Search results for: using an Anisotropic Analytical Algorithm (AAA)

Authors: Lubabatu Sada Sodangi

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The rapid growth in exchange and accessibility of information via the internet makes many organisations acquire data on their own operation. The aim of data mining is to analyse the different behaviour of a dataset using observation. Although, the subset of the dataset being analysed may not display all the behaviours and relationships of the entire data and, therefore, may not represent other parts that exist in the dataset. There is a range of techniques used in data mining to determine the hidden or unknown information in datasets. In this paper, the performance of two algorithms Chi-Square Automatic Interaction Detection (CHAID) and multilayer perceptron (MLP) would be matched using an Adult dataset to find out the percentage of an/the adults that earn > 50k and those that earn <= 50k per year. The two algorithms were studied and compared using IBM SPSS statistics software. The result for CHAID shows that the most important predictors are relationship and education. The algorithm shows that those are married (husband) and have qualification: Bachelor, Masters, Doctorate or Prof-school whose their age is > 41<57 earn > 50k. Also, multilayer perceptron displays marital status and capital gain as the most important predictors of the income. It also shows that individuals that their capital gain is less than 6,849 and are single, separated or widow, earn <= 50K, whereas individuals with their capital gain is > 6,849, work > 35 hrs/wk, and > 27yrs their income will be > 50k. By comparing the two algorithms, it is observed that both algorithms are reliable but there is strong reliability in CHAID which clearly shows that relation and education contribute to the prediction as displayed in the data visualisation.

Keywords: data mining, CHAID, multi-layer perceptron, SPSS, Adult dataset

Procedia PDF Downloads 359

Authors: T. Sattarpour, D. Nazarpour

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This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.

Keywords: active distribution network (ADN), distributed generations (DGs), smart meters (SMs), demand response programs (DRPs), adaptive power factor (APF)

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

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Authors: Amir Shouri, Fereshteh Tabe

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Thoughtful and sequential design strategies will shape the future of human being’s lifestyle. Design, as a product, either being for small furniture on sidewalk or a multi-story structure in urban scale, will be important in creating the sense of quality for citizens of a city. Technology besides economy has played a major role in improving design process and increasing awareness of clients about the character of their required design product. Architects along with other design professionals benefited from improvements in aesthetics and technology in building industry. Accordingly, the expectation platforms of people about the quality of habitable space have risen. However, the question is if the quality of architectural design product has increased with the same speed as technology and client’s expectations. Is it behind or a head of technological and economical improvements? This study will work on developing a model of planning for New York City, from the past to present to future. The role of thoughtful thinking at design stage regardless of where or when it is for; may result in a positive or negative aspect. However, considering design objectives based on the need of human being may help in developing a successful design plan. Technology, economy, culture and people’s support may be other parameters in designing a good product. ‘Design Forms Urban Space’ is going to be done in an analytical, qualitative and quantitative work frame, where it will study cases from all over the world and their achievements compared to New York City’s development. Technology, Organic Design, Materiality, Urban forms, city politics and sustainability will be discussed in different cases in international scale. From design professional’s interest in doing a high quality work for a particular answer to importance of being a follower, the ‘Zero-Carbon City’ in Persian Gulf to ‘Polluted City’ in China, from ‘Urban Scale Furniture’ in cities to ‘Seasonal installations’ of a Megacity, will all be studied with references and detailed look to analysis of each case in order to propose the most resourceful, practical and realistic solutions to questions on ‘A Good Design in a City’, ‘New City Planning and social activities’ and ‘New Strategic Architecture for better Cities’.

Keywords: design quality, urban scale, active city, city installations, architecture for better cities

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Shidrokh Goudarzi, Wan Haslina Hassan

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Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers or vertical handoffs are necessary for seamless mobility. In this paper, we conduct a review of existing vertical handover decision-making mechanisms that aim to provide ubiquitous connectivity to mobile users. To offer a systematic comparison, we categorize these vertical handover measurement and decision structures based on their respective methodology and parameters. Subsequently, we analyze several vertical handover approaches in the literature and compare them according to their advantages and weaknesses. The paper compares the algorithms based on the network selection methods, complexity of the technologies used and efficiency in order to introduce our vertical handover decision framework. We find that vertical handovers on heterogeneous wireless networks suffer from the lack of a standard and efficient method to satisfy both user and network quality of service requirements at different levels including architectural, decision-making and protocols. Also, the consolidation of network terminal, cross-layer information, multi packet casting and intelligent network selection algorithm appears to be an optimum solution for achieving seamless service continuity in order to facilitate seamless connectivity.

