Search results for: automated programming

Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Jeffrey Ansloos

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International trauma education in low and emerging economies requires innovative methods for capacity building in existing social service infrastructures. This study details the findings of a program evaluation used to assess the learning outcomes and community impact of an international trauma-focused graduate degree program in Guyana. Through a collaborative partnership between Lesley University, the Government of Guyana, and UNICEF, a 2-year low-residency masters degree graduate program in trauma-focused assessment, intervention, and treatment was piloted with a cohort of Guyanese mental health professionals. Through an analytical review of the program development, as well as qualitative data analysis of participant interviews and focus-groups, this study will address the efficacy of the programming in terms of preparedness of professionals to understand, evaluate and implement trauma-informed practices across various child, youth, and family mental health service settings. Strengths and limitations of this international trauma-education delivery model will be discussed with particular emphasis on the role of capacity-building interventions, community-based participatory curriculum development, innovative technological delivery platforms, and interdisciplinary education. Implications for further research and subsequent program development will be discussed.

Keywords: mental health promotion, global health promotion, trauma education, innovations in education, child, youth, mental health education

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Authors: Ya-Ju Ju, Li-Chen Yang, Yi-Chun Du, Rong-Ju Cherng

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Introduction: It is estimated that 5-6% of school-aged children may be diagnosed to have developmental coordination disorder (DCD). Children with DCD are characterized with motor skill difficulty which cannot be explained by any medical or intellectual reasons. Such motor difficulties limit children’s participation to sports activity, further affect their physical fitness, cardiopulmonary function and balance, and may lead to obesity. The purpose of the project was to develop an exergaming system for children with DCD aiming to improve their physical fitness, cardiopulmonary function and balance ability. Methods: This study took five steps to build up the system: system planning, tasks selection, tasks programming, system integration and usability test. The system basically adopted virtual reality technique to integrate self-developed training programs. The training programs were developed to brainstorm among team members and after literature review. The selected tasks for training in the system were a combination of fundamental movement tor skill. Results and Discussion: Based on the theory of motor development, we design the training task from easy ones to hard ones, from single tasks to dual tasks. The tasks included walking, sit to stand, jumping, kicking, weight shifting, side jumping and their combination. Preliminary study showed that the tasks presented an order of development. Further study is needed to examine its effect on motor skill and cardiovascular fitness in children with DCD.

Keywords: virtual reality, virtual reality system, developmental coordination disorder, children

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Adam Jenkins

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The Maritime RobotX Challenge provides a platform for postgraduate students conducting research in autonomous robotic systems to participate in an international competition. Although targeted to postgraduate students, the problem domain lends itself to a wide range of different levels of student expertise. In 2022, undergraduate Information Technology students from the University of South Australia undertook the challenge, utilizing a System of the Systems approach to the project's architecture. Each student group produced an independent solution to an identified task, which was then implemented on a Single Board Computer (SBC). A Central Control System then engaged each solution when appropriate, allowing the encapsulated SBC systems to manage each task as it was encountered. This approach facilitated collaboration among the multiple independent student teams over an 18-month period, and the fundamental system-agnostic architecture allowed for both the variance in student solutions and the limitations caused by the global electronics shortage. By adopting this approach, Information Technology teams were able to work independently yet produce an effective solution, leveraging their expertise to develop and construct an autonomous catamaran capable of meeting the competition's demanding requirements while producing a high level of engagement. The System of Systems approach is recommended to other universities interested in competing at this level and engaging students in a real-world problem.

Keywords: case study, robotics, education, programming, system of systems, multi-disciplinary collaboration

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Authors: Khyamling Parane, B. M. Prabhu Prasad, Basavaraj Talawar

