A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

Dynamic equipment workspace generation for improving earthwork safety using real-time location system

Earthwork equipment accounts for a large proportion of the fatalities on construction sites. According to the U.S. Bureau of Labor Statistics, in the period between 1992 and 2002, struck by vehicles and struck by objects (e.g., vehicle parts, vehicle loads, or falling vehicles) were identified as the causes of 30% and 24% of fatal equipment-related accidents on excavations sites, respectively. It is therefore of a paramount importance to improve the safety of construction sites by increasing the peripheral awareness of the operators of earthwork equipment. Several research works have investigated numerous collision avoidance systems that exploit real-time location systems and proximity measurements to mitigate the risk of accidents on excavation sites. However, these systems often detect collisions based on using the workspaces that only account for the geometry and the degrees of freedom of the equipment, and thus disregard the state-dependent characteristics of equipment. This results in reserving a large space for every piece of equipment, which reduces the applicability of these systems in congested sites. Therefore, this paper proposes a novel method for generating dynamic equipment workspaces based on the continuous monitoring of a spectrum of equipment-related information, i.e., the current pose/state of the equipment, and the speed characteristics of each movement. This method uses the required operation stoppage time to determine how much space needs to be reserved for each piece of equipment. A case study is conducted to validate the proposed method. It is shown that the proposed method has a strong potential in capturing the hazardous areas around the equipment and triggering warnings in view of the impending movements of various pieces of equipment. Also, the proposed method proved to have potential applications in actual projects in congested sites where space is limited.     

Peanut FAD2 Genotype and Growing Location Interactions Significantly Affect the Level of Oleic Acid in Seeds

The level of oleic acid is an important parameter in determining seed nutritional quality and oil stability. The level of oleic acid in peanut is genetically controlled by a pair of fatty acid desaturase genes (FAD2A and FAD2B), but the environmental conditions of the production sites can also have a significant effect. To investigate the effect of gene and environment interaction, 45 accessions were grown at three locations for 2 years. Environmental data were collected; individual plants were genotyped with functional SNP markers from FAD2A and FAD2B; and seed level of oleic acid was determined by gas chromatography. Three FAD2A/FAD2B genotypes (448G/no insertion 442A, 448A/no insertion 442A, and 448A/insertion 442A) were identified and designated as G/W, A/W, and A/A, respectively. A/A genotype averaged the highest level of oleic acid (80.0%), followed by A/W (56.0%), and then G/W (40.7%). Analysis of gene and environment interaction revealed that oleic acid phenotype plasticity could be explained by the interaction of FAD2 genotype and photothermal time, which quantified environmental conditions. The A/W genotype was the most sensitive to photothermal time changes. The oleic acid plasticity revealed in this study would be useful for breeders, farmers, and product processors.     

Monitoring and assessment of underground climatic conditions using sensors and GIS tools

Monitoring and assessment of underground climatic conditions are necessary to identify potential hazards and initiate remedial measures in advance that otherwise would lead to disastrous conditions.This paper discusses the concept of real-time monitoring and assessment of climatic conditions in a typical underground mine using sensors and GIS tools by utilizing a laboratory scale model. Typical ventilation parameters including temperature, humidity, and gas concentrations were monitored using sensors in a laboratory setting and various ratios and indices proposed by previous researchers for interpreting fire gases and spontaneous combustion conditions are predicted from the monitored data. GIS tools were used to display this information in real-time on a mine map that would help in creating a safe and comfortable working environment for personnel and equipment working underground.     

Improved TARP development based upon mine specific data

The trigger action response plans(TARPs) are inherent to managing the multiple hazards such as: high gas content with multiple coal seams, high spontaneous combustion(sponcom) propensity, heat and ventilation. TARPs aim to provide assurance and guidance when the situation deviates from the original plan or there is a change conditions that could be hazardous. Over the years, learnings from various incidents has continuously required the coal operations to re-visit the TARP trigger values that were based on historic data or based on guidance values from the industry. In most cases, the background to the basis of TARP statistical data, viz., average, maximum, hourly, daily for the monitoring or sampling location is also unknown. Introduction of real-time monitoring devices to monitor the gases and airflows has provided greater understanding of the hazard scenarios and their controls. This paper analyses the carbon monoxide data from operating longwall mines and compares these with the historic trigger values to understand the changes and determine improvement opportunities while setting trigger levels in the TARPs.As an example of setting trigger values, those used in during the sealing of a panel are explored in terms of setting values based upon the sampling location and the level of risk. It is envisaged that the learning's shared herein would further enhance the understanding and management of multiple hazards in Australian coal mines.     

Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.