Search results for: unknown input observer

Authors: Kanlaya Kong-Ngern, Chutima Homwonk, Sittiruk Roytrakul

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In this research, we compared the proteomic profile of two rice leaf sheaths under salt stress, Thai moderately salt tolerant rice (Leaung Anan), and high salt tolerant rice (Pokkali). Seeds were grown in hydroponic culture for 21 days before NaCl was introduced initially at the level of 12 dS m⁻¹ for 10 days. Then the leaf sheath proteomes were analyzed by 1D-SDS-PAGE and NanoLC-MS/MS. In this study, 873 proteins were detected. Among these proteins, 219 proteins were known proteins and the other proteins were unnamed and unknown proteins. By using Mev software, we found that only 31 proteins in treated plants of both rice cultivars significantly expressed, 21 proteins were up-regulated and 10 proteins were down-regulated. Interestingly, the intensity of the 3 proteins in the Leaung Anan more expressed than in the Pokkali. The results indicate that the up-regulated proteins were more expressed in less tolerant rice may play an important role in helping rice to survive under salt stress.

Keywords: mass spectrometry, proteomics, rice leaf sheaths, salt stress

Procedia PDF Downloads 115

Authors: Benjamin Leiby, Darryl Ahner

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This study demonstrates an alternative stochastic imputation approach for large datasets when preferred commercial packages struggle to iterate due to numerical problems. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The methodology capitalizes on correlation while using model residuals to provide the uncertainty in estimating unknown values. Examination of the methodology provides insight toward choosing linear or nonlinear modeling terms. Static tolerances common in most packages are replaced with tailorable tolerances that exploit residuals to fit each data element. The methodology evaluation includes observing computation time, model fit, and the comparison of known values to replaced values created through imputation. Overall, the country conflict dataset illustrates promise with modeling first-order interactions while presenting a need for further refinement that mimics predictive mean matching.

Keywords: correlation, country conflict, imputation, stochastic regression

Procedia PDF Downloads 108

Authors: R. Mohan, V. Jadhav, A. Ahmed, J. Rivas, A. Kelkar

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Cementitious materials are an excellent example of a composite material with complex hierarchical features and random features that range from nanometer (nm) to millimeter (mm) scale. Multi-scale modeling of complex material systems requires starting from fundamental building blocks to capture the scale relevant features through associated computational models. In this paper, molecular dynamics (MD) modeling is employed to predict the effect of plasticizer additive on the mechanical properties of key hydrated cement constituent calcium-silicate-hydrate (CSH) at the molecular, nanometer scale level. Due to complexity, still unknown molecular configuration of CSH, a representative configuration widely accepted in the field of mineral Jennite is employed. The effectiveness of the Molecular Dynamics modeling to understand the predictive influence of material chemistry changes based on molecular/nanoscale models is demonstrated.

Keywords: cement composite, mechanical properties, molecular dynamics, plasticizer additives

Procedia PDF Downloads 438

Authors: Diego De Almeida Pereira, Diana Borchenko

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Artificial neural networks are used for many applications as they are able to learn complex nonlinear relationships between input and output data. As the number of neurons and layers in a neural network increases, it is possible to represent more complex behaviors. The present study proposes that artificial neural networks are a valuable tool for architecture and engineering professionals concerned with understanding how buildings influence human and social well-being based on theories of environmental psychology.

Keywords: environmental psychology, architecture, neural networks, human and social well-being

Procedia PDF Downloads 465

Authors: Somnath Bhattachryya, Montree Bunruangses, Somchat Sonasang, Preecha Yupapin

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In this study and research on meditation and insight, the design and experiment with electronic circuits to manipulate the meditators' mental circles that call the chakras to have the same size is proposed. The shape of the circuit is 4-ports, called an add-drop multiplexer, that studies the meditation structure called the four-mindfulness foundation, then uses an AC power signal as an input instead of the meditation time function, where various behaviors with the method of re-filtering the signal (successive filtering), like eight noble paths. Start by inputting a signal at a frequency that causes the velocity of the wave on the perimeter of the circuit to cause particles to have the speed of light in a vacuum. The signal changes from electromagnetic waves and matter waves according to the velocity (frequency) until it reaches the point of the relativistic limit. The electromagnetic waves are transformed into photons with properties of wave-particle overcoming the limits of the speed of light. As for the matter wave, it will travel to the other side and cannot pass through the relativistic limit, called a shadow signal (echo) that can have power from increasing speed but cannot create speed faster than light or insight. In the experiment, the only the side where the velocity is positive, only where the speed above light or the corresponding frequency indicates intelligence. Other side(echo) can be done by changing the input signal to the other side of the circuit to get the same result. But there is no intelligence or speed beyond light. It is also used to study the stretching, contraction of time and wormholes that can be applied for teleporting, Bose-Einstein condensate and teleprinting, quantum telephone. The teleporting can happen throughout the system with wave-particle and echo, which is when the speed of the particle is faster than the stretching or contraction of time, the particle will submerge in the wormhole, when the destination and time are determined, will travel through the wormhole. In a wormhole, time can determine in the future and the past. The experimental results using the microstrip circuit have been found to be by the principle of quantum relativity, which can be further developed for both tools and meditation practitioners for quantum technology.