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Fast simulations are critical in reducing time to market in CMPs and SoCs. Several simulators have been used to evaluate the performance and power consumed by Network-on-Chips. Researchers and designers rely upon these simulators for design space exploration of NoC architectures. Our experiments show that simulating large NoC topologies take hours to several days for completion. To speed up the simulations, it is necessary to investigate and optimize the hotspots in simulator source code. Among several simulators available, we choose Booksim2.0, as it is being extensively used in the NoC community. In this paper, we analyze the cache and memory system behaviour of Booksim2.0 to accurately monitor input dependent performance bottlenecks. Our measurements show that cache and memory usage patterns vary widely based on the input parameters given to Booksim2.0. Based on these measurements, the cache configuration having least misses has been identified. To further reduce the cache misses, we use software optimization techniques such as removal of unused functions, loop interchanging and replacing post-increment operator with pre-increment operator for non-primitive data types. The cache misses were reduced by 18.52%, 5.34% and 3.91% by employing above technology respectively. We also employ thread parallelization and vectorization to improve the overall performance of Booksim2.0. The OpenMP programming model and SIMD are used for parallelizing and vectorizing the more time-consuming portions of Booksim2.0. Speedups of 2.93x and 3.97x were observed for the Mesh topology with 30 × 30 network size by employing thread parallelization and vectorization respectively.

Keywords: cache behaviour, network-on-chip, performance profiling, vectorization

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Authors: Dominic Huschke, Rudolf Keil

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New driving functionalities like highly automated driving have a major impact on the electrics/electronics architecture of future vehicles and inevitably lead to higher safety requirements. Partly due to these increased requirements, the vehicle industry is increasingly looking at semiconductor switches as an alternative to conventional melting fuses. The protective functionality of semiconductor switches can be implemented in hardware as well as in software. A current approach discussed in science and industry is the implementation of a model of the protected low voltage power cable on a microcontroller to calculate its temperature. Here, the information regarding the current is provided by the continuous current measurement of the semiconductor switch. The signal to open the semiconductor switch is provided by the microcontroller when a previously defined limit for the temperature of the low voltage power cable is exceeded. A setup for the testing of the described principle for electronic fuse protection of a low voltage power cable is built and successfullyvalidated with experiments afterwards. Here, the evaluation criterion is the deviation of the measured temperature of the low voltage power cable from the specified limit temperature when the semiconductor switch is opened. The analysis is carried out with an assumed ambient temperature as well as with a measured ambient temperature. Subsequently, the experimentally performed investigations are simulated in a virtual environment. The explicit focus is on the simulation of the behavior of the microcontroller with an implemented model of a low voltage power cable in a real-time environment. Subsequently, the generated results are compared with those of the experiments. Based on this, the completely virtual design of the described approach is assumed to be valid.

Keywords: automotive wire harness, electronic fuse protection, low voltage power cable, semiconductor-based fuses, software-based validation

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Authors: S. Boria

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In the last years, the crashworthiness of an automotive body structure can be improved, since the beginning of the design stage, thanks to the development of specific optimization tools. It is well known how the finite element codes can help the designer to investigate the crashing performance of structures under dynamic impact. Therefore, by coupling nonlinear mathematical programming procedure and statistical techniques with FE simulations, it is possible to optimize the design with reduced number of analytical evaluations. In engineering applications, many optimization methods which are based on statistical techniques and utilize estimated models, called meta-models, are quickly spreading. A meta-model is an approximation of a detailed simulation model based on a dataset of input, identified by the design of experiments (DOE); the number of simulations needed to build it depends on the number of variables. Among the various types of meta-modeling techniques, Kriging method seems to be excellent in accuracy, robustness and efficiency compared to other ones when applied to crashworthiness optimization. Therefore the application of such meta-model was used in this work, in order to improve the structural optimization of a bumper for a racing car in composite material subjected to frontal impact. The specific energy absorption represents the objective function to maximize and the geometrical parameters subjected to some design constraints are the design variables. LS-DYNA codes were interfaced with LS-OPT tool in order to find the optimized solution, through the use of a domain reduction strategy. With the use of the Kriging meta-model the crashworthiness characteristic of the composite bumper was improved.

