Narrow Results

In order to improve the teaching level of digital piano collective course for preschool education major in normal universities and provide high-quality music education for children, a knowledge-based system for questionnaires evaluation of digital piano collective course for preschool education major in normal universities is proposed. The system can collect sufficient data and information, the exact orientation of piano learning, the exact choice of teaching mode by questionnaires. A comprehensive analysis of the students’ characteristics, teachers’ post skills and the orientation of digital piano syllabus in the preschool collective course teaching of digital piano in normal universities is made. Moreover, some countermeasures for perfecting digital piano teaching mode are proposed. As a result, it promotes the level of piano teaching in preschool education major, realizes the goal of arousing students’ enthusiasm for study and reduces teachers’ pressure.

Natural disasters have had great impact on human beings. Emergency relief is becoming more important with the increase of the frequency and scale of humanitarian emergencies resulting from more natural disasters because of climate change. In this paper, an interesting hybrid knowledge representation method during the development of a KBS for design of emergency relief structures is presented. It encapsulates ill-structured, semi-structured and structured knowledge that is gathered from literature, human expert and even knowledge gleaned during the system development. All routine as well as cumbrous activities in the emergency relief cycle are covered. The system can provide the user with advice on preliminary plan evaluation, plan optimization, plan evaluation, plan summary and miscellaneous. It would be beneficial to the field of disaster emergency relief decision by focusing on the acquisition and organization of expert knowledge through the development of knowledge-based system.

The intelligent control of cleaning of rice–wheat combined harvester is a complex problem, which includes the initial setting of cleaning control, judgment of cleaning loss state, cause analysis and selection of corresponding control strategies and many other sub-problems. The knowledge contained in these sub-problems, including knowledge representation methods and reasoning strategies, is different. Therefore, this paper decomposes the complex problem of cleaning control into a sub-problem of hierarchical structure, and constructs a knowledge model of cleaning control based on binary tree structure. In this way, the cleaning control problem can be decomposed into a small set of sub-problems by the judgment of the nodes of the binary tree, until the sub-problems are small enough to be solved directly so as to get the solution of the original problem. It is proved by examples that this method is of great significance to improve the efficiency of knowledge acquisition, management and maintenance of the expert system of rice–wheat combine harvester, and to enhance the knowledge service ability of the expert system of rice–wheat combine harvester. This method can also be used for reference in other fields.

In order to analyze the feasibility of integrating Sunan music into children’s music teaching through research, a knowledge-based system for children’s music teaching strategies based on case-based reasoning is proposed. The system can collect sufficient data and information, the exact orientation of piano learning, and the exact choice of teaching modes by questionnaires. Research data show that the vast majority of children like music teaching, and they like 76% more than they do not like, and more parents who express an indifferent attitude to music are 84% more than those who feel important. This experimental study shows that most teachers hold a positive attitude toward the development and use of folk music education resources in kindergartens, and parents do not have enough knowledge about folk music. The infiltration of excellent local culture in children’s music activities can improve students’ cultural literacy, let the new generation understand the tradition and the classics, and let children understand the local culture of southern Jiangsu and inherit the excellent culture of the Chinese nation through the understanding, feeling, and understanding of the local culture of southern Jiangsu. This is the new idea of local cultural heritage in southern Jiangsu and also the responsibility of kindergarten education.

The intelligent regulation and control strategies for rice and wheat combine harvesters’ operation are lacking and the rule of parameter matching is fuzzy in China, around these issues. The dynamic correlation control law among the parameters of rice and wheat, cleaning operation parameters of combine harvesters, the cleaning loss rate and impurity rate, and so on are studied. The intelligent control model for the rice and wheat combine harvester is established based on case-based reasoning (CBR). According to different rice and wheat varieties, water content and other rice and wheat properties, the control scheme of cleaning fan speed, air distributor plate angle and upper sieve opening with low cleaning impurity rate and cleaning loss rate is provided. Through the development of web-based cleaning intelligent control expert system and experimental evaluation, the feasibility and effectiveness of the CBR method in the intelligent control filed of rice and wheat combine harvesters are verified.