Keywords: quantu meditation, insight picture, quantum circuit, absolute time, teleportation

Procedia PDF Downloads 52

Authors: Mostafa Zandi, Ramin Mansouri

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Spillways are one of the most important hydraulic structures of dams that provide the stability of the dam and downstream areas at the time of flood. Morning-Glory spillway is one of the common spillways for discharging the overflow water behind dams, these kinds of spillways are constructed in dams with small reservoirs. In this research, the hydraulic flow characteristics of a morning-glory spillways are investigated with CFD model. Two dimensional unsteady RANS equations were solved numerically using Finite Volume Method. The PISO scheme was applied for the velocity-pressure coupling. The mostly used two-equation turbulence models, k- and k-, were chosen to model Reynolds shear stress term. The power law scheme was used for discretization of momentum, k , and  equations. The VOF method (geometrically reconstruction algorithm) was adopted for interface simulation. The results show that the fine computational grid, the input speed condition for the flow input boundary, and the output pressure for the boundaries that are in contact with the air provide the best possible results. Also, the standard wall function is chosen for the effect of the wall function, and the turbulent model k -ε (Standard) has the most consistent results with experimental results. When the jet is getting closer to end of basin, the computational results increase with the numerical results of their differences. The lower profile of the water jet has less sensitivity to the hydraulic jet profile than the hydraulic jet profile. In the pressure test, it was also found that the results show that the numerical values of the pressure in the lower landing number differ greatly in experimental results. The characteristics of the complex flows over a Morning-Glory spillway were studied numerically using a RANS solver. Grid study showed that numerical results of a 57512-node grid had the best agreement with the experimental values. The desired downstream channel length was preferred to be 1.5 meter, and the standard k-ε turbulence model produced the best results in Morning-Glory spillway. The numerical free-surface profiles followed the theoretical equations very well.

Keywords: morning-glory spillway, CFD model, hydraulic characteristics, wall function

Procedia PDF Downloads 61

Authors: M. Bazin, I. Bouguir, D. Combe, V. Germain, G. Lassade

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The problem of managing a fleet of quad-rotor drones in a completely unknown environment is analyzed in the present paper. This work is following the footsteps of other studies about how should be managed the movements of a swarm of elements that have to stay gathered throughout their activities. In this paper we aim to demonstrate the limitations of a system where absolutely all the calculations and physical movements of our elements are done by one single external element. The strategy of control is an adaptive approach which takes into account the explored environment. This is made possible thanks to a set of command rules which can guide the drones through various missions with defined goal. The result of the mission is independent of the nature of environment and the number of drones in the fleet. This strategy is based on a simultaneous usage of different data: obstacles positions, real-time positions of all drones and relative positions between the different drones. The present work is made with the Robot Operating System and used several open-source projects on localization and usage of drones.

Keywords: cooperative guidance, distributed control, unmanned aerial vehicle, obstacle avoidance

Procedia PDF Downloads 291

Authors: Yusuf Ulaş Kabukçu, Si̇nan Çeli̇k, Onur Salan, Mai̇de Altuntaş, Mert Can Dalkiran, Gökseni̇n Bozdağ, Metehan Bulut, Fati̇h Yaman

