Monitoring applications: An immune inspired algorithm for software-fault detection

Large-scale software systems are in general difficult to manage and monitor. In many cases, these systems display unexpected behavior, especially after being updated or when changes occur in their environment (operating system upgrades or hardware migrations, to name a few). Therefore, to handle a changing environment, it is desirable to base fault detection and performance monitoring on self-adaptive techniques.Several studies have been carried out in the past which, inspired on the immune system, aim at solving complex technological problems. Among them, anomaly detection, pattern recognition, system security and data mining are problems that have been addressed in this framework.There are similarities between the software fault detection problem and the identification of the pathogens that are found in natural immune systems. Being inspired by vaccination and negative and clonal selection observed in these systems, we developed an effective self-adaptive model to monitor software applications analyzing the metrics of system resources.     

Long-term monitoring of natural hydrogen superficial emissions in a brazilian cratonic environment. Sporadic large pulses versus daily periodic emissions

In many basins, hydrogen-emitting structures are now observed, but the estimation of the H₂ flow leading to their formation remains poorly constrained since all data show that the H₂ emissions are variable in space and time. We present here the data of a long-term monitoring campaign with a high density of permanent hydrogen detectors installed in 2 structures in the Minas Gerais State (Brazil). Results show that two kinds of signals are recorded, large sporadic pulses that affect the H₂ content of the soil for one or two days and smaller ones, with a daily periodicity, that last 6 h and during which the near surface soil concentration usually does not exceed 200 ppm. This last signal is very regular in frequency, less in amount, and the daily maximum happens around noon or in the early afternoon. We interpret the large pulses as evidences of a deep hydrogen flux, leaking either from a reservoir located in the subsurface, from an aquifer which is degassing or, although it seems unlikely, directly from the H₂ generation area.The time correlation between the pulse and the increase of the daily signal suggests that this last one corresponds to the slow release of the gas that has been captured by the soil during its transport towards the surface. This daily signal is most likely influenced by external factors such as atmospheric pressure and sub-surface bacterial activity. In map view, the lack of correlation between the highest hydrogen concentrations over time suggests that the soil is very heterogeneous and that preferential pathways exist. The results confirm that long-term monitoring, over a few months, is mandatory to catch a certain number of high pulses and so to have a better estimation of the real flow. Data also suggests that sensors with a large concentration range should be used (from 10 to several 10⁴ ppm). Even if quantification of leakage doesn't help to quantify the subsurface reserves, these new data allow a more precise evaluation of the quantity of H₂ released in surface by these structures, few hundred of kilo per day, and confirm the high H₂ prospectivity of this basin.     

Detection of unapproved genetically modified potatoes in Korea using multiplex polymerase chain reaction

Several genetically modified (GM) potato cultivars with improved traits such as increased amylopectin levels, decreased asparagine levels, and reduced incidence of black spot have been developed. In this study, we describe a multiplex polymerase chain reaction (PCR) method specific for four GM potato events (EH92-527-1, AM04-1020, PH05-026-0048, and E12) that are unauthorized in Korea. The UDP-glucose pyrophosphorylase (UGPase) gene was used as the endogenous reference gene. The specificity of the primer sets was evaluated using GM potatoes and other GM crops. The limit of detection in the developed multiplex PCR was confirmed to be approximately 0.04% (w/w). Thirty-three commercial products containing potato ingredients were tested by the multiplex PCR assay, and GM potato event E12 was found in one of the 33 samples. This result suggested that the developed PCR method could be used to effectively identify unauthorized GM potatoes in Korea.     

Reduction in thickness error of optical coatings by dividing thick layers and monitoring with multiple witness glasses

We present a monitoring strategy based on using two pieces of witness glass, which are brought to the measuring position in a specially chosen sequence, each witness glass is monitored by one single wavelength. To reduce the thickness error, some thick layers are divided into two layers and monitored by different witness glasses. Theoretical analysis and experimental results have demonstrated that the proposed monitoring strategy can achieve spectral performance close to the theoretical design.     

A comparative study of trends of nitrate,chloride and phosphate concentration levels in selected urban rivers

Concerns for the river environment in Malaysia are highlighted. There had been occurrences of inadequate supply for water in big cities and unexpected floods over the last few years. This paper discussed and compared the results of efforts to clean-up Gombak River which passes through several industrial areas and the City of Kuala Lumpur and Penchala River which passes through the industrial areas and the neighboring City of Petaling Jaya. The trends in the levels of nitrates, chlorides and phosphates present in the rivers were analyzed. Over the period of thirteen years from 1997 to 2009, results of efforts which included public participation, engineering and river works and strict statutory regulations by government had shown success in improving the river water quality. Continued efforts from stakeholders must be harnessed to enhance the health of the Gombak River and the Penchala River.     

Field trial of a reinforced landfill cover system: performance and failure

The geotechnical stability of an inclined multilayer capping depends on the shear strength available along the various interfaces. If the slope is very steep an additional reinforcing geosynthetic may be used to obtain a safer condition. Full-scale field trials can provide better resolution data on the reinforcement behaviour than conventional calculation methods based only on laboratory tests. The paper deals with a field trial carried out on multilayer capping, reinforced with a geogrid, in an Italian landfill. The geogrid behaviour was monitored for a month using displacement sensors and pressure cells located along the slope and in the anchor trench. Subsequently, the cover system was led to collapse by cutting the reinforcement and an analysis of the reinforcement behaviour and its relevance in the system stability were studied. This paper discusses in detail the setup of the field trial and the experimental data recorded during installation, monitoring, and failure phases of the system. The deformation behaviour of the geogrid during the entire test was recorded and analysed. The resulting data highlight the effects of the construction process on the geogrid behaviour including the contribution of geogrid creep characteristics until the failure.     

