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The authors showed that the imperfection of precision farming methods, methods of transferring energy from the tractor engine to the working bodies required the development of new methods for reliable and accurate identification of the soil and its properties, as well as the state of machine-tractor units. (Research purpose) To determine the actual energy and economic indicators of a working machine and unit reliably and promptly in the operating conditions of agricultural production by parameters closely related to the energy indicators of an internal combustion engine, the measurement of which did not require large costs. (Materials and methods) 24 indirect parameters reflecting the power and degree of engine load were used for the operational assessment. In this case, the angular accelerations of the internal combustion engine and the turbocharger rotor crankshaft, including those caused by the operation of individual cylinders, the working processes of the generator (their average and extreme values), as well as the amplitude-frequency spectra of these processes, and speed characteristics were taken into account. To improve the accuracy and reliability of the assessment of energy indicators, the authors selected the processes in the frequency and phase domains. (Results and discussion) The considered methods were tested in production conditions during the main processing of the alkaline-chernozem complexes of the Chulym district of the Novosibirsk region. The authors found that the use of a flexible machine-tractor unit based on an energy-rich tractor improved the quality of soil cultivation: the share of soil lumps less than 5 centimeters in size was 73-80 percent; the productivity of the tillage machine increased by 15-20 percent. (Conclusions) The authors proved that with the help of indirectly determined indicators of power and degree of load, it was possible to estimate the actual values of the energy and technical and economic indicators of the working machine and the unit, the quality of the assembly of the unit, the heterogeneity of the soil was much easier and cheaper than using the known force-measuring methods. It was found that the use of digital systems for automatic control of the unit using the indicated indicators made it possible to provide quite simply the setting of the optimal zone of energy modes.

Mathematical modeling and simulation are moving from being powerful development and analysis tools towards having increased roles in operational monitoring, control and decision support, in which models of specific entities are continually updated in the form of a digital twin. However, current digital twins are largely the result of bespoke technical solutions that are difficult to scale. We discuss two exemplar applications that motivate challenges and opportunities for scaling digital twins, and that underscore potential barriers to wider adoption of this technology. Development in digital-twin technology has been rapidly growing across a range of industries and disciplines. However, to ensure a wider and more robust adoption of such technology, various challenges must be addressed by the computational science community.

In order to develop the scientific and technical foundations of creating an artificial intelligence system for monitoring terrorist acts, the paper presents the results of a study of the amplitude-frequency spectra of acoustic emission (AE) of the combustion process of cellulose-containing materials (as one of the main materials used to carry out arson and attacks in organizing terrorist actions in the form of law and order violations in the local territory) of various physicochemical composition. The technical implementation of this effect in the development of automated devices for monitoring acoustic space as part of an operational monitoring system for the terrorist zone, aimed at early identification of the sources of terrorist threats to the normal functioning of the local territory of the state.

Assessment of the information space allows you to determine the possible causes (conditions) of the crisis and the characteristics of the information impact for any information activities that will be planned. One of the promising such scientific studies is the substantiation of a method for monitoring information impact in the interests of providing information activities. The essence of the information impact monitoring method is to assess information activities, namely: identifying information impact; the level of information impact; formation of conclusions and decisions on the need to counteract information impact; planning of countermeasures, approval of a plan of countermeasures; implementation of countermeasures according to the plan. In each information event there are certain indicators by which the characteristics of information are assessed and the conditions are determined by which a destructive orientation can be carried out for any information processes (actions, facts) in the information space of the country. Considering that these components have their own informational correction, in particular, they are found in the information space of the state (actions, facts), or phenomena can reduce the level of the moral and psychological state of the personnel of troops (forces) to a state when the personnel are not able to effectively perform tasks as intended , then in the above method it is proposed to consider these issues in two directions. These areas are the level of operational monitoring of the analysis of current information and the level of operational monitoring of the forecast of the development of information surges.

Because of the inherent complexity of urban brownfield regeneration projects, achieving sustainability objectives is not easy. It requires approaches adapted to the specificities of this type of operation, that allow for structured and regular follow-up, and that are integrated into the project dynamics. In this chapter, we argue that sustainability monitoring can help address this challenge. We start by defining the principles of sustainability evaluation and monitoring. Then, we look deeper at the challenges of an operational monitoring tool from the brownfield regeneration perspective. On this basis, we plead for the necessity of tailor-made operational monitoring tools for this type of operation and define, to this end, three general requirements for said tools. Finally, we make a critical analysis of existing certifications at the neighbourhood scale and different approaches developed for brownfield regeneration projects.