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This study presents a more compact implementation of the 2.4GHz MIT Coffee Can Doppler Radar for 2.6GHz operating frequency. The main difference of our prototype depends on the use of microstrip antennas which makes it possible to transport with a small robotic vehicle. We have designed our radar system with two different channels: Tx and Rx. The system mainly consists of Voltage Controlled Oscillator (VCO) source, low noise amplifiers, microstrip antennas, splitter, mixer, low pass filter, and necessary RF connectors with cables. The two microstrip antennas, one is element for transmitter and the other one is array for receiver channel, was designed, fabricated and verified by experiments. The system has two operation modes: speed detection and range detection. If the switch of the operation mode is ‘Off’, only CW signal transmitted for speed measurement. When the switch is ‘On’, CW is frequency-modulated and range detection is possible. In speed detection mode, high frequency (2.6 GHz) is generated by a VCO, and then amplified to reach a reasonable level of transmit power. Before transmitting the amplified signal through a microstrip patch antenna, a splitter used in order to compare the frequencies of transmitted and received signals. Half of amplified signal (LO) is forwarded to a mixer, which helps us to compare the frequencies of transmitted and received (RF) and has the IF output, or in other words information of Doppler frequency. Then, IF output is filtered and amplified to process the signal digitally. Filtered and amplified signal showing Doppler frequency is used as an input of audio input of a computer. After getting this data Doppler frequency is shown as a speed change on a figure via Matlab script. According to experimental field measurements the accuracy of speed measurement is approximately %90. In range detection mode, a chirp signal is used to form a FM chirp. This FM chirp helps to determine the range of the target since only Doppler frequency measured with CW is not enough for range detection. Such a FMCW Doppler radar may be used in border security of the countries since it is capable of both speed and range detection.

Keywords: doppler radar, FMCW, range detection, speed detection

Procedia PDF Downloads 382

Authors: Omar Hussein El Ayadi, EmadY. El-Mansouri, Mohamed B. Dozan

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Production oil process require specific details i.e. oil composition. Generally, types of oil or differentiation between reservoir fluids depend specifically on composition. The main purpose of this study is to correlate and predict the Libyan oil (reservoir fluid and residual) composition utilizing tri-angle-coordinate plots discovered and tasked with Excel. The reservoir fluid data (61 old + 47 new), the residual oil data (33 new) collected from most of Libyan reservoirs were correlated with each others. Moreover, find a relation between stock tank molecular weight and stock tank oil gravity (oAPI), the molecular weight oh (C7+) versus residual oil gravity (oAPI). The average value of every oil composition was estimated including non-hydrocarbon (H2S, CO2, and N2). Nevertheless, the isomers (i-…) and normal (n-…) structure of (C4) and (C5) were also obtained. The summary of the conclusion is; utilizing excel Microsoft office to draw triangle coordinates to find two unknown component if only one is known. However, it is recommended to use the obtained oil composition plots and equations for any oil composition dependents i.e. optimum separator pressure.

Keywords: PVT, phase behavior, petroleum, chemical engineering

Procedia PDF Downloads 499

Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

Procedia PDF Downloads 670

Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 275

Authors: Sandhya Armoogum

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In distributed systems and in open systems such as the Internet, often mobile code has to run on unknown and potentially hostile hosts. Mobile code such as a mobile agent is vulnerable when executing on remote hosts. The mobile agent may be subjected to various attacks such as tampering, inspection, and replay attack by a malicious host. Much research has been done to provide solutions for various security problems, such as authentication of mobile agent and hosts, integrity and confidentiality of the data carried by the mobile agent. Many of such proposed solutions in literature are not suitable for open systems whereby the mobile code arrives and executes on a host which is not known and trusted by the mobile agent owner. In this paper, we propose the adoption of the reference monitor by hosts in an open system for providing trust and security for mobile code execution. A secure protocol for the distribution of the reference monitor entity is described. This reference monitor entity on the remote host may also provide several security services such as authentication and integrity to the mobile code.

Keywords: security, mobile agents, reference monitor, trust

Procedia PDF Downloads 425

Authors: LaRon A. Scott, Donna M. Gibson

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Data indicate that counselor education programs in the United States do not prepare their students adequately to serve students with disabilities nor provide counseling as a related service. There is a need to train more school counselors to provide related services to students with disabilities, for many reasons, but specifically, school counselors are participating in Individualized Education Programs (IEP) and transition planning meetings for students with disabilities where important academic, mental health and post-secondary education decisions are made. While school counselors input is perceived very important to the process, they may not have the knowledge or training in this area to feel confident in offering required input in these meetings. Using a conceptual research design, a model that can be used to prepare school counseling students as related service providers and effective supports to address transition for students with disabilities was developed as a component of this research. The authors developed the Collaborative Model of Preparing School Counseling Students as Related Service Providers to Students with Disabilities, based on a conceptual framework that involves an integration of Social Cognitive Career Theory (SCCT) and evidenced-based practices based on Self-Determination Theory (SDT) to provide related and transition services and planning with students with disabilities. The authors’ conclude that with five overarching competencies, (1) knowledge and understanding of disabilities, (2) knowledge and expertise in group counseling to students with disabilities, (3), knowledge and experience in specific related service components, (4) knowledge and experience in evidence-based counseling interventions, (5) knowledge and experiencing in evidenced-based transition and career planning services, that school counselors can enter the field with the necessary expertise to adequately serve all students. Other examples and strategies are suggested, and recommendations for preparation programs seeking to integrate a model to prepare school counselors to implement evidenced-based transition strategies in supporting students with disabilities are included

