A relational framework for join implementation in model management systems

Two important issues in the design of relational model banks are the degree to which they should be aggregated or disaggregated and the methods by which disaggregated model banks might be integrated in response to user queries. Three topics relevant to this issue are addressed in this paper. The first is whether a universal model and its projections may possess the lossy join property. We will show that they do not. The second is the development of a relational algebra for the specification of join implementation in model banks, and the third is the realization of such an algebra in a language similar to Query-by-Example.     

A generic and efficient framework for flash-aware spatial indexing

Spatial indexing on flash-based Solid State Drives (SSDs) has become a core aspect in spatial database applications, and has been carried out by flash-aware spatial indices. Although there are some flash-aware spatial indices proposed in the literature, they do not exploit all the benefits of SSDs, leading to loss of efficiency and durability. In this article, we propose eFIND, a new generic and efficient framework for flash-aware spatial indexing. eFIND takes into account the intrinsic characteristics of SSDs by employing (i) a write buffer to avoid expensive random writes, (ii) a flushing algorithm that smartly picks modifications to be flushed in batch to the SSD, (iii) a read buffer to decrease the overhead of random reads, (iv) a temporal control to avoid interleaved reads and writes, and (v) a log-structured approach to provide data durability. Performance tests showed the efficiency of eFIND. Compared to the state of the art, eFIND improved the construction of spatial indices from 43% to 77%, and the spatial query processing from 4% to 23%.     

A multi-stage learning framework for intelligent system

As information technologies advance and user-friendly interfaces develop, the interaction between humans and computers, information devices, and new consumer electronics is increasingly gaining attention. One example that most people can relate to is Apple’s innovation in human–computer interaction which has been used on many products such as iPod and iPhone. Siri, the intelligent personal assistant, is a typical application of machine-learning human–computer interaction.Algorithms in machine learning have been employed in many disciplines, including gesture recognition, speaker recognition, and product recommendation systems. While the existing learning algorithms compute and learn from a large quantity of data, this study proposes an improved learning to rank algorithm named MultiStageBoost. In addition to ranking data through multiple stages, the MultiStageBoost algorithm significantly improves the existing algorithms in two ways. Firstly, it classifies and filters data to small quantities and applies the Boosting algorithm to achieve faster ranking performance. Secondly, it enhances the original binary classification by using the reciprocal of fuzzily weighted membership as the ranking distance.The importance of data is revealed in their ranked positions. Usually data ranked in the front are given more attention than those ranked in the middle. For example, after ranking 10,000 pieces of data, the top 10, or at most 100, are the most important and relevant. Whether the data after the top ones are ranked precisely does not really matter. Due to this reason, this study has made improvement on the conventional methods of the pair-wise ranking approach. Not only are data classified and ranked binarily, they are also given different weights depending on whether they are concordant or discordant. Incorporating the concept of weighting into the ranking distance allows us to increase the precision of ranking. Results from experiments demonstrate that our proposed algorithm outperforms the conventional methods in three evaluation measures: P@n, MAP, and NDCG. MultiStageBoost was then applied to speech recognition. However, we do not aim to improve the technology of speech recognition, but simply hope to provide evidences that MultiStageBoost can be used in the classification and ranking in speech recognition. Experiments show that the recognition optimization procedures established by this study are able to increase the recognition rate to over 95% in the personal computing device and industrial personal computer. It is expected that in the future this voice management system will accurately and effectively identify speakers answering the voice response questionnaire and will successfully carry out the functions in the choice of answers, paying the way for the formation of a virtual customer service person.     

A framework for intelligent test data generation

Test data generation using traditional software testing methods generally requires considerable manual effort and generates only a limited number of test cases before the amount of time expanded becomes unacceptably large. A rule-based framework that will automatically generate test data to achieve maximal branch coverage is presented. The design and discovery of rules used to generate meaningful test cases are also described. The rule-based approach allows this framework to be extended to include additional testing requirements and test case generation knowledge.This work was supported in part by George C. Marshall Space Flight Center, NASA/MSFC, AL 35812 (NASA-NCC8-14).     

