Estimation Accuracy Research Articles

Estimating the accuracy of smartphone app-based removal estimates against actual wood-harvesting data from clear cuttings

Estimating the accuracy of smartphone app-based removal estimates against actual wood-harvesting data from clear cuttings

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.

This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.

Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning

AbstractAs an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.

Multi-mode vehicle pose estimation under different GNSS conditions

The integrated navigation system combining the global navigation satellite system (GNSS) and inertial navigation system (INS) is a crucial method for pose estimation in the field of autonomous driving technologies. Nevertheless, the accuracy of pose estimation is severely compromised when GNSS signals are obstructed or disrupted. To address this issue, this study introduces a multi-mode pose estimation framework designed to ensure accurate pose estimation even under unstable GNSS conditions. By integrating vehicle kinematics model that considers steering characteristics (VKMSC) and the convolutional neural network-long short-term memory (CNN-LSTM) neural network (NN) model into various estimation modes, the framework enhances the robustness of the integrated navigation system against signal interference. The system dynamically selects the optimal estimation strategy based on the degree of GNSS signal disruption. The proposed method has been validated through real-vehicle experiments, which demonstrate its efficacy in providing precise pose estimation across a spectrum of interference scenarios. Under the multipath and non-line-of-sight (MP/NLOS) mode, compared to the integrated navigation system and the fusion of traditional vehicle kinematic models, the proposed method improved positional estimation accuracy by 61.8 % and 19.7 %, respectively. In GNSS outage mode, the proposed method increased the estimation accuracy by 36.5 % and 12.0 %, respectively, compared to the INS navigation system assisted by the VKMSC and CNN-LSTM network model. The proposed method effectively reduces pose estimation errors in the integrated navigation system during interference and suppresses data fluctuations, thereby enhancing the system's precision and robustness.

A novel correlation-based approach for combined estimation of state of charge and state of health of lithium-ion batteries

State of charge(SOC) and state of health(SOH) estimation are two critical aspects for the secure operation and echelon utilisation of lithium-ion batteries. Considering the two states are interrelated, combined estimation methods are desired in practice. However, previous machine learning-based or traditional filter-based techniques have high computational requirements or suffer from convergence issues. To this end, the combined SOC and SOH estimation method of lithium-ion batteries using the novel correlation between SOC and SOH is proposed in this study. Specifically, a correlation between the capacity ratio for constant-current charging process and constant-voltage charging process and capacity loss is established. Extended Kalman filter is adopted to track SOC values at the end of constant current charging process instead of relying on the entire charging process during online application and SOH estimation can be accomplished in only one step based on the established correlation. The results show that the proposed combined SOC and SOH estimation method enables precise SOC estimation during both charging and discharging processes and achieves reliable estimation results during battery degradation. Additionally, the proposed method offers a swift means of estimating SOH with guaranteed estimation accuracy.

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation

Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural net- works (DNNs) can be miscalibrated and overconfident, leading to ”silent failures” that are risky for clinical applications. Bayesian DL provides an intuitive approach to DL failure de- tection, based on posterior probability estimation. However, the posterior is intractable for large medical image segmentation DNNs. To tackle this challenge, we propose a Bayesian learning framework using Hamiltonian Monte Carlo (HMC), tempered by cold posterior (CP) to accommodate medical data augmentation, named HMC-CP. For HMC compu- tation, we further propose a cyclical annealing strategy, capturing both local and global geometries of the posterior distribution, enabling highly efficient Bayesian DNN training with the same computational budget as training a single DNN. The resulting Bayesian DNN outputs an ensemble segmentation along with the segmentation uncertainty. We evaluate the proposed HMC-CP extensively on cardiac magnetic resonance image (MRI) segmentation, using in-domain steady-state free precession (SSFP) cine images as well as out-of-domain datasets of quantitative T1 and T2 mapping. Our results show that the proposed method improves both segmentation accuracy and uncertainty estimation for in- and out-of-domain data, compared with well-established baseline methods such as Monte Carlo Dropout and Deep Ensembles. Additionally, we establish a conceptual link between HMC and the commonly known stochastic gradient descent (SGD) and provide general insight into the uncertainty of DL. This uncertainty is implicitly encoded in the training dynamics but often overlooked. With reliable uncertainty estimation, our method provides a promising direction toward trustworthy DL in clinical applications. We release our code in <a href='https://gitlab.tudelft.nl/yidongzhao/hmc_uncertainty'>https://gitlab.tudelft.nl/yidongzhao/hmc_uncertainty</a>

