Search results for: intelligent computational techniques

Authors: Megan Burfoot, Ali GhaffarianHoseini, Nicola Naismith, Amirhosein GhaffarianHoseini

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Intelligent Acoustic Technology (IAT) is a novel architectural device used in buildings to automatically vary the acoustic conditions of space. IAT is realized by integrating two components: Variable Acoustic Technology (VAT) and an intelligent system. The VAT passively alters the RT by changing the total sound absorption in a room. In doing so, the Reverberation Time (RT) is changed and thus, the sound strength and clarity are altered. The intelligent system detects sound waves in real-time to identify the aural situation, and the RT is adjusted accordingly based on pre-programmed algorithms. IAT - the synthesis of these two components - can dramatically improve acoustic comfort, as the acoustic condition is automatically optimized for any detected aural situation. This paper presents an evaluation of the improvements of acoustic comfort in an existing tertiary classroom located at Auckland University of Technology in New Zealand. This is a pilot case study, the first of its’ kind attempting to quantify the benefits of IAT. Naturally, the potential acoustic improvements from IAT can be actualized by only installing the VAT component of IAT and by manually adjusting it rather than utilizing an intelligent system. Such a simplified methodology is adopted for this case study to understand the potential significance of IAT without adopting a time and cost-intensive strategy. For this study, the VAT is built by overlaying reflective, rotating louvers over sound absorption panels. RT's are measured according to international standards before and after installing VAT in the classroom. The louvers are manually rotated in increments by the experimenter and further RT measurements are recorded. The results are compared with recommended guidelines and reference values from national standards for spaces intended for speech and communication. The results obtained from the measurements are used to quantify the potential improvements in classroom acoustic comfort, where IAT to be used. This evaluation reveals the current existence of poor acoustic conditions in the classroom caused by high RT's. The poor acoustics are also largely attributed to the classrooms’ inability to vary acoustic parameters for changing aural situations. The classroom experiences one static acoustic state, neglecting to recognize the nature of classrooms as flexible, dynamic spaces. Evidently, when using VAT the classroom is prescribed with a wide range of RTs it can achieve. Namely, acoustic requirements for varying teaching approaches are satisfied, and acoustic comfort is improved. By quantifying the benefits of using VAT, it can confidently suggest these same benefits are achieved with IAT. Nevertheless, it is encouraged that future studies continue this line of research toward the eventual development of IAT and its’ acceptance into mainstream architecture.

Keywords: acoustic comfort, classroom acoustics, intelligent acoustics, variable acoustics

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Authors: Mallikarjuna Rao Gundavarapu, Ch. Mallikarjuna Rao, K. Anuradha Bai

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Real Time Video Tracking is a challenging task for computing professionals. The performance of video tracking techniques is greatly affected by background detection and elimination process. Local regions of the image frame contain vital information of background and foreground. However, pixel-level processing of local regions consumes a good amount of computational time and memory space by traditional approaches. In our approach we have explored the concurrent computational ability of General Purpose Graphic Processing Units (GPGPU) to address this problem. The Gaussian Mixture Model (GMM) with adaptive weighted kernels is used for detecting the background. The weights of the kernel are influenced by local regions and are updated by inter-frame variations of these corresponding regions. The proposed system has been tested with GPU devices such as GeForce GTX 280, GeForce GTX 280 and Quadro K2000. The results are encouraging with maximum speed up 10X compared to sequential approach.

Keywords: connected components, embrace threads, local weighted kernel, structuring elements

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Rui Sun, Tamer M. Ismail, Xiaohan Ren, M. Abd El-Salam

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Incineration of municipal solid waste (MSW) is one of the key scopes in the global clean energy strategy. A computational fluid dynamics (CFD) model was established. In order to reveal these features of the combustion process in a fixed porous bed of MSW. Transporting equations and process rate equations of the waste bed were modeled and set up to describe the incineration process, according to the local thermal conditions and waste property characters. Gas phase turbulence was modeled using k-ε turbulent model and the particle phase was modeled using the kinetic theory of granular flow. The heterogeneous reaction rates were determined using Arrhenius eddy dissipation and the Arrhenius-diffusion reaction rates. The effects of primary air flow rate and temperature in the burning process of simulated MSW are investigated experimentally and numerically. The simulation results in bed are accordant with experimental data well. The model provides detailed information on burning processes in the fixed bed, which is otherwise very difficult to obtain by conventional experimental techniques.