Keywords: composite material, crashworthiness, finite element analysis, optimization

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Authors: S. J. Shahidzadeh Tabatabaei, A. M. Fattahi

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Modal analysis of an FGM plate composed of Al2O3 ceramic phase and 304 stainless steel metal phases was performed in this paper by ABAQUS software with the assumption that the behavior of material is elastic and mechanical properties (Young's modulus and density) are variable in the thickness direction of the plate. Therefore, a sub-program was written in FORTRAN programming language and was linked with ABAQUS software. For modal analysis, a finite element analysis was carried out similar to the model of other researchers and the accuracy of results was evaluated after comparing the results. Comparison of natural frequencies and mode shapes reflected the compatibility of results and optimal performance of the program written in FORTRAN as well as high accuracy of finite element model used in this research. After validation of the results, it was evaluated the effect of material (n parameter) on the natural frequency. In this regard, finite element analysis was carried out for different values of n and in simply supported mode. About the effect of n parameter that indicates the effect of material on the natural frequency, it was observed that the natural frequency decreased as n increased; because by increasing n, the share of ceramic phase on FGM plate has decreased and the share of steel phase has increased and this led to reducing stiffness of FGM plate and thereby reduce in the natural frequency. That is because the Young's modulus of Al2O3 ceramic is equal to 380 GPa and Young's modulus of SUS304 steel is 207 GPa.

Keywords: FGM plates, modal analysis, natural frequency, finite element method

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Sarka Langer, Despoina Teli, Blanka Cabovska, Jan-Olof Dalenbäck, Lars Ekberg, Gabriel Bekö, Pawel Wargocki, Natalia Giraldo Vasquez

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The aim of the study was to investigate the impact of various ventilation systems on indoor climate, air pollution, chemistry, and perception. Measurements of thermal environment and indoor air quality were performed in 45 primary school classrooms in Gothenburg, Sweden. The classrooms were grouped into three categories according to their ventilation system: category A) natural or exhaust ventilation or automated window opening; category B) balanced mechanical ventilation systems with constant air volume (CAV); and category C) balanced mechanical ventilation systems with variable air volume (VAV). A questionnaire survey about indoor air quality, perception of temperature, odour, noise and light, and sensation of well-being, alertness focus, etc., was distributed among the 10-12 years old children attending the classrooms. The results (medians) showed statistically significant differences between ventilation category A and categories B and C, but not between categories B and C in air change rates, median concentrations of carbon dioxide, individual volatile organic compounds formaldehyde and isoprene, in-door-to-outdoor ozone ratios and products of ozonolysis of squalene, a constituent of human skin oils, 6-methyl-5-hepten-2-one and decanal. Median ozone concentration, ozone loss -a difference between outdoor and indoor ozone concentrations- were different only between categories A and C. Median concentration of total VOCs and a perception index based on survey responses on perceptions and sensations indoors were not significantly different. In conclusion, ventilation systems have an impact on air change rates, indoor air quality, and chemistry, but the Swedish primary school children’s perception did not differ with the ventilation systems of the classrooms.

Keywords: indoor air pollutants, indoor climate, indoor chemistry, air change rate, perception

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Authors: Maria Angela Bedini, Fabio Bronzini

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Most of the Italian territory is at seismic risk. Many earthquakes have hit Italy, and devastating effects have been generated. The specific objective of the research is to distinguish the negative approaches that have generated unacceptable social situations of marginalization, abandonment, and economic regression, from positive methodological approaches. On the basis of the different situations examined, the study proposes strategies and guidelines to obtain the best possible results, in Italy or abroad, in the event of new earthquakes. At national and international level, many theoretical studies address the aspects of prevention, while the comparisons, carried out in this study, between the techniques and the operative procedures applied and the results obtained are rare. The adopted methodology compares the different pre-earthquake urban-planning approaches, for the emergency (temporary urban planning), and for the post-earthquake (socio-economic-territorial processes) in Italy. Attention is placed on the current consolidated planning and programming acquisitions, pre and post-earthquake. The main results of the study concern the prospects in Italy of protection from seismic risks in the next decades. An integrated settlement system for a new economic and social model, aimed at the rebirth of territories in crisis, is proposed. Finally, the conclusions describe the disciplinary positions, procedures and the fundamental points generally shared by the scientific community for each approach, in order to identify the strategic choices and the disciplinary and management paths that will be followed in the coming decades.

