Search results for: artificial ground freezing

Authors: Ahmad H. Abdelgwad, Ahmed A. Nashat

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Landmine detection is an important and yet challenging problem remains to be solved. Ground Penetrating Radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. The detection methodology of GPR depends mainly on the contrast of the dielectric properties of the searched target and its surrounding soil. This contrast produces a partial reflection of the electromagnetic pulses that are being transmitted into the soil and then being collected by the GPR.  One of the most critical hardware components for the performance of GPR is the antenna system. The current paper explores the design and simulation of a pyramidal horn antenna operating at L-band frequencies (1- 2 GHz) to detect a landmine. A prototype model of the GPR system setup is developed to simulate full wave analysis of the electromagnetic fields in different soil types. The contrast in the dielectric permittivity of the landmine and the sandy soil is the most important parameter to be considered for detecting the presence of landmine. L-band horn antenna is proved to be well-versed in the investigation of landmine detection.

Keywords: full wave analysis, ground penetrating radar, horn antenna design, landmine detection

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Authors: Suzan Salem, Heba Moustafa, Abd El-Aziz Abd El-Aal

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Seismic micro hazard zonation for urban areas is the first step towards a seismic risk analysis and mitigation strategy. Essential here is to obtain a proper understanding of the local subsurface conditions and to evaluate ground-shaking effects. In the present study, an attempt has been made to evaluate the seismic hazard considering local site effects by carrying out detailed geotechnical and geophysical site characterization in El-Fayoum New City. Seismic hazard analysis and microzonation of El-Fayoum New City are addressed in three parts: in the first part, estimation of seismic hazard is done using seismotectonic and geological information. The second part deals with site characterization using geotechnical and shallow geophysical techniques. In the last part, local site effects are assessed by carrying out one-dimensional (1-D) ground response analysis using the equivalent linear method by program SHAKE 2000. Finally, microzonation maps have been prepared. The detailed methodology, along with experimental details, collected data, results and maps are presented in this paper.

Keywords: El-Fayoum, microzonation, seismotectonic, Egypt

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Authors: Kunal Krishna Das, Eddie S. S. Lam

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Preplaced Aggregate Concrete (PAC) is produced by first placing the coarse aggregate into the formwork, followed by injection of grout to fill in the voids in between the coarse aggregates. In this study, tests were carried out to determine the effects of supplementary cementitious materials on the properties of PAC. Cement was partially replaced by ground granulated blast furnace slag (GGBS) and silica fume (SF) at different proportions. Grout properties were determined by the flow cone test and compressive strength test. Grout proportion was optimized statistically. It was applied to form PAC. Hardened properties of PAC, comprising compressive strength, splitting tensile strength, chloride-ion penetration and drying shrinkage, were evaluated. GGBS enhanced the flowability of the grout, whereas SF enhanced the strength of PAC. Both GGBS and SF improved the resistance to chloride-ion penetration with the drawback of increased drying shrinkage. Nevertheless, drying shrinkage was within the range to be classified as low shrinkage concrete.

Keywords: factorial design, ground granulated blast furnace slag, preplaced aggregate concrete, silica fume

Procedia PDF Downloads 117

Authors: Ciro Cursio, Giulia Cursio, Pio Luigi Cursio, Luigi Cursio

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Communication between practitioner and patient is of the utmost importance in aesthetic medicine: as of today, images of previous treatments are the most common tool used by doctors to describe and anticipate future results for their patients. However, using photos of other people often reduces the engagement of the prospective patient and is further limited by the number and quality of pictures available to the practitioner. Pre-existing work solves this issue in two ways: 3D scanning of the area with manual editing of the 3D model by the doctor or automatic prediction of the treatment by warping the image with hand-written parameters. The first approach requires the manual intervention of the doctor, while the second approach always generates results that aren’t always realistic. Thus, in one case, there is significant manual work required by the doctor, and in the other case, the prediction looks artificial. We propose an AI-based algorithm that autonomously generates a realistic prediction of treatment results. For the purpose of this study, we focus on hyaluronic acid treatments in the facial area. Our approach takes into account the individual characteristics of each face, and furthermore, the prediction system allows the patient to decide which area of the face she wants to modify. We show that the predictions generated by our system are realistic: first, the quality of the generated images is on par with real images; second, the prediction matches the actual results obtained after the treatment is completed. In conclusion, the proposed approach provides a valid tool for doctors to show patients what they will look like before deciding on the treatment.