Keywords: heterogeneous wireless networks, vertical handovers, vertical handover metric, decision-making algorithms

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Authors: Victor Alves Barros Galvão, Israel Da Silveira Rego, Antonio Carlos Oliveira, Paulo Gilberto De Paula Toro

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The Brazilian hypersonic aerospace vehicle 14-X B, VHA 14-X B, is a vehicle integrated with the hypersonic airbreathing propulsion system based on supersonic combustion (scramjet), developing in Aerothermodynamics and hypersonic Prof. Henry T. Nagamatsu Laboratory, to conduct demonstration in atmospheric flight at the speed corresponding to Mach number 7 at an altitude of 30km. In the experimental procedure the hypersonic shock tunnel T3 was used, installed in that laboratory. This device simulates the flow over a model is fixed in the test section and can also simulate different atmospheric conditions. The scramjet technology offers substantial advantages to improve aerospace vehicle performance which flies at a hypersonic speed through the Earth's atmosphere by reducing fuel consumption on board. Basically, the scramjet is an aspirated aircraft engine fully integrated that uses oblique/conic shock waves generated during hypersonic flight, to promote the deceleration and compression of atmospheric air in scramjet inlet. During the hypersonic flight, the vehicle VHA 14-X will suffer atmospheric influences, promoting changes in the vehicle's angles of attack (angle that the mean line of vehicle makes with respect to the direction of the flow). Based on this information, a study is conducted to analyze the influences of changes in the vehicle's angle of attack during the atmospheric flight. Analytical theoretical analysis, simulation computational fluid dynamics and experimental investigation are the methodologies used to design a technological demonstrator prior to the flight in the atmosphere. This paper considers analysis of the thermodynamic properties (pressure, temperature, density, sound velocity) in lower surface of the VHA 14-X B. Also, it considers air as an ideal gas and chemical equilibrium, with and without boundary layer, considering changes in the vehicle's angle of attack (positive and negative in relation to the flow) and bi-dimensional expansion wave theory at the expansion section (Theory of Prandtl-Meyer).

Keywords: angle of attack, experimental hypersonic, hypersonic airbreathing propulsion, Scramjet

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Authors: Isabel Moscol, Carlos J. Díaz, Ciro Rodríguez

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Hip replacement becomes necessary when a person suffers severe pain or considerable functional limitations and the best option to enhance their quality of life is through the replacement of the damaged joint. One of the main components in femoral prostheses is the stem which distributes the loads from the joint to the proximal femur. To preserve more bone stock and avoid weakening of the diaphysis, a short starting stem was selected, generated from the intramedullary morphology of the patient's femur. It ensures the implantability of the design and leads to geometric delimitation for personalized optimization with machine learning (ML) and metaheuristic algorithms. The present study attempts to design a cementless short stem to make the strain deviation before and after implantation close to zero, promoting its fixation and durability. Regression models developed to estimate the percentage change of maximum principal stresses were used as objective optimization functions by the metaheuristic algorithm. The latter evaluated different geometries of the short stem with the modification of certain parameters in oblique sections from the osteotomy plane. The optimized geometry reached a global stress shielding (SS) of 18.37% with a determination factor (R²) of 0.667. The predicted results favour implantability integration in the short stem optimization to effectively reduce SS in the proximal femur.

