Search results for: optimal reaction network

Authors: Ahmed Altalb

Abstract:

Fertilizers are natural materials, or industrial contain nutrients, which help to improve soil fertility and is considered (nitrogen, phosphorus, and potassium) is important elements for the growth of crops properly. Fertilization is necessary in order to improve the quality of agricultural products and the recovery in agricultural activities. The use of organic fertilizers and chemical lead to reduce the loss of nutrients in agricultural soils, and this leads to an increase in the production of agricultural crops. Fertilizers are one of the key factors in the increase of agricultural production as well as other factors such as irrigation and improved seeds and Prevention and others; the fertilizers will continue to be a cornerstone of the agriculture in order to produce the food to feed of world population. The use of fertilizers has become commonplace today, especially the chemical fertilizers for the development of agricultural production, due to the provision of nutrients for plants and in high concentrations and easily dissolves in water and ease of use. The choose the right type of fertilizer depends on the soil type and the type of crop. In this subject, find the relationship between the agricultural extension and the optimal use of fertilizers. The extension plays the important role in the advise and educate of farmers in how they optimal use the fertilizers in a scientific way. This article aims to identify the concept the fertilizers. Identify the role of fertilizers in increasing the agricultural production, identify the role of agricultural extension in the optimal use of fertilizers and rural development.

Keywords: agricultural, extension, fertilizers, production

Procedia PDF Downloads 433

Authors: Vahid Nourani, Atefeh Ashrafi

Abstract:

Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

Procedia PDF Downloads 124

Authors: W. Hashim, H. Burok, N. Ghazaly, H. Ahmad Nasir, N. Mohamad Anas, A. F. Ismail, K. L. Yau

Abstract:

Future communication networks require devices that are able to work on a single platform but support heterogeneous operations which lead to service diversity and functional flexibility. This paper proposes two cognitive mechanisms termed cognitive hybrid function which is applied in multiple broadband user terminals in order to maintain reliable connectivity and preventing unnecessary interferences. By employing such mechanisms especially for future hyper-dense network, we can observe their performances in terms of optimized speed and power saving efficiency. Results were obtained from several empirical laboratory studies. It was found that selecting reliable network had shown a better optimized speed performance up to 37% improvement as compared without such function. In terms of power adjustment, our evaluation of this mechanism can reduce the power to 5dB while maintaining the same level of throughput at higher power performance. We also discuss the issues impacting future telecommunication standards whenever such devices get in place.

Keywords: dense network, intelligent network selection, multiple networks, transmit power adjustment

Procedia PDF Downloads 372

Authors: Danilo López, Edwin Rivas, Fernando Pedraza

Abstract:

Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

Procedia PDF Downloads 416

Authors: Wai Keng Teng, Gek Cheng Ngoh, Rozita Yusoff, Mohamed Kheireddine Aroua

Abstract:

As a by-product of the biodiesel industries, glycerol has been vastly generated which surpasses the market demand. It is imperative to develop an efficient glycerol valorization processes in minimizing the net energy requirement and intensifying the biodiesel production. In this study, base-catalyzed transesterification of glycerol with dimethyl carbonate using microwave irradiation as heating method to produce glycerol carbonate was conducted by varing grades of glycerol i.e. 70%, 86% and 99% purity that obtained from biodiesel plant. Metal oxide catalysts were used with varying operating parameters including reaction time, DMC/glycerol molar ratio, catalyst weight %, temperature and stirring speed. From the study on the effect of different operating parameters; it was found that the type of catalyst used has the most significant effect on the transesterification reaction. Admist the metal oxide catalysts examined, CaO gave the best performance. This study indicates the feasibility of producing glycerol carbonate using different grade of glycerol in both conventional thermal activation and microwave irradiation with CaO as catalyst. Microwave assisted transesterification (MAT) of glycerol into glycerol carbonate has demostrated itself as an energy efficient route by achieving 94.3% yield of GC at 65°C, 5 minutes reaction time, 1 wt% CaO and DMC/glycerol molar ratio of 2. The advantages of MAT transesterification route has made the direct utilization of bioglycerol from biodiesel production without the need of purification. This has marked a more economical and less-energy intensive glycerol carbonate synthesis route.

