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.
The current statistical, machine learning, and data mining (data analysis) algorithms available are only suited for analyzing single datasets (studies) in isolation. The findings of the analysis and the knowledge produced (e.g,. important predicting variables and risk-factors, rules for prediction, etc.) are published and this knowledge is then manually synthesized within the human brain. In this paper we argue that this process can be further automated: it is possible to develop algorithms that integratively analyze (co-analyze) data from different studies with different characteristics and measured quantities (variables). To develop such algorithms, we further argue that modeling and inducing causal relations is necessary. We call this process Integrative Causal Analysis or INCA. We present three cases and corresponding existing or newly developed algorithms and techniques that illustrate the feasibility of integrative causal analysis and the key enabling ideas. Specifically, we discuss algorithms for learning causal relations from (a) data obtained over different experimental conditions, (b) data over different variable sets, and (c) data over semantically similar variables that nevertheless cannot be pulled together for various technical reasons. The latter case particularly, often occurs in the setting of analyzing multiple gene-expression datasets. The grand vision of Integrative Causal Analysis is to enable the automated or semi-automated, large-scale integration of a large part of the available data to construct causal models involving a significant part of human concepts.
In this paper, determine the fault type of failed power transformers with a few key gases with artficial neural network (ANN) using Levenberg-Marquardt algorithm is presented. Three Dissolved Gas in oil Analysis (DGA) criteria commonly used in industry was trained and tested with neural network Levenberg-Marquardt algorithm. Three key gases Methane (CH(4)), Ethylene (C(2)H(4)) and Acetylene (C(2)H(2)) were chosen for this study. Percentage of each gas used as inputs of ANN. The output is one of the fault types PD, D1, D2, T1, T2, T3. The results of this study are useful in development of a reliable transformer automated diagnostic system using artificial neural network. Multiple layer feedforward ANN is trained with Levenberg-Marquardt learning algorithm. This algorithm appears to be the fastest method for training moderate-sized feedforward neural networks. We determined best neural network topology and reached 100% diagnostic success.
Early failure detection in motor pumps is an important issue in prediction maintenance. An efficient condition-monitoring scheme is capable of providing warnings and predicting the faults at early stages. Usually, this task is executed by humans, but the logical progression of the condition-monitoring technologies is the automating the diagnosis process. To this end, intelligent diagnosis systems are used. Many researchers have explored artificial intelligence techniques to diagnose failures in general. However, all papers found in literature are related to a specific problem that can appear in many different machines. In real applications, when the expert analyzes a machine, not only one problem appears, but more than one problem may appear together. So, it is necessary to propose new methods to assist diagnosis, looking for a set of occurring faults. In this work, we describe methods to support motor pump failure diagnoses based on parametric net model and ANNs committees, and we propose methods to combine them. We describe a case study realized with a real dataset. The results obtained with these methods are encouraging.
A hybrid algorithm that incorporates two biologically inspired computational intelligence methods was used for the assessment of abdominal pain. Namely, Genetic Algorithms (GA) where used in search for the optimal subset of clinical diagnostic factors that can be given as inputs for Probabilistic Neural Networks (PNN) to perform medical diagnosis based on the clinical data. Thus, the implemented GA was a two-objective one. The first objective was to minimize the number of diagnostic factors that were considered for medical diagnosis. The second objective was to minimize the Mean Square Error of the constructed PNN at the testing phase. The obtained results of the proposed hybrid algorithm are related favorably to the corresponding ones derived by applying Receiver Operating Characteristic analysis. Eventually, it was found that a number up to 60% of the diagnostic factors that are recorded in patient's history may be omitted without any loss in clinical assessment validity, while, at the same time, the performance of the genetically pruned PNN is improved in terms of execution speed and prediction accuracy.
Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (posts, songs, pictures, web links, products etc.). Political blogs can recommend posts to users, based on tags they have in common with other similar users. However, a post in politics may be interpreted in a number of ways by different users. This is because terms, especially in politics, carry an ideological burden and therefore it is very likely for posts to present a semantic ambiguity. The significance of this study is that in contrast to current recommendation algorithms, we apply Higher Order Singular Value Decomposition (HOSVD) on a 3-dimensional tensor to find latent semantic relationships between the three types of entities that exist in a social blogging system: users, posts, and tags. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Wordpress and Technorati). Our results show significant improvements in terms of effectiveness measured through recall/precision.
