Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 16316

Search results for: artificial potential function

16316 Super-ellipsoidal Potential Function for Autonomous Collision Avoidance of a Teleoperated UAV

Authors: Mohammed Qasim, Kyoung-Dae Kim

Abstract:

In this paper, we present the design of the super-ellipsoidal potential function (SEPF), that can be used for autonomous collision avoidance of an unmanned aerial vehicle (UAV) in a 3-dimensional space. In the design of SEPF, we have the full control over the shape and size of the potential function. In particular, we can adjust the length, width, height, and the amount of flattening at the tips of the potential function so that the collision avoidance motion vector generated from the potential function can be adjusted accordingly. Based on the idea of the SEPF, we also propose an approach for the local autonomy of a UAV for its collision avoidance when the UAV is teleoperated by a human operator. In our proposed approach, a teleoperated UAV can not only avoid collision autonomously with other surrounding objects but also track the operator’s control input as closely as possible. As a result, an operator can always be in control of the UAV for his/her high-level guidance and navigation task without worrying too much about the UAVs collision avoidance while it is being teleoperated. The effectiveness of the proposed approach is demonstrated through a human-in-the-loop simulation of quadrotor UAV teleoperation using virtual robot experimentation platform (v-rep) and Matlab programs.

Keywords: artificial potential function, autonomous collision avoidance, teleoperation, quadrotor

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16315 Use of Gaussian-Euclidean Hybrid Function Based Artificial Immune System for Breast Cancer Diagnosis

Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

Abstract:

Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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16314 Solution of the Nonrelativistic Radial Wave Equation of Hydrogen Atom Using the Green's Function Approach

Authors: F. U. Rahman, R. Q. Zhang

Abstract:

This work aims to develop a systematic numerical technique which can be easily extended to many-body problem. The Lippmann Schwinger equation (integral form of the Schrodinger wave equation) is solved for the nonrelativistic radial wave of hydrogen atom using iterative integration scheme. As the unknown wave function appears on both sides of the Lippmann Schwinger equation, therefore an approximate wave function is used in order to solve the equation. The Green’s function is obtained by the method of Laplace transform for the radial wave equation with excluded potential term. Using the Lippmann Schwinger equation, the product of approximate wave function, the Green’s function and the potential term is integrated iteratively. Finally, the wave function is normalized and plotted against the standard radial wave for comparison. The outcome wave function converges to the standard wave function with the increasing number of iteration. Results are verified for the first fifteen states of hydrogen atom. The method is efficient and consistent and can be applied to complex systems in future.

Keywords: Green’s function, hydrogen atom, Lippmann Schwinger equation, radial wave

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16313 Effects of Artificial Intelligence Technology on Children: Positives and Negatives

Authors: Paula C. Latorre Arroyo, Andrea C. Latorre Arroyo

Abstract:

In the present society, children are exposed to and impacted by technology from very early on in various ways. Artificial intelligence (AI), in particular, directly affects them, be it positively or negatively. The concept of artificial intelligence is commonly defined as the technological programming of computers or robotic mechanisms with humanlike capabilities and characteristics. These technologies are often designed as helpful machines or disguised as handy tools that could ultimately steal private information for illicit purposes. Children, being one of the most vulnerable groups due to their lack of experience and knowledge, do not have the ability to recognize or have the malice to distinguish if an apparatus with artificial intelligence is good or bad for them. For this reason, as a society, there must be a sense of responsibility to regulate and monitor different types of uses for artificial intelligence to protect children from potential risks that might arise. This article aims to investigate the many implications that artificial intelligence has in the lives of children, starting from a home setting, within the classroom, and, ultimately, in online spaces. Irrefutably, there are numerous beneficial aspects to the use of artificial intelligence. However, due to its limitless potential and lack of specific and substantial regulations to prevent the illicit use of this technology, safety and privacy concerns surface, specifically regarding the youth. This written work aims to provide an in-depth analysis of how artificial intelligence can both help children and jeopardize their safety. Concluding with resources and data supporting the aforementioned statement.

