Search

Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology. [ABSTRACT FROM PUBLISHER]

Efficient operation of the integrated optimization or automation system in an industrial plant depends mainly on good measurement of product quality. However, measuring or estimating the product quality online in many industrial plants is usually not feasible using the available techniques. In this paper, a data-driven soft-sensor using case-based reasoning (CBR) and fuzzy-similarity rough sets is proposed for product quality estimation. Owning to the sustained learning ability, the modeling of a CBR soft-sensor does not need any additional model correction which is otherwise required by the neural network based methods to overcome the slow time-varying nature of industrial processes. Because the conventional k-nearest neighbor (k-NN) algorithm is strongly influenced by the value of k, an improved k-NN algorithm with dynamic adjustment of case similarity threshold is proposed to retrieve sufficient matching cases for making a correct estimation. Moreover, considering that the estimation accuracy of the CBR soft-sensor system is closely related to the weights of case feature, a feature weighting algorithm using fuzzy-similarity rough sets is proposed in this paper. This feature weighting method does not require any transcendental knowledge, and its computation complexity is only linear with respect to the number of cases and attributes. The developed soft-sensor system has been successfully applied in a large grinding plant in China. And the application results show that the system has achieved satisfactory estimation accuracy and adaptation ability. [ABSTRACT FROM AUTHOR]

This paper presents a case-based Windows graphic package developed by the authors for the education and training ofpower system restoration. In order to overcome the inherent complexities associated with a restoration switching sequence, a case-based reasoning (CBR) algorithm has been developed. An object-oriented Windows-based graphical user interface (GUI) has been developed to communicate with the expert system and to visualize outputs. Simulation results on a part of the Saskatchewan Power (SaskPower) network have been presented in this paper. The restoration practices of the SaskPower network have been collected and stored in a knowledge database called case library. The power flow and the implementation risk analysis tools have been incorporated into the expert system. A risk analysis tool has been used to determine the implementation risk due to the imperfect switching actions. Using the GUI, a user can simulate a blackout event on the system under study. The expert system proposes a restoration proposal after reasoning with the past solutions available in the case library. Using the power flow analysis tool, the user can check the system scenarios after the restoration proposal is implemented. [ABSTRACT FROM PUBLISHER]

The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC. [ABSTRACT FROM AUTHOR]

The effectiveness of a case-based reasoning system for the preliminary design of electromagnetic (EM) devices is heavily dependent on having a highly competent case base. This allows the design space to be explored faster and high-quality decisions to be made. However, good cases are difficult to find. In this paper, we have developed a competent case base for EM devices through a refined combined competence model described in this paper. This case base involves functional, geometrical, and material information for EM devices. The practical significance of this competence model is validated through investigation of a real-world induction motor case base with high dimensionality. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) is an attractive approach to the design of electromagnetic (EM) devices because it draws upon experiential knowledge to tackle new problems. This paper presents a hybrid design model to represent the structural, behavioral, mathematical, and material information for a generic EM device. Using this representation method, a hierarchical case library can be constructed for the preliminary design of EM devices and act as input and design knowledge storage for a CBR-based creative design system. [ABSTRACT FROM AUTHOR]

The health sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available in the market as commercial products. However, many of the projects/systems are intended to be commercialized. [ABSTRACT FROM PUBLISHER]

Our recently proposed cognitive methodology for optical amplifier gain adjustment, that relies on case-based reasoning, showed optical signal-to-noise ratio improvements over time demonstrating the cognition process regardless the deployed amplifier type. In this paper, we extend our preliminary analysis exploring the cognitive methodology benefits for different and larger network topologies. The obtained results show agreement between networks, demonstrating the methodology suitability regardless the network scenario. [ABSTRACT FROM PUBLISHER]