Improved estimation of canopy water status in cotton using vegetation indices along with textural information from UAV-based multispectral images

Precise and timely estimation of crop canopy water status is of great importance for precision irrigation. The unmanned aerial vehicle (UAV)-based remote sensing technology has become increasingly popular for crop canopy water status estimation. Nevertheless, the capability of vegetation indices (VIs) along with textural information from high-resolution imagery for estimating cotton canopy water status has been rarely explored. The VIs, texture features (TFs), and texture indices (TIs) were obtained from UAV multispectral images of cotton with different irrigation levels and nitrogen rates in the southern Xinjiang of China. The performances of three machine learning models, i.e. support vector machine (SVM), back-propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were evaluated for estimating canopy equivalent water thickness (CEWT) and crop water stress index (CWSI) throughout the cotton growing season. The results showed that combining vegetation indices and textural information significantly improved the estimation accuracy of models compared to vegetation indices or textural information alone. The XGBoost_VIs + TFs model exhibited the best accuracy in estimating CEWT (R2 = 0.75, RMSE = 0.01 cm, RE = 19.46 % at upper half-canopy level, and R2 = 0.65, RMSE = 0.02 cm, RE = 24.59 % at all-canopy level), while the XGBoost_VIs + TFs + TIs model performed best in predicting CWSI among the models (R2 = 0.90, RMSE = 0.05, RE = 5.84 %). Although CEWT estimation was fair to some extent, CWSI estimation was more applicable for diagnosing cotton water stress. The CWSI maps created from the optimal XGBoost_VIs + TFs + TIs model intuitively reflected the cotton canopy water status under various irrigation levels and nitrogen rates, which could help farmers implement timely and precision irrigation in cotton production.

Triple-diode solar cell current optimization – An analytical solution based on the Lambert W function

A novel mathematical modeling approach for the Triple-Diode Model (TDM) and its parameter estimation is introduced in this paper. For the first time in the literature, this paper presents two analytical solutions for calculating the current in TDM solar cells using the Lambert W function. Additionally, a new metaheuristic algorithm, the Adaptive Evaporation Rate Water Cycle Algorithm (Adaptive ERWCA), is proposed for estimating TDM solar cell parameters. The proposed algorithm is tested using both the new analytical solutions and a well-known iterative approach for the current-voltage characteristics of the TDM solar cells model. Extensive testing is conducted on three well-known solar cells/modules and a panel under various weather conditions. The results demonstrate that the proposed analytical expressions for the current-voltage characteristics are accurate and highly effective for representing TDM solar cell current. The accuracy of the parameter estimates obtained with the application of the proposed algorithm, considering both analytical solutions and the iterative approach, is compared with existing literature results. It is shown that the proposed algorithm outperforms existing algorithms in solar cell parameter estimation across both the analytical and iterative approaches for current-voltage characteristics. Remarkably, for the RTC France solar cell, the SOLAREX MSX 60 solar module, and the PHOTOWATT-PWP 201 solar module, the obtained Root Mean Square Errors (RMSE) are 0.00075141, 0.01157153, and 0.0010578, respectively. These results are currently the best reported in the literature.

