Search results for: Integration Error

Authors: Jane O. Munonye

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Climate change has both negative and positive effects in agricultural production. For agriculture to be sustainable in adverse climate change condition, some natural measures are needed. The issue is to produce more food with available natural resources and reduce the contribution of agriculture to climate change. The study reviewed climate change and sustainable agriculture in southeast Nigeria. Data from the study were from secondary sources. Ten scientific papers were consulted and data for the review were collected from three. The objectives of the paper were as follows: to review the effect of climate change on one major arable crop in southeast Nigeria (yam; Dioscorea rotundata); evident of climate change impact and methods for sustainable agricultural production in adverse weather condition. Some climatic parameter as sunshine, relative humidity and rainfall have negative relationship with yam production and significant at 10% probability. Crop production was predicted to decline by 25% per hectare by 2060 while livestock production has increased the incidence of diseases and pathogens as the major effect to agriculture. Methods for sustainable agriculture and damage of natural resources by climate change were highlighted. Agriculture needs to be transformed as climate changes to enable the sector to be sustainable. There should be a policy in place to facilitate the integration of sustainability in Nigeria agriculture.

Keywords: Agriculture, climate change, sustainability, yam.

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Authors: Anu Paul, Varghese Paul

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The focal aspire of e-Government (eGovt) is to offer citizen-centered service delivery. Accordingly, the citizenry consumes services from multiple government agencies through national portal. Thus, eGovt is an enterprise with the primary business motive of transparent, efficient and effective public services to its citizenry and its logical structure is the eGovernment Enterprise Architecture (eGEA). Since eGovt is IT oriented multifaceted service-centric system, EA doesn’t do much on an automated enterprise other than the business artifacts. Service-Oriented Architecture (SOA) manifestation led some governments to pertain this in their eGovts, but it limits the source of business artifacts. The concurrent use of EA and SOA in eGovt executes interoperability and integration and leads to Service-Oriented e-Government Enterprise (SOeGE). Consequently, agile eGovt system becomes a reality. As an IT perspective eGovt comprises of centralized public service artifacts with the existing application logics belong to various departments at central, state and local level. The eGovt is renovating to SOeGE by apply the Service-Orientation (SO) principles in the entire system. This paper explores IT perspective of SOeGE in India which encompasses the public service models and illustrated with a case study the Passport service of India.

Keywords: Enterprise Architecture, Service-Oriented e-Government Enterprise, Service Interface Layer, Service Model.

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Authors: C. Villegas-Quezada, J. Climent

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Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.

Keywords: AdaBoost Classifier, Holistic Face Recognition, Minimax Multivariate Approximation, Genetic Algorithm.

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Authors: José M. Cantera, Miguel Jiménez, Genoveva López, Javier Soriano

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With the advent of emerging personal computing paradigms such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide range of mobile devices. Since these devices do not have the same rendering capabilities, Web contents need to be adapted for transparent access from a variety of client agents. Such content adaptation results in better rendering and faster delivery to the client device. Nevertheless, Web content adaptation sets new challenges for semantic markup. This paper presents an advanced components platform, called MorfeoSMC, enabling the development of mobility applications and services according to a channel model based on Services Oriented Architecture (SOA) principles. It then goes on to describe the potential for integration with the Semantic Web through a novel framework of external semantic annotation of mobile Web contents. The role of semantic annotation in this framework is to describe the contents of individual documents themselves, assuring the preservation of the semantics during the process of adapting content rendering, as well as to exploit these semantic annotations in a novel user profile-aware content adaptation process. Semantic Web content adaptation is a way of adding value to and facilitates repurposing of Web contents (enhanced browsing, Web Services location and access, etc).

Keywords: Semantic web, ubiquitous and mobile computing, web content transcoding, semantic markup, mobile computing middleware and services.

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Authors: Karchung, S. Ruangsinchaiwanich

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This paper presents open-loop vector control method of induction motor with space vector pulse width modulation (SVPWM) technique. Normally, the closed loop speed control is preferred and is believed to be more accurate. However, it requires a position sensor to track the rotor position which is not desirable to use it for certain workspace applications. This paper exhibits the performance of three-phase induction motor with the simplest control algorithm without the use of a position sensor nor an estimation block to estimate rotor position for sensorless control. The motor stator currents are measured and are transformed to synchronously rotating (d-q-axis) frame by use of Clarke and Park transformation. The actual control happens in this frame where the measured currents are compared with the reference currents. The error signal is fed to a conventional PI controller, and the corrected d-q voltage is generated. The controller outputs are transformed back to three phase voltages and are fed to SVPWM block which generates PWM signal for the voltage source inverter. The open loop vector control model along with SVPWM algorithm is modeled in MATLAB/Simulink software and is experimented and validated in TMS320F28335 DSP board.

Keywords: Electric drive, induction motor, open-loop vector control, space vector pulse width modulation technique.

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Authors: Shih-Wen Hsiao, Rong-Qi Chen, Chien-Yu Lin

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The topic of surface flattening plays a vital role in the field of computer aided design and manufacture. Surface flattening enables the production of 2D patterns and it can be used in design and manufacturing for developing a 3D surface to a 2D platform, especially in fashion design. This study describes surface flattening based on minimum energy methods according to the property of different fabrics. Firstly, through the geometric feature of a 3D surface, the less transformed area can be flattened on a 2D platform by geodesic. Then, strain energy that has accumulated in mesh can be stably released by an approximate implicit method and revised error function. In some cases, cutting mesh to further release the energy is a common way to fix the situation and enhance the accuracy of the surface flattening, and this makes the obtained 2D pattern naturally generate significant cracks. When this methodology is applied to a 3D mannequin constructed with feature lines, it enhances the level of computer-aided fashion design. Besides, when different fabrics are applied to fashion design, it is necessary to revise the shape of a 2D pattern according to the properties of the fabric. With this model, the outline of 2D patterns can be revised by distributing the strain energy with different results according to different fabric properties. Finally, this research uses some common design cases to illustrate and verify the feasibility of this methodology.

Keywords: Surface flattening, Strain energy, Minimum energy, approximate implicit method, Fashion design.

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Authors: Muhammad Rehan Khalid, Ahmed Farhan Hanif, Adnan Ahmed Khan

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This paper investigates the performance of Multiple- Input Multiple-Output (MIMO) feedback system combined with Orthogonal Frequency Division Multiplexing (OFDM). Two types of codebook based channel feedback techniques are used in this work. The first feedback technique uses a combination of both the long-term and short-term channel state information (CSI) at the transmitter, whereas the second technique uses only the short term CSI. The long-term and short-term CSI at the transmitter is used for efficient channel utilization. OFDM is a powerful technique employed in communication systems suffering from frequency selectivity. Combined with multiple antennas at the transmitter and receiver, OFDM proves to be robust against delay spread. Moreover, it leads to significant data rates with improved bit error performance over links having only a single antenna at both the transmitter and receiver. The effectiveness of these techniques has been demonstrated through the simulation of a MIMO-OFDM feedback system. The results have been evaluated for 4x4 MIMO channels. Simulation results indicate the benefits of the MIMO-OFDM channel feedback system over the one without incorporating OFDM. Performance gain of about 3 dB is observed for MIMO-OFDM feedback system as compared to the one without employing OFDM. Hence MIMO-OFDM becomes an attractive approach for future high speed wireless communication systems.

Keywords: MIMO systems, OFDM, Codebooks, Channel Feedback

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: Earnings management, generalized regression neural networks, linear regression, multi-layer perceptron, Tehran stock exchange.

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Authors: Fahad Alhussein, Huaping Liu

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This paper compares the multipath mitigation performance of code correlation reference waveform receivers with variable and fixed window width, for binary offset carrier and multiplexed binary offset carrier signals typically used in global navigation satellite systems. In the variable window width method, such width is iteratively reduced until the distortion on the discriminator with multipath is eliminated. This distortion is measured as the Euclidean distance between the actual discriminator (obtained with the incoming signal), and the local discriminator (generated with a local copy of the signal). The variable window width have shown better performance compared to the fixed window width. In particular, the former yields zero error for all delays for the BOC and MBOC signals considered, while the latter gives rather large nonzero errors for small delays in all cases. Due to its computational simplicity, the variable window width method is perfectly suitable for implementation in low-cost receivers.

