensory abilities of children with motor speech disorders. Hence, oral sensorimotor evaluation is an important component in the assessment of children with motor speech disorders. To our knowledge, the importance of oral motor examination has been well established, yet the sensory assessment of the oral structures has received less focus. One of the most common motor speech disorders seen in children is developmental dysarthria. The present study aimed to assess the orosensory aspects in children with developmental dysarthria (CDD). The control group consisted of 240 children in the age range of four and eight years which was divided into four subgroups (4-4.11, 5-5.11, 6-6.11 and 7-7.11 years). The experimental group consisted of 15 children who were diagnosed with developmental dysarthria secondary to cerebral palsy who belonged in the age range of four and eight years. The oro-sensory aspects such as response to touch, temperature, taste, texture, and orofacial sensitivity were evaluated and profiled. For this purpose, the authors used the ‘Oral Sensorimotor Evaluation Protocol- Children’ which was developed by the authors. The oro-sensory section of the protocol was administered and the clinical profile of oro-sensory abilities of typically developing children and CDD was obtained for each of the sensory abilities. The oro-sensory abilities of speech articulators such as lips, tongue, palate, jaw, and cheeks were assessed in detail and scored. The results indicated that experimental group had poorer scores on oro-sensory aspects such as light static touch, kinetic touch, deep pressure, vibration and double simultaneous touch. However, it was also found that the experimental group performed similar to control group on few aspects like temperature, taste, texture and orofacial sensitivity. Apart from the oro-motor abilities which has received utmost interest, the variation in the oro-sensory abilities of experimental and control group is highlighted and discussed in the present study. This emphasizes the need for assessing the oro-sensory abilities in children with developmental dysarthria in addition to oro-motor abilities.Keywords: cerebral palsy, developmental dysarthria, orosensory assessment, touch
Procedia PDF Downloads 15514087 Deep Learning for Recommender System: Principles, Methods and Evaluation
Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui
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Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors.Keywords: big data, decision making, deep learning, recommender system
Procedia PDF Downloads 46914086 Enhancement of Indexing Model for Heterogeneous Multimedia Documents: User Profile Based Approach
Authors: Aicha Aggoune, Abdelkrim Bouramoul, Mohamed Khiereddine Kholladi
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Recent research shows that user profile as important element can improve heterogeneous information retrieval with its content. In this context, we present our indexing model for heterogeneous multimedia documents. This model is based on the combination of user profile to the indexing process. The general idea of our proposal is to operate the common concepts between the representation of a document and the definition of a user through his profile. These two elements will be added as additional indexing entities to enrich the heterogeneous corpus documents indexes. We have developed IRONTO domain ontology allowing annotation of documents. We will present also the developed tool validating the proposed model.Keywords: indexing model, user profile, multimedia document, heterogeneous of sources, ontology
Procedia PDF Downloads 33914085 Numerical Modeling of Various Support Systems to Stabilize Deep Excavations
Authors: M. Abdallah
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Urban development requires deep excavations near buildings and other structures. Deep excavation has become more a necessity for better utilization of space as the population of the world has dramatically increased. In Lebanon, some urban areas are very crowded and lack spaces for new buildings and underground projects, which makes the usage of underground space indispensable. In this paper, a numerical modeling is performed using the finite element method to study the deep excavation-diaphragm wall soil-structure interaction in the case of nonlinear soil behavior. The study is focused on a comparison of the results obtained using different support systems. Furthermore, a parametric study is performed according to the remoteness of the structure.Keywords: deep excavation, ground anchors, interaction soil-structure, struts
Procedia PDF Downloads 40814084 Deep Reinforcement Learning for Advanced Pressure Management in Water Distribution Networks
Authors: Ahmed Negm, George Aggidis, Xiandong Ma
