Search results for: deep mixing column

Authors: D. Jagath Kumari, K. Srinivasa Rao

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Reinforced concrete column is an important structural element in a building. Concrete usually performs well in building fires. However, when it is subjected to prolonged fire exposure or unusually high temperatures, and then it will suffer significant distress. Because concrete pre-fire compressive strength generally exceeds design requirements, therefore an average strength reduction can be tolerated. However high temperature reduces the compressive strength of concrete so much that the concrete retains no useful structural strength. Therefore the residual strength and its performance of structure can be assed by NDT testing. In this paper, rebound hammer test and the ultrasonic pulse velocity (UPV) are used to evaluate the residual compressive strength and material integrity of post-fire-curing concrete subjected to elevated temperatures. Also considering the large availability of fly ash in most of the countries, an attempt was made to study the effect of high volume fly ash concrete exposed to elevated temperatures. 32 RC column specimens were made with a M20 grade concrete mix. Out of 32 column specimens 16 column specimens were made with OPC concrete and other 16 column specimens were made with HVFA concrete. All specimens having similar cross-section details. Columns were exposed to fire for temperatures from 100oC to 800o C with increments of 100o C for duration of 3 hours. Then the specimens allowed cooling to room temperature by two methods natural air cooling method and immediate water quenching method. All the specimens were tested identically, for the compressive strengths and material integrity by rebound hammer and ultrasonic pulse velocity meter respectively for study. These two tests were carried out on preheating and post heating of the column specimens. The percentage variation of compressive strengths of RCC columns with the increase in temperature has been studied and compared the results for both OPC and HVFA concretes. Physical observations of the heated columns were observed.

Keywords: HVFA concrete, NDT testing, residual strength

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Jae-Yeong Choi, Gyu-Mok Jeon, Jong-Chun Park, Yong-Jin Cho, Seok-Tae Yoon

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When air flow is injected into water, bubbles are formed in various types inside the water pool along with the air flow rate. The bubbles are floated in equilibrium with forces such as buoyancy, surface tension and shear force. Single bubble generated at low flow rate maintains shape, but bubbles with high flow rate break up to make mixing and turbulence. In addition to this phenomenon, as the hot air bubbles are injected into the water, heat affects the interface of phases. Therefore, the main scope of the present work reveals how to proceed heat transfer between water and hot air bubbles injected into water. In the present study, a series of CFD simulation for the heat transfer of hot bubbles injected through a nozzle near the bottom in a cylindrical water column are performed using a commercial CFD software, STAR-CCM+. The governing equations for incompressible and viscous flow are the continuous and the RaNS (Reynolds- averaged Navier-Stokes) equations and discretized by the FVM (Finite Volume Method) manner. For solving multi-phase flow, the Eulerian multiphase model is employed and the interface is defined by VOF (Volume-of-Fluid) technique. As a turbulence model, the SST k-w model considering the buoyancy effects is introduced. For spatial differencing the 3th-order MUSCL scheme is adopted and the 2nd-order implicit scheme for time integration. As the results, the dynamic behavior of the rising hot bubbles with the flow rate injected and regarding heat transfer mechanism are discussed based on the simulation results.

Keywords: heat transfer, hot bubble injection, eulerian multiphase model, flow rate, CFD (Computational Fluid Dynamics)

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Authors: A. Akbarpour, M. R. Adib Ramezani, M. Zhian, N. Ghorbani Amirabad

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One way to improve the performance of structures against of earthquake is passive control which requires no external power source. In this research, tuned liquid column dampers which are among of systems with the capability to transfer energy between various modes of vibration, are used. For the first time, a liquid column damper for vibration control structure is presented. After modeling this structure in design building software and performing the static and dynamic analysis and obtaining the necessary parameters for the design of tuned liquid column damper, the whole structure will be analyzed in finite elements software. The tuned liquid column dampers are installed on the structure and nonlinear time-history analysis is done in two cases of structures; with and without dampers. Finally the seismic behavior of building in the two cases will be examined. In this study the nonlinear time-history analysis on a twelve-story steel structure equipped with damper subject to records of earthquake including Loma Prieta, Northridge, Imperiall Valley, Pertrolia and Landers was performed. The results of comparing between two cases show that these dampers have reduced lateral displacement and acceleration of levels on average of 10%. Roof displacement and acceleration also reduced respectively 5% and 12%. Due to structural asymmetric in the plan, the maximum displacements of surrounding structures as well as twisting were studied. The results show that the dampers lead to a 10% reduction in the maximum response of structure stories surrounding points. At the same time, placing the dampers, caused to reduce twisting on the floor plan of the structure, Base shear of structure in the different earthquakes also has been reduced on the average of 6%.

