Search results for: hybrid use of reinforcement
Commenced in January 2007
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Edition: International
Paper Count: 2321

Search results for: hybrid use of reinforcement

2261 Seismic Analysis of Structurally Hybrid Wind Mill Tower

Authors: Atul K. Desai, Hemal J. Shah

Abstract:

The tall windmill towers are designed as monopole tower or lattice tower. In the present research, a 125-meter high hybrid tower which is a combination of lattice and monopole type is proposed. The response of hybrid tower is compared with conventional monopole tower. The towers were analyzed in finite element method software considering nonlinear seismic time history load. The synthetic seismic time history for different soil is derived using the SeismoARTIF software. From the present research, it is concluded that, in the hybrid tower, we are not getting resonance condition. The base shear is less in hybrid tower compared to monopole tower for different soil conditions.

Keywords: dynamic analysis, hybrid wind mill tower, resonance condition, synthetic time history

Procedia PDF Downloads 119
2260 Numerical Analysis of the Effect of Geocell Reinforcement above Buried Pipes on Surface Settlement and Vertical Pressure

Authors: Waqed H. Almohammed, Mohammed Y. Fattah, Sajjad E. Rasheed

Abstract:

Dynamic traffic loads cause deformation of underground pipes, resulting in vehicle discomfort. This makes it necessary to reinforce the layers of soil above underground pipes. In this study, the subbase layer was reinforced. Finite element software (PLAXIS 3D) was used to in the simulation, which includes geocell reinforcement, vehicle loading, soil layers and Glass Fiber Reinforced Plastic (GRP) pipe. Geocell reinforcement was modeled using a geogrid element, which was defined as a slender structure element that has the ability to withstand axial stresses but not to resist bending. Geogrids cannot withstand compression but they can withstand tensile forces. Comparisons have been made between the numerical models and experimental works, and a good agreement was obtained. Using the mathematical model, the performance of three different pipes of diameter 600 mm, 800 mm, and 1000 mm, and three different vehicular speeds of 20 km/h, 40 km/h, and 60 km/h, was examined to determine their impact on surface settlement and vertical pressure at the pipe crown for two cases: with and without geocell reinforcement. The results showed that, for a pipe diameter of 600 mm under geocell reinforcement, surface settlement decreases by 94 % when the speed of the vehicle is 20 km/h and by 98% when the speed of the vehicle is 60 km/h. Vertical pressure decreases by 81 % when the diameter of the pipe is 600 mm, while the value decreases to 58 % for a pipe with diameter 1000 mm. The results show that geocell reinforcement causes a significant and positive reduction in surface settlement and vertical stress above the pipe crown, leading to an increase in pipe safety.

Keywords: dynamic loading, finite element, geocell-reinforcement, GRP pipe, PLAXIS 3D, surface settlement

Procedia PDF Downloads 225
2259 Experimental Research on Ductility of Regional Confined Concrete Beam

Authors: Qinggui Wu, Xinming Cao, Guyue Guo, Jiajun Ding

Abstract:

In efforts to study the shear ductility of regional confined concrete beam, 5 reinforced concrete beams were tested to examine its shear performance. These beams has the same shear span ratio, concrete strength, different ratios of tension reinforcement and shapes of stirrup. The purpose of the test is studying the effects of stirrup shape and tension reinforcement ratio on failure mode and shear ductility. The test shows that the regional confined part can be used as an independent part and the rest of the beam is good to work together so that the ductility of the beam is more one time higher than that of the normal confined concrete beam. The related laws of the effect of tension reinforcement ratio and stirrup shapes on beam’s shear ductility are founded.