Keywords: transition education, social cognitive career theory, self-determination, counseling

Procedia PDF Downloads 233

Authors: Naime Boudemagh, Zoheir Hammoudi

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The parameters estimation of the K-distribution is an essential part in radar detection. In fact, presence of interfering targets in reference cells causes a decrease in detection performances. In such situation, the estimate of the shape and the scale parameters are far from the actual values. In the order to avoid interfering targets, we propose an Automatic Censoring (AC) algorithm of radar interfering targets in K-distribution. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The homogeneous population is then used to estimate the unknown parameters by the classical Method of Moment (MOM). The AC algorithm does not need any prior information about the clutter parameters nor does it require both the number and the position of interfering targets. The accuracy of the estimation parameters obtained by this algorithm are validated and compared to various actual values of the shape parameter, using Monte Carlo simulations, this latter show that the probability of censing in multiple target situations are in good agreement.

Keywords: parameters estimation, method of moments, automatic censoring, K distribution

Procedia PDF Downloads 364

Authors: Mahdi Shamsi, Tohid Yousefi Rezaii, Siavash Eftekharifar

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Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising.

Keywords: compressed sensing, data denoising, model order selection, sparse representation

Procedia PDF Downloads 466

Authors: A. El Hamdouni, J. Zbitou, A. Tajmouati, L. El Abdellaoui, A. Errkik, A. Tribak, M. Latrach

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This paper presents a novel fractal antenna structure proposed for UWB (Ultra – Wideband) applications. The frequency band 3.1-10.6 GHz released by FCC (Federal Communication Commission) as the commercial operation of UWB has been chosen as frequency range for this antenna based on coplanar waveguide (CPW) feed and circular shapes fulfilled according to fractal geometry. The proposed antenna is validated and designed by using an FR4 substrate with overall area of 34 x 43 mm2. The simulated results performed by CST-Microwave Studio and compared by ADS (Advanced Design System) show good matching input impedance with return loss less than -10 dB between 2.9 GHz and 11 GHz.

Keywords: Fractal antenna, Fractal Geometry, CPW Feed, UWB, FCC

Procedia PDF Downloads 370

Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

Procedia PDF Downloads 159

Authors: Jair Servín-Aguilar, Yu Tang

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We address the problem of robust attitude tracking for agile satellites under unknown bounded torque disturbances using a double-gimbal variable-speed control-moment gyro (DGVSCMG) driven by a cluster of three permanent magnet synchronous motors (PMSMs). Uniform practical asymptotic stability is achieved at the torque control level first. The desired speed of gimbals and the acceleration of the spin wheel to produce the required torque are then calculated by a velocity-based steering law and tracked at the PMSM speed-control level by designing a speed-tracking controller with compensation for the vibration caused by eccentricity and imbalance due to mechanical imperfection in the DGVSCMG. Uniform practical asymptotic stability of the overall system is ensured by loan relying on the analysis of the resulting cascaded system. Numerical simulations are included to show the performance improvement of the proposed controller.

Keywords: agile satellites, vibration compensation, internal model, stability

Procedia PDF Downloads 100

Authors: Abhimanyu Kumar, Chirag Gupta

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This work presents a comprehensive study on the employment of Model Predictive Control (MPC) for a three-phase voltage-source inverter to regulate the output voltage efficiently. The inverter is modeled via the Clarke Transformation, considering a scenario where the load is unknown. An LC filter model is developed, demonstrating its efficacy in Total Harmonic Distortion (THD) reduction. The system, when implemented with fault-tolerant multilevel inverter topologies, ensures reliable operation even under fault conditions, a requirement that is paramount with the increasing dependence on renewable energy sources. The research also integrates a Fuzzy Logic based fault tolerance system which identifies and manages faults, ensuring consistent inverter performance. The efficacy of the proposed methodology is substantiated through rigorous simulations and comparative results, shedding light on the voltage prediction efficiency and the robustness of the model even under fault conditions.

