Earticle

As the modeling of the architectural system is difficult and inaccurate, the 3D modeling of the light in the architectural system is studied by using Java3D technology. Using java3D perspective projection technique, the observation coordinate system is determined, and real-time viewpoint setting as well as roaming processing is realized. Through multiple view technique, we can observe the movement of building bodies from multiple aspects to obtain vivid simulation effects of light' behavior in the architectural system. Simulation results show that the virtual simulation platform developed based on java3D can improve the accuracy and naturalness of architectural system simulation.

Text mining is a specific method to extract knowledge from structured and unstructured data. This extracted knowledge from text mining process can be used for further usage and discovery. This paper presents the method for extraction information from unstructured text data and the importance of Association Rules Mining, specifically for of Korean language (text) and also, NLP (Natural Language Processing) tools are explained. Association Rules Mining (ARM) can also be used for mining association between itemsets from unstructured data with some modifications. Which can then, help for generating statistical thesaurus, to mine grammatical rules and to search large data efficiently. Although various association rules mining techniques have successfully used for market basket analysis but very few has applied on Korean text. A proposed Korean language mining method calculates and extracts meaningful patterns (association rules) between words and presents the hidden knowledge. First it cleans and integrates data, select relevant data then transform into transactional database. Then data mining techniques are used on data source to extract hidden patterns. These patterns are evaluated by specific rules until we get the valid and satisfactory result. We have tested on Korean news corpus and results have shown that it has worked well, and the results were adequate enough to research further.

Computer music, as an important product of the information and digital age, belongs to the systems engineering combining the musical discipline with computer information control technology science. Through the applied research on digitalized acoustical signals, characteristic analysis of computer music, computer MIDI production technology, digital audio technology and computer music production software, this paper builds up a set of computer music production technology system based on a digital, informational and modernized background, and proves by experiments that this system makes the computer music production technology represent effects of simple and convenient usage, high-degree simulation and efficient extraction.

The first step of accessing computer systems is typing in a user ID and a password. The user management system of the computer system then authenticates the user. Only after authentication is the user allowed to access the computer system. In addition to authentication, authorization is also performed by user management systems. For Internet TV systems, authorization is extremely important because Internet TV channels and videos should only be accessed by authorized users. Furthermore, user management systems record who accessed which TV channels and videos when and how long. This information is used by the billing system. Therefore, we develop a user management system for Internet TV systems. Our u

With the development of multimedia technology and the appearance of information highway, the storage and transmission of digital video has become a less difficult issue. Various sport games bring the explosive expanding of sport video, therefore, efficient analysis of sport video is necessary. An algorithm of detection and location for scoreboard is proposed. Firstly, the template of scoreboard is built using multi-dimensional correlation, it involves the location and the detection parameter. Then the scoreboard is located by the template matching.

Most of pedestrian inertial navigation system estimates displacement based on the integration of inertial sensors measurements. However, due to low-cost sensors and pedestrian dead reckoning inherent characteristics these systems provide huge location estimation errors. To suppress some of these limitations we propose a pedestrian inertial navigation system based on low-cost sensors and on information fusion and learning techniques. The proposed system introduces a step characterization module that characterizes the step according to the activity that the pedestrian is performing. This module performs three characterizations: terrain, direction and length. Thus, in this work are presented and evaluated several machine learning approaches that perform the terrain characterization. The inclusion of this machine learning module led to a significantly better performance of the pedestrian inertial navigation system.

Wireless sensor network is composed of a dense deployment of intelligent sensors in the monitoring area basis points in Ad Hoc network of application system. Based on improved grouping kind of energy efficient routing protocol is a kind of adaptive, distributed routing protocols. Basis points through the sensor and its deployment area to build mathematical model, deduce a given number of basis points improved grouping the computational expressions of the class. Numerical results obtained using the MATLAB software, and the calculation results are analyzed, it is concluded that with the increase of bp number x, the change of the improved grouping class k curve approach gently, as bp number x increases to a certain stage, the overall trend or bp number x increases, the improvement group class k increase, but in a local number x range basis points to increase the impact on the optimal cluster number k.

