Search results for: Flight data recorder (FDR)
6978 Churn Prediction for Telecommunication Industry Using Artificial Neural Networks
Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz
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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.Keywords: Customer relationship management, churn prediction, telecom industry, deep learning, Artificial Neural Networks, ANN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7396977 A Technical Perspective on Roadway Safety in Eastern Province: Data Evaluation and Spatial Analysis
Authors: Muhammad Farhan, Sayed Faruque, Amr Mohammed, Sami Osman, Omar Al-Jabari, Abdul Almojil
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Saudi Arabia in recent years has seen drastic increase in traffic related crashes. With population of over 29 million, Saudi Arabia is considered as a fast growing and emerging economy. The rapid population increase and economic growth has resulted in rapid expansion of transportation infrastructure, which has led to increase in road crashes. Saudi Ministry of Interior reported more than 7,000 people killed and 68,000 injured in 2011 ranking Saudi Arabia to be one of the worst worldwide in traffic safety. The traffic safety issues in the country also result in distress to road users and cause and economic loss exceeding 3.7 billion Euros annually. Keeping this in view, the researchers in Saudi Arabia are investigating ways to improve traffic safety conditions in the country. This paper presents a multilevel approach to collect traffic safety related data required to do traffic safety studies in the region. Two highway corridors including King Fahd Highway 39 kilometre and Gulf Cooperation Council Highway 42 kilometre long connecting the cities of Dammam and Khobar were selected as a study area. Traffic data collected included traffic counts, crash data, travel time data, and speed data. The collected data was analysed using geographic information system to evaluate any correlation. Further research is needed to investigate the effectiveness of traffic safety related data when collected in a concerted effort.
Keywords: Crash Data, Data Collection, Traffic Safety.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23416976 Machine Scoring Model Using Data Mining Techniques
Authors: Wimalin S. Laosiritaworn, Pongsak Holimchayachotikul
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this article proposed a methodology for computer numerical control (CNC) machine scoring. The case study company is a manufacturer of hard disk drive parts in Thailand. In this company, sample of parts manufactured from CNC machine are usually taken randomly for quality inspection. These inspection data were used to make a decision to shut down the machine if it has tendency to produce parts that are out of specification. Large amount of data are produced in this process and data mining could be very useful technique in analyzing them. In this research, data mining techniques were used to construct a machine scoring model called 'machine priority assessment model (MPAM)'. This model helps to ensure that the machine with higher risk of producing defective parts be inspected before those with lower risk. If the defective prone machine is identified sooner, defective part and rework could be reduced hence improving the overall productivity. The results showed that the proposed method can be successfully implemented and approximately 351,000 baht of opportunity cost could have saved in the case study company.Keywords: Computer Numerical Control, Data Mining, HardDisk Drive.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13856975 The Impact of Seasonality on Rainfall Patterns: A Case Study
Authors: Priti Kaushik, Randhir Singh Baghel, Somil Khandelwal
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This study uses whole-year data from Rajasthan, India, at the meteorological divisional level to analyze and evaluate long-term spatiotemporal trends in rainfall and looked at the data from each of the thirteen tehsils in the Jaipur district to see how the rainfall pattern has altered over the last 10 years. Data on daily rainfall from the Indian Meteorological Department (IMD) in Jaipur are available for the years 2012 through 2021. We mainly focus on comparing data of tehsil wise in the Jaipur district, Rajasthan, India. Also analyzed is the fact that July and August always see higher rainfall than any other month. Rainfall usually starts to rise around week 25th and peaks in weeks 32nd or 33rd. They showed that on several occasions, 2017 saw the least amount of rainfall during a long span of 10 years. The greatest rain fell between 2012 and 2021 in 2013, 2019, and 2020.
Keywords: Data analysis, extreme events, rainfall, descriptive case studies, precipitation temperature.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1676974 Enhance the Power of Sentiment Analysis
Authors: Yu Zhang, Pedro Desouza
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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modeling and testing work was done in R and Greenplum in-database analytic tools.
