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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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An Improved Methodology of Soft Drink Discrimination Using an Electronic Nose

An Improved Methodology of Soft Drink Discrimination Using a...

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international conference on Computer Science and Network Technology

作者： Xiaoli Lu Jing Teng Dept. of Inf. Eng. Yanching Inst. of Technol. Langfang China

ISBN: (纸本)9781479905607

An improved methodology of soft drink discrimination using an electronic nose is developed in this study. 4 kinds of soft drinks, namely Coca Cola, Pepsi Cola, Future Cola and Sprite are detected. 3 pattern recognition techniques, PCA (Principle Component Analysis), MDA (Mahalanobis Distances Analysis) and PNN (Probabilistic Neural Network) are employed to verify the effectiveness of the 3 sampling procedures. The results indicated that, sampling by static headspace, 25 samples are misclassified in PNN analysis. The electronic nose cannot discriminate the 3 Colas due to the presence of humidity in the headspace, only Sprite can be discriminated from the Colas. With 21 samples are misclassified in PNN analysis, the EDU (Enrichment and Desorption Unit) cannot improve the results significantly. Sodium carbonate powder is very effective in adsorbing moisture in the samples, which effectively improves the sensitivity and the stability of the electronic nose sensors. Consequently, all the samples are classified correctly in PNN, and the electronic nose can be used in soft drink detection.

关键词： beverages electronic noses neural nets pattern classification principal component analysis Coca Cola Future Cola Mahalanobis distances analysis Pepsi Cola Sprite electronic nose sensor pattern recognition techniques principle component analysis probabilistic neural network sodium carbonate powder soft drink discrimination Electronic noses Powders Principal component analysis Sensitivity Sensors Sprites (computer) Stability analysis electronic nose sodium carbonate soft drink

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2013 3rd international conference on Advanced Materials and Information Technology Processing (AMITP 2013)

作者： M.Hernando Casta?eda M.Claudia Patricia Parra Facultad de Ingeniería Universidad de Pamplona

In order to further illustrate similarity concept and fuzzy method structural,we consider the problem of estimation stump test dynamics parameters in the concrete from data set described in UCI machine learning *** consider this is important for monitoring mixture concrete mechanics in civil engineering by pattern recognition of behavioral(curvature and slope features) of compressive strength and cement–water *** example,to assess aggregates mixture conditions for which the interpretability of the local model parameters is of vital importance and to automatically control or optimize the stump test settings for which the global model.

关键词： concrete slump test identification structural similarity fuzzy set

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An indexing method of mathematical expression retrieval

An indexing method of mathematical expression retrieval

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international conference on Computer Science and Network Technology (ICCSNT)

作者： Xuedong Tian Songqiang Yang Xinfu Li Fang Yang Hebei key laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics & Computer Science Hebei University Baoding China

ISBN: (纸本)9781479905607

As the kernel component of scientific documents, mathematical expressions are becoming a new object of searching engines. Different from normal text, mathematical expressions are composed of various kinds of symbols arranged in nonlinear mode, which results in the limitations of traditional full-text information retrieval used for expression searching. In this paper, we discuss the existing search engine of mathematical expressions and introduce the two-dimensional characteristics of mathematical expressions firstly. Then, a data structure of expressing mathematical formulas is designed which contains not only the symbol code but also the mathematical information among symbols. Finally, the indexing algorithm of mathematical expressions is put forward on the basis of the expression data structure. The experimental result shows the effectiveness of the indexing method proposed in this paper.

关键词： Feature extraction Search engines Data mining Indexing Educational institutions Data structures

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Lateral inhibition based holistic approach to adaptive image enhancement

Lateral inhibition based holistic approach to adaptive image...

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IEEE international Advance Computing conference, IACC

作者： Avijit Dasgupta Ashish Bakshi Kuntal Ghosh Department of Electronics & Communication Engineering Saint Thomas College of Engineering and Technology Kolkata India Machine Intelligence Unit and Center for Soft Computing Research Indian Statistical Institute Kolkata India

ISBN: (纸本)9781467345279

We present a physiologically inspired adaptive algorithm for noise removal in an image while preserving significant amount of edge details. The algorithm is motivated by the classical lateral inhibition based receptive field in the visual system as well as the holistic approach of the well known bilateral filter. We propose an adaptive difference of Gaussian (DoG) filter with varying window size depending upon the edge strengths in the image. Our algorithm has advantages over similar other techniques such as simple Gaussian filter, DoG filter, and is comparable to the bilateral filter in terms of edge enhancement. Furthermore, time complexity of our algorithm is much less than the bilateral filter.