Keywords: total harmonic distortion, fuzzy logic, renewable energy sources, MLI

Procedia PDF Downloads 96

Authors: Hyun-Seok Hong, In-Gun Kim, Ye-Jun Oh, Ju Lee

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This paper deals with the effects of welding performed for the manufacture of laminations in a stator in the case of prototype motors that are manufactured in small quantity. As a result of performing the no-load test for an IPM (interior permanent magnet)-type BLDC (blushless direct current) motor manufactured by welding both inside and outside of the stator, it was found that more DC input than expected was provided. To verify the effects of welding, a stator was re-manufactured by bonding, and DC inputs provided during the no-load test were compared.

Keywords: welding, stator, Eddy current, BLDC

Procedia PDF Downloads 545

Authors: James P. Takona

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Most of the global population has never lived through a pandemic, and thus there is so much that remains unknown about students' capacity for resiliency under such environments and circumstances and what a timeline for full recovery will look like. The session will guide participants to focus on misinformation and disinformation in the context of recent crisis events, with specific reference to how information flows across. Particular focus will be given to the flow of information in mediated technology and platforms with particular reference to K-12 and teacher preparation program environments. The paper will draw on theories and responses from the sociology of disaster, the social psychology of rumoring, and published studies on disinformation and misinformation. Applications will be identified and applied in the context of online information-sharing during crisis events. The session will offer the application of the Center for Contagious Diseases' Crisis and Emergency Risk Communication model to understand the themes and evolution of misinformation and disinformation. The paper will invite session participants to suggest and interact with raised challenges on the impact of dis-and misinformation.

Keywords: Sociology of disaster, misinformation, dis-information, Social Psychology of rumors

Procedia PDF Downloads 75

Authors: Kebaili Bachir

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The reuse of concrete waste as a secondary aggregate could be an efficient solution for sustainable development and long-term environmental protection. The variable nature of waste concrete, with various compressive strengths, can have a negative effect on the final compressive strength of recycled concrete. Accordingly, an experimental test programme was developed to evaluate the effect of parent concrete qualities on the performance of recycled concrete. Three grades with different compressive strengths 10MPa, 20MPa, and 30MPa were considered in the study; moreover, an unknown compressive strength was introduced as well. The trial mixes used 40% secondary aggregates (both course and fine) and 60% of natural aggregates. The compressive strength of the test concrete decrease between 15 and 25% compared to normal concrete with no secondary aggregates. This work proves that the strength properties of the parent concrete have a limited effect on the compressive strength of recycled concrete. Low compressive strength parent concrete when crushed generate a high percentage of recycled coarse aggregates with the less attached mortar and give the same compressive strength as an excellent parent concrete. However, the decrease in compressive strength can be mitigated by increasing the cement content 4% by weight of recycled aggregates used.

Keywords: compressive, concrete, quality, recycled, strength

Procedia PDF Downloads 310

Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

Procedia PDF Downloads 116

Authors: Faruk Emir, Simel Ayyıldız, Cem Şahin

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Behçet’s disease or Behçet’s syndrome is a chronic and multi-systemic inflammatory disease of unknown cause. This syndrome often presents with mucous membrane ulceration and ocular problems. As a systemic disease Behcet includes triple-symptom complex of recurrent oral aphthous ulcers, genital ulcers, and uveitis. Nearly all patients present with some form of painful oral mucocutaneous ulcerations in the form of aphthous ulcers. The aim of the treatment plan for Behçet’s Disease patients is to eliminate oral problems and increase the patient comfort.This clinical report represents the prosthodontic rehabilitation of Behcet’s disease patients mandibular total edentulism with the use of implant supported prosthesis that planned on custom abutments and bar retainers via CAD/CAM technology and patient satisfaction has been achieved in function and aesthetics.

Keywords: Behçet’s disease, CAD/CAM, custom-made manufacturing, titanium milled bar retainer