Industrial Digitalization and Automation (DA), promoted through new production paradigms such as Industry 4.0 currently dominates the strategy agendas of industrial companies. However, practitioners report various problems while implementing DA into their organizations work-routines. We find underlying problems in missing operational monitoring systems to assess and control the DA-implementation status. We therefore propose a holistic monitoring system using 143 developed DA-implementation key performance indicators in nine dimensions. Moreover, we integrate the monitoring system into a continuous improvement approach following the PDCA-circle. First applications show increased objectivity during initial KPI-status assessments, higher acceptance of defined KPI-target-states and increased alignment during definition of future DA-projects.

This paper discusses the issues of creating an aerial survey complex based on an unmanned aerial vehicle. The analysis of the efficiency of using aerial survey complexes based on unmanned aerial vehicles for operational monitoring of the state of natural and man-made territorial complexes is carried out. The paper considers the process of developing a modified control system for unmanned aerial vehicles, as well as the subsequent photogrammetric processing of aerial photographs. The quality of electronic units installed on unmanned aerial vehicles has been determined. The created modified control system for unmanned aerial vehicles and geographically referenced terrain orthophotomaps obtained by photogrammetric methods make it possible to classify the captured objects, perform their vectorization and provide each object with semantic information. The developed modified aerial survey complex will allow real-time monitoring of the state of natural and man-made territorial complexes based on remote sensing data from UAVs.

Pipe conveyors are currently widely employed in continuous transport of raw materials. Contact forces on idler rolls of an idler housing represent an important parameter to be monitored in the process of online operational monitoring. Operational tension force cannot be reached during the experimental research performed on laboratory conditions with regard to construction of the testing equipment. It is impossible for the current experimental rig. The main objective of the manuscript is to monitor dependences of contact forces on tension forces in individual idler rolls of a pipe conveyor idler housing. The manuscript output is in form of contact forces’ prediction for respective idler rolls for two more rates of tension forces and two cases of measurements: with and without material load.

The article discusses the fields and methods of application of unmanned aerial vehicles (UAV). Current legislation in Russia and in the world, significantly limit the use of UAV in monitoring. For the first time, we present a solution to the problem of a monitoring measurement system included in the state register of measuring instruments using the example of the basic UAV model. We conducted an analysis of promising approaches to the creation of UAV metrological and methodological support, as well as ways to adapt their target load to meet the challenges of operational monitoring of air pollution.

Research project “PerManeNt” aims at developing an integrated platform for operational monitoring, smart control, and sustainable energy management of the external aqueduct system of the city of Patras in western Greece, which consists of more than 60 km of pressurized pipeline, 44 pumping wells, 3 springs, 22 regulating tanks, and 14 pumping stations. Given the significance of the existing infrastructure, 5 main pipelines, 7 pumping wells, 9 reservoirs, and 5 pumping stations were selected to be monitored in the context of: a) real-time data collection, processing and visualization, b) near real-time detection of system malfunctioning and automatic alarm generation, and c) generation of short and longer term forecasts for the water demand and corresponding energy consumption rates, based on hydrometeorological data and environmental indices. The development of the integrated platform is expected to have significant scientific, financial, societal and environmental impacts including: i) efficient water resources management and environmental protection, ii) reduction of the operational costs and regulator expenses for system maintenance and management, iii) promotion of citizens’ awareness regarding environmental issues, and iv) significant improvement of the quality of services offered, including pricing and emergency planning.Acknowledgments:This research is co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-4177.

