Bridging the gap between low level ontologies used for data acquisition and high level ontologies used for inference is essential to enable the discovery of high-level links between low-level entities. This is of utmost importance in many applications, where the semantic distance between the observable evidence and the target relations is large. Examples of these applications would be detection of terrorist activity, crime analysis, and technology monitoring, among others. Currently this inference gap has been filled by expert knowledge. However, with the increase of the data and system size, it has become too costly to perform such manual inference. This paper proposes a semi-automatic system to bridge the inference gap using network correlation methods, similar to Bayesian Belief Networks, combined with hierarchical clustering, to group and organize data so that experts can observe and build the inference gap ontologies quickly and efficiently, decreasing the cost of this labor-intensive process. A simple application of this method is shown here, where the co-author collaboration structure ontology is inferred from the analysis of a collection of journal publications on the subject of anthrax. This example uncovers a co-author collaboration structures (a well defined ontology) from a scientific publication dataset (also a well defined ontology). Nevertheless, the evidence of author collaboration is poorly defined, requiring the use of evidence from keywords, citations, publication dates, and paper co-authorship. The proposed system automatically suggests candidate collaboration group patterns for evaluation by experts. Using an intuitive graphic user interface, these experts identify, confirm and refine the proposed ontologies and add them to the ontology database to be used in subsequent processes.
In this study, we propose a novel hybrid intelligent system (HIS) which provides a unified integration of numerical and linguistic knowledge representations. The proposed HIS is hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a linguistic model, i.e., fuzzy expert system, optimized via the genetic algorithm. The ILFN is a self-organizing network with the capability of fast, one-pass, online, and incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN or provided by the domain expert. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy, comprehensibility, completeness, compactness, and consistency. After the system being completely constructed, it can incrementally learn new information in both numerical and linguistic forms. To evaluate the system's performance, the well-known benchmark Wisconsin breast cancer data set was studied for an application to medical diagnosis. The simulation results have shown that the prosed HIS perform better than the individual standalone systems. The comparison results show that the linguistic rules extracted are competitive with or even superior to some well-known methods.
Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recognize the vocalizations of four species of frogs and can identify different individuals within the species of interest, is proposed. For the desired monitoring system, species identification is performed first with the proposed filtering and grouping algorithm. Individual identification, which can estimate frog population within the specific species, is performed in the second stage. Digital signal pre-processing, feature extraction, dimensionality reduction, and neural network pattern classification are performed step by step in this stage. Wavelet Packet feature extraction together with two different dimension reduction algorithms are synergistically integrated to produce final feature vectors, which are to be fed into a neural network classifier. The simulation results show the promising future of deploying an array of continuous, on-line environmental monitoring systems based upon nonintrusive analysis of animal calls.
While most research attention has been focused on fault detection and diagnosis, much less research effort has been dedicated to `general' failure accommodation. Due to the inherent complexity of nonlinear systems, most of model- based analytical redundancy fault diagnosis and accommodation studies deal with the linear system that is subject to simple additive or multiplicative faults. This assumption has limited the effectiveness and usefulness in practical applications. In this research work, the on-line fault accommodation control problems under catastrophic system failures are investigated. The main interest is focused on dealing with the unanticipated system component failures in the most general formulation. Through discrete- time Lyapunov stability theory, the necessary and sufficient conditions to guarantee the system on-line stability and performance under failures are derived and a systematic procedure and technique for proper fault accommodation under the unanticipated failures are developed. A complete architecture of fault diagnosis and accommodation has also been presented by incorporating the developed intelligent fault tolerant control scheme with a cost-effective fault detection scheme and a multiple-model based failure diagnosis process to efficiently handle the false alarms and the accommodation of both the anticipated and unanticipated failures in on-line situations.
In this paper, a method for automatic constructing a fuzzy expert system from numerical data using the ILFN network and the Genetic Algorithm is presented. The Incremental Learning Fuzzy Neural (ILFN) network was developed for pattern classification applications. The ILFN network, employed fuzzy sets and neural network theory, is a fast, one-pass, on-line, and incremental nearing algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly mapped into if-then rule bases. A knowledge base for fuzzy expert systems can then be extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only important features of input patterns needed to provide to a fuzzy rule-based system. Three computer simulations using the Wisconsin breast cancer data set were performed. Using 400 patterns for training and 299 patterns for testing, the derived fuzzy expert system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.
