Search results for: Local Network

Authors: Eric K. W. Aikins

Abstract:

Traditionally, Ghana is a marine fishing country. The fishing industry dominated by artisanal marine fishing helps Ghana to meet its fish and protein requirements. Also, it provides employment for most coastal dwellers that depend on fishing as their main economic enterprise. Nonetheless, the marine fishing industry is confronted with challenges that have contributed to a declining fish production in recent past decade. Bad fishing practices and the general limited knowledge on sustainable management of fisheries resources are the limiting factors that affect sustainable fish production and sustainable marine biodiversity management in Ghana. This paper discusses the challenges and strategies for attaining and maintaining sustainable marine fishing in Ghana as well as the state of marine fishing in Ghana. It concludes that an increase in the level of involvement of local fishers in the management of fisheries resources of the country could help local fishers to employ sustainable fisheries resources exploitation methods that could result in an improvement in the spatio-economic development and wellbeing of affected fishing communities in particular and Ghana in general.

Keywords: Pair trawling, sargassum, spatio-economic development, sustainable marine fishing.

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Authors: Heru Nurwarsito, R. Arief Setyawan, Rakhmadhany Primananda

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Voice Over IP (VoIP) is a technology that could pass the voice traffic and data packet form over an IP network. Network can be used for intranet or Internet. Phone calls using VoIP has advantages in terms of cheaper cost of PSTN phone to more than half, because the cost is calculated by the cost of the global nature of the Internet. Session Initiation Protocol (SIP) is a signaling protocol at the application layer which serves to establish, modify, and terminate a multimedia session involving one or more users. This SIP signaling has SIP message in text form that is used for session management by the SIP components, such as User Agent, Registrar, Redirect Server, and Proxy Server. To build a SIP communication is required SIP Express Router (SER) to be able to receive SIP messages, for handling the basic functions of SIP messages. Problems occur when the NAT through which affects the voice communication will be blocked starting from the sound that is not sent or one side of the sound are sent (half duplex). How that could be used to penetrate NAT is to use a given mediaproxy random RTP port to penetrate NAT.

Keywords: VoIP, SIP, SIP Express Router, NAT, Mediaproxy.

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Authors: H. Ferfera-Harrar, N. Aiouaz, N. Dairi

Abstract:

Superabsorbent polymers received much attention and are used in many fields because of their superior characters to traditional absorbents, e.g., sponge and cotton. So, it is very important but challenging to prepare highly and fast-swelling superabsorbents. A reliable, efficient and low-cost technique for removing heavy metal ions from wastewater is the adsorption using bio-adsorbents obtained from biological materials, such as polysaccharides-based hydrogels superabsorbents. In this study, novel multi-functional superabsorbent composites type semi-interpenetrating polymer networks (Semi-IPNs) were prepared via graft polymerization of acrylamide onto chitosan backbone in presence of gelatin, CTS-g-PAAm/Ge, using potassium persulfate and N,N’-methylene bisacrylamide as initiator and crosslinker, respectively. These hydrogels were also partially hydrolyzed to achieve superabsorbents with ampholytic properties and uppermost swelling capacity. The formation of the grafted network was evidenced by Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Thermogravimetric Analysis (TGA). The porous structures were observed by Scanning Electron Microscope (SEM). From TGA analysis, it was concluded that the incorporation of the Ge in the CTS-g-PAAm network has marginally affected its thermal stability. The effect of gelatin content on the swelling capacities of these superabsorbent composites was examined in various media (distilled water, saline and pH-solutions). The water absorbency was enhanced by adding Ge in the network, where the optimum value was reached at 2 wt. % of Ge. Their hydrolysis has not only greatly optimized their absorption capacity but also improved the swelling kinetic.These materials have also showed reswelling ability. We believe that these super-absorbing materials would be very effective for the adsorption of harmful metal ions from wastewater.

Keywords: Chitosan, gelatin, superabsorbent, water absorbency.

