Search results for: algorithms and data structure

Authors: Emre Akyürek, Anthony Huynh, Tatiana Kalganova

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The Ambidextrous Robot Hand is a robotic device with the purpose to mimic either the gestures of a right or a left hand. The symmetrical behavior of its fingers allows them to bend in one way or another keeping a compliant and anthropomorphic shape. However, in addition to gestures they can reproduce on both sides, an asymmetrical mechanical design with a three tendons routing has been engineered to reduce the number of actuators. As a consequence, control algorithms must be adapted to drive efficiently the ambidextrous fingers from one position to another and to include grasping features. These movements are controlled by pneumatic muscles, which are nonlinear actuators. As their elasticity constantly varies when they are under actuation, the length of pneumatic muscles and the force they provide may differ for a same value of pressurized air. The control algorithms introduced in this paper take both the fingers asymmetrical design and the pneumatic muscles nonlinearity into account to permit an accurate control of the Ambidextrous Robot Hand. The finger motion is achieved by combining a classic PID controller with a phase plane switching control that turns the gain constants into dynamic values. The grasping ability is made possible because of a sliding mode control that makes the fingers adapt to the shape of an object before strengthening their positions.

Keywords: ambidextrous hand, intelligent algorithms, nonlinear actuators, pneumatic muscles, robotics, sliding control

Procedia PDF Downloads 300

Authors: Bounar Nedjemeddine

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Materials with the pyrochlore lattice structure have attracted much recent attention due to their wide applications in ceramic thermal barrier coatings, high-permittivity dielectrics, and potential solid electrolytes in solid-oxide fuel cells. The work described in this paper is devoted to the synthesis and characterization of a pyrochlore structure based on lanthanum (La₂O₃) and tin (SnO₂) oxides of general formula La₂Sn₂O₇, substituted by Sr at the site La. Their structures were determined from X-ray powder diffraction using CELFER analysis. All the compositions present the space group Fd-3m. The substitution of La by Sr in the La₂Sn₂O₇ compound causes a variation of the cell parameters. The difference in charge between La³⁺ and Sr²⁺ and the difference in size cause the cell parameters to decrease from a=10.7165 A° to a=10.6848 A° for the substitution rates (x = 0.05, 0.1, 0.15 ...), which leads to a decrease in the volume of the mesh. For a substitution rate x = 0.25, there is an increase in the cell parameters (a=10.7035A°), which can be explained by a competitiveness of the size effect and the presence of a gap in the structure which go in the opposite direction.

Keywords: solid-oxide fuel cells, structure, pyrochlore, X-ray diffraction

Procedia PDF Downloads 133

Authors: Vahid Bayrami Rad

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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

Procedia PDF Downloads 71

Authors: Sana Hamdi, Emna Bouazizi, Sami Faiz

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In recent years, real-time spatial applications, like location-aware services and traffic monitoring, have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of our computing infrastructure. For instance, in real-time spatial Big Data, users expect to receive the results of each query within a short time period without holding in account the load of the system. But with a huge amount of real-time spatial data generated, the system performance degrades rapidly especially in overload situations. To solve this problem, we propose the use of data partitioning as an optimization technique. Traditional horizontal and vertical partitioning can increase the performance of the system and simplify data management. But they remain insufficient for real-time spatial Big data; they can’t deal with real-time and stream queries efficiently. Thus, in this paper, we propose a novel data partitioning approach for real-time spatial Big data named VPA-RTSBD (Vertical Partitioning Approach for Real-Time Spatial Big data). This contribution is an implementation of the Matching algorithm for traditional vertical partitioning. We find, firstly, the optimal attribute sequence by the use of Matching algorithm. Then, we propose a new cost model used for database partitioning, for keeping the data amount of each partition more balanced limit and for providing a parallel execution guarantees for the most frequent queries. VPA-RTSBD aims to obtain a real-time partitioning scheme and deals with stream data. It improves the performance of query execution by maximizing the degree of parallel execution. This affects QoS (Quality Of Service) improvement in real-time spatial Big Data especially with a huge volume of stream data. The performance of our contribution is evaluated via simulation experiments. The results show that the proposed algorithm is both efficient and scalable, and that it outperforms comparable algorithms.

