Data Mining - Concepts and Techniques.

Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf/

Artificial neural networks in hardware: A survey of two decades of progress

We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) 
 
Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

Structural Health Monitoring Framework Based on Internet of Things: A Survey

In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.

/pdf/wireless-sensor-networks-for-condition-monitoring-in-the-2he64b3jue.pdf

Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey

During early development, neural circuits fire spontaneously, generating activity episodes with complex spatiotemporal patterns. Recordings of spontaneous activity have been made in many parts of the nervous system over the last 25 years, reporting developmental changes in activity patterns and the effects of various genetic perturbations. We present a curated repository of multielectrode array recordings of spontaneous activity in developing mouse and ferret retina. The data have been annotated with minimal metadata and converted into HDF5. This paper describes the structure of the data, along with examples of reproducible research using these data files. We also demonstrate how these data can be analysed in the CARMEN workflow system. This article is written as a literate programming document; all programs and data described here are freely available. 1. We hope this repository will lead to novel analysis of spontaneous activity recorded in different laboratories. 2. We encourage published data to be added to the repository. 3. This repository serves as an example of how multielectrode array recordings can be stored for long-term reuse.

/pdf/a-data-repository-and-analysis-framework-for-spontaneous-3k2zyjh52b.pdf

A data repository and analysis framework for spontaneous neural activity recordings in developing retina

Background: During early development, neural circuits fire spontaneously, generating activity episodes with complex spatiotemporal patterns. Recordings of spontaneous activity have been made in many parts of the nervous system over the last 20 years, reporting developmental changes in activity patterns and the effects of various genetic perturbations. Results: We present a curated repository of multielectrode array recordings of spontaneous activity in developing mouse and ferret retina. The data have been annotated with minimal metadata and converted into the HDF5 format. This paper describes the structure of the data, along with examples of reproducible research using these data files. We also demonstrate how these data can be analysed in the CARMEN workflow system. This article is written as a literate programming document; all programs and data described here are freely available. Conclusions: 1. We hope this repository will lead to novel analysis of spontaneous activity recorded in different laboratories. 2. We encourage published data to be added to the repository. 3. This repository serves as an example of how multielectrode array recordings can be stored for long-term reuse.

https://www.biorxiv.org/content/biorxiv/early/2013/11/15/000455.full.pdf

A number of applications varying from music to document classification, require the similarity between a collection of objects to be calculated. To achieve this, features about these objects are extracted e.g. keywords, shapes, colours, frequency components etc, to produce an N-dimensional feature vector, representing a point in a N-dimensional feature space. A database containing these feature vectors can be constructed, allowing query vectors to be applied and the distance between this vector and those stored in the database to be calculated. From the results of these comparisons, similar objects can now be identified and retrieved from the database for further processing by the application. There exists a number of commonly used distance or similarity measures e.g. city block, Euclidean, weighted cosine distance etc, with varying processing requirements and performance characteristics. This paper investigates the possibility of accelerating these distance measures by using FPGA based hardware IP cores and compares this to a software implementation based on a Sun Blade 2000 computer.

Hardware implementation of similarity functions.

The CARMEN (Code, Analysis, Repository and Modelling for e-Neuroscience) system [1] provides a web based portal platform through which users can share and collaboratively exploit data, analysis code and expertise in neuroscience. The system has been beendeveloped in the UK and currently supports 200 hundred neuroscientists working in a Virtual Environment with an initial focus on electrophysiology data. The proposal here is that the CARMEN system provides an excellent base from which to develop an ‘executable paper’ system. CARMEN has been built by York and Newcastle Universities and is based on over 10 years experience in the construction of eScience based distributed technology. CARMEN started four years ago involving 20 scientific investigators (neuroscientists and computer scientists) at 11 UK Universities (www.CARMEN.org.uk). The project is supported for another 4 years at York and Newcastle, along with a sister project to take the underlying technology and pilot it as a UK platform for supporting the sharing of research outputs in a generic way. An entirely natural extension to the CARMEN system would be its alignment with a publications repository. The CARMEN system is operational on the domain https://portal.CARMEN.org.uk, where it is possible to request a login to try out the system.

Michael Weeks

Papers

Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey

A data repository and analysis framework for spontaneous neural activity recordings in developing retina

A data repository and analysis framework for spontaneous neural activity recordings in developing retina

Hardware implementation of similarity functions.

CARMEN: Code analysis, Repository and Modeling for e-Neuroscience