Video database retrieval based on gestures and its application

The paper describes a retrieval technique for video databases that is based on gestures. The proposed technique gives users a method to create various video content by intuitive interaction. This database system can extract feature parameters from video, perform automatic indexing, retrieve an appropriate result, and replace a presented object with the retrieval result in real time. Furthermore, we implement the fundamental functions of the proposal database. The retrieval accuracy and computational cost of the system are also described.     

A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

In this paper, we extend the work of Kraft et al. to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.     

Collaborative intelligent CAD framework incorporating design history tracking algorithm

This paper shows a new and efficient collaborative intelligent Computer Aided Design (CAD) framework in a theoretical study. While other collaborative CAD frameworks or protocols focus on decreasing the waiting time for updating design or communication methods for design review among collaborative designers, the suggested collaborative design protocol determines the next design ownership criterion with the objective of minimizing redundant design stages and design bottlenecks using the design history. In addition, the suggested framework generates an efficient reverse-engineered process for achieving the final design target with identification of redundant designs and how these can be prevented while resolving other existing collaborative design issues. As a design history mechanism, a feature-based design history tracking algorithm is suggested. In each design stage, the modeling activities are mapped to the related geometry and topology information. This information is reasoned into features using the feature design history graph (FDHG) and modified attribute adjacency graph (MAAG). The identified features are utilized for determining the redundant design stage and how it can be changed efficiently using the tracking algorithm. As the size of the design history increases with the number of collaborative designers and their design stages, this design history mechanism contributes to a decrease in size and captures the characteristics of design using features. As possible directions for future research this suggested framework can provide an insight into the design tendencies of designers and also be used as a reference model for intelligent CAD systems with an extracted design history-based knowledge database.     

Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory

In this paper, we describe a new method for the estimation of the fractal dimension of a geometrical object using fuzzy logic techniques. The fractal dimension is a mathematical concept, which measures the geometrical complexity of an object. The algorithms for estimating the fractal dimension calculate a numerical value using as data a time series for the specific problem. This numerical (crisp) value gives an idea of the complexity of the geometrical object (or time series). However, there is an underlying uncertainty in the estimation of the fractal dimension because we use only a sample of points of the object, and also because the numerical algorithms for the fractal dimension are not completely accurate. For this reason, we have proposed a new definition of the fractal dimension that incorporates the concept of a fuzzy set. This new definition can be considered a weaker definition (but more realistic) of the fractal dimension, and we have named this the "fuzzy fractal dimension." We can apply this new definition of the fractal dimension in conjunction with soft computing techniques for the problem of time series prediction. We have developed hybrid intelligent systems combining neural networks, fuzzy logic, and the fractal dimension, for the problem of time series prediction, and we have achieved very good results.     

Human centricity and information granularity in the agenda of theories and applications of soft computing

Soft computing is an interdisciplinary area that focuses on the design of intelligent systems to process uncertain, imprecise and incomplete information. It mainly builds on fuzzy sets theory, fuzzy logic, neural computing, optimization, evolutionary algorithms, and approximate reasoning et al. Information granularity is in general regarded as a crucial design asset, which helps establish a better rapport of the resulting granular model with the system under modeling. Human centricity is an inherent property of people's view on a system, a process, a machine or a model. Information granularity can be used to reflect people's level of uncertainty and this makes its pivotal role in soft computing. Indeed, the concept of information granularity facilitates the development of theory and application of soft computing immensely. A number of papers pertaining to some recent advances in theoretical development and practical application of information granularity in soft computing are highlighted in this special issue. The main objective of this study is to collect as many as possible researches on human centricity and information granularity in the agenda of theories and applications of soft computing, review the main idea of these literatures, compare the advantages and disadvantages of their methods and try to find the relationships and relevance of these theories and applications.     

Elasticsearch在车牌识别系统中的应用研究

为了提升车牌识别系统中海量识别数据与涉案车辆、交通违法等信息的实时分析、计算与检索性能,在对传统技术分析的基础上,针对现有系统中无法实现大数据分析与检索的问题,提出了利用Elasticsearch大数据实时检索技术构建交通大数据处理平台,在此架构上对交通识别数据进行实际测试。实验结果表明,所用技术能够有效解决目前的交通大数据检索和分析等关键问题,各项指标都满足了用户需求。     

基于DFNN的语义驱动色彩智能设计研究

色彩是表达设计情感的重要手段,进行了色彩语义的量化处理,通过抽取模糊规则获得主要影响因素,根据色彩方案多维小样本的特点,提出了基于广义径向基函数（RBF）的动态模糊神经网络（DFNN）方法,智能模拟色彩方案的设计、选择过程。根据训练数据开发了色彩智能设计原型系统,结合汽车色彩智能设计进行了可行性验证,能够在较高层次上辅助设计师进行色彩设计。     