Keywords: real-time spatial big data, quality of service, vertical partitioning, horizontal partitioning, matching algorithm, hamming distance, stream query

Procedia PDF Downloads 159

Authors: Tesfaye Alamirew Dessie

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Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters.

Keywords: MPC, PID, ARX, pasteurization

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Sanaa I. Abu Alasal, Madleen M. Esbeih, Eman R. Fayyad, Rami S. Gharaibeh, Mostafa Z. Ali, Ahmed A. Freewan, Monther M. Jamhawi

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This research proposes a novel reconstruction protocol for restoring missing surfaces and low-quality edges and shapes in photos of artifacts at historical sites. The protocol starts with the extraction of a cloud of points. This extraction process is based on four subordinate algorithms, which differ in the robustness and amount of resultant. Moreover, they use different -but complementary- accuracy to some related features and to the way they build a quality mesh. The performance of our proposed protocol is compared with other state-of-the-art algorithms and toolkits. The statistical analysis shows that our algorithm significantly outperforms its rivals in the resultant quality of its object files used to reconstruct the desired model.

Keywords: meshes, point clouds, surface reconstruction protocols, 3D reconstruction

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Authors: Asim Qureshi, R. S. Jangid

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Seismic response control of structures is generally achieved by using control devices which either dissipate the input energy or modify the dynamic properties of structure.In this paper, the response of a 20-storey benchmark building supplemented by viscous dampers and Negative Stiffness Device (NSD) is assessed by numerical simulations using the Newmark-beta method. True negative stiffness is an adaptive passive device which assists the motion unlike positive stiffness. The structure used in this study is subjected to four standard ground motions varying from moderate to severe, near fault to far-field earthquakes. The objective of the present study is to show the effectiveness of the adaptive negative stiffness device (NSD and passive dampers together) relative to passive dampers alone. This is done by comparing the responses of the above uncontrolled structure (i.e., without any device) with the structure having passive dampers only and also with the structure supplemented with adaptive negative stiffness device. Various performance indices, top floor displacement, top floor acceleration and inter-storey drifts are used as comparison parameters. It is found that NSD together with passive dampers is quite effective in reducing the response of aforementioned structure relative to structure without any device or passive dampers only. Base shear and acceleration is reduced significantly by incorporating NSD at the cost of increased inter-storey drifts which can be compensated using the passive dampers.

Keywords: adaptive negative stiffness device, apparent yielding, NSD, passive dampers

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Authors: Mohamad Mahdi Namdari

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In today's rapidly changing and evolving business competition, companies and organizations require advanced and efficient tools to manage their markets and sales networks. Big data analysis, quick response in competitive markets, process and operations optimization, and forecasting customer behavior are among the concerns of executive managers. Artificial intelligence, as one of the emerging technologies, has provided extensive capabilities in this regard. The use of artificial intelligence in market and sales network management can lead to improved efficiency, increased decision-making accuracy, and enhanced customer satisfaction. Specifically, AI algorithms can analyze vast amounts of data, identify complex patterns, and offer strategic suggestions to improve sales performance. However, many companies are still distant from effectively leveraging this technology, and those that do face challenges in fully exploiting AI's potential in market and sales network management. It appears that the general public's and even the managerial and academic communities' lack of knowledge of this technology has caused the managerial structure to lag behind the progress and development of artificial intelligence. Additionally, high costs, fear of change and employee resistance, lack of quality data production processes, the need for updating structures and processes, implementation issues, the need for specialized skills and technical equipment, and ethical and privacy concerns are among the factors preventing widespread use of this technology in organizations. Clarifying and explaining this technology, especially to the academic, managerial, and elite communities, can pave the way for a transformative beginning. The aim of this research is to elucidate the capacities of artificial intelligence in market and sales network management, identify its opportunities and benefits, and examine the existing challenges and obstacles. This research aims to leverage AI capabilities to provide a framework for enhancing market and sales network performance for managers. The results of this research can help managers and decision-makers adopt more effective strategies for business growth and development by better understanding the capabilities and limitations of artificial intelligence.