In this paper, the operation process of cleaning of intelligent rice–wheat combine harvester is divided into two key steps: initial setting of cleaning device operation parameters and dynamic control of cleaning device operation parameters. Combined with the operation experience of cleaning control of agricultural machinery operators, the dynamic control knowledge-based system of cleaning device operation parameters was built based on production rule reasoning. The cleaning device of the rice–wheat combine harvester is intelligently controlled based on the dynamic monitoring and control system of the cleaning device operation quality and operation parameters, so as to achieve the purpose of controlling the cleaning operation quality of the rice–wheat combine harvester in the normal range. Through the field experiment results and analysis, it is proved that the intelligent control system of the cleaning device operation parameters based on the dynamic control knowledge-based system of cleaning device operation parameters can effectively keep the cleaning impurity content and loss rate of intelligent rice–wheat combine harvester in the normal range, so as to verify the effectiveness of the intelligent control knowledge-based system of the cleaning device operation parameters.

Since the end of last year, the researchers at the Institute of Systems Science (ISS) started to consider a more ambitious project as part of its multilingual programming objective. This project examines the domain of Chinese Business Letter Writing. With the problem defined as generating Chinese letters to meet business needs, investigations suggest an intersection of 3 possible approaches: knowledge engineering, form processing and natural language processing. This paper attempts to report some of the findings and document the design and implementation issues that have arisen and been tackled as prototyping work progresses.

The image correspondence problem has generally been considered the most difficult step in both stereo and temporal vision. Most existing approaches match area features or linear features extracted from an image pair. The approach described in this paper is novel in that it uses an expert system shell to develop an image correspondence knowledge-based system for the general image correspondence problem. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions in images for every edge or region matching. A computation network is used to represent this knowledge. It allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. Heuristics for determining the correspondences between image features and the problem of handling missing information will be discussed. The values of the individual matching results are used to direct the traversal and pruning of the global matching process. The problem of parallelizing the entire process will be discussed. Experimental results on real-world images show that all matching edges and regions have been identified correctly.

This paper discusses the design and implementation of PLAYMAKER, a knowledge-based system for characterizing hydrocarbon plays. PLAYMAKER is a component of XX (eXpert eXplorer), a workstation-based tool that aids exploration geologists in a number of different tasks: sediment and carbonate simulation, play and field characterization, retrieval and storage of information in a geological database, comparison of the play or field under study with other fields in the database, and report generation. PLAYMAKER is implemented using MIDST (Mixed Inferencing Dempster-Shafer Tool), a rule-based expert system shell that incorporates mixed-initiative and inexact reasoning based on the Dempster-Shafer evidence combination scheme. This paper discusses the effectiveness of a two-level knowledge base structure adopted for the design and implementation of PLAYMAKER.

Thermal infrared images of the ocean obtained from satellite sensors are widely used for the study of ocean dynamics. The derivation of mesoscale ocean information from satellite data depends to a large extent on the correct interpretation of infrared oceanographic images. The difficulty of the image analysis and understanding problem for oceanographic images is due in large part to the lack of precise mathematical descriptions of the ocean features, coupled with the time varying nature of these features and the complication that the view of the ocean surface is typically obscured by clouds, sometimes almost completely. Towards this objective, the present paper describes a hybrid technique that utilizes a nonlinear probabilistic relaxation method and an expert system for the oceanographic image interpretation problem. This paper highlights the advantages of using the contextual information in the feature labeling algorithm. The need for an expert system and its feedback in automatic interpretation of oceanic features is discussed. The paper presents some important results of the series of experiments conducted at the Remote Sensing Branch, of the Naval Oceanographic and Atmospheric Research Laboratory, on the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) imagery data. The results clearly indicate the drastic improvement in labeling due to the oceanographic expert system.