Electroluminescence and infrared imaging of fielded photovoltaic modules: A complementary analysis of series resistance-related defects

Fielded photovoltaic (PV) modules often exhibit series resistance-related defects, mainly due to solder bond degradation and metallization corrosion. To quantitatively analyze the series resistance increase in fielded modules, we conducted a detailed investigation on two sets of modules, employing two complementary techniques, electroluminescence (EL) and infrared (IR) imaging. The dependence of EL image characteristics on module temperature, as revealed in IR images, was a key focus of this study. The first and second sets of modules were exposed in Florida (hot and humid climate) and Arizona (hot and dry climate) over 10 years and 18 years, respectively. The resistive defect patterns obtained using EL and IR images showed a closer correlation for the Florida modules compared to the Arizona modules as the Florida modules primarily experience solder bond degradation. EL and IR images were acquired at five current injection levels (i.e., 0.1, 0.3, 0.5, 0.7, 1.0 x short circuit current) and two exposure times (i.e., 60 s and 300 s) and used to develop and report a new curve fitting method for estimating the external series resistance. The results indicate that inaccurate temperature determinations from IR images can lead to underestimations (up to 23 %) in EL-based external series resistance estimates. For the most accurate series resistance estimation, especially in modules with severe thermal defects and series resistance deterioration, the study recommends obtaining EL and IR images within 60 s of the current injection time. This study also reports a Monte Carlo simulation assessing the impact of EL and IR characteristics on the accuracy of external series resistance estimations.

A Novel Method for Estimating the Undrained Shear Strength of Marine Soil Based on CPTU Tests

The undrained shear strength is an essential parameter in the foundation design of marine structures. Due to the complex marine environment and technical limitations, it is difficult and costly to obtain offshore samples. Piezocone penetration tests (CPTU) are relatively low-cost compared to drilling and sampling methods. Therefore, based on the soil behavior type index (Ic) derived from CPTU results, a model for estimating cone factors (Nkt, Nke) is proposed to improve the accuracy of estimation of undrained shear strength. The result shows that the soil behavior type index (Ic) and cone factors take on a negatively correlated exponential relation. Incorporating a cone factor that varies with the soil behavior type index (Ic) significantly enhances the accuracy of undrained shear strength predictions compared to the conventional method of using a constant cone factor. This approach reduces the root mean square error (RMSE) for Nkt (Nke) from 0.124 (0.126) MPa to 0.056 (0.06) MPa, and the mean absolute error (MAE) from 0.0154 (0.016) MPa to 0.0032 (0.0036) MPa. The method was validated at an additional location and the predictions were in high agreement with the results of the consolidated quick direct shear test. The developed method can serve as an effective tool used in the design of foundations of marine structures.

Research on an Ice Tolerance Control Method for Large Aircraft Based on Adaptive Dynamic Inversion

Considering the effect of icing on aircraft control performance, this paper proposes an adaptive dynamic inverse ice tolerance control method based on piecewise constant. A control allocation algorithm is introduced to compensate for the change of control surface performance caused by icing. This method can achieve satisfactory disturbance estimation accuracy under a given sampling time, and thus ensure a closed-loop system error within an acceptable range. The proposed design method is applied to the design of a flight control law for a transport aircraft, aiming to solve the problem of ice-tolerant flight control, reduce the influence of icing conditions on controllability and safe flight of the transport aircraft, and thus improve the flight quality of the transport aircraft. The simulation results are verified under the influence of both standby ice type and failure ice type, and the interference effect on aircraft aerodynamic parameters is further added. The simulation results show that adaptive dynamic inverse control based on piecewise constant can overcome the influence caused by icing and aerodynamic parameter interference, achieve accurate tracking of command, and provide excellent fault tolerance and robustness, which ensures that the transport aircraft can achieve the desired control performance and safe flight capability.

Estimation of Road Adhesion Coefficient Based on Camber Brush Model

Electric vehicles, with their distinct power systems, weight distribution, and power control strategies compared to traditional vehicles, influence the pressure distribution in the tire contact area, thereby affecting the estimation of road adhesion coefficient. In electric vehicle research, tire adhesion coefficient serves as a measure of the frictional force between the vehicle and the road surface, directly impacting the vehicle’s handling performance. The accurate estimation of the adhesion coefficient aids drivers in better understanding the vehicle’s driving state. However, the existing brush models neglect differences in ground pressure distribution along the width direction of tires during tire camber, potentially leading to inaccuracies in adhesion coefficient estimation. This study proposes a camber brush tire model that considers the width-direction pressure distribution characteristics, aiming to enhance the accuracy of adhesion coefficient estimation under camber conditions. Experimental comparisons between the improved and original models reveal a significant enhancement in estimation precision. Consequently, the findings of this study provide valuable insights for deepening our understanding of tire camber dynamics and for designing control systems for electric vehicles, thereby improving vehicle stability and safety.

