Dong Sik Kim

Under the single contract (SC) used in apartment complexes in the Republic of Korea, the total electricity consumption of an apartment complex is measured by a main electricity meter, and the residential high-voltage (HV) progressive tariff is applied to calculate the total electricity bill. The difference between the total electricity bill charged to the apartment complex and the sum of the electricity bills calculated for individual households constitutes the common-area electricity bill, which is generally allocated equally among households. However, because the residential progressive tariff is applied to the total electricity consumption, the common-area electricity bill depends not only on the electricity consumption of common-area facilities but also on the distribution of electricity consumption among individual households. Therefore, an allocation method that considers the impact of individual households on the common-area electricity bill is required. In this study, the impact value of each household on the common-area electricity bill is quantified by the change in the bill resulting from the inclusion of that household. Based on these impact values, proportional (PROP) and additive (ADD) allocation schemes are proposed, along with their nonnegative (NN) variants to prevent negative charges. A bounding method is also proposed to impose upper and lower bounds on the allocated charges. The proposed schemes were investigated using actual metering data collected from 13,335 households over 24 months, and their characteristics were analyzed and compared. The results show that PROP exhibits the largest changes in the allocated common-area electricity charges, whereas PROP-NN results in relatively smaller changes from the current uniform scheme. ADD and ADD-NN provide intermediate alternatives, with ADD generally showing larger changes than ADD-NN. Thus, PROP is suitable when the impact value is to be reflected strongly, whereas PROP-NN is preferable when relatively small changes in charges are desired. The bounding method is recommended for PROP and can be optionally applied to the other schemes.

Background/Objectives: A dual-layer flat-panel detector (DFD), in which two flat-panel detectors are stacked vertically, enables single-shot dual-energy imaging without a fan-beam scanning and switching mechanism in conventional dual-energy X-ray absorptiometry (DXA), the clinical standard for bone mineral density (BMD). However, because single-shot BMD measurement systems using DFDs show substantial spectral overlap, their BMD measurement performance is inherently inferior to that of dual-shot systems. In this paper, we optimize the tube voltage, metal filter thickness, and CsI(Tl)-scintillator thickness so that the BMD estimation error of the single-shot method is similar to that of the double-shot method. Here, we also optimize the dual-shot approach to serve as a meaningful reference in the comparison. Methods: BMD measurement simulations were performed using a polynomial estimator based on second-order polynomial fitting of dual-energy logarithmic intensities. Performance comparison was based on noise sensitivity, quantified by the condition number and mean square error under a multiplicative noise model, and was further assessed using the equivalent energies and the bone-tissue attenuation ratios. Results: The simulation results indicate that, in the dual-shot approach, decreasing the low tube voltage is the most effective strategy for improving BMD measurement performance, whereas in the single-shot approach, reducing the upper scintillator thickness has the largest impact. Conclusions: When both approaches are evaluated under their respective optimized configurations based on synthetic simulations, the single-shot approach demonstrates that the BMD estimation error is sufficiently similar to that of the dual-shot approach, supporting its potential as a hardware-efficient alternative for BMD measurement.

Objective. A dual-layer flat-panel detector (DFD) allows for the acquisition of dual-energy images with a single x-ray exposure without scanning. In this paper, we aim to perform noise sensitivity analyses on a single-shot approach using DFD in measuring bone mineral density (BMD) and propose a method for calculating BMD using features acquired from a nonlinear response detector without a nonlinearity correction scheme. Approach. We evaluate the condition numbers of the BMD estimate functions and observe the mean squared error (MSE) for ranges of additive and multiplicative errors to compare with a dual-shot FD approach. To accurately describe the BMD surface, we use a fully connected neural network (FCNN) model instead of the conventional multiple regression model. We conduct experiments to observe and model the nonlinear response of DFD, followed by simulations based on a BMD model with nonlinear intensities. Main results. Direct BMD estimation from uncorrected nonlinear intensities using the FCNN produced lower estimation errors than the conventional second-order polynomial estimator. For Case S-sub at a multiplicative noise variance of 10 − 7 , the FCNN reduced the BMD estimation MSE from 1.112 × 10 − 4 to 3.352 × 10 − 5 . Significance. The results support the feasibility of direct FCNN-based BMD estimation from nonlinear DFD signals without applying an explicit nonlinearity correction. The proposed approach may support single-exposure BMD pre-screening, subject to further validation using physical phantoms and clinical data.