Design and Analysis of MEMS Comb Drive Capacitive Accelerometer for SHM and Seismic Applications

This work presents the design of a MEMS accelerometer that is specifically intended for Structural Health Monitoring (SHM) applications where sensing low frequency low amplitude accelerations with high resolution is essential. The surface micromachined comb drive capacitance accelerometer structure has been considered in this design. The simulation experiments conducted on these devices using IntelliSuite MEMS design tool show that it has excellent displacement sensitivity of 21.39 μm/g, a capacitive sensitivity of 1.22 pF/g and voltage sensitivity of 1783 mV/g/V when it is designed to measure 0–0.1 g. Further, it is seen that it has a very low noise floor of 1.32 μg/√Hz and therefore high resolution. Since the accelerations can be as low as 0.04 g in SHM applications, excellent resolution is the primary goal in this design. Further, one more sensor specifically meant for strong motion seismic application has also been reported. This device has a bandwidth of 0–250 Hz and a noise floor of 5.612 μg/√Hz in addition to a sensor level voltage sensitivity of 97.9 mV/g/V. Finally, the comparison of these results with other similar devices reported in the past clearly illustrates the comparable performance of the present devices. Further, these devices, unlike the commercial low frequency accelerometers and other similar devices reported in the past can be fabricated by surface micromachining and CMOS compatible processes.     

An error correction framework for sequences resulting from known state-transition models in Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM), the set of techniques used for disaggregating total electricity consumption in a building into its constituent electrical loads, has recently received renewed interest in the research community, partly due to the roll-out of smart metering technology worldwide. Event-based NILM approaches (i.e., those that are based on first segmenting the power time-series and associating each segment with the operation of electrical appliances) are a commonly implemented solution but are prone to the propagation of errors through the data processing pipeline. Thus, during energy estimation (the final step in the process), many corrections need to be made to account for errors incurred during segmentation, feature extraction and classification (the other steps typically present in event-based approaches). A robust framework for energy estimation should use the labels from classification to (1) model the different state transitions that can occur in an appliance; (2) account for any misclassifications by correcting event labels that violate the extracted model; and (3) accurately estimate the energy consumed by that appliance over a period of time. In this paper, we address the second problem by proposing an error-correcting algorithm which looks at sequences generated by Finite State Machines (FSMs) and corrects for errors in the sequence; errors are defined as state transitions that violate the said FSM. We evaluate our framework on simulated data and find that it improves energy estimation errors. We further test it on data from 43 appliances collected from 19 houses and find that the framework significantly improves errors in energy estimates when compared to the case with no correction in 19 appliances, leaves 17 appliances unchanged, and has a slightly negative impact on 6 appliances.     

Classification into homogeneous groups using combined cluster and discriminant analysis

The classification of observations into groups is a general procedure in modern research. However, when searching for homogeneous groups the difficulty of deciding whether further division of a classification is necessary or not to obtain the desired homogeneous groups arises. The presented method, Combined cluster and discriminant analysis (CCDA), aims to facilitate this decision.CCDA consists of three main steps: (I) a basic grouping procedure; (II) a core cycle where the goodness of preconceived and random classifications is determined; and (III) an evaluation step where a decision has to be made regarding division into sub-groups. These steps of the proposed method were implemented in R in a package, under the name of ccda.To present the applicability of the method, a case study on the water quality samples of Neusiedler See is presented, in which CCDA classified the 33 original sampling locations into 17 homogeneous groups, which could provide a starting point for a later recalibration of the lake's monitoring network.     

MTConnect compliant monitoring for finishing assembly interfaces of large-scale components: A vertical tail section application

Monitoring is a significant issue for finishing the assembly interfaces of large-scale components before final assembly. Acquisition and supervision of the pivotal data is essential to ensure the security and reliability for machining the large and complicated components with high-value. This process is generally cumbersome and time-consuming because there are various types of data coming from different components and sensors. The problem becomes more serious when considering the whole shop floor. Recently, MTConnect has been proven to be an effective method to realize standardized data collection and monitoring process. However, MTConnect is still under development and cannot cover the whole finishing process such as on-machining measuring (OMM) and fixturing. To address the issue, an MTConnect compliant method with extended data models is proposed in this paper to implement a standardized monitoring system. Firstly, a finishing system for the assembly interfaces is introduced, including the framework, workflow and key procedures and data. Then extended MTConnect data models are proposed to represent the finishing system including on-machine touch-trigger probe and sensor-based intelligent fixturing related information. Based on the extended MTConnect data models, a web-based monitoring system is developed for data collection and monitoring by combining an MTConnect agent and an OPC adapter. The proposed approach is validated by collecting and monitoring the key process data using an airplane vertical tail as an application. The advantages of using MTConnect would be more significant when extended to the entire factory and implemented in cloud manufacturing in the future.