Keywords: artificial intelligence, children, privacy, rights, safety

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16312 Predicting the Success of Bank Telemarketing Using Artificial Neural Network

Authors: Mokrane Selma

Abstract:

The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

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16311 Synthesis of a Model Predictive Controller for Artificial Pancreas

Authors: Mohamed El Hachimi, Abdelhakim Ballouk, Ilyas Khelafa, Abdelaziz Mouhou

Abstract:

Introduction: Type 1 diabetes occurs when beta cells are destroyed by the body's own immune system. Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an Artificial Pancreas (AP). Method: In this paper, we present a new formulation of the cost function for a Model Predictive Control (MPC) utilizing a technic which accelerates the speed of control of the AP and tackles the nonlinearity of the control problem via asymmetric objective functions. Finding: The finding of this work consists in a new Model Predictive Control algorithm that leads to good performances like decreasing the time of hyperglycaemia and avoiding hypoglycaemia. Conclusion: These performances are validated under in silico trials.

Keywords: artificial pancreas, control algorithm, biomedical control, MPC, objective function, nonlinearity

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16310 Multiple Relaxation Times in the Gibbs Ensemble Monte Carlo Simulation of Phase Separation

Authors: Bina Kumari, Subir K. Sarkar, Pradipta Bandyopadhyay

Abstract:

The autocorrelation function of the density fluctuation is studied in each of the two phases in a Gibbs Ensemble Monte Carlo (GEMC) simulation of the problem of phase separation for a square well potential with various values of its range. We find that the normalized autocorrelation function is described very well as a linear combination of an exponential function with a time scale τ₂ and a stretched exponential function with a time scale τ₁ and an exponent α. Dependence of (α, τ₁, τ₂) on the parameters of the GEMC algorithm and the range of the square well potential is investigated and interpreted. We also analyse the issue of how to choose the parameters of the GEMC simulation optimally.

Keywords: autocorrelation function, density fluctuation, GEMC, simulation

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16309 Comparative Analysis of Sigmoidal Feedforward Artificial Neural Networks and Radial Basis Function Networks Approach for Localization in Wireless Sensor Networks

Authors: Ashish Payal, C. S. Rai, B. V. R. Reddy

Abstract:

With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m2 grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

Keywords: localization, wireless sensor networks, artificial neural network, radial basis function, multi-layer perceptron, backpropagation, RSSI, GPS

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16308 Fast-Forward Problem in Asymmetric Double-Well Potential

Authors: Iwan Setiawan, Bobby Eka Gunara, Katshuhiro Nakamura

Abstract:

The theory to accelerate system on quantum dynamics has been constructed to get the desired wave function on shorter time. This theory is developed on adiabatic quantum dynamics which any regulation is done on wave function that satisfies Schrödinger equation. We show accelerated manipulation of WFs with the use of a parameter-dependent in asymmetric double-well potential and also when it’s influenced by electromagnetic fields.

Keywords: driving potential, Adiabatic Quantum Dynamics, regulation, electromagnetic field

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16307 Intelligent Swarm-Finding in Formation Control of Multi-Robots to Track a Moving Target

Authors: Anh Duc Dang, Joachim Horn

Abstract:

This paper presents a new approach to control robots, which can quickly find their swarm while tracking a moving target through the obstacles of the environment. In this approach, an artificial potential field is generated between each free-robot and the virtual attractive point of the swarm. This artificial potential field will lead free-robots to their swarm. The swarm-finding of these free-robots dose not influence the general motion of their swarm and nor other robots. When one singular robot approaches the swarm then its swarm-search will finish, and it will further participate with its swarm to reach the position of the target. The connections between member-robots with their neighbours are controlled by the artificial attractive/repulsive force field between them to avoid collisions and keep the constant distances between them in ordered formation. The effectiveness of the proposed approach has been verified in simulations.