Keywords: computational fluid dynamics (CFD) model, waste incineration, municipal solid waste (MSW), fixed bed, primary air

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Authors: Aakash Chhunchha, Assma Begum

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The qualitative aspects of hypersonic flow over a range of blunt bodies have been extensively analyzed in the past. It is well known that the curvature of a body’s geometry in the sonic region predominantly dictates the bow shock shape and its standoff distance from the body, while the surface pressure distribution depends on both the sonic region and on the local body shape. The present study is an extension to analyze the hypersonic flow characteristics over several blunt-nosed bodies using modern Computational Fluid Dynamics (CFD) tools to determine the shock shape and its effect on the heat flux around the body. 4 blunt-nosed models with cylindrical afterbodies were analyzed for a flow at a Mach number of 10 corresponding to the standard atmospheric conditions at an altitude of 50 km. The nose radii of curvature of the models range from a hemispherical nose to a flat nose. Appropriate numerical models and the supplementary convergence techniques that were implemented for the CFD analysis are thoroughly described. The flow contours are presented highlighting the key characteristics of shock wave shape, shock standoff distance and the sonic point shift on the shock. The variation of heat flux, due to different shock detachments for various models is comprehensively discussed. It is observed that the more the bluntness of the nose radii, the farther the shock stands from the body; and consequently, the less the surface heating at the nose. The results obtained from the CFD analyses are compared with approximated theoretical engineering correlations. Overall, a satisfactory agreement is observed between the two.

Keywords: aero-thermodynamics, blunt-nosed bodies, computational fluid dynamics (CFD), hypersonic flow

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Authors: José Alberto García Fernández, Zhimin Du, Xinqiao Jin

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Nowadays, data center industry faces strong challenges for increasing the speed and data processing capacities while at the same time is trying to keep their devices a suitable working temperature without penalizing that capacity. Consequently, the cooling systems of this kind of facilities use a large amount of energy to dissipate the heat generated inside the servers, and developing new cooling techniques or perfecting those already existing would be a great advance in this type of industry. The installation of a temperature sensor matrix distributed in the structure of each server would provide the necessary information for collecting the required data for obtaining a temperature profile instantly inside them. However, the number of temperature probes required to obtain the temperature profiles with sufficient accuracy is very high and expensive. Therefore, other less intrusive techniques are employed where each point that characterizes the server temperature profile is obtained by solving differential equations through simulation methods, simplifying data collection techniques but increasing the time to obtain results. In order to reduce these calculation times, complicated and slow computational fluid dynamics simulations are replaced by simpler and faster finite element method simulations which solve the Burgers‘ equations by backward, forward and central discretization techniques after simplifying the energy and enthalpy conservation differential equations. The discretization methods employed for solving the first and second order derivatives of the obtained Burgers‘ equation after these simplifications are the key for obtaining results with greater or lesser accuracy regardless of the characteristic truncation error.

Keywords: Burgers' equations, CFD simulation, data center, discretization methods, FEM simulation, temperature profile

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Authors: Mesbah Khaled, Bouraoui Ouissal, Benkiniouar Rachid, Belkhiri Lotfi

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Alstonia, which are tropical plants with a wide geographical distribution, have been divided into different sections by different authors based on previous studies of several species within the genus. Monachino divides Alstonia into 5 sections, while Pichon divides it into 3 sections. Several plants belonging to this genus, such as Alstonia brassii, have been used in traditional folk medicine to treat ailments such as fever, malaria and dysentery]. Previous studies focusing on the chemical composition of these plants have successfully identified indol alkaloids with cytotoxic, anti-diabetic and anti-inflammatory properties. The newly discovered monomers are structurally similar to the backbones of picralin, affinisin and macrolin. On the other hand, all recently isolated dimeric compounds have a macrolin moiety. In this study, a computational analysis was performed on a series of novel molecules, including both monomeric and dimeric compounds with different structural frameworks. This investigation represents the first computational study of these molecules using an in silico approach incorporating 2D-QSAR data. The analysis involved various computational techniques, including 2D-QSAR modelling, molecular docking studies and subsequent validation by molecular dynamics simulation and assessment of ADMET properties. The chemical composition was identified by 1D and 2D NMR. Eight new alkaloids were isolated, 5 monomers and 3 dimers. In this section, we focus on the biological activity of 4 new alkaloids belonging to two different skeletons, the affinisine skeleton.