Field operators use their eyes, ears, and nose to detect process behavior and to trigger corrective control actions. For instance: in daily practice, the experienced operator in sulfuric acid treatment of phosphate rock may observe froth color or bubble character to control process material in-flow. Or, similarly, (s)he may use acoustic sound of cavitation or boiling/flashing to increase or decrease material flow rates in tank levels. By contrast, process control computers continue to be limited to taking action on P, T, F, and A signals. Yet, there is sufficient evidence from the fields that visual and acoustic information can be used for control and identification. Smart in-situ sensors have facilitated potential mechanism for factory automation with promising industry applicability. In respond to these critical needs, a generic, structured health monitoring approach is proposed. The system assumes a given sensor suite will act as an on-line health usage monitor and at best provide the real-time control autonomy. The sensor suite can incorporate various types of sensory devices, from vibration accelerometers, directional microphones, machine vision CCDs, pressure gauges to temperature indicators. The decision can be shown in a visual on-board display or fed to the control block to invoke controller reconfigurration.
We propose to design and to evaluate an on-board intelligent health assessment tool for rotorcraft machines, which is capable of detecting, identifying, and accommodating expected system degradations and unanticipated catastrophic failures in rotorcraft machines under an adverse operating environment. A fuzzy-based neural network paradigm with an on-line learning algorithm is developed to perform expert advising for the ground-based maintenance crew. A hierarchical fault diagnosis architecture is advocated to fulfill the time-critical and on- board needs in different levels of structural integrity over a global operating envelope. The research objective is to experimentally demonstrate the feasibility and flexibility of the proposed health monitoring procedure through numerical simulations of bearing faults in USAF MH-53J PAVE LOW helicopter transmissions. The proposed fault detection, identification and accommodation architecture is applicable to various generic rotorcraft machines. The proposed system will greatly reduce the operational and developmental costs and serve as an essential component in an autonomous control system.
This paper discusses the developed Adaptive Neural Control architecture for on-line system identification and real-time adaptive control. After reviewing existing literature involving controls of structural vibration, we report new developments carried out under the adaptive neural control program for the USAF Phillips Laboratory. The new results include a neural control architecture suitable for MIMO systems subjected to tonal disturbances that is capable of optimizing vibration suppression in the presence of sensor or actuator failures. This architecture was demonstrated in a series of tests on the ASTREX facility.
A hybrid neural control system incorporated with feedforward and feedback dynamics is advocated for flexible multibody structures. The proposed neural controller is designed to achieve trajectory slewing of structural member as well as vibration suppression for precision pointing capability. The feedforward path corresponds to the steady-state output of the dynamics while the feedback path stabilizes the transient-state of the motion. In the spirit of model reference adaptive control, we utilize adaptive time-delay radial basis function networks as a building block to allow the neural network to function as an indirect closed-loop controller. The horizon-of-one predictive controllers cooperatively regulates the dynamics of the nonlinear structure to follow the prespecified reference signals asymptotically. The proposed control strategy is validated in the experimental facility, called the Planar Articulating Controls Experiment which consists of a two-link flexible planar structure constrained to move over a granite table. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via a realistic structural test bed.
A distributive neural control system is advocated for flexible multibody structures. The proposed neural controller is designed to achieve trajectory slewing of a structural member as well as vibration suppression for precision pointing capability. The motivation to support such an innovation is to pursue a real-time implementation of a robust and fault tolerant structural controller. The proposed control architecture which takes advantage of the geometric distribution of piezoceramic sensors and actuators has provided a tremendous freedom from computational complexity. In the spirit of model reference adaptive control, we utilize adaptive time-delay radial basis function networks as a building block to allow the neural network to function as an indirect closed-loop controller. The horizon-of-one predictive controllers cooperatively regulates the dynamics of the nonlinear structure to follow the prespecified reference models asymptotically. The proposed control strategy is validated in the experimental facility, called the Planar Articulating Controls Experiment which consists of a two-link flexible planar structure constrained to move over a granite table. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via a realistic structural test bed.
The design of control algorithms for large space structures, possessing nonlinear dynamics which are often time-varying and likely ill-modeled, presents great challenges for all current methodologies. These limitations have led to the pursuit of a robust and fault tolerant structural controller. In the present paper, we propose the use of adaptive time-delay radial basis function (ATDRBF) networks as a learning controller in system identification and dynamic control of flexible structures. The ability of such neural networks to approximate arbitrary continuous functions offers an efficient means of vibration suppression and trajectory maneuvering for precision pointing capability. The ATDRBF network, which incorporates adaptive time-delays and interconnection weights, provides a feasible and flexible modeling technique to effectively capture all of the spatiotemporal interactions among the structure members. In the spirit of model reference adaptive control, we utilize the ATDRBF network as a building block to allow the neural network to function as a closed-loop controller. The controller regulate the dynamics of the nonlinear plant to follow a prespecified reference model asymptotically. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via specific examples.