Search results for: Deep mixed column

Authors: Nur Irmawati, H. A. Mohammed

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In the present study, mixed convection in a horizontal rectangular duct using Al2O3 is numerically investigated. The effects of different Rayleigh number, Reynolds number and radiation on flow and heat transfer characteristics were studied in detail. This study covers Rayleigh number in the range of 2×106≤Ra≤2×107 and Reynolds number in the range of 100≤Re≤1100. Results reveal that the Nusselt number increases as Reynolds and Rayleigh numbers increase. It was also found that the dimensionless temperature distribution increases as Rayleigh number increases.

Keywords: numerical simulation, mixed convection, horizontal rectangular duct, nanofluids

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Authors: Kyoungrean Kim

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Importance of deep-sea mineral resources is dramatically increasing due to the depletion of land mineral resources corresponding to increasing human’s economic activities. Korea has acquired exclusive exploration licenses at four areas which are the Clarion-Clipperton Fracture Zone in the Pacific Ocean (2002), Tonga (2008), Fiji (2011) and Indian Ocean (2014). The preparation for commercial mining of Nautilus minerals (Canada) and Lockheed martin minerals (USA) is expected by 2020. The London Protocol 1996 (LP) under International Maritime Organization (IMO) and International Seabed Authority (ISA) will set environmental guidelines for deep-sea mining until 2020, to protect marine environment. In this research, the applicability of washing/extraction treatment for the remediation of deep-sea mining tailings was mainly evaluated in order to present preliminary data to develop practical remediation technology in near future. Polymetallic nodule samples were collected at the Clarion-Clipperton Fracture Zone in the Pacific Ocean, then stored at room temperature. Samples were pulverized by using jaw crusher and ball mill then, classified into 3 particle sizes (> 63 µm, 63-20 µm, < 20 µm) by using vibratory sieve shakers (Analysette 3 Pro, Fritsch, Germany) with 63 µm and 20 µm sieve. Only the particle size 63-20 µm was used as the samples for investigation considering the lower limit of ore dressing process which is tens to 100 µm. Rhamnolipid and sodium alginate as biosurfactant and aluminum sulfate which are mainly used as flocculant were used as environmentally friendly additives. Samples were adjusted to 2% liquid with deionized water then mixed with various concentrations of additives. The mixture was stirred with a magnetic bar during specific reaction times and then the liquid phase was separated by a centrifugal separator (Thermo Fisher Scientific, USA) under 4,000 rpm for 1 h. The separated liquid was filtered with a syringe and acrylic-based filter (0.45 µm). The extracted heavy metals in the filtered liquid were then determined using a UV-Vis spectrometer (DR-5000, Hach, USA) and a heat block (DBR 200, Hach, USA) followed by US EPA methods (8506, 8009, 10217 and 10220). Polymetallic nodule was mainly composed of manganese (27%), iron (8%), nickel (1.4%), cupper (1.3 %), cobalt (1.3%) and molybdenum (0.04%). Based on remediation standards of various countries, Nickel (Ni), Copper (Cu), Cadmium (Cd) and Zinc (Zn) were selected as primary target materials. Throughout this research, the use of rhamnolipid was shown to be an effective approach for removing heavy metals in samples originated from manganese nodules. Sodium alginate might also be one of the effective additives for the remediation of deep-sea mining tailings such as polymetallic nodules. Compare to the use of rhamnolipid and sodium alginate, aluminum sulfate was more effective additive at short reaction time within 4 h. Based on these results, sequencing particle separation, selective extraction/washing, advanced filtration of liquid phase, water treatment without dewatering and solidification/stabilization may be considered as candidate technologies for the remediation of deep-sea mining tailings.

Keywords: deep-sea mining tailings, heavy metals, remediation, extraction, additives

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Authors: Khamael M. Abualnaja, Lidija Šiller, Ben R. Horrocks

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The structural characterization of silicon nano crystals (SiNCs) have been carried out using transmission electron microscope (TEM) and atomic force microscopy (AFM). SiNCs are crystalline with an average diameter of 65 nm. Erbium trichloride was added to silicon nano crystals using a simple chemical procedure. Erbium is useful in this context because it has a narrow emission band at ⋍1536 nm which corresponds to a standard optical telecommunication wavelength. The optical properties of SiNCs and erbium-mixed SiNCs samples have been characterized using UV-vis spectroscopy, confocal Raman spectroscopy and photoluminescence spectroscopy (PL). SiNCs and erbium-mixed SiNCs samples exhibit an orange PL emission peak at around 595 nm that arise from radiative recombination of Si. Erbium-mixed SiNCs also shows a weak PL emission peak at ⋍1536 nm that attributed to the intra-4f transition in erbium ions. The intensity of the PL peak of Si in erbium-mixed SiNCs is increased in the intensity up to ×3 as compared to pure SiNCs. It was observed that intensity of 1536 nm peak decreased dramatically in the presence of silicon nano crystals and the PL emission peak of silicon nano crystals is increased. Therefore, the resulted data present that the energy transfer from erbium ions to SiNCs due to the chemical mixing method which used in this work.

Keywords: Silicon Nanocrystals (SiNCs), Erbium Ion, photoluminescence, energy transfer

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Authors: Tahmina Sultana, Yasser Hassan

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Vehicle technologies rapidly evolving due to their multifaceted advantages. Adapted different vehicle technologies like connectivity and automation on the same roads with conventional vehicles controlled by human drivers may increase speed disparity in mixed vehicle technologies. Identifying relationships between speed distribution measures of different vehicles and road geometry can be an indicator of speed disparity in mixed technologies. Previous studies proved that speed disparity measures and traffic accidents are inextricably related. Horizontal curves from three geographic areas were selected based on relevant criteria, and speed data were collected at the midpoint of the preceding tangent and starting, ending, and middle point of the curve. Multiple linear mixed effect models (LME) were developed using the instantaneous speed measures representing the speed of vehicles at different points of horizontal curves to recognize relationships between speed variance (standard deviation) and road geometry. A simulation-based framework (Monte Carlo) was introduced to check the speed disparity on horizontal curves in mixed vehicle technologies when consideration is given to the interactions among connected vehicles (CVs), autonomous vehicles (AVs), and non-connected vehicles (NCVs) on horizontal curves. The Monte Carlo method was used in the simulation to randomly sample values for the various parameters from their respective distributions. Theresults show that NCVs had higher speed variation than CVs and AVs. In addition, AVs and CVs contributed to reduce speed disparity in the mixed vehicle technologies in any penetration rates.

Keywords: autonomous vehicles, connected vehicles, non-connected vehicles, speed variance

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Brinda Sharma, Saakshi S. Sarpotdar

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Deep-fat frying is popular standard method that has been studied basically to clarify the complicated mechanisms of fat decomposition at high temperatures and to assess their effects on human health. The aim of this paper is to point out the application of method engineering that has been recently improved our understanding of the fundamental principles and mechanisms concerned at different scales and different times throughout the process: pretreatment, frying, and cooling. It covers the several aspects of deep-fat drying. New results regarding the understanding of the frying method that are obtained as a results of major breakthroughs in on-line instrumentation (heat, steam flux, and native pressure sensors), within the methodology of microstructural and imaging analysis (NMR, MRI, SEM) and in software system tools for the simulation of coupled transfer and transport phenomena. Such advances have opened the approach for the creation of significant information of the behavior of varied materials and to the event of latest tools to manage frying operations via final product quality in real conditions. Lastly, this paper promotes an integrated approach to the frying method as well as numerous competencies like those of chemists, engineers, toxicologists, nutritionists, and materials scientists also as of the occupation and industrial sectors.