Procedia PDF Downloads 312

Authors: Andreas Lind, Aitor Iriondo Pascual, Dan Hogberg, Lars Hanson

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Factory setup lifecycles are most often described and prepared in CAD environments; the preparation is based on experience and inputs from several cross-disciplinary processes. Early in the factory setup preparation, a so-called block layout is created. The intention is to describe a high-level view of the intended factory setup and to claim area reservations and allocations. Factory areas are then blocked, i.e., targeted to be used for specific intended resources and processes, later redefined with detailed factory setup layouts. Each detailed layout is based on the block layout and inputs from cross-disciplinary preparation processes, such as manufacturing sequence, productivity, workers’ workplace requirements, and resource setup preparation. However, this activity is often not carried out with all variables considered simultaneously, which might entail a risk of sub-optimizing the detailed layout based on manual decisions. Therefore, this work aims to realize a digital method for assembly manufacturing layout planning where productivity, area utilization, and ergonomics can be considered simultaneously in a cross-disciplinary manner. The purpose of the digital method is to support engineers in finding optimized designs of detailed layouts for assembly manufacturing factories, thereby facilitating better decisions regarding setups of future factories. Input datasets are company-specific descriptions of required dimensions for specific area reservations, such as defined dimensions of a worker’s workplace, material façades, aisles, and the sequence to realize the product assembly manufacturing process. To test and iteratively develop the digital method, a demonstrator has been developed with an adaptation of existing software that simulates and proposes optimized designs of detailed layouts. Since the method is to consider productivity, ergonomics, area utilization, and constraints from the automatically generated block layout, a multi-objective optimization approach is utilized. In the demonstrator, the input data are sent to the simulation software industrial path solutions (IPS). Based on the input and Lua scripts, the IPS software generates a block layout in compliance with the company’s defined dimensions of area reservations. Communication is then established between the IPS and the software EPP (Ergonomics in Productivity Platform), including intended resource descriptions, assembly manufacturing process, and manikin (digital human) resources. Using multi-objective optimization approaches, the EPP software then calculates layout proposals that are sent iteratively and simulated and rendered in IPS, following the rules and regulations defined in the block layout as well as productivity and ergonomics constraints and objectives. The software demonstrator is promising. The software can handle several parameters to optimize the detailed layout simultaneously and can put forward several proposals. It can optimize multiple parameters or weight the parameters to fine-tune the optimal result of the detailed layout. The intention of the demonstrator is to make the preparation between cross-disciplinary silos transparent and achieve a common preparation of the assembly manufacturing factory setup, thereby facilitating better decisions.

Keywords: factory setup, multi-objective, optimization, simulation

Procedia PDF Downloads 136

Authors: Tomáš Bublík, Miroslav Virius

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This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and sub graph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm.

Keywords: AST, Java, tree matching, scripthon source code recognition

Procedia PDF Downloads 415

Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian

Abstract:

Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.

Keywords: desalting unit, crude oil, neural networks, simulation, recovery, separation

Procedia PDF Downloads 424

Authors: Rosa Nurtazina, Yerkebulan Zhumashov, Maral Tomanova

Abstract:

Humanity is entering an era when 'virtual reality' as the image of the world created by the media with the help of the Internet does not match the reality in many respects, when new communication technologies create a fundamentally different and previously unknown 'global space'. According to these technologies, the state begins to change the basic technology of political communication of the state and society, the state and the state. Nowadays, image of the state becomes the most important tool and technology. Image is a purposefully created image granting political object (person, organization, country, etc.) certain social and political values and promoting more emotional perception. Political image of the state plays an important role in international relations. The success of the country's foreign policy, development of trade and economic relations with other countries depends on whether it is positive or negative. Foreign policy image has an impact on political processes taking place in the state: the negative image of the countries can be used by opposition forces as one of the arguments to criticize the government and its policies.

Keywords: image of the country, country's image classification, function of the country image, country's image components

Procedia PDF Downloads 417

Authors: Chin-Chen Chang, I-Ta Lee, Tsung-Ta Ke, Wen-Kai Tai

Abstract:

Common methods for resizing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image resizing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.

Keywords: energy map, visual saliency, gradient map, seam carving

Procedia PDF Downloads 469

Authors: Burcu Oralhan, Zeki Oralhan, Nilsun Sariyer, Kumru Uyar

Abstract:

Data mining has been becoming more and more important and a wide range of applications in recent years. Data mining is the process of find hidden and unknown patterns in big data. One of the applied fields of data mining is Customer Relationship Management. Understanding the relationships between products and customers is crucial for every business. Customer Relationship Management is an approach to focus on customer relationship development, retention and increase on customer satisfaction. In this study, we made an application of a data mining methods in telecommunication customer relationship management side. This study aims to determine the customers profile who likely to leave the system, develop marketing strategies, and customized campaigns for customers. Data are clustered by applying classification techniques for used to determine the churners. As a result of this study, we will obtain knowledge from international telecommunication industry. We will contribute to the understanding and development of this subject in Customer Relationship Management.

Keywords: customer churn analysis, customer relationship management, data mining, telecommunication industry

Procedia PDF Downloads 299