Zeszyty czasopisma

Tom 22 (2022): Zeszyt 3 (September 2022)

Tom 22 (2022): Zeszyt 2 (June 2022)

Tom 22 (2022): Zeszyt 1 (March 2022)

Tom 21 (2021): Zeszyt 4 (December 2021)

Tom 21 (2021): Zeszyt 3 (September 2021)

Tom 21 (2021): Zeszyt 2 (June 2021)

Tom 21 (2021): Zeszyt 1 (March 2021)

Tom 20 (2020): Zeszyt 6 (December 2020)
Special Zeszyt on New Developments in Scalable Computing

Tom 20 (2020): Zeszyt 5 (December 2020)
Special issue on Innovations in Intelligent Systems and Applications

Tom 20 (2020): Zeszyt 4 (November 2020)

Tom 20 (2020): Zeszyt 3 (September 2020)

Tom 20 (2020): Zeszyt 2 (June 2020)

Tom 20 (2020): Zeszyt 1 (March 2020)

Tom 19 (2019): Zeszyt 4 (November 2019)

Tom 19 (2019): Zeszyt 3 (September 2019)

Tom 19 (2019): Zeszyt 2 (June 2019)

Tom 19 (2019): Zeszyt 1 (March 2019)

Tom 18 (2018): Zeszyt 5 (May 2018)
Special Thematic Zeszyt on Optimal Codes and Related Topics

Tom 18 (2018): Zeszyt 4 (November 2018)

Tom 18 (2018): Zeszyt 3 (September 2018)

Tom 18 (2018): Zeszyt 2 (June 2018)

Tom 18 (2018): Zeszyt 1 (March 2018)

Tom 17 (2017): Zeszyt 5 (December 2017)
Special Zeszyt With Selected Papers From The Workshop “Two Years Avitohol: Advanced High Performance Computing Applications 2017

Tom 17 (2017): Zeszyt 4 (November 2017)

Tom 17 (2017): Zeszyt 3 (September 2017)

Tom 17 (2017): Zeszyt 2 (June 2017)

Tom 17 (2017): Zeszyt 1 (March 2017)

Tom 16 (2016): Zeszyt 6 (December 2016)
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

Tom 16 (2016): Zeszyt 5 (October 2016)
Zeszyt Title: Special Zeszyt on Application of Advanced Computing and Simulation in Information Systems

Tom 16 (2016): Zeszyt 4 (December 2016)

Tom 16 (2016): Zeszyt 3 (September 2016)

Tom 16 (2016): Zeszyt 2 (June 2016)

Tom 16 (2016): Zeszyt 1 (March 2016)

Tom 15 (2015): Zeszyt 7 (December 2015)
Special Zeszyt on Information Fusion

Tom 15 (2015): Zeszyt 6 (December 2015)
Special Zeszyt on Logistics, Informatics and Service Science

Tom 15 (2015): Zeszyt 5 (April 2015)
Special Zeszyt on Control in Transportation Systems

Tom 15 (2015): Zeszyt 4 (November 2015)

Tom 15 (2015): Zeszyt 3 (September 2015)

Tom 15 (2015): Zeszyt 2 (June 2015)

Tom 15 (2015): Zeszyt 1 (March 2015)

Tom 14 (2014): Zeszyt 5 (December 2014)
Special Zeszyt

Tom 14 (2014): Zeszyt 4 (December 2014)

Tom 14 (2014): Zeszyt 3 (September 2014)

Tom 14 (2014): Zeszyt 2 (June 2014)

Tom 14 (2014): Zeszyt 1 (March 2014)

Tom 13 (2013): Zeszyt Special-Zeszyt (December 2013)

Tom 13 (2013): Zeszyt 4 (December 2013)
The publishing of the present issue (Tom 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.

Tom 13 (2013): Zeszyt 3 (September 2013)

Tom 13 (2013): Zeszyt 2 (June 2013)

Tom 13 (2013): Zeszyt 1 (March 2013)

Tom 12 (2012): Zeszyt 4 (December 2012)

Tom 12 (2012): Zeszyt 3 (September 2012)

Tom 12 (2012): Zeszyt 2 (June 2012)

Tom 12 (2012): Zeszyt 1 (March 2012)

Informacje o czasopiśmie
Format
Czasopismo
eISSN
1314-4081
Pierwsze wydanie
13 Mar 2012
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

Tom 16 (2016): Zeszyt 6 (December 2016)
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

Informacje o czasopiśmie
Format
Czasopismo
eISSN
1314-4081
Pierwsze wydanie
13 Mar 2012
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

21 Artykułów
Otwarty dostęp

Preface

Data publikacji: 25 Jan 2017
Zakres stron: 3 - 4

Abstrakt

Otwarty dostęp

The Algebraic Operations and Their Implementation Based on a Two-Layer Cloud Data Model

Data publikacji: 25 Jan 2017
Zakres stron: 5 - 26

Abstrakt

Abstract

The existing cloud data models cannot meet the management requirements of structured data very well including a great deal of relational data, therefore a two-layer cloud data model is proposed. The composite object is defined to model the nested data in the representation layer, while a 4-tuple is defined to model the non-nested data in the storage layer. Referring the relational algebra, the concept of SNO (Simple Nested Object) is defined as basic operational unit of the algebraic operations; the formal definitions of the algebraic operations consisting of the set operations and the query operations on the representation layer are proposed. The algorithm of extracting all SNOs from a CAO (Component-Attribute-Object) set of a composite object is proposed firstly as the foundation, and then as the idea; the pseudo code implementation of algorithms of the algebraic operations on the storage layer are proposed. Logic proof and example proof indicate that the definition and the algorithms of the algebraic operations are correct.

