Reliability Of Evaluation Results Research Articles

Human motion similarity evaluation based on deep metric learning

In order to eliminate the impact of camera viewpoint factors and human skeleton differences on the action similarity evaluation and to address the issue of human action similarity evaluation under different viewpoints, a method based on deep metric learning is proposed in this article. The method trains an automatic encoder-decoder deep neural network model by means of a homemade synthetic dataset, which maps the 2D human skeletal key point sequence samples extracted from motion videos into three potential low-dimensional dense spaces. Action feature vectors independent of camera viewpoint and human skeleton structure are extracted in the low-dimensional dense spaces, and motion similarity metrics are performed based on these features, thereby effectively eliminating the effects of camera viewpoint and human skeleton size differences on motion similarity evaluation. Specifically, when extracting the action information feature vectors using the automatic encoder-decoder network model, a sliding window method is used to divide the key point sequences of each limb part into sequence patches, and the action information feature vectors independent of the camera viewpoint and skeleton structure are extracted in a smaller time unit, so as to obtain a more refined action similarity evaluation result. In addition, the dynamic time warping (DWT) algorithm is exploited to align the sequence of action information feature vectors temporally, which solves the problem of temporal axis discrepancies in realizing similarity metrics based on action information feature vectors. More accurate and reliable human action similarity evaluation results were achieved by the loss function composed of three components, namely, cross-reconstruction loss, reconstruction loss and triplet loss. Finally, the performance of the algorithm is evaluated in a homemade dataset, and the experimental results show that the method could effectively eliminate the influence of the differences in camera viewpoints and human skeleton sizes on the similarity evaluation of actions, and generate more reliable and closer to the human subjective perception of similarity evaluation results for human actions captured from different viewpoints or with varying skeleton sizes.

A multi-phase mission success evaluation approach for maritime autonomous surface ships considering equipment performance degradation and system composition changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Analysis of the frequency–response stability and reliability of a tower-column unstable rock mass on a high and steep slope

The first-order second-moment checking point method was introduced to judge the instability probability and evaluate the stability of the TCURM. The frequency–response stability calculation and the reliability results were compared, and a frequency–response stability and reliability analysis method was proposed. Taking the Zengziyan W12# unstable rock mass in Nanchuan, Chongqing, China, as an example, the calculation shows that the dynamic indexes and geological indexes decrease as the stiffness of the deterioration area decreases. According to the statistical data of the laboratory test and the field investigation, reliability theory is used to evaluate the stability of the TCURM, and the failure probabilities are 80.3% and 96.27% under natural and saturated conditions, which correspond to states of poor stability and instability, respectively. The reliability evaluation results are consistent with the conclusion of the frequency–response stability analysis. The new method can provide a theoretical basis for developing the dynamic monitoring and early warning indicators of the TCURM and disaster prevention and mitigation in mountainous areas.

Investigation on the propagation of uncertainties of a timber floor under human excitation

Due to the characteristics of high stiffness-weight ratio, timber floors are prone to annoying vibrations under human excitation. Given the natural origin of timber, its mechanical properties exhibit significant variability. The randomness inherent in human excitation cannot be overlooked during structural dynamic analysis. Consequently, the adoption of a stochastic approach is imperative for attaining reliable serviceability evaluation results. However, current research on human-induced vibrations in the timber floor, accounting for this randomness, remains inadequate. In this paper, an experimental investigation is conducted on the dynamic properties and human-induced responses of a timber floor composed of glued laminated timber and oriented strand board. A finite element model is developed and subsequently validated for accuracy in terms of modal properties and dynamic responses. The probability density evolution method is introduced for stochastic analysis, which demonstrates that both the natural frequency and dynamic responses of the floor exhibit considerable variability when uncertainty factors are considered. The Kullback–Leibler divergence indices are used to assess the impact of each uncertain variable quantitatively. The results indicate that the longitudinal elastic modulus has the greatest influence on the natural frequency, while the first dynamic load factor, αz1, exerts the most significant impact on dynamic responses. Notably, both material mechanical properties and load model parameters contribute to the uncertainty of dynamic responses, with the influence of the load model parameters being significantly greater than that of material mechanical properties.