Keywords: post-earthquake, seismic emergency, seismic prevention, urban planning interventions in Italy

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Authors: Farhat Jabeen, Syed Asfaq Hussain Bukhari

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In the wake of the contemporary period the basic objective of the research paper points out that socio-cultural scenario of Pakistan reveals that gender-based violence is deep rooted in the society irrespective of language and ethnicity. This paper would reconnaissance the possibility reforms in Pakistan for diminishing of violence. Women are not given their due role, rights, and respect. Furthermore, they are treated as chattels. This presentation will cover the socio-customary practices in the context of discrimination, stigmatization, and violence against women. This paper envisages justice in a broader sense of recognition of rights for women, and masculine structure of society, socio-customary practices and discrimination against women are a very serious concern which needs to be understood as a multidimensional problem. The paper will specially focus on understanding the existing obstacles of women in Pakistan in the constitutional scenario. Women stumble across discrimination and human rights manipulations, voluptuous violation and manipulation including domestic viciousness and are disadvantaged by laws, strategies, and programming that do not take their concerns into considerations. This presentation examines the role of honour killings among Pakistani community. This affects their self-assurance and capability to elevation integrity campaign where gender inequalities and discrimination in social, legal domain are to be put right. This paper brings to light the range of practices, laws and legal justice regarding the status of women and also covers attitude towards compensations for murders/killings, domestic violence, rape, adultery, social behavior and recourse to justice.

Keywords: discrimination, cultural, women, violence

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Authors: Francys Souza, Alberto Ohashi, Dorival Leao

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We present a general solution for finding the ε-optimal controls for non-Markovian stochastic systems as stochastic differential equations driven by Brownian motion, which is a problem recognized as a difficult solution. The contribution appears in the development of mathematical tools to deal with modeling and control of non-Markovian systems, whose applicability in different areas is well known. The methodology used consists to discretize the problem through a random discretization. In this way, we transform an infinite dimensional problem in a finite dimensional, thereafter we use measurable selection arguments, to find a control on an explicit form for the discretized problem. Then, we prove the control found for the discretized problem is a ε-optimal control for the original problem. Our theory provides a concrete description of a rather general class, among the principals, we can highlight financial problems such as portfolio control, hedging, super-hedging, pairs-trading and others. Therefore, our main contribution is the development of a tool to explicitly the ε-optimal control for non-Markovian stochastic systems. The pathwise analysis was made through a random discretization jointly with measurable selection arguments, has provided us with a structure to transform an infinite dimensional problem into a finite dimensional. The theory is applied to stochastic control problems based on path-dependent stochastic differential equations, where both drift and diffusion components are controlled. We are able to explicitly show optimal control with our method.

Keywords: dynamic programming equation, optimal control, stochastic control, stochastic differential equation

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Authors: Ming-Jong Yao, Chin-Sum Shui, Chih-Han Wang

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This paper is developed based on a real-world decision scenario that an industrial gas company that applies the Vendor Managed Inventory model and supplies liquid oxygen with a self-operated heterogeneous vehicle fleet to hospitals in nearby cities. We name it as a Joint Replenishment and Heterogeneous Vehicle Routing Problem with Cyclical Schedule and formulate it as a non-linear mixed-integer linear programming problem which simultaneously determines the length of the planning cycle (PC), the length of the replenishment cycle and the dates of replenishment for each customer and the vehicle routes of each day within PC, such that the average daily operation cost within PC, including inventory holding cost, setup cost, transportation cost, and overtime labor cost, is minimized. A solution method based on genetic algorithm, embedded with an encoding and decoding mechanism and local search operators, is then proposed, and the hash function is adopted to avoid repetitive fitness evaluation for identical solutions. Numerical experiments demonstrate that the proposed solution method can effectively solve the problem under different lengths of PC and number of customers. The method is also shown to be effective in determining whether the company should expand the storage capacity of a customer whose demand increases. Sensitivity analysis of the vehicle fleet composition shows that deploying a mixed fleet can reduce the daily operating cost.

Keywords: cyclic inventory routing problem, joint replenishment, heterogeneous vehicle, genetic algorithm

