Search results for: network estimation.

Authors: Wanwipa Siriwatwechakul, Nutte Teraphongphom, Vatcharani Ngaotheppitak, Sureeporn Kunataned

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The study investigated the hydrophilic to hydrophobic transition of modified polyacrylamide hydrogel with the inclusion of N-isopropylacrylamide (NIAM). The modification was done by mimicking micellar polymerization, which resulted in better arrangement of NIAM chains in the polyacrylamide network. The degree of NIAM arrangement is described by NH number. The hydrophilic to hydrophobic transition was measured through the partition coefficient, K, of Orange II and Methylene Blue in hydrogel and in water. These dyes were chosen as a model for solutes with different degree of hydrophobicity. The study showed that the hydrogel with higher NH values resulted in better solubility of both dyes. Moreover, in temperature above the lower critical solution temperature (LCST) of Poly(N-isopropylacrylamide) (PNIAM)also caused the collapse of NIPAM chains which results in a more hydrophobic environment that increases the solubility of Methylene Blue and decreases the solubility of Orange II in the hydrogels with NIPAM present.

Keywords: Thermo-sensitive hydrogel, partition coefficient, the lower critical solution temperature (LCST), micellar polymerization.

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Authors: Sachin Vrajlal Rajani, Vivek Pandya, Ankit Suvariya

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Green and renewable energy is getting extraordinary consideration today, because of ecological concerns made by blazing of fossil powers. Photovoltaic and wind power generation are the basic decisions for delivering power in this respects. Producing power by the sun based photovoltaic systems is known to the world, yet control makers may get confounded to pick between on-grid and off-grid systems. In this exploration work, an endeavor is made to compare the off-grid (stand-alone) and on-grid (grid-connected) frameworks. The work presents relative examination, between two distinctive PV frameworks situated at V.V.P. Engineering College, Rajkot. The first framework is 100 kW remain solitary and the second is 60 kW network joined. The real-time parameters compared are; output voltage, load current, power in-flow, power output, performance ratio, yield factor, and capacity factor. The voltage changes and the power variances in both frameworks are given exceptional consideration and the examination is made between the two frameworks to judge the focal points and confinements of both the frameworks.

Keywords: Standalone PV systems, grid connected PV systems, comparison, real time data analysis.

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Authors: Yang Wenjuan, Xu Yilun

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Based on the comparative analysis of the relevant evaluation methods of walking environment, this paper selects the combined evaluation method of macro urban morphology analysis and micro urban design quality survey, then investigates and analyzes the walking environment of three rail transit station area in Nanjing to explore the influence factor and internal relation of walkability of rail transit station area. Analysis shows that micro urban design factors have greater impact on the walkability of rail transit station area compared with macro urban morphology factors, the convenience is the key factor in the four aspects of convenience, security, identity and comfortability of the urban design factors, the convenience is not only affected by the block network form, but also related to the quality of the street space. The overall evaluation of walkability comes from the overlapping and regrouping of the walking environment at different levels, but some environmental factors play a leading role. The social attributes of pedestrians also partly influence their walking perception and evaluation.

Keywords: Rail transit station area, walkability, evaluation, influence factors.

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Authors: Christos Spandonidis, Stefanos Tsantilas, Elias Sedikos, Nektarios Galiatsatos, Fotios Giannopoulos, Panagiotis Papadopoulos, Nikolaos Demagos, Dimitrios Reppas, Christos Giordamlis

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In the present paper, a low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises of dedicated low cost, low power hardware and the associated embedded software that enable measurement of particles (PM2.5 and PM10), NO, CO, CO₂ and O₃ concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensor measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the developed platform took place in Thessaloniki, Greece, where 16 devices were installed in various buildings in the city. In the near future, many more of these devices are going to be installed in the greater Thessaloniki area, giving a detailed air quality map of the city.

Keywords: Distributed sensor system, environmental monitoring, Internet of Things, IoT, Smart Cities.