As smart meter technology advances, the advanced metering infrastructure (AMI) enables the collection of high-resolution energy consumption data, such as 15-minute interval electricity and gas usage. While anomaly detection studies have primarily focused on industrial infrastructures, the challenge of identifying specific residential events, such as resident changes, in an unsupervised manner remains largely unaddressed. In this paper, we propose an unsupervised framework to detect the month of a resident change by interpreting significant prediction errors from a deep learning model as anomalies. Our framework utilizes a gated recurrent unit and fully connected layer (GRU-FC) model to predict both monthly consumption patterns and amounts. For training and validation, we used electricity and gas datasets collected from 750 households in Seoul, Republic of Korea, using AMI. These datasets contain ground-truth labels for the actual resident change dates that occurred in the households in 2024. To prepare the datasets for training the GRU-FC models, an efficient outlier removal scheme was developed based on the vector quantization. Combined-loss and complementary decision schemes were also employed to jointly leverage pattern and amount features for both electricity and gas data. When only electricity data was used, the detection accuracy was 80.37%, which increased significantly to 89.19% when electricity and gas datasets were used together. This study not only demonstrates the practical feasibility of detecting resident changes but also highlights the effectiveness of a multi-source, prediction-based approach for general time-series anomaly detection.

Bone mineral density (BMD) measurement plays a crucial role in the diagnosis and management of osteoporosis. The most widely used clinical method is dual-energy x-ray absorptiometry (DXA), where image scanning using fan-beam x-rays requires complex alignment mechanisms and switching of tube voltages and filters for dual-shot x-ray imaging. To simplify the BMD measurement system, a dual-layer flat panel detector (DFD) can be employed to acquire x-ray images with single-shot x-rays without scanning. In this paper, we study the feasibility of a single-shot approach using DFD in measuring BMD based on a second-order polynomial in comparison with conventional DXA scanning. We first synthesize x-ray spectra for different conditions of DFD and next conduct noise sensitivity analysis by adding log-uniform distributed noise to the input log intensities. Condition numbers of the BMD measurement functions and beam-hardening effect according to the soft-tissue thickness are also observed. Through extensive simulations, we observed that the noise sensitivity performance of the single-shot approach based on DFD could be better than the dual-shot approach based on a single flat panel detector if the switching filters are not employed. When the noise variance at the inputs was 10⁻⁹, the single-shot BMD estimate showed a lower mean square error of 7.99 × 10⁻⁴ than the dual-shot case of 13.8 × 10⁻⁴. This approach can be effectively utilized as a preliminary screening method prior to DXA scanning in clinical settings.

Background/Objectives: In digital radiography imaging, dual-layer flat-panel detectors (DFDs), in which two flat-panel detector layers are stacked with a minimal distance between the layers and appropriate alignment, are commonly used in material decompositions as dual-energy applications with a single x-ray exposure. DFDs also enable more efficient use of incident photons, resulting in x-ray images with improved noise power spectrum (NPS) and detection quantum efficiency (DQE) performances as single-energy applications. Purpose: Although the development of DFD systems for material decomposition applications is actively underway, there is a lack of research on whether single-energy applications of DFD can achieve better performance than the single-layer case. In this paper, we experimentally observe the DFD performance in terms of the modulation transfer function (MTF), NPS, and DQE with discussions. Methods: Using prototypes of DFD, we experimentally measure the MTF, NPS, and DQE of the convex combination of the images acquired from the upper and lower detector layers of DFD. To optimize DFD performance, a two-step image registration is performed, where subpixel registration based on the maximum amplitude response to the transform based on the Fourier shift theorem and an affine transformation using cubic interpolation are adopted. The DFD performance is analyzed and discussed through extensive experiments for various scintillator thicknesses, x-ray beam conditions, and incident doses. Results: Under the RQA 9 beam conditions of 2.7 μGy dose, the DFD with the upper and lower scintillator thicknesses of 0.5 mm could achieve a zero-frequency DQE of 75%, compared to 56% when using a single-layer detector. This implies that the DFD using 75 % of the incident dose of a single-layer detector can provide the same signal-to-noise ratio as a single-layer detector. Conclusions: In single-energy radiography imaging, DFD can provide better NPS and DQE performances than the case of the single-layer detector, especially at relatively high x-ray energies, which enables low-dose imaging.