Słowa kluczowe

  • Cloud database
  • data model
  • algebraic operation
  • key-value
  • structured data
Otwarty dostęp

Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

Data publikacji: 25 Jan 2017
Zakres stron: 27 - 42

Abstrakt

Abstract

Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

Słowa kluczowe

  • Data mining
  • high dimensional sparse data
  • simulated annealing
  • clustering validity
Otwarty dostęp

A Game Theory Based Model for Internet Public Opinion’s Embryonic Stage

Data publikacji: 25 Jan 2017
Zakres stron: 43 - 59

Abstrakt

Abstract

The development of internet public opinion presents a certain ecological characteristics. According to the different characteristics, this paper divides internet public opinion into five stages. This paper focuses on the first stage in internet public opinion, from the perspective of qualitative and quantitative analysis, to have a definition of embryonic stage, characteristics, as well as composition. Then this paper analyzes the causes of the formation mechanism in internet public opinion’s embryonic stage with the method of game theory. After that, the concepts of hot source factors are introduced to construct the conceptual model of embryonic stage in internet public opinion. Finally, aiming at different types of hot source factors (internet public opinion events), we put forward the effective means to guide and control the development of embryonic stage.

Słowa kluczowe

  • Internet public opinion
  • embryonic stage
  • game theory
  • formation mechanism
  • conceptual model
Otwarty dostęp

A Fast and Simple Adaptive Bionic Wavelet Transform: ECG Baseline Shift Correction

Data publikacji: 25 Jan 2017
Zakres stron: 60 - 68

Abstrakt

Abstract

An ECG baseline shift correction method is presented on the base of the adaptive bionic wavelet transform. After modifying the bionic wavelet transform according to the characteristics of the ECG signal, we propose a novel adaptive BWT algorithm. Using the contaminated and actual data in the MIT-BIH database, the method of fast and simple adaptive bionic wavelet transform can effectively correct the baseline shift under the premise of maintaining the geometric characteristics of the ECG signal. Evaluation of the proposed method shows that the average improvement SNR of FABWT is 2.187 dB more than the CWT-based best case result.

Słowa kluczowe

  • Baseline shift
  • Bionic Wavelet Transform (BWT)
  • fast algorithm
  • MIT-BIH
Otwarty dostęp

Chinese Text Auto-Categorization on Petro-Chemical Industrial Processes

Data publikacji: 25 Jan 2017
Zakres stron: 69 - 82

Abstrakt

Abstract

There is a huge growth in the amount of documents of corporations in recent years. With this paper we aim to improve classification performance and to support the effective management of massive technical material in the domain-specific field. Taking the field of petro-chemical process as a case, we study in detail the influence of parameters on classification accuracy when using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) Text auto-classification algorithm. Advantages and disadvantages of the two text classification algorithms are presented in the field of petro-chemical processes. Our tests also show that more attention to the professional vocabulary can significantly improve the F1 value of the two algorithms. These results have reference value for the future information classification in related industry fields.

Słowa kluczowe

  • Text classification
  • KNN
  • SVM
  • petro-chemical
  • field-specific knowledge
Otwarty dostęp

Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises

Data publikacji: 25 Jan 2017
Zakres stron: 83 - 97

Abstrakt

Abstract

Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce energy consumption. Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine considering the practical enterprise situation of massive and high-dimension data. The analysis of the real energy consumption data provided by LB Enterprise was undertaken and corresponding calculating experiments were completed using a 32-node cloud computing cluster. The experimental results show that the energy consumption forecasting accuracy of the proposed model is higher than the traditional support vector regression and the generalized neural network algorithm. Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer’s insufficient computing power when facing massive and high-dimensional data without increasing the cost.

Słowa kluczowe

  • Energy consumption forecasting
  • cloud computing
  • online sequential optimization
  • extreme learning machine
  • equipment-manufacturing enterprises
Otwarty dostęp

Determination of the Starting Point in Time Series for Trend Detection Based on Overlapping Trend

Data publikacji: 25 Jan 2017
Zakres stron: 98 - 110

Abstrakt

Abstract

The traditional time series studies consider the time series as a whole while carrying on the trend detection; therefore not enough attention is paid to the stage characteristic. On the other hand, the piecewise linear fitting type methods for trend detection are lacking consideration of the possibility that the same node belongs to multiple trends. The above two methods are affected by the start position of the sequence. In this paper, the concept of overlapping trend is proposed, and the definition of milestone nodes is given on its base; these way not only the recognition of overlapping trend is realized, but also the negative influence of the starting point of sequence is effectively reduced. The experimental results show that the computational accuracy is not affected by the improved algorithm and the time cost is greatly reduced when dealing with the processing tasks on dynamic growing data sequence.

