Search results for: sewer network planning

Authors: Kexin Jia, Youxin Lu

Abstract:

To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).

Keywords: Modulation classification, combined classifier, fuzzy neural network, interclass distance.

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Authors: H. Shayeghi, H. A. Shayanfar, G. Azimi

Abstract:

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

Keywords: Large Neural Network, Short-Term Load Forecasting, Particle Swarm Optimization.

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Authors: Volker Heierhoff, Aurilla Aurelie Bechina Arntzen, Gerrit Muller

Abstract:

It well recognized that one feature that makes a successful company is its ability to successfully align its business goals with its information communication technologies platform. Enterprise Resource Planning (ERP) systems contribute to achieve better performance by integrating various business functions and providing support for information flows. However, the technological systems complexity is known to prevent the business users to exploit in an efficient way the Enterprise Resource Planning Systems (ERP). This paper aims to investigate the role of training in improving the usage of ERP systems. To this end, we have designed an instrument survey to employees of a Norwegian multinational global provider of technology solutions. Based on the analysis of collected data, we have delineated a training model that could be high relevance for both researchers and practitioners as a step towards a better understanding of ERP system implementation.

Keywords: Business User Training, Enterprise resource planning system, Global consulting company, Role and responsibilities

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Authors: Hong-Nam Choi, Hyeong-Wook Song, Sungwan Hong, Hong-Kyu Kim

Abstract:

In Korea, elements that decide the quality of residential environment are not only diverse, but show deviation as well. However, people do not consider these elements and instead, they try to settle the uniformed style of residential environment, which focuses on the construction development of apartment housing and business based plans. Recently, block-unit redevelopment is becoming the standout alternative plan of standardize redevelopment projects, but constructions become inefficient because of indefinite planning criteria.

 In conclusion, the following research is about analyzing and categorizing the development method and legal ground of redevelopment project district and plan determinant and applicable standard; the purpose of this study is to become a basis in compatible analysis of planning standards that will happen in the future.

Keywords: Shape Restrictions, Improvement of Reagulation, Diversity of Residential Environment, Classification of Redevelopment Project, Planning Criteria of Redevelopment, Special Architectural District (SAD).

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Authors: Hiba Hasan, Khalid Raza

Abstract:

Reverse engineering of genetic regulatory network involves the modeling of the given gene expression data into a form of the network. Computationally it is possible to have the relationships between genes, so called gene regulatory networks (GRNs), that can help to find the genomics and proteomics based diagnostic approach for any disease. In this paper, clustering based method has been used to reconstruct genetic regulatory network from time series gene expression data. Supercoiled data set from Escherichia coli has been taken to demonstrate the proposed method.

Keywords: Gene expression, gene regulatory networks (GRNs), clustering, data preprocessing, network visualization.

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Authors: S. Sendhilkumar, N. Mohanasundaram, M. Senthilkumar, S. N. Sivanandam

Abstract:

The operational life of rotating machines has to be extended using a predictive condition maintenance tool. Among various condition monitoring techniques, vibration analysis is most widely used technique in industry. Signals are extracted for evaluating the condition of machine; further diagnostics is carried out with detected signals to extend the life of machine. With help of detected signals, further interpretations are done to predict the occurrence of defects. To study the problem of defects, a test rig with various possibilities of defects is constructed and experiments are performed considering the unbalanced condition. Further, this paper presents an approach for fault diagnosis of unbalance condition using Elman neural network and frequency-domain vibration analysis. Amplitudes with variation in acceleration are fed to Elman neural network to classify fault or no-fault condition. The Elman network is trained, validated and tested with experimental readings. Results illustrate the effectiveness of Elman network in rotor-bearing system.

Keywords: Elman neural network, fault detection, rotating machines, unbalance, vibration analysis.

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Authors: Ritu Ahlawat

Abstract:

Among all geo-hydrological relationships, rainfallrunoff relationship is of utmost importance in any hydrological investigation and water resource planning. Spatial variation, lag time involved in obtaining areal estimates for the basin as a whole can affect the parameterization in design stage as well as in planning stage. In conventional hydrological processing of data, spatial aspect is either ignored or interpolated at sub-basin level. Temporal variation when analysed for different stages can provide clues for its spatial effectiveness. The interplay of space-time variation at pixel level can provide better understanding of basin parameters. Sustenance of design structures for different return periods and their spatial auto-correlations should be studied at different geographical scales for better management and planning of water resources. In order to understand the relative effect of spatio-temporal variation in hydrological data network, a detailed geo-hydrological analysis of Betwa river catchment falling in Lower Yamuna Basin is presented in this paper. Moreover, the exact estimates about the availability of water in the Betwa river catchment, especially in the wake of recent Betwa-Ken linkage project, need thorough scientific investigation for better planning. Therefore, an attempt in this direction is made here to analyse the existing hydrological and meteorological data with the help of SPSS, GIS and MS-EXCEL software. A comparison of spatial and temporal correlations at subcatchment level in case of upper Betwa reaches has been made to demonstrate the representativeness of rain gauges. First, flows at different locations are used to derive correlation and regression coefficients. Then, long-term normal water yield estimates based on pixel-wise regression coefficients of rainfall-runoff relationship have been mapped. The areal values obtained from these maps can definitely improve upon estimates based on point-based extrapolations or areal interpolations.

