Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Challenges and opportunities of introducing Internet of Things and Artificial Intelligence applications into Supply Chain Management

The study examines the challenges and opportunities of introducing Artificial Intelligence (AI) and the Internet of Things (IoT) into the Supply Chain Management (SCM). This research focuses on the Logistic Management. The central research question is “What are the key challenges and opportunities of introducing AI and IoT applications into the Supply Chain Management?” The goal of this research is to collect the most appropriate literature to help create a conceptual framework, which involves the integration of the IoT and AI applications into contemporary supply chain management with the emphasis on the logistics management. Additionally, the role of 5G Network is closely studied in order to indicate its capabilities and the processing capacity that it can provide to the AI and IoT operations. In addition, the semi-structured online interview with the top managers from several companies was conducted in order to identify the degree of readiness of the companies for the AI and IoT applications in SCM. From the retrieved results, the major challenges of integrating the IoT into SCM are the security and privacy issues, the sensitivity of the data and high costs of the implementation at an initial stage. Moreover, the research results have shown that the IoT applications can positively affect the SCM activities, in particular, the high visibility across the SC, an effective traceability and an automated data collection. Furthermore, the predictive analysis of AI programs can help the SCM to eliminate the potential errors and failures in the processes.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Fuzzy rules for off-grid security subsystem

Stand-alone off-grid power supply system is a necessity for applications when electrical power distribution network is inaccessible. Internal and external subject security aspects should be valued, and the system should be optimized to obtain properly working, never-failing and secure local electrical power supply system. Internal security aspects result from both security concept and used technologies. Internal threats contain fire ignition, flooding, electrical cables melting and other threats arising from intrinsic materials or subsystems characteristics, at standard conditions of use. Additional external aspects include anti-theft protection, breaking doors or windows signaling and countermeasure, weather extremes alert, etc. Both internal and external aspects necessarily induce additional off-grid power system or subject monitoring that might be performed by supervising security subsystem. This paper provides a solution for off-grid security subsystem. It consists from pervasive sensor circuits, fuzzy controlled diagnostics and executive actuators. Cooperated fuzzy logic diagnostic and control device enables to evaluate anticipated threats and dynamically generates the most appropriate decisions about real threat existence that leads to improvement of system protection. Proposed fuzzy rules are particularly discussed in the paper

RELAX:a language to address uncertainty in self-adaptive systems requirement

Self-adaptive systems have the capability to autonomously modify their behavior at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specication of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In order to support the rigorous specication of adaptive systems requirements, this paper introduces RELAX, a new requirements language for self- adaptive systems that explicitly addresses uncertainty inherent in adaptive systems. We present the formal semantics for RELAX in terms of fuzzy logic, thus enabling a rigorous treatment of requirements that include uncertainty. RELAX enables developers to identify uncertainty in the requirements, thereby facilitating the design of systems that are, by denition, more exible and amenable to adaptation in a systematic fashion. We illustrate the use of RELAX on smart home applications, including an adaptive assisted living system