Multi-failure mode reliability of monorail vehicle gear transmission system based on multi-index staged degradation

The operational condition of monorail vehicle is diverse and influenced by numerous factors, leading to multiple failure modes and complex performance degradation mechanisms in gear transmission system. Addressing the problem that current reliability evaluation method based on single-stage single-index degradation models fail to accurately evaluate the multi-failure mode reliability of monorail vehicle gear transmission system, this study comprehensively considers the stage and nonlinear characteristics of degradation processes, along with the interdependence of indexes at different stages, a multi-index staged degradation model is established based on nonlinear Wiener process and Vine-Copula function. By analyzing the positions of multi-index stage change points, a multi-failure mode reliability evaluation method of monorail vehicle gear transmission system considering segmented dependence is proposed. And using the Bayes-Gibbs stepwise parameter estimation method proposed in this study, model parameters for Wiener process and Copula function are estimated separately. Finally, the applicability and effectiveness of the proposed method are verified, and the results shows that, compared to traditional single-stage single-index models, the proposed method more accurately describes the dependence and stage patterns of the degradation process, yielding more rational and effective reliability evaluation results.

Synergy of LIDAR and hyperspectral remote sensing: health status assessment of architectural heritage based on normal cloud theory and variable weight theory

Architectural heritage health assessment is the basis of scientific repair and maintenance. However, existing methods do not adequately take into account the fuzziness, randomness and uncertainties unique to architectural heritage assessment. In this paper, a new evaluation model of VM-NCM is constructed by combining variable weight theory and normal cloud model theory. The model enables the combination of qualitative ratings and quantitative calculation, deals with the fuzziness in the assessment process, and resolves the randomness and reflects the uncertainty to a certain extent. Based on constructing the index system combining qualitative and quantitative indexes, the structural index values are acquired by the synergistic coupling of the fine laser point cloud model and finite element structural analysis model. The acquisition of surface index values is completed by the hyperspectral intelligent detection technology of surface materials and diseases. These reduce the generation of ambiguous information in the index detection process. An evaluation study is conducted using the Yingxian wooden pagoda in China as an example. The results show that this method takes into account the fuzziness and randomness in the evaluation process, and obtains more scientific and reliable evaluation results, which provides a research paradigm for assessing the architectural heritage health status.

The Evaluation System and Continuous Improvement of College Physical Education Teaching in Guangdong Province

The purpose of this study is to explore the construction of the evaluation scale of college physical education teaching in Guangdong Province, to ensure that the evaluation tool can provide accurate, reliable and effective evaluation results, thus supporting and guiding the improvement of the quality of college physical education teaching in Guangdong Province. Through the analysis of the validity of evaluation tools, the validity and reliability of the scale, the research found that most of the items were effective in evaluating the ability and performance of PE teachers. The result of factor analysis shows that the items of evaluation tools can be divided into six main factors: 1) Continuous Learning and Improvement; 2) Instructional Strategies and Assessment; 3) Collaborative Learning Environment; 4) Technology Integration; 5) Subject Knowledge and Curriculum Alignment; 6) Professional Ethics and Growth. In addition, it also verified the consistency of evaluation between teachers and managers and between different work units.

Acceleration model considering multi‐stress coupling effect and reliability modeling method based on nonlinear Wiener process

AbstractEstablishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling.

Fully Automatic Fine-Grained Grading of Lumbar Intervertebral Disc Degeneration Using Regional Feature Recalibration Network.

