Search results for: efficiently computable endomorphism

Authors: Fu Lin

Abstract:

We consider the worst-case load-shedding problem in electric power networks where a number of transmission lines are to be taken out of service. The objective is to identify a prespecified number of line outages that lead to the maximum interruption of power generation and load at the transmission level, subject to the active power-flow model, the load and generation capacity of the buses, and the phase-angle limit across the transmission lines. For this nonlinear model with binary constraints, we show that all decision variables are separable except for the nonlinear power-flow equations. We develop an iterative decomposition algorithm, which converts the worst-case load shedding problem into a sequence of small subproblems. We show that the subproblems are either convex problems that can be solved efficiently or nonconvex problems that have closed-form solutions. Consequently, our approach is scalable for large networks. Furthermore, we prove the convergence of our algorithm to a critical point, and the objective value is guaranteed to decrease throughout the iterations. Numerical experiments with IEEE test cases demonstrate the effectiveness of the developed approach.

Keywords: load shedding, power system, proximal alternating linearization method, vulnerability analysis

Procedia PDF Downloads 110

Authors: Olokode Enitan Aishat

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People can perform to the best of their abilities and produce the highest-quality work most effectively and efficiently with the aid of performance management tools. The performance, goal-setting, activation, monitoring, measurement, and evaluation aspects of hospitality operations are key. The hospitality industry, the investors, and management would become irrelevant without performance since the industry would no longer be viable. The goal of this study is to elucidate the quandary for both management and investor, which derives from an intrinsic perspective in which both parties seek to reach and exceed goals while maximizing returns on investment. The desire for achievement and a return on investment is a major conundrum for all parties concerned. It is envisaged that there would be returns on the investments and expenses made in maintaining hospitality facilities with human resources. Secondary research was used to develop the theoretical framework. A random sample of respondents from hotels employee and investors within the city of Abuja was used to collect data, which was then analyzed using SPSS. This study confirms the validity of simple and straightforward common misunderstandings and provides tried and tested strategies for understanding and working together as a team among managers and owners in a business, as this would guarantee a return for business owners and management.

Keywords: performance management, hospitality industry, conflict, alignment of key performance indicator

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Authors: Gholamreza Momenzadeh, Forough Nematolahi

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The Extended Enterprise Resource Planning (ERPII) system usually requires massive amounts of storage space, powerful servers, and large upfront and ongoing investments to purchase and manage the software and the related hardware which are not affordable for organizations. In recent decades, organizations prefer to adapt their business structures with new technologies for remaining competitive in the world economy. Therefore, cloud computing (which is one of the tools of information technology (IT)) is a modern system that reveals the next-generation application architecture. Also, cloud computing has had some advantages that reduce costs in many ways such as: lower upfront costs for all computing infrastructure and lower cost of maintaining and supporting. On the other hand, traditional ERPII is not responding for huge amounts of data and relations between the organizations. In this study, based on a literature study, ERPII is investigated in the context of cloud computing where the organizations operate more efficiently. Also, ERPII conditions have a response to needs of organizations in large amounts of data and relations between the organizations.

Keywords: extended enterprise resource planning, cloud computing, business process, enterprise information integration

Procedia PDF Downloads 190

Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh

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The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.

Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging

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Authors: Wenyu Guo, Malachy O’Rourke, Mark Bowkett, Michael Gilchrist