Keywords: formation control, potential field method, obstacle avoidance, swarm intelligence, multi-agent systems

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16306 Secret Security Smart Lock Using Artificial Intelligence Hybrid Algorithm

Authors: Vahid Bayrami Rad

Abstract:

Ever since humans developed a collective way of life to the development of urbanization, the concern of security has always been considered one of the most important challenges of life. To protect property, locks have always been a practical tool. With the advancement of technology, the form of locks has changed from mechanical to electric. One of the most widely used fields of using artificial intelligence is its application in the technology of surveillance security systems. Currently, the technologies used in smart anti-theft door handles are one of the most potential fields for using artificial intelligence. Artificial intelligence has the possibility to learn, calculate, interpret and process by analyzing data with the help of algorithms and mathematical models and make smart decisions. We will use Arduino board to process data.

Keywords: arduino board, artificial intelligence, image processing, solenoid lock

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16305 An Early Attempt of Artificial Intelligence-Assisted Language Oral Practice and Assessment

Authors: Paul Lam, Kevin Wong, Chi Him Chan

Abstract:

Constant practicing and accurate, immediate feedback are the keys to improving students’ speaking skills. However, traditional oral examination often fails to provide such opportunities to students. The traditional, face-to-face oral assessment is often time consuming – attending the oral needs of one student often leads to the negligence of others. Hence, teachers can only provide limited opportunities and feedback to students. Moreover, students’ incentive to practice is also reduced by their anxiety and shyness in speaking the new language. A mobile app was developed to use artificial intelligence (AI) to provide immediate feedback to students’ speaking performance as an attempt to solve the above-mentioned problems. Firstly, it was thought that online exercises would greatly increase the learning opportunities of students as they can now practice more without the needs of teachers’ presence. Secondly, the automatic feedback provided by the AI would enhance students’ motivation to practice as there is an instant evaluation of their performance. Lastly, students should feel less anxious and shy compared to directly practicing oral in front of teachers. Technically, the program made use of speech-to-text functions to generate feedback to students. To be specific, the software analyzes students’ oral input through certain speech-to-text AI engine and then cleans up the results further to the point that can be compared with the targeted text. The mobile app has invited English teachers for the pilot use and asked for their feedback. Preliminary trials indicated that the approach has limitations. Many of the users’ pronunciation were automatically corrected by the speech recognition function as wise guessing is already integrated into many of such systems. Nevertheless, teachers have confidence that the app can be further improved for accuracy. It has the potential to significantly improve oral drilling by giving students more chances to practice. Moreover, they believe that the success of this mobile app confirms the potential to extend the AI-assisted assessment to other language skills, such as writing, reading, and listening.

Keywords: artificial Intelligence, mobile learning, oral assessment, oral practice, speech-to-text function

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16304 Artificial Intelligence Created Inventions

Authors: John Goodhue, Xiaonan Wei

Abstract:

Current legal decisions and policies regarding the naming as artificial intelligence as inventor are reviewed with emphasis on the recent decisions by the European Patent Office regarding the DABUS inventions holding that an artificial intelligence machine cannot be an inventor. Next, a set of hypotheticals is introduced and examined to better understand how artificial intelligence might be used to create or assist in creating new inventions and how application of existing or proposed changes in the law would affect the ability to protect these inventions including due to restrictions on artificial intelligence for being named as inventors, ownership of inventions made by artificial intelligence, and the effects on legal standards for inventiveness or obviousness.

Keywords: Artificial intelligence, innovation, invention, patent

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16303 Friend or Foe: Decoding the Legal Challenges Posed by Artificial Intellegence in the Era of Intellectual Property

Authors: Latika Choudhary

Abstract:

“The potential benefits of Artificial Intelligence are huge, So are the dangers.” - Dave Water. Artificial intelligence is one of the facet of Information technology domain which despite several attempts does not have a clear definition or ambit. However it can be understood as technology to solve problems via automated decisions and predictions. Artificial intelligence is essentially an algorithm based technology which analyses the large amounts of data and then solves problems by detecting useful patterns. Owing to its automated feature it will not be wrong to say that humans & AI have more utility than humans alone or computers alone.1 For many decades AI experienced enthusiasm as well as setbacks, yet it has today become part and parcel of our everyday life, making it convenient or at times problematic. AI and related technology encompass Intellectual Property in multiple ways, the most important being AI technology for management of Intellectual Property, IP for protecting AI and IP as a hindrance to the transparency of AI systems. Thus the relationship between the two is of reciprocity as IP influences AI and vice versa. While AI is a recent concept, the IP laws for protection or even dealing with its challenges are relatively older, raising the need for revision to keep up with the pace of technological advancements. This paper will analyze the relationship between AI and IP to determine how beneficial or conflictual the same is, address how the old concepts of IP are being stretched to its maximum limits so as to accommodate the unwanted consequences of the Artificial Intelligence and propose ways to mitigate the situation so that AI becomes the friend it is and not turn into a potential foe it appears to be.