Keywords: retrofitting, passive control, tuned liquid column damper, finite element analysis

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Authors: F. Z. Benabid, F. Zouai, N. Kharchi, D. Benachour

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A HDPE/ZnO nano composites have been successfully performed using the co-mixing. The ZnO was first co-mixed with the stearic acid then added to the polymer in the plastograph. The nano composites prepared with the co-mixed ZnO were compared to those prepared with the neat TiO2. The nano composites were characterized by different techniques as the wide-angle X-ray scattering (WAXS). The micro and nano structure/properties relationships were investigated. The present study allowed establishing good correlations between the different measured properties.

Keywords: exfoliation, ZnO, nano composites, HDPE, co-mixing

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Authors: A. Al-Rifaie, Z. W. Guan, S. W. Jones

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This paper presents a method for modelling and analysing end plate beam-to-column connections to obtain the quasi-static behaviour using non-linear dynamic explicit integration. In addition to its importance to study the static behaviour of a structural member, quasi-static behaviour is largely needed to be compared with the dynamic behaviour of such members in order to investigate the dynamic effect by proposing dynamic increase factors (DIFs). The beam-to-column bolted connections contain various contact surfaces at which the implicit procedure may have difficulties converging, resulting in a large number of iterations. Contrary, explicit procedure could deal effectively with complex contacts without converging problems. Hence, finite element modelling using ABAQUS/explicit is used in this study to address the dynamic effect may be produced using explicit procedure. Also, the effect of loading rate and mass scaling are discussed to investigate their effect on the time of analysis. The results show that the explicit procedure is valuable to model the end plate beam-to-column connections in terms of failure mode, load-displacement relationships. Also, it is concluded that loading rate and mass scaling should be carefully selected to avoid the dynamic effect in the solution.

Keywords: quasi-static, end plate, finite elements, connections

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Authors: Chung-Hao Wu, Tung-Dju Lin, Ming-Chin Ho, Minehiro Nishiyama

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This paper discusses reported fire tests of concrete beams and columns, future fire tests of beam/column frames, and an innovative concept for designing a beam/column furnace. The proposed furnace could be designed to maximize the efficiency of fire test procedures and minimize the cost of furnace construction and fuel consumption. ASTM E119 and ISO 834 standards were drafted based on prescriptive codes and have several weaknesses. The first involves a provision allowing the support regions of a test element to be protected from fire exposure. The second deals with the L/30 deflection end point instead of the structural end point (collapse) in order to protect the hydraulic rams from fire damage. Furthermore, designers commonly use the measured fire endurances of interior columns to assess fire ratings of edge and corner columns of the same building. The validity of such an engineering practice is theoretically unsound. Performance-Based Codes (PBC) require verification tests of structural frames including the beam/column joints to overcome these weaknesses but allow the use of element test data as reference only. In the last 30 years, PBC have gained global popularity because the innovative design and flexibility in achieving an ultimate performance goal.

Keywords: fire resistance, concrete structure, beam/column frame, fire tests

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: A. Rajesh Kanna, G. Suresh Kumar, Sathyanaryana N. Gummadi

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There are various sources of energies available worldwide and among them, crude oil plays a vital role. Oil recovery is achieved using conventional primary and secondary recovery methods. In-order to recover the remaining residual oil, technologies like Enhanced Oil Recovery (EOR) are utilized which is also known as tertiary recovery. Among EOR, Microbial enhanced oil recovery (MEOR) is a technique which enables the improvement of oil recovery by injection of bio-surfactant produced by microorganisms. Bio-surfactant can retrieve unrecoverable oil from the cap rock which is held by high capillary force. Bio-surfactant is a surface active agent which can reduce the interfacial tension and reduce viscosity of oil and thereby oil can be recovered to the surface as the mobility of the oil is increased. Research in this area has shown promising results besides the method is echo-friendly and cost effective compared with other EOR techniques. In our research, on laboratory scale we produced bio-surfactant using the strain Pseudomonas putida (MTCC 2467) and injected into designed simple sand packed column which resembles actual petroleum reservoir. The experiment was conducted in order to determine the efficiency of produced bio-surfactant in oil recovery. The column was made of plastic material with 10 cm in length. The diameter was 2.5 cm. The column was packed with fine sand material. Sand was saturated with brine initially followed by oil saturation. Water flooding followed by bio-surfactant injection was done to determine the amount of oil recovered. Further, the injection of bio-surfactant volume was varied and checked how effectively oil recovery can be achieved. A comparative study was also done by injecting Triton X 100 which is one of the chemical surfactant. Since, bio-surfactant reduced surface and interfacial tension oil can be easily recovered from the porous sand packed column.