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Many devices for real-time monitoring of surface water have been developed in the past few years to provide early warning of pollutions and so to decrease the risk of environmental pollution efficiently. One of the most common methodologies used in the detection system is a colorimetric process, in which a container with fixed volume is filled with target ions and reagents to combine a colorimetric dye. The colorimetric ions can sensitively absorb a specific-wavelength radiation beam, and its absorbance rate is proportional to the concentration of the fully developed product, indicating the concentration of target nutrients in the pre-mixed water samples. In order to achieve precise and rapid detection effect, channels with dimensions in the order of micrometers, i.e., microfluidic systems have been developed and introduced into these diagnostics studies. Microfluidics technology largely reduces the surface to volume ratios and decrease the samples/reagents consumption significantly. However, species transport in such miniaturized channels is limited by the low Reynolds numbers in the regimes. Thus, the flow is extremely laminar state, and diffusion is the dominant mass transport process all over the regimes of the microfluidic channels. The objective of this present work has been to analyse the mixing effect and chemistry kinetics in a stop-flow microfluidic device measuring Nitride concentrations in fresh water samples. In order to improve the temporal resolution of the Nitride microfluidic sensor, we have used computational fluid dynamics to investigate the influence that the effectiveness of the mixing process between the sample and reagent within a microfluidic device exerts on the time to completion of the resulting chemical reaction. This computational approach has been complemented by physical experiments. The kinetics of the Griess reaction involving the conversion of sulphanilic acid to a diazonium salt by reaction with nitrite in acidic solution is set in the Laminar Finite-rate chemical reaction in the model. Initially, a methodology was developed to assess the degree of mixing of the sample and reagent within the device. This enabled different designs of the mixing channel to be compared, such as straight, square wave and serpentine geometries. Thereafter, the time to completion of the Griess reaction within a straight mixing channel device was modeled and the reaction time validated with experimental data. Further simulations have been done to compare the reaction time to effective mixing within straight, square wave and serpentine geometries. Results show that square wave channels can significantly improve the mixing effect and provides a low standard deviations of the concentrations of nitride and reagent, while for straight channel microfluidic patterns the corresponding values are 2-3 orders of magnitude greater, and consequently are less efficiently mixed. This has allowed us to design novel channel patterns of micro-mixers with more effective mixing that can be used to detect and monitor levels of nutrients present in water samples, in particular, Nitride. Future generations of water quality monitoring and diagnostic devices will easily exploit this technology.

Keywords: nitride detection, computational fluid dynamics, chemical kinetics, mixing effect

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

Procedia PDF Downloads 95

Authors: Omaima A. Sharaf, Wafaa M. Abd El-Rahim, Hassan Moawad, Michael J. Sadowsky

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Textile industry is one of the important industries in the worldwide. It is known that the eco-friendly industrial and agricultural activities are significant for socio-economic stability of all countries. The absence of appropriate industrial waste water treatments is essential barrier for sustainable development in food and agricultural sectors especially in developing country like Egypt. Thus, the development of enzymatic bioremediation technology for textile dye removal will enhance the collaboration between scientists who develop the technology and industry where this technology will be implemented towards the safe disposal of the textile dye wastes. Highly efficient microorganisms are of most importance in developing and using highly effective biological treatment processes. Bacterial degradation of azo dyes is generally initiated by an enzymatic step that involves cleavage of azo linkages, usually with the aid of an azoreductase as electron donor. Thus, expanding the spectrum of microorganisms with high enzymatic activities as azoreductases and discovering novel azo-dye degrading enzymes, with enhanced stability and superior catalytic properties, are necessary for many environmental and industrial applications. Consequently, the use of molecular tools has become increasingly integrated into the understanding of enzyme properties and characterization. Researchers have utilized a gene cloning and expression methods as a tool to produce recombinant protein for decolorizing dyes more efficiently. Thus, presumptive evidence for the presence of genes encoding azoreductases in the genomes of selected local, and most potent azo-degrading strains were obtained by using specific oligonucleotides primers. These potent strains have been isolated from textile industrial wastewater in Egypt and identified using 16S rRNA sequence analysis as 'Lysinibacillus macrolidesB8, Brevibacillus parabrevisB11, Bacillus coagulansB7, and B. cereusB5'. PCR products of two full length genes designated as (AZO1;621bp and AZO2;534bp) were detected. BLASTx results indicated that AZO1 gene was corresponding to predicted azoreductase from of Bacillus sp. ABP14, complete genome, multispecies azoreductase [Bacillus], It was submitted to the gene bank by an accession no., BankIt2085371 AZO1 MG923210 (621bp; 207 amino acids). AZO1 was generated from the DNA of our identified strains Lysinibacillus macrolidesB8. On the other hand, AZO2 gene was corresponding to a predicted azoreductase from Bacillus cereus strain S2-8. Gene bank accession no. was BankIt2085839 AZO2 MG932081 (534bp;178 amino acids) and it was amplified from our Bacillus coagulansB7. Both genes were successfully cloned into pCR2.1TOPO (Invitrogen) and in pET28b+ vectors, then they transformed into E. coli DH5α and BL21(DE3) cells for heterologous expression studies. Our recombinant azoreductases (AZO1&AZO2) exhibited potential enzyme activity and efficiently decolorized an azo dye (Direct violet). They exhibited pH stability between 6 and 8 with optimum temperature up to 60°C and 37 °C after induction by 1mM and 1.5mM IPTG, for both AZO1 &AZO2, respectively. These results suggested that further optimization and purification of these recombinant proteins by using different heterologous expression systems will give great potential for the sustainable utilization of these recombinant enzymes in several industrial applications especially in wastewater treatments.

