On the decomposition of tabular knowledge systems.

Recently there has been a growing interest in the decomposition of knowledge based systems and decision tables. Much work in this area has adopted an informal approach. In this paper, we first formalize the notion of decomposition, and then we study some interesting classes of decompositions. The proposed classification can be used to formulate design goals to master the decomposition of large decision tables into smaller components. Importantly, carrying out a decomposition eliminates redundant information from the knowledge base, thereby taking away -right from the beginning- a possible source of inconsistency. This, in turn, renders subsequent verification and validation more smoothly.Knowledge; Systems;

Fuzzy Decision-Support System for Safeguarding Tangible and Intangible Cultural Heritage

In the current world economic situation, the maintenance of built heritage has been limited due to a lack of funds and accurate tools for proper management and implementation of these actions. However, in specific local areas, the maintenance and conservation of historical and cultural heritage have become an investment opportunity. In this sense, in this study, a new tool is proposed, for the estimation of the functional service life of heritage buildings in a local region (city of Seville, South Spain). This tool is developed in Art-Risk research project and consists of a free software to evaluate decisions in regional policies, planning and management of tangible and intangible cultural heritage, considering physical, environmental, economic and social resources. This tool provides a ranking of priority of intervention among case studies belonging to a particular urban context. This information is particularly relevant for the stakeholders responsible for the management of maintenance plans in built heritage

A SON Solution for Sleeping Cell Detection Using Low-Dimensional Embedding of MDT Measurements

Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93 accuracy

Computational simulation for concurrent engineering of aerospace propulsion systems

Results are summarized for an investigation to assess the infrastructure available and the technology readiness in order to develop computational simulation methods/software for concurrent engineering. These results demonstrate that development of computational simulation methods for concurrent engineering is timely. Extensive infrastructure, in terms of multi-discipline simulation, component-specific simulation, system simulators, fabrication process simulation, and simulation of uncertainties--fundamental to develop such methods, is available. An approach is recommended which can be used to develop computational simulation methods for concurrent engineering of propulsion systems and systems in general. Benefits and issues needing early attention in the development are outlined

Cyber-Physical System Architecture for Minimizing the Possibility of Producing Bad Products in a Manufacturing System

The new industry 4.0 requires the implementation of several cyber-physical systems to increase the level of productivity in a manufacturing system. This chapter proposes an architecture of a generic manufacturing system that requires the use of techniques of agile production, lean manufacturing, and statistical approaches. The combination of the previous techniques will be implemented in the architecture proposed for minimizing the possibility of producing bad products. Thus, the cyber-physical system architecture proposed will optimize the overall system thanks to the implementation of intelligent modules and control strategies. Moreover, 10 proposed actions will be described in detail. These actions can be implemented in cyber-physical systems that take into account five levels

Restructuring and simplifying rule bases.

Rule bases are commonly acquired, by expert and/or knowledge engineer, in a form which is well suited for acquisition purposes. When the knowledge base is executed, however, a different structure may be required. Moreover, since human experts normally do not provide the knowledge in compact chunks, rule bases often suffer from redundancy. This may considerably harm efficiency. In this paper a procedure is examined to transform rules that are specified in the knowledge acquisition process into an efficient rule base by way of decision tables. This transformation algorithms allows the generation of a minimal rule representation of the knowledge, and verification and optimization of rule bases and other specification (e.g. legal texts, procedural descriptions, ...). The proposed procedures are fully supported by the PROLOGA tool.

A branch and bound algorithm to optimize the representation of tabular decision processe.

Decision situations have various aspects: knowledge acquisition and structuring, knowledge representation, knowledge validation and decision making. It has been recognized in literature that decision tables can play an important role in each of these stages. It is however not necessary to use only one representation formalism during the whole life cycle of an intelligent system. Likewise it is possible that different formats of the same formalism serve different purposes in the development process.Important in this respect is the search for automated and, if possible, optimized transitions between different formats of a formalism and between various formalisms. In this paper a branch and bound algorithm is presented that transforms expanded decision tables, that, because of their explicit enumeration of all decision cases primarily serve an acquisition and verification function, into optimized contracted decision tables, primarily used as target representation of a decision process. An optimal contracted decision table is a contracted decision table with a condition order which results in the minimum number of contracted decision columns.

Embedded intelligence for electrical network operation and control

Integrating multiple types of intelligent, mulitagent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by technical issues confound these data, while the volume and velocity of data prevent manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data. After identifying end-user needs and defining anomalies, we ranked their importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, using other water-quality variables as covariates reduced performance due to complex relationships among variables. Classification of drift and periods of anomalously low or high variability improved when we applied replaced anomalous measurements with forecasts, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies, but were also less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, all feature-based methods produced low false positive rates, but did not and require training or optimization. Rule-based methods successfully detected impossible values and missing observations. Thus, we recommend using a combination of methods to improve anomaly detection performance, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and analysts for optimal outcomes with respect to both detection performance and end-user needs. Our framework is applicable to other types of high frequency time-series data and anomaly detection applications

Maintenance Knowledge Management with Fusion of CMMS and CM

Abstract- Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems. Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution. Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes)