Search results for: transfer matrix approach

Authors: S. Amirhassan Monadjemi, Narges Zarrabi, Naser Neamatbakhsh

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As days go by, we hear more and more about HIV, Ebola, Bird Flu and other dreadful viruses which were unknown a few decades ago. In both detecting and fighting viral diseases ordinary methods have come across some basic and important difficulties. Vaccination is by a sense introduction of the virus to the immune system before the occurrence of the real case infection. It is very successful against some viruses (e.g. Poliomyelitis), while totally ineffective against some others (e.g. HIV or Hepatitis-C). On the other hand, Anti-virus drugs are mostly some tools to control and not to cure a viral disease. This could be a good motivation to try alternative treatments. In this study, some key features of possible physical-based alternative treatments for viral diseases are presented. Electrification of body parts or fluids (especially blood) with micro electric signals with adjusted current or frequency is also studied. The main approach of this study is to find a suitable energy field, with appropriate parameters that are able to kill or deactivate viruses. This would be a lengthy, multi-disciplinary research which needs the contribution of virology, physics, and signal processing experts. It should be mentioned that all the claims made by alternative cures researchers must be tested carefully and are not advisable at the time being.

Keywords: Alternative Cure, Viral disease, HIV, signals, energy filed.

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Authors: Sung Ho Ha, Seong Hyeon Joo, Eun Kyung Kwon

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Until recently, researchers have developed various tools and methodologies for effective clinical decision-making. Among those decisions, chest pain diseases have been one of important diagnostic issues especially in an emergency department. To improve the ability of physicians in diagnosis, many researchers have developed diagnosis intelligence by using machine learning and data mining. However, most of the conventional methodologies have been generally based on a single classifier for disease classification and prediction, which shows moderate performance. This study utilizes an ensemble strategy to combine multiple different classifiers to help physicians diagnose chest pain diseases more accurately than ever. Specifically the ensemble strategy is applied by using the integration of decision trees, neural networks, and support vector machines. The ensemble models are applied to real-world emergency data. This study shows that the performance of the ensemble models is superior to each of single classifiers.

Keywords: Diagnosis intelligence, ensemble approach, data mining, emergency department

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Authors: Melissa C. LaDuke

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The overarching purpose of this study was to determine the relationship between the current format of online delivery for Defense Acquisition University (DAU) courses and Air Force Acquisition (AFA) personnel participation. AFA personnel (hereafter named “student”) were particularly of interest, as they have been mandated to take anywhere from 3 to 30 online courses to earn various DAU specialization certifications. Participants in this qualitative case study were AFA personnel who pursued DAU certifications in science and technology management, program/contract management, and other related fields. Air Force personnel were interviewed about their experiences with online courses. The data gathered were analyzed and grouped into 12 major themes. The themes tied into the theoretical framework and addressed either teacher-centered or student-centered educational practices within DAU. Based on the results of the data analysis, various factors contributed to student perceptions of DAU courses to include the online course construct and relevance to their job. The analysis also found students want to learn the information presented but would like to be able to apply the information learned in meaningful ways.

Keywords: Educational theory, computer-based training, interview, student perceptions, online course design, teacher positionality.

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Authors: G. Partheepan, D.K. Sehgal, R.K. Pandey

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This paper presents a method for determining the uniaxial tensile properties such as Young-s modulus, yield strength and the flow behaviour of a material in a virtually non-destructive manner. To achieve this, a new dumb-bell shaped miniature specimen has been designed. This helps in avoiding the removal of large size material samples from the in-service component for the evaluation of current material properties. The proposed miniature specimen has an advantage in finite element modelling with respect to computational time and memory space. Test fixtures have been developed to enable the tension tests on the miniature specimen in a testing machine. The studies have been conducted in a chromium (H11) steel and an aluminum alloy (AR66). The output from the miniature test viz. load-elongation diagram is obtained and the finite element simulation of the test is carried out using a 2D plane stress analysis. The results are compared with the experimental results. It is observed that the results from the finite element simulation corroborate well with the miniature test results. The approach seems to have potential to predict the mechanical properties of the materials, which could be used in remaining life estimation of the various in-service structures.

Keywords: ABAQUS, finite element, miniature test, tensileproperties

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: Big data, building-value analysis, machine learning, price prediction.

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Authors: Inaki Maulida Hakim, T. Yuri M. Zagloel, Astari Wulandari

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Six sigma is a framework that is used to identify inefficiency so that the cause of inefficiency will be known and right improvement to overcome cause of inefficiency can be conducted. This paper presents result of implementing six sigma to improve piston assembly line in Manufacturing Laboratory, Universitas Indonesia. Six sigma framework will be used to analyze the significant factor of inefficiency that needs to be improved which causes bottleneck in assembly line. After analysis based on six sigma framework conducted, line balancing method was chosen for improvement to overcome causative factor of inefficiency which is differences time between workstation that causes bottleneck in assembly line. Then after line balancing conducted in piston assembly line, the result is increase in efficiency. Efficiency is shown in the decreasing of Defects per Million Opportunities (DPMO) from 900,000 to 700,000, the increasing of level of labor productivity from 0.0041 to 0.00742, the decreasing of idle time from 121.3 seconds to 12.1 seconds, and the increasing of output, which is from 1 piston in 5 minutes become 3 pistons in 5 minutes.

Keywords: Assembly line, efficiency, improvement, line balancing, productivity, six sigma, workstation.

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Authors: Karim Heidari

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Semantic Web Technologies enable machines to interpret data published in a machine-interpretable form on the web. At the present time, only human beings are able to understand the product information published online. The emerging semantic Web technologies have the potential to deeply influence the further development of the Internet Economy. In this paper we propose a scenario based research approach to predict the effects of these new technologies on electronic markets and business models of traders and intermediaries and customers. Over 300 million searches are conducted everyday on the Internet by people trying to find what they need. A majority of these searches are in the domain of consumer ecommerce, where a web user is looking for something to buy. This represents a huge cost in terms of people hours and an enormous drain of resources. Agent enabled semantic search will have a dramatic impact on the precision of these searches. It will reduce and possibly eliminate information asymmetry where a better informed buyer gets the best value. By impacting this key determinant of market prices semantic web will foster the evolution of different business and economic models. We submit that there is a need for developing these futuristic models based on our current understanding of e-commerce models and nascent semantic web technologies. We believe these business models will encourage mainstream web developers and businesses to join the “semantic web revolution."

Keywords: E-Commerce, E-Business, Semantic Web, XML.

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Authors: Mamoun Sqali, Mohamed Wassim Trojet

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The requirements analysis, modeling, and simulation have consistently been one of the main challenges during the development of complex systems. The scenarios and the state machines are two successful models to describe the behavior of an interactive system. The scenarios represent examples of system execution in the form of sequences of messages exchanged between objects and are a partial view of the system. In contrast, state machines can represent the overall system behavior. The automation of processing scenarios in the state machines provide some answers to various problems such as system behavior validation and scenarios consistency checking. In this paper, we propose a method for translating scenarios in state machines represented by Discreet EVent Specification and procedure to detect implied scenarios. Each induced DEVS model represents the behavior of an object of the system. The global system behavior is described by coupling the atomic DEVS models and validated through simulation. We improve the validation process with integrating formal methods to eliminate logical inconsistencies in the global model. For that end, we use the Z notation.

Keywords: Scenarios, DEVS, synthesis, validation and verification, simulation, formal verification, z notation.

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Authors: Hazem M. El-Bakry, Ahmed A. Mohammed

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In this paper, an intelligent algorithm for optimal document archiving is presented. It is kown that electronic archives are very important for information system management. Minimizing the size of the stored data in electronic archive is a main issue to reduce the physical storage area. Here, the effect of different types of Arabic fonts on electronic archives size is discussed. Simulation results show that PDF is the best file format for storage of the Arabic documents in electronic archive. Furthermore, fast information detection in a given PDF file is introduced. Such approach uses fast neural networks (FNNs) implemented in the frequency domain. The operation of these networks relies on performing cross correlation in the frequency domain rather than spatial one. It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations.

Keywords: Information Storage and Retrieval, Electronic Archiving, Fast Information Detection, Cross Correlation, Frequency Domain.

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Authors: Prakash Devale, R. S. Prasad, Amol Dhumane, Pritesh Patil

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Now a days video data embedding approach is a very challenging and interesting task towards keeping real time video data secure. We can implement and use this technique with high-level applications. As the rate-distortion of any image is not confirmed, because the gain provided by accurate image frame segmentation are balanced by the inefficiency of coding objects of arbitrary shape, with a lot factors like losses that depend on both the coding scheme and the object structure. By using rate controller in association with the encoder one can dynamically adjust the target bitrate. This paper discusses about to keep secure videos by mixing signature data with negligible distortion in the original video, and to keep steganographic video as closely as possible to the quality of the original video. In this discussion we propose the method for embedding the signature data into separate video frames by the use of block Discrete Cosine Transform. These frames are then encoded by real time encoding H.264 scheme concepts. After processing, at receiver end recovery of original video and the signature data is proposed.