Keywords: frying, cooling, imaging analysis (NMR, MRI, SEM), deep-fat frying

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Authors: T. Zaki, Fathi S. Soliman

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Desulfurization process is required by most, if not all refineries, to achieve ultra-low sulfur fuel, that contains less than 10 ppm sulfur. A lot of research works and many effective technologies have been studied to achieve deep desulfurization process in moderate reaction environment, such as adsorption desulfurization (ADS), oxidative desulfurization (ODS), biodesulfurization and extraction desulfurization (EDS). Extraction desulfurization using deep eutectic solvents (DESs) is considered as simple, cheap, highly efficient and environmentally friend process. In this work, four DESs were designed and synthesized. Choline chloride (ChCl) was selected as typical hydrogen bond acceptors (HBA), and ethylene glycol (EG), glycerol (Gl), urea (Ur) and thiourea (Tu) were selected as hydrogen bond donors (HBD), from which a series of deep eutectic solvents were synthesized. The experimental data showed that the synthesized DESs showed desulfurization affinities towards the thiophene species in cyclohexane solvent. Ethylene glycol molecules showed more affinity to create hydrogen bond with thiophene instead of choline chloride. Accordingly, ethylene glycol choline chloride DES has the highest extraction efficiency.

Keywords: DES, desulfurization, green solvent, extraction

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: C. Senabre, S. Valero, M. Lopez, E. Velasco, M. Sanchez

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This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology.

Keywords: short-term load forecasting, mixed effects models, neural networks, mixed effects models

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Authors: Muhammad Afief Maruf

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Kacapuri foundation is the traditional shallow foundation of building which has been used since long by traditional communities in Borneo, Indonesia. Kacapuri foundation is a foundation that uses a combination of ironwood (eusideroxylon zwageri) as a column and truss and softwood (Melaleuca leucadendra syn. M. leucadendron) as a raft. In today's modern era, ironwood happened to be a rare item, and it is protected by the Indonesian government. This condition then triggers the idea to maintain the shape of the traditional foundation by modifying the material. The suggestion is replacing the ironwood column with reinforced concrete column. In addition, the number of stem softwood is added to sustain the burden of replacing the column material. Although this modified form of Kacapuri foundation is currently still not been tested in applications in society, some research on the modified Kacapuri foundation has been conducted by some researchers and government unit. This paper will try to give an overview of the theoretical foundation bearing capacity Kacapuri modifications applied to the soft alluvial soil located in Borneo, Indonesia, where the original form of Kacapuri is implemented this whole time. The foundation is modeled buried depth in 2m below the ground surface and also below the ground water level. The calculation of the theoretical bearing capacity and then is calculated based on the bearing capacity equation suggested Skempton, Terzaghi and Ohsuki using the data of soft alluvial soil in Borneo. The result will then compared with the bearing capacity of the Kacapuri foundation original design from some previous research. The results show that the ultimate bearing capacity of the Modified Kacapuri foundation using Skempton equation amounted to 329,26 kN, Terzaghi for 456,804kN, and according Ohsaki amounted to 491,972 kN. The ultimate bearing capacity of the original Kacapuri foundation model based on Skempton equation is 18,23 kN. This result shows that the modification added the ultimate bearing capacity of the foundation, although the replacement of ironwood to reinforced concrete will also add some dead load to the total load itself.

Keywords: bearing capacity, Kacapuri, modified foundation, shallow foundation

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Authors: Sang Hwan Bak, Min Jung Kim, Dong Bok Lee

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Fe-2%Mn-0.5%Si-0.2C steel was welded and corroded at 600, 700 and 800oC for 20 h in 1 atm of N2/H2S/H2O-mixed gas in order to characterize the high-temperature corrosion behavior of the welded joint. Corrosion proceeded fast and almost linearly. It increased with an increase in the corrosion temperature. H2S formed FeS owing to sulfur released from H2S. The scales were fragile and nonadherent.

Keywords: Fe-Mn-Si steel, corrosion, welding, sulfidation, H2S gas

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Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

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Authors: Chun-Ying Lee, Mei-Wen Wu, Li-Yi Cheng, Chiang-Ho Cheng

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This research studies the electroplating of zinc coating in the zinc chloride bath mixed with supercritical CO2. The sodium fluoride (NaF) was used as the bath additive to change the structure and property of the coating, and therefore the roughness and corrosion resistance of the zinc coating was investigated. The surface characterization was performed using optical microscope (OM), X-ray diffractometer (XRD), and α-step profilometer. Moreover, the potentiodynamic polarization measurement in 3% NaCl solution was employed in the corrosion resistance evaluation. Because of the emulsification of the electrolyte mixed in Sc-CO2, the electroplated zinc produced the coating with smoother surface, smaller grain, better throwing power and higher corrosion resistance. The main role played by the NaF was to reduce the coating’s roughness and grain size. In other words, the CO2 mixed with the electrolyte under the supercritical condition performed the similar function as brighter and leveler in zinc electroplating to enhance the throwing power and corrosion resistance of the coating.