Keywords: ratio of tension reinforcement, stirrup shapes, shear ductility, failure mode

Procedia PDF Downloads 297
2258 A Framework of Dynamic Rule Selection Method for Dynamic Flexible Job Shop Problem by Reinforcement Learning Method

Authors: Rui Wu

Abstract:

In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

Procedia PDF Downloads 73
2257 Generic Hybrid Models for Two-Dimensional Ultrasonic Guided Wave Problems

Authors: Manoj Reghu, Prabhu Rajagopal, C. V. Krishnamurthy, Krishnan Balasubramaniam

Abstract:

A thorough understanding of guided ultrasonic wave behavior in structures is essential for the application of existing Non Destructive Evaluation (NDE) technologies, as well as for the development of new methods. However, the analysis of guided wave phenomena is challenging because of their complex dispersive and multimodal nature. Although numerical solution procedures have proven to be very useful in this regard, the increasing complexity of features and defects to be considered, as well as the desire to improve the accuracy of inspection often imposes a large computational cost. Hybrid models that combine numerical solutions for wave scattering with faster alternative methods for wave propagation have long been considered as a solution to this problem. However usually such models require modification of the base code of the solution procedure. Here we aim to develop Generic Hybrid models that can be directly applied to any two different solution procedures. With this goal in mind, a Numerical Hybrid model and an Analytical-Numerical Hybrid model has been developed. The concept and implementation of these Hybrid models are discussed in this paper.

Keywords: guided ultrasonic waves, Finite Element Method (FEM), Hybrid model

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2256 Network Connectivity Knowledge Graph Using Dwave Quantum Hybrid Solvers

Authors: Nivedha Rajaram

Abstract:

Hybrid Quantum solvers have been given prime focus in recent days by computation problem-solving domain industrial applications. D’Wave Quantum Computers are one such paragon of systems built using quantum annealing mechanism. Discrete Quadratic Models is a hybrid quantum computing model class supplied by D’Wave Ocean SDK - a real-time software platform for hybrid quantum solvers. These hybrid quantum computing modellers can be employed to solve classic problems. One such problem that we consider in this paper is finding a network connectivity knowledge hub in a huge network of systems. Using this quantum solver, we try to find out the prime system hub, which acts as a supreme connection point for the set of connected computers in a large network. This paper establishes an innovative problem approach to generate a connectivity system hub plot for a set of systems using DWave ocean SDK hybrid quantum solvers.

Keywords: quantum computing, hybrid quantum solver, DWave annealing, network knowledge graph

Procedia PDF Downloads 88
2255 Flow Performance of Hybrid Cement Based Mortars

Authors: Z. Abdollahnejad, M. Kheradmand, F. Pacheco Torgal

Abstract:

The workability of hybrid alkaline cements is a field of knowledge that still needs further research efforts. This paper reports experimental results of 32 hybrid cement mixes regarding the joint effect of sodium hydroxide concentration, the use of a commercial superplasticizer and a biopolymer on the flow and compressive strength performance. The results show that the use of commercial admixtures led to a slightly increase in the flow of mortars with lower sodium hydroxide concentration.

Keywords: waste reuse, fly ash, waste glass, hybrid cement, biopolymer, polycarboxylate, flow

Procedia PDF Downloads 281
2254 Deep Reinforcement Learning with Leonard-Ornstein Processes Based Recommender System

Authors: Khalil Bachiri, Ali Yahyaouy, Nicoleta Rogovschi

Abstract:

Improved user experience is a goal of contemporary recommender systems. Recommender systems are starting to incorporate reinforcement learning since it easily satisfies this goal of increasing a user’s reward every session. In this paper, we examine the most effective Reinforcement Learning agent tactics on the Movielens (1M) dataset, balancing precision and a variety of recommendations. The absence of variability in final predictions makes simplistic techniques, although able to optimize ranking quality criteria, worthless for consumers of the recommendation system. Utilizing the stochasticity of Leonard-Ornstein processes, our suggested strategy encourages the agent to investigate its surroundings. Research demonstrates that raising the NDCG (Discounted Cumulative Gain) and HR (HitRate) criterion without lowering the Ornstein-Uhlenbeck process drift coefficient enhances the diversity of suggestions.

