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Manufacturing test data analysis - On comparing different classification algorithms

Manufacturing test data analysis - On comparing different cl...

引用

International Symposium on Information Technology Convergence

作者： Lee, Jia Keat Phon-Amnuaisuk, Somnuk Chew, Huat Chin Ho, Chin Kuan Multimedia Univ Fac Informat Technol Cyberjaya Malaysia Intel Corp Bayan Lepaz FIZ George Town Malaysia

ISBN: (纸本)9780769530451

Due to the circuit complexity and the number of parameters involved, test relationship of a Test Program (TP) might not be fully discovered. Traditionally, TP setup are defined based on the domain expertise and gathered experience of an engineer. Such judgment is time consuming and could be inefficient especially when new products and technologies are rapidly developed for the competing market. If the complexity of a TP increases, the undetected interrelationship among tests in a TP will also increase. In this paper, inferences are performed to a huge and complex TP using different classification algorithms, with the primary goal to discover potential test relationships in a fast and efficient way. The mining output can be used as a reference and basis for test engineers to improve TP setup or to reprogram test machine to replace current exhaustive test policy.

关键词： sorting algorithm circuit complexity New products Engineers observational data

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Local and Centralized control strategy for Capacitor Voltage Balancing of Modular Multilevel Converter 5

Local and Centralized control strategy for Capacitor Voltage...

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5th IEEE International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Verdugo, Cristian Candela, Jose I. Rodriguez, Pedro UPC SEER Dept Elect Engn Terrassa Spain Abengoa Res Abengoa Seville Spain

ISBN: (纸本)9781509033881

This paper presents two different control strategies to balance the capacitor voltage of sub-modules in modular multi-level converters. In the centralized control, sub-modules receive the switching state defined by a proposed sorting algorithm in order to control the capacitor voltage. The proposed algorithm reduces the number of switching changes in comparison with the classical sorting algorithm used in literature. The local control based on a PI controller is integrated into the sub-modules, for this reason, all the cells in the arm receive the same modulation index, being modified according to voltage references. Both control strategies are evaluated and validated through simulation results.

关键词： Capacitor voltage balancing Centralized control balance Local control balance Modular Multilevel Converter sorting algorithm

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JA-trie: Entropy-Based Packet Classification 15

JA-trie: Entropy-Based Packet Classification

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15th IEEE International Conference on High Performance Switching and Routing (HPSR)

作者： Antichi, Gianni Callegari, Christian Moore, Andrew W. Giordano, Stefano Anastasi, Enrico Univ Cambridge Comp Lab Cambridge CB2 1TN England Univ Pisa Dept Informat Engn I-56100 Pisa Italy

ISBN: (纸本)9781479916337

Any improvement in packet classification performance is crucial to ensure Internet functions continue to track the ever-increasing link capacities. Packet classification is the foundation of many Internet functions: from fundamental packet-forwarding to advanced features such as Quality of Service enforcement, monitoring and security functions. This work proposes a novel trie-based classification algorithm, named Jump-Ahead Trie (JA-trie), utilizing an entropy-based pre-processing phase and a novel approach to wildcard matching. Through extensive experimental tests, we demonstrate that our proposed algorithm is able to outperform a range of state-of-the-art classification algorithms.

关键词： Data structures Entropy Hardware IP networks Internet Memory management Quality of service Store management packet classification Quality of service Internet Protocol networks Data structures Computer hardware Internets entropy sorting algorithm

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A method based on tree-structured Markov random field for forest area classification

A method based on tree-structured Markov random field for fo...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Cuozzo, G D'Elia, C Puzzolo, V Univ Cassino DAEIMI I-03043 Cassino Italy

ISBN: (纸本)0780387422

The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the usefulness of segmentation processes and object classification is still improving. In this paper, a method based on tree-structured Markov random field (TS-MRF) is applied to Landsat TM images in order to assess the capability of the TS-MRF segmentation algorithm for discriminating forest-non forest covers in a test area located in the Eastern Italian Alps of Trentino. In particular, the regions of interest are selected from the image using a two step process based on a segmentation algorithm and an analysis process. The segmentation is achieved applying a MRF a-prior model, which takes into account the spatial dependencies in the image, and the TS-MRF optimisation algorithm which segments recursively the image in smaller regions using a binary tree structure. The analysis process links to each object identified by the segmentation a set of features related to the geometry (like shape, smoothness, etc.), to the spectral signature and to the neighbour regions (contextual features). These features were used in this study for classifying each object as forest or non-forest thought a simple supervised classification algorithm based on a thresholds built on the feature values obtained from a set of training objects. This method already allowed the detection of the Forest area within the study area with an accuracy of 90%, while better performances could be achieved using more sophisticated classification algorithm, like Neural Networks and Support Vector Machine.