A major step in the process of speech understanding is the acoustic-phonetic decoding which can be defined as the automatic mapping of the continuous speech wave into a set of predetermined linguistic units such as phones, diphones, syllables, etc. This paper relates to the approach of this problem which consists in exploiting an explicit description of all kinds of available knowledge about the speech communication phenomena, in the general framework of an artificial intelligence knowledge-based system.

We will first recall the main difficulties of acoustic-phonetic decoding with a practical example. We will then present the APHODEX system that we have been designing for the past eight years, in terms of software architecture and of knowledge representation and reasoning. The practical evaluation of this system will then be carried out at the different levels of feature extraction, segmentation and labelling. Finally, we will discuss the limitations of our approach and present the ongoing effort to overcome these limitations, especially through the use of abductive reasoning.

The task of object identification is fundamental to the operations of an autonomous vehicle. It can be accomplished by using techniques based on a Multisensor Fusion framework, which allows the integration of data coming from different sensors. In this paper, an approach to the synergic interpretation of data provided by thermal and visual sensors is proposed. Such integration is justified by the necessity for solving the ambiguities that may arise from separate data interpretations.

The architecture of a distributed Knowledge-Based system is described. It performs an Intelligent Data Fusion process by integrating, in an opportunistic way, data acquired with a thermal and a video (b/w) camera. Data integration is performed at various architecture levels in order to increase the robustness of the whole recognition process. A priori models allow the system to obtain interesting data from both sensors; to transform such data into intermediate symbolic objects; and, finally, to recognize environmental situations on which to perform further processing. Some results are reported for different environmental conditions (i.e. a road scene by day and by night, with and without the presence of obstacles).

In this paper, we study the problem of recovering approximate shape from the shading of a three-dimensional object in a single image when knowledge about the object is available. The application of knowledge-based methods to low-level image processing tasks will help overcome problems that arise from processing images using a pixel-based approach. Shape-from-shading has generally been approached by precognitive vision methods where a standard operator is applied to the image based on assumptions about the imaging process and generic properties of what appears. This paper explores some advantages of applying knowledge and hypotheses about what appears in the image. The knowledge and hypotheses used here come from domain knowledge and edge-matching. Specifically, we are able to find solutions to some problems that cannot be solved by other methods and gain advantages in terms of computation speed over similar approaches. Further, we can fully automate the derivation of the approximate shape of an object. This paper demonstrates the efficacy of using knowledge in the basic operation of an early vision operator, and so introduces a new paradigm for computer vision that may be applied to other early vision operators.

In this paper, we present a vision system with a special camera for knowledge-based real-time monitoring of the inside of a fluid bed heat exchanger (FBHE) chamber in a thermal power plant. With the proposed system, it is possible to monitor the internal flux condition and analyze the inner temperature of a chamber. Due to the fact that the mixture of coal and sand inside a chamber flows by very quickly, there is an immense amount of smoke and dust, which make it difficult to capture images and analyze an existing system. An adaptive average method is proposed here to observe the background internal environment of an FBHE chamber. The experimental results show that real-time monitoring is possible, even under hot and dusty conditions. Preliminary experimental results confirm expectations about the possibility and effectiveness of the developed device for commercialized real-time monitoring systems. They demonstrate that a single camera with embedded image processing software can concurrently analyze the degree of fluidization of a mixture and the temperature of the chamber inside, even in extremely harsh and hazardous conditions. We aim to eventually develop an image analysis system that combines image processing and knowledge engineering techniques.

Knowledge about relations plays a crucial role in human’s knowledge. Different methods for representing this type of knowledge have been proposed. However, due to the lack of theoretical foundations, these methods cannot guarantee criteria in knowledge representation such as formality, universality, usability and practicality. They are not adequate to represent the knowledge domains in practice which have many components. Based on formal ontology approach, a knowledge model about relations, called Rela-model, is presented in this paper. It has the components such as concepts, relations between concepts, and rules. The concepts in this model consist of attributes, facts and rules of itself. Each object in a concept has also equipped its behavior to solve problems on it. The methods for solving problems based on Rela-model are also studied. The general problems on this model are the following: Given some objects and facts on them, determine the closure of set of attributes and facts on the objects or determine an object or consider a relation between the objects. The algorithms to solve problems are designed and their properties, such as finiteness, effectiveness, have also been proved. Besides the solid mathematical foundation, Rela-model also has a simple specification language which can effectively represent the knowledge, thus it can be used in many real situations. Our approach is also applied to build two systems: the intelligent problem solver about solid geometry in high school mathematics, and the expert system to diagnose diseases in diabetic microvascular complication.