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Authors: S. Zappacosta, C. Bove, M. Carmela Marinelli, P. di Lauro, K. Spasenovic, L. Ostano, G. Aiello, M. Pietrosanto

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This paper proposes a neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The classification capability was compared with the flood hazard mapping River Basin Plans (Piani Assetto Idrogeologico, acronimed as PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale), encoding four different increasing flood hazard levels. The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

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Authors: I. Bagheri, M.B. Dehpour

Abstract:

The effect of moisture content and loading rate on mechanical strength of 12 brown rice grain varieties was determined. The results showed that the rupture force of brown rice grain decreased by increasing the moisture content and loading rate. The highest rupture force values was obtained at the moisture content of 8% (w.b.) and loading rate of 10 mm/min; while the lowest rupture force corresponded to the moisture content of 14% (w.b.) and loading rate of 15 mm/min. The 12 varieties were divided into three groups, namely local short grain varieties, local long grain varieties and improved long grain varieties. It was observed that the rupture strength of the three groups were statistically different from each other (P<0.01). It was revealed that the brown rice rupture at lower levels of moisture content was in the form of sudden failure with less deformation; while at higher levels of moisture content the grain rupture was in the form of gradually crushing with more deformation.

Keywords: Brown rice, loading rate, moisture content, ruptureforce

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Authors: Lewis E. Hibell, Honghai Liu, David J. Brown

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This paper presents the use of a newly created network structure known as a Self-Delaying Dynamic Network (SDN) to create a high resolution image from a set of time stepped input frames. These SDNs are non-recurrent temporal neural networks which can process time sampled data. SDNs can store input data for a lifecycle and feature dynamic logic based connections between layers. Several low resolution images and one high resolution image of a scene were presented to the SDN during training by a Genetic Algorithm. The SDN was trained to process the input frames in order to recreate the high resolution image. The trained SDN was then used to enhance a number of unseen noisy image sets. The quality of high resolution images produced by the SDN is compared to that of high resolution images generated using Bi-Cubic interpolation. The SDN produced images are superior in several ways to the images produced using Bi-Cubic interpolation.

Keywords: Image Enhancement, Neural Networks, Multi-Frame.

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: Distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: Salim Ouchtati, Bedda Mouldi, Abderrazak Lachouri

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In this paper we present an off line system for the recognition of the handwritten numeric chains. Our work is divided in two big parts. The first part is the realization of a recognition system of the isolated handwritten digits. In this case the study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: the distribution sequence, the Barr features and the centred moments of the different projections and profiles. The second part is the extension of our system for the reading of the handwritten numeric chains constituted of a variable number of digits. The vertical projection is used to segment the numeric chain at isolated digits and every digit (or segment) will be presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The result of the recognition of the numeric chain will be displayed at the exit of the global system.

Keywords: Optical Characters Recognition, Neural networks, Barr features, Image processing, Pattern Recognition, Featuresextraction.

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Authors: Wennan Long, Yuhao Nie, Yunan Li, Adam Brandt

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To fight against climate change, California government issued the Senate Bill No. 100 (SB-100) in 2018 September, which aims at achieving a target of 100% renewable electricity by the end of 2045. A capacity expansion problem is solved in this case study using a binary quadratic programming model. The optimal locations and capacities of the potential renewable power plants (i.e., solar, wind, biomass, geothermal and hydropower), the phase-out schedule of existing fossil-based (nature gas) power plants and the transmission of electricity across the entire network are determined with the minimal total annualized cost measured by net present value (NPV). The results show that the renewable electricity contribution could increase to 85.9% by 2030 and reach 100% by 2035. Fossil-based power plants will be totally phased out around 2035 and solar and wind will finally become the most dominant renewable energy resource in California electricity mix.

Keywords: 100% renewable electricity, California, capacity expansion, binary quadratic programming.

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Authors: Ming-Hui Cheng, Jyh-Horng Jeng

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In this study, a classification-based video super-resolution method using artificial neural network (ANN) is proposed to enhance low-resolution (LR) to high-resolution (HR) frames. The proposed method consists of four main steps: classification, motion-trace volume collection, temporal adjustment, and ANN prediction. A classifier is designed based on the edge properties of a pixel in the LR frame to identify the spatial information. To exploit the spatio-temporal information, a motion-trace volume is collected using motion estimation, which can eliminate unfathomable object motion in the LR frames. In addition, temporal lateral process is employed for volume adjustment to reduce unnecessary temporal features. Finally, ANN is applied to each class to learn the complicated spatio-temporal relationship between LR and HR frames. Simulation results show that the proposed method successfully improves both peak signal-to-noise ratio and perceptual quality.