Keywords: machine learning techniques, metaheuristic algorithms, short-stem design, stress shielding, hip replacement

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Authors: Fereshteh Pourmohammad, Mehdi Zare

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The city of Karaj, having a population of 2.2 millions (est. 2022) is located in the South West of Alborz Mountain Belt in Northern Iran. The region is known to be a highly active seismic zone. This study is focused on the geological and seismological analyses within a radius of 200 km from the center of Karaj. There are identified five seismic zones and seven linear seismic sources. The maximum magnitude was calculated for the seismic zones. Scine tghe seismicity catalog is incomplete, we have used a parametric-historic algorithm and the Kijko and Sellevoll (1992) method was used to calculate seismicity parameters, and the return periods and the probability frequency of recurrence of the earthquake magnitude in each zone obtained for 475-years return period. According to the calculations, the highest and lowest earthquake magnitudes of 7.6 and 6.2 were respectively obtained in Zones 1 and 4. This result is a new and extremely important in view point of earthquake risk in a densely population city. The maximum strong horizontal ground motion for the 475-years return period 0.42g and for 2475-year return period 0.70g also the maximum strong vertical ground motion for 475-years return period 0.25g and 2475-years return period 0.44g was calculated using attenuation relationships. These acceleration levels are new, and are obtained to be about 25% higher than presented values in the Iranian building code.

Keywords: seismic zones, ground motion, return period, hazard analysis

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Authors: Hanlin Liu, Hua Li, Yintan AI

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Network performance prediction (NPP) is essential for the management and optimization of software-defined networking (SDN) and contributes to improving the quality of service (QoS) in SDN to meet the requirements of users. Although current deep learning-based methods can achieve high effectiveness, they still suffer from some problems, such as difficulty in capturing global information of the network, inefficiency in modeling end-to-end network performance, and inadequate graph feature extraction. To cope with these issues, our proposed Graphormer-based architecture for NPP leverages the powerful graph representation ability of Graphormer to effectively model the graph structure data, and a node-edge transformation algorithm is designed to transfer the feature extraction object from nodes to edges, thereby effectively extracting the end-to-end performance characteristics of the network. Moreover, routing oriented centrality measure coefficient for nodes and edges is proposed respectively to assess their importance and influence within the graph. Based on this coefficient, an enhanced feature extraction method and an advanced centrality encoding strategy are derived to fully extract the structural information of the graph. Experimental results on three public datasets demonstrate that the proposed GraphNPP architecture can achieve state-of-the-art results compared to current NPP methods.

Keywords: software-defined networking, network performance prediction, Graphormer, graph neural network

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Authors: Patricia Jiménez, Rafael Corchuelo

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Nowadays the World Wide Web is the most popular source of information that relies on billions of on-line documents. Web mining is used to crawl through these documents, collect the information of interest and process it by applying data mining tools in order to use the gathered information in the best interest of a business, what enables companies to promote theirs. Unfortunately, it is not easy to extract the information a web site provides automatically when it lacks an API that allows to transform the user-friendly data provided in web documents into a structured format that is machine-readable. Rule-based information extractors are the tools intended to extract the information of interest automatically and offer it in a structured format that allow mining tools to process it. However, the performance of an information extractor strongly depends on the search heuristic employed since bad choices regarding how to learn a rule may easily result in loss of effectiveness and/or efficiency. Improving search heuristics regarding efficiency is of uttermost importance in the field of Web Information Extraction since typical datasets are very large. In this paper, we employ an information extractor based on a classical top-down algorithm that uses the so-called Information Gain heuristic introduced by Quinlan and Cameron-Jones. Unfortunately, the Information Gain relies on some well-known problems so we analyse an intuitive alternative, Termini, that is clearly more efficient; we also analyse other proposals in the literature and conclude that none of them outperforms the previous alternative.

Keywords: information extraction, search heuristics, semi-structured documents, web mining.