Keywords: Correlation reference waveform receivers, binary offset carrier, multiplexed binary offset carrier, global navigation satellite systems.

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Authors: Ekow A. Kwofie, Emmanuel K. Anto, Godfred Mensah

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In this paper, CYME Distribution software has been used to assess the impacts of solar Photovoltaic (PV) distributed generation (DG) plant on the Electricity Company of Ghana (ECG) 33 kV sub-transmission network at different PV penetration levels. As ECG begins to encourage DG PV interconnections within its network, there has been the need to assess the impacts on the sub-transmission losses and voltage contribution. In Tema, a city in Accra - Ghana, ECG has a 33 kV sub-transmission network made up of 20 No. 33 kV buses that was modeled. Three different locations were chosen: The source bus, a bus along the sub-transmission radial network and a bus at the tail end to determine the optimal location for DG PV interconnection. The optimal location was determined based on sub-transmission technical losses and voltage impact. PV capacities at different penetration levels were modeled at each location and simulations performed to determine the optimal PV penetration level. Interconnection at a bus along (or in the middle of) the sub-transmission network offered the highest benefits at an optimal PV penetration level of 80%. At that location, the maximum voltage improvement of 0.789% on the neighboring 33 kV buses and maximum loss reduction of 6.033% over the base case scenario were recorded. Hence, the optimal location for DG PV integration within the 33 kV sub-transmission utility network is at a bus along the sub-transmission radial network.

Keywords: Distributed generation photovoltaic, DG PV, optimal location, penetration level, sub-transmission network.

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Authors: Shadrack Mambo, Karim Djouani, Yskandar Hamam, Barend van Wyk, Patrick Siarry

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This paper discusses the current trends in medical image registration techniques and addresses the need to provide a solid theoretical foundation for research endeavours. Methodological analysis and synthesis of quality literature was done, providing a platform for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Research on medical image registration techniques assists clinical and medical practitioners in diagnosis of tumours and lesion in anatomical organs, thereby enhancing fast and accurate curative treatment of patients. Literature review aims to provide a solid theoretical foundation for research endeavours in image registration techniques. Developing a solid foundation for a research study is possible through a methodological analysis and synthesis of existing contributions. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in medical image registration techniques and also communicate to them, the contribution of this research in the field of image processing. The gaps identified in current techniques can be closed by use of artificial neural networks that form learning systems designed to minimise error function. The paper also suggests several areas of future research in the image registration.

Keywords: Image registration techniques, medical images, neural networks, optimisation, transformation.

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Authors: Farhan Beg, Raymond Moberly

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The Indian subcontinent is facing a massive challenge with regards to energy security in its member countries; to provide reliable electricity to facilitate development across various sectors of the economy and consequently achieve the developmental targets. The instability of the current precarious situation is observable in the frequent system failures and blackouts.

The deployment of interconnected electricity ‘Supergrid’ designed to carry huge quanta of power across the Indian sub-continent is proposed in this paper. Not only enabling energy security in the subcontinent it will also provide a platform for Renewable Energy Sources (RES) integration. This paper assesses the need and conditions for a Supergrid deployment and consequently proposes a meshed topology based on Voltage Source High Voltage Direct Current (VSC- HVDC) converters for the Supergrid modeling. Various control schemes for the control of voltage and power are utilized for the regulation of the network parameters. A 3 terminal Multi Terminal Direct Current (MTDC) network is used for the simulations.

Keywords: Super grid, Wind and Solar energy, High Voltage Direct Current, Electricity management, Load Flow Analysis.