Keywords: Super-resolution, classification, spatio-temporal information, artificial neural network.

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Authors: T.P. Andamuthu, Dr. P. Balasubramanie

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The intermittent connectivity modifies the “always on" network assumption made by all the distributed query processing systems. In modern- day systems, the absence of network connectivity is considered as a fault. Since the last upload, it might not be feasible to transmit all the data accumulated right away over the available connection. It is possible that vital information may be delayed excessively when the less important information takes place of the vital information. Owing to the restricted and uneven bandwidth, it is vital that the mobile nodes make the most advantageous use of the connectivity when it arrives. Hence, in order to select the data that needs to be transmitted first, some sort of data prioritization is essential. A continuous query processing system for intermittently connected mobile networks that comprises of a delaytolerant continuous query processor distributed across the mobile hosts has been proposed in this paper. In addition, a mechanism for prioritizing query results has been designed that guarantees enhanced accuracy and reduced delay. It is illustrated that our architecture reduces the client power consumption, increases query efficiency by the extensive simulation results.

Keywords: Broadcast, Location, Mobile host, Mobility, Query.

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Authors: P. Waiyawuththanapoom, P. Tirastittam

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Thailand is the agriculture country as the weather and geography are suitable for agriculture environment. In 2011, the quantity of exported fresh vegetable was 126,069 tons which valued 117.1 million US dollars. Although the fresh vegetable has a high potential in exporting, there also have a lack of knowledge such as chemical usage, land usage, marketing and also the transportation and logistics. Nakorn Pathom province is the area which the farmer and manufacturer of fresh vegetable located. The objectives of this study are to study the basic information of the local fresh vegetable farmers in Nakorn Pathom province, to study the factor which effects the management of the fresh vegetable supply chain in Nakorn Pathom province and to study the problems and obstacle of the fresh vegetable supply chain in Nakorn Pathom province. This study is limited to the flow of the Nakorn Pathom province fresh vegetable from the farmers to the country which import the vegetable from Thailand. The populations of this study are 100 local farmers in Nakorn Pathom province. The result of this study shows that the key process of the fresh vegetable supply chain is in the supply sourcing process and manufacturing process.

Keywords: Exporting, Fresh Vegetable, Nakorn Pathom Province, Supply Chain.

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Authors: Ramaswamy Palaniappan

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In this paper, a second order autoregressive (AR) model is proposed to discriminate alcoholics using single trial gamma band Visual Evoked Potential (VEP) signals using 3 different classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN), Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear Discriminant (LD). Electroencephalogram (EEG) signals were recorded from alcoholic and control subjects during the presentation of visuals from Snodgrass and Vanderwart picture set. Single trial VEP signals were extracted from EEG signals using Elliptic filtering in the gamma band spectral range. A second order AR model was used as gamma band VEP exhibits pseudo-periodic behaviour and second order AR is optimal to represent this behaviour. This circumvents the requirement of having to use some criteria to choose the correct order. The averaged discrimination errors of 2.6%, 2.8% and 11.9% were given by LD, MLP-BP and SFA classifiers. The high LD discrimination results show the validity of the proposed method to discriminate between alcoholic subjects.

Keywords: Linear Discriminant, Neural Network, VisualEvoked Potential.

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Authors: Hae Young Lee, Tae Ho Cho

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In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false reports during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and overhead. In this paper, we propose a fuzzy logic for determining a security threshold value in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the number of partitions in a global key pool, the number of compromised partitions, and the energy level of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.

Keywords: Fuzzy logic, security, sensor network.

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Authors: A. Amrani, O. Allali, A. Ben Hamida, F. Defrance, S. Morland, E. Pineau, T. Lacroix

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The energy planning process is a very complex task that involves several stakeholders and requires the consideration of several local and global factors and constraints. In order to optimize and simplify this process, we propose a tool-based iterative approach applied to district heating planning. We build our tool with the collaboration of a French territory using actual district data and implementing the European incentives. We set up an iterative process including data visualization and analysis, identification and extraction of information related to the area concerned by the operation, design of sustainable planning scenarios leveraging local renewable and recoverable energy sources, and finally, the evaluation of scenarios. The last step is performed by a dynamic digital twin replica of the city. Territory’s energy experts confirm that the tool provides them with valuable support towards sustainable energy planning.