Keywords: affinisine, talcarpine, macroline, cytotoxicity, alkaloids

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Authors: Ahmed Ishtiaq, Maeeda Khalid, Umair Sajjad

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In the fast-moving world, information is the key to success. If information is easily available, then it makes work easy. The Internet is the biggest collection and source of information nowadays, and with every single day, the data on internet increases, and it becomes difficult to find required data. Everyone wants to make his/her website at the top of search results. This can be possible when you have applied some techniques of SEO inside your application or outside your application, which are two types of SEO, onsite and offsite SEO. SEO is an abbreviation of Search Engine Optimization, and it is a set of techniques, methods to increase users of a website on World Wide Web or to rank up your website in search engine indexing. In this paper, we have compared different techniques of Onpage and Offpage SEO, and we have suggested many things that should be changed inside webpage, outside web page and mentioned some most powerful and search engine considerable elements and techniques in both types of SEO in order to gain high ranking on Search Engine.

Keywords: auto-suggestion, search engine optimization, SEO, query, web mining, web crawler

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Authors: Te-Jen Chang, Ping-Sheng Huang, Shan-Ten Cheng, Chih-Lin Lin, I-Hui Pan, Tsung- Hsien Lin

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RSA system is a great contribution for the encryption and the decryption. It is based on the modular exponentiation. We call this system as “a large of numbers for calculation”. The operation of a large of numbers is a very heavy burden for CPU. For increasing the computational speed, in addition to improve these algorithms, such as the binary method, the sliding window method, the addition chain method, and so on, the cluster computer can be used to advance computational speed. The cluster system is composed of the computers which are installed the MPICH2 in laboratory. The parallel procedures of the modular exponentiation can be processed by combining the sliding window method with the addition chain method. It will significantly reduce the computational time of the modular exponentiation whose digits are more than 512 bits and even more than 1024 bits.

Keywords: cluster system, modular exponentiation, sliding window, addition chain

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Authors: Sulaiman Al amro, Ali Alkhalifah

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The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses.

Keywords: computer viruses, virus detection, signature-based, behaviour-based, heuristic-based

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Authors: Alicja Reuben, Amira Annabi

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In this paper, we explore the relationship between the selection of valuation techniques by investment banks and the banks’ risk perceptions and performance in the context of the theory of performativity. We use inferential statistics to study these relationships by building a unique dataset based on the disclosure of 12 investment banks’ 2012-2015 annual financial statements. Moreover, we create two constructs, namely intensity of use and risk perception. We measure the intensity of use as a frequency metric of how often a particular bank adopts valuation techniques for a particular asset or liability. We measure risk perception based on disclosed ranges of values for unobservable inputs. Our results are twofold: we find a significant negative correlation between (1) intensity of use and investment bank performance and (2) intensity of use and risk perception. These results indicate that a performative process takes place, and the valuation techniques are enacting their environment.

Keywords: language, linguistics, performativity, financial techniques

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Authors: Muhammad M. A. S. Mahmoud

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Natural gas sweetening process is a controlled process that must be done at maximum efficiency and with the highest quality. In this work, due to complexity and non-linearity of the process, the H₂S gas separation and the intelligent fuzzy controller, which is used to enhance the process, are simulated in MATLAB – Simulink. The new design of fuzzy control for Gas Separator is discussed in this paper. The design is based on the utilization of linear state-estimation to generate the internal knowledge-base that stores input-output pairs. The obtained input/output pairs are then used to design a feedback fuzzy controller. The proposed closed-loop fuzzy control system maintains the system asymptotically-stability while it enhances the system time response to achieve better control of the concentration of the output gas from the tower. Simulation studies are carried out to illustrate the Gas Separator system performance.