Customer churn analysis has become an important focus of corporate marketing. It will be a great help to profitability if there is a method can find losing customers in time. In the paper, a method based on RFM and Cross-correlation model is proposed. Firstly, the customer’s value is calculated by RFM. Secondly, the typical losing curves of customer value are matched via cross-correlation. And finally, integrated with social network analysis (SNA) and community detection, the group of potential losing customers are revealed. The effectiveness of the presented method has been proven in a dataset of retail sales records.

Many behaviors happen in information protection control, threatening from unauthorized change, destruction, and exposure to integrity, confidentiality, and availability of database, which is the final and core object of control. Like this it approaches database through numerous paths like many applications and home pages and execute query which search, modify, and delete the data. Some of it executes normal queries, but sometimes it maliciously executes the queries for leakage of information, and gives load to database server by executing the query which uses large amount of hardware resources. Traditionally it has limits, using only to find the reason for the problems, such as malignant queries, by collecting security log. Analyzing malignant queries and personal information leakage in diversified views through multidimensional analysis of data is necessary in order to use security log in more various ways. Therefore, this treatise is going to design multidimensional analysis modeling and suggest the technology to analyze in diversified views as an application plan of existing security log so that we can detect malignant queries and personal information leakage through security log analysis. We established the standard of analysis as follows for various analyses. First, we made linkage analysis available, which we cannot know with only simple history search, through analysis of database examination history. Second, we analyze if it repeatedly approached important table for a long time through detection of abnormal pattern or long term leakage via database abnormal access analysis. Third, we understood the flow of elements and data which weigh impact on specific database assets through database impact analysis and made analysis of database assets correlation and data flow analysis available. For analysis this treatise analyzed the log collected by using OLAP tools and used experiment data and operation data in order to verify the efficiency of database security log analysis technology suggested. Also we showed that the analysis method suggested by this treatise is excellent in availability and credibility in detection of malignant queries and personal information leakage, by comparing traditional data analysis method and the analysis method suggested by this treatise.

Delineating brain tumor boundaries from multi-modality magnetic resonance images (MRIs) is a crucial step in brain cancer surgical and treatment planning. In this paper, we propose a fully automatic technique for brain tumor segmentation from multi-modality human brain MRIs. We first use the intensities of different modalities in MRIs to represent the features of both normal and abnormal tissues. Then, the multiple classifier system (MCS) is applied to calculate the probabilities of brain tumor and normal brain tissue in the whole image. At last, the spatial-contextual information is proposed by constraining the classified neighbors to improve the classification accuracy. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results.

It is widely known that consumers' cognitive styles lead to making online shopping behavior react quite differently to various types of information on the online shopping websites. However, empirical results proving such premise from the perspective of eye-tracking are rare in literature. In this sense, this paper deals with two types of consumers’ cognitive styles- field-dependent and field-independent. Field-dependent users tend to follow other people's opinion, while field-independent users show maverick style of making decisions, trying not to be influenced by others. To add rigor to our empirical results, we analyzed users' eye-movements in response to information on the online shopping websites. Empirical results obtained from eye-tracking analysis revealed very robust and promising results. For a utilitarian product like notebook computer, the field-independent users showed higher TFD (Total Fixation Duration) and FC (Fixation Count) to product description information and product image information than the field-dependent users. For a hedonic product like perfume, field-independent users showed higher TFD and FC to product description and product image information than field-dependent users. As expected, field-dependent users showed higher TFD and FC to consumer review information than field-independent users. In this way, it is quite clear that the physiological information obtained from analysis of the eye-tracking data reveals more reliable and unique description of possible influences that consumers' cognitive styles may have on their online shopping behavior.

In the computer-aided medical diagnosis, the problem of missing attribute values in many medical data sets brings a great challenge to data mining. To solve the problem, this paper proposes a method based on density clustering and grey relational analysis. It provides an effective solution for missing medical data. The method uses the characteristic and degree of data samples dynamic relation and the existing attribute value information to impute the missing value, in order to alleviate the difficulty brought by missing data for aided medical diagnosis. By comparison with the experiment, the proposed method can effectively solve the classification problem which causes by missing medical attribute value, accurately predict the patient’s health and provide the help to doctor’s diagnosis.