Cable yarders are often the preferred harvesting system when extracting trees on steep terrain. While the practice of cable logging is well established, productivity is dependent on many stand and terrain variables. Being able to continuously monitor a cable yarder operation would provide the opportunity not only to manage and improve the system, but also to study the effect on operations in different conditions.This paper presents the results of an automated monitoring system that was developed and tested on a series of cable yarder operations. The system is based on the installation of a Geographical Navigation Satellite System (GNSS) onto the carriage, coupled with a data-logging unit and a data analysis program. The analysis program includes a set of algorithms able to transform the raw carriage movement data into detailed timing elements. Outputs include basic aspects such average extraction distance, average inhaul and outhaul carriage speed, but is also able to distinguish number of cycles, cycle time, as well as break the cycles into its distinct elements of outhaul, hook, inhaul and unhook.The system was tested in eight locations; four in thinning operations in Italy and four clear-cut operations in New Zealand, using three different rigging configuration of motorized slack-pulling, motorized grapple and North Bend. At all locations, a manual time and motion study was completed for comparison to the data produced by the newly developed automated system. Results showed that the system was able to identify 98% of the 369 cycles measured. The 8 cycles not detected were directly attributed to the loss of GNSS signal at two Italian sites with tree cover. For the remaining 361 cycles, the difference in gross cycle time was less than 1% and the overall accuracy for the separate elements of the cycle was less than 3% when considered at the rigging system level. The study showed that the data analyses system developed can readily convert GNSS data of the carriage movement into information useful for monitoring and studying cable yarding operations.

The article analyzes the diagnostics of a high-voltage power transformer by means of an acoustic camera. It is one of the basic diagnostics and monitoring of remote measurement and analysis of acoustic emission. The high-voltage power transformer generates acoustic emission, thanks to which we can analyze the operating and fault states of the device. The article shows the use of the acoustic camera for operational monitoring and diagnostics of its life through noise from the source of the transformer winding and the ferromagnetic core.

Forests, one of the most valuable and necessary resources and protect earth’s ecological balance, are a natural habitat to animals and forest products are vital in our lives in many direct and indirect ways. But wildfires can cause critical damage to grounds and many other resources like properties, human life, wildlife in superabundant amounts. Wildfires burn acres of land and destroy everything in their paths in mere minutes. Wildfire destroys homes, animals, trees and plants, wildlife as well as vegetation. The effects of wildfires are numerous and wide-ranging. it causes a hugely significant impact on the economy, environment, heritage and social fabric of rural areas. Naturally caused wildfires can be predicted using factors[3] like temperature, humidity, soil moisture, pressure and many more. In this paper, the prediction of forest fires by machine learning using some operational monitoring over a region and encountering changes in climate using different sensors are advocated. The Wildfire Prediction System (WiPreSy) monitors and records changes in climatic parameters and predicts the intensity of forest fire based on real-time data, thus avoiding the massive loss due to forest fires.

As a consequence of inexpensive sensors, ubiquitous tracking devices, and abundant digital storage, by 2019 smarter cities have become platforms for increasing levels of operational monitoring and ...

Стаття присвячена оптимізації урожайності і забезпеченню екологічної якості сільськогосподарської продукції за допомогою сучасних інформаційно-технічних систем, із урахуванням варіабельності зон управління сільськогосподарським полем. Запропоновано неоднорідність ґрунту представити як ієрархічну підпорядкованість явищ. При цьому питання вимірювання неоднорідності пропонується вирішити шляхом кількісного порівняння об’єктів вимірювання з використанням відповідних критеріїв. За цих умов виникає необхідність у пошуку найбільш ефективного способу для оперативного моніторингу агробіологічного стану сільськогосподарських угідь, що відкриває нові перспективи до ведення органічного землеробства з використанням інтелектуальних сільськогосподарських машин. The article is devoted to optimization of productivity and provision of ecological quality of agricultural products with the help of modern information and technical systems, taking into account the variability of zones of management by agricultural field. It is proposed to represent the heterogeneity of the soil as a hierarchical subordination of phenomena. At the same time, the issue of measuring heterogeneity is proposed to be solved by quantitative comparison of measuring objects using the appropriate criteria. Under these conditions, there is a need to find the most effective way for operational monitoring of agrobiological state of agricultural land, which opens new prospects for organic farming using intelligent agricultural machines.

This study proposes an enhanced damping estimation procedure for flexible cable-supported bridges based on operational modal analysis (OMA). The OMA approach combines the natural excitation techniq...

A practical example of a hermetic cable gland for nuclear power plants is described. A model is constructed for performing diagnostics of the parameters of the hermetic cable gland and a procedure for performing operational monitoring of its state is developed. The proposed device makes it possible to reduce the development risk of emergency situations in the system by increasing the operational reliability of the hermetic cable gland. For this reason, operation monitoring of the electrodynamic state of a hermetic gland in NPP on the basis of an equivalent model is significant and urgent.