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Authors: Javier Barbaran, Manuel Dıaz, Inaki Esteve, Bartolome Rubio

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Wireless Sensor Networks (WSNs) have attracted the attention of many researchers. This has resulted in their rapid integration in very different areas such as precision agriculture,environmental monitoring, object and event detection and military surveillance. Due to the current WSN characteristics this technology is specifically useful in industrial areas where security, reliability and autonomy are basic, such as nuclear power plants, chemical plants, and others. In this paper we present a system based on WSNs to monitor environmental conditions around and inside a nuclear power plant, specifically, radiation levels. Sensor nodes, equipped with radiation sensors, are deployed in fixed positions throughout the plant. In addition, plant staff are also equipped with mobile devices with higher capabilities than sensors such as for example PDAs able to monitor radiation levels and other conditions around them. The system enables communication between PDAs, which form a Mobile Ad-hoc Wireless Network (MANET), and allows workers to monitor remote conditions in the plant. It is particularly useful during stoppage periods for inspection or in the event of an accident to prevent risk situations.

Keywords: MANETs, Mobile computing, Radiation monitoring, Wireless Sensor Networks.

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Authors: Petru Manescu, Joseph Azencot, Michael Beuve, Hamid Ladjal, Jacques Saade, Jean-Michel Morreau, Philippe Giraud, Behzad Shariat

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Organ motion, especially respiratory motion, is a technical challenge to radiation therapy planning and dosimetry. This motion induces displacements and deformation of the organ tissues within the irradiated region which need to be taken into account when simulating dose distribution during treatment. Finite element modeling (FEM) can provide a great insight into the mechanical behavior of the organs, since they are based on the biomechanical material properties, complex geometry of organs, and anatomical boundary conditions. In this paper we present an original approach that offers the possibility to combine image-based biomechanical models with particle transport simulations. We propose a new method to map material density information issued from CT images to deformable tetrahedral meshes. Based on the principle of mass conservation our method can correlate density variation of organ tissues with geometrical deformations during the different phases of the respiratory cycle. The first results are particularly encouraging, as local error quantification of density mapping on organ geometry and density variation with organ motion are performed to evaluate and validate our approach.

Keywords: Biomechanical simulation, dose distribution, image guided radiation therapy, organ motion, tetrahedral mesh, 4D-CT.

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Authors: Taylan Kabbani, Ekrem Duman

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Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining  the financial assets price ”prediction” step and the ”allocation” step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solved the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and achieved a 2.68 Sharpe ratio on the test dataset. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Keywords: Autonomous agent, deep reinforcement learning, MDP, sentiment analysis, stock market, technical indicators, twin delayed deep deterministic policy gradient.

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Authors: A. Joseph, D.B.L. Bong, D.A.A. Mat

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Security has been an important issue and concern in the smart home systems. Smart home networks consist of a wide range of wired or wireless devices, there is possibility that illegal access to some restricted data or devices may happen. Password-based authentication is widely used to identify authorize users, because this method is cheap, easy and quite accurate. In this paper, a neural network is trained to store the passwords instead of using verification table. This method is useful in solving security problems that happened in some authentication system. The conventional way to train the network using Backpropagation (BPN) requires a long training time. Hence, a faster training algorithm, Resilient Backpropagation (RPROP) is embedded to the MLPs Neural Network to accelerate the training process. For the Data Part, 200 sets of UserID and Passwords were created and encoded into binary as the input. The simulation had been carried out to evaluate the performance for different number of hidden neurons and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the results obtained, using Tansig and Purelin in hidden and output layer and 250 hidden neurons gave the better performance. As a result, a password-based user authentication system for smart home by using neural network had been developed successfully.

Keywords: Neural Network, User Authentication, Smart Home, Security

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Authors: Raja Das, M. K. Pradhan

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This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.

Keywords: Electrical-discharge machining, General Regression Neural Network, Back-propagation Neural Network, Radial Overcut.

