Search results for: Intelligent tutoring systems

Authors: Ibrahim Ozkan

Abstract:

In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: Cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading.

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Authors: I. Abuiziah, A. Oulhaj, K. Sebari, D. Ouazar

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This paper presents the problem of modeling and simulating of transient phenomena in conveying pipeline systems based on the rigid column and full elastic methods. Transient analysis is important and one of the more challenging and complicated flow problem in the design and the operation of water pipeline systems. Transient can produce large pressure forces and rapid fluid acceleration into a water pipeline system, these disturbances may result in device failures, system fatigue or pipe ruptures, and even the dirty water intrusion. Several methods have been introduced and used to analyze transient flow, an accurate analysis and suitable protection devices should be used to protect water pipeline systems. The fourth-order Runge-Kutta method has been used to solve the dynamic and continuity equations in the rigid column method, while the characteristics method used to solve these equations in the full elastic method. The results obtained provide that the model is an efficient tool for flow transient analysis and provide approximately identical results by using these two methods. Moreover; using the simple surge tank ”open surge tank” reduces the unfavorable effects of transients.

Keywords: Elastic method, Flow transient, Open surge tank, Pipeline, Protection devices, Numerical model, Rigid column method.

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Authors: V. Sandeep Kumar, S. Anuradha

Abstract:

The high Peak to Average Power Ratio (PAR) in Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) can significantly reduce power efficiency and performance. In this paper, we address the problem of PAPR reduction for FBMC-OQAM systems using Tone Reservation (TR) technique. Due to the overlapping structure of FBMCOQAM signals, directly applying TR schemes of OFDM systems to FBMC-OQAM systems is not effective. We improve the tone reservation (TR) technique by employing sliding window with Active Constellation Extension for the PAPR reduction of FBMC-OQAM signals, called sliding window tone reservation Active Constellation Extension (SW-TRACE) technique. The proposed SW-TRACE technique uses the peak reduction tones (PRTs) of several consecutive data blocks to cancel the peaks of the FBMC-OQAM signal inside a window, with dynamically extending outer constellation points in active(data-carrying) channels, within margin-preserving constraints, in order to minimize the peak magnitude. Analysis and simulation results compared to the existing Tone Reservation (TR) technique for FBMC/OQAM system. The proposed method SW-TRACE has better PAPR performance and lower computational complexity.

Keywords: FBMC-OQAM, peak-to-average power ratio, sliding window, tone reservation Active Constellation Extension.

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Authors: Tomohiro Hachino, Kengo Nagatomo, Hitoshi Takata

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This paper deals with an on-line identification method of continuous-time Hammerstein systems by using the radial basis function (RBF) networks and immune algorithm (IA). An unknown nonlinear static part to be estimated is approximately represented by the RBF network. The IA is efficiently combined with the recursive least-squares (RLS) method. The objective function for the identification is regarded as the antigen. The candidates of the RBF parameters such as the centers and widths are coded into binary bit strings as the antibodies and searched by the IA. On the other hand, the candidates of both the weighting parameters of the RBF network and the system parameters of the linear dynamic part are updated by the RLS method. Simulation results are shown to illustrate the proposed method.

Keywords: Continuous-time System, Hammerstein System, OnlineIdentification, Immune Algorithm, RBF network.

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Authors: Smah Doeban Almotiri

Abstract:

One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2021, until April 4, 2021. Natural language processing (NLP), which is a form of Artificial intelligent was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applied machine learning sentimental method such as Crystal Feel and extended the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: S. Sookpeng, C. J. Martin, D. J. Gentle

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Automatic tube current modulation (ATCM) systems are available for all CT manufacturers and are used for the majority of patients. Understanding how the systems work and their influence on patient dose and image quality is important for CT users, in order to gain the most effective use of the systems. In the present study, a new phantom was used for evaluating dose distribution and image quality under the ATCM operation for the Toshiba Aquilion 64 CT scanner using different ATCM options and a fixed mAs technique. A routine chest, abdomen and pelvis (CAP) protocol was selected for study and Gafchromic film was used to measure entrance surface dose (ESD), peripheral dose and central axis dose in the phantom. The results show the dose reductions achievable with various ATCM options, in relation with the target noise. The doses and image noise distribution were more uniform when the ATCM system was implemented compared with the fixed mAs technique. The lower limit set for the tube current will affect the modulations especially for the lower dose option. This limit prevented the tube current being reduced further and therefore the lower dose ATCM setting resembled a fixed mAs technique. Selection of a lower tube current limit is likely to reduce doses for smaller patients in scans of chest and neck regions.