Next activity prediction is one of the most important problems concerning the operational monitoring of processes, that is, supporting the user in predicting the activity that will be executed as the next step during process execution. However, traditional algorithms do not cope with the presence of parallel activities, thus failing to devise accurate prediction of multiple parallel activities that will be simultaneously executed. Moreover, they often require a trace alignment pre-processing step, which can be infeasible during process executions. In this paper, we propose the ParallAct methodology, in which multi-target regression is used to predict the next parallel activities in event logs without the need of aligning traces during process executions. Experimental results show that the proposed solution achieve more accurate predictions compared to the single-target setting.

This paper describes the approach followed to perform an automatic load monitoring during yarding activities. The goal of this methodology is the weight assessment of the loaded biomass during the inhaul phase in cable logging operations. The approach is based on the development of dedicated mathematical models that assessing the skyline deflection, tension, and slope permit to estimate the hauled load through an indirect approach. To carry out this experience an in-scale standing skyline system with single-span was set-up. The experiment was conducted at 0%, 10% and 20% of slope, respectively, and the skyline deflection was measured during travels with 0, 5, 10, 20, 30 and 40 kg of fully suspend loads. Besides these, the assessment was performed at mounting tensions of 10 and 100 kg. According to the different estimative models and the operative context, the collected results showed a difference between the estimated load and the reference one lower than 20% in terms of mass. In conclusion is possible to affirm that the proposed approach can be a suitable compromise for the indirect estimation of timber during fully suspended yarding operations.

The distinctive features and key areas of analytical tools application for the operational monitoring and security analysis of cyber-physical systems of critical information infrastructure are highlighted. Problems of applying data analytics methods and technologies to ensure the security of cyber-physical systems at enterprises of the fuel and energy industries are described. The architectural solutions of the advanced security analytics platform are proposed. The chapter discusses the use of data analysis methods and technologies to ensure the security of cyber-physical systems at enterprises of the fuel and energy complex. Identified the distinctive features and highlighted the key areas of application of analytical tools of the system of operational monitoring and security analysis of cyber-physical systems of critical information infrastructure. Problem questions are formulated, the possibilities and limitations of advanced analytics tools in solving the tasks of ensuring the security of cyber-physical systems at the enterprises of the fuel and energy complex are defined. Architectural solutions of the advanced cybersecurity analytics platform are described. The presented results are based on the analysis of information from open sources: materials of scientific-practical conferences, analytical and technical reviews on the subject of security of industrial systems, and generalization of practical experience in the development, implementation, and support of integrated security systems at enterprises of the fuel and energy complex.

This paper describes the theoretical approach followed to perform an indirect load mass assessment during yarding activities. The goal of this methodology is the mass assessment of the loaded biomass during the inhaul phase in cable logging operations.The theoretical approach proposed is based on the development and the use of mathematical model which, thanks to the measurement of the skyline deflection, slope and tensions permit to estimate the hauled mass. Indeed, as reported in literature, these parameters, together with the mass of the load, are strongly related each other. For this experiment an in-scale yarding line has been developed in order to control and monitor all these parameters. The precision and accuracy of the obtained results related to the mass estimations have been then compared with similar results obtained through manual surveys using tools commonly used by the forestry community.In conclusion the proposed methodology has shown to be reliable for an indirect mass assessment of loads during yarding operations. Indeed, if the line is adequately installed this monitoring system ensures better accuracy if compared with the common tools employed for timber assessment.

Diagnosing a railway track is a complicated task, both from the point of view of measuring equipment and organization. An important factor here is to ensure the safety of rail traffic while minimizing the cost of diagnostics (including time expenditures). One of the most promising is the approach based on the use of integrable track monitoring systems containing compact, relatively inexpensive sensors operating on different physical principles. This paper is dedicated to the peculiarities of the functioning, processing, presentation and transmission of information in organizing this approach, which is analyzed using the example of the InerTrack system developed with the participation of the authors.