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With the diverse nature of urban cities, customer demand patterns, landscape topologies or even seasonal weather trends; managing our water distribution networks (WDNs) has proved a complex task. These unpredictable circumstances manifest as pipe failures, intermittent supply and burst events thus adding to water loss, energy waste and increased carbon emissions. Whilst these events are unavoidable, advanced pressure management has proved an effective tool to control and mitigate them. Henceforth, water utilities have struggled with developing a real-time control method that is resilient when confronting the challenges of water distribution. In this paper we use deep reinforcement learning (DRL) algorithms as a novel pressure control strategy to minimise pressure violations and leakage under both burst and background leakage conditions. Agents based on asynchronous actor critic (A2C) and recurrent proximal policy optimisation (Recurrent PPO) were trained and compared to benchmarked optimisation algorithms (differential evolution, particle swarm optimisation. A2C manages to minimise leakage by 32.48% under burst conditions and 67.17% under background conditions which was the highest performance in the DRL algorithms. A2C and Recurrent PPO performed well in comparison to the benchmarks with higher processing speed and lower computational effort.Keywords: deep reinforcement learning, pressure management, water distribution networks, leakage management
Procedia PDF Downloads 7714083 Performance Evaluation of Distributed Deep Learning Frameworks in Cloud Environment
Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang
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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks
Procedia PDF Downloads 20014082 Identification of Deep Landslide on Erzurum-Turkey Highway by Geotechnical and Geophysical Methods and its Prevention
Authors: Neşe Işık, Şenol Altıok, Galip Devrim Eryılmaz, Aydın durukan, Hasan Özgür Daş
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In this study, an active landslide zone affecting the road alignment on the Tortum-Uzundere (Erzurum/Turkey) highway was investigated. Due to the landslide movement, problems have occurred in the existing road pavement, which has caused both safety problems and reduced driving comfort in the operation of the road. In order to model the landslide, drilling, geophysical and inclinometer studies were carried out in the field within the scope of ground investigation. Laboratory tests were carried out on soil and rock samples obtained from the borings. When the drilling and geophysical studies were evaluated together, it was determined that the study area has a complex geological structure. In addition, according to the inclinometer results, the direction and speed of movement of the landslide mass were observed. In order to create an idealized geological profile, all field and laboratory studies were evaluated together and then the sliding surface of the landslide was determined by back analysis method. According to the findings obtained, it was determined that the landslide was massively large, and the movement occurred had a deep sliding surface. As a result of the numerical analyses, it was concluded that the Slope angle reduction is the most economical and environmentally friendly method for the control of the landslide mass.Keywords: landslide, geotechnical methods, geophysics, monitoring, highway
Procedia PDF Downloads 6214081 Optimal Allocation of Distributed Generation Sources for Loss Reduction and Voltage Profile Improvement by Using Particle Swarm Optimization
Authors: Muhammad Zaheer Babar, Amer Kashif, Muhammad Rizwan Javed
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Nowadays distributed generation integration is best way to overcome the increasing load demand. Optimal allocation of distributed generation plays a vital role in reducing system losses and improves voltage profile. In this paper, a Meta heuristic technique is proposed for allocation of DG in order to reduce power losses and improve voltage profile. The proposed technique is based on Multi Objective Particle Swarm optimization. Fewer control parameters are needed in this algorithm. Modification is made in search space of PSO. The effectiveness of proposed technique is tested on IEEE 33 bus test system. Single DG as well as multiple DG scenario is adopted for proposed method. Proposed method is more effective as compared to other Meta heuristic techniques and gives better results regarding system losses and voltage profile.Keywords: Distributed generation (DG), Multi Objective Particle Swarm Optimization (MOPSO), particle swarm optimization (PSO), IEEE standard Test System
Procedia PDF Downloads 44814080 Leveraging Deep Q Networks in Portfolio Optimization
Authors: Peng Liu
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Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework.Keywords: deep reinforcement learning, deep Q networks, portfolio optimization, multi-period optimization
Procedia PDF Downloads 1914079 Geochemical Composition of Deep and Highly Weathered Soils Leyte and Samar Islands Philippines
Authors: Snowie Jane Galgo, Victor Asio