Keywords: bio-surfactant, bacteria, interfacial tension, sand column

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Authors: F. Waffo Tchuimmo, G. S. Kwandio Dongoua, C. U. Yves Mbono Samba, O. Dafounansou, L. Nana

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The strength criterion is an important condition of great interest to guarantee the stability of the structural elements. The present work is based on the study of the free vibration of Euler’s Bernoulli column of equal strength in compression while considering its own weight and the axial load in compression and tension subjected to symmetrical boundary conditions. We use the differential quadrature method to investigate the first fifth naturals frequencies parameters of the column according to the different forms of geometrical sections. The results of this work give help in making a judicious choice of type of cross-section and a better boundary condition to guarantee good stability of this type of column in civil constructions.

Keywords: free vibration, equal strength, self-weight, tip force, differential quadrature method

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Authors: Taekyun Kim, Jongho Park, Jinwoong Choi, Sun-Kyu Park

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Recently, the abnormal climate phenomenon has enlarged due to the global warming. As a result, temperature variation is increasing and the term is being prolonged, frequency of high and low temperature is increasing by heat wave and severe cold. Especially for reinforced concrete structure, the corrosion of reinforcement has occurred by concrete crack due to temperature change and the durability of the structure that has decreased by concrete crack. Accordingly, the textile reinforced concrete (TRC) which does not corrode due to using textile is getting the interest and the investigation of TRC is proceeding. The study of TRC structure behavior has proceeded, but the characteristic study of the concrete used in TRC is insufficient. Therefore, characteristic of the concrete by changing mixing ratio is studied in this paper. As a result, mixing ratio with different water-binder ratio has influenced to the strength of concrete. Also, as the water-binder ratio has decreased, strength of concrete has increased.

Keywords: concrete, mixing ratio, textile, TRC

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Authors: Doo Ki Lee, Kumaresh Selvakumar, Man Young Kim, In Jae Song

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The widespread utilization of mixer in selective catalytic reduction (SCR) system marks a remarkable advantage in diesel engines. In the automotive selective catalytic reduction (SCR) system, the de-NOX efficiency can be improved by highly uniform flow with effective turbulent mixing. In this paper, the exhaust pipe is complemented with the swirling mixers of three different vane angles installed at the upstream of the SCR reactor. The attributes of the mixer are established by the variation in flow behavior followed by the drawback owing to the absence of mixer. In particular, the information pertaining to the selection of proper static mixer is provided based on the correlation between the uniformity index (UI) and the pressure drop. The uniform distribution of the flow at the entrance of the SCR reactor aids to determine the configuration which gives high mixing performance and comprehend the function of the mixer.

Keywords: pressure drop, selective catalytic reduction, static mixer, turbulent mixing, uniformity index

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Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

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With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.

Keywords: dialogue management, response generation, deep learning, evaluation

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Authors: Vinayambika S. Bhat, S. Shanmuga Priya, I. Thirunavukkarasu, Shreeranga Bhat

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An attempt has been made to design a decoupling controller for systems with more inputs more outputs with dead time in it. The de-coupler is designed for the chemical process industry 3×3 plant transfer function with dead time. The Quantitative Feedback Theory (QFT) based controller has also been designed here for the 2×2 distillation column transfer function. The developed control techniques were simulated using the MATLAB/Simulink. Also, the stability of the process was analyzed, together with the presence of various perturbations in it. Time domain specifications like setting time along with overshoot and oscillations were analyzed to prove the efficiency of the de-coupler method. The load disturbance rejection was tested along with its performance. The QFT control technique was synthesized based on the stability and performance specifications in the presence of uncertainty in time constant of the plant transfer function through sequential loop shaping technique. Further, the energy efficiency of the distillation column was improved by proper tuning of the controller. A distillation column consumes 3% of the total energy consumption of the world. A suitable control technique is very important from an economic point of view. The real time implementation of the process is under process in our laboratory.