This manuscript proposes an agricultural environment monitoring server system for monitoring information concerning an outdoors agricultural production environment utilizing Wireless Sensor Network (WSN) technology. The proposed agricultural server collects environmental, agricultural land, seeds and soil information on the outdoors through WSN-based environmental and soil sensors, collects snaps information through CCTVs, area information through RFID and collects location information using GPS modules. This collected information is converted into data and saved in to a database through the agricultural server consisting of a sensor manager, which manages information gathered from the WSN sensors, an image information manager, which manages image information collected from CCTVs, RFID and a GPS manager, which processes location information of the agricultural server system, and provides it to producers. In addition, a solar cell-based power supply is implemented for the server system so that it could be used in agricultural environments with insufficient power infrastructure. This agricultural server could even monitor the environmental information on the outdoors, and it could be look forward that the use of such a system could participate to increasing crop yields and improving quality in the agricultural field by supporting the decision making of crop producers through analysis of the collected information.

The article discusses the experience of supporting a regional operational satellite monitoring system. Based on NASA IOPP free & open source software, a set of programs has been created to automate the reception and automated processing of incoming satellite information. The development is primarily focused on data coming from the satellite receiving station, however, it also deals with the interpretation of data received via the Internet. A three-stage scheme of distributed processing and data conversion is implemented. The output goes to the local satellite data storage. A collection of specialized multi-channel raster images is also being formed for quick visualization of images in a geographic information web system. The current version of the system performs processing from eight different sensors installed on several satellites.

The Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment the Large Hadron Collider (LHC) at CERN currently is composed of a large number of distributed hardware and software components (about 3000 machines and more than 25000 applications) which, in a coordinated manner, provide the data-taking functionality of the overall system. During data taking runs, a huge flow of operational data is produced in order to constantly monitor the system and allow proper detection of anomalies or misbehaviors. The Persistent Back-End for the ATLAS Information System of TDAQ (P-BEAST) is a system based on a custom-built time-series database and it is used to archive and retrieve for applications any operational monitoring data. P-BEAST stores about 18 TB of highly compacted and compressed raw monitoring data per year acquired at 200 kHz average information update rate during ATLAS data taking periods. Since P-BEAST has been put into production, 4 years ago, several promising database technologies for fast access to time-series and column-oriented data have become available. InfluxDB and ClickHouse were the most promising candidates for improving the performance and functionality of the current implementation of P-BEAST. This poster presents a the testing methodology and setup and the first batch of results, along with some preliminary conclusions and further work outlook.

SMARTCOMP 2018 IEEE International Conference on Smart Computing, Taormina, ITALIE, 18-/06/2018 - 20/06/2018; The ability to detect critical spots in transportation networks is fundamental to improve traffic operations and road-network resilience in smart cities. Real-Time monitoring of these networks, especially in very large metropolitan areas, is a compelling challenge due to the complexity of computing robustness metrics. This paper presents a framework for identifying vulnerabilities in very-large road networks. The framework adopts graph-based modeling of road networks and exploits big-data techniques and technologies for processing such large and complex graphs. First, we use the framework to prove the existence of a significant correlation between global efficiency and betweenness centrality. Then, we focus on an efficient algorithm, integrated in the framework, to rank the nodes according to this metric for finding potential vulnerabilities of a road network. To keep computation time under a 'quasi' real-Time threshold, a fast, requirement-driven, approximated strategy for computing betweenness centrality is adopted. The evaluation shows that the algorithm, integrated in the framework, exhibits a very good approximation for the most critical nodes, thus being well-suited for on-line operational monitoring.

In this paper, intrusion detection system based on touch traps is presented. Aim of the work — investigate and design a software product for monitoring intruders in various systems for further analysis and prevention of repeated attacks. The work is designed for intrusion detection system that includes procedures for analyzing network honeypots and procedure for systematization received data. The results may be used for: Improving intrusion detection system; Providing users of intrusion detection system tool for operational monitoring of network honeypots; Providing additional tools for systematization of input data. The efficiency of the above-mentioned procedures verified by computer experiment.

Acoustic sensor networks (ASN) are widely used to monitor water leaks in the power generating systems. Since the ASN are used in harsh climatic conditions the failures of microphone elements of ASN are inevitable. That's why the failure detection of ASN elements is a problem of current interest. Two techniques of operational monitoring ASN are developed. Both of them are based on the placement of the test sound source within a network. The signal processing for ASN sensors had to detect the failed element. Techniques are based time difference of arrival (TDOA) estimating at the each pair of ASN elements. TDOA estimates as argmaximum of cross-correlation function (CCF) for signals on each microphone sensors pair. The M-sequence phase-shift keyed signal is applied as a test acoustic signal to ensure high accuracy of the CCF maximum estimation at low signal/noise ratio (SNR). The first technique is based on the isolation principle for TDOA sum at three points. It require to locate the test sound source in the far field. This is not always possible due to technological reasons. For the second proposed technique test sound source can be located near the ASN. It is based on a system of hyperbolic equations solving for each of the four elements of the ASN. Both techniques has been tested in the computer imitation experiment. It was found that for the SNR to –5 dB both techniques show unmistakable indicators of control quality. The second method requires significantly more time control.