Keywords: Data Hiding, Digital Watermarking, video coding H.264, Rate Control, Block DCT.

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Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

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With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of human, vehicle, roadside infrastructure and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the paper proposes a distributed multi-agent C-ITS. The system consists of Roadside Subsystem, Vehicle Subsystem and Personal Subsystem. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: Distributed system, artificial intelligence, multi-agent, Cooperative Intelligent Transportation System.

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Authors: G. K. Pandey, D. Ramadasu, I. Banerjee, V. Vinod, G. Padmakumar, V. Prakash, K. K. Rajan

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Detailed thermal hydraulic investigations are very  essential for safe and reliable functioning of liquid metal cooled fast  breeder reactors. These investigations are further more important for  components with complex profile, since there is no direct correlation  available in literature to evaluate the hydraulic characteristics of such  components directly. In those cases available correlations for similar  profile or geometries may lead to significant uncertainty in the  outcome. Hence experimental approach can be adopted to evaluate  these hydraulic characteristics more precisely for better prediction in  reactor core components.  Prototype Fast Breeder Reactor (PFBR), a sodium cooled pool  type reactor is under advanced stage of construction at Kalpakkam,  India. Several components of this reactor core require hydraulic  investigation before its usage in the reactor. These hydraulic  investigations on full scale models, carried out by experimental  approaches using water as simulant fluid are discussed in the paper. 

Keywords: Fast Breeder Reactor, Cavitation, pressure drop, Reactor components.

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Authors: Panagiotis I. Mallios, Vassilis S. Moustakis

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The research reported herein presents a conceptual model that explores the relationship between social media factors and entrepreneurial intention, with a focus on the Greek startup ecosystem. The significance of the study is that social media gained importance in explaining the entrepreneurial process, and through them, nascent and potential entrepreneurs seem to get inspired and motivated to initiate their businesses. The research methodology employed in this study included a qualitative research approach, utilizing in-depth interviews with a sample of 15 startup entrepreneurs providing valuable retrospective information. The data collected were analyzed using the content analysis method. The major findings of the study are that social media factors such as usefulness, influence, and credibility have a significant impact on entrepreneurial intention. We also found that social media can be a powerful tool for entrepreneurs to access resources, knowledge and networks that can help them in their venture creation. Overall, this research contributes to the entrepreneurship literature by uncovering the relationship between social media factors and entrepreneurial intention and has implications for entrepreneurial education, policymakers, and official partners, highlighting the potential of social media to enhance the startup ecosystem.

Keywords: Entrepreneurial intention, entrepreneurship, social media, startup ecosystem.

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Authors: N. Moradian, E. Timofeev, R. Tahir

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In this work, startability of the Busemann intake family with weak/strong conical shock, as most efficient intakes, via overboard mass spillage method is theoretically analyzed. Masterix and Candifix codes are used to numerically simulate few models of this type of intake and verify the theoretical results. Portions of the intake corresponding to various flow capture angles are considered to have mass spillage in the starting process of this intake. This approach allows for overboard mass spillage via a V-shaped slot with the tip of V coinciding with the focal point of the Busemann flow. The theoretical results, achieved using two different theories, of self-started Busemann takes with weak/strong conical shock show that significant improve in intake startability using overboard spillage technique. The starting phenomena of Busemann intakes with weak conical shock and seven different capture angles are numerically simulated at freestream Mach number of 3 to find the minimum area ratios of self-started intakes. The numerical results confirm the theoretical ones achieved by authors.

Keywords: Busemann intake, conical shock, overboard spillage, startability.