Keywords: prediction, hyaluronic acid, treatment, artificial intelligence

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Authors: Nadir Atayev, Mehman Hasanov

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Future spacecraft will require a structural change in the way data is transmitted due to the increase in the volume of data required for space communication. Current radio frequency communication systems are already facing a bottleneck in the volume of data sent to the ground segment due to their technological and regulatory characteristics. To overcome these issues, free space optics communication plays an important role in the integrated terrestrial space network due to its advantages such as significantly improved data rate compared to traditional RF technology, low cost, improved security, and inter-satellite free space communication, as well as uses a laser beam, which is an optical signal carrier to establish satellite-ground & ground-to-satellite links. In this approach, there is a need for high-speed and energy-efficient systems as a base platform for sending high-volume video & audio data. Nano Satellite and its branch CubeSat platforms have more technical functionality than large satellites, wheres cover an important part of the space sector, with their Low-Earth-Orbit application area with low-cost design and technical functionality for building networks using different communication topologies. Along the research theme developed in this regard, the output parameter indicators for the FSO of the optical communication transceiver subsystem on the existing CubeSat platforms, and in the direction of improving the mentioned parameters of this communication methodology, 3D optical switch and laser beam controlled optical transponder with 2U CubeSat structural subsystems and application in the Low Earth Orbit satellite network topology, as well as its functional performance and structural parameters, has been studied accordingly.

Keywords: cubesat, free space optics, nano satellite, optical laser communication.

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Authors: Emmanuel Uba, Lily Chimuanya, Maryam Tar

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Orthographic engineering is no longer the preserve of the Short Messaging Service (SMS), which is characterised by limited space. Such stylistic creativity or deviation is fast creeping into real-time messaging, popularly known as Instant Messaging (IM), despite the large number of characters allowed. This occurs at various linguistic levels: phonology, morphology, syntax, etc. Nigerians are not immune to this linguistic stylisation. This study investigates the phonological and meta-phonological conventions of the messages sent and received via WhatsApp by Nigerian graduates. This is ontological study of 250 instant messages collected from 98 graduates from different ethnic groups in Nigeria. The selection and analysis of the messages are based on figure and ground principle. The results reveal the use of accent stylisation, phoneme substitution, blending, consonantisation (a specialised form of deletion targeting vowels), numerophony (using a figure/number, usually 1-10, to represent a word or syllable that has the same sound) and phonetic respelling in the IMs sent by Nigerians. The study confirms the existence of linguistic creativity.

Keywords: figure and ground principle, instant messaging, linguistic stylisation, meta-phonology

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Authors: Jae Ni Jang, Young Uk Kim

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Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Damou Mehdi, Nouri Keltoum, Feham Mohammed, Khazini Mohammed, Bouazza Tayb Habibi Chawki

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The Half mode SIW filter is treated by two softwares (HFSS (High Frequency Structure Simulator) and CST (Computer Simulation Technology)). The filter HMSIW has a very simple structure and a very compact size. The simulated results by CST are presented and compared with the results simulated by a high-frequency structure simulator. Good agreement between the simulated CST and simulated results by HFSS is observed. By cascading two of them according to design requirement, a X-band bandpass filter is designed and simulated to meet compact size, low insertion loss, good return loss as well as second harmonic suppression. As an example, we designed the proposed HMSIW filter at X band by HFSS. The filter has a pass-band from 7.3 GHz to 9.8 GHz, and its relative operating fraction bandwidth is 29.5 %. There are one transmission zeros are located at 14.4 GHz.

Keywords: substrate integrated waveguide, filter, HMSIW, defected ground structures (DGS), simulation BPF

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Authors: Musa H. Arslan, Murat Ceylan, Tayfun Koyuncu

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In this study, an artificial intelligence-based (ANN based) analytical method has been developed for analyzing earthquake performances of the reinforced concrete (RC) buildings. 66 RC buildings with four to ten storeys were subjected to performance analysis according to the parameters which are the existing material, loading and geometrical characteristics of the buildings. The selected parameters have been thought to be effective on the performance of RC buildings. In the performance analyses stage of the study, level of performance possible to be shown by these buildings in case of an earthquake was determined on the basis of the 4-grade performance levels specified in Turkish Earthquake Code- 2007 (TEC-2007). After obtaining the 4-grade performance level, selected 23 parameters of each building have been matched with the performance level. In this stage, ANN-based fast evaluation algorithm mentioned above made an economic and rapid evaluation of four to ten storey RC buildings. According to the study, the prediction accuracy of ANN has been found about 74%.