Keywords: artificial intelligence, market management, sales network, big data analysis, decision-making, digital marketing

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Authors: Najrullah Khan, Athar Ali Khan

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The Topp-Leone distribution was introduced by Topp- Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized exponential (TPGE) distribution. A real survival data set is used for illustrations. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools. The main aim of this paper is to describe and illustrate the Bayesian modelling approach to the analysis of survival data. Emphasis is placed on the modeling of data and the interpretation of the results. Crucial to this is an understanding of the nature of the incomplete or 'censored' data encountered. Analytic approximation and simulation tools are covered here, but most of the emphasis is on Markov chain based Monte Carlo method including independent Metropolis algorithm, which is currently the most popular technique. For analytic approximation, among various optimization algorithms and trust region method is found to be the best. In this paper, TPGE model is also used to analyze the lifetime data in Bayesian paradigm. Results are evaluated from the above mentioned real survival data set. The analytic approximation and simulation methods are implemented using some software packages. It is clear from our findings that simulation tools provide better results as compared to those obtained by asymptotic approximation.

Keywords: Bayesian Inference, JAGS, Laplace Approximation, LaplacesDemon, posterior, R Software, simulation

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Authors: Aziz Zoubir, Sefir Yamina, Djelti Redouan, Bentata Samir

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The effect of a uniform electric field across multibarrier systems (GaAs/AlxGa1-xAs) is exhaustively explored by a computational model using exact Airy function formalism and the transfer-matrix technique. In the case of biased Dimer Fibonacci Height Barrier superlattices (DFHBSL) structure a strong reduction in transmission properties was observed and the width of the miniband structure linearly decreases with the increase of the applied bias. This is due to the confinement of the states in the miniband structure, which becomes increasingly important (Wannier-Stark effect).

Keywords: Dimer Fibonacci Height Barrier superlattices, singular extended states, exact Airy function, transfer matrix formalism

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Authors: M. Hossain, A. H. Khan, M. A. R. Sarkar

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Primary containment structure is one of the five safety layers of a nuclear facility which is needed to be designed in such a manner that it can withstand the pressure and excessive radioactivity during accidental situations. It is also necessary to ensure minimization of cost with maximum possible safety in order to make the design economically feasible and attractive. This paper attempts to identify the optimum design conditions for primary containment structure considering both mechanical and radiation safety keeping the economic aspects in mind. This work takes advantage of commercial simulation software to identify the suitable conditions without the requirement of costly experiments. Generated data may be helpful for further studies.

Keywords: PWR, concrete containment, finite element approach, neutron attenuation, Von Mises stress

Procedia PDF Downloads 190

Authors: Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou

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pscmsForecasting is an open-source web service that implements a variety of time series forecasting algorithms and exposes them to the user via the ubiquitous HTTP protocol. It allows developers to enhance their applications by adding time series forecasting functionalities through an intuitive and easy-to-use interface. This paper provides some background on time series forecasting and gives details about the implemented algorithms, aiming to enhance the end user’s understanding of the underlying methods before incorporating them into their applications. A detailed description of the web service’s interface and its various parameterizations is also provided. Being an open-source project, pcsmsForecasting can also be easily modified and tailored to the specific needs of each application.

Keywords: time series, forecasting, web service, open source

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Authors: Hamed Sarbazy, Zohre Gholipour Haftkhani, Ali Safari, Pejman Hosseiniun

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Scientific and industrial progress in the past two decades has resulted in energy distribution systems based on power electronics, as an enabling technology in various industries and building management systems can be considered. Grading and standardization module power electronics systems and its use in a distributed control system, a strategy for overcoming the limitations of using this system. The purpose of this paper is to investigate strategies for scheduling and control structure of standard modules is a power electronic systems. This paper introduces the classical control methods and disadvantages of these methods will be discussed, The hierarchical control as a mechanism for distributed control structure of the classification module explains. The different levels of control and communication between these levels are fully introduced. Also continue to standardize software distribution system control structure is discussed. Finally, as an example, the control structure will be presented in a DC distribution system.