关键词： Image edge detection Filter banks Adaptive filters Filtering algorithms Noise Smoothing methods

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ALP: An authentication and leak prediction model for Cloud Computing privacy

ALP: An authentication and leak prediction model for Cloud C...

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IEEE international Advance Computing conference, IACC

作者： Mohammad Farhatullah Department of CSE Dr.L.Bullayya College of Engineering for Women Visakhapatnam India

ISBN: (纸本)9781467345279

The current paper is an attempt to integrate privacy preservation with privacy leak detection in the context of text mining. This may be considered as extending the Storage as a Service feature of Cloud Computing wherein the user in the role of content creator submits the documents to be stored. There exists a facility to cluster these documents based on the concept-based mining algorithm. The clustered documents are usually available in the form a tree. When a user in the role of a subscriber requests to access a document, this request for access will have to go through an authentication procedure based on the Leakage Free Redactable Signature Scheme. Access control information is being maintained in the form of a Cloud user access control list. A privacy detection leak module which detects privacy leaks depending upon the pattern of previous privacy leaks is also being proposed. This information is then used to update the cloud user access control list and users responsible for privacy leaks are prevented from accessing the cloud service. The current document being requested by the user together with the information from the access control list is used to decide which part of the redacted trees have to be made available to the user as a response. Thus this combination of this authentication procedure and privacy leak detection can be used to ensure the privacy of the sensitive information stored by the user in the cloud.

关键词： Privacy Authentication Leak detection Cloud computing Predictive models pattern recognition Computational modeling

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3D shape retrieval using point bidirectional features

3D shape retrieval using point bidirectional features

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international conference on Computer Science and Network Technology (ICCSNT)

作者： Hui Chen Jingqi Yan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479905607

In this paper, we describe a novel 3D shape retrieval method based on new features. The features are extracted for 3D points based on a 2D attribute space which consists of two bidirectional 3D shape attributes, one of which along the shape surface direction and the other along the shape content direction perpendicular to the former. We define this features as Point Bidirectional Features (PBFs), which can reflect not only the global shape but also the local content. For simplicity, we choose the geodesic distance (GD) and the shape diameter function (SDF) to construct PBFs. Given a database of 3D shapes and a query shape, the proposed retrieval method adopts a shape similarity measurement based on 3D points matching with PBFs to decide which is the most similar shape from the database. To reduce cost of computation and feature storage, a shape simplification algorithm is integrated into this method. Additionally, a simple but effective point correspondence mechanism with K-Nearest Neighbor (KNN) assignment is designed for this retrieval method. Experimental results demonstrate the effectiveness of the proposed features and method.

关键词： Shape Three-dimensional displays Histograms Solid modeling Computational modeling Feature extraction Databases

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Swarm Robots Reinforcement Learning Convergence Accuracy-Based Learning Classifier Systems with Gradient Descent (XCS-GD)

Swarm Robots Reinforcement Learning Convergence Accuracy-Bas...

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international conference on Computer Science and Network Technology

作者： Jie Shao Haixia Lin Kaibian Zhang Dept. of Inf. Eng. Zhengzhou Chenggong Univ. of Finance & Econ. Zhengzhou China

ISBN: (纸本)9781479905607

This paper presented a novel approach XCS-GD to research on swarm robots reinforcement learning convergence. XCS-GD combines covering operator and genetic algorithm. XCS-GD is responsible for adjusting precision and reducing search space according to some reward obtained from the environment, XCS-GD's innovation discovery component is responsible for discovering new better reinforcement learning rules. The experiment and simulation showed that XCS-GD approach can achieved convergence very quickly in swarm robots reinforcement learning.

关键词： collision avoidance convergence genetic algorithms gradient methods learning (artificial intelligence) mathematical operators multi-robot systems pattern classification search problems XCS-GD approach XCS-GD innovation discovery component covering operator genetic algorithm gradient descent learning classifier systems precision adjustment reinforcement learning rule discovery search space reduction swarm robot reinforcement learning convergence accuracy Collision avoidance Convergence Genetic algorithms Learning (artificial intelligence) Path planning Robot kinematics Accuracy-based learning classifier system with Gradient descent (XCS-GD) Fitness function Reinforcement learning swarm robots

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AI*IA 2013: Advances in Artificial intelligence: XIIIth international conference of the Italian Association for Artificial intelligence, Turin, Italy, December 4-6, 2013. Proceedings 1

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丛书名： Lecture Notes in Computer Science

2013年

作者： Mario Alviano, Carmine Dodaro, Francesco Ricca (auth.), Matteo Baldoni, Cristina Baroglio, Guido Boella, Roberto Micalizio (eds.)

ISBN: (数字)9783319035246

ISBN: (纸本)9783319035239;9783319035246

This book constitutes the refereed proceedings of the 13th international conference of the Italian Association for Artificial intelligence, AI*IA 2013, held in Turin, Italy, in December 2013. The 45 revised full papers were carefully reviewed and selected from 86 submissions. The conference covers broadly the many aspects of theoretical and applied Artificial intelligence as follows: knowledge representation and reasoning, machine learning, natural language processing, planning, distributed AI: robotics and MAS, recommender systems and semantic Web and AI applications.

关键词：人工智能l。机器人学；数据采矿和知识发现；数理逻辑和正式语言；由抽象设备的计算；用户接口和人的计算机相互作用；模式识别

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Degradation Mode recognition for Battery of Electric Vehicle Based on ART2 Network

Degradation Mode Recognition for Battery of Electric Vehicle...

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2013 3rd international conference on Mechanical Materials and Manufacturing Engineering(ICMMME 2013)

作者： Gang Yu Mimi Guo Shenzhen Key Laboratory of Digital Manufacturing Technology School of Mechanical Engineering and Automation Harbin Institute of Technology (HIT) Shenzhen Graduate School

In the process of describing the degradation process of battery of electric vehicle,degradation models under dynamic running conditions need to be analyzed in order to effectively forecast the remaining useful mileage and life of *** paper proposed a new method of pattern recognition based on ART2 network to distinguish the degradation mode of the battery of electric ***,the degradation mechanism is analyzed of the battery to extract the features of degradation *** the learning process of ART2 is designed for recognition of the degradation *** that,HMM is applied to research the dynamic changes of the degradation mode and predict future degradation mode ***,experimental analysis based on battery’s charge and discharge data proves the effectiveness of the proposed approach.

关键词： Battery Degradation mode ART2 pattern recognition

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An Efficient Comparative machine Learning-based Metagenomics Binning Technique Via Using Random Forest

An Efficient Comparative Machine Learning-based Metagenomics...

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IEEE international conference on Computational lntelligence and Virtual Environments for Measurement Systems and Applications

作者： Helal Saghir Dalila B. Megherbi Center for Human/Machine Intelligence Networking and Distributed Systems Department of Electrical and Computer Engineering University of Massachusetts

ISBN: (纸本)9781467347020

Metagenomics is the study of microorganisms collected directly from natural environments. Metagenomics studies use DNA fragments obtained directly from a natural environment using whole genome shotgun (WGS) sequencing. Sequencing random fragments obtained from whole genome shotgun into taxa-based groups is known as binning. Currently, there are two different methods of binning: sequence similarity methods and sequence composition methods. Sequence similarity methods are usually based on sequence alignment to known genome like BLAST, or MEGAN. As only a very small fraction of species is available in the current databases, similarity methods do not yield good results. As a given database of organisms grows, the complexity of the search will also grow. Sequence composition methods are based on compositional features of a given DNA sequence like K-mers, or other genomic signature(s). Most of these current methods for binning have two major issues: they do not work well with short sequences and closely related genomes. In this paper we propose new machine learning related predictive DNA sequence feature selection algorithms to solve binning problems in more accurate and efficient ways. In this work we use Oligonucleotide frequencies from 2-mers to 4-mers as features to differentiate between sequences. 2-mers produces 16 features, 3-mers produces 64 features and 4-mers produces 256 features. We did not use feature higher than 4-mers as the number of feature increases exponentially and for 5-mers the number of feature would be 1024 features. We found out that the 4-mers produces better results than 2-mers and 3-mers. The data used in this work has an average length of 250, 500, 1000, and 2000 base pairs. Experimental results of the proposed algorithms are presented to show the potential value of the proposed methods. The proposed algorithm accuracy is tested on a variety of data sets and the classification/prediction accuracy achieved is between 78% - 99% for various simul

关键词： Bioinformatics Metagenomics Binning Next generation Sequencing pattern Classification machine learning Random forest Bagged decision tree Computational intelligence Reduction methods Forwaord sequential feature selection Ttest Metagenomics Artificial intelligence Computational intelligence pattern recognition Dataset Bioinformatics REDUCTION METHODS machine Learning

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