关键词： forestry vegetation mapping terrain mapping geophysical signal processing land use planning image classification image segmentation tree data structures Markov processes feature extraction tree-structured Markov random field forest area classification forest cover classification land use management pixel based classifications middle resolution images object classification Landsat TM images TS-MRF segmentation algorithm forest-nonforest cover discrimination Eastern Italian Alps Trentino Italy image spatial dependencies TS-MRF optimisation algorithm binary tree structure object identification spectral signature contextual features supervised classification algorithm forest area detection neural networks support vector machine Land use planning image segmentation Spectrum signatures wooded area Trentino tree data structures sorting algorithm object recognition geophysical signal processing Land use management Support Vector Network terrain mapping vegetation mapping Image classification Neural network

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A Detailed Power Loss Analysis of Modular Multilevel Converter 30

A Detailed Power Loss Analysis of Modular Multilevel Convert...

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30th Annual IEEE Applied Power Electronics Conference and Exposition (APEC)

作者： Erturk, Feyzullah Hava, Ahmet M. Middle E Tech Univ Elect & Elect Engn Dept TR-06531 Ankara Turkey

ISBN: (纸本)9781479967353

This paper thoroughly examines the semiconductor power loss characteristics of modular multilevel converters (MMC). Power loss behavior is examined under different pulse width modulation (PWM) methods and operating conditions. The effects of stored energy level, circulating current control utilization, power factor and submodule voltage balancing method on power loss are studied. Furthermore, unbalanced power losses and specific semiconductor stresses within a submodule are visualized by investigating the loss distribution in a submodule. The paper aids in understanding and design of MMC converter modules, selecting PWM and control methods with power loss perspective.

关键词： modular multilevel converter phase disposition (PD) PWM phase shift PWM power loss sorting algorithm

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Sentence-based Plot Classification for Online Review Comments

Sentence-based Plot Classification for Online Review Comment...

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IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (WI-IAT)

作者： Iwai, Hidenari Hijikata, Yoshinori Ikeda, Kaori Nishida, Shogo Osaka Univ Grad Sch Engn Sci Suita Osaka 565 Japan

ISBN: (纸本)9781479941438

Many shopping sites provide functions to submit a user review for a purchased item. Reviews of items including stories such as novels and movies sometimes contain spoilers (undesired and revealing plot descriptions) along with the opinions of the review author. In this paper, we propose a system that helps users see reviews without seeing such plot descriptions. This system classifies each sentence in a user review as plot-related or non-plot-related and hides plot descriptions from user reviews. We tested five common machine-learning algorithms to ascertain the appropriate algorithm to address this problem. We also proposed a method of generalizing people's names, which we think is strongly related to the plot description. We verified its contribution to the classification results. Finally, we implemented a display interface of user reviews in which users can control the level of plot hiding.

关键词： customer satisfaction learning (artificial intelligence) pattern classification retail data processing user interfaces classilication results display interface machine-learning algorithms online review comments plot descriptions plot hiding purchased item review author sentence-based plot classification shopping sites user review Classification algorithms DVD Educational institutions Logistics Machine learning algorithms Motion pictures Support vector machines bag of words machine learning plot classification sentiment analysis text mining Machine learning algorithms Cinema business data processing sorting algorithm Educational institutions text mining User Interface Device Component Pattern recognition Consumer Satisfaction Logistics Support Vector Network Films Digital versatile discs DIGITAL VIDEO DISC Consumers

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Increasing Flexibility of Time Triggered Ethernet based Systems by Optimal Greedy Scheduling Approach 20

Increasing Flexibility of Time Triggered Ethernet based Syst...

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20th IEEE Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Wisniewski, Lukasz Schumacher, Markus Jasperneite, Juergen Diedrich, Christian Ostwestfalen Lippe Univ Appl Sci inIT Inst Ind IT D-32657 Lemgo Germany Fraunhofer IOSB INA Applicat Ctr Ind Automat D-32657 Lemgo Germany Univ Magdeburg Inst Automat Technol D-39106 Magdeburg Germany

ISBN: (纸本)9781467379298

Many aspects have to be considered while achieving flexibility of production systems. One of them is flexible communication system that on one hand fulfils requirements of a particular industrial application and on the other hand allows integration of new network components optimally in a seamless way. In this paper, an approach to increase, rather static, Time Triggered Ethernet based systems is proposed. It is based on a greedy scheduling approach that can be implemented directly in a processing limited hardware such as IO Controller. This, together with the discovery and auto configuration mechanism proposed in Ill allows to shift the engineering process typically performed offline using engineering tool to the field level. Furthermore, it has been proven that the proposed scheduling approach is not only simple but also powerful. It is able to calculate an optimal schedule for any kind of tree graph and the complexity of the algorithm is defined by the complexity of the used sorting algorithm.