Keywords: Catchment's runoff estimates, influence area regional regression coefficients, runoff yield series,

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Authors: Abhishek Bansal, G. N. Pillai

Abstract:

This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response.

Keywords: Fault identification, distribution networks, high impedance arc-faults, feature vector, LVQ networks.

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Authors: John Kabuba

Abstract:

The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ionexchange.

Keywords: Clinoptilolite, loading, modeling, Neural network.

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Authors: M. Khatami Rad, N. Jamali, M. Torabizadeh, A. Noshadi

Abstract:

In this paper, an artificial neural network simulator is employed to carry out diagnosis and prognosis on electric motor as rotating machinery based on predictive maintenance. Vibration data of the primary failed motor including unbalance, misalignment and bearing fault were collected for training the neural network. Neural network training was performed for a variety of inputs and the motor condition was used as the expert training information. The main purpose of applying the neural network as an expert system was to detect the type of failure and applying preventive maintenance. The advantage of this study is for machinery Industries by providing appropriate maintenance that has an essential activity to keep the production process going at all processes in the machinery industry. Proper maintenance is pivotal in order to prevent the possible failures in operating system and increase the availability and effectiveness of a system by analyzing vibration monitoring and developing expert system.

Keywords: Condition based monitoring, expert system, neural network, fault detection, vibration monitoring.

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Authors: S. Pazi, C. Chatwin, R. Young, P. Birch

Abstract:

Tanzania is a developing country, which significantly lags behind the rest of the world in information communications technology (ICT), especially for the Internet. Internet connectivity to the rest of the world is via expensive satellite links, thus leaving the majority of the population unable to access the Internet due to the high cost. This paper introduces the concept of an optical WDM network for Internet infrastructure in Tanzania, so as to reduce Internet connection costs, and provide Internet access to the majority of people who live in both urban and rural areas. We also present a proposed optical WDM network, which mitigates the effects of system impairments, and provide simulation results to show that the data is successfully transmitted over a longer distance using a WDM network.

Keywords: Internet infrastructure, Internet access, Internettraffic, WDM.

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Authors: Chaimongkol Limpianchob

Abstract:

This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case.

Keywords: Aromatic coconut, supply chain management, production planning, mixed-integer linear programming.

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Authors: Semanur Soyyiğit Kaya, Ercan Eren

Abstract:

Energy has a prominent role for development of nations. Countries which have energy resources also have strategic power in the international trade of energy since it is essential for all stages of production in the economy. Thus, it is important for countries to analyze the weaknesses and strength of the system. On the other side, international trade is one of the fields that are analyzed as a complex network via network analysis. Complex network is one of the tools to analyze complex systems with heterogeneous agents and interaction between them. A complex network consists of nodes and the interactions between these nodes. Total properties which emerge as a result of these interactions are distinct from the sum of small parts (more or less) in complex systems. Thus, standard approaches to international trade are superficial to analyze these systems. Network analysis provides a new approach to analyze international trade as a network. In this network, countries constitute nodes and trade relations (export or import) constitute edges. It becomes possible to analyze international trade network in terms of high degree indicators which are specific to complex networks such as connectivity, clustering, assortativity/disassortativity, centrality, etc. In this analysis, international trade of crude oil and coal which are types of fossil fuel has been analyzed from 2005 to 2014 via network analysis. First, it has been analyzed in terms of some topological parameters such as density, transitivity, clustering etc. Afterwards, fitness to Pareto distribution has been analyzed via Kolmogorov-Smirnov test. Finally, weighted HITS algorithm has been applied to the data as a centrality measure to determine the real prominence of countries in these trade networks. Weighted HITS algorithm is a strong tool to analyze the network by ranking countries with regards to prominence of their trade partners. We have calculated both an export centrality and an import centrality by applying w-HITS algorithm to the data. As a result, impacts of the trading countries have been presented in terms of high-degree indicators.

Keywords: Complex network approach, fossil fuel, international trade, network theory.