Słowa kluczowe

  • Overlapping trend
  • milestone nodes
  • trend detection
  • calculate cost
Otwarty dostęp

Improved Chirp Scaling Algorithm for Processing Squinted Mode Synthetic Aperture Sonar Data

Data publikacji: 25 Jan 2017
Zakres stron: 111 - 122

Abstrakt

Abstract

The conventional Chirp Scaling Algorithm is mainly used in side looking or small squint angle mode of synthetic aperture imaging. In application of synthetic sonar, the large squint angle imaging is often required and the range-azimuth coupling is serious. On the basis of studying the principle of the conventional Chirp Scaling Algorithm, we improved the imaging algorithm in large squint mode. We analysed the structure of the echo frequency spectrum and the compensation of phase factor. The improved imaging algorithm designs a more precision of phase compensation factor, and eliminates the high order degree of range and azimuth coupling in the specific mapping band. Any target point is simulated in imaging region by using the improved algorithm. The simulation conclusion showed that the improved Chirp Scaling Algorithm is able to meet the imaging focus and is more suitable to slant imaging as compared with the traditional algorithm.

Słowa kluczowe

  • Synthetic aperture sonar
  • Chirp Scaling Algorithm
  • range-azimuth coupling
  • secondary range compression
  • Taylor series
  • Fresnel assumption
Otwarty dostęp

Low Cost Locating Method of Wireless Sensor Network in Precision Agriculture

Data publikacji: 25 Jan 2017
Zakres stron: 123 - 132

Abstrakt

Abstract

The wireless sensor network covers more scale with more sensor nodes for larger scale agriculture. The article describes improvement of DV-Hop Algorithm to locate the nodes with quadrilateral range positioning method, so that the difficulty of dilatation method in agriculture actual application to be solved. The analog test for the algorithm is conducted and is mainly developed for the average locating error with illustration and discussion on the proportion relations of average error, average connectivity and anchor nodes. According to the analog results, the algorithm obtains better effect on the average locating error, which improves the accuracy of the algorithm.

Słowa kluczowe

  • Wireless sensor network
  • agriculture
  • low cost location
  • DV-Hop
Otwarty dostęp

Head Pose Estimation Based on Robust Convolutional Neural Network

Data publikacji: 25 Jan 2017
Zakres stron: 133 - 145

Abstrakt

Abstract

Head pose estimation plays an important role in face recognition. However, it faces vast challenges on account of the initialization, facial feature points’ location accuracy and so on. Inspired by the observation that head pose angles change smoothly and continuously, we present a method based on a robust convolutional neural network for head pose estimation. The proposed network architecture consists of three levels and each level has three convolutional neural networks. The first level is a global one; it predicts the head pose quickly as a preliminary estimation. The following two levels are local ones; they refine the estimation achieved from the previous level step by step. Higher and higher resolution image with different input regions are taken as input in our network. At last, a multi-level regression is employed to combine the estimations from each level. The whole process is conducted in a cascade way to improve the head pose estimation performance directly with three angles together. We perform large experiments on nine challenging benchmark datasets. The experimental results demonstrate that our method performs better than the compared methods.

Słowa kluczowe

  • Head pose estimation
  • convolutional neural network
  • cascade network
  • multi-level regression
  • deep learning
Otwarty dostęp

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Data publikacji: 25 Jan 2017
Zakres stron: 146 - 159

Abstrakt

Abstract

With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic and industry. Aiming at the problem of low accuracy in personalized tourism recommendation system, this paper presents a personalized algorithm for tourist attraction recommendation – RecUFG Algorithm, which combines user collaborative filtering technology with friends trust relationships and geographic context. This algorithm fully exploits social relations and trust friendship between users, and by means of the geographic information between user and attraction location, recommends users most interesting attractions. Experimental results on real data sets demonstrate the feasibility and effectiveness of the algorithm. Compared with the existing recommendation algorithm, it has a higher prediction accuracy and customer satisfaction.

Słowa kluczowe

  • Location services
  • social network
  • personalized recommendation
  • user context
  • social computing
Otwarty dostęp

Generation Method and Application of Product-Oriented Medial Axis

Data publikacji: 25 Jan 2017
Zakres stron: 160 - 174

Abstrakt

Abstract

In this paper, the generation method of the medial axis in the arbitrary quadrilateral surface is proposed. It can provide a solution for the simplification of the complex fillet feature and the generation of the mesh in the model. By using the locus method associated with moving Frenet frame, we realize the simple and fast algorithm for generating the medial axis. As for the engineering problem, B-rep 3D solid models with clear boundary definition are mostly applied; the information of vertex, side and surface of the model, which is clearly stored in the model file, can be used to simplify the traditional locus method for generating the medial axis, in order to reduce the amount of data required by the generation. In this paper, we use the clear boundary information in the B-rep model as the condition for generating the medial axis and the characteristics of the bisector to eliminate the calculation of the branch points, reducing the factors affecting the accuracy of the medial axis. In order to ensure the accuracy of the medial axis, the density of the insertion points can be used for control.

