A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification

This paper presents a novel power-band-based data segmentation (PBDS) method to enhance the identification of meter phase and meter-transformer pairing. Meters that share the same transformer or are on the same phase typically exhibit strongly correlated voltage profiles. However, under high power consumption, there can be significant voltage drops along the line connecting a customer to the distribution transformer. These voltage drops significantly decrease the correlations among meters on the same phase or supplied by the same transformer, resulting in high misidentification rates. To address this issue, we propose using power bands to select highly correlated voltage segments for computing correlations, rather than relying solely on correlations computed from the entire voltage waveforms. The algorithm's performance is assessed by conducting tests using data gathered from 13 utility feeders. To ensure the credibility of the identification results, utility engineers conduct field verification for all 13 feeders. The verification results unequivocally demonstrate that the proposed algorithm surpasses existing methods in both accuracy and robustness.Comment: Submitted to the IEEE Transactions on Power Delivery. arXiv admin note: text overlap with arXiv:2111.1050

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mild-temperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods.Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers

A real-time location-based construction labor safety management system

The construction industry continues to record a high number of accidents compared to other industries. Furthermore, the ramifications of construction accidents are growing in terms of both economic loss and loss of life with trends toward larger-scale, more complex projects. For this reason, there is an increasing awareness of the importance of safety management in the construction industry, and the need for more effective safety management techniques. This paper introduces a real-time location-based construction labor safety management system that tracks and visualizes workers’ locations in real-time and sends early warnings to endangered workers. The system is developed by integrating: a real-time locating system (RTLS) for tracking of workers’ location; a location monitoring system for mapping the workers location on a computerized building model; and alarm technology for sending early warnings. The developed system has been applied to an apartment project and an RTLS technology test center in Korea, and proved to be effective in tracking and monitoring workers in real-time and preventing construction accidents. It is envisioned that the developed system will enable proactive construction safety management in South Korea and the methodologies developed in this study will be applicable to other contexts with minimal customization

An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation

This paper presents a novel iterative, bidirectional, gradient boosting (bidirectional-GB) algorithm for estimating the baseline of the Conservation Voltage Reduction (CVR) program. We define the CVR baseline as the load profile during the CVR period if the substation voltage is not lowered. The proposed algorithm consists of two key steps: selection of similar days and iterative bidirectional-GB training. In the first step, pre- and post-event temperature profiles of the targeted CVR day are used to select similar days from historical non-CVR days. In the second step, the pre-event and post-event similar days are used to train two GBMs iteratively: a forward-GBM and a backward-GBM. After each iteration, the two generated CVR baselines are reconciled and only the first and the last points on the reconciled baseline are kept. The iteration repeats until all CVR baseline points are generated. We tested two gradient boosting methods (i.e., GBM and LighGBM) with two data resolutions (i.e., 15- and 30-minute). The results demonstrate that both the accuracy and performance of the algorithm are satisfactory.Comment: 5 pages, 8 figures, 2 table

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri

Recurrent odontogenic ghost cell carcinoma (OGCC) at a reconstructed fibular flap : a case report with immunohistochemical findings

Odontogenic ghost cell carcinoma (OGCC), a malignant counterpart of the odontogenic ghost cell tumor (OGCT), with aggressive growth characteristics, is exceedingly rare. A painful swelling in the jaw with local paresthesia is the most common symptom. We described a case of 47-year Korean woman who had a rare central epithelial odontogenic ghost cell carcinoma which recurred at reconstructed fibular flap. Immunohistochemical differences between OGCT and OGCC analyzed using primary and recurred surgical specimen. On the basis of this case, the tumor started as an OGCT and transformed into OGCC with highly aggressive, rapidly growing, infiltrative tumors. Our findings suggest that some of the cytokines produced by ghost cells may play important roles in causing extensive bone resorption in the odontogenic ghost cell carcinoma. Wide local excision with histologically clean margins is the treatment mode of selection. Also, we recommend close long-term surveillance of OGCT because of high recurrence and potential for malignancy transformation. © Medicina Oral

Interferometric detection of prostate specific antigen based on enzyme immunoassay

AbstractInterferometric detection of Prostate-specific antigen (PSA) based on enzyme immunoassay are investigated. Refractive index changes of substrate are measured for PSA detection. Michelson scheme of optical interferometer was used so as to be applicable to a disposable fluidic chip. When interferometer is used for the measurements of refractive index changes, the detection is over 8 times more sensitive than that of absorbance changes for the same amount of target protein

Direct Conversion of Mouse Fibroblasts into Cholangiocyte Progenitor Cells

Disorders of the biliary epithelium, known as cholangiopathies, cause severe and irreversible liver diseases. The limited accessibility of bile duct precludes modeling of several cholangiocyte-mediated diseases. Therefore, novel approaches for obtaining functional cholangiocytes with high purity are needed. Previous work has shown that the combination of Hnf1β and Foxa3 could directly convert mouse fibroblasts into bipotential hepatic stem cell-like cells, termed iHepSCs. However, the efficiency of converting fibroblasts into iHepSCs is low, and these iHepSCs exhibit extremely low differentiation potential into cholangiocytes, thus hindering the translation of iHepSCs to the clinic. Here, we describe that the expression of Hnf1α and Foxa3 dramatically facilitates the robust generation of iHepSCs. Notably, prolonged in vitro culture of Hnf1α- and Foxa3-derived iHepSCs induces a Notch signaling-mediated secondary conversion into cholangiocyte progenitor-like cells that display dramatically enhanced differentiation capacity into mature cholangiocytes. Our study provides a robust two-step approach for obtaining cholangiocyte progenitor-like cells using defined factors