In the smelting process of fused magnesium furnaces (FMFs), frequent changes in the raw material granule size and impurity constituent will cause the arc resistance between the lower end of the electrode and the surface of the molten pool to vary, and thus, the smelting currents fluctuate. Consequently, abnormal conditions, which can arise if the setpoints of electrode currents are not properly adjusted on time, will causethe performance to deteriorate or even the overall operation to stall. Through analysis of the characteristics of different operating conditions, this paper presents a data-driven abnormal condition identification and self-healing control system. The proposed system extracts the identification rules according to the current tracking error, as well as the rate and duration of the current fluctuations, and identifies the abnormal conditions based on rule-based reasoning. The self-healing control is developed using case-based reasoning to correct the current setpoints based on the identification results. The outputs of the control loop track the corrected setpoints, thereby forcing the process to recover from the abnormal conditions. The proposed method and the developed control system have been applied to a real FMF, and substantial improvement is achieved with many benefits provided to the factory. The implementation results show that occurrence of abnormal conditions has been reduced by more than 50%, and the product quality has been increased by more than 2%. [ABSTRACT FROM PUBLISHER]

Theories of evidence have already been applied more or less successfully in the fusion of remote sensing images. These attempts were based on the classical evidential reasoning which works under the condition that all sources of evidence and their fusion results are related to the same invariable (static) frame of discernment. When working with multitemporal remote sensing images, some change occurrences are possible between two images obtained at a different period of time, and these changes need to be detected efficiently in particular applications. The classical evidential reasoning is adapted for working with an invariable frame of discernment over time, but it cannot efficiently detect nor represent the occurrence of change from heterogeneous remote sensing images when the frame is possibly changing over time. To overcome this limitation, dynamic evidential reasoning (DER) is proposed for the sequential fusion of multitemporal images. A new state-transition frame is defined in DER, and the change occurrences can be precisely represented by introducing a statetransition operator. Two kinds of dynamical combination rules working in the free model and in the constrained model are proposed in this new framework for dealing with the different cases. Moreover, the prior probability of state transitions is taken into account, and the link between DER and Dezert-Smarandache theory is presented. The belief functions used in DER are defined similarly to those defined in the Dempster-Shafer theory. As shown in the last part of this paper, DER is able to estimate efficiently the correct change detections as a postprocessing technique. Two applications are given to illustrate the interest of DER: The first example is based on a set of two SPOT images acquired before and after a flood, and the second example uses three QuickBird images acquired during an earthquake event. [ABSTRACT FROM AUTHOR]

This paper proposes a novel scheme for evaluating an emotional response to color images. The proposed scheme uses case-based reasoning in which the prototypical color images for each emotion are stored as cases and are compared with the images to be evaluated. In the comparison, the similarities in terms of image descriptors play an important role, and their combination is crucial for the construction of a proper similarity measure. In the training phase of the proposed scheme, the weights that represent the unequal importance of each descriptor is determined in order to obtain a similarity measure that can be used to evaluate and classify a color image with respect to a pair of emotions. Prior to classification, the representative color images are chosen for each emotion by human subjects and are stored as cases. The stored images are compared with an image to be classified using the constructed similarity measure to determine which emotion is appropriate between a pair of emotions. In this study, we used color and texture descriptors recommended by MPEG-7, represented as high-dimensional vectors. In the training, we proposed a method based on the rough approximation and the fuzzy inter- and intra-similarities to determine the weights that represent the unequal importance of the complex MPEG-7 descriptors. Experimental results show a promising performance for the proposed scheme, and better performance could be achieved by including more prototypical images as cases. [ABSTRACT FROM PUBLISHER]

In this paper, we describe and evaluate a Web service discovery framework using OWL-S advertisements, combined with the distinction between service and Web service of the WSMO Discovery Framework. More specifically, we follow the Web service discovery model, which is based on abstract and lightweight semantic Web service descriptions, using the Service Profile ontology of OWL-S. Our goal is to determine fast an initial set of candidate Web services for a specific request. This set can then be used in more fine-grained discovery approaches, based on richer Web service descriptions. Our Web service matchmaking algorithm extends object-based matching techniques used in Structural Case-based Reasoning, allowing 1) the retrieval of Web services not only based on subsumption relationships, but exploiting also the structural information of OWL ontologies and 2) the exploitation of Web services classification in Profile taxonomies, performing domain-dependent discovery. Furthermore, we describe how the typical paradigm of Profile input/output annotation with ontology concepts can be extended, allowing ontology roles to be considered as well. We have implemented our framework in the OWLS-SLR system, which we extensively evaluate and compare to the OWLS-MX matchmaker. [ABSTRACT FROM AUTHOR]

Assessing the similarity between cases is a key aspect of the retrieval phase in case-based reasoning (CBR). In most CBR work, similarity is assessed based on feature value descriptions of cases using similarity metrics, which use these feature values. In fact, it might be said that this notion of a feature value representation is a defining part of the CBR worldview—it underpins the idea of a problem space with cases located relative to each other in this space. Recently, a variety of similarity mechanisms have emerged that are not founded on this feature space idea. Some of these new similarity mechanisms have emerged in CBR research and some have arisen in other areas of data analysis. In fact, research on kernel-based learning is a rich source of novel similarity representations because of the emphasis on encoding domain knowledge in the kernel function. In this paper, we present a taxonomy that organizes these new similarity mechanisms and more established similarity mechanisms in a coherent framework. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for retrieving promising candidate solutions in case-based problem solving. Our method, referred to as credible case-based inference, makes use of so-called similarity profiles as a formal model of the key hypothesis underlying case-based reasoning (CBR), namely, the assumption that similar problems have similar solutions. Proceeding from this formalization, it becomes possible to derive theoretical properties of the corresponding inference scheme in a rigorous way. In particular, it can be shown that, under mild technical conditions, a set of candidates covers the true solution with high probability. Thus, the approach supports an important subtask in CBR, namely, to generate potential solutions for a new target problem in a sound manner and hence contributes to the methodical foundations of CBR. Due to its generality, it can be employed for different types of performance tasks and can easily be integrated in existing CBR systems. [ABSTRACT FROM AUTHOR]

This paper introduces a framework of experienced-based decision making as an extension of case-based decision making, a recently proposed alternative to expected utility theory. In experienced-based decision making, an agent faced with a new decision problem acts on the basis of experience gathered from previous problems in the past, either through predicting the utility of potential actions or through establishing a direct relationship between decision problems and appropriate actions. In the paper, a realization of the latter approach in the form of "satisficing" decision trees is proposed. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to acquiring and using problem specific knowledge during a genetic algorithm (GA) search. A GA augmented with a case-based memory of past problem solving attempts learns to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a GA's population with appropriate intermediate solutions to similar previously solved problems. Perhaps, counterintuitively, simply injecting solutions to previously solved problems does not produce very good re- suits. We provide a framework for evaluating this GA-based machine-learning system and show experimental results on a set of design and optimization problems. These results demonstrate the performance gains from our approach and indicate that our system learns to take less time to provide quality solutions to a new problem as it gains experience from solving other similar problems in design and optimization. [ABSTRACT FROM AUTHOR]

Exploratory learning environments (ELEs) promote a view of learning that encourages students to construct and/or explore models and observe the effects of modifying their parameters. The freedom given to learners in this exploration context leads to a variety of learner approaches for constructing models and makes modelling of learner behavior a challenging task. To address this issue, we propose a learner modelling mechanism for monitoring learners’ actions when constructing/exploring models by modelling sequences of actions reflecting different strategies in solving a task. This is based on a modified version of case-based reasoning for problems with multiple solutions. In our formulation, approaches to explore the task are represented as sequences of simple cases linked by temporal and dependency relations, which are mapped to the learners’ behavior in the system by means of appropriate similarity metrics. This paper presents the development and validation of the modelling mechanism. The model was validated in the context of an ELE for mathematical generalisation using data from classroom sessions and pedagogically-driven learning scenarios. [ABSTRACT FROM PUBLISHER]

We examine the problem of constructing and maintaining a map of an autonomous vehicle's environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In the paper, the map is based on a two-dimensional (2-D) occupancy grid. The sensor readings are fused into the map using the Dempster-Shafer inference rule. This evidential approach with its multivalued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments. [ABSTRACT FROM PUBLISHER]

This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network's prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population.