Słowa kluczowe

  • The medial axis
  • moving Frenet frame
  • mesh generation
  • the model feature simplification
Otwarty dostęp

Structural Robustness of Unidirectional Dependent Networks Based on Attack Strategies

Data publikacji: 25 Jan 2017
Zakres stron: 175 - 184

Abstrakt

Abstract

Current works have been focused on the robustness of single network and interdependent networks. However, to be more correct, the dependence of many real systems should be described as unidirectional. To study the structural robustness of networks with unidirectional dependence, the dependent networks named UDN are proposed, the description of the propagation of failures in them is given, as well as the introduction of the attack strategies that the probability of a node being attacked depends on the degree (DP attack) or on the betweenness (BP attack) of this node. The simulated results show that UDN is more vulnerable to BP attack when is first attacked a node with high betweenness. Compared with the Interacting Networks (IN), the UDN is more fragile under the two attack’s strategies.

Słowa kluczowe

  • Structural robustness
  • two-layer networks
  • cascading failure
Otwarty dostęp

Indirect Detection Method of Rotor Position Based on DE-SVM

Data publikacji: 25 Jan 2017
Zakres stron: 185 - 193

Abstrakt

Abstract

In view of the defects and deficiencies of existing detection methods of rotor position for Switched Reluctance Motor (SRM), an indirect Detection Method (DM) based on DE-SVM for Support Vector Machine (SVM) rotor position is proposed. This method uses the three-phase current and flux linkage within the full angle domain of SRM as input and rotor position angle as output, and utilizes the strong nonlinear mapping capability of SVM to create a predication model for these three parameters offline. The strong global optimization capability of Differential Evolution (DE) Algorithm is then employed based on the deviation between actual rotor position and model output to optimize the prediction model online, thereby realizing sensorless detection of SRM rotor position. The simulation result shows that this method can accurately predict the position of SRM rotor.

Słowa kluczowe

  • SRM
  • rotor position detection
  • SVM
  • DE Algorithm
Otwarty dostęp

Multiple Manifolds Clustering via Local Linear Analysis

Data publikacji: 25 Jan 2017
Zakres stron: 194 - 206

Abstrakt

Abstract

Clustering on multiple manifolds serves as an analysis of the data lying on multiple manifolds. The smoothness and local linearity of data samples are utilized to define the local linear degree which is motivated by Principal Component Analysis (PCA) and Depth First Search (DFS). Then, Multiple Manifolds Clustering (LMMC) is proposed on the base of the Local Linear Analysis (LLA) via this definition and neighbor-growing algorithm, which are especially effective under the condition of interactions. Instead of addressing problems of complex optimization and K-means operation, LMMC is simple and efficient compared with traditional manifold clustering. The algorithm can achieve superior performance on complex subspace and manifolds clustering datasets. Meanwhile, comparative experiments are given to show the effectiveness and efficiency of this algorithm.

Słowa kluczowe

  • Manifolds learning
  • clustering algorithm
  • PCA
  • DFS
  • neighborhood
Otwarty dostęp

Depth Data Reconstruction Based on Gaussian Mixture Model

Data publikacji: 25 Jan 2017
Zakres stron: 207 - 219

Abstrakt

Abstract

Depth data is an effective tool to locate the intelligent agent in space because it accurately records the 3D geometry information on the surface of the scanned object, and is not affected by factors like shadow and light. However, if there are many planes in the work scene, it is difficult to identify objects and process the resulting huge amount of data. In view of this problem and targeted at object calibration, this paper puts forward a depth data calibration method based on Gauss mixture model. The method converts the depth data to point cloud, filters the noise and collects samples, which effectively reduces the computational load in the following steps. Besides, the authors cluster the point cloud vector with the Gaussian mixture model, and obtain the target and background planes by using the random sampling consensus algorithm to fit the planes. The combination of target Region Of Intelligent agent (ROI) and point cloud significantly reduces the computational load and improves the computing speed. The effect and accuracy of the algorithm is verified by the test of the actual object.

Słowa kluczowe

  • Depth data
  • point cloud
  • normal vector clustering
  • Gaussian mixture model
  • random sampling consensus algorithm
  • object calibration
  • CAMShift
Otwarty dostęp

Analysis of Various ESDD of Contaminant Insulator Flashover Acoustic Signal by Wavelet Packet

Data publikacji: 25 Jan 2017
Zakres stron: 220 - 231

Abstrakt

Abstract

With the formation of China’s large power grid, the security of the network is particularly important. The contaminant flashover of insulators has a serious impact on the operation safety of a high voltage power network. In this paper, the acoustic signals’ characteristics of the contaminant insulators flashover are analyzed, and, as a result, the correlation between the acoustic signal and the contaminant insulator flashover is established. To experiment with contaminant insulator for three different Equivalent Salt Deposit Densities (ESDD), acoustic signals were collected separately. Then, the contaminant insulators’ acoustic signals of flashover were analyzed by wavelet packet. The characteristics of the signals were obtained, and they can be judged for contaminant flashover warning.