Keywords: azoreductases, decolorization, enzyme activity, gene cloning and expression

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Authors: Tiexin Wang, Sebastien Truptil, Frederick Benaben

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Model transformation, as a pivotal aspect of Model-driven engineering, attracts more and more attentions both from researchers and practitioners. Many domains (enterprise engineering, software engineering, knowledge engineering, etc.) use model transformation principles and practices to serve to their domain specific problems; furthermore, model transformation could also be used to fulfill the gap between different domains: by sharing and exchanging knowledge. Since model transformation has been widely used, there comes new requirement on it: effectively and efficiently define the transformation process and reduce manual effort that involved in. This paper presents an automatic model transformation methodology based on semantic and syntactic comparisons, and focuses particularly on granularity issue that existed in transformation process. Comparing to the traditional model transformation methodologies, this methodology serves to a general purpose: cross-domain methodology. Semantic and syntactic checking measurements are combined into a refined transformation process, which solves the granularity issue. Moreover, semantic and syntactic comparisons are supported by software tool; manual effort is replaced in this way.

Keywords: automatic model transformation, granularity issue, model-driven engineering, semantic and syntactic comparisons

Procedia PDF Downloads 363

Authors: Heba M. Wagih, Hoda M. O. Mokhtar

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Social Network has recently played a significant role in both scientific and social communities. The growing adoption of social network applications has been a relevant source of information nowadays. Due to its popularity, several research trends are emerged to service the huge volume of users including, Location-Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. LBSNs applications are among the highly demanded applications that do not focus only on analyzing the spatiotemporal positions in a given raw trajectory but also on understanding the semantics behind the dynamics of the moving object. LBSNs are possible means of predicting human mobility based on users social ties as well as their spatial preferences. LBSNs rely on the efficient representation of users’ trajectories. Hence, traditional raw trajectory information is no longer convenient. In our research, we focus on studying human behavior trajectory which is the major pillar in location recommendation systems. In this paper, we propose an ontology design patterns with their underlying description logics to efficiently annotate human behavior trajectories.

Keywords: human behavior trajectory, location-based social network, ontology, social network

Procedia PDF Downloads 426

Authors: Mohamad Farhad Bakhtiyariyan

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One of the manner to earning profit and having a high income, is investing in an acceptable market. Every the thinker brain knows, investing in the business world today, maybe, have a manifold profit or lead to failure. So, before entering in the investment market, we must have a comprehensive and sufficient awareness, know markets, acquainted with the main industrial activities, know the rules and regulation and consider the conditions of society. Qatar, as a one of the richest countries in the world, can be a good destination for investment. The inflation rate, taxes, easiness of the importing, company registration, ease of exporting process, profitable and appropriate markets, simple and applicable rules, all of this has made Qatar, one of the best and gainful investment countries. Above all, Qatar 2022 world cup event, has led of investment in this country efficiently and profitable method. In this paper, first, we have introduced the Qatar and its location, also looked at the countries international markets during the world cup and we have described the impact of the world cup on business, and then the laws and regulations of the Qatar in the field of investment, company registration, ownership by foreigners, obtaining residency by investors, export and import process in second part its examined, and in third part, major investment markets, principal industrial activities in Qatar, markets affected by the world cup and the main needs of this country in various fields during the world cup, have been investigated.

Keywords: investment, Qatar, markets, world cup

Procedia PDF Downloads 151

Authors: Samson Davoyan, Ashot Davoyan, Ani Khachatryan

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The indexes (Global Competitiveness Index, Economic Freedom Index, Human Development Index, etc.) developed by different international and non-government organizations in time and space express the quantitative and qualitative features of different fields of various reforms implemented in different countries. The main objective of our research is to develop new methodology that we will use to create integral index based on many indexes and that will include many areas of reforms. To achieve our aim we have used econometric methods (regression model for panel data method). The basis of our methodology is the development of the new integral index based on quantitative assessment of the change of two main parameters: the score of the countries by different indexes and the change of the ranks of countries for following two periods of time. As a result of the usage of methods for analyzes we have defined the indexes that are used to create the new integral index and the scales for each of them. Analyzing quantitatively and qualitatively analysis through the integral index for more than 100 countries for 2009-2014, we have defined comparative efficiency that helps to conclude in which directions countries have implemented reforms more effectively compared to others and in which direction reforms have implemented less efficiently.