关键词： Tree graphs Ethernet networks greedy method sorting algorithm Engineering tools Network components field level IO Flexibility

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Novel Context-aware Classification for Highly Accurate Automatic Toll Collection 30

Novel Context-aware Classification for Highly Accurate Autom...

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30th IEEE Intelligent Vehicles Symposium (IV)

作者： Khata, Marouan Shvai, Nadiya Hasnat, Abul Llanza, Arcadi Sanogo, Assan Meicler, Antoine Nakib, Amir VINCI Autoroutes Cyclope AI Rueil Malmaison France Univ Paris Est Lab LISSI Vitry Sur Seine France

ISBN: (纸本)9781728105604

Toll Vehicle Classification is an important task. Indeed, it has many uses in traffic management and toll collection systems. In this paper, Vinci Autoroutes group Networks (the biggest French Highways concession) are considered, where every year, millions of vehicles are classified in real-time. Then, a small decrease in classification performance can have serious economic losses. Therefore, the accuracy and the time complexity become critical for the toll collection system. The current classification algorithm uses the scene features' to detect vehicles classes. However, it requires a large labeled datasets, and has a limitations when multiple vehicles are in the scene. Herein, we propose a novel context-aware vehicle classification method that takes profit from the semantic spatial relationship of the objects. The experiments show that our method is performing as accurately as the existing model with significantly lower labeled datasets (74 times smaller). Moreover,the obtained accuracy of the proposed method is 99.97% compared to 99.79% achieved by the current method when using the same training set.

关键词： feature extraction image classification object detection road vehicles traffic engineering computing ubiquitous computing road vehicles Object detection Ubiquitous computing Image classification Feature extraction Toll collection Vehicle classification engineering computing Land transportation Context awareness vehicle complexity classes Traffic management sorting algorithm

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Classification of sonar data for a mobile robot using neural networks

Classification of sonar data for a mobile robot using neural...

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IEEE International Joint Symposia on Intelligence and Systems

作者： Diep, D Johannet, A Bonnefoy, P Harroy, F Loiseau, P LGI2P EERIE EMA F-30000 Nimes France

ISBN: (纸本)0818685484

We study in this paper an innovative architecture of ultrasonic sensor, in conjunction with a neural network-based classification algorithm, in order to recognize some geometric obstacles encountered by a mobile robot. The ultrasonic sensor is made of the association of an array of ultrasonic transducers, building an acoustic antenna, and providing acoustic scans with a fine resolution. The neural network is a multilayer perceptron which was trained with a set of features extracted from the sonar data. Results show that, by selecting appropriate features, the network can be trained to classify some geometric shapes, like wall, corners and edges.

关键词： Ultrasonic sensors Mobile robots Neural network sonar Multilayer perceptrons Geometric forms ultrasonic transducers sorting algorithm Acoustics

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Using ABC algorithm for Classification and Analysis on Effects of Control Parameters

Using ABC Algorithm for Classification and Analysis on Effec...

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18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Dilmac, Selim Korurek, Mehmet Istanbul Tech Univ Fen Bilimleri Enstitusu Maslak Sariyer Is Turkey

ISBN: (纸本)9781479975723

In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH ECG database. In addition to it, validity indices and effects of some control parameters such as MCN, Limit are examined. It is observed that, selected features have significiant effect on classification success rate of classifier. If there is high overlap between the classes, success rate of classifier decreases. However observed results indicate that ABC algorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, MABC algorithm based classifier provides higher classification success rates versus ABC algorithm, independent from Limit and MCN values.

关键词： classification data analysis electrocardiography feature extraction feature selection learning (artificial intelligence) medical information systems medical signal processing signal classification swarm intelligence ABC algorithm effectiveness BUPA IRIS MABC algorithm based classifier MCN parameter MITBIH ECG database PIMA SCTR control parameter UCI Machine Learning Repository WINE analysis algorithm artificial bee colony algorithm based classifier class overlap classification algorithm classification success rate control parameter effect feature effect limit parameter multidimensional dataset classification validity index Classification algorithms Educational institutions Electrocardiography Indexes Iris Machine learning algorithms Particle swarm optimization Feature extraction sorting algorithm Electrocardiography Particle swarm optimization Wine medical information systems control parameter INTEGRATED RISK INFORMATION SYSTEM Machine learning algorithms medical signal processing signal classification Educational institutions Classifiers activity based costing Iris

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