A theoretical framework for an intelligent design catalogue

Product catalogues constitute a valuable source of information for engineers engaged in design activities. Unfortunately, these catalogues provide only limited support to engineers in the earlier, conceptual stages of design. This research proposes the intelligent design catalogue consisting of a virtual design environment linked to catalogues of standard components. Engineers develop their design concepts within the virtual environment and refer to the catalogues as these concepts are refined. The selected components are assembled within the design environment. The intelligent design catalogue provides search aids as well as assessment tools. The theoretical framework draws on several engineering areas. Manufacturing demonstrates how process plans can be developed in a virtual environment independently of the machines on the shop floor just as products can be conceptually designed independently of the standard components available. The standard components themselves can be grouped borrowing from classification schemes of group technology. Object-oriented programming (OOP) provides an environment for the development of the software that runs the intelligent design catalogue. As the objects of OOP parallel standard components, OOP also serves as a design paradigm after which the catalogue can be modelled. Design theory suggests frameworks for developing a (semi-) hierarchical structure for cataloguing parts.     

A framework for intelligent design of manufacturing cells

One of the major thrusts of agile/lean/responsive manufacturing strategies of the twentyfirst century is to introduce advanced information technology into manufacturing. This paper presents a framework for robust manufacturing system design with the integration of simulation, neural networks and knowledge-based expert system tools. An operation/ cost-driven cell design methodology was applied to concurrently consider cell physical design and the complexity of cell control functions. Simulation was exercised to estimate performance measures based on input parameters and given cell configurations. A rulebased expert system was employed to store the acquired expert knowledge regarding the relation between cell control complexities, cost of cell controls, performance measures and cell configuration. Neural networks were applied to predict the cell design configuration and corresponding complexities of cell control functions. Training of neural networks was performed with both forward and backward methods by using the same pair of data sets. Hence, trained neural networks will be able to predict either input or output parameters. This innovative new design methodology was illustrated via a successful implementation exercise resulting in actually acquiring an automated cell at industrial settings. The experience learned from this exercise indicates that the proposed design methodology works well as an effective decision support system for cell designers and the management in determining appropriate cell configuration and cell control functions at the design stage.     

A probabilistic framework for semantic video indexing, filtering,and retrieval

Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories. Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. We therefore formulate the multimedia content access problem as a multimedia pattern recognition problem. We propose a probabilistic framework for semantic video indexing, which call support filtering and retrieval and facilitate efficient content-based access. To map low-level features to high-level semantics we propose probabilistic multimedia objects (multijects). Examples of multijects in movies include explosion, mountain, beach, outdoor, music etc. Semantic concepts in videos interact and to model this interaction explicitly, we propose a network of multijects (multinet). Using probabilistic models for six site multijects, rocks, sky, snow, water-body forestry/greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this framework to semantic indexing. We demonstrate how detection performance can be significantly improved using the multinet to take interconceptual relationships into account. We also show how the multinet can fuse heterogeneous features to support detection based on inference and reasoning     

A unifying framework for invariant pattern recognition

We introduce a group-theoretic model of invariant pattern recognition, the Group Representation Network. We show that many standard invariance techniques can be viewed as GRNs, including the DFT power spectrum, higher order neural network and fast translation-invariant transform.     

S-MRST: a novel framework for indexing uncertain data

This paper studies the problem of probabilistic range query over uncertain data. Although existing solutions could support such query, it still has space for improvement. In this paper, we firstly propose a novel index called S-MRST for indexing uncertain data. For one thing, via using an irregular shape for bounding uncertain data, it has a stronger space pruning ability. For another, by taking the gradient of probability density function into consideration, S-MRST is also powerful in terms of probability pruning ability. More important, S-MRST is a general index which could support multiple types of probabilistic queries. Theoretical analysis and extensive experimental results demonstrate the effectiveness and efficiency of the proposed index.     

A theoretic framework for intelligent expert systems in medical encounter evaluation

Abstract: This paper describes a novel approach to implementation of a medical diagnosis expert system that can assist physicians with their daily practices. Differential artificial intelligence techniques are incorporated into a multi-stage expert system to best represent the various phases of the patient diagnosis process. A weighted scoring system is used to represent the subjective analysis stage, while a rule-based fuzzy expert system is employed to both interpret laboratory tests and imaging findings and suggest the final diagnosis. A model of various patient flow scenarios is presented to demonstrate the functionality of the proposed expert system. An actual example of patient walkthrough is used to demonstrate various computation steps from recording the patient chief complaint to arriving at the final diagnosis. It is shown that the conclusion arrived at by using the proposed system is consistent with a common diagnosis of a third party specialist who is asked to evaluate the performance of the system.     

Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

An automata-theoretic framework for intelligent systems

A theoretical framework for the analysis and synthesis of intelligent systems is presented. The basic models of automata theory and adaptive systems are highlighted, then extended and interpreted in the context of intelligent system design. In particular, the basic models are generalized to intelligent systems incorporating self-learning behavior. The utility of this approach is demonstrated by its use as a rigorous basis for (1) the discussion of information minimization in automatic systems, (2) the formal structure of adaptive systems and (3) implications for a general theory of intelligent systems.     

Knowledge framework for intelligent manufacturing systems

Nowadays, competition is experienced not only among companies but among global supply chains and business networks. There is a demand for intelligent world-class solutions capable of reinforcing partnerships and collaborations with an improved cross-cultural understanding. However due to the proliferation of terminology, organizations from similar business environments have trouble cooperating, and are experiencing difficulties exchanging electronically vital information, such as product and manufacturing data, even when using international standards. To address similar interoperability problems, the Intelligent manufacturing systems program () is providing an opportunity to develop industry-led R&D initiatives, building common semantics and integrated solutions. The SMART-fm project was one of those initiatives. It led to the development of the international standard for product data representation and exchange in the furniture sector (ISO 10303-236) and identified the challenge of semantic interoperability which is today a major challenge in modern enterprise integration. This paper presents a knowledge framework to address that challenge and make interoperable intelligent manufacturing systems a reality. It proposes to use semantically enriched international product data standards, and knowledge representation elements as a basis for achieving seamless enterprise interoperability.     

面向虚拟采办的智能决策支持系统概念框架

提出一种面向虚拟采办全寿命周期、全系统、全方位决策的智能决策支持系统SBA—IDSS概念框架．作为真实世界采办最终需求的抽象描述,它属于与实现无关的规范性模型体系,由它定义应用领域、用户概念和环境特征．同时它是数字世界中拟实现的初始工程模型,包括与实现无关的设计可行性模型、规划模型及功能顶层分解．该框架支持系统的自组织、自适应智能行为,预期可按需组成模型与仿真、文件、知识、通信及数据驱动的各类实用决策支持系统．该框架通过一项SBA实例的验证,得到可用性的正面评价．     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

A knowledge-based intelligent framework for anterior cruciate ligament rehabilitation monitoring

This study presents an integration of knowledge-based system and intelligent methods to develop a recovery monitoring framework for post anterior cruciate ligament (ACL) injured/reconstructed subjects. The case based reasoning methodology has been combined with fuzzy clustering and intelligent classification techniques in order to develop a knowledge base and a learning model for identifying the recovery stage of ACL-reconstructed subjects and objectively monitoring the progress during the convalescence regimen. The system records kinematics and neuromuscular signals from lower limbs of healthy and ACL-reconstructed subjects using self adjusted non-invasive body-mounted wireless sensors. These bio-signals are synchronized and integrated, and a combined feature set is generated by performing data transformation using wavelet decomposition and feature reduction techniques. The knowledge base stores the subjects’ profiles, their recovery sessions’ data and problem/solution pairs for different activities monitored during the course of rehabilitation. Fuzzy clustering technique has been employed to form the initial groups of subjects at similar stage of recovery. In order to classify the recovery stage of subjects (i.e. retrieval of similar cases), adaptive neuro-fuzzy inference system (ANFIS), fuzzy unordered rule induction algorithm (FURIA) and support vector machine (SVM) have been applied and compared. The system has been successfully tested on a group of healthy and post-operated athletes for analyzing their performance in two activities (ambulation at various speeds and one leg balance testing) selected from the rehabilitation protocol. The case adaptation and retention is a semi-automatic process requiring input from the physiotherapists and physiatrists. This intelligent framework can be utilized by physiatrists, physiotherapists, sports trainers and clinicians for multiple purposes including maintaining athletes’ profile, monitoring progress of recovery, classifying recovery status, adapting recovery protocols and predicting/comparing athletes’ sports performance. Further, the knowledge base can easily be extended and enhanced for monitoring different types of sports activities.     

A framework for applying intelligent agents to support electronic trading

The purpose of this paper is to study how Intelligent Agents (IAs) can be used to facilitate electronic trading. An IA is a software program designed for performing a specific task based on its own knowledge and the message it received. Given the increased complexity of Internet services, many IAs are useful to make electronic markets more effective.In the paper, activities and structures of electronic markets are reviewed and discussed with respect to the coordination mechanism and primitive activities. This is followed by an analysis of IAs useful for electronic commerce (EC). A three-layer architecture for organizing IAs for EC is developed. Finally, application of the framework to support EC and related issues are presented. The findings are useful for implementing a more effective environment for EC.