Keywords: application management, hardware management, power electronics, building blocks

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Authors: Maher Ali Rusho, Sudipta Halder

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The purpose of the study is to develop optimal solutions for models of parallel computing systems using the Java language. During the study, programmes were written for the examined models of parallel computing systems. The result of the parallel sorting code is the output of a sorted array of random numbers. When processing data in parallel, the time spent on processing and the first elements of the list of squared numbers are displayed. When processing requests asynchronously, processing completion messages are displayed for each task with a slight delay. The main results include the development of optimisation methods for algorithms and processes, such as the division of tasks into subtasks, the use of non-blocking algorithms, effective memory management, and load balancing, as well as the construction of diagrams and comparison of these methods by characteristics, including descriptions, implementation examples, and advantages. In addition, various specialised libraries were analysed to improve the performance and scalability of the models. The results of the work performed showed a substantial improvement in response time, bandwidth, and resource efficiency in parallel computing systems. Scalability and load analysis assessments were conducted, demonstrating how the system responds to an increase in data volume or the number of threads. Profiling tools were used to analyse performance in detail and identify bottlenecks in models, which improved the architecture and implementation of parallel computing systems. The obtained results emphasise the importance of choosing the right methods and tools for optimising parallel computing systems, which can substantially improve their performance and efficiency.

Keywords: algorithm optimisation, memory management, load balancing, performance profiling, asynchronous programming.

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Authors: Juan S. Mantilla, Peter Thomson

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Seismic events may result in discomfort on occupants of the buildings, structural damage or even buildings collapse. Traditional design aims to reduce dynamic response of structures by increasing stiffness, thus increasing the construction costs and the design forces. Structural control systems arise as an alternative to reduce these dynamic responses. A commonly used control systems in buildings are the passive friction dampers, which adds energy dissipation through damping mechanisms induced by sliding friction between their surfaces. Passive friction dampers are usually implemented on the diagonal of braced buildings, but such devices have the disadvantage that are optimal for a range of sliding force and out of that range its efficiency decreases. The above implies that each passive friction damper is designed, built and commercialized for a specific sliding/clamping force, in which the damper shift from a locked state to a slip state, where dissipates energy through friction. The risk of having a variation in the efficiency of the device according to the sliding force is that the dynamic properties of the building can change as result of many factor, even damage caused by a seismic event. In this case the expected forces in the building can change and thus considerably reduce the efficiency of the damper (that is designed for a specific sliding force). It is also evident than when a seismic event occurs the forces in each floor varies in the time what means that the damper's efficiency is not the best at all times. Semi-Active Friction devices adapt its sliding force trying to maintain its motion in the slipping phase as much as possible, because of this, the effectiveness of the device depends on the control strategy used. This paper deals with the development and performance evaluation of a low cost Semiactive Variable Friction Damper (SAVFD) in reduced scale to reduce vibrations of structures subject to earthquakes. The SAVFD consist in a (1) hydraulic brake adapted to (2) a servomotor which is controlled with an (3) Arduino board and acquires accelerations or displacement from (4) sensors in the immediately upper and lower floors and a (5) power supply that can be a pair of common batteries. A test structure, based on a Benchmark structure for structural control, was design and constructed. The SAVFD and the structure are experimentally characterized. A numerical model of the structure and the SAVFD is developed based on the dynamic characterization. Decentralized control algorithms were modeled and later tested experimentally using shaking table test using earthquake and frequency chirp signals. The controlled structure with the SAVFD achieved reductions greater than 80% in relative displacements and accelerations in comparison to the uncontrolled structure.

