Search results for: fault location andfault classification.

Authors: Mohammad Reza Tabatabaei, Mohammad Bagher Haddadi, Mojtaba Saeedimoghadam, Ali Vaseghi Ardekani

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Economic Dispatch (ED) is one of the most challenging problems of power system since it is difficult to determine the optimum generation scheduling to meet the particular load demand with the minimum fuel costs while all constraints are satisfied. The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and system equality and inequality constraints. In this paper, an efficient and practical steady-state genetic algorithm (SSGAs) has been proposed for solving the economic dispatch problem. The objective is to minimize the total generation fuel cost and keep the power flows within the security limits. To achieve that, the present work is developed to determine the optimal location and size of capacitors in transmission power system where, the Participation Factor Algorithm and the Steady State Genetic Algorithm are proposed to select the best locations for the capacitors and determine the optimal size for them.

Keywords: Economic Dispatch, Lagrange, Capacitors Placement, Losses Reduction, Genetic Algorithm.

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Authors: Onesmus Muvengei, John Kihiu, Bernard Ikua

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This paper numerically investigates the effects of input speed on the overall dynamic characteristics of a multi-body system with differently located revolute clearance joints without friction. A typical planar slider-crank mechanism is used as a demonstration case in which the effects of the input speed on the dynamic performance of the mechanism with a revolute clearance joint between the crank and connecting rod, and between the connecting rod and slider are separately investigated with comprehensive observations numerically presented. It is observed that, changing the driving speed of a multibody system makes the behavior of the system to change from either periodic to chaotic, or chaotic to periodic depending on which joint has clearance. The location of the clearance revolute joint and the operating speed of a multi-body system play a crucial role in predicting accurately the dynamic responses of the system. Therefore the dynamic behavior of one clearance revolute joint cannot be used as a general case for a mechanical system.

Keywords: Chaotic behavior, Contact-impact forces, Dynamic response, Multi-body mechanical system, Periodic behavior, Poincare maps, Quasi-periodic behavior, Revolute clearance joint

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Authors: B. O. Olawale, C. R. Chatwin, R. C. D. Young, P. M. Birch, F. O. Faithpraise, A. O. Olukiran

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In this paper, we present a four-step ortho-rectification procedure for real-time geo-referencing of video data from a low-cost UAV equipped with a multi-sensor system. The basic procedures for the real-time ortho-rectification are: (1) decompilation of the video stream into individual frames; (2) establishing the interior camera orientation parameters; (3) determining the relative orientation parameters for each video frame with respect to each other; (4) finding the absolute orientation parameters, using a self-calibration bundle and adjustment with the aid of a mathematical model. Each ortho-rectified video frame is then mosaicked together to produce a mosaic image of the test area, which is then merged with a well referenced existing digital map for the purpose of geo-referencing and aerial surveillance. A test field located in Abuja, Nigeria was used to evaluate our method. Video and telemetry data were collected for about fifteen minutes, and they were processed using the four-step ortho-rectification procedure. The results demonstrated that the geometric measurement of the control field from ortho-images is more accurate when compared with those from original perspective images when used to pin point the exact location of targets on the video imagery acquired by the UAV. The 2-D planimetric accuracy when compared with the 6 control points measured by a GPS receiver is between 3 to 5 metres.

Keywords: Geo-referencing, ortho-rectification, video frame, self-calibration, UAV, target tracking.

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Authors: Dimitrios E. Kontaxis, George Litainas, Dimitrios P. Ptochos, Vaggelis P. Ptochos, Sotirios P. Ptochos, Dimitrios Beletsis, Konstantinos Kritikakis, Milan Sunaric

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Real-time monitoring of the supply chain conditions and procedures is a critical element for the optimal coordination and safety of the deliveries, as well as for the minimization of the delivery time and cost. Real time monitoring requires IoT data streams, which are related to the conditions of the products and the means of transport (e.g., location, temperature/humidity conditions, kinematic state, ambient light conditions, etc.). These streams are generated by battery-based IoT tracking devices, equipped with appropriate sensors, and are transmitted to a cloud-based back-end system. Proper handling and processing of the IoT data streams, using predictive and artificial intelligence algorithms, can provide significant and useful results, which can be exploited by the supply chain stakeholders in order to enhance their financial benefits, as well as the efficiency, security, transparency, coordination and sustainability of the supply chain procedures. The technology, the features and the characteristics of a complete, proprietary system, including hardware, firmware and software tools - developed in the context of a co-funded R&D program - are addressed and presented in this paper. 