Keywords: base-catalyzed transesterification, glycerol, glycerol carbonate, microwave irradiation

Procedia PDF Downloads 283

Authors: S. Shalaweh, P. Bouic, F. Weitz, R. Henkel

Abstract:

More than 3000 plants of notable phyto-therapeutic value grow in South Africa; these include Cissampelos capensis, commonly known in Afrikaans as dawidjie or dawidjiewortel. C. capensis is the most significant and popular medicinal plant used by the Khoisan as well as other rural groups in the Western region of South Africa. Its rhizomes are traditionally used to treat male fertility problems. Yet, no studies have investigated the effects of this plant or its extracts on human spermatozoa. Therefore, this study aimed at investigating the effects of C. capensis rhizome extract (CRE) fractions on ejaculated human spermatozoa in vitro. Spermatozoa from a total of 77 semen samples were washed with human tubular fluid medium supplemented with bovine serum albumin (HTF-BSA) and incubated for 2 hourswith 20 µg/ml progesterone (P4) followed by incubation with different concentrations (0, 0.05, 0.5, 5, 50, 200 µg/ml) of fractionated CRE (F1=0% MeOH, F2=30% MeOH, F3=60% MeOH and F4=100% MeOH) for 1.5 hours at 37°C. A sample without addition of CRE fractions served as control. Samples were analyzed for sperm motility, reactive oxygen species (ROS), DNA-fragmentation, acrosome reaction and capacitation. Results showed that F1 resulted in significantly higher values for ROS, capacitation and hyper-activation compared to F2, F3, and F4 with P4-stimulated samples generally having higher values. No significant effect was found for the other parameters. In conclusion, alkaloids present in F1 of CRE appear to have triggered sperm intrinsic ROS production leading to sperm capacitation and acrosome reaction induced by P4.

Keywords: capacitaion, acrosome reaction, DNA fragmentation, ROS

Procedia PDF Downloads 301

Authors: Rim Chang Hyon, Song Hak Jin, Song Kwang Hyok, Jong Ki Hun

Abstract:

The calibration of the articulated arm coordinate measuring machine (AACMM) is key to improving calibration accuracy and saving calibration time. To reduce the time consumed for calibration, we should choose the proper calibration gauges and develop a reasonable calibration method. In addition, we should get the exact optimal solution by accurately removing the rough errors within the experimental data. In this paper, we present a calibration method of the portable coordinate measuring arm (PCMA) using the 1.2m long bar guage with cone-holes. First, we determine the locations of the bar gauge and establish an optimal objective function for identifying the structural parameter errors. Next, we make a mathematical model of the calibration algorithm and present a new mathematical method to remove the rough errors within calibration data. Finally, we find the optimal solution to identify the kinematic parameter errors by using Levenberg-Marquardt algorithm. The experimental results show that our calibration method is very effective in saving the calibration time and improving the calibration accuracy.

Keywords: AACMM, kinematic model, parameter identify, measurement accuracy, calibration

Procedia PDF Downloads 73

Authors: Mehrdad Shahrbanozadeh, Gholam Abbas Barani, Saeed Shojaee

Abstract:

In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM.

Keywords: seepage, dam foundation, finite element method, neural network, seep 3D model

Procedia PDF Downloads 467

Authors: Julien Romieux, Fabio Verdicchio

Abstract:

Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: cooperative signal processing, signal representation and approximation, power management, wireless sensor networks

Procedia PDF Downloads 386

Authors: Marc S. Paolella, Pawel Polak

Abstract:

The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery.

Keywords: comfort, financial crises, portfolio optimization, risk monitoring

Procedia PDF Downloads 519

Authors: Muhammad Yusuf Abduh, Robert Manurung, Hero Jan Heeres

Abstract:

Biodiesel can be produced in small scale mobile units which are designed with local input and demand. Unlike the typical biodiesel production plants, mobile biodiesel unit consiss of a biodiesel production facility placed inside a standard cargo container and mounted on a truck so that it can be transported to a region near the location of raw materials. In this paper, we review the existing concept and unit for the development of community-scale and mobile production of biodiesel. This includes the main reactor technology to produce biodiesel as well as the pre-treatment prior to the reaction unit. The pre-treatment includes the oil-expeller unit to obtain oil from the oilseeds as well as the quality control of the oil before it enters the reaction unit. This paper also discusses the post-treatment after the production of biodiesel. It includes the refining and purification of biodiesel to meet the product specification set by the biodiesel industry.

Keywords: biodiesel, community scale, mobile biodiesel unit, reactor technology

Procedia PDF Downloads 231

Authors: Anshul Gupta, T. Shankar

Abstract:

In this paper, we had analyzed the call drop to provide a good quality of service to user. By optimizing it we can increase the coverage area and also the reduction of interference and congestion created in a network. Basically handover is the transfer of call from one cell site to another site during a call. Here we have analyzed the whole network by two method-statistic model and analytic model. In statistic model we have collected all the data of a network during busy hour and normal 24 hours and in analytic model we have the equation through which we have to find the call drop probability. By avoiding unnecessary handovers we can increase the number of calls per hour. The most important parameter is co-efficient of variation on which the whole paper discussed.

Keywords: coefficient of variation, mean, standard deviation, call drop probability, handover

Procedia PDF Downloads 485

Authors: Agryzkov Taras, Oliver Jose L., Tortosa Leandro, Vicent Jose

Abstract:

This paper aims to demonstrate how the consideration of cities as a special kind of complex network, called urban network, may lead to the use of design tools coming from network theories which, in fact, results in a quite sustainable approach. There is no doubt that the irruption in contemporary thought of Gaia as an essential political agent proposes a narrative that has been extended to the field of creative processes in which, of course, the activity of Urban Design is found. The rationalist paradigm is put in crisis, and from the so-called sciences of complexity, its way of describing reality and of intervening in it is questioned. Thus, a new way of understanding reality surges, which has to do with a redefinition of the human being's own place in what is now understood as a delicate and complex network. In this sense, we know that in these systems of connected and interdependent elements, the influences generated by them originate emergent properties and behaviors for the whole that, individually studied, would not make sense. We believe that the design of cities cannot remain oblivious to these principles, and therefore this research aims to demonstrate the potential that they have for decision-making in the urban environment. Thus, we will see an example of action in the field of public mobility, another example in the design of commercial areas, and a third example in the field of redensification of sprawl areas, in which different aspects of network theory have been applied to change the urban design. We think that even though these actions have been developed in European cities, and more specifically in the Mediterranean area in Spain, the reflections and tools could have a broader scope of action.

Keywords: graphs, complexity sciences, urban networks, urban design

Procedia PDF Downloads 151

Authors: Zhiwei Yan, Jong-Hyouk Lee, Yong-Jin Park, Xiaodong Lee

Abstract:

In this paper, we analyze NEtwork MObility (NEMO) supporting problems in Content-Centric Networking (CCN), and propose the CCN-NEMO which can well support the deployment of the content-centric paradigm in large-scale mobile Internet. The CCN-NEMO extends the signaling message of the basic CCN protocol, to support the mobility discovery and fast trigger of Interest re-issuing during the network mobility. Besides, the Mobile Router (MR) is extended to optimize the content searching and relaying in the local subnet. These features can be employed by the nested NEMO to maximize the advantages of content retrieving with CCN. Based on the analysis, we compare the performance on handover latency between the basic CCN and our proposed CCN-NEMO. The results show that our scheme can facilitate the content-retrieving in the NEMO scenario with improved performance.

Keywords: NEMO, CCN, mobility, handover latency

Procedia PDF Downloads 465

Authors: Yating Yang, Xue Zhang, Chengli Zhao

Abstract:

In the increasingly interconnected global environment brought about by globalization, it is crucial for countries to timely grasp the development motivations in relevant research fields of other countries and seize development opportunities. Motivation, as the intrinsic driving force behind actions, is abstract in nature, making it difficult to directly measure and evaluate. Drawing on the ideas of social influence network theory, the research motivations of a country can be understood as the driving force behind the development of its science and technology sector, which is simultaneously influenced by both the country itself and other countries/regions. In response to this issue, this paper improves upon Friedkin's social influence network theory and applies it to motivation description, constructing a dynamic alliance network and hostile network centered around the United States and China, as well as a sensitivity matrix, to remotely assess the changes in national research motivations under the influence of international relations. Taking artificial intelligence as a case study, the research reveals that the motivations of most countries/regions are declining, gradually shifting from a neutral attitude to a negative one. The motivation of the United States is hardly influenced by other countries/regions and remains at a high level, while the motivation of China has been consistently increasing in recent years. By comparing the results with real data, it is found that this model can reflect, to some extent, the trends in national motivations.

Keywords: influence network theory, remote assessment, relation matrix, dynamic sensitivity matrix

Procedia PDF Downloads 64

Authors: Tim Farrelly

Abstract:

In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time.

Keywords: deep learning, object detection, machine vision applications, sport, network design

Procedia PDF Downloads 140

Authors: Suprit Pradhan, Sushil Pradhan

Abstract:

Photosynthesis is a physiological process where green plants prepare their food from carbon dioxide from the atmosphere and water being absorbed from the soil in presence of sun light and chlorophyll. From this definition it is clear that four reactants (Carbon Dioxide, Water, Light and Chlorophyll) are essential for the process to proceed and the product is a sugar or carbohydrate ultimately stored as starch. The entire process has “Light Reaction” (Photochemical) and “Dark Reaction” (Biochemical). Biochemical reactions are very much complicated being catalysed by various enzymes and the path of carbon is known as “Calvin Cycle” according to the name of its discover. The overall reaction which is now universally accepted can be explained like this. Six molecules of carbon dioxide react with twelve molecules of water in presence of chlorophyll and sun light to give only one molecule of sugar (Carbohydrate) six molecules of water and six molecules of oxygen is being evolved in gaseous form. This is the accepted equation and also chemically balanced. However while teaching the subject the author came across a new balanced equation from among the students who happened to be the daughter of the author. In the new balanced equation in place of twelve water molecules in the reactant side seven molecules can be expressed and accordingly in place of six molecules of water in the product side only one molecule of water is produced. The energetics of the photosynthesis as related to the environment and the newly reported balanced chemical equation has been discussed in detail in the present research paper presentation in this international conference on energy, environmental and chemical engineering.

Keywords: biochemistry, enzyme , isotope, photosynthesis

Procedia PDF Downloads 507

Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

Abstract:

Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

Procedia PDF Downloads 119

Authors: Zohreh Moghaddas, Morteza Yazdani, Farhad Hosseinzadeh

Abstract:

Nowadays, data envelopment analysis (DEA) models featuring network structures have gained widespread usage for evaluating the performance of production systems and activities (Decision-Making Units (DMUs)) across diverse fields. By examining the relationships between the internal stages of the network, these models offer valuable insights to managers and decision-makers regarding the performance of each stage and its impact on the overall network. To further empower system decision-makers, the inverse data envelopment analysis (IDEA) model has been introduced. This model allows the estimation of crucial information for estimating parameters while keeping the efficiency score unchanged or improved, enabling analysis of the sensitivity of system inputs or outputs according to managers' preferences. This empowers managers to apply their preferences and policies on resources, such as inputs and outputs, and analyze various aspects like production, resource allocation processes, and resource efficiency enhancement within the system. The results obtained can be instrumental in making informed decisions in the future. The top result of this study is an analysis of infeasibility and incorrect estimation that may arise in the theory and application of the inverse model of data envelopment analysis with network structures. By addressing these pitfalls, novel protocols are proposed to circumvent these shortcomings effectively. Subsequently, several theoretical and applied problems are examined and resolved through insightful case studies.

Keywords: inverse models of data envelopment analysis, series network, estimation of inputs and outputs, efficiency, resource allocation, sensitivity analysis, infeasibility

Procedia PDF Downloads 42

Authors: B. J. Kim, K. W. Nam, S. J. Lee

Abstract:

Traditional social network services provide users with more information than is needed, and it is not easy to verify the authenticity of the information. This paper proposes a system that can only post messages where users are located to enhance the reliability of social networking services. The proposed system implements a Google Map API to post postings on the map and to read postings within a range of distances from the users’ location. The proposed system will only provide alerts, memories, and information about locations within a given range depending on the users' current location, providing reliable information that they believe will be necessary in real time. It is expected that the proposed system will be able to meet the real demands of users and create a more reliable social network services environment.

Keywords: social network, location, reliability, posting

Procedia PDF Downloads 251

Authors: Farnaz Alaeimoghadam, Farzad Alaeimoghadam

Abstract:

In low-lactose milk, due to lactose hydrolysis and its conversion to monosaccharides like glucose and galactose, the Maillard reaction (non-enzymatic browning) occurs more readily compared to non-hydrolyzed milk. This reaction incurs off-flavor and dark color, as well as a decrease in the nutritional value of milk. The target of this research was to evaluate the effect of natural antioxidants in diminishing the browning in low-lactose milk. In this research, three antioxidants, namely ascorbic acid, gallic acid, and pantothenic acid in the concentration range of 0-1 mM/L, either in combination with each other or separately, were added to low-lactose milk. After heat treatment (120 0C for 3 min.), milk samples incubated at 55 0C for one day and then stored at 4 0C for 9 days. Quality indices, including total phenol content, antioxidant activity, color indices, and sensory characters, were measured during intervals of 0, 2, 5, 7, and 9 days. Results of this research showed that the effect of storage time and adding antioxidants were significant on pH, antioxidant activity, total phenolic compounds either before or after heating, index L*, color change, and sensational characteristics (p < 0.05); however, acidity, a* and b* indices, chroma, and hue angle showed no significant changes (p > 0.05). The findings showed that the simultaneous application of gallic acid and ascorbic in the diminishing of non-enzymatic browning and color change, increasing pH, longevity, and antioxidant activity after heat treatment, and augmenting phenolic compounds before heat treatment was better than that of pantothenic acid.

Keywords: Maillard, low-lactose milk, non-enzymatic browning, natural antioxidant

Procedia PDF Downloads 132

Authors: Cristian Pauna

Abstract:

Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

Procedia PDF Downloads 156

Authors: Tesfaw Belayneh Abebe

Abstract:

Integrating low-altitude earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN) with the assistance of reconfigurable intelligent surfaces (RIS), we investigate the problem of how to enhance throughput through integrated LEO satellites and UAVs with the assistance of RIS. We propose a method to jointly optimize the associations with the LEO satellite, the 3D trajectory of the UAV, and the phase shifts of the RIS to maximize communication throughput for RIS-assisted integrated LEO satellite and UAV-enabled wireless communications, which is challenging due to the time-varying changes in the position of the LEO satellite, the high mobility of UAVs, an enormous number of possible control actions, and also the large number of RIS elements. Utilizing a multi-agent double deep Q-network (MADDQN), our approach dynamically adjusts LEO satellite association, UAV positioning, and RIS phase shifts. Simulation results demonstrate that our method significantly outperforms baseline strategies in maximizing throughput. Lastly, thanks to the integrated network and the RIS, the proposed scheme achieves up to 65.66x higher peak throughput and 25.09x higher worst-case throughput.

Keywords: integrating low-altitude earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN), reconfigurable intelligent surfaces (RIS), multi-agent double deep Q-network (MADDQN)

Procedia PDF Downloads 41

Authors: Joel Y. Y. Loh, Geoffrey A. Ozin, Charles A. Mims, Nazir P. Kherani

Abstract:

A major goal in the emerging field of solar fuels is to realize an ‘artificial leaf’ – a material that converts light energy in the form of solar photons into chemical energy – using CO2 as a feedstock to generate useful chemical species. Enabling this technology will allow the greenhouse gas, CO2, emitted from energy and manufacturing production exhaust streams to be converted into valuable solar fuels or chemical products. Indium Oxide (In2O3) with surface hydroxyl (OH) groups have been shown to reduce CO2 in the presence of H2 to CO with a reaction rate of 15 μmol gcat−1 h−1. The likely mechanism is via a Frustrated Lewis Pair sites heterolytically splitting H2 to be absorbed and form protonic and hydric sites that can dissociate CO2. In this study, we investigate the dependence of oxygen composition of In2O3 on the CO2 reduction rate. In2O3-x films on quartz fiber paper were DC sputtered with an Indium target and varying O2/Ar plasma mixture. OH surface groups were then introduced by immersing the In2O3-x samples in KOH. We show that hydroxylated In2O3-x reduces more CO2 than non-hydroxylated groups and that a hydroxylated and higher O2/Ar ratio sputtered In2O3-x has a higher reaction rate of 45 μmol gcat-1 h-1. We show by electrical resistivity-temperature curves that H2 is adsorbed onto the surface of In2O3 whereas CO2 itself does not affect the indium oxide surface. We also present activation and ionization energy levels of the hydroxylated In2O3-x under vacuum, CO2 and H2 atmosphere conditions.

Keywords: solar fuels, photocatalysis, indium oxide nanoparticles, carbon dioxide

Procedia PDF Downloads 233

Authors: Semanur Savruk, Onur Akbatı

Abstract:

Integration of various communication protocols into an automation system causes a rise in setup and maintenance cost and make to control network devices in difficulty. The gateway becomes necessary for reducing complexity in network topology. In this study, Modbus RTU/Modbus TCP industrial ethernet gateway design and implementation are presented with ARM embedded system and FreeRTOS real-time operating system. The Modbus gateway can perform communication with Modbus RTU and Modbus TCP devices over itself. Moreover, the gateway can be adjustable with the user-interface application or messaging interface. Conducted experiments and the results are presented in the paper. Eventually, the proposed system is a complete, low-cost, real-time, and user-friendly design for monitoring and setting devices and useful for meeting remote control purposes.