Keywords: earthquake response, friction damper, semiactive control, shaking table

Procedia PDF Downloads 379

Authors: Ameni Mejri, Moufida Hajjaj, Salem Hasnaoui, Ridha Bouallegue

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The technology advances have most lately made the millimeter wave (mmWave) communication possible. Due to the huge amount of spectrum that is available in MmWave frequency bands, this promising candidate is considered as a key technology for the deployment of 5G cellular networks. In order to enhance system capacity and achieve spectral efficiency, very large antenna arrays are employed at mmWave systems by exploiting array gain. However, it has been shown that conventional beamforming strategies are not suitable for mmWave hardware implementation. Therefore, new features are required for mmWave cellular applications. Unlike traditional multiple-input-multiple-output (MIMO) systems for which only digital precoders are essential to accomplish precoding, MIMO technology seems to be different at mmWave because of digital precoding limitations. Moreover, precoding implements a greater number of radio frequency (RF) chains supporting more signal mixers and analog-to-digital converters. As RF chain cost and power consumption is increasing, we need to resort to another alternative. Although the hybrid precoding architecture has been regarded as the best solution based on a combination between a baseband precoder and an RF precoder, we still do not get the optimal design of hybrid precoders. According to the mapping strategies from RF chains to the different antenna elements, there are two main categories of hybrid precoding architecture. Given as a hybrid precoding sub-array architecture, the partially-connected structure reduces hardware complexity by using a less number of phase shifters, whereas it sacrifices some beamforming gain. In this paper, we treat the hybrid precoder design in mmWave MIMO systems as a problem of matrix factorization. Thus, we adopt the alternating minimization principle in order to solve the design problem. Further, we present our proposed algorithm for the partially-connected structure, which is based on the iterative hard thresholding method. Through simulation results, we show that our hybrid precoding algorithm provides significant performance gains over existing algorithms. We also show that the proposed approach reduces significantly the computational complexity. Furthermore, valuable design insights are provided when we use the proposed algorithm to make simulation comparisons between the hybrid precoding partially-connected structure and the fully-connected structure.

Keywords: alternating minimization, hybrid precoding, iterative hard thresholding, low-complexity, millimeter wave communication, partially-connected structure

Procedia PDF Downloads 325

Authors: Lan Zhang, Cailing Zhang

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In recent years, many face anti-spoofing algorithms have high detection accuracy when detecting 2D face anti-spoofing or 3D mask face anti-spoofing alone in the field of face anti-spoofing, but their detection performance is greatly reduced in multidimensional and cross-datasets tests. The rPPG method used for face anti-spoofing uses the unique vital information of real face to judge real faces and face anti-spoofing, so rPPG method has strong stability compared with other methods, but its detection rate of 2D face anti-spoofing needs to be improved. Therefore, in this paper, we improve an rPPG(Remote Photoplethysmography) method(MrPPG) for face anti-spoofing which through color space fusion, using the correlation of pulse signals between real face regions and background regions, and introducing the cyclic neural network (LSTM) method to improve accuracy in 2D face anti-spoofing. Meanwhile, the MrPPG also has high accuracy and good stability in face anti-spoofing of multi-dimensional and cross-data datasets. The improved method was validated on Replay-Attack, CASIA-FASD, Siw and HKBU_MARs_V2 datasets, the experimental results show that the performance and stability of the improved algorithm proposed in this paper is superior to many advanced algorithms.

Keywords: face anti-spoofing, face presentation attack detection, remote photoplethysmography, MrPPG

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: Muhammet Emre Turan, Sait Özçelik, Yavuz Sun

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In railway rails, residual stress was measured and the values of residual stress were associated with hardness and micro structure in this study. At first, three rails as one meter long were taken and residual stresses were measured by cutting method according to the EN 13674-1 standardization. In this study, strain gauge that is an electrical apparatus was used. During the cutting, change in resistance in rail gave us residual stress value via computer program. After residual stress measurement, Brinell hardness distribution were performed for head parts of rails. Thus, the relationship between residual stress and hardness were established. In addition to that, micro structure analysis was carried out by optical microscope. The results show that, the micro structure and hardness value was changed with residual stress.

Keywords: residual stress, hardness, micro structure, rail, strain gauge

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Authors: Shirin Ghasemi

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The structure of Asadi’s poem in Garshasb-Nameh coordinates with the structure of human psyche based on Jung’s perspective. The poetic stories of Asadi in Garshasb-Nameh is contrasted to human psyche according to Jung’s view in psychology which indicated the similarity of poetic structure of stories of Garshasb-Nameh to analytical psychology of Jung. In fact, by studying the stories of this collection the reader travels with him and finds it consistent with the human psyche. To demonstrate this, the story of Jamshid marriage with Kuhrang’s daughter and the story of Garshasb marriage with King’s daughter are selected. These two stories illustrate the poetic structure and the human psyche based on Jung’s analytical psychology perspective.