Inversion with stratigraphy-guided deep learning

Property estimation in seismic exploration traditionally relies on seismic inversion, which is an ill-posed problem. However, recent advances in deep learning (DL), specifically supervised neural networks, indicate promise for accuracy improvements. Building upon this, stratigraphy-guided deep learning (SGDL) is a novel method that encodes stratigraphic units as discrete features within the DL model. Our primary objective is to evaluate SGDL in a scenario with available geologic data and field data calibration, such as well tops and horizons. We conduct a case study predicting porosity and acoustic impedance from poststack seismic data. Robustness evaluations demonstrate a 20% average enhancement in the correlation for acoustic impedance across 10 test wells from the Volve data set. We find that SGDL inversion outperforms traditional and other DL methods, reaching a 91% correlation for one benchmark blind well. These results offer compelling evidence that the incorporation of stratigraphic units as features in the DL model contributes to further enhancing the accuracy of property estimation. In summary, SGDL represents a novel approach that integrates DL with geologic data, offering significant enhancements in property estimation accuracy within the field of seismic inversion.

Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras.

Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters and for 3D perception. However, conventional image-based calibration techniques are not applicable due to the asynchronous, binary output of the sensor. The current standard for calibrating event cameras relies on either blinking patterns or event-based image reconstruction algorithms. These approaches are difficult to deploy in factory settings and are affected by noise and artifacts degrading the calibration performance. To bridge these limitations, we present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras utilizing the asymmetric circle grid, for its robustness to out-of-focus scenes. E-Calib introduces an efficient reweighted least squares (eRWLS) method for feature extraction of the calibration pattern circles with sub-pixel accuracy and robustness to noise. In addition, a modified hierarchical clustering algorithm is devised to detect the calibration grid apart from the background clutter. The proposed method is tested in a variety of rigorous experiments for different event camera models, on circle grids with different geometric properties, on varying calibration trajectories and speeds, and under challenging illumination conditions. The results show that our approach outperforms the state-of-the-art in detection success rate, reprojection error, and pose estimation accuracy.

On the Security of Selectively Encrypted HEVC Video Bitstreams

With the growing applications of video, ensuring its security has become of utmost importance. Selective encryption (SE) has gained significant attention in the field of video content protection due to its compatibility with video codecs, favorable visual distortion, and low time complexity. However, few studies consider SE security under cryptographic attacks. To fill this gap, we analyze the security concerns of encrypted bitstreams by SE schemes and propose two known plaintext attacks (KPAs). Then the corresponding defense is presented against the KPAs. To validate the effectiveness of the KPA, it is applied to attack two existing SE schemes with superior visual degradation in HEVC videos. Firstly, the encrypted bitstreams are generated using the HEVC encoder with SE (HESE). Secondly, the video sequences are encoded using H.265/HEVC. During encoding, the selected syntax elements are recorded. Then the recorded syntax elements are imported into the HEVC decoder using decryption (HDD). By utilizing the encryption parameters and the imported data in the HDD, it becomes possible to reconstruct a significant portion of the original syntax elements before encryption. Finally, the reconstructed syntax elements are compared with the encrypted syntax elements in the HDD, allowing the design of a pseudo-key stream (PKS) through the inverse of the encryption operations. The PKS is used to decrypt the existing SE scheme, and the experimental results provide evidence that the two existing SE schemes are vulnerable to the proposed KPAs. In the case of single bitstream estimation (SBE), the average correct rate of key stream estimation exceeds 93%. Moreover, with multi-bitstream complementation (MBC), the average estimation accuracy can be further improved to 99%.