A system for multiple camera networks is proposed for continuous monitoring of ecosystems by processing image time series. The system is built around the Finnish Meteorological Image PROcessing Toolbox (FMIPROT), which includes data acquisition, processing and visualization from multiple camera networks. The toolbox has a user-friendly graphical user interface (GUI) for which only minimal computer knowledge and skills are required to use it. Images from camera networks are acquired and handled automatically according to the common communication protocols, e.g., File Transfer Protocol (FTP). Processing features include GUI based selection of the region of interest (ROI), automatic analysis chain, extraction of ROI based indices such as the green fraction index (GF), red fraction index (RF), blue fraction index (BF), green-red vegetation index (GRVI), and green excess (GEI) index, as well as a custom index defined by a user-provided mathematical formula. Analysis results are visualized on interactive plots both on the GUI and hypertext markup language (HTML) reports. The users can implement their own developed algorithms to extract information from digital image series for any purpose. The toolbox can also be run in non-GUI mode, which allows running series of analyses in servers unattended and scheduled. The system is demonstrated using an environmental camera network in Finland.

Summary An innovative approach for the automation of operational monitoring activities in agricultural and forestry tasks is described and discussed in this article. This approach can be considered as a solution for Precision Agriculture and Precision Forestry applications and can be used as an information and communication technology (ICT) tool for the management aims by a variety of agricultural and forestry companies. The aim of the proposed concept is to develop a system, composed of both hardware and software units, with the ability to collect and manage operative raw data and then to translate them into operational information that will be used in decision-making processes. All the procedures will be carried out automatically, in order to ensure an objective compilation of the field activity register. Thus, the entrepreneur will have all the operative information automatically updated in a dedicated database system. All the obtained documents can then be used for certification and traceability processes, if required by the procedural guideline, as well as to satisfy any other management tasks, including the estimation of the actual operative costs of the farm.

BIGDATA 2017 IEEE International Conference on Big Data, Boston, ETATS-UNIS, 11-/12/2017 - 14/12/2017; Betweenness centrality is a metric widely used in several domains (social, biological, transportation, computer) to identify critical nodes of networks. Its exact computation is very demanding, with an O(nm) time complexity for unweighted graphs (where n is the number of nodes and m is the number of edges). Such complexity becomes an obstacle to the adoption of betweenness centrality for continuous monitoring of critical nodes in very large networks. Several solutions have been proposed to reduce computation time, mainly via parallelism, approximation or incremental recalculation. In this paper, we propose an algorithm for computing approximated values of betweenness that allows for tuning its performance on the basis of a tolerable error. The algorithm aims at reducing the number of single-source shortest-paths explorations via a pivot-based technique that exploits topological properties of graphs and clustering. It is evaluated by identifying the vulnerabilities (critical nodes) of a real-world, very-large road network. The evaluation shows that the approximation error does not significantly affect the most critical nodes, thus making the algorithm well-suited for on-line operational monitoring of road networks.

Copernicus is a European Union (EU) led initiative designed to establish a European capacity for the provision and use of operational monitoring information for environment and security applications. Within the Copernicus program, ESA is responsible for the development of the Space Component and Ground Segment..

The structure of the wireless sensor network (WSN) for operational monitoring of soil moisture and temperature on the agricultural field with high accuracy is considered in the article. The evaluation of wireless sensor networks' technical capabilities for agricultural applications is given; the advantages and disadvantages of their use in the field conditions are noted, taking into account the influence of different factors on the quality of communication. To solve the problems of radio communication in the soil and deploying a sensor device completely below the ground surface, the prototype model of a sensor node with a retractable antenna has been developed. Sensor nodes were communicated via the ZigBee protocol and the integrated spatial attribute information was transmitted into the database.