Keywords: development, rank, reforms, comparative, index, economic, corruption, social, program

Procedia PDF Downloads 297

Authors: Sana Shokat, Rabia Riaz, Raja Shoaib Hussain, Saba Shabir

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Digital divide is usually measured in terms of gap between those who can efficiently use new technological tools, such as Internet, and those who cannot. It is also hypothesized that web 2.0 tools motivate people to use technology i.e. Social networking sites can play an important role in bridging digital gap. This study was to determine the presence of digital divide in urban and rural areas of district Muzaffrabad, Azad Jammu & Kashmir taking internet usage as the key element. A cross-sectional community based survey was conducted involving 384 respondents from city Muzaffrabad and village Garhi Doppta. The existence of digital divide was accessed on the basis of the questionnaires given. Chi- square test was used to find the association of different demographic and ICT related factors with internet usage. Age based and area based divide still exist among the targeted population but gender based digital divide is vanishing from the intended area of study. Outcomes of the survey also revealed that web 2.0-based web sites are also becoming popular and attracting people to use internet. Trend of using internet and communication technologies can be increased by solving the highlighted problems.

Keywords: Azad Jammu and Kashmir, digital divide, ICT, information and communication technology, Web2.0

Procedia PDF Downloads 338

Authors: Bentoutou Houari, Boutte Aissa, Belaidi El Yazid, Limam Lakhdar

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The integration of a load switch with a DC/DC buck converter using LM2596 for power distribution in low-cost educational nanosatellites is a technique that aims to efficiently manage the power distribution system in these small spacecraft. The converter is based on the LM2596 regulator and designed to step down the input voltage of +16.8V to +12V, +5V, and +3.3V output, which are suitable for the nanosatellite's various subsystems. The load switch is based on MOSFET and is used to turn on or off the power supply to a particular load and protect the nanosatellite from power surges. A prototype of a +12V DC/DC buck converter with a high side load switch has been realized and tested, which meets our requirements and shows a good efficiency of 89%. In addition, the prototype features a capacitor between the source and gate of the MOSFET, which has effectively reduced the inrush current, demonstrating the effectiveness of this approach in reducing surges of current when the load is connected. The output current and voltage were measured at 0.7A and 11.89V, respectively, making this design suitable for use in low-cost educational nanosatellites.

Keywords: DC/DC buck converter, load switch, LM2596, electrical power subsystems, nanosatellite, inrush current

Procedia PDF Downloads 73

Authors: Li Chen

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For an aerofoil or hydrofoil in high Reynolds number flows, broadband noise is generated efficiently as the result of the turbulence convecting over the trailing edge. This noise can be related to the surface pressure fluctuations, which can be predicted by either CFD or empirical models. However, in reality, the aerofoil or hydrofoil often operates at an angle of attack. Under this situation, the flow is subjected to an Adverse-Pressure-Gradient (APG), and as a result, a flow separation may occur. This study is to assess trailing-edge noise models for such flows. In the present work, the trailing-edge noise from a 2D airfoil at 6 degree of angle of attach is investigated. Under this condition, the flow is experiencing a strong APG, and the flow separation occurs. The flow over the airfoil with a chord of 300 mm, equivalent to a Reynold Number 4x10⁵, is simulated using RANS with the SST k-ɛ turbulent model. The predicted surface pressure fluctuations are compared with the published experimental data and empirical models, and show a good agreement with the experimental data. The effect of the APG on the trailing edge noise is discussed, and the associated trailing edge noise is calculated.

Keywords: aero-acoustics, adverse-pressure gradient, computational fluid dynamics, trailing-edge noise

Procedia PDF Downloads 311

Authors: M. Ndeley

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The performance of global manufacturing supply chains depends on the interaction of production and transport processes. Currently, the scheduling of these processes is done separately without considering mutual requirements, which leads to no optimal solutions. An integrated scheduling of both processes enables the improvement of supply chain performance. The integrated production and transport scheduling problem (PTSP) is NP-hard, so that heuristic methods are necessary to efficiently solve large problem instances as in the case of global manufacturing supply chains. This paper presents a heuristic scheduling approach which handles the integration of flexible production processes with intermodal transport, incorporating flexible land transport. The method is based on a graph that allows a reformulation of the PTSP as a shortest path problem for each job, which can be solved in polynomial time. The proposed method is applied to a supply chain scenario with a manufacturing facility in South Africa and shipments of finished product to customers within the Country. The obtained results show that the approach is suitable for the scheduling of large-scale problems and can be flexibly adapted to different scenarios.