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Geochemical composition of soils provides vital information about their origin and development. Highly weathered soils are widespread in the islands of Leyte and Samar but limited data have been published in terms of their nature, characteristics and nutrient status. This study evaluated the total elemental composition, properties and nutrient status of eight (8) deep and highly weathered soils in various parts of Leyte and Samar. Sampling was done down to 3 to 4 meters deep. Total amounts of Al₂O₃, As₂O₃, CaO, CdO, Cr₂O₃, CuO, Fe₂O₃, K₂O, MgO, MnO, Na₂O, NiO, P₂O₅, PbO, SO₃, SiO₂, TiO₂, ZnO and ZrO₂ were analyzed using an X-ray analytical microscope for eight soil profiles. Most of the deep and highly weathered soils have probably developed from homogenous parent materials based on the regular distribution with depth of TiO₂ and ZrO₂. Two of the soils indicated high variability with depth of TiO₂ and ZrO₂ suggesting that these soils developed from heterogeneous parent material. Most soils have K₂O and CaO values below those of MgO and Na₂O. This suggests more losses of K₂O and CaO have occurred since they are more mobile in the weathering environment. Most of the soils contain low amounts of other elements such as CuO, ZnO, PbO, NiO, CrO and SO₂. Basic elements such as K₂O and CaO are more mobile in the weathering environment than MgO and Na₂O resulting in higher losses of the former than the latter. Other elements also show small amounts in all soil profile. Thus, this study is very useful for sustainable crop production and environmental conservation in the study area specifically for highly weathered soils which are widespread in the Philippines.Keywords: depth function, geochemical composition, highly weathered soils, total elemental composition
Procedia PDF Downloads 25614078 Reconnaissance Geophysical Study on the Southeastern Part of Al-Qashah Aera, Kingdom of Saudi Arabia
Authors: Ali Al-Bakri, Mohammed Sazid
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The investigated study area locates about 72 km from Jeddah city, Makkah district, Kingdom of Saudi Arabia. The study mainly aimed to define only in detail the most significant zones of possible mineralization and outline their subsurface parameters (location and strike) in the southeast part of Jabal Al-Qashah. Several geophysical methods have been conducted to carry out the goal. Among these methods are the ground magnetic method, self-potential (SP) method, and induced polarization (IP) method. Integrating these methods aims to help in delineating the possible mineralization in the study area. The magnetic survey was conducted along 17 profiles where these profiles were chosen to be perpendicular to the strike of the quartz shear zone. Self-potential was applied along with five profiles covering the study area. At the same time, induced polarization was used along with one profile located at the western side of the study area corresponding to some magnetic and SP profiles. The most interesting zones of mineralization were successfully determined by comparing the results of residual magnetic profile (3), SP profile (1), and IP profile, where geological structures control some mineralization.Keywords: geophysical methods, magnetic method, self-potential, induced polarization, Jabal Al-Qashah
Procedia PDF Downloads 12314077 Effect of Aerobic Training on Visfatin Levels and Lipid Profile in Obese Women
Authors: Banaeifar Abdolali, Rahmanimoghadam Neda, Sohyli Shahram
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Obesity is an increase in body fat , in addition it has been introduced as a risk factor for the progress of lipid disorders, hypertension, cardiovascular disease and type 2 diabetes (1,2). In recent years, Adipose tissue is now recognized as an endocrine organ that secretes many cytokines such as: interleukin 6, leptin, and visfatin (3). Visfatin is an adipocytokine that release from adiposities. It is unidentified whether training also influences concentrations of visfatin. Purpose: The purpose of this study was to examine the effects of 12 weeks of aerobic training on visfatin levels and lipid profile in obese women. Method: Thirty two obese women (age = 37.8 ± 13.2 years, body mass index = of 39.4 ± 6.4 kg/m2 .) volunteered to participate in a 12-wk exercise program. They were randomly assigned to either a training (n = 16) or control (n = 14) group. The training group exercised for 70 minutes per session, 3 days per week during the 12 week training program. The control group was asked to maintain their normal daily activities. Samples were obtained before and at the end of training program. We use t.paire and independent,test for data analyzes. Results: Exercise training resulted in a decrease in body weight (p < 0.05), percent body fat (% fat) and BMI (p < 0.05), fasting glucose level and visfatin concentration decreased but wasn’t significant (p > 0.05). Also the levels of triglyceride, total cholesterol and low-density lipoprotein cholesterol did not change significantly. Conclution: In conclusion, three month aerobic training program used in this study was very effective for producing significant benefits to body composition and HDL.c but didn’t significant chenging visfatin levels and lipid profile in these obese women.Keywords: aerobic training, visfatin, lipid profile, women
Procedia PDF Downloads 459