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Authors: Kate N. Okeke

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The ever-growing needs of man requiring satisfaction have pushed him strongly towards industrialization which has and is still leaving environmental degradation and its attendant negative impacts in its wake. It is, therefore, not surprising that the enjoyment of fundamental rights like food supply, security of lives and property, freedom of worship, health and education have been drastically affected by such degradation. In recognition of the imperative need to protect the environment and human rights, many global instruments and constitutions have recognized the right to a healthy and sustainable environment. Some environmental advocates and quite a number of literatures on the subject matter call for the recognition of environmental rights via rule of law as a vital means of achieving positive outcomes on the subject matter. However, although there are numerous countries with constitutional environmental provisions, most of them such as Nigeria, have shown poor environmental performance. A notable problem is the fact that the constitution which recognizes environmental rights appears in its other provisions to contradict its provisions by making enforceability of the environmental rights unattainable. While adopting a descriptive, analytical, comparative and explanatory study design in reviewing a successful positive environmental outcome via the rule of law, this article argues that rule of law on a balance of scale, weighs more than just environmental rights recognition and therefore should receive more attention by environmental lawyers and advocates. This is because with rule of law, members of a society are sure of getting the most out of the environmental rights existing in their legal system. Members of Niger-Delta communities of Nigeria will benefit from the environmental rights existing in Nigeria. They are exposed to environmental degradation and pollution with effects such as acidic rainfall, pollution of farmlands and clean water sources. These and many more are consequences of oil and gas exploration. It will also pave way for solving the violence between cattle herdsmen and farmers in the Middle Belt and other regions of Nigeria. Their clashes are over natural resource control. Having seen that environmental rule of law is vital to sustainable development, this paper aims to contribute to discussions on how best the vehicle of rule law can be driven towards achieving positive environmental outcomes. This will be in reliance on other enforceable provisions in the Nigerian Constitution. Other domesticated international instruments will also be considered to attain sustainable environment and development.

Keywords: environment, rule of law, constitution, sustainability

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Authors: Georgios Koronis, Arlindo Silva

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This study seeks to understand the mechanical behavior of a natural fiber reinforced composite (epoxy/flax) in more depth, utilizing both experimental and numerical methods. It is attempted to identify relationships between the design parameters and the product performance, understand the effect of noise factors and reduce process variations. Optimization of the mechanical performance of manufactured goods has recently been implemented by numerous studies for green composites. However, these studies are limited and have explored in principal mass production processes. It is expected here to discover knowledge about composite’s manufacturing that can be used to design artifacts that are of low batch and tailored to niche markets. The goal is to reach greater consistency in the performance and further understand which factors play significant roles in obtaining the best mechanical performance. A prediction of response function (in various operating conditions) of the process is modeled by the DoE. Normally, a full factorial designed experiment is required and consists of all possible combinations of levels for all factors. An analytical assessment is possible though with just a fraction of the full factorial experiment. The outline of the research approach will comprise of evaluating the influence that these variables have and how they affect the composite mechanical behavior. The coupons will be fabricated by the vacuum infusion process defined by three process parameters: flow rate, injection point position and fiber treatment. Each process parameter is studied at 2-levels along with their interactions. Moreover, the tensile and flexural properties will be obtained through mechanical testing to discover the key process parameters. In this setting, an experimental phase will be followed in which a number of fabricated coupons will be tested to allow for a validation of the design of the experiment’s setup. Finally, the results are validated by performing the optimum set of in a final set of experiments as indicated by the DoE. It is expected that after a good agreement between the predicted and the verification experimental values, the optimal processing parameter of the biocomposite lamina will be effectively determined.

Keywords: design of experiments, flax fabrics, mechanical performance, natural fiber reinforced composites

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Authors: Xinhuang Wu, Yousef Sardahi

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A hybrid and optimal multi-loop control structure combining linear and nonlinear control algorithms are introduced in this paper to regulate the position of a quadcopter unmanned aerial vehicle (UAV) driven by four brushless DC motors. To this end, a nonlinear mathematical model of the UAV is derived and then linearized around one of its operating points. Using the nonlinear version of the model, a sliding mode control is used to derive the control laws of the motor thrust forces required to drive the UAV to a certain position. The linear model is used to design two controllers, XG-controller and YG-controller, responsible for calculating the required roll and pitch to maneuver the vehicle to the desired X and Y position. Three attitude controllers are designed to calculate the desired angular rates of rotors, assuming that the Euler angles are minimal. After that, a many-objective optimization problem involving 20 design parameters and ten objective functions is formulated and solved by HypE (Hypervolume estimation algorithm), one of the widely used many-objective optimization algorithms approaches. Both stability and performance constraints are imposed on the optimization problem. The optimization results in terms of Pareto sets and fronts are obtained and show that some of the design objectives are competing. That is, when one objective goes down, the other goes up. Also, Numerical simulations conducted on the nonlinear UAV model show that the proposed optimization method is quite effective.