Keywords: production and transport scheduling problem, graph based scheduling, integrated scheduling

Procedia PDF Downloads 451

Authors: Gaye Ibrahima, Mendy Gervais, Seck Diaraf, Ouya Samuel

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In this paper, we consider the influence maximization in social networks. Here we give importance to initial diffuser called the seeds. The goal is to find efficiently a subset of k elements in the social network that will begin and maximize the information diffusion process. A new approach which treats the social network before to determine the seeds, is proposed. This treatment eliminates the information feedback toward a considered element as seed by extracting an acyclic spanning social network. At first, we propose two algorithm versions called SCG − algoritm (v1 and v2) (Spanning Connected Graphalgorithm). This algorithm takes as input data a connected social network directed or no. And finally, a generalization of the SCG − algoritm is proposed. It is called SG − algoritm (Spanning Graph-algorithm) and takes as input data any graph. These two algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, two seeds set are determined and those given by our approach give a better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.

Keywords: acyclic spanning graph, centrality measures, information feedback, influence maximization, social network

Procedia PDF Downloads 216

Authors: Seung Yoon Hyun, Hyeong Seung An, Myeong Ho Choi, Sung Hwan Bae, Yu Jong Sim

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In 2015, the southern part of the Korean Peninsula has 8.6 million poles, 1.25 million km power lines, and 2 million transformers, etc. It is the massive amount of distribution equipments which could cover a round-trip distance from the earth to the moon and 11 turns around the earth. These distribution equipments are spread out like capillaries and supplying power to every corner of the Korean Peninsula. In order to manage these huge power facility efficiently, KEPCO use DAS (Distribution Automation System) to operate distribution power system since 1997. DAS is integrated system that enables to remotely supervise and control breakers and switches on distribution network. Using DAS, we can reduce outage time and power loss. KEPCO has about 160,000 switches, 50%(about 80,000) of switches are automated, and 41 distribution center monitoring&control these switches 24-hour 365 days to get the best efficiency of distribution networks. However, the rapid increasing renewable energy sources become the problem in the efficient operation of distributed power system. (currently 2,400 MW, 75,000 generators operate in distribution power system). In this paper, it suggests the way to interconnect between renewable energy source and distribution power system.

Keywords: distribution, renewable, connect, DAS (Distribution Automation System)

Procedia PDF Downloads 587

Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

Procedia PDF Downloads 271

Authors: Gholamreza Namavar, Ali Bayati

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Nowadays according to increasing the population all around the world, consuming of fossil fuels increased dramatically. Many believe that most of the atmospheric pollution comes by using fossil fuels. The process of natural sources entering cities show one of the large challenges in consumption sources management. Nowadays, everyone considered about the consumption of fossil fuels and also reduction of consumption civil energy in megacities that play a key role in solving serious problems such as air pollution, producing greenhouse gasses, global warming and damage ozone layer. In construction industry we should use the materials with the lowest need to energy for making and carrying them, and also the materials which need the lowest energy and expenses to recycling. In this way, the kind of usage material, the way of processing, regional materials and the adaption with environment is critical. Otherwise, the isolation should be use and mention in long term. Accordingly, in this article we investigates the new ways in order to reduce environmental pollution and save more energy by using materials that are not harmful to the environment, fully insulated materials in buildings, sustainable and diversified buildings, suitable urban design and using solar energy more efficiently in order to reduce energy consumption.