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Authors: M. Ismail, Cao Yihua, Zhao Ming, Abu Bakar

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Heavy rainfall greatly affects the aerodynamic performance of the aircraft. There are many accidents of aircraft caused by aerodynamic efficiency degradation by heavy rain. In this Paper we have studied the heavy rain effects on the aerodynamic efficiency of cambered NACA 64-210 and symmetric NACA 0012 airfoils. Our results show significant increase in drag and decrease in lift. We used preprocessing software gridgen for creation of geometry and mesh, used fluent as solver and techplot as postprocessor. Discrete phase modeling called DPM is used to model the rain particles using two phase flow approach. The rain particles are assumed to be inert. Both airfoils showed significant decrease in lift and increase in drag in simulated rain environment. The most significant difference between these two airfoils was the NACA 64-210 more sensitivity than NACA 0012 to liquid water content (LWC). We believe that the results showed in this paper will be useful for the designer of the commercial aircrafts and UAVs, and will be helpful for training of the pilots to control the airplanes in heavy rain.

Keywords: airfoil, discrete phase modeling, heavy rain, Reynolds

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Authors: A. R. Seifi

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This work presents a new algorithm based on a combination of fuzzy (FUZ), Dynamic Programming (DP), and Genetic Algorithm (GA) approach for capacitor allocation in distribution feeders. The problem formulation considers two distinct objectives related to total cost of power loss and total cost of capacitors including the purchase and installation costs. The novel formulation is a multi-objective and non-differentiable optimization problem. The proposed method of this article uses fuzzy reasoning for sitting of capacitors in radial distribution feeders, DP for sizing and finally GA for finding the optimum shape of membership functions which are used in fuzzy reasoning stage. The proposed method has been implemented in a software package and its effectiveness has been verified through a 9-bus radial distribution feeder for the sake of conclusions supports. A comparison has been done among the proposed method of this paper and similar methods in other research works that shows the effectiveness of the proposed method of this paper for solving optimum capacitor planning problem.

Keywords: Capacitor planning, Fuzzy logic method, Genetic Algorithm, Dynamic programming, Radial Distribution feeder

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Authors: Yasaswi Palagummi, Sareh Rowlands

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Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GZSL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets - AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models.

Keywords: Generalised Zero-shot Learning, Inductive Learning, Shifted-Window Attention, Swin Transformer, Vision Transformer.

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Authors: Vishal Awasthi, Atul Kumar Agnihotri

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Face is a non-intrusive strong biometrics for identification of original and dummy facial by different artificial means. Face recognition is extremely important in the contexts of computer vision, psychology, surveillance, pattern recognition, neural network, content based video processing. The availability of a widespread face database is crucial to test the performance of these face recognition algorithms. The openly available face databases include face images with a wide range of poses, illumination, gestures and face occlusions but there is no dummy face database accessible in public domain. This paper presents a face detection algorithm based on the image segmentation in terms of distance from a fixed point and template matching methods. This proposed work is having the most appropriate number of nodal points resulting in most appropriate outcomes in terms of face recognition and detection. The time taken to identify and extract distinctive facial features is improved in the range of 90 to 110 sec. with the increment of efficiency by 3%.

Keywords: Face recognition, Principal Component Analysis, PCA, Linear Discriminant Analysis, LDA, Improved Support Vector Machine, iSVM, elastic bunch mapping technique.

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Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim

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Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.

Keywords: Control chart pattern recognition, Multi-resolution wavelet analysis, Bi-directional Kohonen network, Back-propagation network, Feature extraction.

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Zuriati Ismail, Naomie Salim

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Seeking and sharing knowledge on online forums have made them popular in recent years. Although online forums are valuable sources of information, due to variety of sources of messages, retrieving reliable threads with high quality content is an issue. Majority of the existing information retrieval systems ignore the quality of retrieved documents, particularly, in the field of thread retrieval. In this research, we present an approach that employs various quality features in order to investigate the quality of retrieved threads. Different aspects of content quality, including completeness, comprehensiveness, and politeness, are assessed using these features, which lead to finding not only textual, but also conceptual relevant threads for a user query within a forum. To analyse the influence of the features, we used an adopted version of voting model thread search as a retrieval system. We equipped it with each feature solely and also various combinations of features in turn during multiple runs. The results show that incorporating the quality features enhances the effectiveness of the utilised retrieval system significantly.

Keywords: Content quality, Forum search, Thread retrieval, Voting techniques.