Keywords: optimal control, many-objective optimization, sliding mode control, linear control, cascade controllers, UAV, drones

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Authors: Shiri Elisheva Decktor, Sharon Hornstein

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Highway merges and unmarked junctions are key components in any urban road network, which can act as bottlenecks and create traffic disruption. Inefficient highway merges may trigger traffic instabilities such as stop-and-go waves, pose safety conditions and lead to longer journey times. These phenomena occur spontaneously if the average vehicle density exceeds a certain critical value. This study focuses on modeling the traffic using a microscopic traffic flow model. A hybrid traffic model, which combines human-driven and controlled vehicles is assumed. The controlled vehicles obey different driving policies when approaching the merge, or in the vicinity of other vehicles. We developed a co-simulation model in SUMO (Simulation of Urban Mobility), in which the human-driven cars are modeled using the IDM model, and the controlled cars are modeled using a dedicated controller. The scenario chosen for this study is a closed track with one merge and one exit, which could be later implemented using a scaled infrastructure on our lab setup. This will enable us to benchmark the results of this study obtained in simulation, to comparable results in similar conditions in the lab. The metrics chosen for the comparison of the performance of our algorithm on the overall traffic conditions include the average speed, wait time near the merge, and throughput after the merge, measured under different travel demand conditions (low, medium, and heavy traffic).

Keywords: highway merges, traffic modeling, SUMO, driving policy

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Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control

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Authors: Gabriel Sitler, Yousef Sardahi, Asad Salem

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This paper presents an optimal and robust sliding mode controller (SMC) to regulate the position of the knee joint angle for patients suffering from knee injuries. The controller imitates the role of active orthoses that produce the joint torques required to overcome gravity and loading forces and regain natural human movements. To this end, a mathematical model of the shank, the lower part of the leg, is derived first and then used for the control system design and computer simulations. The design of the controller is carried out in optimal and multi-objective settings. Four objectives are considered: minimization of the control effort and tracking error; and maximization of the control signal smoothness and closed-loop system’s speed of response. Optimal solutions in terms of the Pareto set and its image, the Pareto front, are obtained. The results show that there are trade-offs among the design objectives and many optimal solutions from which the decision-maker can choose to implement. Also, computer simulations conducted at different points from the Pareto set and assuming knee squat movement demonstrate competing relationships among the design goals. In addition, the proposed control algorithm shows robustness in tracking a standard gait signal when accounting for uncertainty in the shank’s parameters.

Keywords: optimal control, multi-objective optimization, sliding mode control, wearable knee exoskeletons

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Authors: Israel B. Bitton

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“Memory” is associated with various phenomena, from physical to mental, personal to collective and historical to cultural. As part of a broader exploration of memory studies in philosophy and science (slated for academic publication October 2021), this specific study employs analytical methods of cognitive psychology and philosophy of memory to theorize that A) the primary human will (drive) is to significance, in that every human action and expression can be rooted in a most primal desire to be cosmically significant (however that is individually perceived); and B) that the will to significance manifests as the will to memorability, an innate desire to be remembered by others after death. In support of these broad claims, a review of various popular culture “touchpoints”—historic and contemporary records spanning literature, film and television, traditional news media, and social media—is presented to demonstrate how this very theory is repeatedly and commonly expressed (and has been for a long time) by many popular public figures as well as “everyday people.” Though developed before COVID, the crisis only increased the theory’s relevance: so many people were forced to die alone, leaving them and their loved ones to face even greater existential angst than what ordinarily accompanies death since the usual expectations for one’s “final moments” were shattered. To underscore this issue of, and response to, what can be considered a sociocultural “memory gap,” this study concludes with a summary of several projects launched by journalists at the height of the pandemic to document the memorable human stories behind COVID’s tragic warped speed death toll that, when analyzed through the lens of Viktor E. Frankl’s psychoanalytical perspective on “existential meaning,” shows how countless individuals were robbed of the last wills and testaments to their self-significance and memorability typically afforded to the dying and the aggrieved. The resulting insight ought to inform how government and public health officials determine what is truly “non-essential” to human health, physical and mental, at times of crisis.

