Aiming at the drawbacks of the heavy workload and lengthy calibration cycle of transmission control unit (TCU) development, a fast optimizing and test method of clutch–clutch shift calibration parameters is developed based on the “V&rdquo...
Shift-scheduling calibration of automatic transmission (AT) vehicles is vital for both driving experience and automobile industry. Shifting schedules are usually calibrated with the consideration of fuel economy and drivability while neglecting the i...
Reliable uncertainty quantification matters for high-stakes tabular classification, yet the comparative influence of architecture and post-hoc calibration on uncertainty quality remains underexplored, particularly outside in-distribution conditions....
Cross-calibration using an autocollimator and a polygon is the traditional method for calibrating a rotary encoder. These angles, which can be calibrated using this method, are limited by the pitch angle of the polygon, which is 15° for a 24-face...
As the number of gears in automatic transmissions (AT) increases, the calibration parameters in the gear shift control process of the transmission control unit (TCU) increase exponentially, significantly increasing the calibration workload during eng...
Planar targets with known features have been widely used for camera calibration in various vision systems. This paper utilizes phase-shifting wedge grating (PWG) arrays as an active calibration target. Features points are encoded into the carrier pha...
Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertaint...
Camera parameters can’t be estimated accurately using traditional calibration methods if the camera is substantially defocused. To tackle this problem, an improved approach based on three phase-shifting circular grating (PCG) arrays is proposed in th...
Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can...
Sensor relocation can induce structural domain shift because gearbox vibration depends on fault excitation and the transmission path. This study evaluates transferability using 180 source recordings representing ten states, six accelerometer position...
Modern intrusion detection systems (IDSs) are often evaluated under matched training and test conditions, whereas deployment environments involve changing traffic distributions, heterogeneous feature-generation pipelines, and shifting attack prevalen...
Conformal prediction provides distribution-free coverage under exchangeability, but global calibration can conceal heterogeneous forecast errors. We introduce attribution-state conformal forecasting (ASCF), which learns calibration strata from residu...
In this study, we present a non-invasive and contactless method for estimating the internal temperature of Cr:LiCAF laser crystals using temperature-dependent shifts in fluorescence emission peaks. A high-resolution calibration dataset was created wi...
Fuel economy efficiency is one of the most important parameters for vehicle powertrains, which is of particular interest for heavy-duty powertrain calibration. Conventionally, this work relies heavily on road tests, which cost more and may lead to lo...
Detection of the Doppler shift of airglow radiation in the middle and upper atmosphere is one of the most important methods for remote sensing of the atmospheric wind field. Laboratory and routine field calibration of an optical interferometer for wi...
Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attribu...
Background: Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness. Consequently, numerous deep learning models have been developed for the early detection of DR. Safety-critical applications employed in medical diagnosis m...
Multi-digital camera systems (MDCS) are constantly being improved to meet the increasing requirement of high-resolution spatial data. This study identifies the insufficiencies of traditional MDCSs and proposes a new category MDCS based on tilt-shift...
The performance of modern digital cameras approaches physical limits and enables high-precision measurements in optical metrology and in computer vision. All camera-assisted geometrical measurements are fundamentally limited by the quality of camera...
Overlapped radiation-detector pulses must be resolved without conflating accurate pulse counting with reliable recovery of their latent components. This study examines how reconstruction fidelity and predictive confidence respond to controlled change...
Stable discrimination can coexist with unreliable probabilities, abstention policies, and uncertainty sets under distribution shift. We evaluate this mismatch with CRIT-AID, an executable multi-domain reliability-audit framework that separates model...
Background: Artificial intelligence (AI) has shown promise in breast ultrasound image analysis, but most evidence still comes from single-dataset studies. Clinical translation requires evaluation under heterogeneous acquisition and curation condition...
This study investigates the establishment and calibration method of the rut depth (RD) prediction model of inverted asphalt pavements (IAPs), based on full-scale accelerated pavement testing (APT), which facilitates the accurate and reliable design o...
Camera-distribution monitoring and semantic out-of-distribution detection assign different meanings to weather and illumination changes. We evaluate specified departures from a camera reference distribution and distinguish the image representation fr...
Quality monitoring in diagnostic-device manufacturing requires datasets that preserve temporal, hierarchical, and calibration-related structure while avoiding information leakage in predictive evaluation. This study presents the Diabetes-QMS Benchmar...
This paper presents a new method to improve the long-term frequency stability of an oven-controlled crystal oscillator (OCXO) without an external reference source. The frequency drift of the crystal oscillator can be obtained in real time by measurin...