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Authors: R. Sahraian, A. Karampour Haghighi, E. Ghasemi

Abstract:

Process planning and production scheduling play important roles in manufacturing systems. In this paper a multiobjective mixed integer linear programming model is presented for the integrated planning and scheduling of multi-product. The aim is to find a set of high-quality trade-off solutions. This is a combinatorial optimization problem with substantially large solution space, suggesting that it is highly difficult to find the best solutions with the exact search method. To account for it, a PSO-based algorithm is proposed by fully utilizing the capability of the exploration search and fast convergence. To fit the continuous PSO in the discrete modeled problem, a solution representation is used in the algorithm. The numerical experiments have been performed to demonstrate the effectiveness of the proposed algorithm.

Keywords: Integrated process planning and scheduling, multi objective, MILP, Particle swarm optimization

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Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: Convolutional Neural Network, Deep Learning, Deep Learning Based FER, Facial Emotion Recognition.

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Authors: H. Zormati, J. Chebil, J. Bel Hadj Taher

Abstract:

The goal of this paper is to present an addressing scheme that allows for assigning a unique IPv6 address to each node in the Internet of Things (IoT) network. This scheme guarantees uniqueness by extracting the clock skew of each communication device and converting it into an IPv6 address. Simulation analysis confirms that the presented scheme provides reductions in terms of energy consumption, communication overhead and response time as compared to four studied addressing schemes Strong DAD, LEADS, SIPA and CLOSA.

Keywords: Addressing, IoT, IPv6, network, nodes.

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Authors: H. Soleimanjahi, M. J. Nategh, S. Falahi

Abstract:

Deciding the numerous parameters involved in designing a competent artificial neural network is a complicated task. The existence of several options for selecting an appropriate architecture for neural network adds to this complexity, especially when different applications of heterogeneous natures are concerned. Two completely different applications in engineering and medical science were selected in the present study including prediction of workpiece's surface roughness in ultrasonic-vibration assisted turning and papilloma viruses oncogenicity. Several neural network architectures with different parameters were developed for each application and the results were compared. It was illustrated in this paper that some applications such as the first one mentioned above are apt to be modeled by a single network with sufficient accuracy, whereas others such as the second application can be best modeled by different expert networks for different ranges of output. Development of knowledge about the essentials of neural networks for different applications is regarded as the cornerstone of multidisciplinary network design programs to be developed as a means of reducing inconsistencies and the burden of the user intervention.

Keywords: Artificial Neural Network, Malignancy Diagnosis, Papilloma Viruses Oncogenicity, Surface Roughness, UltrasonicVibration-Assisted Turning.

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Authors: Wen Liang Chang

Abstract:

This paper provides a replacement policy for warranty products with different failure rate from the consumer-s viewpoint. Assume that the product is replaced once within a finite planning horizon, and the failure rate of the second product is lower than the failure rate of the first product. Within warranty period (WP), the failed product is corrected by minimal repair without any cost to the consumers. After WP, the failed product is repaired with a fixed repair cost to the consumers. However, each failure incurs a fixed downtime cost to the consumers over a finite planning horizon. In this paper, we derive the model of the expected total disbursement cost within a finite planning horizon and some properties of the optimal replacement policy under some reasonable conditions are obtained. Finally, numerical examples are given to illustrate the features of the optimal replacement policy under various maintenance costs.

Keywords: Planning horizon, Free-repair warranty, Minimal repair, Replacement.

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Authors: A. Harifi, A. Aghagolzadeh

Abstract:

The main problem for recognition of handwritten Persian digits using Neural Network is to extract an appropriate feature vector from image matrix. In this research an asymmetrical segmentation pattern is proposed to obtain the feature vector. This pattern can be adjusted as an optimum model thanks to its one degree of freedom as a control point. Since any chosen algorithm depends on digit identity, a Neural Network is used to prevail over this dependence. Inputs of this Network are the moment of inertia and the center of gravity which do not depend on digit identity. Recognizing the digit is carried out using another Neural Network. Simulation results indicate the high recognition rate of 97.6% for new introduced pattern in comparison to the previous models for recognition of digits.

Keywords: Pattern recognition, Persian digits, NeuralNetwork.

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Authors: Mario Gonzalez, David Dominguez, Francisco B. Rodriguez

Abstract:

An attractor neural network on the small-world topology is studied. A learning pattern is presented to the network, then a stimulus carrying local information is applied to the neurons and the retrieval of block-like structure is investigated. A synaptic noise decreases the memory capability. The change of stability from local to global attractors is shown to depend on the long-range character of the network connectivity.