Keywords: Climate change, data management, decision support, digital twin, district heating, energy planning, renewables, smart city.

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Authors: Fereidoun Nowshiravan Rahatabad, Mohammad Ali Nekoui, Mohammad Reza Hashemi Golpaygani, AliFallah, Mehdi Kazemzadeh Narbat

Abstract:

The people are differed by their capabilities, skills and mental agilities. The evolution of human from childhood when they are completely dependent up to adultness the time they gradually set the dependency free is too complicated, by considering they have all started from almost one point but some become cleverer and some less. The main control command of a cybernetic hand should be posted by remaining healthy organs of disabled Person. These commands can be from several channels, which their recording and detecting are different and need complicated study. In this research, we suppose that, this stage has been done or in the other words, the command has been already sent and detected. So the main goal is to control a long hand, upper elbow hand missing, by an interest angle define by disabled. It means that, the system input is the position desired by disables and the output is the elbow-joint angle variation. Therefore the goal is a suitable control design based on neural network theory in order to meet the given mapping.

Keywords: Control - system design, Upper limb prosthesis, neuralnetwork.

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Authors: Hassan Haleh, Zohreh Nasiri, Parisa Farahpour

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During recent years, the traditional learning approaches have undergone fundamental changes due to the emergence of new technologies such as multimedia, hypermedia and telecommunication. E-learning is a modern world phenomenon that has come into existence in the information age and in a knowledgebased society. E-learning has developed significantly within a short period of time. Thus it is of a great significant to secure information, allow a confident access and prevent unauthorized accesses. Making use of individuals- physiologic or behavioral (biometric) properties is a confident method to make the information secure. Among the biometrics, fingerprint is more acceptable and most countries use it as an efficient methods of identification. This article provides a new method to compare the fingerprint comparison by pattern recognition and image processing techniques. To verify fingerprint, the shortest distance method is used together with perceptronic multilayer neural network functioning based on minutiae. This method is highly accurate in the extraction of minutiae and it accelerates comparisons due to elimination of false minutiae and is more reliable compared with methods that merely use directional images.

Keywords: Fingerprint, minutiae, extraction of properties, multilayer neural network

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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In recent years, new techniques for solving complex problems in engineering are proposed. One of these techniques is JPSO algorithm. With innovative changes in the nature of the jump algorithm JPSO, it is possible to construct a graph-based solution with a new algorithm called G-JPSO. In this paper, a new algorithm to solve the optimal control problem Fletcher-Powell and optimal control of pumps in water distribution network was evaluated. Optimal control of pumps comprise of optimum timetable operation (status on and off) for each of the pumps at the desired time interval. Maximum number of status on and off for each pumps imposed to the objective function as another constraint. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The proposed algorithm results were compared well with the ant colony algorithm, genetic and JPSO results. This shows the robustness of proposed algorithm in finding near optimum solutions with reasonable computational cost.

Keywords: G-JPSO, operation, optimization, pumping station, water distribution networks.

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Authors: Ghobad Gorji, Hasan Golabi

Abstract:

The goal of this paper is to describe the design of an ultra-wideband (UWB) system that is optimized for the low-rate wireless personal area network application. To this aim, we propose a system based on direct chaotic communication (DCC) technology. Based on this system, a 2-GHz wide chaotic signal is produced into the UWB spectrum lower band, i.e., 3.1–5.1 GHz. For this system, two simple modulation schemes, namely chaotic on-off keying (COOK) and differential chaos shift keying  (DCSK) are evaluated first. We propose a modulation scheme, namely Double DCSK, to improve the performance of UWB DCC. Different characteristics of these systems, with Monte Carlo simulations based on the Additive White Gaussian Noise (AWGN) and the IEEE 802.15.4a standard channel models, are compared.

Keywords: Ultra-wideband, UWB, Direct Chaotic Communication, DCC, IEEE 802.15.4a, COOK, DCSK.