Keywords: supercritical CO2, zinc-electroplating, sodium fluoride, electroplating

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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Authors: Areej R. Banjar, Abraham G. Campbell

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The use of Mixed Reality (MR) in teaching and training is growing popular and can improve students’ ability to perform technical procedures. This short paper outlines the creation of an interactive educational MR 3D application that aims to improve the quality of instruction for dentistry students. This application is called MRImplantology and aims to teach the fundamentals and preoperative planning of dental implant placement. MRImplantology uses cone-beam computed tomography (CBCT) images as the source for 3D dental models that dentistry students will be able to freely manipulate within a 3D MR world to aid their learning process.

Keywords: augmented reality, education, dentistry, cone-beam computed tomography CBCT, head mounted display HMD, mixed reality

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Authors: Sakshi Gupta, Seema Joshi

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Thiosemicarbazones (TSCs) have garnered significant attention in coordination chemistry due to their versatile coordination modes and pharmacological properties. Mixed ligand complexes of TSCs represent a promising area of research, offering enhanced antimicrobial activities compared to their parent compounds. This review provides an overview of the synthesis, characterization, and antimicrobial properties of mixed ligand complexes incorporating thiosemicarbazones. The synthesis of mixed ligand complexes typically involves the reaction of a metal salt with TSC ligands and additional ligands, such as nitrogen- or oxygen-based ligands. Various transition metals, including copper, nickel, and cobalt, have been employed to form mixed ligand complexes with TSCs. Characterization techniques such as spectroscopy, X-ray crystallography, and elemental analysis are commonly utilized to confirm the structures of these complexes. One of the key advantages of mixed ligand complexes is their enhanced antimicrobial activity compared to pure TSC compounds. The synergistic effect between the TSC ligands and additional ligands contributes to increased efficacy, possibly through improved metal-ligand interactions or enhanced membrane permeability. Furthermore, mixed ligand complexes offer the potential for selective targeting of microbial species while minimizing toxicity to mammalian cells. This selectivity arises from the specific interactions between the metal center, TSC ligands, and biological targets within microbial cells. Such targeted antimicrobial activity is crucial for developing effective treatments with minimal side effects. Moreover, the versatility of mixed ligand complexes allows for the design of tailored antimicrobial agents with optimized properties. By varying the metal ion, TSC ligands, and additional ligands, researchers can fine-tune the physicochemical properties and biological activities of these complexes. This tunability opens avenues for the development of novel antimicrobial agents with improved efficacy and reduced resistance. In conclusion, mixed ligand complexes of thiosemicarbazones represent a promising class of compounds with potent antimicrobial activities. Further research in this field holds great potential for the development of novel therapeutic agents to combat microbial infections effectively.

Keywords: metal complex, thiosemicarbazones, mixed ligand, selective targeting, antimicrobial activity