Keywords: artificial intelligence, earthquake, performance, reinforced concrete

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Authors: Kai-Yi Cho, Fa-Shian Chang, Lih-Tyng Hwang, Chih-Feng Liu, Jeng-Nan Lee, Shun-Min Wang, Jhu-Wei Ji

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This paper presents a ground robot which has the tracked transformative structures of the motion mechanism. The robot has a good ability to adapt to the terrain, due to the front end of the track can be deformed, it can more easily pass the more complex area, such as to climb stairs and ramp areas. Usually in the disaster area, where the terrain is generally broken and complicated, there will be many slopes, broken walls, rubble, and obstacles, then if you want the robot through this area, you need to have a good off-road performance for possible complex terrain, this robot with the transformative tracked mechanism has a strong adaptability, it can overcome the limitation of the terrains to be a good rescue robot. Also, the robot has a good flexibility in the shape of contact with the ground; that can adapt the varied terrain by the deformable track, thus able to pass the different terrains, that was verified through the experiments on a test-platform and a field test. The prototype of the robot system has been developed, and experiments are carried out to verify the validity of the proposed design.

Keywords: tracked robot, rescue robot, transformation mechanism, deformable track, hull design

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Authors: O. Maximova, I. L. Danilovich, E. B. Deeva, K. Y. Bukhteev, A. A. Vorobyova, I. V. Morozov, O. S. Volkova, E. A. Zvereva, I. V. Solovyev, S. A. Nikolaev, D. Phuyal, M. Abdel-Hafiez, Y. C. Wang, J. Y. Lin, J. M. Chen, D. I. Gorbunov, K. Puzniak, B. Lake, A. N. Vasiliev

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The low-dimensional magnetic systems tend to reveal exotic spin liquid ground states or form peculiar types of long-range order. Among systems of vivid interest are those characterized by the triangular motif in two dimensions. The realization of either ordered or disordered ground state in a triangular, honeycomb, or kagome lattices is are dictated by the competition of exchange interactions, also being sensitive to anisotropy and the spin value of magnetic ions. While the low-spin Heisenberg systems may arrive at a spin liquid long-range entangled quantum state with emergent gauge structures, the high-spin Ising systems may establish the rigid non-collinear structures. This study presents the case of chiral non-coplanar inverted umbrella-type ferrimagnet formed in cobalt nitrate Co(NO₃)₂ below T

Keywords: chiral magnetic structures, low dimensional magnetic systems, umbrella-type ferrimagnets, chiral non-coplanar magnetic structures

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Justin Bin

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Artificial intelligence (AI) has been rapidly rising in usage recently, already active in the daily lives of millions, from distinguished AIs like the popular ChatGPT or Siri to more obscure, inconspicuous AIs like those used in social media or internet search engines. As upcoming generations grow immersed in emerging technology, AI will play a vital role in their development. Namely, the education sector, an influential portion of a person’s early life as a student, faces a vast ocean of possibilities concerning the implementation of AI. The main purpose of this study is to analyze the effect that AI will have on the future of the educational field. More particularly, this study delves deeper into the following three categories: school admissions, the productivity of students, and ethical concerns (role of human teachers, purpose of schooling itself, and significance of diplomas). This study synthesizes research and data on the current effects of AI on education from various published literature sources and journals, as well as estimates on further AI potential, in order to determine the main, overarching effects it will have on the future of education. For this study, a systematic organization of data in terms of type (quantitative vs. qualitative), the magnitude of effect implicated, and other similar factors were implemented within each area of significance. The results of the study suggest that AI stands to change all the beforementioned subgroups. However, its specific effects vary in magnitude and favorability (beneficial or harmful) and will be further discussed. The results discussed will reveal to those affiliated with the education field, such as teachers, counselors, or even parents of students, valuable information on not just the projected possibilities of AI in education but the effects of those changes moving forward.