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Authors: Divya Agarwal, Pushpendra S. Bharti

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Autonomous mobile robots (AMR) are expected as smart tools for operations in every automation industry. Path planning and obstacle avoidance is the backbone of AMR as robots have to reach their goal location avoiding obstacles while traversing through optimized path defined according to some criteria such as distance, time or energy. Path planning can be classified into global and local path planning where environmental information is known and unknown/partially known, respectively. A number of sensors are used for data collection. A number of algorithms such as artificial potential field (APF), rapidly exploring random trees (RRT), bidirectional RRT, Fuzzy approach, Purepursuit, A* algorithm, vector field histogram (VFH) and modified local path planning algorithm, etc. have been used in the last three decades for path planning and obstacle avoidance for AMR. This paper makes an attempt to review some of the path planning and obstacle avoidance algorithms used in the field of AMR. The review includes comparative analysis of simulation and mathematical computations of path planning and obstacle avoidance algorithms using MATLAB 2018a. From the review, it could be concluded that different algorithms may complete the same task (i.e. with a different set of instructions) in less or more time, space, effort, etc.

Keywords: Autonomous mobile robots, obstacle avoidance, path planning, and processing time.

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Authors: Kazeem A. Adebiyi

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Despite many success stories of manufacturing safety, many organizations are still reluctant, perceiving it as cost increasing and time consuming. The clear contributor may be due to the use of lagging indicators rather than leading indicator measures. The study therefore proposes a combinatorial model for determining the best safety strategy. A combination theory and cost benefit analysis was employed to develop a monetary saving / loss function in terms value of preventions and cost of prevention strategy. Documentations, interviews and structured questionnaire were employed to collect information on Before-And-After safety programme records from a Tobacco company between periods of 1993-2001(for pre-safety) and 2002-2008 (safety period) for the model application. Three combinatorial alternatives A, B, C were obtained resulting into 4, 6 and 4 strategies respectively with PPE and Training being predominant. A total of 728 accidents were recorded for a 9 year period of pre-safety programme and 163 accidents were recorded for 7 years period of safety programme. Six preventions activities (alternative B) yielded the best results. However, all the years of operation experienced except year 2004. The study provides a leading resources for planning successful safety programme

Keywords: Combination, Manufacturing Safety, Monetary Savings, Prevention Strategies.

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Authors: A. Khosravi, S. Ghadirian, J. S. McCartney

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Evaluation of the seismic-induced settlement of an unsaturated soil layer depends on several variables, among which the small strain shear modulus, G_max, and soil’s state of stress have been demonstrated to be of particular significance. Recent interpretation of trends in G_max revealed considerable effects of the degree of saturation and hydraulic hysteresis on the shear stiffness of soils in unsaturated states. Accordingly, the soil layer is expected to experience different settlement behaviors depending on the soil saturation and seasonal weathering conditions. In this study, a semi-empirical formulation was adapted to extend an existing G_max model to infer hysteretic effects along different paths of the SWRC including scanning curves. The suitability of the proposed approach is validated against experimental results from a suction-controlled resonant column test and from data reported in literature. The model was observed to follow the experimental data along different paths of the SWRC, and showed a slight hysteresis in shear modulus along the scanning curves.

Keywords: Hydraulic hysteresis, Scanning path, Small strain shear modulus, Unsaturated soil.

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Authors: Ja'far Alqatawna, Jawed Siddiqi, Babak Akhgar, Mohammad Hjouj Btoush

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A great deal of research works in the field information systems security has been based on a positivist paradigm. Applying the reductionism concept of the positivist paradigm for information security means missing the bigger picture and thus, the lack of holism which could be one of the reasons why security is still overlooked, comes as an afterthought or perceived from a purely technical dimension. We need to reshape our thinking and attitudes towards security especially in a complex and dynamic environment such as e- Business to develop a holistic understanding of e-Business security in relation to its context as well as considering all the stakeholders in the problem area. In this paper we argue the suitability and need for more inductive interpretive approach and qualitative research method to investigate e-Business security. Our discussion is based on a holistic framework of enquiry, nature of the research problem, the underling theoretical lens and the complexity of e-Business environment. At the end we present a research strategy for developing a holistic framework for understanding of e-Business security problems in the context of developing countries based on an interdisciplinary inquiry which considers their needs and requirements.

Keywords: e-Business Security, Complexity, Methodological considerations, interpretive qualitative research and Case study method.

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Authors: Md. Zahangir Alom, Mei-Lan Piao, Md. Ashraful Alam, Nam Kim, Jae-Hyeung Park

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A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.

