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Assessment of driver stress, crucial for road safety, can greatly benefit from the analysis of multimodal physiological signals. However, fusing such heterogeneous data poses significant challenges, particularly in intermediate fusion where noise can also be fused. In this study, we address this challenge by exploring a 1D convolutional neural network (CNN) with self-attention mechanisms on multimodal data. Electrocardiogram (ECG) signals (256 Hz) and respiration (RESP) signals (128 Hz) were obtained from ten subjects using textile electrodes while driving in different scenarios, namely normal driving and phone usage (calling). The obtained multimodal data is preprocessed and then applied to a self-attention mechanism (SAM) CNN (SAMcNN) to identify driver stress. Experiments are validated using Leave-one-outsubject cross validation. The proposed approach is capable of classifying driver stress. It is observed that shorter segments yield an accuracy of 64.16% compared to longer segment lengths. Thus, exploring self-attention mechanisms for multimodal signals using wearable shirts facilitates non-intrusive monitoring in real-world driving scenarios. [ABSTRACT FROM AUTHOR]

In modern industries, pipelines play a crucial role, both as an essential element in energy transportation (water, gas and electricity) and also in the distribution of these resources. The large size of piping infrastructures, their age and unpredictable external factors are the main difficulties in monitoring the piping system. In this context, the detection and the localization of leaks are challenging but essential, as leaks lead to substantial economic losses. Current methods have many limitations, involving invasive procedures, working only with short pipes or requiring a system shutdown. This paper presents a non-intrusive method based on acoustic signal processing. Leak detection is performed using matched filters, while localization is performed based on the phase diagram representation method and diagram-based entropy computation. Our continuous monitoring system was used for two months and a full comparison with the video inspection-based technique was conducted. The results indicate that this method has a high accuracy, regardless of the length of the pipe. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) generation systems are susceptible to various types of faults. Our objective is to identify unusual operating conditions in a photovoltaic string using only the voltage and current generated at its terminals. To achieve this, we collected voltage and current samples produced by a PV string consisting of six panels during typical operation and four possible types of faults: full panel shading, partial panel shading, panel short-circuits, and electrical arcs in conductor cables. The first three fault groups were further subdivided into six faults, one for each PV panel, resulting in 20 different operating conditions. We collected samples of the electrical system's generation under each state in different climatic situations. We used the resulting dataset to train a convolutional neural network (CNN) and a classic machine learning method, K-Nearest Neighbors (KNN). The results showed that the CNN's ability to learn the characteristics that identify each of the 20 operating conditions resulted in an average accuracy of 95.78%, while the KNN, taking into account previously defined features, achieved an accuracy of 86.34%. [ABSTRACT FROM AUTHOR]

Occupancy detection for buildings is crucial to improving energy efficiency, user comfort, and space utility. However, existing methods require dedicated system setup, continuous calibration, and frequent maintenance. With the instrumentation of electricity meters in millions of homes and offices, however, power measurement presents a unique opportunity for a non-intrusive and cost-effective way to detect occupant presence. This study develops solutions to the problems when no data or limited data is available for training, as motivated by difficulties in ground truth collection. Experimental evaluations on data from both residential and commercial buildings indicate that the proposed methods for binary occupancy detection are nearly as accurate as models learned with sufficient data, with accuracies of approximately 78 to 93 percent for residences and 90 percent for offices. This study shows that power usage contains valuable and sensitive user information, demonstrating a virtual occupancy sensing approach with minimal system calibration and setup. [ABSTRACT FROM AUTHOR]

Copyright of Ingeniería y Universidad is the property of Pontificia Universidad Javeriana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new method based on Higher-order Statistics for non-intrusive residential electrical load identification. Basically, the proposed method extracts cumulants of second and fourth order from the electric current signal of the residential electrical loads and presents these cumulants to a previously trained artificial neural network for classification. The neural network output identifies the residential electric load class of the processed signal. This study considered eleven different classes of residential electrical loads. Results were carried out from experimental electric signals and the achieved overall performance was over to 97%. [ABSTRACT FROM PUBLISHER]

Copyright of Revista Ingeniería e Investigación is the property of Universidad Nacional de Colombia, Facultad de Ingenieraia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a new approach is proposed for the assessment of a vortex in fluid flow using an active acoustic system. The experiment is done on a reduced scale experimental pipe with ultrasonic transceivers. The purpose of the paper is to point out the presence of cavitation appearing in this pipe using wide band signals in the active acoustic system mode. Therefore, the Recurrence Plot Analysis (RPA) method which constitutes an interesting tool for the characterization of a dynamical system is used in terms of signal processing. The investigation is done varying the flow from steady flow to cavitation flow and backwards. [ABSTRACT FROM AUTHOR]