Keywords: IoT embedded electronics, real-time monitoring, tracking device, sensor platform

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition (HAR) modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view Football datasets. Our HAR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH Multi-view Football datasets, respectively.

Keywords: Computer vision, human motion analysis, random forest, machine learning.

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled direct normal irradiance field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: Artificial Neural Networks, Resilient Propagation, Solar Radiation, Time Series Forecasting.

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Authors: Ick Hoon Jang, Hoon Jae Lee, Dae Hoon Kwon, Ui Young Pak

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In this paper, we propose a texture feature-based language identification using wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features and FFT (fast Fourier transform) feature. In the proposed method, wavelet subbands are first obtained by wavelet transform from a test image and denoised by Donoho-s soft-thresholding. BDIP and BVLC operators are next applied to the wavelet subbands. FFT blocks are also obtained by 2D (twodimensional) FFT from the blocks into which the test image is partitioned. Some significant FFT coefficients in each block are selected and magnitude operator is applied to them. Moments for each subband of BDIP and BVLC and for each magnitude of significant FFT coefficients are then computed and fused into a feature vector. In classification, a stabilized Bayesian classifier, which adopts variance thresholding, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method with the three operations yields excellent language identification even with rather low feature dimension.

Keywords: BDIP, BVLC, FFT, language identification, texture feature, wavelet transform.

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Authors: Ghassan H. Halasa

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Elementary particles are created in pairs of equal and opposite momentums at a reference frame at the speed of light. The speed of light reference frame is viewed as a point in space as observed by observer at rest. This point in space is the bang location of the big bang theory. The bang in the big bang theory is not more than sustained flow of pairs of positive and negative elementary particles. Electrons and negative charged elementary particles are ejected from this point in space at velocities faster than light, while protons and positively charged particles obtain velocities lower than light. Subsonic masses are found to have real and positive charge, while supersonic masses are found to be negative and imaginary indicating that the two masses are of different entities. The electron-s super-sonic speed, as viewed by rest observer was calculated and found to be less than the speed of light and is little higher than the electron speed in Bohr-s orbit. The newly formed hydrogen gas temperature was found to be in agreement with temperatures found on newly formed stars. Universe expansion was found to be in agreement. Partial mass and charge elementary particles and particles with momentum only were explained in the context of this theoretical approach.

Keywords: Evolution of Matter, Multidimensional spaces, relativity, Big Bang Theory

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Authors: Md Abdullah Al Faruque, Ram Balachandar

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An experimental study with four different types of bed conditions was carried out to understand the effect of roughness in open channel flow at two different Reynolds numbers. The bed conditions include a smooth surface and three different roughness conditions, which were generated using sand grains with a median diameter of 2.46 mm. The three rough conditions include a surface with distributed roughness, a surface with continuously distributed roughness and a sand bed with a permeable interface. A commercial two-component fibre-optic LDA system was used to conduct the velocity measurements. The variables of interest include the mean velocity, turbulence intensity, correlation between the streamwise and the wall normal turbulence, Reynolds shear stress and velocity triple products. Quadrant decomposition was used to extract the magnitude of the Reynolds shear stress of the turbulent bursting events. The effect of roughness was evident throughout the flow depth. The results show that distributed roughness has the greatest roughness effect followed by the sand bed and the continuous roughness. Compared to the smooth bed, the streamwise turbulence intensity reduces but the vertical turbulence intensity increases at a location very close to the bed due to the introduction of roughness. Although the same sand grain is used to create the three different rough bed conditions, the difference in the turbulence intensity is an indication that the specific geometry of the roughness has an influence on turbulence structure.

Keywords: Open channel flow, smooth bed, rough bed, Reynolds number, turbulence.

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Authors: Brian H. Marsh

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Nitrogen fertility is an important component for optimum potato yield and quality. Best management practices are necessary in regards to N applications to achieve these goals without applying excess N with may contribute to ground water contamination. Eight potato fields in the Southern San Joaquin Valley were sampled for nitrogen inputs and uptake, tuber and vine dry matter and residual soil nitrate-N. The fields had substantial soil nitrate-N prior to the potato crop. Nitrogen fertilizer was applied prior to planting and in irrigation water as needed based on in-season petiole sampling in accordance with published recommendations. Average total nitrogen uptake was 237 kg ha^-1 on 63.5 Mg ha^-1 tuber yield and nitrogen use efficiency was very good at 81 percent. Sixty-nine percent of the plant nitrogen was removed in tubers. Soil nitrate-N increased 14 percent from pre-plant to post-harvest averaged across all fields and was generally situated in the upper soil profile. Irrigation timing and amount applied did not move water into the lower profile except for a single location where nitrate also moved into the lower soil profile. Pre-plant soil analysis is important information to be used. Rotation crops having deeper rooting growth would be able to utilize nitrogen that remained in the soil profile.