Background: Deep-learning systems for endoscopic image classification are commonly evaluated with random data splits, which may overestimate performance under acquisition shift; uncertainty-aware selective prediction may improve reliability by allowi...
The 24 GHz continuous-wave (CW) Doppler radar sensor (DRS) is widely used for measuring the instantaneous speed of moving objects by using a non-contact approach, and has begun to be used in train-borne movable speed measurements in recent years in C...
Low-cost air quality sensors can expand PM2.5 monitoring networks but require calibration against regulatory-grade monitors to correct systematic bias towards trustworthy observations. Although interest in predictive uncertainty for air quality estim...
Multidimensional reliability scores have been proposed as better criteria than confidence for deferring clinical predictions to clinicians. We evaluated reliability-gated deferral for sepsis, mortality, and vasopressor prediction in intensive care pa...
The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wav...
Behavioral ransomware detection often achieves high accuracy under standard evaluation settings, but such results may not generalize under distribution shift or when previously unseen ransomware families are encountered. This study evaluates detectio...
Background/Objectives: High in-domain accuracy of diabetic retinopathy (DR) grading models does not guarantee a similar performance for a new population. We quantify how an APTOS-trained grader degrades on an independent cohort and examine why. Metho...
Pulse oximetry enables oxygen saturation estimation (
Distribution-shift alarms do not establish classifier harm. We evaluated the detectability, frozen-head relevance, and performance deterioration of two public DenseNet-121 chest-radiography classifiers under 30 controlled image conditions. Signals co...
The Feng Yun-3B (FY-3B) Infrared Atmospheric Sounder (IRAS) provides key infrared observations for numerical weather prediction (NWP) and climate applications, but long-term consistency is affected by three factors: spectral response function (SRF) c...
For airborne synthetic aperture radar (SAR) polarimetric calibration (PolCAL) based on distributed targets, it is important to ensure the removal of both the polarimetric distortion and terrain slope effect. This paper proposes a new technique for Po...
Accurate photovoltaic (PV) power forecasting degrades when models are deployed across sites or seasons, primarily due to distribution shift (amplitude bias and scale mismatch) and anomalous contamination—with pronounced amplitude–phase er...
The High-Resolution Infrared Radiation Sounder (HIRS) on NOAA and MetOp A/B satellites has been observing the Earth continuously for over four decades, providing essential data for operational numerical weather prediction, retrieval of atmospheric ve...
Longitudinal administrative prediction is vulnerable to temporal leakage, institutional shortcut learning, and distribution shift. We evaluate a reliability-aware multiview framework for predicting unsuccessful public tenders using 176,470 finalized...
Real-time monitoring of the cutting force in the machining process is critical for improving machining accuracy, optimizing the machining process, and optimizing tool lifetime; however, the dynamometers are too expensive to be widely used by machine...
This paper presents a low power Gaussian Frequency-Shift Keying (GFSK) transceiver (TRX) with high efficiency power management unit and integrated Single-Pole Double-Throw switch for Bluetooth low energy application. Receiver (RX) is implemented with...
Large language models (LLMs) now generate fluent natural language and source code, creating challenges for authorship attribution, academic integrity, and software supply-chain security. Most existing detectors for AI-generated content are evaluated...
Holographic synthetic aperture radar (HoloSAR) introduces a cutting-edge three-dimensional (3-D) imaging mode to the field of synthetic aperture radar (SAR), enriching the scattering information of targets by observing them across multiple spatial di...
A calibrated phase-shifting digital holographic microscope system capable of improving the quality of reconstructed images is proposed. Phase-shifting errors are introduced in phase-shifted holograms for numerous reasons, such as the non-linearity of...
Learning-enabled autonomous controllers may fail when a compact world model remains confident outside its training distribution. We present the conformal disagreement-regulated latent world model (CDR-LWM). The method converts ensemble disagreement i...
This study proposes a field-calibrated, NDT-integrated BIM modeling framework to improve the reliability of post-earthquake assessment for reinforced concrete (RC) buildings. The approach combines destructive and nondestructive testing (NDT) data&mda...
Wind turbine SCADA anomaly detectors are commonly calibrated using data from the same asset or development period, whereas deployment requires transfer across turbines with different operating distributions. This mismatch can produce optimistic thres...
Forecast accuracy alone is an incomplete proxy for operational value when load distributions change. This paper presents ARLOS, an auditable forecast–uncertainty–decision framework that combines static and adaptive XGBoost forecasts, roll...
Respiratory diseases are a major global health challenge. However, identification of respiratory diseases is often limited by subjectivity, environmental noise and inter-clinician variability. This study presents an explainable multimodal deep learni...
of 23