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Authors: Ehsan Mohseni

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The aim of this thesis is to provide numerical analyses of reinforced concrete beams-column joints with/without CFRP (Carbon Fiber Reinforced Polymer) in order to achieve a better understanding of the behaviour of strengthened beamcolumn joints. A comprehensive literature survey prior to this study revealed that published studies are limited to a handful only; the results are inconclusive and some are even contradictory. Therefore in order to improve on this situation, following that review, a numerical study was designed and performed as presented in this thesis. For the numerical study, dimensions, end supports, and characteristics of the beam and column models were the same as those chosen in an experimental investigation performed previously where ten beamcolumn joint were tested tofailure. Finite element analysis is a useful tool in cases where analytical methods are not capable of solving the problem due to the complexities associated with the problem. The cyclic behaviour of FRP strengthened reinforced concrete beam-columns joints is such a case. Interaction of steel (longitudinal and stirrups), concrete and FRP, yielding of steel bars and stirrups, cracking of concrete, the redistribution of stresses as some elements unload due to crushing or yielding and the confinement of concrete due to the presence of FRP are some of the issues that introduce the complexities into the problem.Numerical solutions, however, can provide further in formation about the behaviour in lieu of the costly experiments or complex closed form solutions. This thesis presents the results of a numerical study on beam-column joints subjected to cyclic loads that are strengthened with CFRP wraps or strrips in a variety of configurations. The analyses are performed by Abaqus finite element program and are calibrated with the experiments. A range of issues in beam-column joints including the cracking load, the ultimate load, lateral load-displacement curves of joints, are investigated.The numerical results for different configurations of strengthening are compared. Finally, the computed numerical results are compared with those obtained from experiments. the cracking load, the ultimate load, lateral load-displacement curves obtained from numerical analysis for all joints were in very good agreement with the corresponding experimental ones.The results obtained from the numerical analysis in most cases implies that this method is conservative and therefore can be used in design applications with confidence.

Keywords: numerical analysis, strengthening, CFRP, reinforced concrete joints

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Authors: Vinay Agrawal, Suyash Garg, Ravindra Nagar, Vinay Chandwani

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Open ground storey with floating columns is a typical feature in the modern multistory constructions in urban India. Such features are very much undesirable in buildings built in seismically active areas. The present study proposes a feasible solution to mitigate the effects caused due to non-uniformity of stiffness and discontinuity in load path and to simultaneously hold the functional use of the open storey particularly under the floating column, through a combination of various lateral strengthening systems. An investigation is performed on an example building with nine different analytical models to bring out the importance of recognising the presence of open ground storey and floating columns. Two separate analyses on various models of the building namely, the equivalent static analysis and the response spectrum analysis as per IS: 1893-2002 were performed. Various measures such as incorporation of Chevron bracings and shear walls, strengthening the columns in the open ground storey, and their different combinations were examined. The analysis shows that, in comparison to two short ones separated by interconnecting beams, the structural walls are most effective when placed at the periphery of the buildings and used as one long structural wall. Further, it can be shown that the force transfer from floating columns becomes less horizontal when the Chevron Bracings are placed just below them, thereby reducing the shear forces in the beams on which the floating column rests.

Keywords: equivalent static analysis, floating column, open ground storey, response spectrum analysis, shear wall, stiffness irregularity

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Gee-Cheol Kim, Joo-Won Kang

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Structural characteristics of spatial structure are different from that of rahmen structures and it has many factors that are unpredictable experientially. Both horizontal and vertical earthquake should be considered because of seismic behaviour characteristics of spatial structures. This experimental study is conducted about seismic response characteristics of roof structure according to the effect of columns or walls, through scale model of arch structure that has the basic dynamic characteristics of spatial structure. Though remarkable response is not occurred for horizontal direction in the region of higher frequency than the region of frequency that seismic energy is concentrated, relatively large response is occurred in vertical direction. It is proved that seismic response of arch structure with column is varied according to property of column.

Keywords: arch structure, seismic response, shaking table, spatial structure

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Authors: Ladaporn Pithuk

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This study aims to investigate the marketing mixed factors that affecting on expectations about commercial transactions through social networks. The research method will using quantitative research, data was collected by questionnaires to person have experience access to trading over the internet for 400 sample by purposive sampling method. Data was analyzed by descriptive statistic including percentage, mean, standard deviation and using quality function deployment for hypothesis testing. Finding the most significant interrelationship between marketing mixed factors and commercial transactions expectations through social networks are product and place the relationship of five ties product and place (location) is involved in almost all will make the site a model that meets the needs of the user visit. In terms of price, the promotion, privacy, personalization and providing a process technical. This will make operations more efficient, reduce confusion, duplication, delays in data transmission, including the creation of different elements in products and services.

Keywords: commercial transactions expectations, marketing mixed factors, social networks, consumer behavior

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

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Authors: Shuo Wang

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The hydrogeological parameters of the engineering site and the hydraulic connection between the aquifers can be obtained by the pumping test. Through the recovery test, the characteristics of water level recovery and the law of surface subsidence recovery can be understood. The above two tests can provide the basis for subsequent engineering design. At present, the deformation of deep soil caused by pumping tests is often neglected. However, some studies have shown that the maximum settlement subject to groundwater drawdown is not necessarily on the surface but in the deep soil. In addition, the law of settlement recovery of each soil layer subject to water level recovery is not clear. If the deformation-sensitive structure is deep in the test site, safety accidents may occur. In this study, the pumping test and recovery test of a confined aquifer in Shanghai are introduced. The law of measured groundwater changes and surface subsidence are analyzed. In addition, the fluid-solid coupling model was established by ABAQUS based on the Biot consolidation theory. The models are verified by comparing the computed and measured results. Further, the variation law of water level and the deformation law of deep soil during pumping and recovery tests under different site conditions and different times and spaces are discussed through the above model. It is found that the maximum soil settlement caused by pumping in a confined aquifer is related to the permeability of the overlying aquitard and pumping time. There is a lag between soil deformation and groundwater changes, and the recovery rate of settlement deformation of each soil layer caused by the rise of water level is different. Finally, some possible research directions are proposed to provide new ideas for academic research in this field.

Keywords: coupled hydro-mechanical analysis, deep ground settlement, numerical simulation, pumping test, recovery test

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Sowmiyaa V. S., Tiruvengala Padma, Dhanasekaran B.

Abstract:

Due to urbanization, traffic congestion, air pollution and fuel consumption underground metros are constructed in urban cities nowadays. These metros reduce the commutation time and makes the daily transportation in urban cities hassle free. To construct the underground metros deep excavations are to be carried out. These excavations should be supported by an appropriate earth retaining structures to provide stability and to prevent deformation failures. The failure of deep excavations is catastrophic and hence appropriate caution need to be carried out during design and construction stages. This paper covers the construction aspects, equipment, quality control, design aspects of one of the earth retaining systems the Diaphragm Walls.

Keywords: underground metros, diaphragm wall, quality control of diaphragm wall, design aspects of diaphragm wall

Procedia PDF Downloads 81

Authors: Chaitanya Varma, Arpan Mehar

Abstract:

The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways.

Keywords: highway, mixed traffic flow, modeling, operating speed

Procedia PDF Downloads 439

Authors: Yongrong Li, Ralf Domroes

Abstract:

Due to significant energy savings, enablement of higher machining speed as well as environmentally friendly features, Minimum Quantity Lubrication (MQL) has been used for many machining processes efficiently. However, in the deep hole drilling field (small tool diameter D < 5 mm) and long tool (length L > 25xD) it is always a bottle neck for a single channel MQL system. The single channel MQL, based on the Venturi principle, faces a lack of enough oil quantity caused by dropped pressure difference during the deep hole drilling process. In this paper, a system concept based on a bypass design has explored its possibility to dynamically reach the required pressure difference between the air inlet and the inside of aerosol generator, so that the deep hole drilling demanded volume of oil can be generated and delivered to tool tips. The system concept has been investigated in static and dynamic laboratory testing. In the static test, the oil volume with and without bypass control were measured. This shows an oil quantity increasing potential up to 1000%. A spray pattern test has demonstrated the differences of aerosol particle size, aerosol distribution and reaction time between single channel and bypass controlled single channel MQL systems. A dynamic trial machining test of deep hole drilling (drill tool D=4.5mm, L= 40xD) has been carried out with the proposed system on a difficult machining material AlSi7Mg. The tool wear along a 100 meter drilling was tracked and analyzed. The result shows that the single channel MQL with a bypass control can overcome the limitation and enhance deep hole drilling with a small tool. The optimized combination of inlet air pressure and bypass control results in a high quality oil delivery to tool tips with a uniform and continuous aerosol flow.

Keywords: deep hole drilling, green production, Minimum Quantity Lubrication (MQL), near dry machining

Procedia PDF Downloads 180