Keywords: Potato, nitrogen fertilization, leaching potential, irrigation management

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Authors: José M. Cantera, Miguel Jiménez, Genoveva López, Javier Soriano

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With the advent of emerging personal computing paradigms such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide range of mobile devices. Since these devices do not have the same rendering capabilities, Web contents need to be adapted for transparent access from a variety of client agents. Such content adaptation is exploited for either an individual element or a set of consecutive elements in a Web document and results in better rendering and faster delivery to the client device. Nevertheless, Web content adaptation sets new challenges for semantic markup. This paper presents an advanced components platform, called SMC, enabling the development of mobility applications and services according to a channel model based on the principles of Services Oriented Architecture (SOA). It then goes on to describe the potential for integration with the Semantic Web through a novel framework of external semantic annotation that prescribes a scheme for representing semantic markup files and a way of associating Web documents with these external annotations. The role of semantic annotation in this framework is to describe the contents of individual documents themselves, assuring the preservation of the semantics during the process of adapting content rendering. Semantic Web content adaptation is a way of adding value to Web contents and facilitates repurposing of Web contents (enhanced browsing, Web Services location and access, etc).

Keywords: Semantic web, ubiquitous and mobile computing, web content transcoding. semantic mark-up, mobile computing, middleware and services.

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Authors: Sheena Christabel Pravin, M. Palanivelan

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Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.

Keywords: Bilingual, children who stutter, children with language impairment, Hidden Markov Models, multi-layer perceptron, linguistic disfluencies, stuttering disfluencies.

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Authors: Malinwo Estone Ayikpa

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Electric power systems are likely to operate with minimum losses and voltage meeting international standards. This is made possible generally by control actions provide by automatic voltage regulators, capacitors and transformers with on-load tap changer (OLTC). With the development of photovoltaic (PV) systems technology, their integration on distribution networks has increased over the last years to the extent of replacing the above mentioned techniques. The conventional analysis and simulation tools used for electrical networks are no longer able to take into account control actions necessary for studying distributed PV generation impact. This paper presents an unbalanced optimal power flow (OPF) model that minimizes losses with association of active power generation and reactive power control of single-phase and three-phase PV systems. Reactive power can be generated or absorbed using the available capacity and the adjustable power factor of the inverter. The unbalance OPF is formulated by current balance equations and solved by primal-dual interior point method. Several simulation cases have been carried out varying the size and location of PV systems and the results show a detailed view of the impact of PV distributed generation on distribution systems.

Keywords: Distribution system, losses, photovoltaic generation, primal-dual interior point method, reactive power control.

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Authors: Ali A. Al-Jazaairry, Tahsin T. Sabbagh

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Presence of cavities in soil predictably induces ground deformation and changes in soil stress, which might influence adjacent existing pile foundations, though the effect of twin cavities on a nearby pile needs to be understood. This research is an attempt to identify the behaviour of piles subjected to axial load and embedded in cavitied clayey soil. A series of finite element modelling were conducted to investigate the performance of piled foundation located in such soils. The validity of the numerical simulation was evaluated by comparing it with available field test and alternative analytical model. The study involved many parameters such as twin cavities size, depth, spacing between cavities, and eccentricity of cavities from the pile axis on the pile performance subjected to axial load. The study involved many cases; in each case, a critical value has been found in which cavities’ presence has shown minimum impact on the behaviour of pile. Load-displacement relationships of the affecting parameters on the pile behaviour were presented to provide helpful information for designing piled foundation situated near twin underground cavities. It was concluded that the presence of the cavities within the soil mass reduces the ultimate capacity of pile. This reduction differs according to the size and location of the cavity.

Keywords: Axial load, clay, finite element, pile, twin cavities, ultimate capacity.