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential for the diagnosis and treatment design of high-incidence low back pain. However, the grading accuracy is still challenged by lacking the fine-grained degenerative details, which is mainly due to the existing grading methods are easily dominated by the salient nucleus pulposus regions in LIVD, overlooking the inconspicuous degeneration changes of the surrounding structures. In this study, a novel regional feature recalibration network (RFRecNet) is proposed to achieve accurate and reliable LIVD degeneration grading. Detection transformer (DETR) is first utilized to detect all LIVDs and then input to the proposed RFRecNet for the fine-grained grading. To obtain sufficient features from both the salient nucleus pulposus and the surrounding regions, a regional cube-based feature boosting and suppression (RC-FBS) module is designed to adaptively recalibrate the feature extraction and utilization from the various regions in LIVD, and a feature diversification (FD) module is proposed to capture the complementary semantic information from the multi-scale features for the comprehensive fine-grained degeneration grading. Extensive experiments were conducted on a clinically collected dataset, which consists of 500 MR scans with a total of 10225 LIVDs. An average grading accuracy of 90.5%, specificity of 97.5%, sensitivity of 90.8%, and Cohen's kappa correlation coefficient of 0.876 are obtained, which indicate that the proposed framework is promising to provide doctors with reliable and consistent fine-grained quantitative evaluation results of the LIVD degeneration conditions for the optimal surgical plan design.

Dynamic risk assessment of re-construction dust from abandoned industrial buildings: A human-dust interaction perspective

Health risk assessment (HRA) is a well-established and widely used tool for quantitatively assessing the health status and likelihood of future illness and death of an individual or group. The dynamic properties of reconstruction dust from abandoned industrial buildings significantly affect HRA results. In recent years, dynamic health risk assessment (DHRA) has become an emerging research field. However, research on DHRA is still in the preliminary stage, and the research and application of its dynamic factors lack systematic analysis. Examining data transformation pathways through the U.S. Environmental Agency (USEPA) standard HRA framework, this study formalizes two distinct elements of dynamic assessment, namely, dynamic construction procedures and dynamic construction behaviour from a human-dust interaction perspective. Then, a DHRA framework for reconstruction dust of abandoned industrial buildings is proposed so that the aforementioned dynamic evaluation factors can be integrated into the HRA framework. Finally, feasible application solutions and data availability of dynamic factors are discussed, and case studies are carried out to better support DHRA application. The results show that the evaluation results are sensitive to different degrees of dynamic evaluation factors, and DHRA can provide more authentic and reliable evaluation results.

Reliability evaluation of non-isolated high gain interleaved DC-DC converter

A high gain DC-DC converter is the crucial part in renewable energy systems (RES) and in electric vehicular systems. The reliability of those high-gain converters needs to be assessed for the long-term operation of renewable energy systems. This article presents the reliability analysis of non-isolated high gain interleaved DC-DC converter. The analysis primarily relies on calculating the mean time between failures (MTBF). Based on military handbook (MIL-HDBK-217) criteria, the reliability calculation is performed. Stress factors and predicted failure rate for each component of presented converter is evaluated and tabulated. Reliability evaluation is performed for 1.5 kW hardware prototype. Based on reliability evaluation results, a reliable converter with better operating life time has been introduced.

On the relationship between imprint and reliability in Hf0.5Zr0.5O2 based ferroelectric random access memory

The detrimental effect of imprint, which can cause misreading problem, has hindered the application of ferroelectric HfO2. In this work, we present results of a comprehensive reliability evaluation of Hf0.5Zr0.5O2-based ferroelectric random access memory. The influence of imprint on the retention and endurance is demonstrated. Furthermore, a solution in circuity is proposed to effectively solve the misreading problem caused by imprint.

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.

Towards more reliable evaluation in pedestrian detection by rethinking “ignore regions”

It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors fail. When we perform an error analysis based on the traditional evaluation strategy, we find that it produces many misleading false positives, which in fact cover occluded pedestrians. The reason for this is that we usually have two kinds of annotations in the dataset: regular pedestrians (detection targets) labeled by full-body boxes and ignored pedestrians (NOT detection targets) labeled by visible boxes. Ignored pedestrians are labeled as an additional category termed the “ignore region”. Nevertheless, our detectors always predict a full-body box for each pedestrian. This gap results in the following case: when a detector successfully predicts a full-body box for those ignored pedestrians, a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored pedestrian. This becomes even more harmful as the detector improves and becomes more capable of locating occluded pedestrians. To alleviate this issue, we devise a new pedestrian detection pipeline, which considers the additional visible box at both the detection and evaluation stages. During detection, we predict an extra visible box apart from the full-body box for every instance; during evaluation, we employ visible boxes instead of full-body boxes to match the “ignore region”. We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results.