Keywords: recommender systems, reinforcement learning, deep learning, DDPG, Leonard-Ornstein process

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2253 Overview of Different Approaches Used in Optimal Operation Control of Hybrid Renewable Energy Systems

Authors: K. Kusakana

Abstract:

A hybrid energy system is a combination of renewable energy sources with back up, as well as a storage system used to respond to given load energy requirements. Given that the electrical output of each renewable source is fluctuating with changes in weather conditions, and since the load demand also varies with time; one of the main attributes of hybrid systems is to be able to respond to the load demand at any time by optimally controlling each energy source, storage and back-up system. The induced optimization problem is to compute the optimal operation control of the system with the aim of minimizing operation costs while efficiently and reliably responding to the load energy requirement. Current optimization research and development on hybrid systems are mainly focusing on the sizing aspect. Thus, the aim of this paper is to report on the state-of-the-art of optimal operation control of hybrid renewable energy systems. This paper also discusses different challenges encountered, as well as future developments that can help in improving the optimal operation control of hybrid renewable energy systems.

Keywords: renewable energies, hybrid systems, optimization, operation control

Procedia PDF Downloads 342
2252 Effect of Cryogenic Treatment on Hybrid Natural Fiber Reinforced Polymer Composites

Authors: B. Vinod, L. J. Sudev

Abstract:

Natural fibers as reinforcement in polymer matrix material are gaining lot of attention in recent years. Natural fibers like jute, sisal, coir, hemp, banana etc. have attracted substantial importance as a potential structural material because of its attractive features along with its good mechanical properties. Cryogenic applications of natural fiber reinforced polymer composites are gaining importance. These materials need to possess good mechanical and physical properties at cryogenic temperatures to meet the high requirements by the cryogenic engineering applications. The objective of this work is to investigate the mechanical behavior of hybrid hemp/jute fibers reinforced epoxy composite material at liquid nitrogen temperature. Hybrid hemp/jute fibers reinforced polymer composite is prepared by hand lay-up method and test specimens are cut according to ASTM standards. These test specimens are dipped in liquid nitrogen for different time durations. The tensile properties, flexural properties and impact strength of the specimen are tested immediately after the specimens are removed from liquid nitrogen container. The experimental results indicate that the cryogenic treatment of the polymer composite has a significant effect on the mechanical properties of this material. The tensile properties and flexural properties of the hybrid hemp/jute fibers epoxy composite at liquid nitrogen temperature is higher than at room temperature. The impact strength of the material decreased after subjecting it to liquid nitrogen temperature.

Keywords: liquid nitrogen temperature, polymer composite, tensile properties, flexural properties

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2251 An Experimental Investigation of Bond Properties of Reinforcements Embedded in Geopolymer Concrete

Authors: Jee-Sang Kim, Jong Ho Park

Abstract:

Geopolymer concretes are a new class of construction materials that have emerged as an alternative to Ordinary Portland cement concrete. Considerable researches have been carried out on material development of geopolymer concrete, however, a few studies have been reported on the structural use of them. This paper presents the bond behaviors of reinforcement embedded in fly ash based geopolymer concrete. The development lengths of reinforcement for various compressive strengths of concrete, 20, 30 and 40 MPa, and reinforcement diameters, 10, 16, and 25 mm are investigated. Total 27 specimens were manufactured and pull-out test according to EN 10080 was applied to measure bond strength and slips between concrete and reinforcements. The average bond strengths decreased from 23.06MPa to 17.26 MPa, as the diameters of reinforcements increased from 10mm to 25mm. The compressive strength levels of geopolymer concrete showed no significant influence on bond strengths in this study. Also, the bond-slip relations between geopolymer concrete and reinforcement are derived using non-linear regression analysis for various experimental conditions.

Keywords: bond-slip relation, bond strength, geopolymer concrete, pull-out test

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2250 Weight Loss Degradation of Hybrid Blends LLDPE/Starch/PVA Upon Exposure to UV Light and Soil Burial

Authors: Rahmah M., Noor Zuhaira Abd Aziz, Farhan M., Mohd Muizz Fahimi M.

Abstract:

Polybag and mulch film for agricultural field pose environmental wastage upon disposal. Thus a degradable polybag was designed with hybrid sago starch (SS) and polyvinyl alcohol (PVA). Two Different blended composition of SS and PVA Hybrid have been compounded. Then, the hybrids blended are mixed with linear line density polyethylene (LLDPE) resin to fabricate polybag film through conventional film blowing process. Hybrid blends was compounded at different ratios. Samples of LLDPE, SS and PVA hybrid film were exposed to UV light and soil burial. The weight loss were determined during degradation process. Hybrid film by degradation of starch was found to decrease on esterification. However the hybrid film showed greater degradation in soil and uv radiation up to 60% of SS. Weight loss were also determined in control humidity oven with 70% humidity and temperature set up at 30 °C and left in humidity chamber for a month.

