Answer Set Programming (ASP) is a powerful paradigm based on logic programming for non-monotonic reasoning. Current ASP implementations are restricted to “grounded range-restricted function-free normal programs” and use an evaluation strategy that is “bottom-up” (i.e., not goal-driven). Recent introduction of coinductive Logic Programming (co-LP) has allowed the development of top-down goal evaluation strategies for ASP. In this paper we present this novel goal-directed, top-down approach to executing predicate answer set programs with co-LP. Our method eliminates the need for grounding, allows functions, and effectively handles a large class of predicate answer set programs including possibly infinite ones.
Most current machine learning systems for medical decision support do not produce any indication of how reliable each of their predictions is. However, an indication of this kind is highly desirable especially in the medical field. This paper deals with this problem by applying a recently developed technique for assigning confidence measures to predictions, called conformal prediction, to the problem of acute abdominal pain diagnosis. The data used consist of a large number of hospital records of patients who suffered acute abdominal pain. Each record is described by 33 symptoms and is assigned to one of nine diagnostic groups. The proposed method is based on Neural Networks and for each patient it can produce either the most likely diagnosis together with an associated confidence measure, or the set of all possible diagnoses needed to satisfy a given level of confidence.
One solution to the problem of one-step-ahead prediction of the electricity power consumption in suburban areas is proposed. It is based on implementation of artificial neural networks (ANN) that are properly structured for prediction. A structure named Extended Feed Forward Accommodated for Prediction (EFFAP) ANN is introduced. A proper arrangement of training data is advised. After implementation, promising results were obtained giving, in most instances, predictions with an error less than 1%. In the worst situation observed, the discrepancy between the target and predicted values is 9% the most which we consider as acceptable. The method proposed implements ANNs that are generally widely known but creates new structure which is fully original. In comparison to existing solutions implemented on similar concepts our method is based on much smaller amount of measured data and/or exhibits incomparably simpler ANNs. That allows for its application in dynamic (on-line) forecasting system since unchanged network structure is used during time. In addition, it simplifies the initial solution creation for the training process that, again, enables automatization of the forecast. The method was integrated into a remote-power-reading, billing, control, and planning system. It is our opinion that the method proposed may be implemented with equal success to one-step-ahead prediction of broader class of time series exhibiting inherent quasi-periodical properties.
It is well established that quasi-brittle materials experience visco-elastic creep strain under sustained loads. The creep strain represents the non-instantaneous strain that occurs with time when the stress is sustained. Most of the existing creep prediction models could achieve relatively low accuracy level because of the creep dependency oil large number of parameters (e.g. relative humidity, stress level, age of loading). In addition, creep strain behavior is considered as a time-dependent visco-elastic property of masonry structures. This manuscript investigates the potential use of recurrent neural networks (RNN) for predicting creep of structural masonry. The main merit of using RNN is that RNN paradigm assembles the time-dependent process within its architecture during training. Thus, RNN becomes more capable of capturing time-dependent nonlinear relationships than the existing creep prediction model. Several network architectures are examined to enhance models' performance. The results showed that RNN architectures can reduce the creep prediction error by 30% when compared to feed-forward neural network models.
This paper presents a new approach for fault detection in power transmission line using support vector machine (SVM) technique. This method uses fault current samples for half cycle from the inception of fault. The line currents are applied as inputs to SVM for fault detection. The SVM is trained with linear, polynomial and Radial Basis Function (RBF) Kernel. The feasibility of the proposed method has been tested on a 110-kV, 21.687-km transmission line for all the ten types of fault using MATLAB Simulink. Upon testing on 640 fault cases with varying fault resistance, fault inception angle and fault distance the performance of the proposed method is quite promising. The proposed method is also tested with real time data recorded by digital fault recorder (DFR). The results encourage the use of this proposed method for detection of various faults in transmission line accurately.