Keywords: distillation, energy, MIMO process, time delay, robust stability

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Authors: Atef Ahmad Bleibel

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Most codes of practice, like ACI 318-14, require the use of strut-and-tie modeling to analyze and design reinforced concrete deep beams. Though, investigations that conducted on deep beams do not include ring deep beams of influential parameters. This work presents an analytical parametric study using strut-and-tie modeling stated by ACI 318-14 to predict load capacity of 20 reinforced concrete ring deep beam specimens with different parameters. The parameters that were under consideration in the current work are ring diameter (Dc), number of supports (NS), width of ring beam (bw), concrete compressive strength (f'c) and width of bearing plate (Bp). It is found that the load capacity decreases by about 14-36% when ring diameter increases by about 25-75%. It is also found that load capacity increases by about 62-189% when number of supports increases by about 33-100%, while the load capacity increases by about 25-75% when the beam ring width increases by about 25-75%. Finally, it is found that load capacity increases by about 24-76% when compressive strength increases by about 24-76%, while the load capacity increases by about 5-16% when Bp increases by about 25-75%.

Keywords: load parameters, reinforced concrete, ring deep beam, strut and tie

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Authors: Hyungoo Kang, Minsung Kim, Jinkoo Kim

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This study investigates the performance of a 2D and 3D steel moment frame subjected to vehicle collision at a first story column using LS-DYNA. The finite element models of vehicles provided by the National Crash Analysis Center (NCAC) are used for numerical analysis. Nonlinear dynamic time history analysis of the 2D and 3D model structures are carried out based on the arbitrary column removal scenario, and the vertical displacement of the damaged structures are compared with that obtained from collision analysis. The analysis results show that the model structure remains stable when the speed of the vehicle is 40km/h. However, at the speed of 80 and 120km/h both the 2D and 3D structures collapse by progressive collapse. The vertical displacement of the damaged joint obtained from collision analysis is significantly larger than the displacement computed based on the arbitrary column removal scenario.

Keywords: vehicle collision, progressive collapse, FEM, LS-DYNA

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Authors: A. Sekhar, N. Ramakrishna Raju

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This paper is a result of ground improvement using deep vibro techniques with combination of sand and stone columns performed on a highly liquefaction susceptible site (70 to 80% sand strata and balance silt) with low bearing capacities due to high settlements located (earth quake zone V as per IS code) at Madhepura, Bihar state in northern part of India. Initially, it was envisaged with bored cast in-situ/precast piles, stone/sand columns. However, after detail analysis to address both liquefaction and improve bearing capacities simultaneously, it was analyzed the deep vibro techniques with combination of sand and stone columns is excellent solution for given site condition which may be first time in India. First after detail soil investigation, pre eCPT test was conducted to evaluate the potential depth of liquefaction to densify silty sandy soils to improve factor of safety against liquefaction. Then trail test were being carried out at site by deep vibro compaction technique with sand and stone columns combination with different spacings of columns in triangular shape with different timings during each lift of vibro up to ground level. Different spacings and timing was done to obtain the most effective spacing and timing with vibro compaction technique to achieve maximum densification of saturated loose silty sandy soils uniformly for complete treated area. Then again, post eCPT test and plate load tests were conducted at all trail locations of different spacings and timing of sand and stone columns to evaluate the best results for obtaining the required factor of safety against liquefaction and the desired bearing capacities with reduced settlements for construction of industrial structures. After reviewing these results, it was noticed that the ground layers are densified more than the expected with improved factor of safety against liquefaction and achieved good bearing capacities for a given settlements as per IS codal provisions. It was also worked out for cost-effectiveness of lightly loaded single storied structures by using deep vibro technique with sand column avoiding stone. The results were observed satisfactory for resting the lightly loaded foundations. In this technique, the most important is to mitigating liquefaction with improved bearing capacities and reduced settlements to acceptable limits as per IS: 1904-1986 simultaneously up to a depth of 19M. To our best knowledge it was executed first time in India.

Keywords: ground improvement, deep vibro techniques, liquefaction, bearing capacity, settlement

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Authors: Nima Khosravi

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This paper describes an integrated optimization technique with concurrent use of sequential quadratic programming, genetic algorithm, and simulated annealing particle swarm optimization for the design and optimization of a beam column. In this research, the comparison between 4 different types of optimization methods. The comparison is done and it is found out that all the methods meet the required constraints and the lowest value of the objective function is achieved by SQP, which was also the fastest optimizer to produce the results. SQP is a gradient based optimizer hence its results are usually the same after every run. The only thing which affects the results is the initial conditions given. The initial conditions given in the various test run were very large as compared. Hence, the value converged at a different point. Rest of the methods is a heuristic method which provides different values for different runs even if every parameter is kept constant.

Keywords: beam column, genetic algorithm, particle swarm optimization, sequential quadratic programming, simulated annealing

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Delowar Hossain, Genci Capi

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This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.

Keywords: deep learning, genetic algorithm, object recognition, robot grasping

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Authors: A. Al-Rifaie, Z. W. Guan, S. W. Jones

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Beam to column connection can be considered as the most important structural part that affects the response of buildings to progressive collapse. However, many studies were conducted to investigate the beam to column connection under accidental loads such as fire, blast and impact load to investigate the connection response. The study is a part of a PhD plan to investigate different types of connections under lateral impact load. The conventional test setups, such as cruciform setup, were designed to apply shear forces and bending moment on the connection, whilst, in the lateral impact case, the connection is subjected to combined tension and moment. Hence, a review is presented to introduce the previous test setup that is used to investigate the connection behaviour. Then, the design and fabrication of the novel test setup is presented. Finally, some trial test results to investigate the efficiency of the proposed setup are discussed. The final results indicate that the setup was efficient in terms of the simplicity and strength.

Keywords: connections, impact load, drop hammer, testing methods

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Authors: Amos Olajide Oluyege, Emmanuel Uchechukwu Opara, Sunday Adeniyi Adedutan, Joseph Adeola Fuwape

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This study was conducted to determine the durability of wood-cement particleboards when exposed to attack by subterranean termites, Macrotermes subhylinus. The boards were made from Terminalia superba and Gmelina arborea wood sawdust at nominal board densities (BD) of 1000, 900, and 800 kg/m³ using wood-cement mixing ratios (MR) of 3:1, 2.5:1, 2:1, and 1:1. Above ground durability tests against termite attack were carried out according to ASTM D 2017 for 14 weeks. Results of visual assessment of the wood cement particleboards show that all the board samples had a visual rating that was not less than 7 (i.e., moderate attack) for both species irrespective of the MR and BD. T. superba boards were found to have higher resistance to termite attack compared to their G. arborea counterparts. The mean values for weight loss following exposure ranged from 1.93 to 6.13% and 3.24 to 12.44%. Analysis of variance (ANOVA) results of the weight loss assessment revealed a significant (p < 0.05) effect of species and mixing ratio on the weight loss of the boards due to termite attack with F(₁,₇₂) = 92.890 and P = 0.000 and F(₃,₇₂) = 8.318 and p = 0.000, while board density did not have any significant effect (p > 0.05) with F (₂,₇₂) = 1.307 and p = 0.277. Thus, boards made from a higher mixing ratio had better resistance against termite attacks. Thus, it can be concluded that the durability of cement-bonded particleboards when exposed to subterranean termite attack is not only dependent on the quality of the wood raw material (species) but also on the enhanced protection imparted by the cement matrix; the protection increased with increase in cement/wood mixing ratio.

Keywords: cement-bonded particleboard, mixing ratio, board density, Gmelina arborea, Terminalia superba

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Authors: Sunil Bandaru, Suresh Yv, Srinivas Vanapalli

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Heavy machinery especially compactors has more vibrations induced from the compactor mechanism than the engines. Since the operator’s comfort is most important in any of the machines, this paper shows interest in studying the vibrations on the steering wheel for a double drum compactor. As there are no standard procedures available for testing vibrations on the steering wheel of double drum compactors, this paper tries to evaluate the vibrations on the steering wheel by considering most of the possibilities. In addition to the feasibility for the operator to adjust the steering wheel tilt as in the case of automotive, there is an option for the operator to change the orientation of the operator platform for the complete view of the road’s edge on both the ends of the front and rear drums. When the orientation is either +/-180°, the operator will be closer to the compactor mechanism; also there is a possibility for the shuffle in the modes with respect to the operator. Hence it is mandatory to evaluate the vibrations levels in both cases. This paper attempts to evaluate the vibrations on the steering wheel by considering the two operator platform positions and three steering wheel tilting angles.