Keywords: artificial intelligence, education, schools, teachers

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Authors: Azidah Abu Ziden, Adu Ifedayo Emmanuel

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This study investigated the optical board as an artificial technology for peer teaching in a Nigerian university. A design and development research (DDR) design was adopted, which entailed the planning and testing of instructional design models adopted to produce the optical board. This research population involved twenty-five (25) peer-teaching students at a Nigerian university consisting of theatre arts, religion, and language education-related disciplines. Also, using a random sampling technique, this study selected eight (8) students to work on the optical board. Besides, this study introduced a research instrument titled lecturer assessment rubric containing 30-mark metrics for evaluating students’ teaching with the optical board. In this study, it was discovered that the optical board affords students acquisition of self-employment skills through their exposure to the peer teaching course, which is a teacher training module in Nigerian universities. It is evident in this study that students were able to coordinate their design and effectively develop the optical board without lecturer’s interference. This kind of achievement in this research shows that the Nigerian university curriculum had been designed with contents meant to spur students to create jobs after graduation, and effective implementation of the readily available curriculum contents is enough to imbue students with the needed entrepreneurial skills. It was recommended that the Federal Government of Nigeria (FGN) must discourage the poor implementation of Nigerian university curriculum and invest more in the betterment of the readily available curriculum instead of considering a synonymously acclaimed new curriculum for regurgitated teaching and learning process.

Keywords: optical board, artificial technology, peer teaching, educational technology, Nigeria, Malaysia, university, glass, wood, electrical, improvisation

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Authors: Amir Mirzaei

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The powertrain system is one of the most basic and essential components in a machine. The occurrence of motion is practically impossible without the presence of this system. When power is generated by the engine, it is transmitted by the powertrain system to the wheels, which are the last parts of the system. Powertrain system has different components according to the type of use and design. When the force generated by the engine reaches to the wheels, the amount of frictional force between the tire and the ground determines the amount of traction and non-slip or the amount of slip. At various levels, such as icy, muddy, and snow-covered ground, the amount of friction coefficient between the tire and the ground decreases dramatically and considerably, which in turn increases the amount of force loss and the vehicle traction decreases drastically. This condition is caused by the phenomenon of slipping, which, in addition to the waste of energy produced, causes the premature wear of driving tires. It also causes the temperature of the transmission oil to rise too much, as a result, causes a reduction in the quality and become dirty to oil and also reduces the useful life of the clutches disk and plates inside the transmission. this issue is much more important in road construction and mining machinery than passenger vehicles and is always one of the most important and significant issues in the design discussion, in order to overcome. One of these methods is the automatic spin regulator system which is abbreviated as ASR. The importance of this method and its structure and function have solved one of the biggest challenges of the powertrain system in the field of construction and mining machinery. That this research is examined.

Keywords: automatic spin regulator, ASR, methods of reducing slipping, methods of preventing the reduction of the useful life of clutches disk and plate, methods of preventing the premature dirtiness of transmission oil, method of preventing the reduction of the useful life of tires

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Authors: Mohammed Eltayeb Salih Elamin

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This study discusses the concept of the artificial heart using engineering concepts, of the fluid mechanics and the characteristics of the non-Newtonian fluid. For the purpose to serve heart patients and improve aspects of their lives and since the Statistics review according to world health organization (WHO) says that heart disease and blood vessels are the first cause of death in the world. Statistics shows that 30% of the death cases in the world by the heart disease, so simply we can consider it as the number one leading cause of death in the entire world is heart failure. And since the heart implantation become a very difficult and not always available, the idea of the artificial heart become very essential. So it’s important that we participate in the developing this idea by searching and finding the weakness point in the earlier designs and hoping for improving it for the best of humanity. In this study a pump was designed in order to pump blood to the human body and taking into account all the factors that allows it to replace the human heart, in order to work at the same characteristics and the efficiency of the human heart. The pump was designed on the idea of the diaphragm pump. Three models of blood obtained from the blood real characteristics and all of these models were simulated in order to study the effect of the pumping work on the fluid. After that, we study the properties of this pump by using Ansys15 software to simulate blood flow inside the pump and the amount of stress that it will go under. The 3D geometries modeling was done using SOLID WORKS and the geometries then imported to Ansys design modeler which is used during the pre-processing procedure. The solver used throughout the study is Ansys FLUENT. This is a tool used to analysis the fluid flow troubles and the general well-known term used for this branch of science is known as Computational Fluid Dynamics (CFD). Basically, Design Modeler used during the pre-processing procedure which is a crucial step before the start of the fluid flow problem. Some of the key operations are the geometry creations which specify the domain of the fluid flow problem. Next is mesh generation which means discretization of the domain to solve governing equations at each cell and later, specify the boundary zones to apply boundary conditions for the problem. Finally, the pre–processed work will be saved at the Ansys workbench for future work continuation.