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Authors: Abdelgawad, Salah El-Tahawy

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This paper aimed to establish econometrical equation models for the Nile delta region in Egypt, which will represent a basement for future predictions of Lumpy skin disease outbreaks and its pathway in relation to climate change. Data of lumpy skin disease (LSD) outbreaks were collected from the cattle farms located in the provinces representing the Nile delta region during 1 January, 2015 to December, 2015. The obtained results indicated that there was a significant association between the degree of the LSD outbreaks and the investigated climate factors (temperature, wind speed, and humidity) and the outbreaks peaked during the months of June, July, and August and gradually decreased to the lowest rate in January, February, and December. The model obtained depicted that the increment of these climate factors were associated with evidently increment on LSD outbreaks on the Nile Delta of Egypt. The model validation process was done by the root mean square error (RMSE) and means bias (MB) which compared the number of LSD outbreaks expected with the number of observed outbreaks and estimated the confidence level of the model. The value of RMSE was 1.38% and MB was 99.50% confirming that this established model described the current association between the LSD outbreaks and the change on climate factors and also can be used as a base for predicting the of LSD outbreaks depending on the climatic change on the future.

Keywords: LSD, climate factors, econometric models, Nile Delta.

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Authors: A. Majeed, P. Ali

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Before designing an electrical system, the estimation of load is necessary for unit sizing and demand-generation balancing. The system could be a stand-alone system for a village or grid connected or integrated renewable energy to grid connection, especially as there are non–electrified villages in developing countries. In the classical model, the energy demand was found by estimating the household appliances multiplied with the amount of their rating and the duration of their operation, but in this paper, information exists for electrified villages could be used to predict the demand, as villages almost have the same life style. This paper describes a method used to predict the average energy consumed in each two months for every consumer living in a village by Artificial Neural Network (ANN). The input data are collected using a regional survey for samples of consumers representing typical types of different living, household appliances and energy consumption by a list of information, and the output data are collected from administration office of Piramagrun for each corresponding consumer. The result of this study shows that the average demand for different consumers from four villages in different months throughout the year is approximately 12 kWh/day, this model estimates the average demand/day for every consumer with a mean absolute percent error of 11.8%, and MathWorks software package MATLAB version 7.6.0 that contains and facilitate Neural Network Toolbox was used.

Keywords: Artificial neural network, load estimation, regional survey, rural electrification.

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Authors: Z. M. Najmi

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Over the years, the question around Artificial Intelligence has always been one with many answers. Whether by means of use in business and industry or complicated algorithmic programming, management of these technologies has always been the core focus. More recently, technologies have been questioned in industry and society alike as to whether they have improved human-centred design, assisted choices and objectives, and had a hand in systematic processes across the board. With these questions the answer may lie within AI technologies, and the steps needed in removing common human error. Elements such as Machine Learning, Deep Learning, Recommender Systems and Natural Language Processing will all be features to consider moving forward. Our previous intervention with AI applications has resulted in increased productivity, however, raised concerns for the continuation of traditional human-centred occupations. Emerging technologies such as Augmented Reality and Virtual Reality have all played a part in this during AI’s prominent rise. As mentioned, AI has been constantly under the microscope; the benefits and drawbacks may seem endless is wide, but AI is something we must take notice of and adapt into our everyday lives. The aim of this paper is to give an overview of the technologies surrounding A.I. and its’ related technologies. A comprehensive review has been written as a timeline of the developing events and key points in the history of Artificial Intelligence. This research is gathered entirely from secondary research, academic statements of knowledge and gathered to produce an understanding of the timeline of AI.

Keywords: Artificial Intelligence, Deep Learning, Augmented Reality, Reinforcement Learning, Machine Learning, Supervised Learning.

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Authors: Lalla Nezha Lakmiti, Abdelkahar Zahid

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Due to the setbacks on the international scene and the wave of cacophonic financial scandals affecting large international groups, the new Islamic finance industry is not immune despite its initial resistance. The purpose of this paper is to understand and analyze the meaning of the Corporate Governance (CG) concept in Moroccan Islamic banking systems with specific reference to their institutions. The research objective is to identify also the path taken and adopted by these banks recently set up in Morocco. The foundation is rooted in shari'a, in particular, no stakeholder (the shareholding approach) must be harmed, and the ethical value is reflected into these parties’ behavior. We chose a qualitative method, semi-structured interviews where six managers provided answers about their banking systems. Since these respondents held a senior position (directors) within their organizations, it is felt that they are well placed and have the necessary knowledge to provide us with information to answer the questions asked. The results identified the orientation of participating banks and assessing how governance works, while determining which party is fovoured: shareholders, stakeholders or both. This study discusses the favorable condition to the harmonization of the regulations and therefore a better integration between Islamic finance and conventional ones in the economic context of Morocco.