Feasibility of using Ku-band helicopter-borne microwave radar for stem volume and biomass estimation in boreal forest

Under the global climate challenges, accurate assessment of forest variables is becoming increasingly important for effective forest management and monitoring. While long-wavelength satellite radar data (P-, L-band) has been widely used for this purpose, the potential of airborne Ku-band microwave radar remains underexplored. In this paper, an airborne Ku-band frequency modulated continuous wave (FMCW) profiling radar data was used to predict forest variables including plot-level basal-area weighted mean canopy height (Hg), mean diameter at breast height (DBHg), stem volume (V) and above ground biomass (AGB) using the random forest method. In addition, the maximum canopy heights were computed from the radar-derived results. The estimated results were compared with 55 manually-measured and accurately matched field plots consisting of thousands of trees. The comparison results show that the estimation accuracy is at a good level, with the relative Root Mean Square Errors (RMSE%) and the coefficient of determination (R2) of the four variables were Hg (10.04 %, 0.75), DBHg (17.25 %, 0.63), V (21.11 %, 0.67) and AGB (19.75 %, 0.63), respectively. The estimation of plot-level maximum heights resulted in a relative RMSE of 7.95 % and R2 of 0.89. Through comparing with previous similar studies, the accuracies of estimating the four variables from airborne Ku-band data are comparable to those of high-precision satellite microwave radar data. The study results indicate that the canopy gaps and adequate penetration of airborne Ku-band signals into the canopy enable the characterization of the canopy layer and the ground floor, allowing for accurate estimation of forest variables in boreal forests.

Determining the exergy and energy efficiency of an organic Rankine cycle using fuzzy logic method

AbstractThe Organic Rankine Cycle (ORC) serves as a pivotal technology for energy conversion, harnessing high‐temperature organic liquids sourced from heat reservoirs to propel turbines and generate electricity. This process not only facilitates the conversion of heat into mechanical energy but also significantly mitigates environmental impacts. ORC stands out as the preferred technology for enhancing energy efficiency and leveraging low‐temperature resources optimally. With the advent of artificial intelligence, particularly fuzzy logic, these systems have witnessed integration, providing a robust solution to address uncertainties. Unlike traditional logic, which offers binary outcomes, fuzzy logic offers a more adaptable approach by accommodating uncertainty, thus enabling modeling of complex real‐world situations. In this study, utilizing the fuzzy logic method, we estimated the energy and exergy efficiency of the ORC, resulting in an impressive 90% estimation accuracy.

Disassembly time estimation for used smartphones based on Maynard operation sequence technology

The disassembly phase plays a crucial role in improving sustainability and circular performance, in particular when carried out through manual disassembly. At present, the Maynard operation sequence technique (MOST) for determining disassembly time can avoid subjectivity and is aligned with actual disassembly actions. However, the MOST suffers from inaccurate action analysis and the omission of some disassembly actions, resulting in lower accuracy for the disassembly time estimation of used smartphones with existing index values. To address this issue, the paper proposes an improved method based on the MOST to improve the accuracy of used smartphone disassembly time estimation. Based on the improved general movement index table, the improved tool usage index table, and the established disassembly time calculation model, the disassembly time for a given sequence of actions and target parts is estimated by the proposed method. One of the innovative aspects of the proposed method is the definition of seven disassembly action units, which can provide better insights into which disassembly action is the most time-consuming, and how the product design could be improved. The proposed method is validated through a case study involving manual disassembly for four types of used smartphones, namely Redmi 1, Huawei Enjoy 5, Meizu MX4 Pro, and iPhone 6. The experimental results show that the overall accuracy of the proposed method is approximately 98% in disassembly time estimation, which is improved by 5.7% compared to MOST. Moreover, the distribution analysis for each unit revealed that disconnection is the most time-consuming action followed by surface treatment, and action distance-gain control and action distance-placement. If the Redmi 1 employs the same back cover connection as the Huawei Enjoy 5, the disconnecting time can be shortened by 18.6% of the total disassembly time. This work verifies the applicability of the proposed method and guides for used smartphone design improvements.