Keywords: Asadi Tusi, Garshasb-Nameh, Jung, analytical psychology

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Authors: Mohamed Ramdani, Hassen Abdellaoui, Abdenour Boudrassen

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Mobile platform’s attitude estimation approaches mainly based on combined positioning techniques and developed algorithms; which aim to reach a fast and accurate solution. In this work, we describe the design and the implementation of an attitude determination (AD) process, using only measurements from GPS sensors. The major issue is based on smoothed GPS code data using Hatch filter and raw carrier-phase measurements integrated into attitude algorithm based on vectors measurement using least squares (LSQ) estimation method. GPS dataset from a static experiment is used to investigate the effectiveness of the presented approach and consequently to check the accuracy of the attitude estimation algorithm. Attitude results from GPS multi-antenna over short baselines are introduced and analyzed. The 3D accuracy of estimated attitude parameters using smoothed measurements is over 0.27°.

Keywords: attitude determination, GPS code data smoothing, hatch filter, carrier-phase measurements, least-squares attitude estimation

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Authors: K. Umbleja, M. Ichino

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Symbolic data mining has been developed to analyze data in very large datasets. It is also useful in cases when entry specific details should remain hidden. Symbolic data mining is quickly gaining popularity as datasets in need of analyzing are becoming ever larger. One type of such symbolic data is a histogram, which enables to save huge amounts of information into a single variable with high-level of granularity. Other types of symbolic data can also be described in histograms, therefore making histogram a very important and general symbolic data type - a method developed for histograms - can also be applied to other types of symbolic data. Due to its complex structure, analyzing histograms is complicated. This paper proposes a method, which allows to compare two histogram-valued variables and therefore find a dissimilarity between two histograms. Proposed method uses the Ichino-Yaguchi dissimilarity measure for mixed feature-type data analysis as a base and develops a dissimilarity measure specifically for histogram data, which allows to compare histograms with different number of bins and bin widths (so called general histogram). Proposed dissimilarity measure is then used as a measure for clustering. Furthermore, linkage method based on weighted averages is proposed with the concept of cluster compactness to measure the quality of clustering. The method is then validated with application on real datasets. As a result, the proposed dissimilarity measure is found producing adequate and comparable results with general histograms without the loss of detail or need to transform the data.

Keywords: dissimilarity measure, hierarchical clustering, histograms, symbolic data analysis

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Authors: G. Rajeshwari, V. D. M. Jabez Daniel

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Radio Frequency Identification systems are used for automated identification in various applications such as automobiles, health care and security. It is also called as the automated data collection technology. RFID readers are placed in any area to scan large number of tags to cover a wide distance. The placement of the RFID elements may result in several types of collisions. A major challenge in RFID system is collision avoidance. In the previous works the collision was avoided by using algorithms such as ALOHA and tree algorithm. This work proposes collision reduction and increased throughput through reading enhancement method with tree algorithm. The reading enhancement is done by improving interrogation procedure and increasing the data handling capacity of RFID reader with parallel processing. The work is simulated using Xilinx ISE 14.5 verilog language. By implementing this in the RFID system, we can able to achieve high throughput and avoid collision in the reader at a same instant of time. The overall system efficiency will be increased by implementing this.

Keywords: antenna, anti-collision protocols, data management system, reader, reading enhancement, tag

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

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Health analytics (HA) is used in healthcare systems for effective decision-making, management, and planning of healthcare and related activities. However, user resistance, the unique position of medical data content, and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. The success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose an HA process model with features from the rational unified process (RUP) model and agile methodology.