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Authors: Edison A. Bonifaz

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In this work, a numerical procedure is proposed to design dense multi-material structures using the Directed Energy Deposition (DED) process. A thermo-mechanical finite element model to predict thermal cycles and residual stresses is presented. A numerical layer build-up procedure coupled with a moving heat flux was constructed to minimize strains and residual stresses that result in the multi-layer deposition of an AISI 316 austenitic steel on an AISI 304 austenitic steel substrate. To simulate the DED process, the automated interface of the ABAQUS AM module was used to define element activation and heat input event data as a function of time and position. Of this manner, the construction of ABAQUS user-defined subroutines was not necessary. Thermal cycles and thermally induced stresses created during the multi-layer deposition metal AM pool crystallization were predicted and validated. Results were analyzed in three independent metal layers of three different experiments. The one-way heat and material deposition toolpath used in the analysis was created with a MatLab path script. An optimal combination of feedstock and heat input printing parameters suitable for fabricating multi-material dense structures in the directed energy deposition metal AM process was established. At constant power, it can be concluded that the lower the heat input, the lower the peak temperatures and residual stresses. It means that from a design point of view, the one-way heat and material deposition processing toolpath with the higher welding speed should be selected.

Keywords: event series, thermal cycles, residual stresses, multi-pass welding, abaqus am modeler

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Authors: Hamidullah Binol, Abdullah Bal

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Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: food (ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods

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Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

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Authors: Bita Payami-Shabestari, Dariush Eslami

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The purpose of this paper is to develop a multi-product economic production quantity model under vendor management inventory policy and restrictions including limited warehouse space, budget, and number of orders, average shortage time and maximum permissible shortage. Since the “costs” cannot be predicted with certainty, it is assumed that data behave under uncertain environment. The problem is first formulated into the framework of a bi-objective of multi-product economic production quantity model. Then, the problem is solved with three multi-objective decision-making (MODM) methods. Then following this, three methods had been compared on information on the optimal value of the two objective functions and the central processing unit (CPU) time with the statistical analysis method and the multi-attribute decision-making (MADM). The results are compared with statistical analysis method and the MADM. The results of the study demonstrate that augmented-constraint in terms of optimal value of the two objective functions and the CPU time perform better than global criteria, and goal programming. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The contribution of this research is the use of random costs data in developing a multi-product economic production quantity model under vendor management inventory policy with several constraints.

Keywords: economic production quantity, random cost, supply chain management, vendor-managed inventory

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Authors: Rupali, Anil Kumar Jaiswal

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Cloud computing can be defined as one of the prominent technologies that lets a user change, configure and access the services online. it can be said that this is a prototype of computing that helps in saving cost and time of a user practically the use of cloud computing can be found in various fields like education, health, banking etc.  Cloud computing is an internet dependent technology thus it is the major responsibility of Cloud Service Providers(CSPs) to care of data stored by user at data centers. Scheduling in cloud computing environment plays a vital role as to achieve maximum utilization and user satisfaction cloud providers need to schedule resources effectively.  Job scheduling for cloud computing is analyzed in the following work. To complete, recreate the task calculation, and conveyed scheduling methods CloudSim3.0.3 is utilized. This research work discusses the job scheduling for circulated processing condition also by exploring on this issue we find it works with minimum time and less cost. In this work two load balancing techniques have been employed: ‘Throttled stack adjustment policy’ and ‘Active VM load balancing policy’ with two brokerage services ‘Advanced Response Time’ and ‘Reconfigure Dynamically’ to evaluate the VM_Cost, DC_Cost, Response Time, and Data Processing Time. The proposed techniques are compared with Round Robin scheduling policy.

Keywords: physical machines, virtual machines, support for repetition, self-healing, highly scalable programming model

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Authors: Angel G. De Leon Hernandez

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A structure is defined as a physical system or, in certain cases, an arrangement of connected elements, capable of bearing certain loads. The structures are presented in every part of the daily life, e.g., in the designing of buildings, vehicles and mechanisms. The main goal of a structure designer is to develop a secure, aesthetic and maintainable system, considering the constraint imposed to every case. With the advances in the technology during the last decades, the capabilities of solving engineering problems have increased enormously. Nowadays the computers, play a critical roll in the structural analysis, pitifully, for university students the vast majority of these software are inaccessible due to the high complexity and cost they represent, even when the software manufacturers offer student versions. This is exactly the reason why the idea of developing a more reachable and easy-to-use computing tool. This program is designed as a tool for the university students enrolled in courser related to the structures analysis and designs, as a complementary instrument to achieve a better understanding of this area and to avoid all the tedious calculations. Also, the program can be useful for graduated engineers in the field of structural design and analysis. A graphical user interphase is included in the program to make it even simpler to operate it and understand the information requested and the obtained results. In the present document are included the theoretical basics in which the program is based to solve the structural analysis, the logical path followed in order to develop the program, the theoretical results, a discussion about the results and the validation of those results.

