Finding Unexpected Events in Staring Continuous-Dwell Sensor Data Streams Via Adaptive Prediction

This research produced a Predictive Anomaly Detector (PAD). It is an adaptive prediction-based approach to detecting unexpected events in data streams drawn from staring continuous-dwell sensors. The underlying technology is spectrum independent and does not depend on correlated data (neither temporal nor spatial) to achieve improved detection and extraction in highly robust environments. ( robust environment refers to the data stream\u27s control law being variable and the spectral content covering a wide range of wavelengths.) The resulting approach uses a network of simple building-block equations (basis functions) to predict the non-event data and thereby present subtle sub-streams to a detection model as potential events of interest. The prediction model is automatically created from sequential observations of the data stream. Once model construction is complete, it continues to evolve as new samples arrive. Each sample value that is sufficiently different from the model\u27s predicted value is postulated as an unexpected event. A subsequent detection model uses a set of rules to confirm unexpected events while ignoring outliers. Intruder detection in robust video scenes is the main focus, although one demonstration achieved voice detection in a noisy audio signal. These demonstrations are coupled to a concept of operations that emphasizes the spectrum-independence of this approach and its integration with other processing requirements such as target recognition and tracking. Primary benefits delivered by this work include the ability to process large data volumes for obscured or buried information within highly active environments. The fully automated nature of this technique helps mitigate manning shortfalls typically associated with sorting through large volumes of surveillance data using trained analysts. This approach enables an organization to perform automated cueing for these analysts so that they spend less time examining data where nothing of interest exists. This maximizes the value of skilled personnel by using them to assess data with true potential. In this way, larger data volumes can be processed in a shorter period of time leading to a higher likelihood that important events and signals will be found, analyzed, and acted upon

A method and application of machine learning in design

This thesis addresses the issue of developing machine learning techniques for the acquisition and organization of design knowledge to be used in knowledge-based design systems. It presents a general method of developing machine learning tools in the design domain. An identification tree is introduced to distinguish different approaches and strategies of machine learning in design. Three existing approaches are identified: the knowledge-oriented, the learner-oriented, and the design-oriented approach. The learner-oriented approach is critical, which focuses on the development of new machine learning tools for design knowledge acquisition. Four strategies that are suitable for this approach are: specialization, generalization, integration and exploration. A general method, called MLDS (Machine Learning in Design with 5 steps), of developing machine learning techniques in the design domain is presented. It consists of the following steps: 1) identify source data and target knowledge; 2) determine source representation and target representation; 3) identify the background knowledge available; 4) identify the features of data, knowledge and domain; and 5) develop (specialize, generalize, integrate or explore) a machine learning tool. The method is elaborated step by step and the dependencies between the components are illustrated with a corresponding framework. To assist in characterising the data, knowledge and domain, a set of formal measures are introduced. They include density of dataset, size of description space, homogeneity of dataset, complexity of domain, difficulty of domain, stability of domain, and usage of knowledge. Design knowledge is partitioned into two main types: empirical and causal. Empirical knowledge is modelled as empirical associations in categories of design attributes or empirical mappings between these meaningful categories. Eight types of empirical mappings are distinguished. Among them the mappings from one multiple dimensional space to another are recognized as the most important for both knowledge-based design systems and machine learning in design. The MLDS method is applied to the preliminary design of a learning model for the integration of design cases and design prototypes. Both source and target representations use the framework of design prototypes. The function-behaviour-structure categorization of design prototypes is used as background knowledge to improve both supervised and unsupervised learning in this task. Many-to-many mappings and time- or order-dependent data are discovered as the most important characteristics of the design domain for machine learning. Multiple attribute prediction and the capture of design concept ‘drift’ are identified as challenging tasks for machine learning in design. After the possibilities and limitations of solving the problem by modifying existing learning methods (both supervised and unsupervised) are considered, a learning model is created by integrating several learning techniques. The basic scheme of this model is that of goal-driven concept formation, which consists of flexible categorization, extensive generalization, temporary suspension, and cognitively-based sequence prediction in design. The learning process is described as follows: each time one category of attributes is treated as the predictive feature set and the remaining as the predicted feature set; a conceptual hierarchy or decision tree is constructed incrementally according the predictive features of design cases (but statistical information is generalized with both feature sets); whenever the predictive or the predicted feature set of a node becomes homogeneous, the construction process at that branch will temporarily suspend until a new case arrives and breaks this homogeneity; frequency—based prediction at indeterminate nodes is replaced with a cognitively-based sequence prediction, which allows the more recent cases to have stronger influence on the determination of the default or predicted values. An advantage of this scheme is that with the single learning algorithm, all the types of empirical mappings between function, behaviour and structure or between design problem specification and design solution description can be generalized from design cases. To enrich the indexing facilities in a conceptual hierarchy and improve its case retrieval ability, extensive generalization based memory organizations are investigated as alternatives for concept formation. An integration of the above learning techniques reduces the memory requirement of some existing extensive generalization models to a level applicable to practical problems in the design domain. The MLD5 method is particularly useful in the preliminary design of a learning system for the identification of a learning problem and of suitable strategies for solving the problem in the domain. Although the MLDS method is developed and demonstrated in the context of design, it is independent of any particular design problems and is applicable to some other domains as well. The cognitive model of sequence-based prediction developed with this method can be integrated with general concept formation methods to improve their performance in those domains where concepts drift or knowledge changes quickly, and where the degree of indeterminacy is high