A method for the operational monitoring of the spatial distributions of the structural-morphological parameters (SMPs) of the mucosa of internal organs is developed. This is important for early diagnosis of oncological illnesses. The method is based on obtaining endoscopic images of tissue in diffusely reflected light with wavelengths 450, 480, 577, 600,660, and 700 nm and processing the pixels of the images obtained using the stable regressions between the relative spectral characteristics of the images and the tissue parameters.

Each application of Precision Agriculture or Forestry should be supported by a technological platform able to perform, in an integrated way, the following data-information cycle functions: 1) data collection; 2) data processing; 3) data analysis and evaluation; 4) use of information. In accordance to this view, information are data that are usefully used in a decision making process or within a reporting protocol destined to users external to the enterprise (certification tasks). In order to manage the platform in a complete and efficient manner an adequate information system is needed. Firstly, the paper shows a classification of the possible monitoring solutions based on the different enterprise typologies, highlighting the main technological and interpretative requirements. Secondly, some case studies related to the application of operational monitoring in orchards and forestry are introduced, mainly focusing on some peculiar aspects of the algorithms developed for the implementation of the inference engines.

This article covers the importance of employing a cross-platform and cross-application log management approach rather than a siloed approach to collecting and reviewing logs for simplifying security and operational monitoring as well as compliance initiatives.

Sample processing is a highly challenging stage in the monitoring of waterborne pathogens. This step is time-consuming, requires highly trained technicians and often results in low recovery rates of pathogens. In the UK but also in other parts of the world, Cryptosporidium is the only pathogen directly tested for in routine operational monitoring. The traditional sampling process involves the filtration of 1000 L of water, semi-automated elution of the filters and membranes with recovery rates of about 30–40% typically. This paper explores the use of megasonic sonication in an attempt to increase recovery rates and reduce both the time required for processing and the number of labour-intensive steps. Results demonstrate that megasonic energy assisted elution is equally effective as the traditional manual process in terms of recovery rates. Major advantages are however offered in terms of reduction of the elution volume enabling the current centrifugation stage to be avoided. This saves time, equipment and staff costs and critically removes the step in the process that would be most challenging to automate, paving the way thereby for highly effective automated solutions to pathogens monitoring.

Control object is a collection of hardware (machines, machines, equipment, vehicles), which requires the organization of specially organized actions to achieve the desired results of operation [1,2]. The goal of process automation is to improve the processing of cotton technical and economic indicators and the reliability of the technological machines through automatic and automated maintenance of the most efficient mode of technological processing of raw cotton. The optimal technological regime of processing raw cotton should be carried out within the framework of planned and technological constraints with possibly fewer operating personnel.

A new probabilistic performance assessment procedure using operational monitoring data is proposed. In the procedure, multiple finite element models are identified from the weighted aggregation formulation for multi-objective optimization problem, each of which can represent uncertainty condition of operational monitoring data. By applying principal component analysis andK-means clustering on numerous candidate models, they are grouped according to their similarity and contributions in performance assessment. And then bridge’s performance can be assessed in probabilistic approach using the FE models in the classified groups, at which specific types of operational monitoring data uncertainty is represented. Yeondae Bridge, a steel-box girder highway bridge in Korea, is taken as an illustrative example. Load rating factor is evaluated as performance index of the bridge, and compared to rating factors by sophisticated model and baseline mode to verify effectiveness of proposed method.

An analysis is made of sources of methodological error encountered in the design of differential manometers and in operational monitoring of the discharge of materials and energy carriers. It is shown that these sources of error can be eliminated.

The Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment is a very complex distributed computing system, composed of more than 20000 applications running on more than 2000 computers. The TDAQ Controls system has to guarantee the smooth and synchronous operations of all the TDAQ components and has to provide the means to minimize the downtime of the system caused by runtime failures. During data taking runs, streams of information messages sent or published by running applications are the main sources of knowledge about correctness of running operations. The huge flow of operational monitoring data produced is constantly monitored by experts in order to detect problems or misbehaviours. Given the scale of the system and the rates of data to be analyzed, the automation of the system functionality in the areas of operational monitoring, system verification, error detection and recovery is a strong requirement. To accomplish its objective, the Controls system includes some high-level components which are based on advanced software technologies, namely the rule-based Expert System and the Complex Event Processing engines. The chosen techniques allow to formalize, store and reuse the knowledge of experts and thus to assist the shifters in the ATLAS control room during the data-taking activities.