Keywords: Hebbian learning, image recognition, small world, spatial information.

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Authors: Maryam Jamela Ismail, Rosdiazli Ibrahim, Idris Ismail

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In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive review on published research on the energy consumption prediction which focuses on structures and the parameters used in developing Neural Network models. This paper is then focused on the parameter selection of the neural network prediction model development for energy consumption and analysis on the result. The most reliable model that gives the most accurate result is proposed for the prediction. The result shows that the proposed neural network energy prediction model is able to demonstrate an adequate performance with least Root Mean Square Error.

Keywords: Energy Prediction, Multilayer Feedforward, Levenberg-Marquardt, Root Mean Square Error (RMSE)

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Authors: Pradeep Kumar, Abdul Wahid

Abstract:

Accurate software reliability prediction not only enables developers to improve the quality of software but also provides useful information to help them for planning valuable resources. This paper examines the performance of three well-known data mining techniques (CART, TreeNet and Random Forest) for predicting software reliability. We evaluate and compare the performance of proposed models with Cascade Correlation Neural Network (CCNN) using sixteen empirical databases from the Data and Analysis Center for Software. The goal of our study is to help project managers to concentrate their testing efforts to minimize the software failures in order to improve the reliability of the software systems. Two performance measures, Normalized Root Mean Squared Error (NRMSE) and Mean Absolute Errors (MAE), illustrate that CART model is accurate than the models predicted using Random Forest, TreeNet and CCNN in all datasets used in our study. Finally, we conclude that such methods can help in reliability prediction using real-life failure datasets.

Keywords: Classification, Cascade Correlation Neural Network, Random Forest, Software reliability, TreeNet.

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Authors: Ákos Malatinszky, Szilvia Ádám

Abstract:

Climate change causes severe effects on natural habitats, especially wetlands. These challenges require the adaptation of their management to probable effects of climate change. A compilation of necessary changes in land management was collected in a Hungarian area being both national park and Natura 2000 SAC and SCI site in favor of increasing the resilience and reducing vulnerability. Several factors, such as ecological aspects, nature conservation and climatic adaptation should be combined with social and economic factors during the process of developing climate change adapted management on vulnerable wetlands. Planning adaptive management should be determined by a priority order of conservation aims and evaluation of factors at the determined planning unit. Mowing techniques, frequency and exact date should be observed as well as grazing species and their breed, due to different grazing, group forming and trampling habits. Integrating landscape history and historical land development into the planning process is essential.

Keywords: Adaptation, climate change, management, wetland.

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Authors: Yuan-Jye Tseng, Fang-Yu Yu, Feng-Yi Huang

Abstract:

A green design for assembly model is presented to integrate design evaluation and assembly and disassembly sequence planning by evaluating the three activities in one integrated model. For an assembled product, an assembly sequence planning model is required for assembling the product at the start of the product life cycle. A disassembly sequence planning model is needed for disassembling the product at the end. In a green product life cycle, it is important to plan how a product can be disassembled, reused, or recycled, before the product is actually assembled and produced. Given a product requirement, there may be several design alternative cases to design the same product. In the different design cases, the assembly and disassembly sequences for producing the product can be different. In this research, a new model is presented to concurrently evaluate the design and plan the assembly and disassembly sequences. First, the components are represented by using graph based models. Next, a particle swarm optimization (PSO) method with a new encoding scheme is developed. In the new PSO encoding scheme, a particle is represented by a position matrix defining an assembly sequence and a disassembly sequence. The assembly and disassembly sequences can be simultaneously planned with an objective of minimizing the total of assembly costs and disassembly costs. The test results show that the presented method is feasible and efficient for solving the integrated design evaluation and assembly and disassembly sequence planning problem. An example product is implemented and illustrated in this paper.

Keywords: green design, assembly and disassembly sequence planning, green design for assembly, particle swarm optimization.

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Authors: Vrince Vimal, Madhav J. Nigam

Abstract:

Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime.

Keywords: WSN, random deployment, clustering, isolated nodes, network lifetime.

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Authors: F. Titouna, S. Benferhat, K. Aksa, C. Titouna

Abstract:

A traffic light gives security from traffic congestion,reducing the traffic jam, and organizing the traffic flow. Furthermore,increasing congestion level in public road networks is a growingproblem in many countries. Using Intelligent Transportation Systemsto provide emergency vehicles a green light at intersections canreduce driver confusion, reduce conflicts, and improve emergencyresponse times. Nowadays, the technology of wireless sensornetworks can solve many problems and can offer a good managementof the crossroad. In this paper, we develop a new approach based onthe technique of clustering and the graphical possibilistic fusionmodeling. So, the proposed model is elaborated in three phases. Thefirst one consists to decompose the environment into clusters,following by the fusion intra and inter clusters processes. Finally, wewill show some experimental results by simulation that proves theefficiency of our proposed approach.KeywordsTraffic light, Wireless sensor network, Controller,Possibilistic network/Bayesain network.