Keywords: agile methodology, health analytics, unified process model, UML

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Authors: Davoud Maleki, Neda Zamani

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The purpose of this research was to investigate the factors affecting the organizational pathology of knowledge production in Islamic Azad University. The present research method is quantitative. It was a survey type and applied research in terms of its purpose. The statistical population of the present study included all full-time professors of the Islamic Azad Universities in the North, South, East, West and Central regions, including the Islamic Azad Universities of Sari, Isfahan, Kerman, Khorramabad and Shiraz, and their total number was 1389, based on the Cochran formula. 305 people were selected as the sample size by random sampling method. The research tool was a researcher-made questionnaire, whose validity was calculated from the professors' point of view and its reliability was calculated based on Cronbach's alpha and was 0.89. For data analysis, confirmatory factor analysis and structural equations were used with Smart3 Pls software. The findings showed that the variables of strategy, structure and process directly and the variable of strategy explained indirectly through the variables of structure and process 96.8% of the pathology of knowledge production. Also, structure 49.6% and process variable 58.4% explain the pathology of knowledge production. 38% of knowledge production changes related to the direct effect of strategy, 39% of knowledge production changes Related to the effect of structure, 32% of the changes in knowledge production are related to the direct effect of the process, 70.5% of the changes related to the structure are related to the direct effect of the strategy, 36.5% of the changes related to the process are related to the direct effect of the strategy, 46.3 Percentage of process variable changes It is related to the direct effect of the structure. According to the obtained results, it can be acknowledged that the pathology model of knowledge production in Islamic Azad University can be used as an effective model in the pathology of knowledge production and can improve the scientific level of knowledge producers.

Keywords: pathology of knowledge production, strategic issues, process issues, Islamic Azad University

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Authors: Luciano Roberto Fernandez-Sola, Cesar Augusto Arredondo-Velez, Miguel Angel Jaimes-Tellez

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This paper explores the modifications on peak acceleration, velocity and displacement profiles over the structure due to dynamic soil-structure interaction (DSSI). A shear beam model is used for the structure. Soil-foundation flexibility (inertial interaction) is considered by a set of springs and dashpots at the structure base. Kinematic interaction is considered using transfer functions. Impedance functions are computed using simplified expressions for rigid foundations. The research studies the influence of the slenderness ratio on the value of the peak floor response. It is shown that the modifications of peak floor responses are not the same for acceleration, velocity and displacement. This is opposite to the hypothesis used by methods included in several building codes. Results show that modifications produced by DSSI on different response quantities are not equal.

Keywords: peak floor intensities, dynamic soil-structure interaction, buildings with flexible base, kinematic and inertial interaction

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Authors: Heungsu Lee, Youngseok Kim, Jonghwa Yi, Chul Park

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A landing pier is subjected to safety assessment by visual inspection and design data, but it is difficult to check the damage in real-time. In this study, real - time damage detection and safety evaluation methods were studied. As a result of structural analysis of the arbitrary landing pier structure, the inflection point of deformation and moment occurred at 10%, 50%, and 90% of pile length. The critical value of Fiber Bragg Grating (FBG) sensor was set according to the safety factor, and the FBG sensor application method for real - time safety evaluation was derived.

Keywords: FBG sensor, harbor structure, maintenance, safety evaluation system

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Authors: Karthik Mittal

Abstract:

This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics.

Keywords: breast cancer, computational biology, unsupervised machine learning, k-means, PCA

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Authors: Davide Forcellini

Abstract:

The most recent development of earthquake engineering is based on concept of design consisting in prescribed performance rather than the more traditional prescriptive approaches. The paper aims to assess the effects of isolation devices and soil structure interaction on a benchmark bridge adopting a Performance-Based Earthquake Engineering methodology. Several isolated configurations of abutments and pier connections are compared performing the most representative isolation devices. Isolation systems suitability depends on many factors, mainly connected with ground effects. In this regard, the second purpose of this paper is to assess the effects of soil-structure interaction (SSI) on the studied bridge configurations. Contributions of isolation technique and soil structure interaction are assessed evaluating the resistance effects applied to Peak Ground Acceleration (PGA) levels in terms of cost and time repair quantities.

Keywords: base isolation, bridge, earthquake engineering, non linearity, PBEE methodology, seismic assessment, soil structure interaction

Procedia PDF Downloads 437