This paper presents the architecture and initial usability results of an advanced insulin bolus calculator for diabetes (ABC4D), which provides personalized insulin recommendations for people with diabetes by differentiating between various diabetes scenarios and automatically adjusting its parameters over time. The proposed platform comprises two main components: a smartphone-based patient platform allowing manual input of glucose and variables affecting blood glucose levels (e.g., meal carbohydrate content and exercise) and providing real-time insulin bolus recommendations; and a clinical revision platform to supervise the automatic adaptations of the bolus calculator parameters. The system implements a previously in silico validated bolus calculator algorithm based on case-based reasoning, which uses information from similar past events (i.e., cases) to suggest improved personalized insulin bolus recommendations and automatically learns from new events. Usability of ABC4D was assessed by analyzing the system usage at the end of a six-week pilot study (n = 10). Further feedback on the use of ABC4D has been obtained from each participant at the end of the study from a usability questionnaire. On average, each participant requested 115 $\pm$ 21 insulin recommendations, of which 103 $\pm$ 28 (90%) were accepted. The clinical revision software proposed a total of 754 case revisions, where 723 (96%) adaptations were approved by a clinical expert and updated in the patient platform. [ABSTRACT FROM PUBLISHER]

This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from $166\pm 39$ to $150\pm 16$ in the adult populations ($p=0.03$) and from $167\pm 25$ to $162\pm 23$ in the adolescent population ($p=0.06$). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population. [ABSTRACT FROM PUBLISHER]

Life cycle assessment (LCA) is a fundamental tool used in eco-design. However, it can be costly and resource intensive. We take steps toward the automation of the inventory and impact analyses stages of LCA via the proposal and development of a case-based reasoning (CBR) procedure to estimate the ecological effects of a product. The case memory in CBR, which contains representations and ecological effects of known products, is organized using an extension of the function-behavior-structure (FBS) representation for products. The extension includes ecological characteristics and values. We develop similarity metrics to measure the distance between cases in the case memory and the new product. The k-medoids algorithm is used to cluster the case memory, our metrics enable cluster retrieval and case selection, and multiple linear regression analysis is employed for adaptation. Using a database of 100 fans, we test the accuracy of the proposed approach on a cross flow fan not in the database. The method gives ecological effect estimates within 3% of the true values when there are similar fans in the retrieved cluster and about 7% when the retrieved cluster does not contain similar fans. [ABSTRACT FROM PUBLISHER]

Human decision-making involves cognitive processes of selection, evaluation, and interpretation among candidate solutions in order to solve decision problems. Nonintelligent decision support systems (DSS) lack automatic interpretations, at least in a low level scale, which can lead to undesired solutions. To tackle this limitation, hence producing enhanced decision making, a hybrid intelligent decision support approach is presented, which combines case-based reasoning cycle, semiotic concepts, and self-organizing maps. In addition, a novel sign deconstruction mechanism is introduced as foundation of the new approach and affords better interpretability and contextualization of candidate solutions without compromising efficiency and precision. The obtained results confirm that our proposed approach has the potential to be readily applicable to decision problems, particularly the ones that are of subjective nature. Moreover, the put forward approach may integrate some unlikely elements of linguistics and cognitive science which could fundamentally help the enhancement of DSS. [ABSTRACT FROM AUTHOR]

Background: Given information on just a few prior projects, how do we learn the best and fewest changes for current projects? Aim: To conduct a case study comparing two ways to recommend project changes. 1) Data farmers use Monte Carlo sampling to survey and summarize the space of possible outcomes. 2) Case-based reasoners (CBR) explore the neighborhood around test instances. Method: We applied a state-of-the data farmer (SEESAW) and a CBR tool ((\cal W2)) to software project data. Results: CBR with (\cal W2) was more effective than SEESAW's data farming for learning best and recommended project changes, effectively reducing runtime, effort, and defects. Further, CBR with (\cal W2) was comparably easier to build, maintain, and apply in novel domains, especially on noisy data sets. Conclusion: Use CBR tools like (\cal W2) when data are scarce or noisy or when project data cannot be expressed in the required form of a data farmer. Future Work: This study applied our own CBR tool to several small data sets. Future work could apply other CBR tools and data farmers to other data (perhaps to explore other goals such as, say, minimizing maintenance effort). [ABSTRACT FROM PUBLISHER]

A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing (IC) is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user's soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns during the recommendation process, we propose two novel exponential Reinforcement Learning (RL) approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a Global Weighting (GW) approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches. [ABSTRACT FROM AUTHOR]