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Authors: Rujira Kongnuy, Puntani Pongsumpun, I-Ming Tang

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Dengue fever is an important human arboviral disease. Outbreaks are now reported quite often from many parts of the world. The number of cases involving pregnant women and infant cases are increasing every year. The illness is often severe and complications may occur. Deaths often occur because of the difficulties in early diagnosis and in the improper management of the diseases. Dengue antibodies from pregnant women are passed on to infants and this protects the infants from dengue infections. Antibodies from the mother are transferred to the fetus when it is still in the womb. In this study, we formulate a mathematical model to describe the transmission of this disease in pregnant women. The model is formulated by dividing the human population into pregnant women and non-pregnant human (men and non-pregnant women). Each class is subdivided into susceptible (S), infectious (I) and recovered (R) subclasses. We apply standard dynamical analysis to our model. Conditions for the local stability of the equilibrium points are given. The numerical simulations are shown. The bifurcation diagrams of our model are discussed. The control of this disease in pregnant women is discussed in terms of the threshold conditions.

Keywords: Dengue disease, local stability, mathematical model, pregnancy.

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Authors: Ali Anaissi, Paul J. Kennedy, Madhu Goyal

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Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.

Keywords: Linear Dimension Reduction, Non-Linear Dimension Reduction, Principal Component Analysis, Biologists.

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial neural network, competitive dynamics, logistic regression, text classification, text mining.

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Authors: Yuan Zheng

Abstract:

3D model-based vehicle matching provides a new way for vehicle recognition, localization and tracking. Its key is to construct an evaluation function, also called fitness function, to measure the degree of vehicle matching. The existing fitness functions often poorly perform when the clutter and occlusion exist in traffic scenarios. In this paper, we present a practical and efficient fitness function. Unlike the existing evaluation functions, the proposed fitness function is to study the vehicle matching problem from both local and global perspectives, which exploits the pixel gradient information as well as the silhouette information. In view of the discrepancy between 3D vehicle model and real vehicle, a weighting strategy is introduced to differently treat the fitting of the model’s wireframes. Additionally, a normalization operation for the model’s projection is performed to improve the accuracy of the matching. Experimental results on real traffic videos reveal that the proposed fitness function is efficient and robust to the cluttered background and partial occlusion.

Keywords: 3D-2D matching, fitness function, 3D vehicle model, local image gradient, silhouette information.

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Authors: Ahmad Fadel Klaib, Zurinahni Zainol, Nurul Hashimah Ahamed, Rosma Ahmad, Wahidah Hussin

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Bioinformatics and Cheminformatics use computer as disciplines providing tools for acquisition, storage, processing, analysis, integrate data and for the development of potential applications of biological and chemical data. A chemical database is one of the databases that exclusively designed to store chemical information. NMRShiftDB is one of the main databases that used to represent the chemical structures in 2D or 3D structures. SMILES format is one of many ways to write a chemical structure in a linear format. In this study we extracted Antimicrobial Structures in SMILES format from NMRShiftDB and stored it in our Local Data Warehouse with its corresponding information. Additionally, we developed a searching tool that would response to user-s query using the JME Editor tool that allows user to draw or edit molecules and converts the drawn structure into SMILES format. We applied Quick Search algorithm to search for Antimicrobial Structures in our Local Data Ware House.

Keywords: Exact String-matching Algorithms, NMRShiftDB, SMILES Format, Antimicrobial Structures.

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Authors: C. Thongsopa, P. Sukphongchirakul, A. Intarapanich, P. Jarataku

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Radio wave propagation on the road surface is a major problem on wireless sensor network for traffic monitoring. In this paper, we compare receiving signal strength on two scenarios 1) an empty road and 2) a road with a vehicle. We investigate the effect of antenna polarization and antenna height to the receiving signal strength. The transmitting antenna is installed on the road surface. The receiving signal is measured 360 degrees around the transmitting antenna with the radius of 2.5 meters. Measurement results show the receiving signal fluctuation around the transmitting antenna in both scenarios. Receiving signal with vertical polarization antenna results in higher signal strength than horizontal polarization antenna. The optimum antenna elevation is 1 meter for both horizon and vertical polarizations with the vehicle on the road. In the empty road, the receiving signal level is unvarying with the elevation when the elevation is greater than 1.5 meters.