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Authors: Rehab F. Abdel-Kader, Rabab M. Ramadan, Rawya Y. Rizk

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The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the logpolar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the proposed system outperforms other face recognition methods. The overall recognition rate for the rotated test images being 97%, demonstrating that the extracted feature vector is an effective rotation invariant feature set with minimal set of selected features.

Keywords: Discrete Cosine Transform, Face Recognition, Feature Extraction, Log Polar Transform, Particle SwarmOptimization.

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Authors: Tim Nedyalkov

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A business decision to move to the cloud brings fundamental changes in how an organization develops and delivers its Information Technology solutions. The accelerated pace of digital transformation across businesses and government agencies increases the reliance on cloud-based services. Collecting, managing, and retaining large amounts of data in cloud environments make information security and data privacy protection essential. It becomes even more important to understand what key factors drive successful cloud adoption following the commencement of the Privacy Amendment Notifiable Data Breaches (NDB) Act 2017 in Australia as the regulatory changes impact many organizations and industries. This quantitative correlational research investigated the governance, risk management, and compliance factors contributing to cloud security success. The factors influence the adoption of cloud computing within an organizational context after the commencement of the NDB scheme. The results and findings demonstrated that corporate information security policies, data storage location, management understanding of data governance responsibilities, and regular compliance assessments are the factors influencing cloud computing adoption. The research has implications for organizations, future researchers, practitioners, policymakers, and cloud computing providers to meet the rapidly changing regulatory and compliance requirements.

Keywords: Cloud compliance, cloud security, cloud security governance, data governance, privacy protection.

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Authors: A. Samraj, S. Sayeed, J. E. Raja., J. Hossen, A. Rahman

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Monitoring the tool flank wear without affecting the throughput is considered as the prudent method in production technology. The examination has to be done without affecting the machining process. In this paper we proposed a novel work that is used to determine tool flank wear by observing the sound signals emitted during the turning process. The work-piece material we used here is steel and aluminum and the cutting insert was carbide material. Two different cutting speeds were used in this work. The feed rate and the cutting depth were constant whereas the flank wear was a variable. The emitted sound signal of a fresh tool (0 mm flank wear) a slightly worn tool (0.2 -0.25 mm flank wear) and a severely worn tool (0.4mm and above flank wear) during turning process were recorded separately using a high sensitive microphone. Analysis using Singular Value Decomposition was done on these sound signals to extract the feature sound components. Observation of the results showed that an increase in tool flank wear correlates with an increase in the values of SVD features produced out of the sound signals for both the materials. Hence it can be concluded that wear monitoring of tool flank during turning process using SVD features with the Fuzzy C means classification on the emitted sound signal is a potential and relatively simple method.

Keywords: Fuzzy c means, Microphone, Singular ValueDecomposition, Tool Flank Wear.

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Authors: Fan Li, Chun-Hsien Chen, Li Pheng Khoo

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Operators of vessel traffic service (VTS) center provides three different types of services; namely information service, navigational assistance and traffic organization to vessels. To provide these services, operators monitor vessel traffic through computer interface and provide navigational advice based on the information integrated from multiple sources, including automatic identification system (AIS), radar system, and closed circuit television (CCTV) system. Therefore, this information is crucial in VTS operation. However, what information the VTS operator actually need to efficiently and properly offer services is unclear. The aim of this study is to investigate into information requirements for VTS operation. To achieve this aim, field observation was carried out to elicit the information requirements for VTS operation. The study revealed that the most frequent and important tasks were handling arrival vessel report, potential conflict control and abeam vessel report. Current location and vessel name were used in all tasks. Hazard cargo information was particularly required when operators handle arrival vessel report. The speed, the course, and the distance of two or several vessels were only used in potential conflict control. The information requirements identified in this study can be utilized in designing a human-computer interface that takes into consideration what and when information should be displayed, and might be further used to build the foundation of a decision support system for VTS.

Keywords: Vessel traffic service, information requirements, hierarchy task analysis, field observation.