Keywords: gateway, industrial communication, modbus, network

Procedia PDF Downloads 134

Authors: Yassir Abdelrazig, Ren Moses

Abstract:

Roadway planning and design is a very complex process involving five key phases before a project is completed; planning, project development, final design, right-of-way, and construction. The planning phase for a new roadway transportation project is a very critical phase as it greatly affects all latter phases of the project. A location study is usually performed during the preliminary planning phase in a new roadway project. The objective of the location study is to develop alignment alternatives that are cost efficient considering land acquisition and construction costs. This paper describes a methodology to develop optimal preliminary roadway alignments utilizing spatial-data. Four optimization criteria are taken into consideration; roadway length, land cost, land slope, and environmental impacts. The basic concept of the methodology is to convert the proposed project area into a grid, which represents the search space for an optimal alignment. The aforementioned optimization criteria are represented in each of the grid’s cells. A spatial-data optimization technique is utilized to find the optimal alignment in the search space based on the four optimization criteria. Two case studies for new roadway projects in Duval County in the State of Florida are presented to illustrate the methodology. The optimization output alignments are compared to the proposed Florida Department of Transportation (FDOT) alignments. The comparison is based on right-of-way costs for the alignments. For both case studies, the right-of-way costs for the developed optimal alignments were found to be significantly lower than the FDOT alignments.

Keywords: gemoetric design, optimization, planning, roadway planning, roadway design

Procedia PDF Downloads 333

Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

Abstract:

Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

Procedia PDF Downloads 82

Authors: Gurmail Singh

Abstract:

Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

Procedia PDF Downloads 123

Authors: Shokoufeh Monjezi Kouchak

Abstract:

Cognitive radio network (CRN) is future network .Without this network wireless devices can’t work appropriately in the next decades. Today, wireless devices use static spectrum access methods and these methods don’t use spectrums optimum so we need use dynamic spectrum access methods to solve shortage spectrum challenge and CR is a great device for DSA but first of all its challenges should be solved .security is one of these challenges .In this paper we provided a survey about CR security. You can see this survey in tables 1 to 7 .After that we proposed a multi criteria authentication method in CRN. Our criteria in this method are: sensing results, following sending data rules, position of secondary users and no talk zone. Finally we compared our method with other authentication methods.

Keywords: authentication, cognitive radio, security, radio networks

Procedia PDF Downloads 383

Authors: Helen Dilman, Eyal Oz, Shmuel Hayun, Nahum Frage

Abstract:

The conventional approach for manufacturing silicon carbide and boron carbide reaction bonded composites is based on infiltrating a ceramic porous preform with molten silicon. The relatively high melting temperature of the silicon infiltrating medium is a drawback of the process. The present contribution is concerned with an approach that allows obtaining reaction bonded composites by pressure-less infiltration at a significantly lower (850-1000oC) temperature range. This approach was applied for the fabrication of fully dense SiC/(Si-Al) and (SiC+B4C)/(Si-Al) composites. The key feature of the approach is based on using Si alloys with low melting temperature and the Mg-vapor atmosphere, under which an adequate wetting between ceramics and liquid alloys for the infiltration process is achieved. In the first set of the experiments ceramic performs compacted from multimodal SiC powders (with the green density of about 27 vol. %) without free carbon addition were infiltrated by Si-20%Al alloy at 950oC. In the second set, 19 vol. % of a fine boron carbide powder was added to SiC powders as a source of carbon. The green density of the SiC-B4C preforms was about 23-25 vol. %. In both cases, successful infiltration was achieved and the composites were fully dense. The density of the composites was about 3g/cm3. For the SiC based composites the hardness value was 750±150HV, Young modulus-280GPa and bending strength-240±30MPa. These values for (SiC-B4C)/(Si-Al) composites (1460±200HV, 317GPa and 360±20MPa) were significantly higher due to the formation of novel ceramics phases. Microstructural characteristics of the composites and their phase composition will be discussed.

Keywords: boron carbide, composites, infiltration, low temperatures, silicon carbide

Procedia PDF Downloads 544