A Cooperative Response system, which can detect and correct users’ misconceptions exhibited in database retrieval, is one of the most promising ways for applying the knowledge engineering approach to traditional database query systems. Misconceptions are generally regarded as users’ false beliefs about a database and are considered as a main source of failures in the database retrieval. How to handle users’ false beliefs has recently become an important point to overcome in advanced database front-end development.

While many efforts have been made to develop cooperative response systems, most of the work has placed emphasis on the detection of a user’s false beliefs revealed in a single query. No attention has been paid to handle users’ inconsistent beliefs which would be revealed in a sequence of contextual queries. In this paper, we propose a user-beliefs-modeling approach for detecting the inconsistent beliefs exhibited in two contextual queries. As an extension of the existing cooperative response systems, the system proposed in this paper can generate informative responses to correct users’ inconsistent beliefs.

Software testing is an important step in the development of complex systems. The construction of test cases using traditional methods usually requires considerable manual effort. QUEST/Ada—Query Utility Environment for Software Testing of Ada, is a prototype test case generation system that uses various heuristics-based approaches to generate test cases. The system, which is designed for unit testing, generates test cases by monitoring the branch coverage progress and intelligently modifying existing test cases to achieve additional coverage. Three heuristics-based approaches along with a random test case generation method were studied to compare their branch coverage performance. Although some constraints are imposed by the prototype, the framework provides a useful foundation for further heuristics-based test case generation research. The design of the system, the heuristic rules used in the system, and an evaluation of each rule’s performance are presented.

This paper presents the design and implementation of a knowledge-based tool for performance tuning of the UNIX operating system. The tool, called BMS, provides an intelligent support and maintenance for identifying performance bottlenecks in UNIX and recommending solutions to the problems. Currently, it handles problems in UNIX resource management, such as memory utilization, disk utilization, CPU scheduling and I/O devices. BMS has been implemented in the EXSYS environment and tested on UNIX V.3. Preliminary results have indicated that such a knowledge-based tool to operating system performance tuning (1) is viable; (2) increases the productivity of system maintenance personnel and reduces the cost of training; and (3) offers a better service to operating system users by providing prompt recommendations to solutions of their system performance problems.

In this paper, we are concerned with the automatic recognition of Arabic phonetic macro-classes and complex phonemes by multi-layer sub-neural-networks (SNN) and knowledge-based system (SARPH). Our interest goes to the particularities of the Arabic language such as geminate and emphatic consonants and the vowel duration. These particularities are unanimously considered as the main root of failure of Automatic Speech Recognition (ASR) systems dedicated to standard Arabic. The purely automatic method constituted by the SNNs is confronted to an approach based on the user phonetic knowledge expressed by SARPH rules. For the acoustical analysis of speech as well as for the segmentation task, auditory models have been used. The ability of systems has been tested in experiments using stimuli uttered by 6 native Algerian speakers. The results show that SNNs achieved well in pure identification while in the case of semantically relevant duration the knowledge-based system performs better.

Identifying classes and objects in an object-oriented (OO) software development method requires a great amount of domain-specific knowledge and OO developing experiences to achieve the work. Experienced developers always have heuristic solutions to different problems. However, novice developers have difficulties developing their desired OO software systems.

We propose a method that uses a knowledge-based system with the identification knowledge to support developers to obtain classes and objects that are suitable for one special domain problem. With the help of the identification knowledge, the developers can model the system easily and complete the rest of development work quickly.