Nest monitoring is commonly used to gather important seabird demographic data, but monitoring itself can affect seabird behaviour and offspring survival. The effect of monitoring on grey-headed albatrossesThalassarche chrysostomaat Subantarctic Marion Island was investigated. The behaviour of chicks close to fledging and of brooding adults was recorded in research and non-research sections of the colony. This was done prior to, during and after researcher/s entered the research sections of the colony. During disturbance, both chicks and adults in research sections were more likely to be standing and to be more responsive than during the pre-disturbance phase. However, adults appeared to be more sensitised to disturbance and this is likely because of being previously handled. Offspring survival in research sections was compared with survival in (a) sections less-frequently entered (2004/2005) and (b) sections monitored through fixed-point photography (2005/2006 and 2006/2007). In February of each of the three years of study, offspring survival was 62.5%, 55.8% and 71.8% respectively. Disturbance level, size of section and the interaction of these two factors did not explain significant amounts of the among-section survival variance, but rainfall explained almost all of the temporal variation in offspring survival in 2006/2007. Although research activities influenced the short-term behavioural responses of chicks and adults in the research sections of the colony, it did not seem to influence the survival of offspring. Nevertheless, fixed-point photography is recommended as a non-intrusive monitoring method. [ABSTRACT FROM AUTHOR]

Sand monitoring gives the benefits of avoiding equipment erosion and production failure in the oil industry. This paper presents the design and implementation of a reliable and cost-effective sand monitoring system for measuring sand production in gas and oil flows in real time. The designed monitoring system involves two acoustic emission (AE) sensors and one Doppler sensor for gauging the sand impaction and the velocity of sand particles in a nonintrusive manner, respectively. It is implemented on a field programmable gate array (FPGA) as a prototype for real-time data acquisition and processing, and evaluated using a testbed pump skid available in our laboratory. Relying on a low-cost FPGA board to integrate all acquisition and processing functionality, our monitoring system can measure the sand production amount on-the-fly reliably with high accuracy, according to our experimental evaluation. It is likely to achieve better accuracy than one without the Doppler sensor. The proposed monitoring system is affordable for wide deployment, given its high accuracy, good resilience to surrounding noise, and low cost (when compared with commercially available systems whose price tags can be some tenfold higher). [ABSTRACT FROM PUBLISHER]

Remote, non-intrusive monitoring of elusive mammals remains problematic, particularly in running waters. The utility of using submerged infrared counters for monitoring non-intrusively the activity of Eurasian otters Lutra lutra was assessed in three tributaries of the River Dee (Beltie, Cattie, Feardar; Scotland) during 2003–2004. Otters passing through the infrared counters were strongly nocturnal and displayed a bimodal diel activity pattern. Seasonal activity indices varied fourfold between tributaries and peaked during the salmonid breeding season. The median time elapsing between consecutive night visits was 2.02±0.79 days and did not differ between tributaries. The median head–body length of adult otters was estimated at 75.0±1.1 cm, whereas median upstream swimming speed was calculated at 0.97±0.01 m s−1. Minimum census estimates revealed the activity of at least two adults in the Beltie, two adults and three juveniles in the Cattie, and two adults with one juvenile in the Feardar. Our study indicates that, under suitable conditions, infrared technology can be used effectively to examine non-intrusively the activity of free-ranging otters in running waters, offering some advantages over previous, more intrusive techniques that relied on the collection of spraints, the use of radioisotopes or the tracking of marked individuals. [ABSTRACT FROM AUTHOR]

Detection of vehicles on the road can contribute to the establishment of an intelligent transportation management system to allow smooth transportation and the reduction of road accidents. Thus far, an efficient and low-cost polymer flexible pressure sensor for vehicle detection is lacking. This paper presents a flexible sensor for vehicle sensing and demonstrates a wireless system for monitoring vehicles on the road. A vehicle sensor was fabricated by sandwiching a polyurethane material between aluminum top/bottom electrodes. The sensing mechanism was based on changes in capacitance due to variation in the distance between the two electrodes at an applied external pressure. A clear response against a pressure load of 0.65 Mpa was observed, which is the same pressure as that of the car tire area in contact with the road. Significantly, the sensor was easy to embed on the road line due to its mechanical flexibility and large size. A field test was carried out by embedding the sensor on the road and crossing the sensor with a car. Moreover, the signal displayed on the tablet indicated that the sensing system can be used for wireless detection of the axle, speed, or weight of the vehicle on the road. The findings suggest that the flexible pressure sensor is a promising tool for use as a low-cost vehicle detector in future intelligent transportation management. [ABSTRACT FROM AUTHOR]

Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers. [ABSTRACT FROM AUTHOR]