Keywords: cognitive psychology, covid, neuroscience, philosophy of memory

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

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Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

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Authors: Om Prakash Bharti, Aanchal Verma, R. K. Saket

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The Doubly-Fed Induction Generator (DFIG) is suggested for Wind Energy Conversion System (WECS) to extract wind power. DFIG is preferably employed due to its robustness towards variable wind and rotor speed. DFIG has the adaptable property because the system parameters are smoothly dealt with, including real power, reactive power, DC-link voltage, and the transient and dynamic responses, which are needed to analyze constantly. The analysis becomes more prominent during any unusual condition in the electrical power system. Hence, the study and improvement in the system parameters and transient response performance of DFIG are required to be accomplished using some controlling techniques. For fulfilling the task, the present work implements and compares the optimization methods for the design of the DFIG controller for WECS. The bio-inspired optimization techniques are applied to get the optimal controller design parameters for DFIG-based WECS. The optimized DFIG controllers are then used to retrieve the transient response performance of the six-order DFIG model with a step input. The results using MATLAB/Simulink show the betterment of the Firefly algorithm (FFA) over other control techniques when compared with the other controller design methods.

Keywords: doubly-fed induction generator, wind turbine, wind energy conversion system, induction generator, transfer function, proportional, integral, derivatives

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Authors: Julius K. Gane, Mohamad N. Nahil, Paul T. Williams

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In today’s world of rapid population growth and a changing climate, one way to mitigate various negative effects is via renewable energy solutions. Energy and power as basic requirements in almost all human endeavours are also the banes of the changing climate and the impacts thereof. Thus it is crucial to develop innovative and environmentally friendly energy options to ameliorate various negative repercussions. Upgrading of fast pyrolysis bio-oil via hydro-treatment offers such opportunities, as quality renewable liquid transportation fuels can be produced. The process, however, is typically accompanied by bio-char formation as a by-product. The goal of this work was to study the yield and some properties of bio-chars formed from a hydrotreatment process, with an overall aim to promote the valuable utilization of wastes or by-products from renewable energy technologies. It is assumed that bio-chars that have comparable energy contents with coals will be more desirable as solid energy materials due to renewability and environmental friendliness. Therefore, the analytical work in this study focused mainly on determining the higher heating value (HHV) of the chars. The method involved the reaction of bio-oil in an autoclave supplied by the Parr Instrument Company, IL, USA. Two main parameters (different temperatures and resident times) were investigated. The chars were characterized using a Thermo EA2000 CHNS analyser, then oxygen contents and HHVs computed based on the literature. From the results, these bio-chars can readily serve as feedstocks for the production of renewable solid fuels. Their HHVs ranged between 29.26-39.18 MJ/kg, affected by different temperatures and retention times. There was an inverse relationship between the oxygen content and the HHVs of the chars. It can, therefore, be concluded that it is possible to optimize the process efficiency of the hydrotreatment process used through the production of renewable energy materials from the 'waste’ char by-products. Future work should consider developing a suitable balance between the primary objective of bio-oil upgrading processes (which is to improve the quality of the liquid fuels) and the conversion of its solid wastes into value-added products such as smokeless briquettes.

Keywords: bio-char, renewable solid biofuels, valorisation, waste-to-energy

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Authors: Omar Bouhassoun, Cristian Garrido, César Hueso

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The radiation shielding design is an optimization problem with multiple -constrained- objective functions (radiation dose, weight, price, etc.) that depend on several parameters (material, thickness, position, etc.). The classical approach for shielding design consists of a brute force trial-and-error process subject to previous designer experience. Therefore, the result is an empirical solution but not optimal, which can degrade the overall performance of the shielding. In order to automate the shielding design procedure, the IDOM Shielding Optimization Tool (ISOT) has been developed. This software combines optimization algorithms with the capabilities to read/write input files, run calculations, as well as parse output files for different radiation transport codes. In the first stage, the software was established to adjust the input files for two well-known Monte Carlo codes (MCNP and Serpent) and optimize the result (weight, volume, price, dose rate) using multi-objective genetic algorithms. Nevertheless, its modular implementation easily allows the inclusion of more radiation transport codes and optimization algorithms. The work related to the development of ISOT and its verification on a simple 3D multi-layer shielding problem using both MCNP and Serpent will be presented. ISOT looks very promising for achieving an optimal solution to complex shielding problems.