Keywords: stiffness matrix method, structural analysis, Matlab® applications, programming

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Authors: A. Sohouli, A. Suleman

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This study focuses on reliability analysis of variable stiffness composite laminate structures to investigate the potential structural improvement compared to conventional (straight fibers) composite laminate structures. A computational framework was developed which it consists of a deterministic design step and reliability analysis. The optimization part is Discrete Material Optimization (DMO) and the reliability of the structure is computed by Monte Carlo Simulation (MCS) after using Stochastic Response Surface Method (SRSM). The design driver in deterministic optimization is the maximum stiffness, while optimization method concerns certain manufacturing constraints to attain industrial relevance. These manufacturing constraints are the change of orientation between adjacent patches cannot be too large and the maximum number of successive plies of a particular fiber orientation should not be too high. Variable stiffness composites may be manufactured by Automated Fiber Machines (AFP) which provides consistent quality with good production rates. However, laps and gaps are the most important challenges to steer fibers that effect on the performance of the structures. In this study, the optimal curved fiber paths at each layer of composites are designed in the first step by DMO, and then the reliability analysis is applied to investigate the sensitivity of the structure with different standard deviations compared to the straight fiber angle composites. The random variables are material properties and loads on the structures. The results show that the variable stiffness composite laminate structures are much more reliable, even for high standard deviation of material properties, than the conventional composite laminate structures. The reason is that the variable stiffness composite laminates allow tailoring stiffness and provide the possibility of adjusting stress and strain distribution favorably in the structures.

Keywords: material optimization, Monte Carlo simulation, reliability analysis, response surface method, variable stiffness composite structures

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Authors: Alaa Sheta

Abstract:

Economic Load Dispatch (ELD) is one of the vital optimization problems in power system planning. Solving the ELD problems mean finding the best mixture of power unit outputs of all members of the power system network such that the total fuel cost is minimized while sustaining operation requirements limits satisfied across the entire dispatch phases. Many optimization techniques were proposed to solve this problem. A famous one is the Quadratic Programming (QP). QP is a very simple and fast method but it still suffer many problem as gradient methods that might trapped at local minimum solutions and cannot handle complex nonlinear functions. Numbers of metaheuristic algorithms were used to solve this problem such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). In this paper, another meta-heuristic search algorithm named Differential Evolution (DE) is used to solve the ELD problem in power systems planning. The practicality of the proposed DE based algorithm is verified for three and six power generator system test cases. The gained results are compared to existing results based on QP, GAs and PSO. The developed results show that differential evolution is superior in obtaining a combination of power loads that fulfill the problem constraints and minimize the total fuel cost. DE found to be fast in converging to the optimal power generation loads and capable of handling the non-linearity of ELD problem. The proposed DE solution is able to minimize the cost of generated power, minimize the total power loss in the transmission and maximize the reliability of the power provided to the customers.

Keywords: economic load dispatch, power systems, optimization, differential evolution

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Authors: Odaleia de O. Farias, Suzelene C. Marinho, Ecleidson B. Fragoso, Daniel S. Lima, Francisco R. S. Lira, Lara S. Araújo, Gabriel dos S. D. Soares

Abstract:

In populous cities, prehospital care services that use vehicles alternative to ambulances are needed in order to reduce costs and improve response time to occurrences in areas with large concentration of people, such as leisure and tourism spaces. In this context, it was implanted a program called BIKE VIDA, that is innovative quick access and assistance program. The aim of this study is to describe the implantation and initial profile of occurrences performed by an urgency/emergency pre-hospital care service through paramedics on bicycles. It is a cross-sectional, descriptive study carried out in the city of Fortaleza, Ceara, Brazil. The data included service records from July to August 2017. Ethical aspects were respected. The service covers a perimeter of 4.5 km, divided into three areas with perimeter of 1.5 km for each paramedic, attending from 5 am to 9 pm. Materials transported by bicycles include External Automated Defibrillator - DEA, portable oxygen, oximeter, cervical collar, stethoscope, sphygmomanometer, dressing and immobilization materials and personal protective equipment. Occurrences are requested directly by calling the emergency number 192 or through direct approach to the professional. In the first month of the program, there were 93 emergencies/urgencies, mainly in the daytime period (71,0%), in males (59,7%), in the age range of 26 to 45 years (46,2%). The main nature was traumatic incidents (53.3%). Most of the cases (88,2%) did not require ambulance transport to the hospital, and there were two deaths. Pre-hospital service through bicycles is an innovative strategy in Brazil and has shown to be promising in terms of reducing costs and improving the quality of the services offered.