Keywords: Computed Tomography (CT), Automatic Tube Current Modulation (ATCM), Automatic Exposure Control (AEC).

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

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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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Authors: Bagher Rahimpour Cami, Hamid Hassanpour, Hoda Mashayekhi

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Nowadays websites provide a vast number of resources for users. Recommender systems have been developed as an essential element of these websites to provide a personalized environment for users. They help users to retrieve interested resources from large sets of available resources. Due to the dynamic feature of user preference, constructing an appropriate model to estimate the user preference is the major task of recommender systems. Profile matching and latent factors are two main approaches to identify user preference. In this paper, we employed the latent factor and profile matching to cluster the user profile and identify user preference, respectively. The method uses the Distance Dependent Chines Restaurant Process as a Bayesian nonparametric framework to extract the latent factors from the user profile. These latent factors are mapped to user interests and a weighted distribution is used to identify user preferences. We evaluate the proposed method using a real-world data-set that contains news tweets of a news agency (BBC). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach related to existing methods, and its ability to effectively evolve over time.

Keywords: Content-based recommender systems, dynamic user modeling, extracting user interests, predicting user preference.

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Authors: Mohamad Mahdavi, Mojtaba Mahdavi

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This paper tries to represent a new method for computing the reliability of a system which is arranged in series or parallel model. In this method we estimate life distribution function of whole structure using the asymptotic Extreme Value (EV) distribution of Type I, or Gumbel theory. We use EV distribution in minimal mode, for estimate the life distribution function of series structure and maximal mode for parallel system. All parameters also are estimated by Moments method. Reliability function and failure (hazard) rate and p-th percentile point of each function are determined. Other important indexes such as Mean Time to Failure (MTTF), Mean Time to repair (MTTR), for non-repairable and renewal systems in both of series and parallel structure will be computed.

Keywords: Reliability, extreme value, parallel, series, lifedistribution

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Authors: Kemal Polat

Abstract:

In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: Fuzzy C-means clustering, Fuzzy C-means clustering based attribute weighting, Pima Indians diabetes dataset, SVM.

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Authors: M. A. Hajeeh

Abstract:

Pumping systems are an integral part of water desalination plants, their effective functioning is vital for the operation of a plant. In this research work, the reliability and availability of pressurized pumps in a reverse osmosis desalination plant are studied with the objective of finding configurations that provides optimal performance. Six configurations of a series system with different number of warm and cold standby components were examined. Closed form expressions for the mean time to failure (MTTF) and the long run availability are derived and compared under the assumption that the time between failures and repair times of the primary and standby components are exponentially distributed. Moreover, a cost/ benefit analysis is conducted in order to identify a configuration with the best performance and least cost. It is concluded that configurations with cold standby components are preferable especially when the pumps are of the size.

Keywords: Availability, Cost/ benefit, Mean time to failure, Pumps.

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Authors: Moustafa Osman Mohammed

Abstract:

This paper introduces Luhmann’s autopoietic social systems starting with the original concept of autopoiesis by biologists and scientists, including the modification of general systems based on socialized medicine. A specific type of autopoietic system is explained in the three existing groups of the ecological phenomena: interaction, social and medical sciences. This hypothesis model, nevertheless, has a nonlinear interaction with its natural environment ‘interactional cycle’ for the exchange of photon energy with molecular without any changes in topology. The external forces in the systems environment might be concomitant with the natural fluctuations’ influence (e.g. radioactive radiation, electromagnetic waves). The cantilever sensor deploys insights to the future chip processor for prevention of social metabolic systems. Thus, the circuits with resonant electric and optical properties are prototyped on board as an intra–chip inter–chip transmission for producing electromagnetic energy approximately ranges from 1.7 mA at 3.3 V to service the detection in locomotion with the least significant power losses. Nowadays, therapeutic systems are assimilated materials from embryonic stem cells to aggregate multiple functions of the vessels nature de-cellular structure for replenishment. While, the interior actuators deploy base-pair complementarity of nucleotides for the symmetric arrangement in particular bacterial nanonetworks of the sequence cycle creating double-stranded DNA strings. The DNA strands must be sequenced, assembled, and decoded in order to reconstruct the original source reliably. The design of exterior actuators have the ability in sensing different variations in the corresponding patterns regarding beat-to-beat heart rate variability (HRV) for spatial autocorrelation of molecular communication, which consists of human electromagnetic, piezoelectric, electrostatic and electrothermal energy to monitor and transfer the dynamic changes of all the cantilevers simultaneously in real-time workspace with high precision. A prototype-enabled dynamic energy sensor has been investigated in the laboratory for inclusion of nanoscale devices in the architecture with a fuzzy logic control for detection of thermal and electrostatic changes with optoelectronic devices to interpret uncertainty associated with signal interference. Ultimately, the controversial aspect of molecular frictional properties is adjusted to each other and forms its unique spatial structure modules for providing the environment mutual contribution in the investigation of mass temperature changes due to pathogenic archival architecture of clusters.

Keywords: Autopoiesis, quantum photonics, portable energy, photonic structure, photodynamic therapeutic system.

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Authors: P. K. Singhal, R. Naresh, V. Sharma

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This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: Artificial bee colony algorithm, economic dispatch, unit commitment, wind power.

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Authors: P. K. Singhal, R. Naresh, V. Sharma

Abstract:

This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: Artificial bee colony algorithm, economic dispatch, unit commitment, wind power.

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Authors: Keunhong Chae, Seokho Yoon

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In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments.

Keywords: Frequency offset estimation, maximum-likelihood, non-Gaussian noise environment, OFDM, training symbol.

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Authors: S. Farzi

Abstract:

Recently, a lot of attention has been devoted to advanced techniques of system modeling. PNN(polynomial neural network) is a GMDH-type algorithm (Group Method of Data Handling) which is one of the useful method for modeling nonlinear systems but PNN performance depends strongly on the number of input variables and the order of polynomial which are determined by trial and error. In this paper, we introduce GPNN (genetic polynomial neural network) to improve the performance of PNN. GPNN determines the number of input variables and the order of all neurons with GA (genetic algorithm). We use GA to search between all possible values for the number of input variables and the order of polynomial. GPNN performance is obtained by two nonlinear systems. the quadratic equation and the time series Dow Jones stock index are two case studies for obtaining the GPNN performance.

Keywords: GMDH, GPNN, GA, PNN.

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Authors: Kayijuka Idrissa

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This work investigates a mathematical study for traffic flow and traffic density in Kigali city roads and the data collected from the national police of Rwanda in 2012. While working on this topic, some mathematical models were used in order to analyze and compare traffic variables. This work has been carried out on Kigali roads specifically at roundabouts from Kigali Business Center (KBC) to Prince House as our study sites. In this project, we used some mathematical tools to analyze the data collected and to understand the relationship between traffic variables. We applied the Poisson distribution method to analyze and to know the number of accidents occurred in this section of the road which is from KBC to Prince House. The results show that the accidents that occurred in 2012 were at very high rates due to the fact that this section has a very narrow single lane on each side which leads to high congestion of vehicles, and consequently, accidents occur very frequently. Using the data of speeds and densities collected from this section of road, we found that the increment of the density results in a decrement of the speed of the vehicle. At the point where the density is equal to the jam density the speed becomes zero. The approach is promising in capturing sudden changes on flow patterns and is open to be utilized in a series of intelligent management strategies and especially in noncurrent congestion effect detection and control.

Keywords: Statistical methods, Poisson distribution, car moving techniques, traffic flow.