Keywords: intellectual property rights, information technology, algorithm, artificial intelligence

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16302 Jung GPT: Unveiling the Therapeutic Potential of Artificial Intelligence

Authors: Eman Alhajjar, Albatool Jamjoom, Fatmah Bugshan

Abstract:

This research aims to investigate the artificial intelligence (AI) application Jung GPT and how helpful it is, as a therapy AI, to users. Jung GPT has the potential to make mental health care more accessible and cheaper while also providing tailored support and advice. However, it is not intended to be a substitute for human therapists. Jung GPT is instructed to understand a wide range of concepts, including emojis, sensitive subjects, and various languages. Furthermore, participants were asked to fill out a survey based on their experience with Jung GPT. Additionally, analysis of the responses indicated that Jung GPT was helpful in identifying and exploring challenges, and the use of Jung GPT by participants in the future is highly possible. The results demonstrate that Jung GPT does help in recognizing challenges or problems within the users. On this basis, it is recommended that individuals use Jung GPT to explore their thoughts, feelings, and challenges. Moreover, further research is needed to better evaluate the effectiveness of Jung GPT.

Keywords: Jung GPT, artificial intelligence, therapy, mental health, AI application

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16301 A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks: Prediction of Influential Factors on Eating Behaviors

Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

Abstract:

Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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16300 Artificial Intelligence Based Predictive Models for Short Term Global Horizontal Irradiation Prediction

Authors: Kudzanayi Chiteka, Wellington Makondo

Abstract:

The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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16299 Climate Changes Impact on Artificial Wetlands

Authors: Carla Idely Palencia-Aguilar

Abstract:

Artificial wetlands play an important role at Guasca Municipality in Colombia, not only because they are used for the agroindustry, but also because more than 45 species were found, some of which are endemic and migratory birds. Remote sensing was used to determine the changes in the area occupied by water of artificial wetlands by means of Aster and Modis images for different time periods. Evapotranspiration was also determined by three methods: Surface Energy Balance System-Su (SEBS) algorithm, Surface Energy Balance- Bastiaanssen (SEBAL) algorithm, and Potential Evapotranspiration- FAO. Empirical equations were also developed to determine the relationship between Normalized Difference Vegetation Index (NDVI) versus net radiation, ambient temperature and rain with an obtained R2 of 0.83. Groundwater level fluctuations on a daily basis were studied as well. Data from a piezometer placed next to the wetland were fitted with rain changes (with two weather stations located at the proximities of the wetlands) by means of multiple regression and time series analysis, the R2 from the calculated and measured values resulted was higher than 0.98. Information from nearby weather stations provided information for ordinary kriging as well as the results for the Digital Elevation Model (DEM) developed by using PCI software. Standard models (exponential, spherical, circular, gaussian, linear) to describe spatial variation were tested. Ordinary Cokriging between height and rain variables were also tested, to determine if the accuracy of the interpolation would increase. The results showed no significant differences giving the fact that the mean result of the spherical function for the rain samples after ordinary kriging was 58.06 and a standard deviation of 18.06. The cokriging using for the variable rain, a spherical function; for height variable, the power function and for the cross variable (rain and height), the spherical function had a mean of 57.58 and a standard deviation of 18.36. Threatens of eutrophication were also studied, given the unconsciousness of neighbours and government deficiency. Water quality was determined over the years; different parameters were studied to determine the chemical characteristics of water. In addition, 600 pesticides were studied by gas and liquid chromatography. Results showed that coliforms, nitrogen, phosphorous and prochloraz were the most significant contaminants.