To improve the accuracy of the state evaluation is the most important for the relay protection state maintenance. Therefore, a fuzzy synthetic evaluation of relay protection based on variable weight value is proposed in this paper. Pick up the major factor of the device and secondary circuit of relay protection to establish a reasonable index system of state evaluation. Then according to the variable weight comprehensive concept of factor space theory, we introduce the balance function to adjust the weight coefficient of each factor. Final, get the membership function in triangle trapezoid distribution to show the condition information, and select synthetic operator, then an evaluation system of relay protection with objectively and accurately is proposed. Running examples show that after dealing with the variable weight, some factors weight value become larger or smaller, so running state of evaluate factor may be changed, make it close to the real running state.

This paper addresses how we can build a sustainable and profitable E-learning application for language learners. Most of applications in this kind have been developed authors’ viewpoint. They usually neglect what users really want. To be an application sustainable and profitable, it should satisfy users’ need. This naturally induces that applications should provide what users want according to their preferences. But existing applications are not capable of satisfying each user’s preferences. For an alternative, this paper suggests a split interface-contents structure for applications for language learning. By splitting like this, it is possible to open a possibility of supplying required new contents continuously to users through the interface. This structure also gives a clue for a revenue model. In this new model, consumption is taken place continuously whenever users consume contents provided by the applications.

In order to better establish no-reference video quality assessment model considering the network packet loss and further gain a better QoE evaluation to meet the needs of the user’s, so we build NS2+ MyEvalvid simulation platform to study the scale characteristic of the network packet loss, scale characteristic of packet loss through the influence of packet loss rate to influence QoE. The experimental results show that, packet loss processes has long-range dependence, the number of superimposed source N, shape parameter, Hurst parameter, the output link speed have impacts on long-range dependence. We came to the conclusion that when superimposed source N is more, shape parameter is smaller, Hurst parameter is bigger, the output link speed is smaller, packet loss’s long range dependence is larger, packet loss rate is high.

Lightweight cryptography is equipped as security component, to secure those pervasive devices that are security and privacy sensitive. It has been conclusively proven that unprotected cryptographic implementations are vulnerable to side-channel attacks. In practice, area resource smaller than 3,000GE (5,000GE sometimes) may be available for security components in pervasive devices. This paper presents an ultra-lightweight first-order side-channel resistant crypto of KLEIN, which is a new family of lightweight block cipher that has advantages in both of software and hardware performances. The serial implementation of masked KLEIN requires 2102GE, while parallel implementation requires 4451GE, which are suitable for resource-constrained pervasive devices. Experimental results show that it is secure under First-order Power Analysis Attack, but still vulnerable to High-order Side-channel Attacks, with an exponential increase of the SCA data complexity.

In this paper, a new method for spatial features based image retrieval is proposed. These are the features specifying location of an image segment in an image and relative spatial location between two image segments etc. Spatial feature adds extra capability of the system in addition to texture, color and shape based object recognition system. Heuristic and semantic features of the image segments are identified from low level color, texture descriptors and directly stored into the image database and easily retrieved also. Position of image segments in the image is detected and stored in the image database that will be accessed for retrieval process later. Object ontology is used to determine the spatial relation between heuristics. These spatial relations and corresponding concept are identified and stored. Spatial rank between two segments will be calculated and stored along with segment ids. Spatial rank is calculated by a new technique. Proper concepts are associated with image segment based on ontology based spatial relation.

In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper. In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm. In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here. The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.

With the recent advancements and developments of various media applying new computing technology, smartphone application contents are recognized as new educational media that can develop learners’ space perception ability. Spatial perception ability (SPA) is a popular research subject that is being actively being studied among earth science education scholars using augmented reality. The purpose of this study is to investigate how an astronomy education program using smartphone applications improves 6th-grade students’ space perception ability. Results suggest that the astronomy education program using smartphone applications effectively increased spatial perception and students’ scientific attitudes. Through the results of the research, it was possible to know that the creative experiences using the smartphone applications was positively influential to the enhancement of the spatial perception ability and scientific attitude and problem solving ability of sixty 6th grade elementary school students.