Keywords: Traffic light, Wireless sensor network, Controller, Possibilistic network/Bayesain network.

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Authors: Fatih Eren

Abstract:

This research aims to find an answer to the question of which knowledge and skills do the Turkish urban planners need in their business practice. Understanding change in cities, making a prediction, making an urban decision and putting it into practice, working together with actors from different organizations from various academic disciplines, persuading people to accept something and developing good personal and professional relationships have become very complex and difficult in today’s world. The truth is that urban planners work in many institutions under various positions which are not similar to each other by field of activity and all planners are forced to develop some knowledge and skills for success in their business in Turkey. This study targets to explore what urban planners do in the global information age. The study is the product of a comprehensive nation-wide research. In-depth interviews were conducted with 174 experienced urban planners, who work in different public institutions and private companies under varied positions in the Turkish Planning System, to find out knowledge and skills needed by next-generation urban planners. The main characteristics of next-generation urban planners are defined; skills that planners needed today are explored in this paper. Findings show that the positivist (traditional) planning approach has given place to anti-positivist planning approaches in the Turkish Planning System so next-generation urban planners who seek success and want to carve out a niche for themselves in business life have to equip themselves with innovative skills. The result section also includes useful and instructive findings for planners about what is the meaning of being an urban planner and what is the ideal content and context of planning education at universities in the global age.

Keywords: The global information age, urban planners, innovative job skills, planning education.

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Authors: T. T. Manikandan, Rajeev Sukumaran

Abstract:

Coral Reefs are very important for the majority of marine ecosystems. But, such vital species are under major threat due to the factors such as ocean acidification, overfishing, and coral bleaching. To conserve the coral reefs, reef restoration activities are carried out across the world. After reef restoration, various parameters have to be monitored in order to ensure the overall effectiveness of the reef restoration. Underwater Wireless Sensor Network (UWSN) based  monitoring is widely adopted for such long monitoring activities. Since monitoring of coral reef restoration activities is time sensitive, the QoS guarantee offered by the network with respect to delay is vital. So this research focuses on the analytical modeling of network layer delay using Stochastic Network Calculus (SNC). The core focus of the proposed model will be on the analysis of stochastic dependencies between the network flow and deriving the stochastic delay bounds for the flows that traverse in tandem in UWSNs. The derived analytical bounds are evaluated for their effectiveness using discrete event simulations.

Keywords: Coral Reef Restoration, SNC, SFA, PMOO, Tandem of Queues, Delay Bound.

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Authors: Mohammad Mahdi Paydar, Armin Cheraghalipour, Mostafa Hajiaghaei-Keshteli

Abstract:

Nowadays, in advanced countries, agriculture as one of the most significant sectors of the economy, plays an important role in its political and economic independence. Due to farmers' lack of information about products' demand and lack of proper planning for harvest time, annually the considerable amount of products is corrupted. Besides, in this paper, we attempt to improve these unfavorable conditions via designing an effective supply chain network that tries to minimize total costs of agricultural products along with minimizing shortage in demand points. To validate the proposed model, a stochastic optimization approach by using a branch and bound solver of the LINGO software is utilized. Furthermore, to accumulate the data of parameters, a case study in Mazandaran province placed in the north of Iran has been applied. Finally, using ɛ-constraint approach, a Pareto front is obtained and one of its Pareto solutions as best solution is selected. Then, related results of this solution are explained. Finally, conclusions and suggestions for the future research are presented.

Keywords: Perishable products, stochastic optimization, agricultural supply chain, ɛ-constraint.

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Authors: M. J. Park, S. H. Lee, C. H. Lee, O. M. Kwon

Abstract:

This paper studies a secondary voltage control framework of the microgrids based on the consensus for a communication network of multiagent. The proposed control is designed by the communication network with one-way links. The communication network is modeled by a directed graph. At this time, the concept of sampling is considered as the communication constraint among each distributed generator in the microgrids. To analyze the sampling effects on the secondary voltage control of the microgrids, by using Lyapunov theory and some mathematical techniques, the sufficient condition for such problem will be established regarding linear matrix inequality (LMI). Finally, some simulation results are given to illustrate the necessity of the consideration of the sampling effects on the secondary voltage control of the microgrids.

Keywords: Microgrids, secondary control, multiagent, sampling, LMI.

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