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Authors: N. David, H. O. Gao

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Air pollution is a serious environmental threat on a global scale and can cause harm to human health, morbidity and premature mortality. Reliable monitoring and control systems are therefore necessary to develop coping skills against the hazards associated with this phenomenon. However, existing environmental monitoring means often do not provide a sufficient response due to practical and technical limitations. Commercial microwave links that form the infrastructure for transmitting data between cell phone towers can be harnessed to map rain at high tempo-spatial resolution. Rainfall causes a decrease in the signal strength received by these wireless communication links allowing it to be used as a built-in sensor network to map the phenomenon. In this study, we point to the potential that lies in this system to indirectly monitor areas where air pollution is reduced. The relationship between pollutant wash-off and rainfall provides an opportunity to acquire important spatial information about air quality using existing cell-phone tower signals. Since the density of microwave communication networks is high relative to any dedicated sensor arrays, it could be possible to rely on this available observation tool for studying precipitation scavenging on air pollutants, for model needs and more.

Keywords: Air pollution, commercial microwave links, rainfall.

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Authors: W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sathasivam

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This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

Keywords: Convergence, Modeling, Neural Networks, Preconditioned Conjugate Gradient.

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Authors: Ramaswamy Palaniappan

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In this paper, several improvements are proposed to previous work of automated classification of alcoholics and nonalcoholics. In the previous paper, multiplayer-perceptron neural network classifying energy of gamma band Visual Evoked Potential (VEP) signals gave the best classification performance using 800 VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the dataset is extended to include 3560 VEP signals from 102 subjects: 62 alcoholics and 40 non-alcoholics. Three modifications are introduced to improve the classification performance: i) increasing the gamma band spectral range by increasing the pass-band width of the used filter ii) the use of Multiple Signal Classification algorithm to obtain the power of the dominant frequency in gamma band VEP signals as features and iii) the use of the simple but effective knearest neighbour classifier. To validate that these two modifications do give improved performance, a 10-fold cross validation classification (CVC) scheme is used. Repeat experiments of the previously used methodology for the extended dataset are performed here and improvement from 94.49% to 98.71% in maximum averaged CVC accuracy is obtained using the modifications. This latest results show that VEP based classification of alcoholics is worth exploring further for system development.

Keywords: Alcoholic, Multilayer-perceptron, Nearest neighbour, Gamma band, MUSIC, Visual evoked potential.

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Authors: E. L. Suarez, D. E. Meeroff, Y. Yong

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Current flooding risk modeling focuses on resilience, defined as the probability of recovery from a severe flooding event. However, the long-term damage to property and well-being by nuisance flooding and its long-term effects on communities are not typically included in risk assessments. An approach was developed to address the probability of recovering from a severe flooding event combined with the probability of community performance during a nuisance event. A consolidated model, namely the conflation flooding recovery (&FR) model, evaluates risk-coping mitigation strategies for communities based on the recovery time from catastrophic events, such as hurricanes or extreme surges, and from everyday nuisance flooding events. The &FR model assesses the variation contribution of each independent input and generates a weighted output that favors the distribution with minimum variation. This approach is especially useful if the input distributions have dissimilar variances. The &FR is defined as a single distribution resulting from the product of the individual probability density functions. The resulting conflated distribution resides between the parent distributions, and it infers the recovery time required by a community to return to basic functions, such as power, utilities, transportation, and civil order, after a flooding event. The &FR model is more accurate than averaging individual observations before calculating the mean and variance or averaging the probabilities evaluated at the input values, which assigns the same weighted variation to each input distribution. The main disadvantage of these traditional methods is that the resulting measure of central tendency is exactly equal to the average of the input distribution’s means without the additional information provided by each individual distribution variance. When dealing with exponential distributions, such as resilience from severe flooding events and from nuisance flooding events, conflation results are equivalent to the weighted least squares method or best linear unbiased estimation. The combination of severe flooding risk with nuisance flooding improves flood risk management for highly populated coastal communities, such as in South Florida, USA, and provides a method to estimate community flood recovery time more accurately from two different sources, severe flooding events and nuisance flooding events.

Keywords: Community resilience, conflation, flood risk, nuisance flooding.

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks, bit-serial neural processor, FPGA, Neural Processing Element.