In radiography, dual-layer detectors are commonly used to separate objects such as bones and tissues. Additionally, the use of dual-layer detectors allows for X-ray images with improved detection quantum efficiency (DQE) performance. In this paper, we observe the DQE performance of a dual-layer detector, in which two flat-panel detector layers are stacked with a minimal distance between the layers. To design an aligned dual-layer detector (ADD), a subpixel registration based on an amplitude response is employed between the images acquired from the upper and lower layers, and the lower image is translated based on the Fourier shift theorem. The performance of ADD is evaluated by observing a convex combination of the upper and lower images in terms of the modulation transfer function (MTF), noise power spectrum, and DQE. We also observe the contrast performance by using a contrast-detail phantom of CDRAD. ADD can obtain a better performance than that of the single upper detector layer especially at relatively high x-ray tube voltages. For example, under the RQA 9 condition, ADD can achieve a DQE of 75:0% compared to 56:1% for a single layer detector at zero frequency. Additionally, the problem of non-uniform MTF and DQE across the pixels of the entire detector and the artifact problem caused by the antiscatter grid are also discussed.

As the adoption of electric vehicles (EVs) rapidly increases, the expansion of charging infrastructure has become a critical issue. Unlike internal combustion engine vehicles, EV charging is sensitive to factors such as the time and location for charging, depending on the charging speed and capacity of the battery. Therefore, recommending an appropriate charging station that comprehensively considers not only the user’s preference but also the charging time, waiting time, charging fee rates, and power supply status is crucial for the user’s convenience. Currently, charging station recommendation services suggest suitable charging stations near a designated location and provide information on charging capacity, fee rates, and availability of chargers. Furthermore, research is being conducted on EV charging station recommendations that take into account various charging environments, such as power grid and renewable energy conditions. To solve these optimization problems, a large amount of information about the user’s history and conditions is required. In this paper, we propose a real-time charging station recommendation method based on minimal and simple current information while driving to the destination. We first propose a preference objective function that considers the factors of distance, time, and fees, and then analyze the recommendation results based on both synthetic and real-world charging environments. We also observe the recommendation results for different combinations of the weights for these factors. If we set all the weights equally, we can obtain appropriate recommendations for charging stations that reflect driving distance, trip time, and charging fees in a balanced way. On the other hand, as the number of charging stations in a given area increases, it has been found that gradually increasing the weighting of charging fees is necessary to alleviate the phenomenon of rising fee rates and provide balanced recommendations.

Background: For a single exposure in radiography, a dual-layer flat-panel detector (DFD) can provide spectral images and efficiently utilize the transmitted X-ray photons to improve the detective quantum efficiency (DQE) performance. In this paper, to acquire high DQE performance, we present a registration method for X-ray images acquired from a DFD, considering only spatial translations and scale factors. The conventional registration methods have inconsistent estimate accuracies depending on the captured object scene, even when using entire pixels, and have deteriorated frequency performance because of the interpolation method employed. Methods: The proposed method consists of two steps; the first step is conducting a spatial translation according to the Fourier shift theorem with a subpixel registration, and the second step is conducting a scale transformation using cubic interpolation to process the X-ray projections. To estimate the subpixel spatial translation, a maximum-amplitude method using a small portion of the slant-edge phantom is used. Results: The performance of the proposed two-step method is first theoretically analyzed and then observed by conducting extensive experiments and measuring the noise power spectrum and DQE. An example for registering chest images is also shown. For a DFD, the proposed method shows a better registration result than the conventional one-step registration. The DQE improvement was more than 56% under RQA 9 compared to the single flat-panel detector case. Conclusions: The proposed two-step registration method can efficiently provide aligned image pairs from the DFD to improve the DQE performance at low doses and, thus, increase the accuracy of clinical diagnosis.

In digital radiography imaging, pixel binning is an effective way to reduce the amount of image data for transmission or storage, and is particularly effective for application to dynamic digital radiography detectors for fluoroscopic imaging. Traditional pixel binning methods can be described as the process of downsampling an image that has been processed with a moving average (MA) filter for anti-aliasing purposes. In this paper, the performance of pixel binning is analyzed in terms of the noise power spectrum (NPS), modulation transfer function (MTF), and detective quantum efficiency (DQE) based on theoretical derivations. The performance of the pixel binning method is analyzed by performing theoretical analysis and Monte Carlo simulations through parametric modeling for NPS and MTF. Using x-ray images acquired from direct and indirect conversion detectors, the performances of pixel binning are compared and analyzed. Through theoretical analysis and experiments, we demonstrate that the pixel binning method can approach the DQE performance achieved by downsampling after applying an anti-aliasing filter. However, we found that using a better anti-aliasing filter instead of MA can improve DQE performance, especially for direct conversion detectors.