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Authors: Maziar Ahmad Sharbafi, Caro Lucas, Abolfazel Toroghi Haghighat, Omid AmirGhiasvand, Omid Aghazade

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RoboCup Rescue simulation as a large-scale Multi agent system (MAS) is one of the challenging environments for keeping coordination between agents to achieve the objectives despite sensing and communication limitations. The dynamicity of the environment and intensive dependency between actions of different kinds of agents make the problem more complex. This point encouraged us to use learning-based methods to adapt our decision making to different situations. Our approach is utilizing reinforcement leaning. Using learning in rescue simulation is one of the current ways which has been the subject of several researches in recent years. In this paper we present an innovative learning method implemented for Police Force (PF) Agent. This method can cope with the main difficulties that exist in other learning approaches. Different methods used in the literature have been examined. Their drawbacks and possible improvements have led us to the method proposed in this paper which is fast and accurate. The Brain Emotional Learning Based Intelligent Controller (BELBIC) is our solution for learning in this environment. BELBIC is a physiologically motivated approach based on a computational model of amygdale and limbic system. The paper presents the results obtained by the proposed approach, showing the power of BELBIC as a decision making tool in complex and dynamic situation.

Keywords: Emotional learning, rescue, simulation environment, RoboCup, multi-agent system.

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Authors: Vahid Reza Nafisi, Mohammad Hasan Moradi, Mohammad Hosain Nasr-Esfahani

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The conventional assessment of human semen is a highly subjective assessment, with considerable intra- and interlaboratory variability. Computer-Assisted Sperm Analysis (CASA) systems provide a rapid and automated assessment of the sperm characteristics, together with improved standardization and quality control. However, the outcome of CASA systems is sensitive to the method of experimentation. While conventional CASA systems use digital microscopes with phase-contrast accessories, producing higher contrast images, we have used raw semen samples (no staining materials) and a regular light microscope, with a digital camera directly attached to its eyepiece, to insure cost benefits and simple assembling of the system. However, since the accurate finding of sperms in the semen image is the first step in the examination and analysis of the semen, any error in this step can affect the outcome of the analysis. This article introduces and explains an algorithm for finding sperms in low contrast images: First, an image enhancement algorithm is applied to remove extra particles from the image. Then, the foreground particles (including sperms and round cells) are segmented form the background. Finally, based on certain features and criteria, sperms are separated from other cells.

Keywords: Computer-Assisted Sperm Analysis (CASA), Sperm identification, Tail detection, Elliptic shape model.

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Authors: Kyung-Jung Lee, Young-Hun Ki, Hyun-Sik Ahn

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In this paper, we propose a hardware and software design method for automotive Electronic Control Units (ECU) considering the functional safety. The proposed ECU is considered for the application to Electro-Mechanical Actuator systems and the validity of the design method is shown by the application to the Electro-Mechanical Brake (EMB) control system which is used as a brake actuator in Brake-By-Wire (BBW) systems. The importance of a functional safety-based design approach to EMB ECU design has been emphasized because of its safety-critical functions, which are executed with the aid of many electric actuators, sensors, and application software. Based on hazard analysis and risk assessment according to ISO26262, the EMB system should be ASIL-D-compliant, the highest ASIL level. To this end, an external signature watchdog and an Infineon 32-bit microcontroller TriCore are used to reduce risks considering common-cause hardware failure. Moreover, a software design method is introduced for implementing functional safety-oriented monitoring functions based on an asymmetric dual core architecture considering redundancy and diversity. The validity of the proposed ECU design approach is verified by using the EMB Hardware-In-the-Loop (HILS) system, which consists of the EMB assembly, actuator ECU, a host PC, and a few debugging devices. Furthermore, it is shown that the existing sensor fault tolerant control system can be used more effectively for mitigating the effects of hardware and software faults by applying the proposed ECU design method.

Keywords: BBW (Brake-By-wire), EMB (Electro-Mechanical Brake), Functional Safety, ISO26262.