Keywords: LLDPE, PVA, sago starch, degradation, soil burial, uv radiation

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2249 Pultrusion of Side by Side Glass/Polypropylene Fibers: Study of Flexural and Shear Properties

Authors: Behrooz Ataee, Mohammad Golzar

Abstract:

The main purpose of using side by side (SBS) hybrid yarn in pultrusion thermoplastic method is reprisal the effect of high viscosity in melted thermoplastic and reduction of distance between reinforced fiber and melted thermoplastic. SBS hybrid fiber yarn composed of thermoplastic fibers and fiber reinforcement should be produced in the preparation of pultruded thermoplastic composites prepreg to reach better impregnation. An experimental set-up was designed and built to pultrude continues polypropylene and glass fiber to get obtain a suitable impregnated round prepregs. In final stage, the round prepregs come together to produce rectangular profile. Higher fiber volume fraction produces higher void volume fraction, however the second stage of the production process of rectangular profile and the cold die decrease 50% of the void volume fraction. Results show that whit increasing void volume fraction, flexural and shear strength decrease. Also, under certain conditions of parameters the pultruded profiles exhibit better flexural and shear strength. The pulling speed seems to have the greatest influence on the profile quality. In addition, adding cold die strongly increases the surface quality of rectangular profile.

Keywords: thermoplastic pultrusion, hybrid pultrusion, side-by-side fibers, impregnation

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2248 Mechanical Properties of Hybrid Cement Based Mortars Containing Two Biopolymers

Authors: Z. Abdollahnejad, M. Kheradmand, F. Pacheco-Torgal

Abstract:

The use of bio-based admixtures on construction materials is a recent trend that is gaining momentum. However, to our knowledge, no studies have been reported concerning the use of biopolymers on hybrid cement based mortars. This paper reports experimental results regarding the study of the influence of mix design of 43 hybrid cement mortars containing two different biopolymers on its mechanical performance. The results show that the use of the biopolymer carrageenan is much more effective than the biopolymer xanthan concerning the increase in compressive strength. An optimum biopolymer content was found.

Keywords: waste reuse, fly ash, waste glass, hybrid cement, biopolymers, mechanical strength

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2247 Somatic Hybridization of between Citrus and Murraya paniculata Cells Applied by Electro-Fusion

Authors: Hasan Basri Jumin

Abstract:

Protoplasts isolated from embryogenic callus of Citrus sinensis were electrically used with mesophyll protoplasts isolated from seedless Citrus relatives. Hybrid of somatic embryos plantlets was obtained after 7 months of culture. Somatic hybrid plants were regenerated into normal seedlings and successfully transferred to soil after strictly acclimatization in the glass pot. The somatic hybrid plants were obtained by screening on the basis of chromosomes count. The number of chromosome of root tip counting revealed plantlets tetraploids (2n = 4x = 36) and the other were diploids (2n = 2x = 18) morphologically resembling the mesophyll parent. This somatic hybrid will be utilized as a possible pollen parent for improving the Citrus sinensis. A complete protoplast-to-plant system of somatic hybrid was developed for Citrus sinensis and Citrus relatives which could facilitate the transfer of nuclear and cytoplasmic genes from this species into cultivated Citrus through protoplast fusion.

Keywords: chromosome, Murraya paniculata, protoplast fusion, somatic hybrid, tetrapoliod

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2246 Experimental Studies on Prestressed Precast Concrete Bridge Piers

Authors: C. Shim, C. Koem, S. Park, S. Lee

Abstract:

This paper deals with experimental studies on pre stressed precast concrete columns with continuous reinforcing bars and pre stressing tendons. Design requirements on minimum transverse reinforcement ratio are not included in current design codes. Pre stressing introduces additional compression to the column. Precast columns with different transverse reinforcement ratios were tested to derive adequate design requirement. Displacement ductility of the pre stressed precast columns was evaluated and compared with previous studies. Design of axial steels including reinforcing bars and pre stressing tendons influenced on the seismic performance. Without significant increase of transverse reinforcement ratio, the specimens showed required displacement ductility without reduction of their flexural strength. Design recommendations for precast bridge piers were derived.

