Search Results (6)

Industrial tariffs and demand-response (DR) programs pay for two quantities—daily energy and daily peak demand—yet short-term forecasting research addresses mostly the first, and rarely under the constraints of small industrial sites: strongly bimodal operation and only one to two years of records. We propose the Two-Stage Adaptive Framework (TSAF), a metering-only method that detects bimodality, classifies each day as active or inactive from its partial-day consumption, and fits a regression model to active days only; the same 21 features serve both targets. On 15 min data from ten plating factories of the Ansan Plating Industrial Complex (19 months), TSAF reaches 16.2% mean MAPE on daily energy against 58.7% for a day-ahead reference, and no deep-learning model beats the 22-parameter Ridge regressor. On daily peak, evaluated on active days, a joint multi-factory Transformer reaches 8.4% MAPE against a 14.0% constant-predictor floor, and a training-free tabular foundation model (TabPFN) reaches a comparable 7.0% without cross-factory data; intraday peak timing (≈2 h mean error) marks the limit of the meter-only design. External validation on 36 stratified UCI clients delimits the framework’s scope, and a weather ablation, specified in advance of estimation, finds no significant gain (p = 0.23). After adjusting for Stage 1 misclassification and intraday dispatch feasibility, the ten factories gain about 60 million KRW (US$46,000) per year and avoid 15.4 tCO₂ under Korean market conditions. Because TSAF needs only the smart-meter feed, power suppliers and DR aggregators can deploy it without access to customer-facility internals. Full article

Integrating depth sensors into mobile robots enables automated gait monitoring with potential applications in neurological disorders. This pilot study aims to evaluate the preliminary feasibility of robot-assisted gait assessment using Azure Kinect against Vicon, including individuals with multiple sclerosis, while simultaneously examining between-system, within-system, and environmental effects. A total of 20 participants were recruited to complete the eight-meter straight-line and 32 m corridor walking tests in the laboratory on the same day. Following independent data acquisition by both systems, temporal alignment was achieved through foot-event anchoring and interval trimming. On a unified timeline, 8 joint kinematic signals and 26 descriptors were extracted. Generalized estimating equations were applied, with a Bonferroni correction implemented for the 26 parallel tests to control the family error rate. The results showed: The spatiotemporal gait metrics exhibited general stability between systems and environments. Vicon better revealed variations in hip and pelvic amplitudes and restricted extension phenotypes, while the robotic system demonstrated greater sensitivity to knee posture and relative swing amplitude. The corridor environment induced an increase in stride length and a reduced step time compared to the laboratory, accompanied by a greater peak of hip and knee flexion and a greater forward lean of the trunk, with a largely preserved temporal organization. Within the Vicon-referenced framework, Azure Kinect-based robotic assessment demonstrated preliminary feasibility for capturing gait-related characteristics in individuals with multiple sclerosis. However, due to the limited number of analyzed MS participants, these findings should be interpreted as exploratory rather than as definitive clinical validation. The two systems exhibit complementary kinematic advantages. We recommend adopting an evaluation protocol that combines laboratory baseline with corridor validation, supplemented by descriptor-level mapping for cross-system data integration when necessary. This approach may support future tiered assessment, disease progression monitoring, and efficacy evaluation, but larger clinical cohorts are required to confirm its applicability in individuals with multiple sclerosis. Full article

The conflict between the deployment cost of high-precision intelligent standard instruments and the accuracy of intelligent verification is an unavoidable challenge in the intelligent transformation of charging-pile metering verification. To address this issue, this paper proposes a joint estimation method for charging-pile metering errors based on limited standard data. Specifically, correlated data sample groups are constructed based on the charging records of electric vehicles at different piles, a regularized least-squares convex programming model for joint estimation is established, and the Alternating Least Squares (ALS) algorithm is introduced to solve the model. Simulation results demonstrate that with only 10% of charging piles equipped with standard instruments, the estimation Root Mean Square Error (RMSE) is as low as 0.0069, and the overall identification accuracy reaches 91.25%, whose performance is significantly superior to that of the single-pile independent analysis scheme. Unlike conventional single-pile independent strategies that cannot identify systematic errors or leverage inter-pile data correlation, the proposed method employs an Alternating Least Squares (ALS) algorithm to fuse high-precision standard device data with pile-side reported data, achieving an overall identification accuracy of 91.25%, an F1-score of 72.00%, and an RMSE of 0.0069 for error estimation on a network of 80 charging piles with only 10% standard device coverage—significantly outperforming single-pile independent analysis. Full article