Keywords: DEM, evapotranspiration, geostatistics, NDVI

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16298 Potential Field Functions for Motion Planning and Posture of the Standard 3-Trailer System

Authors: K. Raghuwaiya, S. Singh, B. Sharma, J. Vanualailai

Abstract:

This paper presents a set of artificial potential field functions that improves upon; in general, the motion planning and posture control, with theoretically guaranteed point and posture stabilities, convergence and collision avoidance properties of 3-trailer systems in a priori known environment. We basically design and inject two new concepts; ghost walls and the Distance Optimization Technique (DOT) to strengthen point and posture stabilities, in the sense of Lyapunov, of our dynamical model. This new combination of techniques emerges as a convenient mechanism for obtaining feasible orientations at the target positions with an overall reduction in the complexity of the navigation laws. The effectiveness of the proposed control laws were demonstrated via simulations of two traffic scenarios.

Keywords: artificial potential fields, 3-trailer systems, motion planning, posture, parking and collision, free trajectories

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16297 Intellectual Property in Digital Environment

Authors: Balamurugan L.

Abstract:

Artificial intelligence (AI) and its applications in Intellectual Property Rights (IPR) has been significantly growing in recent years. In last couple of years, AI tools for Patent Research and Patent Analytics have been well-stabilized in terms of accuracy of references and representation of identified patent insights. However, AI tools for Patent Prosecution and Patent Litigation are still in the nascent stage and there may be a significant potential if such market is explored further. Our research is primarily focused on identifying potential whitespaces and schematic algorithms to automate the Patent Prosecution and Patent Litigation Process of the Intellectual Property. The schematic algorithms may assist leading AI tool developers, to explore such opportunities in the field of Intellectual Property. Our research is also focused on identification of pitfalls of the AI. For example, Information Security and its impact in IPR, and Potential remediations to sustain the IPR in the digital environment.

Keywords: artificial intelligence, patent analytics, patent drafting, patent litigation, patent prosecution, patent research

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16296 Synthesizing an Artificial Loess for Geotechnical Investigations of Collapsible Soil Behavior

Authors: Hamed Sadeghi, Pouya A. Panahi, Hamed Nasiri, Mohammad Sadeghi

Abstract:

Collapsible soils like loess comprise an important category of problematic soils for construction purposes and sustainable development. As a result, research on both geological and geotechnical aspects of this type of soil have been in progress for decades. However, considerable natural variability in physical properties of in-situ loess strata even in a single block sample challenges the fundamental laboratory investigations. The reason behind this is that it is somehow impossible to remove the effect of a specific factor like void ratio from fair comparisons to come with a reliable conclusion. In order to cope with this limitation, two types of artificially made dispersive and calcareous loess are introduced which can be easily reproduced in any soil mechanics laboratory provided that all its compositions are known and controlled. The collapse potential is explored for a variety of soil water salinity and lime content and comparisons are made against the natural soil behavior. Trends are reported for the influence of pore water salinity on collapse potential under different osmotic flow conditions. The most important advantage of artificial loess is the ease of controlling cementing agent content like calcite or dispersive potential for studying their influence on mechanical soil behavior.

Keywords: artificial loess, unsaturated soils, collapse potential, dispersive clays, laboratory tests

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16295 Prediction of Formation Pressure Using Artificial Intelligence Techniques

Authors: Abdulmalek Ahmed

Abstract:

Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

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16294 Description of the Non-Iterative Learning Algorithm of Artificial Neuron

Authors: B. S. Akhmetov, S. T. Akhmetova, A. I. Ivanov, T. S. Kartbayev, A. Y. Malygin

Abstract:

The problem of training of a network of artificial neurons in biometric appendices is that this process has to be completely automatic, i.e. the person operator should not participate in it. Therefore, this article discusses the issues of training the network of artificial neurons and the description of the non-iterative learning algorithm of artificial neuron.