Keywords: Aw-SpPC, Expressoin Recognition, Face context, Face Detection, PCA

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Authors: Carole A. El Raheb, Hassan K. Abdel-Salam, Ingi Elcherif

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An architect carrying the design process alone is the main reason for the deterioration of the quality of the architectural product as the complexity of the projects makes it a multi-disciplinary work; then, the Integrative Design Process (IDP) must be applied in the architectural firm especially from the early design phases to improve the product’s quality and to eliminate the ignorance of the principles of design causing the occurrence of low-grade buildings. The research explores the Integrative Design (ID) principles that fit in the architectural practice. Constraints facing this application are presented with strategies and solutions to overcome them. A survey questionnaire was conducted to collect data from a number of recognized Egyptian Architecture, Engineering and Construction (AEC) firms that explores their opinions on using the IDP. This survey emphasizes the importance of the IDP in firms and presents the reasons preventing the firms from applying the IDP. The aim here is to investigate the potentials of integrating this approach into architectural firms emphasizing the importance of this application which ensures the realization of the project’s goal and eliminates the reduction in the project’s quality.

Keywords: Application, architectural firms, integrative design principles, integrative design process, the project quality.

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Authors: Aleksandar Zhelyazkov, Daniele Zonta, Helmut Wenzel, Peter Furtner

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In the current research structural health monitoring is considered for addressing the critical issue of post-earthquake damage detection. A non-standard approach for damage detection via acoustic emission is presented - acoustic emissions are monitored in the low frequency range (up to 120 Hz). Such emissions are termed high-frequency transients. Further a damage indicator defined as the Time-Ratio Damage Indicator is introduced. The indicator relies on time-instance measurements of damage initiation and deformation peaks. Based on the time-instance measurements a procedure for estimation of the maximum drift ratio is proposed. Monitoring data is used from a shaking-table test of a full-scale reinforced concrete bridge pier. Damage of the experimental column is successfully detected and the proposed damage indicator is calculated.

Keywords: Acoustic emission, Damage detection, Shaking table test, Structural health monitoring, High-frequency transients.

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Authors: Angela Unna Chukwu, Samuel Oluwafemi Oyamakin

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We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a shape parameter (allometric). This was achieved by convoluting hyperbolic sine function on the intrinsic rate of growth in the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while the independence of the error term was confirmed using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE and AIC confirmed the predictive power of the Hyperbolic Gompertz growth models over its source model.

Keywords: Height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz.

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Authors: Umair Shafique, Hafiz Usman Zia, Fiaz Majeed, Samina Naz, Javeria Ahmed, Maleeha Zainab

Abstract:

The Internet of Things (IoT) has become a hot topic for the last couple of years. This innovative technology has shown promising progress in various areas and the world has witnessed exponential growth in multiple application domains. Researchers are working to investigate its aptitudes to get the best from it by harnessing its true potential. But at the same time, IoT networks open up a new aspect of vulnerability and physical threats to data integrity, privacy, and confidentiality. It is due to centralized control, data silos approach for handling information, and a lack of standardization in the IoT networks. As we know, blockchain is a new technology that involves creating secure distributed ledgers to store and communicate data. Some of the benefits include resiliency, integrity, anonymity, decentralization, and autonomous control. The potential for blockchain technology to provide the key to managing and controlling IoT has created a new wave of excitement around the idea of putting that data back into the hands of the end-users. In this manuscript, we have proposed a model that combines blockchain and IoT networks to address potential security and privacy issues in the healthcare domain and how various stakeholders will interact with the system.

Keywords: Internet of Things, IoT, blockchain, data integrity, authentication, data privacy.

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Authors: Sajad Farokhi, Siti Mariyam Shamsuddin, Jan Flusser, Usman Ullah Sheikh

Abstract:

Although face recognition seems as an easy task for human, automatic face recognition is a much more challenging task due to variations in time, illumination and pose. In this paper, the influence of time-lapse on visible and thermal images is examined. Orthogonal moment invariants are used as a feature extractor to analyze the effect of time-lapse on thermal and visible images and the results are compared with conventional Principal Component Analysis (PCA). A new triangle square ratio criterion is employed instead of Euclidean distance to enhance the performance of nearest neighbor classifier. The results of this study indicate that the ideal feature vectors can be represented with high discrimination power due to the global characteristic of orthogonal moment invariants. Moreover, the effect of time-lapse has been decreasing and enhancing the accuracy of face recognition considerably in comparison with PCA. Furthermore, our experimental results based on moment invariant and triangle square ratio criterion show that the proposed approach achieves on average 13.6% higher in recognition rate than PCA.

Keywords: Infrared Face recognition, Time-lapse, Zernike moment invariants

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