Keywords: optimization, shielding, nuclear, genetic algorithm

Procedia PDF Downloads 84

Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

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In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

Procedia PDF Downloads 317

Authors: Zair Firdaous, Fourka Mohamed, Elfelsoufi Zoubir

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The arrival of e-commerce has changed the supply chain management on the operational level as well as on the organization and strategic and even tactical decisions of the companies. The optimization of resources is an issue that is needed on the tactical and operational strategic plan. This work considers the allocation of resources in the case of pure players that have launched online sales. The aim is to improve the level of customer satisfaction and maintaining the benefits of e-retailer and of its cooperators and reducing costs and risks. We first modeled the B2C chain with all operations that integrates and possible scenarios since online retailers offer a wide selection of personalized service. The personalized services that online shopping companies offer to the clients can be embodied in many aspects, such as the customizations of payment, the distribution methods, and after-sales service choices. Every aspect of customized service has several modes. At that time, we analyzed the optimization problems of supply chain resource in customized online shopping service mode. Then, we realized an optimization model and algorithm for the development based on the analysis of the of the B2C supply chain resources. It is a multi-objective optimization that considers the collaboration of resources in operations, time and costs but also the risks and the quality of services as well as dynamic and uncertain characters related to the request.

Keywords: supply chain resource, e-commerce, pure-players, optimization

Procedia PDF Downloads 230

Authors: Ronal Muresano, Andrea Pagano

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Nowadays, the mathematical/statistical applications are developed with more complexity and accuracy. However, these precisions and complexities have brought as result that applications need more computational power in order to be executed faster. In this sense, the multicore environments are playing an important role to improve and to optimize the execution time of these applications. These environments allow us the inclusion of more parallelism inside the node. However, to take advantage of this parallelism is not an easy task, because we have to deal with some problems such as: cores communications, data locality, memory sizes (cache and RAM), synchronizations, data dependencies on the model, etc. These issues are becoming more important when we wish to improve the application’s performance and scalability. Hence, this paper describes an optimization method developed for Systemic Model of Banking Originated Losses (SYMBOL) tool developed by the European Commission, which is based on analyzing the application's weakness in order to exploit the advantages of the multicore. All these improvements are done in an automatic and transparent manner with the aim of improving the performance metrics of our tool. Finally, experimental evaluations show the effectiveness of our new optimized version, in which we have achieved a considerable improvement on the execution time. The time has been reduced around 96% for the best case tested, between the original serial version and the automatic parallel version.

Keywords: algorithm optimization, bank failures, OpenMP, parallel techniques, statistical tool

Procedia PDF Downloads 346

Authors: C. O. Igberi, M. U. Amadi

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This research paper on the impact of import restriction on rice production in Nigeria is aimed at finding/proffering valid solutions to the age long problem of rice self-sufficiency, through a better understanding of policy measures used in the past, in this case, the effectiveness of rice import restriction of the early 90’s. It tries to answer the questions of; import restriction boosting domestic rice production and the macroeconomic determining factors of Gross Domestic Rice Product (GDRP). The research probe is investigated through literature and analytical frameworks, such that time series data on the GDRP, Gross Fixed Capital Formation (GFCF), average foreign rice producers’ prices(PPF), domestic producers’ prices (PPN) and the labour force (LABF) are collated for analysis (with an import restriction dummy variable, POL1). The research objectives/hypothesis are analysed using; Cointegration, Vector Error Correction Model (VECM), Impulse Response Function (IRF) and Granger Causality Test(GCT) methodologies. Results show that in the short-run error correction specification for GDRP, a percentage (1%) deviation away from the long-run equilibrium in a current quarter is only corrected by 0.14% in the subsequent quarter. Also, the rice import restriction policy had no significant effect on the GDRP at this time. Other findings show that the policy period has, in fact, had effects on the PPN and LABF. The choice variables used are valid macroeconomic factors that explain the GDRP of Nigeria, as adduced from the IRF and GCT, and in the long-run. Policy recommendations suggest that the import restriction is not disqualified as a veritable tool for improving domestic rice production, rather better enforcement procedures and strict adherence to the policy dictates is needed. Furthermore, accompanying policies which drive public and private capital investment and accumulation must be introduced. Also, employment rate and labour substitution in the agricultural sector should not be drastically changed, rather its welfare and efficiency be improved.