Słowa kluczowe

  • Contaminant insulator flashover
  • acoustic signal
  • Equivalent Salt Deposit Density (ESDD)
  • wavelet packet
Otwarty dostęp

An Internal Clustering Validation Index for Boolean Data

Data publikacji: 25 Jan 2017
Zakres stron: 232 - 244

Abstrakt

Abstract

Internal clustering validation is recognized as one of the vital issues essential to clustering applications, especially when external information is not available. Existing measures have their limitations in different application circumstances. There are still some deficiencies for Internal Validation of Boolean clustering. This paper proposes a new Clustering Validation index based on Type of Attributes for Boolean data (CVTAB). It evaluates the clustering quality in the light of Dissimilarity of two clusters for Boolean Data (DBD). The attributes in the Boolean Data are categorized into three types: Type A, Type O and Type E representing respectively the attribute values 1,0 and not the same for all the objects in the set. When two clusters are composed into one, DBD applies the numbers of attributes with the types changed and the numbers of objects changed to measure dissimilarity of two clusters. CVTAB evaluates the clustering quality without respect to external information

Słowa kluczowe

  • Clustering Validation index based on Type of Attributes for Boolean data (CVTAB)
  • Dissimilarity for Boolean Data (DBD)
  • internal clustering validation index
  • Boolean data
  • high dimensional data
Otwarty dostęp

Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

Data publikacji: 25 Jan 2017
Zakres stron: 245 - 255

Abstrakt

Abstract

In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

Słowa kluczowe

  • Spark
  • recommendation system
  • collaborative filtering
  • alternating least squares
Otwarty dostęp

Realization of Humanoid Robot Playing Golf

Data publikacji: 25 Jan 2017
Zakres stron: 256 - 266

Abstrakt

Abstract

Aiming at the golf tournament technical requirements in International Humanoid robot Olympic Game (IHOG), two-freedom-degrees “head & eye” system based on monocular vision robot MF-AI is developed. The new robot equipped with the “head & eye” system can identify the location of the golf ball and the hole, and can measure the distance of the two objects. Experiments show that the two-freedom-degrees “head & eye” system improves the accuracy of hitting the golf ball into the hole. Moreover, the system can identify the target, can calibrate distances, can measure robot’s moving and look for the hole, can adjust the position of the robot and hit the ball effectively.

Słowa kluczowe

  • Humanoid robot
  • monocular vision
  • international humanoid robot
  • olympic game
21 Artykułów
Otwarty dostęp

Preface

Data publikacji: 25 Jan 2017
Zakres stron: 3 - 4

Abstrakt

Otwarty dostęp

The Algebraic Operations and Their Implementation Based on a Two-Layer Cloud Data Model

Data publikacji: 25 Jan 2017
Zakres stron: 5 - 26

Abstrakt

Abstract

The existing cloud data models cannot meet the management requirements of structured data very well including a great deal of relational data, therefore a two-layer cloud data model is proposed. The composite object is defined to model the nested data in the representation layer, while a 4-tuple is defined to model the non-nested data in the storage layer. Referring the relational algebra, the concept of SNO (Simple Nested Object) is defined as basic operational unit of the algebraic operations; the formal definitions of the algebraic operations consisting of the set operations and the query operations on the representation layer are proposed. The algorithm of extracting all SNOs from a CAO (Component-Attribute-Object) set of a composite object is proposed firstly as the foundation, and then as the idea; the pseudo code implementation of algorithms of the algebraic operations on the storage layer are proposed. Logic proof and example proof indicate that the definition and the algorithms of the algebraic operations are correct.

Słowa kluczowe

  • Cloud database
  • data model
  • algebraic operation
  • key-value
  • structured data
Otwarty dostęp

Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

Data publikacji: 25 Jan 2017
Zakres stron: 27 - 42

Abstrakt

Abstract

Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

Słowa kluczowe

  • Data mining
  • high dimensional sparse data
  • simulated annealing
  • clustering validity
Otwarty dostęp

A Game Theory Based Model for Internet Public Opinion’s Embryonic Stage

Data publikacji: 25 Jan 2017
Zakres stron: 43 - 59

Abstrakt

Abstract

The development of internet public opinion presents a certain ecological characteristics. According to the different characteristics, this paper divides internet public opinion into five stages. This paper focuses on the first stage in internet public opinion, from the perspective of qualitative and quantitative analysis, to have a definition of embryonic stage, characteristics, as well as composition. Then this paper analyzes the causes of the formation mechanism in internet public opinion’s embryonic stage with the method of game theory. After that, the concepts of hot source factors are introduced to construct the conceptual model of embryonic stage in internet public opinion. Finally, aiming at different types of hot source factors (internet public opinion events), we put forward the effective means to guide and control the development of embryonic stage.

Słowa kluczowe

  • Internet public opinion
  • embryonic stage
  • game theory
  • formation mechanism
  • conceptual model
Otwarty dostęp

A Fast and Simple Adaptive Bionic Wavelet Transform: ECG Baseline Shift Correction

Data publikacji: 25 Jan 2017
Zakres stron: 60 - 68

Abstrakt

Abstract

An ECG baseline shift correction method is presented on the base of the adaptive bionic wavelet transform. After modifying the bionic wavelet transform according to the characteristics of the ECG signal, we propose a novel adaptive BWT algorithm. Using the contaminated and actual data in the MIT-BIH database, the method of fast and simple adaptive bionic wavelet transform can effectively correct the baseline shift under the premise of maintaining the geometric characteristics of the ECG signal. Evaluation of the proposed method shows that the average improvement SNR of FABWT is 2.187 dB more than the CWT-based best case result.