On technology for functional requirement-based reference design retrieval in engineering knowledge management

Engineering design is a knowledge-intensive process, and includes conceptual design, detailed design, engineering analysis, assembly design, process design, and performance evaluation. Each of these tasks involves various aspects of technical knowledge and experience. Whether this technical knowledge and experience can be effectively shared is key to increasing product development capability and quality, and also to reducing the duration and cost of the development cycle. Consequently, providing engineering designers various query methods for retrieving engineering knowledge is one of the most important tasks in engineering knowledge management.The study develops a technology for functional requirement-based reference design retrieval as a decision support mechanism, which can assist engineering designers to retrieve relevant design and associated knowledge for reference in conducting functional requirements of a product. This study involves the following tasks: (i) designing a functional requirement-based reference design retrieval process, (ii) developing techniques related to the technology for functional requirement-based reference design retrieval, and (iii) implementing a functional requirement-based reference design retrieval mechanism. The retrieval process includes the steps of functional requirement-based query, case searching and matching, and case ranking. The technology involves (i) a structured query model for functional requirements, (ii) an index structures for historical design cases, (iii) functional requirement-based case searching and matching mechanisms, (iv) a functional requirement-based case ranking mechanism, and (v) a case-based representation of designed entities. Finally, the experimental example with indexing and retrieving similar designed entities is conducted to demonstrate the proposed techniques worked efficiently.     

Content-based audio classification and retrieval using a fuzzy logic system: towards multimedia search engines

 In recent years, available audio corpora are rapidly increasing from fast growing Internet and digital libraries. How to classify and retrieve sound files relevant to the user's interest from large databases is crucial for building multimedia web search engines. In this paper, content-based technology has been applied to classify and retrieve audio clips using a fuzzy logic system, which is intuitive due to the fuzzy nature of human perception of audio, especially audio clips with mixed types. Two features selected from various extracted features are used as input to a constructed fuzzy inference system (FIS). The outputs of the FIS are two types of hierarchical audio classes. The membership functions and rules are derived from the distributions of extracted audio features. Speech and music can thus be discriminated by the FIS. Furthermore, female and male speech can be separated by another FIS, whereas percussion can be distinguished from other music instruments. In addition, we can use multiple FISs to form a “fuzzy tree” for retrieval of more types of audio clips. With this approach, we can classify and retrieve generic audios more accurately, using fewer features and less computation time, compared to other existing approaches.     

智能化信息检索技术的分析及设计

随着人们对信息化技术的要求提高,信息化检索技术也呈现智能化的发展,研究智能化信息检索技术能够提升人们在信息检索中的便捷性,获得更好的信息服务。同时,信息检索设计涉及复杂的专业技术,提升信息检索的智能化也是信息设计的重要内容。本文主要分析了信息检索技术的基础理论,并针对信息检索技术的发展趋势及要求进行了阐述,最后研究了智能化信息检索技术设计,从而为信息检索技术的发展提供理论参考。     

A feature-based fixture design methodology for the manufacturing of aircraft structural parts

In fixture design for the manufacturing of aircraft structural parts, there are various challenges and serious problems in industry, such as difficulty in design knowledge capture and reuse, and the arbitrary or non-standard nature of design, which influence the efficiency and quality of fixture design. This paper proposes a feature-based fixture design methodology in which previous fixture design cases and design rules are described in association with features and thus the design knowledge is integrated with geometric information of aircraft structural parts, which are the main concern of this project. In this methodology, machining features of the structural parts and their associated attributes are identified by feature recognition technique from the 3 dimensional (3D) part models defined based on model-based definition (MBD) technique. The feature-based part information models are then established and are used to retrieve previous fixture design cases and design rules stored in the knowledge base. Fixture designers will choose the appropriate retrieved design cases as the starting point for new fixture design or use previous designs to assess his/her current design. In this way the process of new fixture design can be improved. In the current stage of the research, a prototype feature-based fixture design system based on CATIA for the manufacturing of aircraft structural parts has been developed and used by a large airplane manufacturer.     