Disaggregating end-use electricity consumption from aggregate meter data remains a fundamental challenge in non-intrusive load monitoring, particularly in smart buildings where heating, ventilation, and air-conditioning systems dominate demand and direct sub-metering is often unavailable. Contextual variables such as weather and calendar information provide valuable explanatory signals, but in low-frequency settings, these drivers are typically insufficient to fully characterise building operation. As a result, attribution strategies that implicitly assume complete explainability can lead to unstable driver contributions and reduced physical interpretability when building behaviour is non-stationary or partially unobserved. This paper introduces MD-ADD, a multi-driver automatic dependency disaggregation framework designed for low-frequency smart meter data in commercial and public buildings. The framework supports joint attribution of multiple contextual drivers. It explicitly represents unexplained energy as a meaningful component of the decomposition. It combines robust baseline estimation, leakage-resistant out-of-fold contextual modelling, conservative driver attribution without hard mass-balance constraints, and uncertainty quantification using block bootstrap resampling. A consistency mechanism is included to restrict driver attributions to temporal scales compatible with their expected physical influence. The framework is evaluated on the ADRENALIN Load Disaggregation Challenge dataset, which contains multi-resolution electricity and weather data from commercial and public buildings, using normalized mean absolute error alongside stability and residual-structure diagnostics. Rather than optimising solely for pointwise accuracy, the proposed formulation emphasises robustness, interpretability, and diagnostic transparency, making it suitable for decision-support and analytical workflows under realistic low-frequency monitoring conditions. Full article

This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. Simulations demonstrate that the B3I signal achieves a significantly enhanced range resolution (tens of meters) compared to the B1I signal (hundreds of meters), attributable to its wider bandwidth. Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. Experimental results show that four-satellite configurations outperform three-satellite setups, achieving <10 m position error for uniform motion and <18 m for maneuvering targets, with velocity errors within ±2 m/s using four satellites. The joint detection framework for multi-satellite, multi-target scenarios demonstrates an improved detection accuracy and robust localization performance. Full article

Background: Ankle joint dorsiflexion range of motion is essential to normal gait. Ankle equinus has been implicated in a number of foot and ankle pathologies included Achilles tendonitis, plantar fasciitis, ankle injury, forefoot pain, and foot ulceration. Reliable measurement of ankle joint dorsiflexion range of motion, both clinically and in a research setting, is important. Methods: The primary aim of this study was to investigate the intertester reliability of an innovative device for measuring ankle joint dorsiflexion range of motion. A total of 31 (n = 31) participants volunteered to take part in this study. A paired t-test was performed to assess for systematic differences between the mean measures of each rater. Intertester reliability was evaluated using the intraclass correlation coefficient (ICC) and their 95% confidence intervals. Results: A paired t-test demonstrated that the mean ankle joint dorsiflexion range of motion did not significantly differ between raters. The ankle joint ROM mean for rater 1 was 4.65 SD (3.71) and rater 2 was 4.67 SD (3.91). Intertester reliability for the use of the Dorsi-Meter was excellent and demonstrated a very narrow range of error. The ICC (95%CI) was 0.991 (0.980 to 0.995) the SEM (in degrees) was 0.07, the MDC95, in degrees was 0.19 and 95% LOA, degrees was –1.49 to 1.46. Conclusions: We found the intertester reliability of the Dorsi-Meter to demonstrate higher levels of intertester reliability compared to previous studies investigating other devices. We reported the MDC values to provide an estimate of the smallest amount of change in the ankle joint dorsiflexion range of motion that must be achieved to reflect a true change, outside the error of the test. The Dorsi-Meter has been established as an appropriate reliable device to measure ankle joint dorsiflexion for clinicians and researchers with very small minimal detectable change and limits of agreement. Full article