Keywords: Artificial heart, computational fluid dynamic heart chamber, design, pump

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Authors: Seyed Sadegh Naseralavi, Sohrab Nemati, Ehsan Khojastehfar, Sadegh Balaghi

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In the present research, various formulations of wavelet transform are applied on acceleration time history of earthquake. The mentioned transforms decompose the strong ground motion into low and high frequency parts. Since the high frequency portion of strong ground motion has a minor effect on dynamic response of structures, the structure is excited by low frequency part. Consequently, the seismic response of structure is predicted consuming one half of computational time, comparing with conventional time history analysis. Towards reducing the computational effort needed in seismic optimization of structure, seismic optimization of a shear frame structure is conducted by applying various forms of mentioned transformation through genetic algorithm.

Keywords: time history analysis, wavelet transform, optimization, earthquake

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Authors: Deyu Zhou, Xiao Xue, Lizhen Cui

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With the increasing collaboration among various services and the growing complexity of user demands, there are more and more factors affecting the stable development of the cloud manufacturing service economic system (CMSE). This poses new challenges to the evolution analysis of the CMSE. Many researchers have modeled and analyzed the evolution process of CMSE from the perspectives of individual learning and internal factors influencing the system, but without considering other important characteristics of the system's individuals (such as heterogeneity, bounded rationality, etc.) and the impact of external environmental factors. Therefore, this paper proposes an integrated artificial social model for the cloud manufacturing service economic system, which considers both the characteristics of the system's individuals and the internal and external influencing factors of the system. The model consists of three parts: the Agent model, environment model, and rules model (Agent-Environment-Rules, AER): (1) the Agent model considers important features of the individuals, such as heterogeneity and bounded rationality, based on the adaptive behavior mechanisms of perception, action, and decision-making; (2) the environment model describes the activity space of the individuals (real or virtual environment); (3) the rules model, as the driving force of system evolution, describes the mechanism of the entire system's operation and evolution. Finally, this paper verifies the effectiveness of the AER model through computational and experimental results.

Keywords: cloud manufacturing service economic system (CMSE), AER model, artificial social modeling, integrated framework, computing experiment, agent-based modeling, social networks

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Ciro Cursio, Pio Luigi Cursio, Giulia Cursio, Isabella Chiardi, Luigi Cursio

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Introduction: Full-face aesthetic treatments often present a difficult task: since different patients possess different anatomical and tissue characteristics, there is no guarantee that the same treatment will have the same effect on multiple patients; additionally, full-face rejuvenation and beautification treatments require not only a high degree of technical skill but also the ability to choose the right product for each area and a keen artistic eye. Method: We present an artificial intelligence-based algorithm that can generate realistic post-treatment 3D models based on the patient’s requests together with the doctor’s input. These 3-dimensional predictions can be used by the practitioner for two purposes: firstly, they help ensure that the patient and the doctor are completely aligned on the expectations of the treatment; secondly, the doctor can use them as a visual guide, obtaining a natural result that would normally stem from the practitioner's artistic skills. To this end, the algorithm is able to predict injection zones, the type and quantity of hyaluronic acid, the injection depth, and the technique to use. Results: Our innovation consists in providing an objective visual representation of the patient that is helpful in the patient-doctor dialogue. The patient, based on this information, can express her desire to undergo a specific treatment or make changes to the therapeutic plan. In short, the patient becomes an active agent in the choices made before the treatment. Conclusion: We believe that this algorithm will reveal itself as a useful tool in the pre-treatment decision-making process to prevent both the patient and the doctor from making a leap into the dark.