Keywords: artificial neuron, biometrics, biometrical applications, learning of neuron, non-iterative algorithm

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16293 Artificial Reproduction System and Imbalanced Dataset: A Mendelian Classification

Authors: Anita Kushwaha

Abstract:

We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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16292 Critical Evaluation of the Transformative Potential of Artificial Intelligence in Law: A Focus on the Judicial System

Authors: Abisha Isaac Mohanlal

Abstract:

Amidst all suspicions and cynicism raised by the legal fraternity, Artificial Intelligence has found its way into the legal system and has revolutionized the conventional forms of legal services delivery. Be it legal argumentation and research or resolution of complex legal disputes; artificial intelligence has crept into all legs of modern day legal services. Its impact has been largely felt by way of big data, legal expert systems, prediction tools, e-lawyering, automated mediation, etc., and lawyers around the world are forced to upgrade themselves and their firms to stay in line with the growth of technology in law. Researchers predict that the future of legal services would belong to artificial intelligence and that the age of human lawyers will soon rust. But as far as the Judiciary is concerned, even in the developed countries, the system has not fully drifted away from the orthodoxy of preferring Natural Intelligence over Artificial Intelligence. Since Judicial decision-making involves a lot of unstructured and rather unprecedented situations which have no single correct answer, and looming questions of legal interpretation arise in most of the cases, discretion and Emotional Intelligence play an unavoidable role. Added to that, there are several ethical, moral and policy issues to be confronted before permitting the intrusion of Artificial Intelligence into the judicial system. As of today, the human judge is the unrivalled master of most of the judicial systems around the globe. Yet, scientists of Artificial Intelligence claim that robot judges can replace human judges irrespective of how daunting the complexity of issues is and how sophisticated the cognitive competence required is. They go on to contend that even if the system is too rigid to allow robot judges to substitute human judges in the recent future, Artificial Intelligence may still aid in other judicial tasks such as drafting judicial documents, intelligent document assembly, case retrieval, etc., and also promote overall flexibility, efficiency, and accuracy in the disposal of cases. By deconstructing the major challenges that Artificial Intelligence has to overcome in order to successfully invade the human- dominated judicial sphere, and critically evaluating the potential differences it would make in the system of justice delivery, the author tries to argue that penetration of Artificial Intelligence into the Judiciary could surely be enhancive and reparative, if not fully transformative.

Keywords: artificial intelligence, judicial decision making, judicial systems, legal services delivery

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16291 Assessment of the Natural and Human Potential of the Municipality of Tirana for the Development of Agritourism

Authors: Dritan Lloçi, Xhulia Bygjymi

Abstract:

The topic is about one of the new trends with the greatest expectations in the field of tourism, such as agritourism. It is chosen exactly this type of tourism to address as this issue is one of the newest trends not only for Tirana or Albania but also beyond. The other reason is that this topic is quite current and challenging for the reality in which we find ourselves, and the opportunities for research work and to make our own contribution are quite large. It is chosen Tirana because seeing the many opportunities it offers for the development of agritourism as a result of the rich natural potential it offers; the fact that it is the capital of Albania makes this space absorb a good part of the investments in the rural tourism sector but not alone. The study is organized into several main issues regarding the natural and human potentials of the area, which are in function of the development of agrotourism. The first issue has to do with the natural potentials of the municipality of Tirana and how they can be used for agritourism. The second issue has to do with the cultural potential that the municipality of Tirana possesses, causing tourist flows to be more concentrated in this geographical-administrative space. The third issue has to do with the human potential that is a function of agrotourism. So the way of life, hospitality, cooking, etc.

Keywords: agrotourism, natural potential, agrotourism farms, tirana municipality, tourism development

Procedia PDF Downloads 30
16290 Motion Planning and Posture Control of the General 3-Trailer System

Authors: K. Raghuwaiya, B. Sharma, J. Vanualailai

Abstract:

This paper presents a set of artificial potential field functions that improves upon; in general, the motion planning and posture control, with theoretically guaranteed point and posture stabilities, convergence and collision avoidance properties of the general 3-trailer system in a priori known environment. We basically design and inject two new concepts; ghost walls and the distance optimization technique (DOT) to strengthen point and posture stabilities, in the sense of Lyapunov, of our dynamical model. This new combination of techniques emerges as a convenient mechanism for obtaining feasible orientations at the target positions with an overall reduction in the complexity of the navigation laws. Simulations are provided to demonstrate the effectiveness of the controls laws.