Keywords: gas separator, gas sweetening, intelligent controller, fuzzy control

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Authors: Yuri Bizzoni, Marianne Reboul

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Our program compares French and Italian translations of Homer’s Odyssey, from the XVIth to the XXth century. We focus on the third point, showing how distributional semantics systems can be used both to improve alignment between different French translations as well as between the Greek text and a French translation. Although we focus on French examples, the techniques we display are completely language independent.

Keywords: classical receptions, computational linguistics, distributional semantics, Homeric poems, machine translation, translation studies, text alignment

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Authors: Sijie Fu, Pascal-Henry Biwolé, Christian Mathis

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Among modern airflow measurement methods, Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV), as visualized and non-instructive measurement techniques, are playing more important role. This paper conducts a comparative experimental study for airflow measurement employing both techniques with the same condition. Velocity vector fields, velocity contour fields, voticity profiles and turbulence profiles are selected as the comparison indexes. The results show that the performance of both PIV and PTV techniques for airflow measurement is satisfied, but some differences between the both techniques are existed, it suggests that selecting the measurement technique should be based on a comprehensive consideration.

Keywords: airflow measurement, comparison, PIV, PTV

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Authors: Piyanud Chompookard

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This research aims to study the effect of self-compassion techniques for individuals with depression (A pilot study). A quasi-experimental research with pretest-posttest is used to design this work. The research includes 30 participants, divided into the experimental group (ten samples) and the control group (twenty samples). The experimental group received a self-compassion techniques with an appropriate treatment for a total six times. The control group received an appropriate treatment. The measurement of this study using the Hamilton Rating Scale for Depression (Thai version). There are significant differences in levels of depression after received a self-compassion techniques with an appropriate treatment (p<.01). And there are significant differences in levels of depression between the experimental group and the control group (p<.01).

Keywords: depression, self compassion techniques, psychotherapy, pilot study

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Authors: Xochilt Ramirez-Garcia, Mario Garcia-Valdez

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Contextual recommendations are implemented in Recommender Systems to improve user satisfaction, recommender system makes accurate and suitable recommendations for a particular situation reaching personalized recommendations. The context provides information relevant to the Recommender System and is used as a filter for selection of relevant items for the user. This paper presents a Context-aware Recommender System, which uses techniques based on Collaborative Filtering and Content-Based, as well as fuzzy rules, to recommend items inside the context. The dataset used to test the system is Trip Advisor. The accuracy in the recommendations was evaluated with the Mean Absolute Error.

Keywords: algorithms, collaborative filtering, intelligent systems, fuzzy logic, recommender systems

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Authors: R. Gupta, P. Jain, S. Das

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One way to approach construction scheduling optimization problem is to focus on the individual aspects of planning, which can be broadly classified as time scheduling, crew and resource management, and cost control. During the last four decades, construction planning has seen a lot of research, but to date, no paper had attempted to summarize the literature available under important heads. This paper addresses each of aspects separately, and presents the findings of an in-depth literature of the various planning techniques. For techniques dealing with time scheduling, the authors have adopted a rough chronological documentation. For crew and resource management, classification has been done on the basis of the different steps involved in the resource planning process. For cost control, techniques dealing with both estimation of costs and the subsequent optimization of costs have been dealt with separately.

Keywords: construction planning techniques, time scheduling, resource planning, cost control

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Authors: Navdeep Singh Randhawa, Shally Sharma

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Wireless Sensor Network is an autonomous technology emanating in the current scenario at a fast pace. This technology faces a number of defiance’s and energy management is one of them, which has a huge impact on the network lifetime. To sustain energy the different types of routing protocols have been flourished. The classical routing protocols are no more compatible to perform in complicated environments. Hence, in the field of routing the intelligent algorithms based on nature systems is a turning point in Wireless Sensor Network. These nature-based algorithms are quite efficient to handle the challenges of the WSN as they are capable of achieving local and global best optimization solutions for the complex environments. So, the main attention of this paper is to develop a routing algorithm based on some swarm intelligent technique to enhance the performance of Wireless Sensor Network.