Słowa kluczowe

  • Baseline shift
  • Bionic Wavelet Transform (BWT)
  • fast algorithm
  • MIT-BIH
Otwarty dostęp

Chinese Text Auto-Categorization on Petro-Chemical Industrial Processes

Data publikacji: 25 Jan 2017
Zakres stron: 69 - 82

Abstrakt

Abstract

There is a huge growth in the amount of documents of corporations in recent years. With this paper we aim to improve classification performance and to support the effective management of massive technical material in the domain-specific field. Taking the field of petro-chemical process as a case, we study in detail the influence of parameters on classification accuracy when using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) Text auto-classification algorithm. Advantages and disadvantages of the two text classification algorithms are presented in the field of petro-chemical processes. Our tests also show that more attention to the professional vocabulary can significantly improve the F1 value of the two algorithms. These results have reference value for the future information classification in related industry fields.

Słowa kluczowe

  • Text classification
  • KNN
  • SVM
  • petro-chemical
  • field-specific knowledge
Otwarty dostęp

Cloud Computing and Extreme Learning Machine for a Distributed Energy Consumption Forecasting in Equipment-Manufacturing Enterprises

Data publikacji: 25 Jan 2017
Zakres stron: 83 - 97

Abstrakt

Abstract

Energy consumption forecasting is a kind of fundamental work of the energy management in equipment-manufacturing enterprises, and an important way to reduce energy consumption. Therefore, this paper proposes an intellectualized, short-term distributed energy consumption forecasting model for equipment-manufacturing enterprises based on cloud computing and extreme learning machine considering the practical enterprise situation of massive and high-dimension data. The analysis of the real energy consumption data provided by LB Enterprise was undertaken and corresponding calculating experiments were completed using a 32-node cloud computing cluster. The experimental results show that the energy consumption forecasting accuracy of the proposed model is higher than the traditional support vector regression and the generalized neural network algorithm. Furthermore, the proposed forecasting algorithm possesses excellent parallel performance, overcomes the shortcoming of a single computer’s insufficient computing power when facing massive and high-dimensional data without increasing the cost.

Słowa kluczowe

  • Energy consumption forecasting
  • cloud computing
  • online sequential optimization
  • extreme learning machine
  • equipment-manufacturing enterprises
Otwarty dostęp

Determination of the Starting Point in Time Series for Trend Detection Based on Overlapping Trend

Data publikacji: 25 Jan 2017
Zakres stron: 98 - 110

Abstrakt

Abstract

The traditional time series studies consider the time series as a whole while carrying on the trend detection; therefore not enough attention is paid to the stage characteristic. On the other hand, the piecewise linear fitting type methods for trend detection are lacking consideration of the possibility that the same node belongs to multiple trends. The above two methods are affected by the start position of the sequence. In this paper, the concept of overlapping trend is proposed, and the definition of milestone nodes is given on its base; these way not only the recognition of overlapping trend is realized, but also the negative influence of the starting point of sequence is effectively reduced. The experimental results show that the computational accuracy is not affected by the improved algorithm and the time cost is greatly reduced when dealing with the processing tasks on dynamic growing data sequence.

Słowa kluczowe

  • Overlapping trend
  • milestone nodes
  • trend detection
  • calculate cost
Otwarty dostęp

Improved Chirp Scaling Algorithm for Processing Squinted Mode Synthetic Aperture Sonar Data

Data publikacji: 25 Jan 2017
Zakres stron: 111 - 122

Abstrakt

Abstract

The conventional Chirp Scaling Algorithm is mainly used in side looking or small squint angle mode of synthetic aperture imaging. In application of synthetic sonar, the large squint angle imaging is often required and the range-azimuth coupling is serious. On the basis of studying the principle of the conventional Chirp Scaling Algorithm, we improved the imaging algorithm in large squint mode. We analysed the structure of the echo frequency spectrum and the compensation of phase factor. The improved imaging algorithm designs a more precision of phase compensation factor, and eliminates the high order degree of range and azimuth coupling in the specific mapping band. Any target point is simulated in imaging region by using the improved algorithm. The simulation conclusion showed that the improved Chirp Scaling Algorithm is able to meet the imaging focus and is more suitable to slant imaging as compared with the traditional algorithm.

Słowa kluczowe

  • Synthetic aperture sonar
  • Chirp Scaling Algorithm
  • range-azimuth coupling
  • secondary range compression
  • Taylor series
  • Fresnel assumption
Otwarty dostęp

Low Cost Locating Method of Wireless Sensor Network in Precision Agriculture

Data publikacji: 25 Jan 2017
Zakres stron: 123 - 132

Abstrakt

Abstract

The wireless sensor network covers more scale with more sensor nodes for larger scale agriculture. The article describes improvement of DV-Hop Algorithm to locate the nodes with quadrilateral range positioning method, so that the difficulty of dilatation method in agriculture actual application to be solved. The analog test for the algorithm is conducted and is mainly developed for the average locating error with illustration and discussion on the proportion relations of average error, average connectivity and anchor nodes. According to the analog results, the algorithm obtains better effect on the average locating error, which improves the accuracy of the algorithm.