Keywords: hyaluronic acid, fillers, full face, artificial intelligence, 3D

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Authors: Mary Allagoa, Abir Al-Tabbaa

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The study explores the use of binders and additives, such as Portland cement, pulverized fuel ash, ground granulated blast furnace slag, and MgO, to decrease the concentration and leachability of pollutants in contaminated site soils. The research investigates their effectiveness and associated risks of using the binders, with a focus on Total Heavy metals (THM) and Total Petroleum Hydrocarbon (TPH). The goal of this research is to evaluate the performance and effectiveness of binders and additives in remediating soil pollutants. The study aims to assess the suitability of the mixtures for ground improvement purposes, determine the optimal dosage, and investigate the associated risks. The research utilizes physical (unconfined compressive strength) and chemical tests (batch leachability test) to assess the efficacy of the binders and additives. A completely randomized design one-way ANOVA is used to determine the significance within mix binders of THM. The study also employs incremental lifetime cancer risk assessments (ILCR) and other indexes to evaluate the associated risks. The study finds that Ground Granulated Blast Furnace Slag (GGBS): MgO is the most effective binder for remediation, particularly when using low dosages of MgO combined with higher dosages of GGBS binders on TPH. The results indicate that binders and additives can encapsulate and immobilize pollutants, thereby reducing their leachability and toxicity. The mean unconfined compressive strength of the soil ranges from 285.0- 320.5 kPa, while THM levels are less than 10 µg/l in GGBS: MgO and CEM: PFA but below 1 µg/l in CEM I based. The ILCR ranged from 6.77E-02 - 2.65E-01 and 5.444E-01 – 3.20 E+00, with the highest values observed under extreme conditions. The hazard index (HI), Risk allowable daily dose intake (ADI), and Risk chronic daily intake (CDI) were all less than 1 for the THM. The study identifies MgO as the best additive for use in soil remediation.

Keywords: risk ADI, risk CDI, ILCR, novel binders, additives binders, hazard index

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Authors: Matthew Cruickshank, Chaaruchandra Korde, Roger P. West, John Reddy

Abstract:

Precast concrete element exports are growing in importance in Ireland’s concrete industry and with the increased global focus on reducing carbon emissions, the industry is exploring more sustainable alternatives such as using ground granulated blast-furnace slag (GGBS) as a partial replacement of Portland cement. It is well established that GGBS, with low early age strength development, has limited use in precast manufacturing due to the need for early de-moulding, cutting of pre-stressed strands and lifting. In this dichotomy, the effects of temperature, admixture, are explored to try to achieve the required very early age strength. Testing of the strength of mortars is mandated in the European cement standard, so here with 50% GGBS and Super Fine GGBS, with three admixture conditions (none, conventional accelerator, novel accelerator) and two early age curing temperature conditions (20°C and 35°C), standard mortar strengths are measured at six ages (16 hours, 1, 2, 3, 7, 28 days). The present paper will describe the effort towards developing maturity curves to aid in understanding the effect of these accelerating admixtures and GGBS fineness on slag cement mortars, allowing prediction of their strength with time and temperature. This study is of particular importance to the precast industry where concrete temperature can be controlled. For the climatic conditions in Ireland, heating of precast beds for long hours will amount to an additional cost and also contribute to the carbon footprint of the products. When transitioned from mortar to concrete, these maturity curves are expected to play a vital role in predicting the strength of the GGBS concrete at a very early age prior to demoulding.

Keywords: accelerating admixture, early age strength, ground granulated blast-furnace slag, GGBS, maturity, precast concrete

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Authors: E. Mello, C. Ribellato, E. Mohamedelhassan

Abstract:

This study investigated the improvement in concrete properties with addition of cellulose, steel, carbon and PET fibers. Each fiber was added at four percentages to the fresh concrete, which was moist-cured for 28-days and then tested for compressive, flexural and tensile strengths. Changes in strength and increases in cost were analyzed. Results showed that addition of cellulose caused a decrease between 9.8% and 16.4% in compressive strength. This range may be acceptable as cellulose fibers can significantly increase the concrete resistance to fire, and freezing and thawing cycles. Addition of steel fibers to concrete increased the compressive strength by up to 20%. Increases 121.5% and 80.7% were reported in tensile and flexural strengths respectively. Carbon fibers increased flexural and tensile strengths by up to 11% and 45%, respectively. Concrete strength properties decreased after the addition of PET fibers. Results showed that improvement in strength after addition of steel and carbon fibers may justify the extra cost of fibers.