Keywords: displacement ductility, flexural strength, prestressed precast column, transverse reinforcement

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2245 A Paradigm Shift in Energy Policy and Use: Exergy and Hybrid Renewable Energy Technologies

Authors: Adavbiele Airewe Stephen

Abstract:

Sustainable energy use is exploiting energy resources within acceptable levels of global resource depletion without destroying the ecological balance of an area. In the context of sustainability, the rush to quell the energy crisis of the fossil fuels of the 1970's by embarking on nuclear energy technology has now been seen as a disaster. In the circumstance, action (policy) suggested in this study to avoid future occurrence is exergy maximization/entropy generation minimization and the use is renewable energy technologies that are hybrid based. Thirty-two (32) selected hybrid renewable energy technologies were assessed with respect to their energetic efficiencies and entropy generation. The results indicated that determining which of the hybrid technologies is the most efficient process and sustainable is a matter of defining efficiency and knowing which of them possesses the minimum entropy generation.

Keywords: entropy, exergy, hybrid renewable energy technologies, sustainability

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2244 Hybrid Lubri-Coolants as an Alternatives to Mineral Based Emulsion in Machining Aerospace Alloy Ti-6Al-4V

Authors: Muhammad Jamil, Ning He, Wei Zhao

Abstract:

Ti-6Al-4V has poor thermal conductivity (6.7W/mK) accumulates shear and friction heat at the tool-chip interface zone. To dissipate the heat generation and friction effect, cryogenic cooling, Minimum quantity lubrication (MQL), nanofluids, hybrid cryogenic-MQL, solid lubricants, etc are applied frequently to underscore their significant effect on improving the machinability of Ti-6Al-4V. Nowadays, hybrid lubri-cooling is getting attention from researchers to explore their effect on machining Ti-6Al-4V.

Keywords: hybrid lubri-cooling, tool wear, surface roughness, minimum quantity lubrication

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2243 Sizing of Hybrid Source Battery/Supercapacitor for Automotive Applications

Authors: Laid Degaa, Bachir Bendjedia, Nassim Rizoug, Abdelkader Saidane

Abstract:

Energy storage system is a key aspect for the development of clean cars. The work proposed here deals with the modeling of hybrid storage sources composed of a combination of lithium-ion battery and supercapacitors. Simulation results show the performance of the active model for a hybrid source and confirm the feasibility of our approach. In this context, sizing of the electrical energy supply is carried out. The aim of this sizing is to propose an 'optimal' solution that improves the performance of electric vehicles in term of weight, cost and aging.

Keywords: battery, electric vehicles, energy, hybrid storage, supercapacitor

Procedia PDF Downloads 759
2242 Q-Learning of Bee-Like Robots Through Obstacle Avoidance

Authors: Jawairia Rasheed

Abstract:

Modern robots are often used for search and rescue purpose. One of the key areas of interest in such cases is learning complex environments. One of the key methodologies for robots in such cases is reinforcement learning. In reinforcement learning robots learn to move the path to reach the goal while avoiding obstacles. Q-learning, one of the most advancement of reinforcement learning is used for making the robots to learn the path. Robots learn by interacting with the environment to reach the goal. In this paper simulation model of bee-like robots is implemented in NETLOGO. In the start the learning rate was less and it increased with the passage of time. The bees successfully learned to reach the goal while avoiding obstacles through Q-learning technique.