Keywords: emergency, response time, prehospital care, urgency

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Authors: Jie Zhang, Qianyu Guo, Tieyi Zhang, Zhiyong Feng, Xiaohong Li

Abstract:

With the widespread popularity of mobile devices and the development of artificial intelligence (AI), deep learning (DL) has been extensively applied in Android apps. Compared with traditional Android apps (traditional apps), deep learning based Android apps (DL-based apps) need to use more third-party application programming interfaces (APIs) to complete complex DL inference tasks. However, existing methods (e.g., FlowDroid) for detecting sensitive information leakage in Android apps cannot be directly used to detect DL-based apps as they are difficult to detect third-party APIs. To solve this problem, we design DLtrace; a new static information flow analysis tool that can effectively recognize third-party APIs. With our proposed trace and detection algorithms, DLtrace can also efficiently detect privacy leaks caused by sensitive APIs in DL-based apps. Moreover, using DLtrace, we summarize the non-sequential characteristics of DL inference tasks in DL-based apps and the specific functionalities provided by DL models for such apps. We propose two formal definitions to deal with the common polymorphism and anonymous inner-class problems in the Android static analyzer. We conducted an empirical assessment with DLtrace on 208 popular DL-based apps in the wild and found that 26.0% of the apps suffered from sensitive information leakage. Furthermore, DLtrace has a more robust performance than FlowDroid in detecting and identifying third-party APIs. The experimental results demonstrate that DLtrace expands FlowDroid in understanding DL-based apps and detecting security issues therein.

Keywords: mobile computing, deep learning apps, sensitive information, static analysis

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Authors: Latif Yanar, Muharrem Kaçan

Abstract:

In this study, an Analytic Hierarchy Process and Analytic Network Process Decision Support System (DSS) model for determination of a navy base place in another country is proposed together with a decision support software (DESTEC 1.0) developed using C Sharp programming language. The proposed software also has the ability of performing the fuzzy models (Fuzzy AHP and Fuzzy ANP) of the proposed DSS to cope with the ambiguous and linguistic nature of the model. The AHP and ANP model, for a decision support for selecting the best place among the alternatives, including the criteria and alternatives, is developed and solved by the experts from Turkish Navy and Turkish academicians related to international relations branches of the universities in Turkey. Also, the questionnaires used for weighting of the criteria and the alternatives are filled by these experts.Some of our alternatives are: economic and political stability of the third country, the effect of another super power in that country, historical relations, security in that country, social facilities in the city in which the base will be built, the transportation security and difficulty from a main city that have an airport to the city will have the base etc. Over 20 criteria like these are determined which are categorized in social, political, economic and military aspects. As a result all the criteria and three alternatives are evaluated by different people who have background and experience to weight the criteria and alternatives as it must be in AHP and ANP evaluation system. The alternatives got their degrees all between 0 – 1 and the total is 1. At the end the DSS advices one of the alternatives as the best one to the decision maker according to the developed model and the evaluations of the experts.

Keywords: analytic hierarchical process, analytic network process, fuzzy logic, naval base place selection, multiple criteria decision making

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Authors: Rindra Rantoson, Hichem Nouira, Nabil Anwer, Charyar Mehdi-Souzani

Abstract:

Multiple measurements by means of various data acquisition systems are generally required to measure the shape of freeform workpieces for accuracy, reliability and holisticity. The obtained data are aligned and fused into a common coordinate system within a registration technique involving coarse and fine registrations. Standardized iterative methods have been established for fine registration such as Iterative Closest Points (ICP) and its variants. For coarse registration, no conventional method has been adopted yet despite a significant number of techniques which have been developed in the literature to supply an automatic rough matching between data sets. Two main issues are addressed in this paper: the coarse registration and the fine registration. For coarse registration, two novel automated methods based on the exploitation of discrete curvatures are presented: an enhanced Hough Transformation (HT) and an improved Ransac Transformation. The use of curvature features in both methods aims to reduce computational cost. For fine registration, a new variant of ICP method is proposed in order to reduce registration error using curvature parameters. A specific distance considering the curvature similarity has been combined with Euclidean distance to define the distance criterion used for correspondences searching. Additionally, the objective function has been improved by combining the point-to-point (P-P) minimization and the point-to-plane (P-Pl) minimization with automatic weights. These ones are determined from the preliminary calculated curvature features at each point of the workpiece surface. The algorithms are applied on simulated and real data performed by a computer tomography (CT) system. The obtained results reveal the benefit of the proposed novel curvature-based registration methods.

Keywords: discrete curvature, RANSAC transformation, hough transformation, coarse registration, ICP variant, point-to-point and point-to-plane minimization combination, computer tomography

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Authors: Ugoh Kelechi Melford, Anene O.

Abstract:

Introduction: Evidence has shown that there are increasing rates of new HIV and other STI infections occurring among Men who have Sex with Men (MSM) in Nigeria, with the prevalence 3 times higher than the general population as reported by the 2011 National Integrated Bio Behavioral Surveillance Survey. The poor state of health care and support services hinders our effort to control the high rates of these new infections among MSM. Methods: The Initiative for Improved Male Health (IMH-Initiative) works to provide a safe space for young MSM living with HIV to access comprehensive palliative care and support, as well as referrals for other services through drama and dance competitions. An STI assessment was conducted in IMH-Initiative’s Community Center in Calabar, for gay men and other MSM. An STI history was conducted for all clients who visited the community clinic specifically for HCT and STI counseling and referrals within a 5 month period, and their data were collated. Results: 61 MSM were diagnosed, and reported the following in the last 6 months. 49 where living with HIV. 46 had previous histories of untreated anal warts. 20 had previous histories of treated Gonorrhea by self-medication and herbs. 21 had untreated boils and rashes around the genitals. 10 clients where living with HIV, and reported untreated penile and rectal gonorrhea. All clients indicated that there were not comfortable discussing STI infections with staff of public hospitals. Conclusion: It is evident that a reasonable number of STI infections among MSM are not completely treated or ignored. This thereby increases the individual’s risk of HIV infection, and cripples HIV prevention programming in Nigeria. HIV programs targeting MSM must incorporate STI syndromic management, so as to increase access to non-stigmatized diagnosis and treatment of STIs. Also, access to STI drugs for clients cannot be overemphasized.

Keywords: MSM, IBBSS, STI, IMH

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Authors: Claudius Peter Van Wyk

Abstract:

Given the increasingly manifest phenomena, as described in complexity theory, of volatility, uncertainty, complexity and ambiguity (VUCA), Taleb's notion of 'antifragility, has a significant contribution to make to positive psychology applied to wellbeing. Antifragility is argued to be fundamentally different from the concepts of resiliency; as the ability to recover from failure, and robustness; as the ability to resist failure. Rather it describes the capacity to reorganise in the face of stress in such a way as to cope more effectively with systemic challenges. The concept, which has been applied in disciplines ranging from physics, molecular biology, planning, engineering, and computer science, can now be considered for its application in individual human and social wellbeing. There are strong correlations to Antonovsky's model of 'salutogenesis' in which an attitude and competencies are developed of transforming burdening factors into greater resourcefulness. We demonstrate, from the perspective of neuroscience, how technology measuring nervous system coherence can be coupled to acquired psychodynamic approaches to not only identify contextual stressors, utilise biofeedback instruments for facilitating greater coherence, but apply these insights to specific life stressors that compromise well-being. Employing an on-going case study with BMW South Africa, the neurological mapping is demonstrated together with 'reframing' and emotional anchoring techniques from neurolinguistic programming. The argument is contextualised in the discipline of psychoneuroimmunology which describes the stress pathways from the CNS and endocrine systems and their impact on immune function and the capacity to restore homeostasis.

Keywords: antifragility, complexity, neuroscience, psychoneuroimmunology, salutogenesis, volatility

Procedia PDF Downloads 342