Keywords: Corporate governance, participating banks, stakeholders, shareholders, and interests.

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Authors: Pardeep Singh, Sanjay Sharma

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Content-Based Image Retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of difering sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever increasing quantities and used for diagnostics and therapy. In several articles, content based access to medical images for supporting clinical decision making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into Picture Archiving and Communication Systems (PACS) have been created. This paper gives an overview of soft computing techniques. New research directions are being defined that can prove to be useful. Still, there are very few systems that seem to be used in clinical practice. It needs to be stated as well that the goal is not, in general, to replace text based retrieval methods as they exist at the moment.

Keywords: CBIR, GA, Rough sets, CBMIR

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Authors: M. M. Shabani, O. J. Nydal, R. Larsen

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Gas hydrates can agglomerate and block multiphase oil and gas pipelines when water is present at hydrate forming conditions. Using "Cold Flow Technology", the aim is to condition gas hydrates so that they can be transported as a slurry mixture without a risk of agglomeration. During the pipeline shut down however, hydrate particles may settle in bends and build hydrate plugs. An experimental setup has been designed and constructed to study the flow of such plugs at start up operations. Experiments have been performed using model fluid and model hydrate particles. The propagations of initial plugs in a bend were recorded with impedance probes along the pipe. The experimental results show a dispersion of the plug front. A peak in pressure drop was also recorded when the plugs were passing the bend. The evolutions of the plugs have been simulated by numerical integration of the incompressible mass balance equations, with an imposed mixture velocity. The slip between particles and carrier fluid has been calculated using a drag relation together with a particle-fluid force balance.

Keywords: Cold Flow Technology, Gas Hydrate Plug Flow Experiments, One Dimensional Incompressible Two Fluid Model, Slurry Flow in Inclined Pipes and Bends, Transient Slurry Flow.

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: Neural network, gravitational resistance, pattern recognition, non-linear.

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Authors: Sulaiman Al Amro

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There are currently considerable challenges concerning data security and privacy, particularly in relation to modern technologies. This includes the virtual world known as the Metaverse, which consists of a virtual space that integrates various technologies, and therefore susceptible to cyber threats such as malware, phishing, and identity theft. This has led recent studies to propose the development of Metaverse forensic frameworks and the integration of advanced technologies, including machine learning for intrusion detection and security. In this context, the application of first-order logic offers a formal and systematic approach to defining the conditions of cyberattacks, thereby contributing to the development of effective detection mechanisms. In addition, formalizing the rules and patterns of cyber threats has the potential to enhance the overall security posture of the Metaverse and thus the integrity and safety of this virtual environment. The current paper focuses on the primary actions employed by avatars for potential attacks, including Interval Temporal Logic (ITL) and behavior-based detection to detect an avatar’s abnormal activities within the Metaverse. The research established that the proposed framework attained an accuracy of 92.307%, resulting in the experimental results demonstrating the efficacy of ITL, including its superior performance in addressing the threats posed by avatars within the Metaverse domain.

Keywords: Cyberattacks, detection, first-order logic, Metaverse, privacy, security.