A comprehensive assessment method for manned confined space layouts taking into account operational posture comfort supported by artificial intelligence algorithm

Given its specialized design application, the layout design of confined spaces for human occupancy has a significant impact on the efficiency of operators, especially in the design processes aimed at aviation, aerospace, navigation, and other significant engineering fields. The influence of operational posture on layout comfort is crucial, yet existing assessment methods have limited considerations for the impact of operational posture on layout comfort. Therefore, this study proposes a layout decision support method that integrates posture recognition, simulation, and simulation-based comfort comprehensive assessment. By combining intelligent recognition technology, simulation technology, and comprehensive evaluation methods, it aims to provide a highly reliable comprehensive evaluation result for layout design and decision recommendations. To validate the effectiveness of the method, we conducted a comfort experimental evaluation involving seven individuals in the layout design of human-occupied confined spaces at Northwestern Polytechnical University. The results indicate that the proposed method offers a more objective and comprehensive means for layout design and optimization. This Improvement method not only addresses the deficiencies in existing spatial layout evaluation methods and provides a theoretical basis for architectural layout planning evaluation but also offers new perspectives and methodological support for scientific layout design and planning.

An integrated hesitant 2-tuple linguistic Pythagorean fuzzy decision-making method for single-pilot operations mechanism evaluation

Single-pilot operations (SPO), i.e., one pilot on board the flight in charge of all the operations, has received extensive attention in recent years. SPO involves several participants including the pilot, the autopilot, and the ground operator, and it is of great significance to determine the roles of these participants under the SPO mechanism. However, the evaluation of SPO mechanism has received little attention. Aiming at providing a reliable approach for SPO mechanism evaluation under uncertainty, this paper proposes a novel hesitant 2-tuple linguistic Pythagorean fuzzy decision-making method based on hesitant fuzzy linguistic term sets (HFLTS), 2-tuple linguistic information, Pythagorean fuzzy sets (PFS), and VIKOR method to handle the SPO mechanism evaluation problem. Firstly, based on the analysis of SPO, the evaluation criteria system for SPO mechanism is established. Then, considering the uncertain information in the SPO mechanism evaluation process, the HFLTS is used to represent the linguistic judgments of experts under uncertainty. Next, the HFLTSs of different experts are converted into 2-tuple linguistic information and PFSs to model the overall judgments of different criteria. Finally, the extended VIKOR method is adopted to evaluate and rank different SPO mechanisms. A case study of SPO mechanism evaluation for the ground proximity warning system (GPWS) is used to demonstrate the effectiveness and feasibility of the proposed method, and the SPO mechanism that the autopilot being responsible for GPWS is evaluated by the proposed method to be optimal. Sensitivity and comparison analyses further confirm that the proposed method could provide reliable and reasonable SPO mechanism evaluation results. In conclusion, the proposed method presents a novel and effective way to evaluate and select appropriate SPO mechanisms.

6G-enabled open-pit mine security: toward wise evaluation, monitoring, and early warning

The mining of open pit can easily cause geological disasters such as landslide and debris flow. It is widely acknowledged that communication technology could solve the existing problems in engineering practice from the aspects of disaster monitoring, deformation monitoring, landslide warning and emergency communication. This research paper introduces a fully integrated monitoring and early warning solution tailored for mining regions. By synergizing 6G and Global Navigation Satellite System (GNSS) technologies, the system effectively addresses signal transmission challenges in complex environments. The utilization of 6G’s high-speed, low-latency, and extensive connectivity capabilities enables efficient communication in these settings. The study focuses on a specific open-pit mine located in a cold region of China, utilizing it as a case study to demonstrate the system’s effectiveness in enhancing slope safety through comprehensive monitoring and early warning mechanisms. Apart from considering traditional dump characteristics and external factors, the system also introduces an innovative early warning index for detecting slope changes. Applying the Analytic Hierarchy Process (AHP) and Fuzzy comprehensive evaluation method ensures reliable evaluation results, facilitating slope assessment, monitoring, and early warning procedures in water-rich open-pit mines situated in cold areas.