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Authors: Mochammad Dewo, Sumarsono Sudarto

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The Exponential Triple Smoothing Algorithm approach nowadays, which is used to anticipate the export value of Indonesia's two major commodities, coal and palm oil, has a Mean Percentage Absolute Error (MAPE) value of 30-50%, which may be considered as a "reasonable" forecasting mistake. Forecasting errors of more than 30% shall have a domino effect on industrial output, as extra production adds to raw material, manufacturing and storage expenses. Whereas, reaching an "excellent" classification with an error value of less than 10% will provide new investors and exporters with confidence in the commercial development of related sectors. Industrial growth will bring out a positive impact on economic development. It can be applied for other commodities if the forecast error is less than 10%. The purpose of this project is to create a forecasting technique that can produce precise forecasting results with an error of less than 10%. This research analyzes forecasting methods such as ARIMA (Autoregressive Integrated Moving Average), ANN (Artificial Neural Network) and LSTM (Long-Short Term Memory). By providing a MAPE of 1%, this study reveals that ANN is the most successful strategy for forecasting coal and palm oil commodities in Indonesia.

Keywords: ANN, Artificial Neural Network, ARIMA, Autoregressive Integrated Moving Average, export value, forecast, LSTM, Long Short Term Memory.

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Authors: Maria C. Proença, Marco Aniceto, Pedro N. Santos, José C. Freitas

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A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed.

Keywords: Automatic detection, wavelets, defects, fracture lines.

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Authors: Mohammad T. Shervani-Tabar, Navid Shervani-Tabar

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Tip vortex cavitation is one of well known patterns of cavitation phenomenon which occurs in axial pumps. This pattern of cavitation occurs due to pressure difference between the pressure and suction sides of blades of an axial pump. Since the pressure in the pressure side of the blade is higher than the pressure in its suction side, thus a very small portion of liquid flow flows back from pressure side to the suction side. This fact is cause of tip vortex cavitation and gap cavitation that may occur in axial pumps. In this paper the results of our experimental investigation about movement of tip vortex cavitation along blade edge due to reduction of pump flow rate in an axial pump is reported. Results show that reduction of pump flow rate in conjunction with increasing of outlet pressure causes movement of tip vortex cavitation along blade edge towards the blade tip. Results also show that by approaching tip vortex cavitation to the blade tip, vortex tip pattern of cavitation replaces with a cavitation phenomenon on the blade tip. Furthermore by further reduction of pump flow rate and increasing of outlet pressure, an unstable cavitation phenomenon occurs between each blade leading edge and the next blade trailing edge.

Keywords: Axial Flow Pump, Cavitation, Gap Cavitation, Tip Vortex Cavitation.

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: Video tracking, particle filter, greedy snake, neural network.

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial neural networks, digital image processing, pattern recognition.

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Authors: Cris Bravo

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Social entrepreneurship is a new and exciting topic that holds a great promise in helping alleviate the social problems of the world. As a new subject, the meaning of the term is too broad and this is counterproductive in trying to build understanding around the concept. The purpose of this study is to identify and compare the elements of social entrepreneurship as defined by seven international organizations leading social entrepreneurship projects: Ashoka Foundation, Skoll Foundation, Schwab Foundation and Yunus Center; as well as from three other institutions fostering social entrepreneurship: Global Social Benefit Institute, BRAC University, and Socialab. The study used document analysis from Skoll Foundation, Schwab Foundation, Yunus Center and Ashoka Foundation; and open ended interview to experts from the Global Social Benefit Institute at Santa Clara University in United States, BRAC University from Bangladesh, and Socialab from Argentina. The study identified three clearly differentiated schools of thought, based on their views on revenue, scalability, replicability and geographic location. While this study is by no means exhaustive, it provides an indication of the patterns of ideas fostered by important players in the field. By clearly identifying the similarities and differences in the concept of social entrepreneurship, research and practitioners are better equipped to build on the subject, and to promote more adequate and accurate social policies to foster the development of social entrepreneurship.

Keywords: Replicability, revenue, scalability, schools of thought, social entrepreneurship.

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Authors: Levente Varga, Dávid Deritei, Mária Ercsey-Ravasz, Răzvan Florian, Zsolt I. Lázár, István Papp, Ferenc Járai-Szabó

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One of the major shortcomings of widely used scientometric indicators is that different disciplines cannot be compared with each other. The issue of cross-disciplinary normalization has been long discussed, but even the classification of publications into scientific domains poses problems. Structural properties of citation networks offer new possibilities, however, the large size and constant growth of these networks asks for precaution. Here we present a new tool that in order to perform cross-field normalization of scientometric indicators of individual publications relays on the structural properties of citation networks. Due to the large size of the networks, a systematic procedure for identifying scientific domains based on a local community detection algorithm is proposed. The algorithm is tested with different benchmark and real-world networks. Then, by the use of this algorithm, the mechanism of the scientometric indicator normalization process is shown for a few indicators like the citation number, P-index and a local version of the PageRank indicator. The fat-tail trend of the article indicator distribution enables us to successfully perform the indicator normalization process.