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Authors: Ming-Jong Tsai, Y. L. Cheng, Y. L. Kuo, S. Y. Hsiao, J .W. Chen, P. H. Liu, D. H. Chen

Abstract:

The 3D printing is a combination of digital technology, material science, intelligent manufacturing and control of opto-mechatronics systems. It is called the third industrial revolution from the view of the Economist Journal. A color 3D printing machine may provide the necessary support for high value-added industrial and commercial design, architectural design, personal boutique, and 3D artist’s creation. The main goal of this paper is to develop photo-curable color 3D manufacturing technology and system implementation. The key technologies include (1) Photo-curable color 3D additive manufacturing processes development and materials research (2) Piezo type ink-jet head control and Opto-mechatronics integration technique of the photo-curable color 3D laminated manufacturing system. The proposed system is integrated with single Piezo type ink-jet head with two individual channels for two primary UV light curable color resins which can provide for future colorful 3D printing solutions. The main research results are 16 grey levels and grey resolution of 75 dpi. 

Keywords: 3d printing, additive manufacturing, color, photo-curable, Piezo type ink-jet, UV Resin.

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Authors: J. P. Dubois, O. M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.

Keywords: Least square-support vector machine, on-off keying, matched filter, maximum likelihood detector, wireless infrared communication.

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Authors: Abhishek Soni, A. Kumaraswamy, M. S. Mahesh

Abstract:

Bullet penetration in steel plate is investigated with the help of three-dimensional, non-linear, transient, dynamic, finite elements analysis using explicit time integration code LSDYNA. The effect of large strain, strain-rate and temperature at very high velocity regime was studied from number of simulations of semi-spherical nose shape bullet penetration through single layered circular plate with 2 mm thickness at impact velocities of 500, 1000, and 1500 m/s with the help of Johnson Cook material model. Mie-Gruneisen equation of state is used in conjunction with Johnson Cook material model to determine pressure-volume relationship at various points of interests. Two material models viz. Plastic-Kinematic and Johnson- Cook resulted in different deformation patterns in steel plate. It is observed from the simulation results that the velocity drop and loss of kinetic energy occurred very quickly up to perforation of plate, after that the change in velocity and changes in kinetic energy are negligibly small. The physics behind this kind of behaviour is presented in the paper.

Keywords: AISI 4340 steel, ballistic impact simulation, bullet penetration, non-linear FEM.

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Authors: K. Saravanan

Abstract:

An electrical power system has some negative aspects such as flickering and deviations of voltage/power. This can be eliminated using energy storage devices that will provide a backup energy at the time of voltage/power deviations. Energy-storage devices get charging when system voltage/power is higher than reference value and discharging when system voltage/power is lower than reference value, it is acting as catalysts to provide energy boost. In this paper, a dynamic control of Unified Power Flow Controller (UPFC) integrated with superconducting magnetic energy storage (SMES) is developed to improve the power quality, power oscillation damping, and dynamic voltage stability through the transmission line. UPFC inter-connected to SMES through an interface with DC-DC chopper. This inter-connected system is capable of injecting (absorbing) the real and reactive power into (from) the system at the beginning of stability problems. In this paper, the simulation results of UPFC integrated with SMES and UPFC integrated with fuel cells (FCs) are compared using MATLAB/Simulink software package.

Keywords: UPFC, SMES, power system stability, flexible ac transmission systems, fuel cells, chopper.

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Authors: G. Aloisio, L. T. De Paolis, A. De Mauro, A. Mongelli

Abstract:

One of the essential requirements of a realistic surgical simulator is to reproduce haptic sensations due to the interactions in the virtual environment. However, the interaction need to be performed in real-time, since a delay between the user action and the system reaction reduces the immersion sensation. In this paper, a prototype of a coronary stent implant simulator is present; this system allows real-time interactions with an artery by means of a specific haptic device. To improve the realism of the simulation, the building of the virtual environment is based on real patients- images and a Web Portal is used to search in the geographically remote medical centres a virtual environment with specific features in terms of pathology or anatomy. The functional architecture of the system defines several Medical Centres in which virtual environments built from the real patients- images and related metadata with specific features in terms of pathology or anatomy are stored. The searched data are downloaded from the Medical Centre to the Training Centre provided with a specific haptic device and with the software necessary both to manage the interaction in the virtual environment. After the integration of the virtual environment in the simulation system it is possible to perform training on the specific surgical procedure.

Keywords: Medical Simulation, Web Portal, Virtual Reality.

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