Keywords: Sentiment Analysis, Social Media, Twitter, Amazon, Data Mining, Machine Learning, Text Mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35046973 Lineup Optimization Model of Basketball Players Based on the Prediction of Recursive Neural Networks
Authors: Wang Yichen, Haruka Yamashita
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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.Keywords: Recurrent Neural Network, players lineup, basketball data, decision making model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8066972 New Multisensor Data Fusion Method Based on Probabilistic Grids Representation
Authors: Zhichao Zhao, Yi Liu, Shunping Xiao
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A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion method, the JPDM method dose not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRLB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.
Keywords: Cramer-Rao lower bound (CRLB), data fusion, probabilistic grids, joint probability density matrix, localization, sensor network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17936971 Sampled-Data Model Predictive Tracking Control for Mobile Robot
Authors: Wookyong Kwon, Sangmoon Lee
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In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.Keywords: Model predictive control, sampled-data control, linear parameter varying systems, LPV.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12606970 Talent Management through Integration of Talent Value Chain and Human Capital Analytics Approaches
Authors: Wuttigrai Ngamsirijit
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Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.Keywords: Decision making, human capital analytics, talent management, talent value chain.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9506969 Enhancing K-Means Algorithm with Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance
Authors: S. Deelers, S. Auwatanamongkol
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In this paper, we propose an algorithm to compute initial cluster centers for K-means clustering. Data in a cell is partitioned using a cutting plane that divides cell in two smaller cells. The plane is perpendicular to the data axis with the highest variance and is designed to reduce the sum squared errors of the two cells as much as possible, while at the same time keep the two cells far apart as possible. Cells are partitioned one at a time until the number of cells equals to the predefined number of clusters, K. The centers of the K cells become the initial cluster centers for K-means. The experimental results suggest that the proposed algorithm is effective, converge to better clustering results than those of the random initialization method. The research also indicated the proposed algorithm would greatly improve the likelihood of every cluster containing some data in it.Keywords: Clustering algorithm, K-means algorithm, Datapartitioning, Initial cluster centers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28516968 Semi-Supervised Outlier Detection Using a Generative and Adversary Framework
Authors: Jindong Gu, Matthias Schubert, Volker Tresp
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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.Keywords: Outlier detection, generative adversary networks, semi-supervised learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10616967 Methodology of the Turkey’s National Geographic Information System Integration Project
Authors: Buse A. Ataç, Doğan K. Cenan, Arda Çetinkaya, Naz D. Şahin, Köksal Sanlı, Zeynep Koç, Akın Kısa
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With its spatial data reliability, interpretation and questioning capabilities, Geographical Information Systems make significant contributions to scientists, planners and practitioners. Geographic information systems have received great attention in today's digital world, growing rapidly, and increasing the efficiency of use. Access to and use of current and accurate geographical data, which are the most important components of the Geographical Information System, has become a necessity rather than a need for sustainable and economic development. This project aims to enable sharing of data collected by public institutions and organizations on a web-based platform. Within the scope of the project, INSPIRE (Infrastructure for Spatial Information in the European Community) data specifications are considered as a road-map. In this context, Turkey's National Geographic Information System (TUCBS) Integration Project supports sharing spatial data within 61 pilot public institutions as complied with defined national standards. In this paper, which is prepared by the project team members in the TUCBS Integration Project, the technical process with a detailed methodology is explained. In this context, the main technical processes of the Project consist of Geographic Data Analysis, Geographic Data Harmonization (Standardization), Web Service Creation (WMS, WFS) and Metadata Creation-Publication. In this paper, the integration process carried out to provide the data produced by 61 institutions to be shared from the National Geographic Data Portal (GEOPORTAL), have been trying to be conveyed with a detailed methodology.
Keywords: Data specification, geoportal, GIS, INSPIRE, TUCBS, Turkey’s National Geographic Information System.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6766966 Exploring SSD Suitable Allocation Schemes Incompliance with Workload Patterns
Authors: Jae Young Park, Hwansu Jung, Jong Tae Kim
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In the Solid-State-Drive (SSD) performance, whether the data has been well parallelized is an important factor. SSD parallelization is affected by allocation scheme and it is directly connected to SSD performance. There are dynamic allocation and static allocation in representative allocation schemes. Dynamic allocation is more adaptive in exploiting write operation parallelism, while static allocation is better in read operation parallelism. Therefore, it is hard to select the appropriate allocation scheme when the workload is mixed read and write operations. We simulated conditions on a few mixed data patterns and analyzed the results to help the right choice for better performance. As the results, if data arrival interval is long enough prior operations to be finished and continuous read intensive data environment static allocation is more suitable. Dynamic allocation performs the best on write performance and random data patterns.