Design of robust knowledge bases of fuzzy controllers for intelligent control of substantially nonlinear dynamic systems: II. A soft computing optimizer and robustness of intelligent control systems

The structure of intelligent control system (ICS) is analyzed, and the interrelations with conventional problems of the theory and practice of application of control systems are described. The analysis of the results of simulation of typical structures of intelligent control systems has allowed us to establish the following fact. The application of the technique of designing (presented in Part I), which is based on a fuzzy neural network (FNN), does not guarantee in general that the required accuracy of approximation of the training signal (TS) will be reached. As a result, under an essential change of external conditions, the sensitivity level of the controlled plant (CP) increases, which, on the whole, leads to a decrease in the robustness of the intelligent control system, and, as a consequence, to a loss of reliability (accuracy) of achieving the control goal. To eliminate the specified drawback of the neural network, a soft computing optimizer (SCO), which uses the technique of soft computing and allows one to eliminate the drawback, is applied, which results in an increase in the robustness level of the structure of the intelligent control system. The structure of the soft computing optimizer, which contains as a particular case the required configuration of an optimal fuzzy neural network, is considered. The main specific features of the functional operation of the soft computing optimizer and the stages of the process of designing robust knowledge bases (KB) of fuzzy controllers (FC) are described. The methodology of joint stochastic and fuzzy simulation of automatic control system based on the developed tool of the soft computing optimizer is discussed in order to test the robustness and to estimate the limiting structural capabilities of intelligent control systems. The efficiency of the control processes with application of the soft computing optimizer is demonstrated by particular typical examples (benchmarks) of models of dynamic controlled plants under the conditions of incomplete information about the parameters of the structure of the controlled plant and under the presence of unpredicted (abnormal) control situations. Examples of industrial application of robust intelligent control systems in actual control systems designed based on the soft computing optimizer are presented. Practical recommendations for improving the robustness level of intelligent control systems by using new types of computations and simulation are given     

A Case-Based Framework for Interactive Capture and Reuse of Design Knowledge

Aerospace design is a complex task requiring access to large amounts of specialized information. Consequently, intelligent systems that support and amplify the abilities of human designers by capturing and presenting relevant information can profoundly affect the speed and reliability of design generation. This article describes research on supporting aerospace design by integrating a case-based design support framework with interactive tools for capturing expert design knowledge through concept mapping. In the integrated system, interactive concept mapping tools provide crucial functions for generating and examining design cases and navigating their hierarchical structure, while CBR techniques facilitate retrieval and aid interactive adaptation of designs. Our goal is both to provide a useful design aid and to develop general interactive techniques to facilitate case acquisition and adaptation. Experiments illuminate the performance of the system's context-sensitive retrieval during interactive case adaptation and the conditions under which it provides the most benefit.     

Remote house calls [disease detector]

An innovative bio-robotic system DDX for neuropsychophysical health-condition detection is presented. The proposed fuzzy logic solution is portable without losing efficiency and accuracy in diagnosis and also provides the ability to transfer diagnoses through a remote communication interface in order to monitor the daily health of a patient. The system is an intelligent machine based on soft computing techniques, and its efficiency can be improved considering more patterns of examples for functions, calibration, or, moreover, by using self-learning techniques.     

改进的区间值模糊集交互式遗传算法

区间值模糊集的交互式遗传算法,能有效缓解用户的疲劳,同时避免用户因一时无法给出确定值而浪费掉的时间,大大加快了收敛速度。首先采用区间值模糊集的方法表示对个体进行评价的适应度值,即为区间适应度值,然后对其进行排序,按照排序结果采用交互式遗传算法进行全局搜索。整个过程符合人的思维过程,能有效搜索到用户满意的个体。将该方法应用于图像检索系统中,结果表明该方法有效地提高了检索速度,并且取得了较好的检索结果。     

Indexing shapes in image databases using the centroid–radii model

In content-based image retrieval systems, the content of an image such as color, shapes and textures are used to retrieve images that are similar to a query image. Most of the existing work focus on the retrieval effectiveness of using content for retrieval, i.e., study the accuracy (in terms of recall and precision) of using different representations of content. In this paper, we address the issue of retrieval efficiency, i.e., study the speed of retrieval, since a slow system is not useful for large image databases. In particular, we look at using the shape feature as the content of an image, and employ the centroid–radii model to represent the shape feature of objects in an image. This facilitates multi-resolution and similarity retrievals. Furthermore, using the model, the shape of an object can be transformed into a point in a high-dimensional data space. We can thus employ any existing high-dimensional point index as an index to speed up the retrieval of images. We propose a multi-level R-tree index, called the Nested R-trees (NR-trees) and compare its performance with that of the R-tree. Our experimental study shows that NR-trees can reduce the retrieval time significantly compared to R-tree, and facilitate similarity retrieval. We note that our NR-trees can also be used to index high-dimensional point data commonly found in many other applications.     

基于颜色直方图熵值及分块主色的图像检索

为了克服在检索相同背景中物体颜色不同的图像时无法利用颜色特征而必须利用物体的形状特征这一缺陷,并进一步提高图像的检索效果,本文提出了一种基于颜色直方图熵值及分块主色的图像检索方法。实验结果表明,该方法不仅可以提高图像检索的查准率与查全率,而且对于检索相同背景中物体颜色不同的图像时只利用颜色特性即可达 检索目的。