Counterfeiting and forging currencies is a serious threat to any economy. Even though currency exists as a variation of coins, banknotes, and electronic data, many economies remain threatened by counterfeiting which is made possible by the ongoing technological advancements in reprographic equipment available to the general public. Clearly, counterfeit currency detection is not a task that can be neglected. Digital image processing is one of the most common and effective techniques used to distinguish counterfeit banknotes from genuine ones. A new approach is presented in this paper using the bit-plane slicing technique to extract the most significant data from counterfeit banknote images with the application of an edge detector algorithm. The proposed technique consists of decomposing original images of 256 gray levels into their equivalent 8 binary images. This is useful in analyzing the relative importance contributed by each bit of the original image. Higher order bit levels are evaluated for grayscale banknote images with the application of Canny edge detection algorithm. The results are then compared with genuine banknotes and with other existing techniques used for detecting counterfeit notes. Unlike existing research, it was observed that the edges obtained using bit-plane sliced images are more accurate and can be detected faster than obtaining them from the original image without being sliced. The detection of counterfeit currency was also achieved by following the process of using Canny edge detection, image segmentation, and feature extraction.

A novel Real-world trajectory based tactic analysis method is proposed, which can automatically analyze the offensive tactics of soccer game from the perspective of professionals. First, a real-world trajectory extraction method is proposed, and then an offensive pattern recognition method is described based on the definition of the ball states. Our experiments on user study by soccer professionals demonstrate that the defined offensive patterns can be used to analyze soccer tactics effectively in terms of conciseness, clarity and usability.

Feature selection algorithm plays a crucial role in intrusion detection, data mining and pattern recognition. According to some evaluation criteria, it gets optimal feature subset by deleting unrelated and redundant features of the original data set. Aiming at solving the problems about the low accuracy, the high false positive rate and the long detection time of the existing feature selection algorithm. In this paper, we come up with a feature selection algorithm towards efficient intrusion detection, this algorithm combines the correlation algorithm and redundancy algorithm to chooses the optimal feature subset. Experimental results show that the algorithm shows almost and even better than the traditional feature selection algorithm on the different classifiers.

In particle filter framework, there are many parameters need to be set to have the object tracking work best. On the other hand, the success of an object tracking is also determined by the characteristics of the video being used. Different videos might require different values of parameters to obtain best performance. This paper investigates thoroughly the effect of parameters in object tracking within particle filter framework for videos with different characteristics. We also propose a new method in selecting particle number and matrix value that used in propagation model. This work finds best parameters for video with particular characteristics and contributes in providing guideline of parameter setting in particle filter-based object tracking.

A novel image restoration method based on homology continuity is proposed in this paper. We view images as a collection of gray scale points, taking advantage of the homology continuity principle to combine each point and its fuzzy point derived by drop mass function to constitute the path direction and obtain distinct restore points. The gather of all the restore points is the sharply focused image of the original image. At last, we found every picture by iteration method is clearer than the last by experiment with the method proposed in the paper. The result verifies its feasibility.

In this paper, we introduce a local storage system using Xrootd in which the heterogeneous storage types are combined into a single access point that help clients access to the storage system efficiently without knowing complex architecture underneath. This system is expanding into global storage union federating the storage systems of the WLCG. The WLCG, a collaboration of computing centers to deal with large scale of data produced by the detectors using the LHC at CERN, has distributed storage resources with different protocols and management systems. The heterogeneity of the distributed storage resources prevents efficient access to data and transfer activities between computing centers. The demand on federating storage resources into one single access point has been arising within the WLCG. We introduce the current movement to federating storage resources has been started and expanding into the whole WLCG collaboration upon the successful federation and operation carried by the computing centers supporting ALICE experiment.

Automatic image annotation is one of crucial and attractive field of image retrieval. Classification process is part of the important phase in automatic image annotation (AIA). With the explosive growth of methods in this research area, this paper proposes 5 processing steps before image annotation using Amazon dataset, i.e., image segmentation, object identification, feature extraction, feature selection and image features classification. A lot of research has been done in creating numbers of different approaches and algorithm for image segmentation. Otsu is one of the most well known method in image segmentation region based. The proposed model aims to provide the highest accuracy after undergo those processing steps. This paper conducted several experiments for image classification starting from image segmentation in order to demonstrate usefulness and competiveness among different type of classifiers. It also target to study the effect of morphological operation and feature selection to the accuracy. For the classification experiment, it was tested using four types of classifiers: BayesNet, NaiveBayesUpdateable, RandomTree and IBk.