This manuscript attempts to acquaint with the suitability of Artificial Neural Networks (ANN) especially Radial Basis Function RBF to predict water quality parameters. Search for optimal model parameters within RBF-NN is carried out in two steps, each of which can be made to be more efficient and much faster than in MLP. This study focused on electrical conductivity, total dissolved solids and turbidity as the main water quality parameters for rivers. The employed model proved its capabilities to mimic the inter-relationship between such water quality parameters. Normalization and partitioning for the row data have been carried out to accelerate the training process and to achieve pre-defined Sum Square Error SSE equal to 10(-4). The results show that the proposed RBF-NN after normalization and partitioning outperformed the linear regression model and achieve Mean Absolute Prediction Error MAPE equal to 8.3%.
This paper introduces a new control approach based on multi-layered perceptron to handle reactive power of the system with the help of a synchronous motor compensator. The neural system was trained with backpropagation with momentum. The perceptron parameters were obtained from an off-line training and then inserted in a microcontroller for the correction. The results have shown that the approach presented in this work provides accurate, simple, low-cost and fast correction than the conventional compensators available in the literature.
This paper introduces a new web ant-colony based search methodology, called the Ant-Seeker. As many other successfully applied biological-inspired algorithms and metaphors, Ant-Seeker is capable of tracing relevant information in web graphs, imitating the mission of real ants when they seek food far from their nests. The methodology uses swarm intelligence joined with soft computing techniques, as well as information retrieval theory in directed web crawling (web harvesting techniques). We describe the theoretical background and technologies used, as well as all the necessary amendments and considerations for applying ant colony-based algorithms to the Web. Finally, we describe an initial assessment of Ant-Seeker in selected sub-universes of the Web.
Voltage sag can cause hours of downtime, substantial loss of product and also can attribute to malfunctions, instabilities and shorter lifetime of the load. Accurate voltage sag source location can help to minimize the loss and problems caused by voltage sag in a power distribution system. This paper presents a development of a current component index algorithm to locate the source of voltage sag in a power distribution system. The product of the RMS current and the power factor angle at the monitoring point is employed for the sag source location. A graph of this product against time is plotted. The voltage sag source location is determined by examining the magnitude of the current component index at the beginning of the sag. The prototype of the method is also described in the paper. The proposed method has been verified by simulations and the results are proven to be in agreement when compared with the slope of the line fitting parameters of current and voltage method. This paper only focuses on a single source implementation.
This paper presents a novel methodology for unsupervised mining of patterns of pulmonary infections from plain chest radiographs. The methodology is based on a hierarchical scheme of partitional clusterings fusing information represented by non-negative intensity and textural image features. Partitional clustering is realized by factorization of the feature matrices under non-negativity constraints. In order to enhance its clustering performance a dyadic cluster merging approach has been devised. The spatial distribution of the mined radiographic patterns comprises a cue for the assessment of the extent of an infection. The results of the comprehensive experimentation performed show that the proposed approach achieves an accuracy of 94.1%, outperforming both representative unsupervised and supervised mining approaches.
Fuzzy logic is employed to develop a rule-based approach to detect, isolate and characterize sensor failures in electric power systems. Redundant sensor validation using instantaneous and episodic signal validation is used in the detection of abrupt and incipient faults, respectively. In addition, sensor anomaly characterization is accomplished via a fuzzy logic system incorporating diverse statistical signatures. Sensor anomalies are characterized as spikes and/or jumps. Simulation results from fault detection, isolation and characterization of sinusoidal and non-sinusoidal data are presented.
This paper describes an autonomous agent conceived for automating the decision making process for pricing products. Product pricing involves the interaction of decision makers with different - possibly conflicting - points of view. Our approach allows for applying individual pricing policies to each product by taking into account different points of view expressed through different arguments and the dynamic environment of the application. This is done through the use of argumentation technology. The agent development process using the Agent Systems Engineering Methodology (ASEME) is also presented.
In this paper, the architecture of the existing Quad Tree Network (QUAD) has been modified in an attempt to design a better fault-tolerant Multistage Interconnection Network (MIN) with improved bandwidth and hence probability of acceptance. Another significant feature of the proposed network (M_QUAD) is that there is only a slight degradation in its performance parameters (i.e. bandwidth and probability of acceptance) even under various faulty conditions. Bandwidth and probability of acceptance of the designed MIN has been evaluated under both scenarios (i.e. when the network is fault-free and fully functional and also under faulty conditions). Performance evaluation of the existing MIN i.e. Quad has also been done under various scenarios. The detailed analysis and architectural designing has been done for generalized network of size NxN and also specifically for M_QUAD of size 16x16. The detailed performance analysis has been done for M_QUAD as well as for existing Quad tree network of size 16x16. The requirement of the minimum number of buffers for each SE has also been modeled using queuing M/M/1 model. A very prominent feature of the designed MIN is that it remains fully operational (i.e. without any input-output connections lost) on the basis of fault-tolerant sub-network only i.e the MIN is highly fault-tolerant.