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Authors: Brahim Hamid, Bertrand Le Saec, Mohamed Mosbah

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The vertex connectivity of a graph is the smallest number of vertices whose deletion separates the graph or makes it trivial. This work is devoted to the problem of vertex connectivity test of graphs in a distributed environment based on a general and a constructive approach. The contribution of this paper is threefold. First, using a preconstructed spanning tree of the considered graph, we present a protocol to test whether a given graph is 2-connected using only local knowledge. Second, we present an encoding of this protocol using graph relabeling systems. The last contribution is the implementation of this protocol in the message passing model. For a given graph G, where M is the number of its edges, N the number of its nodes and Δ is its degree, our algorithms need the following requirements: The first one uses O(Δ×N2) steps and O(Δ×logΔ) bits per node. The second one uses O(Δ×N2) messages, O(N2) time and O(Δ × logΔ) bits per node. Furthermore, the studied network is semi-anonymous: Only the root of the pre-constructed spanning tree needs to be identified.

Keywords: Distributed computing, fault-tolerance, graph relabeling systems, local computations, local knowledge, message passing system, networks, vertex connectivity.

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Authors: J. Andrade-Garda, A. Anguera, J. Ares-Casal, M. Hidalgo-Lorenzo, J.-A. Lara, D. Lizcano, S. Suárez-Garaboa

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In the current constantly changing economic context, collaborative networks allow partners to undertake projects that would not be possible if attempted by them individually. These projects usually involve the performance of a group of tasks (named roles) that have to be distributed among the partners. Thus, an allocation/matching problem arises that will be referred to as Role-Partner Allocation problem. In real life this situation is addressed by negotiation between partners in order to reach ad hoc agreements. Besides taking a long time and being hard work, both historical evidence and economic analysis show that such approach is not recommended. Instead, the allocation process should be automated by means of a centralized matching scheme. However, as a preliminary step to start the search for such a matching mechanism (or even the development of a new one), the problem and its core components must be specified. To this end, this paper establishes (i) the definition of the problem and its constraints, (ii) the key features of the involved elements (i.e., roles and partners); and (iii) how to create preference lists both for roles and partners. Only this way it will be possible to conduct subsequent methodological research on the solution method.     

Keywords: Collaborative network, matching, partner, preference list, role.

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Authors: Mohd Fikri Azli bin Abdullah, Ali Fahmi Perwira Negara, Md. Shohel Sayeed, Deok-Jai Choi, Kalaiarasi Sonai Muthu

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Rapid advancement in computing technology brings computers and humans to be seamlessly integrated in future. The emergence of smartphone has driven computing era towards ubiquitous and pervasive computing. Recognizing human activity has garnered a lot of interest and has raised significant researches- concerns in identifying contextual information useful to human activity recognition. Not only unobtrusive to users in daily life, smartphone has embedded built-in sensors that capable to sense contextual information of its users supported with wide range capability of network connections. In this paper, we will discuss the classification algorithms used in smartphone-based human activity. Existing technologies pertaining to smartphone-based researches in human activity recognition will be highlighted and discussed. Our paper will also present our findings and opinions to formulate improvement ideas in current researches- trends. Understanding research trends will enable researchers to have clearer research direction and common vision on latest smartphone-based human activity recognition area.

Keywords: Classification algorithms, Human Activity Recognition (HAR), Smartphones

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Authors: Mahdi Hayatdavudi, Ali Reza Rajabi, Mohammad Hassan Raouf, Mojtaba Saeedimoghadam, Amir Habibi

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Generally, distributed generation units refer to small-scale electric power generators that produce electricity at a site close to the customer or an electric distribution system (in parallel mode). From the customers’ point of view, a potentially lower cost, higher service reliability, high power quality, increased energy efficiency, and energy independence can be the key points of a proper DG unit. Moreover, the use of renewable types of distributed generations such as wind, photovoltaic, geothermal or hydroelectric power can also provide significant environmental benefits. Therefore, it is of crucial importance to study their impacts on the distribution networks. A marked increase in Distributed Generation (DG), associated with medium voltage distribution networks, may be expected. Nowadays, distribution networks are planned for unidirectional power flows that are peculiar to passive systems, and voltage control is carried out exclusively by varying the tap position of the HV/MV transformer. This paper will compare different DG control methods and possible network reconfiguration aimed at assessing their effect on voltage profiles.

Keywords: Distribution Feeder Reconfiguration (DFR), Distributed Generator (DG), Voltage Profile, Control.