Keywords: artificial potential fields, 3-trailer systems, motion planning, posture

Procedia PDF Downloads 389
16289 The Implementation of Secton Method for Finding the Root of Interpolation Function

Authors: Nur Rokhman

Abstract:

A mathematical function gives relationship between the variables composing the function. Interpolation can be viewed as a process of finding mathematical function which goes through some specified points. There are many interpolation methods, namely: Lagrange method, Newton method, Spline method etc. For some specific condition, such as, big amount of interpolation points, the interpolation function can not be written explicitly. This such function consist of computational steps. The solution of equations involving the interpolation function is a problem of solution of non linear equation. Newton method will not work on the interpolation function, for the derivative of the interpolation function cannot be written explicitly. This paper shows the use of Secton method to determine the numerical solution of the function involving the interpolation function. The experiment shows the fact that Secton method works better than Newton method in finding the root of Lagrange interpolation function.

Keywords: Secton method, interpolation, non linear function, numerical solution

Procedia PDF Downloads 340
16288 Designing Function Knitted and Woven Upholstery Textile With SCOPY Film

Authors: Manar Y. Abd El-Aziz, Alyaa E. Morgham, Amira A. El-Fallal, Heba Tolla E. Abo El Naga

Abstract:

Different textile materials are usually used in upholstery. However, upholstery parts may become unhealthy when dust accrues and bacteria raise on the surface, which negatively affects the user's health. Also, leather and artificial leather were used in upholstery but, leather has a high cost and artificial leather has a potential chemical risk for users. Researchers have advanced vegie leather made from bacterial cellulose a symbiotic culture of bacteria and yeast (SCOBY). SCOBY remains a gelatinous, cellulose biofilm discovered floating at the air-liquid interface of the container. But this leather still needs some enhancement for its mechanical properties. This study aimed to prepare SCOBY, produce bamboo rib knitted fabrics with two different stitch densities, and cotton woven fabric then laminate these fabrics with the prepared SCOBY film to enhance the mechanical properties of the SCOBY leather at the same time; add anti-microbial function to the prepared fabrics. Laboratory tests were conducted on the produced samples, including tests for function properties; anti-microbial, thermal conductivity and light transparency. Physical properties; thickness and mass per unit. Mechanical properties; elongation, tensile strength, young modulus, and peel force. The results showed that the type of the fabric affected significantly SCOBY properties. According to the test results, the bamboo knitted fabric with higher stitch density laminated with SCOBY was chosen for its tensile strength and elongation as the upholstery of a bed model with antimicrobial properties and comfortability in the headrest design. Also, the single layer of SCOBY was chosen regarding light transparency and lower thermal conductivity for the creation of a lighting unit built into the bed headboard.

Keywords: anti-microbial, bamboo, rib, SCOPY, upholstery

Procedia PDF Downloads 26
16287 Study of Icons in Enterprise Application Software Context

Authors: Shiva Subhedar, Abhishek Jain, Shivin Mittal

Abstract:

Icons are not merely decorative elements in enterprise applications but very often used because of their many advantages such as compactness, visual appeal, etc. Despite these potential advantages, icons often cause usability problems when they are designed without consideration for their many potential downsides. The aim of the current study was to examine the effect of articulatory distance – the distance between the physical appearance of an interface element and what it actually means. In other words, will the subject find the association of the function and its appearance on the interface natural or is the icon difficult for them to associate with its function. We have calculated response time and quality of identification by varying icon concreteness, the context of usage and subject experience in the enterprise context. The subjects were asked to associate icons (prepared for study purpose) with given function options in context and out of context mode. Response time and their selection were recorded for analysis.

Keywords: HCI, icons, icon concreteness, icon recognition

Procedia PDF Downloads 218