Patent intelligence is an effective approach that enterprises cluster innovation network realizes through quantitative analysis. The paper builds the operational monitoring methods of enterprises cluster innovation network based on patent information and synthetically applying network and patent map analysis methods, and illustrates operation characteristics and existing problems of cluster innovation network taking Harbin equipment manufacturing industrial clusters as an example, so as to offer scientific theoretical methods guidance and empirical reference for risks monitor and management of enterprises cluster innovation network from the angle of patent.

Network intrusion detection systems (NIDSs) critically rely on processing a great deal of state. Often much of this state resides solely in the volatile processor memory accessible to a single user-level process on a single machine. In this work, we highlight the power of independent state, i.e., internal fine-grained state that can be propagated from one instance of a NIDS to others running either concurrently or subsequently. Independent state provides us with a wealth of possible applications that hold promise for enhancing the capabilities of NIDSs. We discuss an implementation of independent state for the Bro NIDS and examine how we can then leverage independent state for distributed processing, load parallelization, selective preservation of state across restarts and crashes, dynamic reconfiguration, high level policy maintenance, and support for profiling and debugging. We have experimented with each of these applications in several large environments and are now working to integrate them into the sites' operational monitoring. A performance evaluation shows that our implementation is suitable for use even in large scale environments

International audience; During the last decade, operational monitoring and forecasting systems have been developed in all the European seas. The exchange of data and products and the development of services for a wide community of users pose some fundamental issues, whose solution has become a priority in integrated and GMES referring projects, such as the MERSEA European project. These projects aim to develop a European system for operational monitoring and forecasting on global and regional scales of ocean physics, bio-chemistry and ecosystems. GMES system and its operational projects need to federate resources and expertise coming from diverse organizations working on different Earth Sciences fields (e.g. satellite data processing, in situ observing systems, data management, ocean and ecosystem modeling, etc.). Therefore, it is required a Marine Information Management (MIM) system capable of facilitating the regular real-time exchange of high quality information, data and products. Moreover, MIM system must provide appropriate information for a wide range of external users both in real-time and delayed mode. In this paper an architecture based on the OPeNDAP/THREDDS technology is proposed as a solution for these operational systems. In this context, a catalog merging solution is introduced for the MIM system, which results in the design and development of the THREDDS Dataset Merger (TDM): a set of services meant to merge THREDDS Dataset Inventory Catalogs, so to achieve a unique catalog service for a whole database federation. TDM service merges distributed and autonomous THREDDS catalogs in order to work out a virtual merged catalog. The TDM service was extended in order to provide automatic catalogs synchronization. This service allows extending the pull-based TDM paradigm to support push-based applications. Some security issues are also considered.

An advanced algorithm capable of executing the extraction, categorization and fusion of aircraft sensor data to achieve improvements in sensor capabilities has been developed. Integration of this strategy with standard sensors results in an adaptive sensor capable of improving its functionality with time. The first of these adaptive sensors is currently undergoing developmental flight testing onboard several Navy SH-60 helicopters. Its function is to determine aircraft gross weight during steady hover conditions. This study describes the methodology necessary for the development of an adaptive sensor and its implementation, as well as the value of the adaptation process. Results from the aircraft gross weight adaptive sensor are evaluated using forty two validation flights originating from three distinct aircraft. Application of this technology to other aircraft systems is also discussed.

The most commonly used fire detection methods based on AVHRR (Advanced Very High Resolution Radiometer aboard NOAA satellites) observations have been applied, in this work, to the Italian Peninsula, in order to assess their effectiveness and robustness in an operational scheme for fire monitoring in different areas. The analysis developed so far shows that unsuccessful results obtained from existing detection methods mainly depend on the use of generalized, fixed threshold values, in the fire-detection tests. A new fire-detection technique, based on the most general RAT (Robust AVHRR Technique) approach, is proposed in this work. It seems able to join good performances, typically associated with techniques based on locally tuned thresholds, with high operational exportability, achieved by an automated implementation scheme completely based on AVHRR data hands. Improvements achievable by using this new approach over the Italian Peninsula, have been evaluated (for several forest- fire events and different observational conditions) by comparison with the historical records of the Italian Forestry Service.

The author mistakenly spelt Nadine Brisson as Nadine Gobron in the Acknowledgements of [1]. [...]