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Authors: Gergely Treplan, Long Tran-Thanh, Janos Levendovszky

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In this paper, a reliable cooperative multipath routing algorithm is proposed for data forwarding in wireless sensor networks (WSNs). In this algorithm, data packets are forwarded towards the base station (BS) through a number of paths, using a set of relay nodes. In addition, the Rayleigh fading model is used to calculate the evaluation metric of links. Here, the quality of reliability is guaranteed by selecting optimal relay set with which the probability of correct packet reception at the BS will exceed a predefined threshold. Therefore, the proposed scheme ensures reliable packet transmission to the BS. Furthermore, in the proposed algorithm, energy efficiency is achieved by energy balancing (i.e. minimizing the energy consumption of the bottleneck node of the routing path) at the same time. This work also demonstrates that the proposed algorithm outperforms existing algorithms in extending longevity of the network, with respect to the quality of reliability. Given this, the obtained results make possible reliable path selection with minimum energy consumption in real time.

Keywords: wireless sensor networks, reliability, cooperativerouting, Rayleigh fading model, energy balancing

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Authors: Kavi K. Khedo, R. K. Subramanian

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Wireless sensor networks (WSN) are currently receiving significant attention due to their unlimited potential. These networks are used for various applications, such as habitat monitoring, automation, agriculture, and security. The efficient nodeenergy utilization is one of important performance factors in wireless sensor networks because sensor nodes operate with limited battery power. In this paper, we proposed the MiSense hierarchical cluster based routing algorithm (MiCRA) to extend the lifetime of sensor networks and to maintain a balanced energy consumption of nodes. MiCRA is an extension of the HEED algorithm with two levels of cluster heads. The performance of the proposed protocol has been examined and evaluated through a simulation study. The simulation results clearly show that MiCRA has a better performance in terms of lifetime than HEED. Indeed, MiCRA our proposed protocol can effectively extend the network lifetime without other critical overheads and performance degradation. It has been noted that there is about 35% of energy saving for MiCRA during the clustering process and 65% energy savings during the routing process compared to the HEED algorithm.

Keywords: Clustering algorithm, energy consumption, hierarchical model, sensor networks.

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Authors: Li Li, Kai-Hsuan Chu

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It is well known that the real estate price depends on a lot of factors. Each house current value is dependent on the location, room number, transportation, living convenience, year and surrounding environments. Although, there are different experienced models for housing agent to estimate the price, it is a case by case study without overall dynamic variation investigation. However, many economic parameters may more or less influence the real estate price variation. Here, the influences of most macroeconomic parameters on real estate price are investigated individually based on least-square scheme and grey correlation strategy. Then those parameters are classified into leading indices, simultaneous indices and laggard indices. In addition, the leading time period is evaluated based on least square method. The important leading and simultaneous indices can be used to establish an artificial intelligent neural network model for real estate price variation prediction. The real estate price variation of Taipei, Taiwan during 2005 ~ 2017 are chosen for this research data analysis and validation. The results show that the proposed method has reasonable prediction function for real estate business reference.

Keywords: Real estate price, least-square, grey correlation, macroeconomics.

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Authors: Elif Derya UBEYLI, Inan GULER

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A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.

Keywords: Chaotic signal, Electroencephalogram (EEG) signals, Feature extraction/selection, Lyapunov exponents

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: Deep learning, long-short-term memory, energy, renewable energy load forecasting.

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Authors: Kyomin Lee, Joohee Kim, Sangho Kang

Abstract:

The commercial nuclear power reactor in South Korea, Kori Unit 1, which was a 587 MWe pressurized water reactor that started operation since 1978, was permanently shut down in June 2017 without an additional operating license extension. The Kori 1 Unit is scheduled to become the nuclear power unit to enter the decommissioning phase. In this study, the preliminary evaluation of the decommissioning wastes for the Kori Unit 1 was performed based on the following series of process: firstly, the plant inventory is investigated based on various documents (i.e., equipment/ component list, construction records, general arrangement drawings). Secondly, the radiological conditions of systems, structures and components (SSCs) are established to estimate the amount of radioactive waste by waste classification. Third, the waste management strategies for Kori Unit 1 including waste packaging are established. Forth, selection of the proper decontamination and dismantling (D&D) technologies is made considering the various factors. Finally, the amount of decommissioning waste by classification for Kori 1 is estimated using the DeCAT program, which was developed by KEPCO-E&C for a decommissioning cost estimation. The preliminary evaluation results have shown that the expected amounts of decommissioning wastes were less than about 2% and 8% of the total wastes generated (i.e., sum of clean wastes and radwastes) before/after waste processing, respectively, and it was found that the majority of contaminated material was carbon or alloy steel and stainless steel. In addition, within the range of availability of information, the results of the evaluation were compared with the results from the various decommissioning experiences data or international/national decommissioning study. The comparison results have shown that the radioactive waste amount from Kori Unit 1 decommissioning were much less than those from the plants decommissioned in U.S. and were comparable to those from the plants in Europe. This result comes from the difference of disposal cost and clearance criteria (i.e., free release level) between U.S. and non-U.S. The preliminary evaluation performed using the methodology established in this study will be useful as a important information in establishing the decommissioning planning for the decommissioning schedule and waste management strategy establishment including the transportation, packaging, handling, and disposal of radioactive wastes.