Keywords: reinforlearning of bee like robots for reaching the goalcement learning for randomly placed obstacles, obstacle avoidance through q-learning, q-learning for obstacle avoidance,

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2241 Seismic Behavior and Loss Assessment of High–Rise Buildings with Light Gauge Steel–Concrete Hybrid Structure

Authors: Bing Lu, Shuang Li, Hongyuan Zhou

Abstract:

The steel–concrete hybrid structure has been extensively employed in high–rise buildings and super high–rise buildings. The light gauge steel–concrete hybrid structure, including light gauge steel structure and concrete hybrid structure, is a new–type steel–concrete hybrid structure, which possesses some advantages of light gauge steel structure and concrete hybrid structure. The seismic behavior and loss assessment of three high–rise buildings with three different concrete hybrid structures were investigated through finite element software, respectively. The three concrete hybrid structures are reinforced concrete column–steel beam (RC‒S) hybrid structure, concrete–filled steel tube column–steel beam (CFST‒S) hybrid structure, and tubed concrete column–steel beam (TC‒S) hybrid structure. The nonlinear time-history analysis of three high–rise buildings under 80 earthquakes was carried out. After simulation, it indicated that the seismic performances of three high–rise buildings were superior. Under extremely rare earthquakes, the maximum inter–storey drifts of three high–rise buildings are significantly lower than 1/50. The inter–storey drift and floor acceleration of high–rise building with CFST‒S hybrid structure were bigger than those of high–rise buildings with RC‒S hybrid structure, and smaller than those of high–rise building with TC‒S hybrid structure. Then, based on the time–history analysis results, the post-earthquake repair cost ratio and repair time of three high–rise buildings were predicted through an economic performance analysis method proposed in FEMA‒P58 report. Under frequent earthquakes, basic earthquakes and rare earthquakes, the repair cost ratio and repair time of three high-rise buildings were less than 5% and 15 days, respectively. Under extremely rare earthquakes, the repair cost ratio and repair time of high-rise buildings with TC‒S hybrid structure were the most among three high rise buildings. Due to the advantages of CFST-S hybrid structure, it could be extensively employed in high-rise buildings subjected to earthquake excitations.

Keywords: seismic behavior, loss assessment, light gauge steel–concrete hybrid structure, high–rise building, time–history analysis

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2240 The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning

Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

Abstract:

Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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2239 Utilizing Hybrid File Mapping for High-Performance I/O

Authors: Jaechun No

Abstract:

As the technology of NAND flash memory rapidly grows, SSD is becoming an excellent alternative for storage solutions, because of its high random I/O throughput and low power consumption. These SSD potentials have drawn great attention from IT enterprises that seek for better I/O performance. However, high SSD cost per capacity makes it less desirable to construct a large-scale storage subsystem solely composed of SSD devices. An alternative is to build a hybrid storage subsystem where both HDD and SSD devices are incorporated in an economic manner, while employing the strengths of both devices. This paper presents a hybrid file system, called hybridFS, that attempts to utilize the advantages of HDD and SSD devices, to provide a single, virtual address space by integrating both devices. HybridFS not only proposes an efficient implementation for the file management in the hybrid storage subsystem but also suggests an experimental framework for making use of the excellent features of existing file systems. Several performance evaluations were conducted to verify the effectiveness and suitability of hybridFS.

Keywords: hybrid file mapping, data layout, hybrid device integration, extent allocation

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2238 Distributed System Computing Resource Scheduling Algorithm Based on Deep Reinforcement Learning

Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

Abstract:

As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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2237 Valorization of Industrial Wastes on Hybrid Low Embodied Carbon Cement Based Mortars

Authors: Z. Abdollahnejad, M. Mastali, F. Pacheco-Torgal

Abstract:

Waste reuse is crucial in a context of circular economy and zero waste sustainable needs. Some wastes deserve further studies by the scientific community not only because they are generated in high amount but also because they have a low reuse rate. This paper reports results of 32 hybrid cement mortars based on fly ash and waste glass. They allow to explore the influence of mix design on the cost and on the embodied carbon of the hybrid cement mortars. The embodied carbon data for all constituents were taken from the database Ecoinvent. This study led to the development of a mixture with just 70 kg CO2e.