Keywords: wireless sensor network, routing, swarm intelligence, MPRSO

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Authors: Mehdi Ghommem, Daniel Garcia, Nathan Collier, Victor Calo

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We develop a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM). The computational framework uses the Python programming language which has easy integration with the scripts requiring computationally-expensive operations written in Fortran. The mixed-language approach enables high performance in terms of solution time and high flexibility in terms of easiness of code adaptation to different system configurations and applications. This computational tool is intended to predict the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges...) subject to an incoming air. We simulate different aerodynamic problems to validate and illustrate the usefulness and effectiveness of the developed computational tool.

Keywords: unsteady aerodynamics, numerical simulations, mixed-language approach, potential flow

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Authors: Harold Jideofor

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The application of Project Management (PM) tools and techniques in the public sector is gradually becoming an important issue in developing economies, especially in a country like Nigeria where projects of different size and structures are undertaken. The paper examined the application of the project management practice in the public sector in Nigeria. The PM lifecycles, tools, and techniques were presented. The study was carried out in Lagos because of its metropolitan nature and rapidly growing economy. Twenty-three copies of questionnaire were administered to 23 public institutions in Lagos to generate primary data. The descriptive analysis techniques using percentages and table presentations coupled with the coefficient of correlation were used for data analysis. The study revealed that application of PM tools and techniques is an essential management approach that tends to achieve specified objectives within specific time and budget limits through the optimum use of resources. Furthermore, the study noted that there is a lack of in-depth knowledge of PM tools and techniques in public sector institutions sampled, also a high cost of the application was also observed by the respondents. The study recommended among others that PM tools and techniques should be applied gradually especially in old government institutions where resistance to change is perceived to be high.

Keywords: project management, public sector, practice, Nigeria

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Authors: G. Valentis, Q. Berthelot

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Nowadays the trend to develop ever more intelligent and autonomous systems often takes its inspiration in the living beings on Earth. Some simple isolated systems are able, once brought together, to form a strong and reliable system. When trying to adapt the idea to man-made systems it is not possible to include in their program everything the system may encounter during its life cycle. It is, thus, necessary to make the system able to take decisions based on other criteria such as its past experience, i.e. to make the system learn on its own. However, at some point the acquired knowledge depends also on environment. So the question is: if system environment is modified, how could the system respond to it quickly and appropriately enough? Here, starting from reinforcement learning to rate its decisions, and using adaptive learning algorithms for gain and loss reward, the system is made able to respond to changing environment and to adapt its knowledge as time passes. Application is made to a robot finding an exit in a labyrinth.

Keywords: reinforcement learning, neural network, autonomous systems, adaptive learning, changing environment

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Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

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5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

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Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

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The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

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Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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Authors: Hartani Kada, Merah Abdelkader

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Aiming at the high nonlinearities and unmatched uncertainties of the intelligent electric vehicles’ dynamic system, this paper presents a lateral motion control algorithm for intelligent electric vehicles with four in-wheel motors. A fuzzy logic procedure is presented and formulated to realize lateral control in lane change. The vehicle dynamics model and a desired target tracking model were established in this paper. A fuzzy logic controller was designed for integrated active front steering (AFS) and direct yaw moment control (DYC) in order to improve vehicle handling performance and stability, and a fuzzy controller for the automatic steering problem. The simulation results demonstrate the strong robustness and excellent tracking performance of the control algorithm that is proposed.

Keywords: fuzzy logic, lateral control, AFS, DYC, electric car technology, longitudinal control, lateral motion

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Authors: Sheng-Shih Wang, Wei-Ting Wang

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This paper designs an intelligent guidance system, based on wireless sensor networks, for efficient parking in parking lots. The proposed system consists of a parking space allocation subsystem, a parking space monitoring subsystem, a driving guidance subsystem, and a vehicle detection subsystem. In the system, we propose a novel and effective virtual coordinate system for sensing and displaying devices to determine the proper vacant parking space and provide the precise guidance to the driver. This study constructs a ZigBee-based wireless sensor network on Arduino platform and implements the prototype of the proposed system using Arduino-based complements. Experimental results confirm that the proposed prototype can not only work well, but also provide drivers the correct parking information.