Keywords: architectural design, insulation, sustainable construction, reducing energy consumption

Procedia PDF Downloads 221

Authors: Mohammadreza Azarnoush, Ali Bayati, Jamileh Azarnoush

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Nowadays according to increasing the population all around the world, consuming of fossil fuels increased dramatically. Many believe that most of the atmospheric pollution comes by using fossil fuels. The process of natural sources entering cities shows one of the large challenges in consumption sources management. Nowadays, everyone considers the consumption of fossil fuels and also reduction of consumption civil energy in megacities as playing a key role in solving serious problems such as air pollution, producing greenhouse gasses, global warming, and damage ozone layer. In the construction industry, we should use the materials with the lowest need to energy for making and carrying them, and also the materials which need the lowest energy and expenses to recycling. In this way, the kind of usage material, the way of processing, regional materials, and the adoption to the environment is critical. Otherwise, the isolation should be use and mention in the long term. Accordingly, in this article, we investigate the new ways in order to reduce environmental pollution and save more energy by using materials that are not harmful to the environment, fully insulated materials in buildings, sustainable and diversified buildings, suitable urban design and using solar energy more efficiently in order to reduce energy consumption.

Keywords: building design, construction masonry, insulation, sustainable construction

Procedia PDF Downloads 387

Authors: Badreddine Chemali, Boualem Tiliouine

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This article presents the main results of a numerical investigation on the uncertainty of dynamic response of structures with statistically correlated random damping Gamma distributed. A computational method based on a Linear Statistical Model (LSM) is implemented to predict second order statistics for the response of a typical industrial building structure. The significance of random damping with correlated parameters and its implications on the sensitivity of structural peak response in the neighborhood of a resonant frequency are discussed in light of considerable ranges of damping uncertainties and correlation coefficients. The results are compared to those generated using Monte Carlo simulation techniques. The numerical results obtained show the importance of damping uncertainty and statistical correlation of damping coefficients when obtaining accurate probabilistic estimates of dynamic response of structures. Furthermore, the effectiveness of the LSM model to efficiently predict uncertainty propagation for structural dynamic problems with correlated damping parameters is demonstrated.

Keywords: correlated random damping, linear statistical model, Monte Carlo simulation, uncertainty of dynamic response

Procedia PDF Downloads 248

Authors: Katsuyuki Umezawa, Makoto Nakazawa, Manabu Kobayashi, Yutaka Ishii, Michiko Nakano, Shigeichi Hirasawa

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This research aims to develop a self-study system equipped with an artificial teacher who gives advice to students by detecting the learners and to evaluate language learning in a unified framework. 'Detecting the learners' means that the system understands the learners' learning conditions, such as each learner’s degree of understanding, the difference in each learner’s thinking process, the degree of concentration or boredom in learning, and problem solving for each learner, which can be interpreted from learning behavior. In this paper, we propose a system to efficiently collect brain waves from learners by focusing on only the brain waves among the biological information for 'detecting the learners'. The conventional Electroencephalograph (EEG) measurement method during learning using a simple EEG has the following disadvantages. (1) The start and end of EEG measurement must be done manually by the experiment participant or staff. (2) Even when the EEG signal is weak, it may not be noticed, and the data may not be obtained. (3) Since the acquired EEG data is stored in each PC, there is a possibility that the time of data acquisition will be different in each PC. This time, we developed a system to collect brain wave data on the server side. This system overcame the above disadvantages.

Keywords: artificial teacher, e-learning, self-study system, simple EEG

Procedia PDF Downloads 115

Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

Procedia PDF Downloads 369

Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

Procedia PDF Downloads 114

Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

Procedia PDF Downloads 417

Authors: Biji Jacob

Abstract:

Vector quantization algorithm which uses minimum distance calculation for codebook generation, a time consuming calculation performed on each pixel values leads to computation complexity. The codebook is updated by comparing the distance of each vector to their centroid vector and measure for their closeness. In this paper vector quantization is modified based on vector difference algorithm for image enhancement purpose. In the proposed scheme, vector differences between the vectors are considered as the new generation vectors or new codebook vectors. The codebook is updated by comparing the new generation vector with a threshold value having minimum error with the parent vector. The minimum error decides the fitness of each newly generated vector. Thus the codebook is generated in an adaptive manner and the fitness value is determined for the suppression of the degraded portion of the image and thereby leads to the enhancement of the image through the adaptive searching capability of the vector quantization through vector difference algorithm. Experimental results shows that the vector difference scheme efficiently modifies the vector quantization algorithm for enhancing the image with peak signal to noise ratio (PSNR), mean square error (MSE), Euclidean distance (E_dist) as the performance parameters.