Keywords: Dynamic allocation, NAND Flash based SSD, SSD parallelism, static allocation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19826965 WebAppShield: An Approach Exploiting Machine Learning to Detect SQLi Attacks in an Application Layer in Run-Time
Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl
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In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method as a Web-App is developed for auto-generated data replication to provide a twin of the targeted data structure. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi", has been developed. A special login form has been developed with a special instance of the data validation; this verification process secures the web application from its early stages. The system has been tested and validated, and up to 99% of SQLi attacks have been prevented.
Keywords: SQL injection, attacks, web application, accuracy, database, WebAppShield.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4236964 Adaptive Kernel Principal Analysis for Online Feature Extraction
Authors: Mingtao Ding, Zheng Tian, Haixia Xu
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The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard KPCA.This formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.
Keywords: adaptive method, kernel principal component analysis, online extraction, recursive algorithm
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15426963 Proposing an Efficient Method for Frequent Pattern Mining
Authors: Vaibhav Kant Singh, Vijay Shah, Yogendra Kumar Jain, Anupam Shukla, A.S. Thoke, Vinay KumarSingh, Chhaya Dule, Vivek Parganiha
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Data mining, which is the exploration of knowledge from the large set of data, generated as a result of the various data processing activities. Frequent Pattern Mining is a very important task in data mining. The previous approaches applied to generate frequent set generally adopt candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper will also look for hardware approach of cache coherence to improve efficiency of the above process. The process of data mining is helpful in generation of support systems that can help in Management, Bioinformatics, Biotechnology, Medical Science, Statistics, Mathematics, Banking, Networking and other Computer related applications. This paper proposes the use of both upward and downward closure property for the extraction of frequent item sets which reduces the total number of scans required for the generation of Candidate Sets.Keywords: Data Mining, Candidate Sets, Frequent Item set, Pruning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16726962 Danger Theory and Intelligent Data Processing
Authors: Anjum Iqbal, Mohd Aizaini Maarof
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Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). Classically it is believed that IS strives to discriminate between self and non-self. Most of the existing AIS research is based on this approach. Danger Theory (DT) argues this approach and proposes that IS fights against danger producing elements and tolerates others. We, the computational researchers, are not concerned with the arguments among immunologists but try to extract from it novel abstractions for intelligent computation. This paper aims to follow DT inspiration for intelligent data processing. The approach may introduce new avenue in intelligent processing. The data used is system calls data that is potentially significant in intrusion detection applications.Keywords: artificial immune system, danger theory, intelligent processing, system calls
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18716961 Using Artificial Neural Network to Forecast Groundwater Depth in Union County Well
Authors: Zahra Ghadampour, Gholamreza Rakhshandehroo
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A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.Keywords: Neural networks, groundwater depth, forecast.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25086960 Organizational Data Security in Perspective of Ownership of Mobile Devices Used by Employees for Works
Authors: B. Ferdousi, J. Bari
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With advancement of mobile computing, employees are increasingly doing their job-related works using personally owned mobile devices or organization owned devices. The Bring Your Own Device (BYOD) model allows employees to use their own mobile devices for job-related works, while Corporate Owned, Personally Enabled (COPE) model allows both organizations and employees to install applications onto organization-owned mobile devices used for job-related works. While there are many benefits of using mobile computing for job-related works, there are also serious concerns of different levels of threats to the organizational data security. Consequently, it is crucial to know the level of threat to the organizational data security in the BOYD and COPE models. It is also important to ensure that employees comply with the organizational data security policy. This paper discusses the organizational data security issues in perspective of ownership of mobile devices used by employees, especially in BYOD and COPE models. It appears that while the BYOD model has many benefits, there are relatively more data security risks in this model than in the COPE model. The findings also showed that in both BYOD and COPE environments, a more practical approach towards achieving secure mobile computing in organizational setting is through the development of comprehensive cybersecurity policies balancing employees’ need for convenience with organizational data security. The study helps to figure out the compliance and the risks of security breach in BYOD and COPE models.
