Process State Monitoring Using Self-Organizing Maps

Kasslin, Mika; Kangas, Jari; Simula, Olli

doi:10.1016/b978-0-444-89488-5.50152-4

Cited by 49 publications

(17 citation statements)

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“…ANN are able to identify and learn correlations between input data and corresponding target output values. They are able to predict the outcome of new independent data sets making them a useful tool in predictive modeling [121]. ANN are well suited for the irrigation decision support problem that can often be complex and stochastic in nature.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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“…By calculating the quantization error on this map, it is possible to detect the existence of a fault: an increase in the quantization error of the best-matching unit indicates a faulty situation. A similar approach has been used in process control [Try91] and in monitoring the operation of devices [Kas92]. Localization of the fault is possible by examining the measured values during a short period preceeding the fault detection.…”

Section: Monitoring System Based On the Self-organizing Mapmentioning

confidence: 98%

Icann ’94

1994

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The use of registered names, trademarks etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use.The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. PrefaceThis book contains the proceedings of the International Conference on Artificial Neural Networks which was held between May 26 and 291994 in Sorrento, organized by the Department of Theoretical Physics of the University of Salerno, IIASS and the Istituto Italiano per gli Studi Filosofici (Napoli), and cosponsored by ENNS, JNNS, IEEE and SIREN. It is the fourth in a series after Helsinki (1991), Brighton (1992) and Amsterdam (1993). The conference is the main event of the European Neural Network Society, which is fostering the growth of the interdisciplinary community ofEuropean researchers and supporting the interaction and cooperation with other international societies in the same scientific area.Neural network is a living proof that the re-birth in science is possible. The first life was driven by a handful of pioneers who, in the fifties and early sixties, discovered the analogies between machines and biological systems as regards communication, control, computing. In a sense, this was not completely new, and we are sure that the mechanical toys of the eighteenth century are a beautiful although naive witness of its illuministic roots. However, the idea was a daring one, and the technological! methodological constraints of the time did not allow it to flourish and trigger a virtuous growth cycle through significant successes in the real world.We are now in the second life. Computation is cheap; software tools are powerful; mathematical tools, developed by computer-aware people, accumulated steadily. Moreover, the brain is less of a black box and can teach us some things. Researchers in many fields have demonstrated in the last decades that they are willing to try again: mathematicians, physicists, psychologists, neuroscientists, and, last but not least engineers who have the crucial task to prove the feasibility of the field and make things work. Of course, there is hype, not all promises will be maintained, but even if only 10% of them succeed, as is happening now in many application areas, this will allow the second wave to expand and consolidate. We can forsee the limits, mainly due to technology, and we can already guess the real breakthroughs, which will make the third wave possible and make neural networks as pervasive as are microcomputers now, will happen when new materials, self-organizing, adaptable wet-ware become available. For all these reasons it is mostly appropriate to dedicate this conference to Eduardo R. Caianiello, one of the pioneers of the first wave and an eminent researcher of the second one. Eduardo R. Caianiello, who was also ...

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“…Kasslin et al (1992), for instance, uses components maps together with trajectories for process state monitoring. Here the values for one parameter are visualized as gray values on a map.…”

Section: Temporal Sequence Processing Without Modifying the Basic Sommentioning

confidence: 99%

Classification and Clustering in Business Cycle Analysis.

Heilemann¹,

Weihs²

2007

Schriften Des Rheinisch-Westfälischen Instituts Für Wirtschaftsforschung

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PrefaceThe analysis of cyclical macroeconomic phenomena is an important field of econometric research. In the recent past, research interests have de-emphasized quantitative forecasting exercises and have addressed the qualitative diagnosis of the relative stance of the economy regarding "upswing", "recession", or "boom" periods, i. e. the classification of the state of the economy into a limited number of discrete states. In this context the principal challenge is to reduce the multifaceted and sometimes abundant quantitative information about the business cycle to such qualitative statements in an efficient way. For more than six years this task was the focus of the project "Multivariate determination and analysis of business cycles" within the SFB 475 "Reduction of complexity in multivariate data structures", funded by the German Research Foundation (DFG).The necessity for complexity reduction is, of course, not unique to business cycle analysis but is studied in many fields and in a number of ways. This broad interest in the reduction of problem dimensionality and in the appropriate combination of data and of theory caused the RWI Essen and the Statistical Department of the University of Dortmund in January 2002 to hold a workshop at the RWI Essen where the findings of this and similar projects were presented and discussed. The present publication collects revised versions of the papers presented at this workshop. Although the workshop took place some five years ago, these papers mark an importment juncture in the development of business cycle research.The RWI Essen thanks the editors of the volume and the authors for their contributions and the other participants for lively discussions. Particular thanks have to go to Joachim Schmidt who supervised the production of the volume and to Anette Hermanowski and Claudia Lohkamp whose support of the production of the manuscripts and of the organisation of the workshop was indispensable.Essen,

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Process State Monitoring Using Self-Organizing Maps

Cited by 49 publications

References 0 publications

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Icann ’94

Classification and Clustering in Business Cycle Analysis.

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