Keywords: FEA, CAE, steering column, steering column orientation position

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Authors: Merve Sagiroglu

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The earthquake-resistant steel structure design is required taking into account the behavior of beam-column connections besides the basic properties of the structure such as material and geometry. Beam-column connections play an important role in the behavior of frame systems. Taking into account the behaviour of connection in analysis and design of steel frames is important due to presenting the actual behavior of frames. So, the behavior of the connections should be well known. The most important force which transmitted by connections in the structural system is the moment. The rotational deformation is customarily expressed as a function of the moment in the connection. So, the moment-rotation curves are the best expression of behaviour of the beam-to-column connections. The designed connections form various moment-rotation curves according to the elements of connection and the shape of placement. The only way to achieve this curve is with real-scale experiments. The experiments of some connections have been carried out partially and are formed in the databank. It has been formed the models using this databank to express the behavior of connection. In this study, theoretical studies have been carried out to model a real behavior of the top and seat angles connections with angles. Two stiffeners in the top and seat angle to increase the stiffness of the connection, and two stiffeners in the beam web to prevent local buckling are used in this beam-to-column connection. Mathematical models have been performed using the database of the beam-to-column connection experiments previously by authors. Using the data of the tests, it has been aimed that analytical expressions have been developed to obtain the moment-rotation curve for the connection details whose test data are not available. The connection has been dimensioned in various shapes and the effect of the dimensions of the connection elements on the behavior has been examined.

Keywords: top and seat angle connection, stiffener, moment-rotation curves, analytical study

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Authors: A. P. Joshi, H. V. Warrior, J. P. Panda

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This paper is a continuation of the work carried out by various turbulence modelers in Oceanography on the topic of oceanic turbulent mixing. It evaluates the evolution of ocean water temperature and salinity by the appropriate modeling of turbulent mixing utilizing proper prescription of eddy viscosity. Many modelers in past have suggested including terms like shear, buoyancy and vorticity to be the parameters that decide the slow pressure strain correlation. We add to it the fact that dissipation anisotropy also modifies the correlation through eddy viscosity parameterization. This recalibrates the established correlation constants slightly and gives improved results. This anisotropization of dissipation implies that the critical Richardson’s number increases much beyond unity (to 1.66) to accommodate enhanced mixing, as is seen in reality. The model is run for a couple of test cases in the General Ocean Turbulence Model (GOTM) and the results are presented here.

Keywords: Anisotropy, GOTM, pressure-strain correlation, Richardson critical number

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Authors: H. S. Youm, S. G. Hong

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This paper presents the test results on 5 slab-column connection specimens with Ultra High Performance Fiber-Reinforced Concrete (UHPFRC) overlay including 1 control specimen to investigate retrofitting effect of UHPFRC overlay on the punching shear capacity. The test parameters were the thickness of the UHPFRC overlay and the amount of steel re-bars in it. All specimens failed in punching shear mode with abrupt failure aspect. The test results showed that by adding a thin layer of UHPFRC over the Reinforced Concrete (RC) substrates, considerable increases in global punching shear resistance up to 82% and structural rigidity were achieved. Furthermore, based on the cracking patterns the composite systems appeared to be governed by two failure modes: 1) diagonal shear failure in RC section and 2) debonding failure at the interface.

Keywords: punching shear strength, retrofit, slab-column connection, UHPFRC, UHPFRC overlay

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: Salamatu Sani

Abstract:

This study is an investigation of how code-mixing manifests in the language development of a Nigerian child, especially in the Hausa speaking environment. It is hinged on the fact that the environment influences the first language acquired by a child regardless of the cultural and/or linguistic background of the parents. The child under investigation has been subjected to close monitoring on her speech hitherto. It is a longitudinal study covering a period of twelve months (January 2018 to December 2018); that was when the subject was between twenty-four and thirty months of age. The speeches have been recorded by means of a tape recorder, video, and a diary. The study employs as a theoretical framework, emergentism, which is an eclectic of the behaviourist and the mentalist theories to the study of language development, for analysis. This is in agreement with the positions of Skinner and Watson. Sequel to this investigation, it was discovered the environment is a major factor that influences the exposure of a child to a language more than the other factors and that, if a child is exposed to more than one language, there is a great tendency for such a child to code-mix and code-switch in her speech production. The child under investigation, in spite of the linguistic background of her parents, speaks the Hausa Language much better than the other languages around her though with remarkable code-mixing with other languages around her such as English and Ebira languages. The study concludes that although a child is born with the innate ability to acquire a particular language, the environment plays a key role to trigger the innate ability and consequently, the child is exposed to the acquisition of the dominant language around her at a particular given time.

Keywords: bilingual, code-mixing, emergentism, environment, Hausa

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Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

Abstract:

In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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