Keywords: Data security, mobile computing, BYOD, COPE, cybersecurity policy, cybersecurity compliance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3406959 New Security Approach of Confidential Resources in Hybrid Clouds
Authors: Haythem Yahyaoui, Samir Moalla, Mounir Bouden, Skander Ghorbel
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Nowadays, cloud environments are becoming a need for companies, this new technology gives the opportunities to access to the data anywhere and anytime. It also provides an optimized and secured access to the resources and gives more security for the data which is stored in the platform. However, some companies do not trust Cloud providers, they think that providers can access and modify some confidential data such as bank accounts. Many works have been done in this context, they conclude that encryption methods realized by providers ensure the confidentiality, but, they forgot that Cloud providers can decrypt the confidential resources. The best solution here is to apply some operations on the data before sending them to the provider Cloud in the objective to make them unreadable. The principal idea is to allow user how it can protect his data with his own methods. In this paper, we are going to demonstrate our approach and prove that is more efficient in term of execution time than some existing methods. This work aims at enhancing the quality of service of providers and ensuring the trust of the customers.
Keywords: Confidentiality, cryptography, security issues, trust issues.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14626958 A Novel Web Metric for the Evaluation of Internet Trends
Authors: Radek Malinský, Ivan Jelínek
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Web 2.0 (social networking, blogging and online forums) can serve as a data source for social science research because it contains vast amount of information from many different users. The volume of that information has been growing at a very high rate and becoming a network of heterogeneous data; this makes things difficult to find and is therefore not almost useful. We have proposed a novel theoretical model for gathering and processing data from Web 2.0, which would reflect semantic content of web pages in better way. This article deals with the analysis part of the model and its usage for content analysis of blogs. The introductory part of the article describes methodology for the gathering and processing data from blogs. The next part of the article is focused on the evaluation and content analysis of blogs, which write about specific trend.Keywords: Blog, Sentiment Analysis, Web 2.0, Webometrics
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35326957 Simulated Annealing Algorithm for Data Aggregation Trees in Wireless Sensor Networks and Comparison with Genetic Algorithm
Authors: Ladan Darougaran, Hossein Shahinzadeh, Hajar Ghotb, Leila Ramezanpour
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In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.
Keywords: Data aggregation, wireless sensor networks, energy efficiency, simulated annealing algorithm, genetic algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16746956 Encoding and Compressing Data for Decreasing Number of Switches in Baseline Networks
Authors: Mohammad Ali Jabraeil Jamali, Ahmad Khademzadeh, Hasan Asil, Amir Asil
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This method decrease usage power (expenditure) in networks on chips (NOC). This method data coding for data transferring in order to reduces expenditure. This method uses data compression reduces the size. Expenditure calculation in NOC occurs inside of NOC based on grown models and transitive activities in entry ports. The goal of simulating is to weigh expenditure for encoding, decoding and compressing in Baseline networks and reduction of switches in this type of networks. KeywordsNetworks on chip, Compression, Encoding, Baseline networks, Banyan networks.