Keywords: import restriction, gross domestic rice production, cointegration, VECM, Granger causality, impulse response function

Procedia PDF Downloads 179

Authors: Wen Li, Zhengyu Bai, Qi Zhang

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The demand for smart scheduling platform in agriculture, particularly in the scheduling process of machinery equipment, is high. With the continuous development of agricultural machinery equipment technology, a large number of agricultural machinery equipment and agricultural machinery cooperative service organizations continue to appear in China. The large area of cultivated land and a large number of agricultural activities in the central and western regions of China have made the demand for smart and efficient agricultural machinery equipment scheduling platforms more intense. In this study, we design and implement a platform for agricultural machinery equipment scheduling to allocate agricultural machinery equipment resources reasonably. With agricultural machinery equipment scheduling platform taken as the research object, we discuss its research significance and value, use the service blueprint technology to analyze and characterize the agricultural machinery equipment schedule workflow, the network analytic method to obtain the demand platform function requirements, and divide the platform functions through the platform function division diagram. Simultaneously, based on the case-based reasoning (CBR) algorithm, the equipment scheduling module of the agricultural machinery equipment scheduling platform is realized; finally, a design scheme of the agricultural machinery equipment scheduling platform architecture is provided, and the visualization interface of the platform is established via VB programming language. It provides design ideas and theoretical support for the construction of a modern agricultural equipment information scheduling platform.

Keywords: case-based reasoning, service blueprint, system design, ANP, VB programming language

Procedia PDF Downloads 144

Authors: Slamet Setioboro, Rahutami Kusumaningsih, Prabowo Setiyawan, Danna Darmayadi

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Designing of cold-formed steel capacity connections often based on the formula used for hot rolled steel. It makes the result of the actual capacity connection doesn’t accurate anymore. When the hot rolled steel receives the axial load pull, it will have different characteristics. As the result, there will be failure result when designing Truss structure made of hot rolled steel. This research aims to determine the capacity of actual cold-formed steel connections section which is loaded by the axial tensile force. It will test the appeal of the connection using bolt grafting tool and screw grafting tool. The variations of the test will be on the type of connection (single and double slap), the number of the connection tools and connection configuration. Bold and screw connections failure mode observed in this research are different each other. Failure mode of bolted connections includes sliding pivot plate, tearing at the plate and cutting of the bolt head. While the failure mode of screw connections includes tilting, hole-bearing, pull over and cutting the screw body out. This research was conducted using a laboratory test of HW2-600S Universal Testing Machine model with ASTM E8. It has done in the materials testing laboratory of Mechanical Engineering Department, Faculty of Engineering UNNES. The results obtained through the laboratory diversification towards theoretical calculations using the standards specified in ISO 7971-2013 Cold-Rolled Steel Structures. Based on the research, it can be concluded that the effective connection in receiving force strength is bolted connections neither single nor double plate. The method used is by applying 4 bolts through 2 parallel lines configuration. Furthermore, this connection deals with the consequences of holding the highest Pmaks, lowest failure risk and getting a little kind of mode of failure.

Keywords: axial load, cold-formed steel, capacity connections, bolted connections, screw connections

Procedia PDF Downloads 262

Authors: T. Hari Prasath, P. Ithaya Rani

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In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods.

Keywords: detecting face, Gabor filter, multi-class AdaBoost classifier, Z-score normalization

Procedia PDF Downloads 250

Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

Procedia PDF Downloads 216