Keywords: Citation networks, scientometric indicator, cross-field normalization, local cluster detection.

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Authors: Sofien Chtourou, Mohamed Chtourou, Omar Hammami

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Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.

Keywords: Address, data set, memory, prediction, recurrentneural network.

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Authors: Alisha Khanal, Gokhan Saygili

Abstract:

It is commonly observed that aftershocks follow the mainshock. Aftershocks continue over a period of time with a decreasing frequency and typically there is not sufficient time for repair and retrofit between a mainshock–aftershock sequence. Usually, aftershocks are smaller in magnitude; however, aftershock ground motion characteristics such as the intensity and duration can be greater than the mainshock due to the changes in the earthquake mechanism and location with respect to the site. The seismic performance of slopes is typically evaluated based on the sliding displacement predicted to occur along a critical sliding surface. Various empirical models are available that predict sliding displacement as a function of seismic loading parameters, ground motion parameters, and site parameters but these models do not include the aftershocks. The seismic risks associated with the post-mainshock slopes ('damaged slopes') subjected to aftershocks is significant. This paper extends the empirical sliding displacement models for flexible slopes subjected to earthquake mainshock-aftershock sequences (a multi hazard approach). A dataset was developed using 144 pairs of as-recorded mainshock-aftershock sequences using the Pacific Earthquake Engineering Research Center (PEER) database. The results reveal that the combination of mainshock and aftershock increases the seismic demand on slopes relative to the mainshock alone; thus, seismic risks are underestimated if aftershocks are neglected.

Keywords: Seismic slope stability, sliding displacement, mainshock, aftershock, landslide, earthquake.

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Authors: Gheida J. Shahrour, Martin J. Russell

Abstract:

The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that intersubject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: Person Recognition, Topic Recognition, Culture Recognition, 3D Body Movement Signals, Variability Compensation.

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Authors: R. Mokhtari Malekabadi, S. Ghaed Rahmati, S. Aram

Abstract:

Optimal cultural site selection is one of the ways that can lead to the promotion of citizenship culture in addition to ensuring the health and leisure of city residents. This study examines the social and cultural needs of the community and optimal cultural site allocation and after identifying the problems and shortcomings, provides a suitable model for finding the best location for these centers where there is the greatest impact on the promotion of citizenship culture. On the other hand, non-scientific methods cause irreversible impacts to the urban environment and citizens. But modern efficient methods can reduce these impacts. One of these methods is using geographical information systems (GIS). In this study, Analytical Hierarchy Process (AHP) method was used to locate the optimal cultural site. In AHP, three principles (decomposition), (comparative analysis), and (combining preferences) are used. The objectives of this research include providing optimal contexts for passing time and performing cultural activities by Shiraz residents and also proposing construction of some cultural sites in different areas of the city. The results of this study show the correct positioning of cultural sites based on social needs of citizens. Thus, considering the population parameters and radii access, GIS and AHP model for locating cultural centers can meet social needs of citizens.

Keywords: Analytical Hierarchy Process (AHP), geographical information systems (GIS), Cultural site, locating, Shiraz.

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Authors: A. Kourou, A. Ioakeimidou, V. Avramea

Abstract:

Planning for natural disasters and emergencies is something every school or educational institution must consider, regardless of its size or location. Preparedness is the key to save lives if a disaster strikes. School disaster management mirrors individual and family disaster prevention, and wider community disaster prevention efforts. This paper presents the usage of E-PreS System as a helpful, managerial tool during the school earthquake drill, in order to support schools in developing effective disaster and emergency plans specific to their local needs. The project comes up with a holistic methodology using real-time evaluation involving different categories of actors, districts, steps and metrics. The main outcomes of E-PreS project are the development of E-PreS web platform that host the needed data of school emergency planning; the development of E-PreS System; the implementation of disaster drills using E-PreS System in educational premises and local schools; and the evaluation of E-PreS System. Taking into consideration that every disaster drill aims to test and valid school plan and procedures; clarify and train personnel in roles and responsibilities; improve interagency coordination; identify gaps in resources; improve individual performance; and identify opportunities for improvement, E-PreS Project was submitted and approved by the European Commission (EC).

Keywords: Disaster drills, earthquake preparedness, E-PreS system, school emergency plans.

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