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Authors: M. Ovinis, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs

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The use of machine vision to inspect the outcome of surgical tasks is investigated, with the aim of incorporating this approach in robotic surgery systems. Machine vision is a non-contact form of inspection i.e. no part of the vision system is in direct contact with the patient, and is therefore well suited for surgery where sterility is an important consideration,. As a proof-of-concept, three primary surgical tasks for a common neurosurgical procedure were inspected using machine vision. Experiments were performed on cadaveric pig heads to simulate the two possible outcomes i.e. satisfactory or unsatisfactory, for tasks involved in making a burr hole, namely incision, retraction, and drilling. We identify low level image features to distinguish the two outcomes, as well as report on results that validate our proposed approach. The potential of using machine vision in a surgical environment, and the challenges that must be addressed, are identified and discussed.

Keywords: Visual inspection, machine vision, robotic surgery.

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Authors: Wei Yee Teoh, Chee Wei Tan

Abstract:

The issue of unintentional islanding in PV grid interconnection still remains as a challenge in grid-connected photovoltaic (PV) systems. This paper discusses the overview of popularly used anti-islanding detection methods, practically applied in PV grid-connected systems. Anti-islanding methods generally can be classified into four major groups, which include passive methods, active methods, hybrid methods and communication base methods. Active methods have been the preferred detection technique over the years due to very small non-detected zone (NDZ) in small scale distribution generation. Passive method is comparatively simpler than active method in terms of circuitry and operations. However, it suffers from large NDZ that significantly reduces its performance. Communication base methods inherit the advantages of active and passive methods with reduced drawbacks. Hybrid method which evolved from the combination of both active and passive methods has been proven to achieve accurate anti-islanding detection by many researchers. For each of the studied anti-islanding methods, the operation analysis is described while the advantages and disadvantages are compared and discussed. It is difficult to pinpoint a generic method for a specific application, because most of the methods discussed are governed by the nature of application and system dependent elements. This study concludes that the setup and operation cost is the vital factor for anti-islanding method selection in order to achieve minimal compromising between cost and system quality.

Keywords: Active method, hybrid method, islanding detection, passive method, photovoltaic (PV), utility method

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Authors: Seshi Reddy Kasu, Florian Misoc

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The use of hydroelectric pump-storage system at large scale, MW-size systems, is already widespread around the world. Designed for large scale applications, pump-storage station can be scaled-down for small, remote residential applications. Given the cost and complexity associated with installing a substation further than 100 miles from the main transmission lines, a remote, independent and self-sufficient system is by far the most feasible solution. This article is aiming at the design of wind and solar power generating system, by means of pumped-storage to replace the wind and /or solar power systems with a battery bank energy storage. Wind and solar pumped-storage power generating system can reduce the cost of power generation system, according to the user's electricity load and resource condition and also can ensure system reliability of power supply. Wind and solar pumped-storage power generation system is well suited for remote residential applications with intermittent wind and/or solar energy. This type of power systems, installed in these locations, could be a very good alternative, with economic benefits and positive social effects. The advantage of pumped storage power system, where wind power regulation is calculated, shows that a significant smoothing of the produced power is obtained, resulting in a power-on-demand system’s capability, concomitant to extra economic benefits.

Keywords: Battery bank, photo-voltaic, pump-storage, wind energy.

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Authors: Li Zong-Cheng

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The deviation between the target state variable and the practical state variable should be used to form the state tending factor of complex systems, which can reflect the process for the complex system to tend rationalization. Relating to the system of basic equations of complete factor synergetics consisting of twenty nonlinear stochastic differential equations, the two new models are considered to set, which should be called respectively the rationalizing tendency model and the non- rationalizing tendency model. Therefore we can extend the theory of programming with the objective function & constraint condition suitable only for the realm of man-s activities into the new analysis with the tendency function & constraint condition suitable for all the field of complex system.

Keywords: complex system, complete factor synergetics, basicequation, rationalizing tendency model, non-rationalizing tendencymodel.

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Authors: Yogesh C. Sharma, A. Seetharaman

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The decision-making process in humans is a complicated system influenced by a variety of intrinsic and extrinsic factors. Human decisions have a ripple effect on subsequent decisions. In this study, the scope of human decision making is limited to employees. In an organisation, a person makes a variety of decisions from the time they are hired to the time they retire. The goal of this research is to identify various elements that influence decision making. In addition, the environment in which a decision is made is a significant aspect of the decision-making process. Employees in today's workplace use artificial intelligence (AI) systems for automation and decision augmentation. The impact of AI systems on the decision-making process is examined in this study. This research is designed based on a systematic literature review. Based on gaps in the literature, limitations and the scope of future research have been identified. Based on these findings, a research framework has been designed to identify various factors affecting employee decision making. Employee decision making is influenced by technological advancement, data-driven culture, human trust, decision automation-augmentation and workplace motivation. Hybrid human-AI systems require development of new skill sets and organisational design. Employee psychological safety and supportive leadership influences overall job satisfaction.