Keywords: Networks on chip, Compression, Encoding, Baseline networks, Banyan networks
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19726955 Sampled-Data Control for Fuel Cell Systems
Authors: H. Y. Jung, Ju H. Park, S. M. Lee
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Sampled-data controller is presented for solid oxide fuel cell systems which is expressed by a sector bounded nonlinear model. The proposed control law is obtained by solving a convex problem satisfying several linear matrix inequalities. Simulation results are given to show the effectiveness of the proposed design method.Keywords: Sampled-data control, Sector bound, Solid oxide fuel cell, Time-delay.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17156954 Automatic Detection and Spatio-temporal Analysis of Commercial Accumulations Using Digital Yellow Page Data
Authors: Yuki. Akiyama, Hiroaki. Sengoku, Ryosuke. Shibasaki
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In this study, the locations and areas of commercial accumulations were detected by using digital yellow page data. An original buffering method that can accurately create polygons of commercial accumulations is proposed in this paper.; by using this method, distribution of commercial accumulations can be easily created and monitored over a wide area. The locations, areas, and time-series changes of commercial accumulations in the South Kanto region can be monitored by integrating polygons of commercial accumulations with the time-series data of digital yellow page data. The circumstances of commercial accumulations were shown to vary according to areas, that is, highly- urbanized regions such as the city center of Tokyo and prefectural capitals, suburban areas near large cities, and suburban and rural areas.Keywords: Commercial accumulations, Spatio-temporal analysis, Urban monitoring, Yellow page data
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12516953 EEG Waves Classifier using Wavelet Transform and Fourier Transform
Authors: Maan M. Shaker
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The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.Keywords: Bioinformatics, DWT, EEG waves, FFT.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 55426952 Obstacle Classification Method Based On 2D LIDAR Database
Authors: Moohyun Lee, Soojung Hur, Yongwan Park
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We propose obstacle classification method based on 2D LIDAR Database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width and intensity data; the first classification was processed by the width data; the second classification was processed by the intensity data; classification was processed by comparing to database; result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.
Keywords: Obstacle, Classification, LIDAR, Segmentation, Width, Intensity, Database.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 34366951 An Empirical Mode Decomposition Based Method for Action Potential Detection in Neural Raw Data
Authors: Sajjad Farashi, Mohammadjavad Abolhassani, Mostafa Taghavi Kani
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Information in the nervous system is coded as firing patterns of electrical signals called action potential or spike so an essential step in analysis of neural mechanism is detection of action potentials embedded in the neural data. There are several methods proposed in the literature for such a purpose. In this paper a novel method based on empirical mode decomposition (EMD) has been developed. EMD is a decomposition method that extracts oscillations with different frequency range in a waveform. The method is adaptive and no a-priori knowledge about data or parameter adjusting is needed in it. The results for simulated data indicate that proposed method is comparable with wavelet based methods for spike detection. For neural signals with signal-to-noise ratio near 3 proposed methods is capable to detect more than 95% of action potentials accurately.
Keywords: EMD, neural data processing, spike detection, wavelet decomposition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23656950 Platform-as-a-Service Sticky Policies for Privacy Classification in the Cloud
Authors: Maha Shamseddine, Amjad Nusayr, Wassim Itani
Abstract:
In this paper, we present a Platform-as-a-Service (PaaS) model for controlling the privacy enforcement mechanisms applied on user data when stored and processed in Cloud data centers. The proposed architecture consists of establishing user configurable ‘sticky’ policies on the Graphical User Interface (GUI) data-bound components during the application development phase to specify the details of privacy enforcement on the contents of these components. Various privacy classification classes on the data components are formally defined to give the user full control on the degree and scope of privacy enforcement including the type of execution containers to process the data in the Cloud. This not only enhances the privacy-awareness of the developed Cloud services, but also results in major savings in performance and energy efficiency due to the fact that the privacy mechanisms are solely applied on sensitive data units and not on all the user content. The proposed design is implemented in a real PaaS cloud computing environment on the Microsoft Azure platform.Keywords: Privacy enforcement, Platform-as-a-Service privacy awareness, cloud computing privacy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7406949 DIFFER: A Propositionalization approach for Learning from Structured Data
Authors: Thashmee Karunaratne, Henrik Böstrom
Abstract:
Logic based methods for learning from structured data is limited w.r.t. handling large search spaces, preventing large-sized substructures from being considered by the resulting classifiers. A novel approach to learning from structured data is introduced that employs a structure transformation method, called finger printing, for addressing these limitations. The method, which generates features corresponding to arbitrarily complex substructures, is implemented in a system, called DIFFER. The method is demonstrated to perform comparably to an existing state-of-art method on some benchmark data sets without requiring restrictions on the search space. Furthermore, learning from the union of features generated by finger printing and the previous method outperforms learning from each individual set of features on all benchmark data sets, demonstrating the benefit of developing complementary, rather than competing, methods for structure classification.Keywords: Machine learning, Structure classification, Propositionalization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1209