Keywords: Characterization, classification, decommissioning, decontamination and dismantling, Kori 1, radioactive waste.

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Authors: Yasemin Gülşen Yılmaz, Süleyman Hakan Yılmaz, Muhammet Erbay

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Social media is among the most important means of communication. Social media offers individuals and groups with an opportunity for participatory socialization over the internet, which is free of any time and place restrictions. Social media is a kind of interactive communication and bilateral social network. Various communication contents can be shared and put into mass circulation easily and quickly through social media. These sharings are not only limited to individuals but also happen to groups, institutions, and different constitutions. Their contents consist of any type of written message, audio and video files. We are living in the social media era now. It is not surprising that social media which has extensive communication facilities and massive prevalence is used in politics. Therefore, the use of social media (Facebook) by political parties during the Turkish general elections held on June 7, 2015, has been chosen as our research subject. Four parties namely, AKP, CHP, MHP and HDP who have the majority of votes in Turkey and participate in elections in Konya have been selected for our study. Their provincial centers’ and parliamentary candidates` use of social media (Facebook) on the last three days prior to the election have been examined and subjected to a qualitative analysis by means of content analysis.

Keywords: Social media, June 7 general elections, politics, Facebook.

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Authors: Taehyun Yoon, Keechul Jung

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There are many researches to detect collision between real object and virtual object in 3D space. In general, these techniques are need to huge computing power. So, many research and study are constructed by using cloud computing, network computing, and distribute computing. As a reason of these, this paper proposed a novel fast 3D collision detection algorithm between real and virtual object using 2D intersection area. Proposed algorithm uses 4 multiple cameras and coarse-and-fine method to improve accuracy and speed performance of collision detection. In the coarse step, this system examines the intersection area between real and virtual object silhouettes from all camera views. The result of this step is the index of virtual sensors which has a possibility of collision in 3D space. To decide collision accurately, at the fine step, this system examines the collision detection in 3D space by using the visual hull algorithm. Performance of the algorithm is verified by comparing with existing algorithm. We believe proposed algorithm help many other research, study and application fields such as HCI, augmented reality, intelligent space, and so on.

Keywords: Collision Detection, Computer Vision, Human Computer Interaction, Visual Hull

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Authors: Jairo A. Muñoz, Alexander Komissarov, Alexander Gromov

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In this investigation, a hypoeutectic AlSi11Cu alloy was printed. This alloy was obtained in powder form with an average particle size of 40 µm. Bars 20 mm in diameter and 100 mm in length were printed with the building direction parallel to the bars' longitudinal direction. The microstructural characterization demonstrated an Al matrix surrounded by a Si network forming a coral-like pattern. The microstructure of the alloy showed a heterogeneous behavior with a mixture of columnar and equiaxed grains. Likewise, the texture indicated that the columnar grains were preferentially oriented towards the building direction, while the equiaxed followed a texture dominated by the cube component. On the other hand, the as-printed material strength showed higher values than those obtained in the same alloy using conventional processes such as casting. In addition, strength and ductility differences were found in the printed material, depending on the measurement direction. The highest values were obtained in the radial direction (565 MPa maximum strength and 4.8% elongation to failure). The lowest values corresponded to the transverse direction (508 MPa maximum strength and 3.2 elongation to failure), which corroborate the material anisotropy.

Keywords: Additive manufacturing, aluminium alloy, melting pools, tensile test.

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Authors: C. Chitra, P. Subbaraj

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The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.

Keywords: Multiobjective optimization, Non-dominated SortingGenetic Algorithm, Routing, Weighted sum.

