Exhaustive Search-based Model for Hybrid Sensor Network

A new model for a cluster of hybrid sensors network with multi sub-clusters is proposed. The model is in particular relevant to the early warning system in a large scale monitoring system in, for example, a nuclear power plant. It mainly addresses to a safety critical system which requires real-time processes with high accuracy. The mathematical model is based on the extended conventional search algorithm with certain interactions among the nearest neighborhood of sensors. It is argued that the model could realize a highly accurate decision support system with less number of parameters. A case of one dimensional interaction function is discussed, and a simple algorithm for the model is also given.Comment: 6 pages, Proceeding of the International Conference on Intelligent & Advanced Systems 2012 pp. 557-56

How Resilient Are Our Societies? Analyses, Models, and Preliminary Results

Traditional social organizations such as those for the management of healthcare and civil defence are the result of designs and realizations that matched well with an operational context considerably different from the one we are experiencing today: A simpler world, characterized by a greater amount of resources to match less users producing lower peaks of requests. The new context reveals all the fragility of our societies: unmanageability is just around the corner unless we do not complement the "old recipes" with smarter forms of social organization. Here we analyze this problem and propose a refinement to our fractal social organizations as a model for resilient cyber-physical societies. Evidence to our claims is provided by simulating our model in terms of multi-agent systems.Comment: Paper submitted for publication in the Proc. of SERENE 2015 (http://serene.disim.univaq.it/2015/

Perspectives of Integrated “Next Industrial Revolution” Clusters in Poland and Siberia

Rozdział z: Functioning of the Local Production Systems in Central and Eastern European Countries and Siberia. Case Studies and Comparative Studies, ed. Mariusz E. Sokołowicz.The paper presents the mapping of potential next industrial revolution clusters in Poland and Siberia. Deindustrialization of the cities and struggles with its consequences are one of the fundamental economic problems in current global economy. Some hope to find an answer to that problem is associated with the idea of next industrial revolution and reindustrialization initiatives. In the paper, projects aimed at developing next industrial revolution clusters are analyzed. The objective of the research was to examine new industrial revolution paradigm as a platform for establishing university-based trans-border industry clusters in Poland and Siberia47 and to raise awareness of next industry revolution initiatives.Monograph financed under a contract of execution of the international scientific project within 7th Framework Programme of the European Union, co-financed by Polish Ministry of Science and Higher Education (title: “Functioning of the Local Production Systems in the Conditions of Economic Crisis (Comparative Analysis and Benchmarking for the EU and Beyond”)). Monografia sfinansowana w oparciu o umowę o wykonanie projektu między narodowego w ramach 7. Programu Ramowego UE, współfinansowanego ze środków Ministerstwa Nauki i Szkolnictwa Wyższego (tytuł projektu: „Funkcjonowanie lokalnych systemów produkcyjnych w warunkach kryzysu gospodarczego (analiza porównawcza i benchmarking w wybranych krajach UE oraz krajach trzecich”))

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

Point Cloud Processing Algorithms for Environment Understanding in Intelligent Vehicle Applications

Understanding the surrounding environment including both still and moving objects is crucial to the design and optimization of intelligent vehicles. In particular, acquiring the knowledge about the vehicle environment could facilitate reliable detection of moving objects for the purpose of avoiding collisions. In this thesis, we focus on developing point cloud processing algorithms to support intelligent vehicle applications. The contributions of this thesis are three-fold.;First, inspired by the analogy between point cloud and video data, we propose to formulate a problem of reconstructing the vehicle environment (e.g., terrains and buildings) from a sequence of point cloud sets. Built upon existing point cloud registration tool such as iterated closest point (ICP), we have developed an expectation-maximization (EM)-like technique that can automatically mosaic multiple point cloud sets into a larger one characterizing the still environment surrounding the vehicle.;Second, we propose to utilize the color information (from color images captured by the RGB camera) as a supplementary source to the three-dimensional point cloud data. Such joint color and depth representation has the potential of better characterizing the surrounding environment of a vehicle. Based on the novel joint RGBD representation, we propose training a convolution neural network on color images and depth maps generated from the point cloud data.;Finally, we explore a sensor fusion method that combines the results given by a Lidar based detection algorithm and vehicle to everything (V2X) communicated data. Since Lidar and V2X respectively characterize the environmental information from complementary sources, we propose to get a better localization of the surrounding vehicles by a linear sensor fusion method. The effectiveness of the proposed sensor fusion method is verified by comparing detection error profiles

Electronic market as a strategic lever of an innovation virtual system - an integrative approach to territorial innovations management

During the last years, electronic market has become established very quickly in all areas of the business world. Moreover, according to the most recent forecasts, it will grow exponentially during the years. ?Electronic market? phenomenon highlights the most significant effect of the Information and Communication Technologies development: space and time independence of the economic and social processes; every people, every social group, every Organization can communicate or can share information, knowledge, objectives, anywhere and anytime. In this new socioeconomic context, a re-thinking of local system economic growth models becomes necessary. In this paper we present Innovation Virtual System, as a new model for local systems development. Innovation System is conceived as a set of interacting Organizations, embedded in a dense web of social and economic relationships, skilled at creating, acquiring and transferring knowledge and at adapting their behavior according to knowledge about their external and internal settings. More specifically, we try to identify the effects of electronic market on these ?knowledge creating? Organizations, that is on their internal learning circuits and on their external relationships. Particularly we focus in the Internet based electronic market, highlighting the differences between Internet and the previous computing and communication environment, in order to give a clearer understanding of Internet as the strategic infrastructure of electronic market. After describing the impact of the Internet based electronic market on a single Organization, we present a framework of a local system collective learning process, and we describe some of the opportunities offered by the Internet based electronic market to this process.