Słowa kluczowe

  • Wireless sensor network
  • agriculture
  • low cost location
  • DV-Hop
Otwarty dostęp

Head Pose Estimation Based on Robust Convolutional Neural Network

Data publikacji: 25 Jan 2017
Zakres stron: 133 - 145

Abstrakt

Abstract

Head pose estimation plays an important role in face recognition. However, it faces vast challenges on account of the initialization, facial feature points’ location accuracy and so on. Inspired by the observation that head pose angles change smoothly and continuously, we present a method based on a robust convolutional neural network for head pose estimation. The proposed network architecture consists of three levels and each level has three convolutional neural networks. The first level is a global one; it predicts the head pose quickly as a preliminary estimation. The following two levels are local ones; they refine the estimation achieved from the previous level step by step. Higher and higher resolution image with different input regions are taken as input in our network. At last, a multi-level regression is employed to combine the estimations from each level. The whole process is conducted in a cascade way to improve the head pose estimation performance directly with three angles together. We perform large experiments on nine challenging benchmark datasets. The experimental results demonstrate that our method performs better than the compared methods.

Słowa kluczowe

  • Head pose estimation
  • convolutional neural network
  • cascade network
  • multi-level regression
  • deep learning
Otwarty dostęp

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Data publikacji: 25 Jan 2017
Zakres stron: 146 - 159

Abstrakt

Abstract

With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic and industry. Aiming at the problem of low accuracy in personalized tourism recommendation system, this paper presents a personalized algorithm for tourist attraction recommendation – RecUFG Algorithm, which combines user collaborative filtering technology with friends trust relationships and geographic context. This algorithm fully exploits social relations and trust friendship between users, and by means of the geographic information between user and attraction location, recommends users most interesting attractions. Experimental results on real data sets demonstrate the feasibility and effectiveness of the algorithm. Compared with the existing recommendation algorithm, it has a higher prediction accuracy and customer satisfaction.

Słowa kluczowe

  • Location services
  • social network
  • personalized recommendation
  • user context
  • social computing
Otwarty dostęp

Generation Method and Application of Product-Oriented Medial Axis

Data publikacji: 25 Jan 2017
Zakres stron: 160 - 174

Abstrakt

Abstract

In this paper, the generation method of the medial axis in the arbitrary quadrilateral surface is proposed. It can provide a solution for the simplification of the complex fillet feature and the generation of the mesh in the model. By using the locus method associated with moving Frenet frame, we realize the simple and fast algorithm for generating the medial axis. As for the engineering problem, B-rep 3D solid models with clear boundary definition are mostly applied; the information of vertex, side and surface of the model, which is clearly stored in the model file, can be used to simplify the traditional locus method for generating the medial axis, in order to reduce the amount of data required by the generation. In this paper, we use the clear boundary information in the B-rep model as the condition for generating the medial axis and the characteristics of the bisector to eliminate the calculation of the branch points, reducing the factors affecting the accuracy of the medial axis. In order to ensure the accuracy of the medial axis, the density of the insertion points can be used for control.

Słowa kluczowe

  • The medial axis
  • moving Frenet frame
  • mesh generation
  • the model feature simplification
Otwarty dostęp

Structural Robustness of Unidirectional Dependent Networks Based on Attack Strategies

Data publikacji: 25 Jan 2017
Zakres stron: 175 - 184

Abstrakt

Abstract

Current works have been focused on the robustness of single network and interdependent networks. However, to be more correct, the dependence of many real systems should be described as unidirectional. To study the structural robustness of networks with unidirectional dependence, the dependent networks named UDN are proposed, the description of the propagation of failures in them is given, as well as the introduction of the attack strategies that the probability of a node being attacked depends on the degree (DP attack) or on the betweenness (BP attack) of this node. The simulated results show that UDN is more vulnerable to BP attack when is first attacked a node with high betweenness. Compared with the Interacting Networks (IN), the UDN is more fragile under the two attack’s strategies.

Słowa kluczowe

  • Structural robustness
  • two-layer networks
  • cascading failure
Otwarty dostęp

Indirect Detection Method of Rotor Position Based on DE-SVM

Data publikacji: 25 Jan 2017
Zakres stron: 185 - 193

Abstrakt

Abstract

In view of the defects and deficiencies of existing detection methods of rotor position for Switched Reluctance Motor (SRM), an indirect Detection Method (DM) based on DE-SVM for Support Vector Machine (SVM) rotor position is proposed. This method uses the three-phase current and flux linkage within the full angle domain of SRM as input and rotor position angle as output, and utilizes the strong nonlinear mapping capability of SVM to create a predication model for these three parameters offline. The strong global optimization capability of Differential Evolution (DE) Algorithm is then employed based on the deviation between actual rotor position and model output to optimize the prediction model online, thereby realizing sensorless detection of SRM rotor position. The simulation result shows that this method can accurately predict the position of SRM rotor.