Keywords: concrete, compressive strength, fibers, flexural strength, tensile strength

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

Abstract:

The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

Procedia PDF Downloads 92

Authors: S. Mehrab Amiri, Nasser Talebbeydokhti

Abstract:

Modeling sediment transport processes by means of numerical approach often poses severe challenges. In this way, a number of techniques have been suggested to solve flow and sediment equations in decoupled, semi-coupled or fully coupled forms. Furthermore, in order to capture flow discontinuities, a number of techniques, like artificial viscosity and shock fitting, have been proposed for solving these equations which are mostly required careful calibration processes. In this research, a numerical scheme for solving shallow water and Exner equations in fully coupled form is presented. First-Order Centered scheme is applied for producing required numerical fluxes and the reconstruction process is carried out toward using Monotonic Upstream Scheme for Conservation Laws to achieve a high order scheme.  In order to satisfy C-property of the scheme in presence of bed topography, Surface Gradient Method is proposed. Combining the presented scheme with fourth order Runge-Kutta algorithm for time integration yields a competent numerical scheme. In addition, to handle non-prismatic channels problems, Cartesian Cut Cell Method is employed. A trained Multi-Layer Perceptron Artificial Neural Network which is of Feed Forward Back Propagation (FFBP) type estimates sediment flow discharge in the model rather than usual empirical formulas. Hydrodynamic part of the model is tested for showing its capability in simulation of flow discontinuities, transcritical flows, wetting/drying conditions and non-prismatic channel flows. In this end, dam-break flow onto a locally non-prismatic converging-diverging channel with initially dry bed conditions is modeled. The morphodynamic part of the model is verified simulating dam break on a dry movable bed and bed level variations in an alluvial junction. The results show that the model is capable in capturing the flow discontinuities, solving wetting/drying problems even in non-prismatic channels and presenting proper results for movable bed situations. It can also be deducted that applying Artificial Neural Network, instead of common empirical formulas for estimating sediment flow discharge, leads to more accurate results.

Keywords: artificial neural network, morphodynamic model, sediment continuity equation, shallow water equations

Procedia PDF Downloads 176

Authors: M. S. V. K. V. Prasad, G. Siva Praveena, P. V. V. Prasada Rao

Abstract:

It is known that the groundwater quality is very important parameter because it is the main factor determining its suitability for drinking, agricultural and industrial purposes. Water Quality Index (WQI) has been calculated for ground water samples taken from Narsapur-Mogalthur mandals, West Godavari district, Andhra Pradesh, India, from 10 different locations in the pre-monsoon season as well as post monsoon. The water samples were analyzed for pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Hardness (TH), major cations like calcium, magnesium, sodium, potassium and anions like chloride, nitrate and sulphate in the laboratory using the standard methods given by the American Public Health Association (APHA). The overall quality of water in the study area is somewhat good for all constituents. Drinking water at almost all the locations was found to be slightly contaminated, except a few locations during the year 2014. It was found that some effective measures are urgently required for water quality management in this region.

Keywords: Water Quality Index, Physico-chemical parameters, Quality rating, monsoon

Procedia PDF Downloads 312

Authors: Siddhartha Paul, Aman Harlalka, Ashim K. Dey

Abstract:

Soft clayey soil possesses poor bearing capacity and high compressibility because of which foundations cannot be directly placed over soft clay. Normally pile foundations are constructed to carry the load through the soft soil up to the hard stratum below. Pile construction is costly and time consuming. In order to increase the properties of soft clay, many ground improvement techniques like stone column, preloading with and without sand drains/band drains, etc. are in vogue. Time is a constraint for successful application of these improvement techniques. Another way to improve the bearing capacity of soft clay and to reduce the settlement possibility is to apply geocells below the foundation. The geocells impart rigidity to the foundation soil, reduce the net load intensity on soil and thus reduce the compressibility. A well designed geocell reinforced soil may replace the pile foundation. The present paper deals with the applicability of geocells on improvement of the bearing capacity. It is observed that a properly designed geocell may increase the bearing capacity of soft clay up to two and a half times.

Keywords: bearing capacity, geo-cell, ground improvement, soft clay

Procedia PDF Downloads 301

Authors: Hailyie Tekleselase

Abstract:

Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 109