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Authors: Fantazi Walid, Ezzedine Tahar, Bargaoui Zoubeida

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This research presents the main ideas to implement an intelligent system composed by communicating wireless sensors measuring environmental data linked to drought indicators (such as air temperature, soil moisture , etc...). On the other hand, the setting up of a spatio temporal database communicating with a Web mapping application for a monitoring in real time in activity 24:00 /day, 7 days/week is proposed to allow the screening of the drought parameters time evolution and their extraction. Thus this system helps detecting surfaces touched by the phenomenon of drought. Spatio-temporal conceptual models seek to answer the users who need to manage soil water content for irrigating or fertilizing or other activities pursuing crop yield augmentation. Effectively, spatiotemporal conceptual models enable users to obtain a diagram of readable and easy data to apprehend. Based on socio-economic information, it helps identifying people impacted by the phenomena with the corresponding severity especially that this information is accessible by farmers and stakeholders themselves. The study will be applied in Siliana watershed Northern Tunisia.

Keywords: WSN, database spatio-temporal, GIS, web-mapping, indicator of drought.

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: Data mining, hybrid storage system, recurrent neural network, support vector machine.

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Authors: Shuen-Tai Wang, Ying-Chuan Chen, Yu-Ching Lin

Abstract:

There has been renewal of interest in the relation between Green IT and cloud computing in recent years. Cloud computing has to be a highly elastic environment which provides stable services to users. The growing use of cloud computing facilities has caused marked energy consumption, putting negative pressure on electricity cost of computing center or data center. Each year more and more network devices, storages and computers are purchased and put to use, but it is not just the number of computers that is driving energy consumption upward. We could foresee that the power consumption of cloud computing facilities will double, triple, or even more in the next decade. This paper aims at resource allocation and scheduling technologies that are short of or have not well developed yet to reduce energy utilization in cloud computing platform. In particular, our approach relies on recalling services dynamically onto appropriate amount of the machines according to user’s requirement and temporarily shutting down the machines after finish in order to conserve energy. We present initial work on integration of resource and power management system that focuses on reducing power consumption such that they suffice for meeting the minimizing quality of service required by the cloud computing platform.

Keywords: Cloud computing, energy utilization, power consumption, resource allocation.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: Azadeh Maroufmashat, Sourena Sattari Khavas, Halle Bakhteeyar

Abstract:

Multi-energy systems will enhance the system reliability and power quality. This paper presents an integrated approach for the design and operation of distributed energy resources (DER) systems, based on energy hub modeling. A multi-objective optimization model is developed by considering an integrated view of electricity and natural gas network to analyze the optimal design and operating condition of DER systems, by considering two conflicting objectives, namely, minimization of total cost and the minimization of environmental impact which is assessed in terms of CO2 emissions. The mathematical model considers energy demands of the site, local climate data, and utility tariff structure, as well as technical and financial characteristics of the candidate DER technologies. To provide energy demands, energy systems including photovoltaic, and co-generation systems, boiler, central power grid are considered. As an illustrative example, a hotel in Iran demonstrates potential applications of the proposed method. The results prove that increasing the satisfaction degree of environmental objective leads to increased total cost.

Keywords: Multi objective optimization, DER systems, Energy hub, Cost, CO2 emission.

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Authors: Yao-Tien Wang, Hsiang-Fu Yu, Dung Chen Chiou

Abstract:

In wireless and mobile communications, this progress provides opportunities for introducing new standards and improving existing services. Supporting multimedia traffic with wireless networks quality of service (QoS). In this paper, a grey-fuzzy controller for radio resource management (GF-RRM) is presented to maximize the number of the served calls and QoS provision in wireless networks. In a wireless network, the call arrival rate, the call duration and the communication overhead between the base stations and the control center are vague and uncertain. In this paper, we develop a method to predict the cell load and to solve the RRM problem based on the GF-RRM, and support the present facility has been built on the application-level of the wireless networks. The GF-RRM exhibits the better adaptability, fault-tolerant capability and performance than other algorithms. Through simulations, we evaluate the blocking rate, update overhead, and channel acquisition delay time of the proposed method. The results demonstrate our algorithm has the lower blocking rate, less updated overhead, and shorter channel acquisition delay.

Keywords: radio resource management, grey prediction, fuzzylogic control, wireless networks, quality of service.

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Authors: Megha Gupta, Nupur Prakash

Abstract:

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

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

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