Keywords: codebook, image enhancement, vector difference, vector quantization

Procedia PDF Downloads 237

Authors: Jackson Parker Galvan, Wenxuan Guo

Abstract:

Unmanned aerial vehicle (UAV) technology is being increasingly adopted in high-throughput plant phenotyping for applications in plant breeding and precision agriculture. Winter wheat is an important cover crop for reducing soil erosion and protecting the environment in the Southern High Plains. Efficiently quantifying plant leaf area and biomass provides critical information for producers to practice site-specific management of crop inputs, such as water and fertilizers. The objective of this study was to estimate wheat biomass and leaf area index using UAV images. This study was conducted in an irrigated field in Garza County, Texas. High-resolution images were acquired on three dates (February 18, March 25, and May 15th ) using a multispectral sensor onboard a Matrice 600 UAV. On each data of image acquisition, 10 random plant samples were collected and measured for biomass and leaf area. Images were stitched using Pix4D, and ArcGIS was applied to overlay sampling locations and derive data for sampling locations.

Keywords: precision agriculture, UAV plant phenotyping, biomass, leaf area index, winter wheat, southern high plains

Procedia PDF Downloads 68

Authors: Tao Yin, Zeqiang Zhang, Wei Liang, Yanqing Zeng, Yu Zhang

Abstract:

To accelerate the remanufacturing process of electronic waste products, this study designs a partial disassembly line with the multi-robotic station to effectively dispose of excessive wastes. The multi-robotic partial disassembly line is a technical upgrade to the existing manual disassembly line. Balancing optimization can make the disassembly line smoother and more efficient. For partial disassembly line balancing with the multi-robotic station (PDLBMRS), a mixed-integer programming model (MIPM) considering the robotic efficiency differences is established to minimize cycle time, energy consumption and hazard index and to calculate their optimal global values. Besides, an enhanced NSGA-II algorithm (HNSGA-II) is proposed to optimize PDLBMRS efficiently. Finally, MIPM and HNSGA-II are applied to an actual mixed disassembly case of two types of computers, the comparison of the results solved by GUROBI and HNSGA-II verifies the correctness of the model and excellent performance of the algorithm, and the obtained Pareto solution set provides multiple options for decision-makers.

Keywords: waste disposal, disassembly line balancing, multi-robot station, robotic efficiency difference, HNSGA-II

Procedia PDF Downloads 194

Authors: Edward Sarich, Jack Ryan

Abstract:

In contrast to learning new vocabulary incidentally in one’s first language, foreign language vocabulary is often acquired purposefully, because a lack of natural exposure requires it to be studied in an artificial environment. It follows then that foreign language vocabulary may be more efficiently acquired if it is purpose-driven, or linked to a clear and desirable outcome. The research described in this paper relates to the early stages of what is seen as a long-term effort to measure the effectiveness of a methodology for purpose-driven foreign language vocabulary instruction, specifically by analyzing whether directed studying from high-frequency vocabulary lists leads to an improvement in Test of English for International Communication (TOEIC) scores. The research was carried out in two sections of a first-year university English composition class at a small university in Japan. The results seem to indicate that purposeful study from relevant high-frequency vocabulary lists can contribute to raising TOEIC scores and that the test preparation methodology used in this study was thought by students to be beneficial in helping them to prepare to take this high-stakes test.

Keywords: corpus vocabulary, language asssessment, second language vocabulary acquisition, TOEIC test preparation

Procedia PDF Downloads 124

Authors: Long Chen

Abstract:

With the proliferation of E-Learning, collaborative learning becomes more and more popular in various teaching and learning occasions. Studies over the years have proved that actively participating in classroom discussion can enhance student's learning experience, consolidating their knowledge and understanding of the class content. Collaborative learning can also allow students to share their resources and knowledge by exchanging, absorbing, and observing one another's opinions and ideas. Community Question Answering (CQA) services are particularly suitable paradigms for collaborative learning, since it is essentially an online collaborative learning platform where one can get information from multiple sources for he/her to choose from. However, current CQA services have only achieved limited success in collaborative learning due to the uncertainty of answers' quality. In this paper, we predict the quality of answers in a CQA service, i.e. Yahoo! Answers, for the use of online education and distance learning, which would enable a student to find relevant answers and potential answerers more effectively and efficiently, and thus greatly increase students' user experience in CQA services. Our experiment reveals that the quality of answers is influenced by a series of factors such as asking time, relations between users, and his/her experience in the past. We also show that by modelling user's profile with our proposed personalized features, student's satisfaction towards the provided answers could be accurately estimated.

Keywords: Community Question Answering, Collaborative Learning, Log File, Co-Training

Procedia PDF Downloads 416