Keywords: Arduino, parking guidance, wireless sensor network, ZigBee

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Authors: John Allard, Alex Rich, Iris Aguilar, Zachary Dodds

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With this paper we present several experiences deploying novel, low-cost resources for computing with 3D spatial models. Certainly, computing with 3D models undergirds some of our field’s most important contributions to the human experience. Most often, those are contrived artifacts. This work extends that tradition by focusing on novel resources that deliver uncontrived models of a system’s current surroundings. Atop this new capability, we present several projects investigating the student-accessibility of the computational tools for reasoning about the 3D space around us. We conclude that, with current scaffolding, real-world 3D models are now an accessible and viable foundation for creative computational work.

Keywords: 3D vision, matterport model, real-world 3D models, mathematical and computational methods

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Authors: Doyoung Kim, Hyo Seon Park

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Studies of System Identification(SI) based on Structural Health Monitoring(SHM) have actively conducted for structural safety. Recently SI techniques have been rapidly developed with output-only SI paradigm for estimating modal parameters. The features of these output-only SI methods consist of Frequency Domain Decomposition(FDD) and Stochastic Subspace Identification(SSI) are using the algorithms based on orthogonal decomposition such as singular value decomposition(SVD). But the SVD leads to high level of computational complexity to estimate modal parameters. This paper proposes the technique to estimate mode shape with lower computational cost. This technique shows pseudo modal Operating Deflections Shape(ODS) through bandpass filter and suggests time history Modal Assurance Criterion(MAC). Finally, mode shape could be estimated from pseudo modal ODS and time history MAC. Analytical simulations of vibration measurement were performed and the results with mode shape and computation time between representative SI method and proposed method were compared.

Keywords: modal assurance criterion, mode shape, operating deflection shape, system identification

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Authors: Waziri Victor Onomza, John K. Alhassan, Idris Ismaila, Noel Dogonyaro Moses

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This paper describes the problem of building secure computational services for encrypted information in the Cloud Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy, confidentiality, availability of the users. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute theoretical presentations in high-level computational processes that are based on number theory and algebra that can easily be integrated and leveraged in the Cloud computing with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, homomorphic, homomorphic encryption scheme

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Authors: Kishor T. Zingre, Seshadhri Srinivasan

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Energy consumption by the building sector is increasing at an alarming rate throughout the world and leading to more building-related CO₂ emissions into the environment. In buildings, the main contributors to energy consumption are heating, ventilation, and air-conditioning (HVAC) systems, lighting, and electrical appliances. It is hypothesised that the energy efficiency in buildings can be achieved by implementing sustainable technologies such as i) enhancing the thermal resistance of fabric materials for reducing heat gain (in hotter climates) and heat loss (in colder climates), ii) enhancing daylight and lighting system, iii) HVAC system and iv) occupant localization. Energy performance of various sustainable technologies is highly dependent on climatic conditions. This paper investigated the use of machine learning techniques for accurate prediction of air-conditioning energy in seasonal climates. The data required to train the machine learning techniques is obtained using the computational simulations performed on a 3-story commercial building using EnergyPlus program plugged-in with OpenStudio and Google SketchUp. The EnergyPlus model was calibrated against experimental measurements of surface temperatures and heat flux prior to employing for the simulations. It has been observed from the simulations that the performance of sustainable fabric materials (for walls, roof, and windows) such as phase change materials, insulation, cool roof, etc. vary with the climate conditions. Various renewable technologies were also used for the building flat roofs in various climates to investigate the potential for electricity generation. It has been observed that the proposed technique overcomes the shortcomings of existing approaches, such as local linearization or over-simplifying assumptions. In addition, the proposed method can be used for real-time estimation of building air-conditioning energy.

Keywords: building energy efficiency, energyplus, machine learning techniques, seasonal climates

Procedia PDF Downloads 96