Słowa kluczowe

  • SRM
  • rotor position detection
  • SVM
  • DE Algorithm
Otwarty dostęp

Multiple Manifolds Clustering via Local Linear Analysis

Data publikacji: 25 Jan 2017
Zakres stron: 194 - 206

Abstrakt

Abstract

Clustering on multiple manifolds serves as an analysis of the data lying on multiple manifolds. The smoothness and local linearity of data samples are utilized to define the local linear degree which is motivated by Principal Component Analysis (PCA) and Depth First Search (DFS). Then, Multiple Manifolds Clustering (LMMC) is proposed on the base of the Local Linear Analysis (LLA) via this definition and neighbor-growing algorithm, which are especially effective under the condition of interactions. Instead of addressing problems of complex optimization and K-means operation, LMMC is simple and efficient compared with traditional manifold clustering. The algorithm can achieve superior performance on complex subspace and manifolds clustering datasets. Meanwhile, comparative experiments are given to show the effectiveness and efficiency of this algorithm.

Słowa kluczowe

  • Manifolds learning
  • clustering algorithm
  • PCA
  • DFS
  • neighborhood
Otwarty dostęp

Depth Data Reconstruction Based on Gaussian Mixture Model

Data publikacji: 25 Jan 2017
Zakres stron: 207 - 219

Abstrakt

Abstract

Depth data is an effective tool to locate the intelligent agent in space because it accurately records the 3D geometry information on the surface of the scanned object, and is not affected by factors like shadow and light. However, if there are many planes in the work scene, it is difficult to identify objects and process the resulting huge amount of data. In view of this problem and targeted at object calibration, this paper puts forward a depth data calibration method based on Gauss mixture model. The method converts the depth data to point cloud, filters the noise and collects samples, which effectively reduces the computational load in the following steps. Besides, the authors cluster the point cloud vector with the Gaussian mixture model, and obtain the target and background planes by using the random sampling consensus algorithm to fit the planes. The combination of target Region Of Intelligent agent (ROI) and point cloud significantly reduces the computational load and improves the computing speed. The effect and accuracy of the algorithm is verified by the test of the actual object.

Słowa kluczowe

  • Depth data
  • point cloud
  • normal vector clustering
  • Gaussian mixture model
  • random sampling consensus algorithm
  • object calibration
  • CAMShift
Otwarty dostęp

Analysis of Various ESDD of Contaminant Insulator Flashover Acoustic Signal by Wavelet Packet

Data publikacji: 25 Jan 2017
Zakres stron: 220 - 231

Abstrakt

Abstract

With the formation of China’s large power grid, the security of the network is particularly important. The contaminant flashover of insulators has a serious impact on the operation safety of a high voltage power network. In this paper, the acoustic signals’ characteristics of the contaminant insulators flashover are analyzed, and, as a result, the correlation between the acoustic signal and the contaminant insulator flashover is established. To experiment with contaminant insulator for three different Equivalent Salt Deposit Densities (ESDD), acoustic signals were collected separately. Then, the contaminant insulators’ acoustic signals of flashover were analyzed by wavelet packet. The characteristics of the signals were obtained, and they can be judged for contaminant flashover warning.

Słowa kluczowe

  • Contaminant insulator flashover
  • acoustic signal
  • Equivalent Salt Deposit Density (ESDD)
  • wavelet packet
Otwarty dostęp

An Internal Clustering Validation Index for Boolean Data

Data publikacji: 25 Jan 2017
Zakres stron: 232 - 244

Abstrakt

Abstract

Internal clustering validation is recognized as one of the vital issues essential to clustering applications, especially when external information is not available. Existing measures have their limitations in different application circumstances. There are still some deficiencies for Internal Validation of Boolean clustering. This paper proposes a new Clustering Validation index based on Type of Attributes for Boolean data (CVTAB). It evaluates the clustering quality in the light of Dissimilarity of two clusters for Boolean Data (DBD). The attributes in the Boolean Data are categorized into three types: Type A, Type O and Type E representing respectively the attribute values 1,0 and not the same for all the objects in the set. When two clusters are composed into one, DBD applies the numbers of attributes with the types changed and the numbers of objects changed to measure dissimilarity of two clusters. CVTAB evaluates the clustering quality without respect to external information

Słowa kluczowe

  • Clustering Validation index based on Type of Attributes for Boolean data (CVTAB)
  • Dissimilarity for Boolean Data (DBD)
  • internal clustering validation index
  • Boolean data
  • high dimensional data
Otwarty dostęp

Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

Data publikacji: 25 Jan 2017
Zakres stron: 245 - 255

Abstrakt

Abstract

In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

Słowa kluczowe

  • Spark
  • recommendation system
  • collaborative filtering
  • alternating least squares
Otwarty dostęp

Realization of Humanoid Robot Playing Golf

Data publikacji: 25 Jan 2017
Zakres stron: 256 - 266

Abstrakt

Abstract

Aiming at the golf tournament technical requirements in International Humanoid robot Olympic Game (IHOG), two-freedom-degrees “head & eye” system based on monocular vision robot MF-AI is developed. The new robot equipped with the “head & eye” system can identify the location of the golf ball and the hole, and can measure the distance of the two objects. Experiments show that the two-freedom-degrees “head & eye” system improves the accuracy of hitting the golf ball into the hole. Moreover, the system can identify the target, can calibrate distances, can measure robot’s moving and look for the hole, can adjust the position of the robot and hit the ball effectively.

Słowa kluczowe

  • Humanoid robot
  • monocular vision
  • international humanoid robot
  • olympic game

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