Keywords: Wave propagation, wireless sensor network.

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Authors: J. K. Shrestha, A. Benta, R. B. Lopes, N. Lopes

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This paper presents a multi-objective model for addressing two main objectives in designing rural roads networks: minimization of user operation costs and maximization of population covered. As limited budgets often exist, a reasonable trade-off must be obtained in order to account for both cost and social benefits in this type of networks. For a real-world rural road network, the model is solved, where all non-dominated solutions were obtained. Afterwards, an analysis is made on the (possibly) most interesting solutions (the ones providing better trade-offs). This analysis, coupled with the knowledge of the real world scenario (typically provided by decision makers) provides a suitable method for the evaluation of road networks in rural areas of developing countries.

Keywords: Multi-objective, user operation cost, population covered, rural road network.

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Authors: Prasan Pitiranggon, Nunthika Benjathepanun, Somsri Banditvilai, Veera Boonjing

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Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IFTHEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.

Keywords: Fuzzy Rule Base, Rule Extraction, Rule Generation, Support Vector Machine.

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Authors: F. Caliskan

Abstract:

This paper presents a Neural Network (NN) identification of icing parameters in an A340 aircraft and a reconfiguration technique to keep the A/C performance close to the performance prior to icing. Five aircraft parameters are assumed to be considerably affected by icing. The off-line training for identifying the clear and iced dynamics is based on the Levenberg-Marquard Backpropagation algorithm. The icing parameters are located in the system matrix. The physical locations of the icing are assumed at the right and left wings. The reconfiguration is based on the technique known as the control mixer approach or pseudo inverse technique. This technique generates the new control input vector such that the A/C dynamics is not much affected by icing. In the simulations, the longitudinal and lateral dynamics of an Airbus A340 aircraft model are considered, and the stability derivatives affected by icing are identified. The simulation results show the successful NN identification of the icing parameters and the reconfigured flight dynamics having the similar performance before the icing. In other words, the destabilizing icing affect is compensated.

Keywords: Aircraft Icing, Stability Derivatives, Neural NetworkIdentification, Reconfiguration.

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Authors: Md. Ashikur Rahman Khan, M. M. Rahman

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In electrical discharge machining (EDM), a complete and clear theory has not yet been established. The developed theory (physical models) yields results far from reality due to the complexity of the physics. It is difficult to select proper parameter settings in order to achieve better EDM performance. However, modelling can solve this critical problem concerning the parameter settings. Therefore, the purpose of the present work is to develop mathematical model to predict performance characteristics of EDM on Ti-5Al-2.5Sn titanium alloy. Response surface method (RSM) and artificial neural network (ANN) are employed to develop the mathematical models. The developed models are verified through analysis of variance (ANOVA). The ANN models are trained, tested, and validated utilizing a set of data. It is found that the developed ANN and mathematical model can predict performance of EDM effectively. Thus, the model has found a precise tool that turns EDM process cost-effective and more efficient.

Keywords: Analysis of variance, artificial neural network, material removal rate, modelling, response surface method, surface finish.

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Authors: M. E. Soto-López, I. Gaxiola-Avendaño, A. Reyes-Salazar, E. Bojórquez, S. E. Ruiz

Abstract:

The seismic responses of steel buildings with semirigid post-tensioned connections (PC) are estimated and compared with those of steel buildings with typical rigid (welded) connections (RC). The comparison is made in terms of global and local response parameters. The results indicate that the seismic responses in terms of interstory shears, roof displacements, axial load and bending moments are smaller for the buildings with PC connection. The difference is larger for global than for local parameters, which in turn varies from one column location to another. The reason for this improved behavior is that the buildings with PC dissipate more hysteretic energy than those with RC. In addition, unlike the case of buildings with WC, for the PC structures the hysteretic energy is mostly dissipated at the connections, which implies that structural damage in beams and columns is not significant. According to these results, steel buildings with PC are a viable option in high seismicity areas because of their smaller response and self-centering connection capacity as well as the fact that brittle failure is avoided.

Keywords: Inter-story drift, Nonlinear time-history analysis, Post-tensioned connections, Steel buildings.

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