Keywords: Employee decision making, artificial intelligence, environment, human trust, technology innovation, psychological safety.

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Authors: Zhiying Zhang, Chongdu Cho

Abstract:

Accurate evaluation of damping ratios involving soilstructure interaction (SSI) effects is the prerequisite for seismic design of in-situ buildings. This study proposes a combined approach to identify damping ratios of SSI systems based on ambient excitation technique. The proposed approach is illustrated with main test process, sampling principle and algorithm steps through an engineering example, as along with its feasibility and validity. The proposed approach is employed for damping ratio identification of 82 buildings in Xi-an, China. Based on the experimental data, the variation range and tendency of damping ratios of these SSI systems, along with the preliminary influence factor, are shown and discussed. In addition, a fitting curve indicates the relation between the damping ratio and fundamental natural period of SSI system.

Keywords: Damping ratio, seismic design, soil-structure interaction, system parameter identification.

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Authors: Noura Al-Ajmi, Mohammed A. Almulla

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With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit.

Keywords: Headache diagnosis system, treatment recommender system, rule-based expert system.

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Authors: Mohamed Omar

Abstract:

Superelastic Shape Memory Alloy (SMA) is accepted when it used as connection in steel structures. The seismic behaviour of steel frames with SMA is being assessed in this study. Three eightstorey steel frames with different SMA systems are suggested, the first one of which is braced with diagonal bracing system, the second one is braced with nee bracing system while the last one is which the SMA is used as connection at the plastic hinge regions of beams. Nonlinear time history analyses of steel frames with SMA subjected to two different ground motion records have been performed using Seismostruct software. To evaluate the efficiency of suggested systems, the dynamic responses of the frames were compared. From the comparison results, it can be concluded that using SMA element is an effective way to improve the dynamic response of structures subjected to earthquake excitations. Implementing the SMA braces can lead to a reduction in residual roof displacement. The shape memory alloy is effective in reducing the maximum displacement at the frame top and it provides a large elastic deformation range. SMA connections are very effective in dissipating energy and reducing the total input energy of the whole frame under severe seismic ground motion. Using of the SMA connection system is more effective in controlling the reaction forces at the base frame than other bracing systems. Using SMA as bracing is more effective in reducing the displacements. The efficiency of SMA is dependant on the input wave motions and the construction system as well.

Keywords: Finite element analysis, seismic response, shapesmemory alloy, steel frame, superelasticity

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

Abstract:

Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: Deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator.

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Authors: Patil Ashwini, Archana Thosar

Abstract:

This paper addresses the mathematical model of wind energy system useful for designing fault tolerant control. To serve the demand of power, large capacity wind energy systems are vital. These systems are installed offshore where non planned service is very costly. Whenever there is a fault in between two planned services, the system may stop working abruptly. This might even lead to the complete failure of the system. To enhance the reliability, the availability and reduce the cost of maintenance of wind turbines, the fault tolerant control systems are very essential. For designing any control system, an appropriate mathematical model is always needed. In this paper, the two-mass model is modified by considering the frequent mechanical faults like misalignments in the drive train, gears and bearings faults. These faults are subject to a wear process and cause frictional losses. This paper addresses these faults in the mathematics of the wind energy system. Further, the work is extended to study the variations of the parameters namely generator inertia constant, spring constant, viscous friction coefficient and gear ratio; on the pole-zero plot which is related with the physical design of the wind turbine. Behavior of the wind turbine during drive train faults are simulated and briefly discussed.

Keywords: Mathematical model of wind energy system, stability analysis, shaft stiffness, viscous friction coefficient, gear ratio, generator inertia, fault tolerant control.

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