Using soft computing tools for piezometric level prediction

The safety assessment of dams is a complex task that is made possible thanks to a constant monitoring of pertinent parameters. Once collected, the data is processed by statistical analysis models in order to describe the behaviour of the structure. The aim of those models is to detect early signs of abnormal behaviour so as to take corrective actions when required. Because of the uniqueness of each structure, the behavioural models need to adapt to each of these structures, thus flexibility is required. Simultaneously, generalisation capacities are sought, so a trade-off has to be found. This flexibility is even more important when the analysed phenomenon is characterised by non-linear features, as it is the case for the piezometric levels (PL) monitored at the rockconcrete interface of the arch dam that this study focuses on. In that case, the linear models that are classically used by engineers show insufficient performances. Consequently, interest naturally grows for the advanced learning algorithms known as machine learning techniques. In this work, the aim is to compare the predictive performances and generalization capacities of three different Data Mining algorithms that are likely to be used for monitoring purposes: Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Multiple Regression (MR). The achieved results show that SVM and ANN stand out as the most efficient algorithms, when it comes to analysing non-linear monitored phenomenon. Through a global sensitivity analysis, the influence of the models’ attributes was measured, evidencing a high impact of Z (relative trough) in PL prediction.info:eu-repo/semantics/publishedVersio

Understandable Database Mining In Imprecise Domains

An unsupervised database mining methodology is under development. A particular goal is that the process be understandable. This methodology tries to identify relationships having the most information value through a progressive reduction of cognitive dissonance. This work is dependent on soft computing tools

Memristor Crossbar-based Hardware Implementation of IDS Method

Ink Drop Spread (IDS) is the engine of Active Learning Method (ALM), which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system subjected to modeling. In spite of its excellent potential in solving problems such as classification and modeling compared to other soft computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of IDS method based on the memristor crossbar structure. In addition of simplicity, being completely real-time, having low latency and the ability to continue working after the occurrence of power breakdown are some of the advantages of our proposed circuit.Comment: 16 pages, 13 figures, Submitted to IEEE Transaction on Fuzzy System

Case-based reasoning: concepts, features and soft computing

Here we first describe the concepts, components and features of CBR. The feasibility and merits of using CBR for problem solving is then explained. This is followed by a description of the relevance of soft computing tools to CBR. In particular, some of the tasks in the four REs, namely Retrieve, Reuse, Revise and Retain, of the CBR cycle that have relevance as prospective candidates for soft computing applications are explained

Possible Applications of Neural Networks in Managing Urban Road Networks

Life-cycle management of urban road networks as a part of an urban system is a very complex process from the management standpoint of social, technical and economic aspects. The complexity and multidisciplinarity of such a problem suggest the need for using soft computing tools as well as multicriteria analysis and group decision-making. Recently, there is a significant increase in using various soft computing tools, especially neural networks, for different prediction purposes in the field of road construction planning and management. Along with known advantages of such a prediction method, yet some applications showed the shortcomings. In that sense, the focus of this research is on possible applications of neural networks related to the life-cycle phases during the management of urban road projects. This is done in both horizontal (projects‘ life-cycle phases) and vertical (hierarchical decisionmaking levels) approach. The final aim of the research is to compare and highlight the possible applications of neural networks as a prediction tool and support for decision-making in urban road management

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

Implementation of DMAs in Intermittent Water Supply Networks Based on Equity Criteria

[EN] Intermittent supply is a common way of delivering water in many developing countries. Limitations on water and economic resources, in addition to poor management and population growth, limit the possibilities of delivering water 24 h a day. Intermittent water supply networks are usually designed and managed in an empirical manner, or using tools and criteria devised for continuous supply systems, and this approach can produce supply inequity. In this paper, an approach based on the hydraulic capacity concept, which uses soft computing tools of graph theory and cluster analysis, is developed to define sectors, also called district metered areas (DMAs), to produce an equitable water supply. Moreover, this approach helps determine the supply time for each sector, which depends on each sector¿s hydraulic characteristics. This process also includes the opinions of water company experts, the individuals who are best acquainted with the intricacies of the network.Ilaya-Ayza, AE.; Martins-Alves, C.; Campbell-Gonzalez, E.; Izquierdo Sebastián, J. (2017). Implementation of DMAs in Intermittent Water Supply Networks Based on Equity Criteria. Water. 9(11):1-20. doi:10.3390/w9110851S12091

Automatic RADAR Target Recognition System at THz Frequency Band. A Review

The development of technology for communication in the THz frequency band has seen rapid progress recently. Due to the wider bandwidth a THz frequency RADAR provides the possibility of higher precision imaging compared to conventional RADARs. A high resolution RADAR operating at THz frequency can be used for automatically detecting and segmenting concealed objects. Recent advancements in THz circuit integration have opened up a wide range of possibilities for on chip applications, like of security and surveillance. The development of various sources and detectors for generation and detection of THz frequency has been driven by other techniques such as spectroscopy, imaging and impulse ranging. One of the central vision of this type of security system aims at ambient intelligence: the computation and communication carried out intelligently. The need for higher mobility with limited size and power consumption has led to development of nanotechnology based THz generators. In addition to this some of the soft computing tools are used for detection of radar target automatically based on some algorithms named as ANN, RNN, Neuro-Fuzzy and Genetic algorithms. This review article includes UWB radar for THz signal, its characteristics and application, Nanotechnology for THz generation and issues related to ATR