Keywords: waste reuse, fly ash, waste glass, hybrid cements, cost, embodied carbon

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2236 Curriculum-Based Multi-Agent Reinforcement Learning for Robotic Navigation

Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

Abstract:

Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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2235 Synthesis of Polystyrene Grafted Filler Nanoparticles: Effect of Grafting on Mechanical Reinforcement

Authors: M. Khlifa, A. Youssef, A. F. Zaed, A. Kraft, V. Arrighi

Abstract:

A series of PS-nanoparticles were prepared by grafting PS from both aggregated silica and colloidally silica using atom-transfer radical polymerisation (ATRP). The mechanical behaviour of the nanocomposites have been examined by differential scanning calorimetry (DSC)and dynamic mechanical thermal analysis (DMTA).

Keywords: ATRP, nanocomposites, polystyrene, reinforcement

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2234 Reinforcing Effects of Natural Micro-Particles on the Dynamic Impact Behaviour of Hybrid Bio-Composites Made of Short Kevlar Fibers Reinforced Thermoplastic Composite Armor

Authors: Edison E. Haro, Akindele G. Odeshi, Jerzy A. Szpunar

Abstract:

Hybrid bio-composites are developed for use in protective armor through positive hybridization offered by reinforcement of high-density polyethylene (HDPE) with Kevlar short fibers and palm wood micro-fillers. The manufacturing process involved a combination of extrusion and compression molding techniques. The mechanical behavior of Kevlar fiber reinforced HDPE with and without palm wood filler additions are compared. The effect of the weight fraction of the added palm wood micro-fillers is also determined. The Young modulus was found to increase as the weight fraction of organic micro-particles increased. However, the flexural strength decreased with increasing weight fraction of added micro-fillers. The interfacial interactions between the components were investigated using scanning electron microscopy. The influence of the size, random alignment and distribution of the natural micro-particles was evaluated. Ballistic impact and dynamic shock loading tests were performed to determine the optimum proportion of Kevlar short fibers and organic micro-fillers needed to improve impact strength of the HDPE. These results indicate a positive hybridization by deposition of organic micro-fillers on the surface of short Kevlar fibers used in reinforcing the thermoplastic matrix leading to enhancement of the mechanical strength and dynamic impact behavior of these materials. Therefore, these hybrid bio-composites can be promising materials for different applications against high velocity impacts.

Keywords: hybrid bio-composites, organic nano-fillers, dynamic shocking loading, ballistic impacts, energy absorption

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2233 Optimization of a Hybrid PV-Diesel Mini grid System: A Case Study of Vimtim-Mubi, Nigeria

Authors: Julius Agaka Yusufu

Abstract:

This study undertakes the development of an optimal PV-diesel hybrid power system tailored to the specific energy landscape of Vimtim Mubi, Nigeria, utilizing real-world wind speed, solar radiation, and diesel cost data. Employing HOMER simulation, the research meticulously assesses the technical and financial viability of this hybrid configuration. Additionally, a rigorous performance comparison is conducted between the PV-diesel system and the conventional grid-connected alternative, offering crucial insights into the potential advantages and economic feasibility of adopting hybrid renewable energy solutions in regions grappling with energy access and reliability challenges, with implications for sustainable electrification efforts in similar communities worldwide.

Keywords: Vimtim-Nigeria, homer, renewable energy, PV-diesel hybrid system.

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2232 Performance Analysis of a Hybrid DF-AF Hybrid RF/FSO System under Gamma Gamma Atmospheric Turbulence Channel Using MPPM Modulation

Authors: Hechmi Saidi, Noureddine Hamdi

Abstract:

The performance of hybrid amplify and forward - decode and forward (AF-DF) hybrid radio frequency/free space optical (RF/FSO) communication system, that adopts M-ary pulse position modulation (MPPM) techniques, is analyzed. Both exact and approximate symbol-error rates (SERs) are derived. The random variations of the received optical irradiance, produced by the atmospheric turbulence, is modeled by the gamma-gamma (GG) statistical distribution. A closed-form expression for the probability density function (PDF) is derived for the whole above system is obtained. Thanks to the use of hybrid AF-DF hybrid RF/FSO configuration and MPPM, the effects of atmospheric turbulence is mitigated; hence the capacity of combating atmospheric turbulence and the transmissitted signal quality are improved.

Keywords: free space optical, gamma gamma channel, radio frequency, decode and forward, error pointing, M-ary pulse position modulation, symbol error rate

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