Assessing the manufacturable 32-channel cochlear electrode array: evaluation results for clinical trials.

Reliability evaluation results of a manufacturable 32-channel cochlear electrode array are reported in this paper. Applying automated laser micro-machining process and a layer-by-layer silicone deposition scheme, authors developed the manufacturing methods of the electrode array for fine patterning and mass production. The developed electrode array has been verified through the requirements specified by the ISO Standard 14708-7. And the insertion trauma of the electrode array has been evaluated based on human temporal bone studies. According to the specified requirements, the electrode array was assessed through elongation & insulation, flexural, and fatigue tests. In addition, Temporal bone study was performed using eight fresh-frozen cadaver temporal bones with the electrode arrays inserted via the round window. Following soaking in saline condition, the impedances between conducting wires of the electrode array were measured over 100 kΩ (the pass/fail criterion). After each required test, it was shown that the electrode array maintained the electrical continuity and insulation condition. The average insertion angle of the electrode array inside the scala tympani was 399.7°. The human temporal bone studies exhibited atraumatic insertion rate of 60.3% (grade 0 or 1). The reliability of the manufacturable electrode array is successfully verified in mechanical, electrical, and histological aspects. Following the completion of a 32-channel cochlear implant system, the performance and stability of the 32-channel electrode array will be evaluated in clinical trials.

Investigation into Mining Economic Evaluation Approaches Based on the Rosenblueth Point Estimate Method

Conducting technical and economic evaluations is important for mining investment and mining operation decision-making. Traditional economic evaluation methods rarely address the issue of evaluation reliability and usually require complex calculations to obtain the optimal solution. In this study, the Rosenblueth point estimate method for reliability evaluation of engineering project schemes is introduced. Combined with the cash flow method for economic evaluation of mines, the Rosenblueth point estimate method for evaluating the reliability of mining economy is established. Based on the technical and economic index of the case mine, taking the ore grade as a sensitivity indicator, empirical research on established models and methods was carried out. The results of the economic reliability evaluation and the variation rules obtained using the Rosenblueth point estimate method model were basically consistent with the actual production and operation rules of mining enterprise. The similar results also proved that the proposed model has good applicability and reliability for mining economic evaluation. Using the proposed RPEM economic reliability model, the economic reliability of a certain iron mine in Liaoning Province was calculated to be 99.95, which was a huge improvement compared with the traditional evaluation method. Additionally, the calculation process of the proposed model for economic reliability evaluation is simple and the accuracy is controllable. The economic reliability of the project can be calculated based on changes in sensitivity indicators, and the value range of sensitivity indicators can also be calculated through the required reliability. The obtained results and the proposed evaluation model provide a decision-making basis for mining investment projects and operation management.

UTILIZING THE EMBODIMENT FUNCTION RELATION AND TOLERANCE MODEL FOR ROBUST CONCEPT DESIGN

AbstractThe early use of Robust Design (RD) supports the development of product concepts with low sensitivity to variation, which offers advantages for reducing the risk of costly iterations. Due to the lack of approaches for early evaluation of product robustness, the embodiment-function-relation and tolerance (EFRT-) model was developed, which combines the contact and channel approach and tolerance graphs. The information exchange of both approaches offers a high potential for reliable robustness evaluation results. However, that potential currently relies unused, since the link between applicable robustness criteria and the extended information is missing. To solve this problem, four research steps were determined: (1) understanding of robustness, (2) collection of RD principles, (3) identification of EFRT-model information and (4) mapping of RD principles and information. The results show nine adapted RD principles, the identified model information for the robustness evaluation, the evaluation criteria as well as their mapping. Utilizing the mapping and the proposed criteria in this contribution, a more comprehensive robustness evaluation in early stages is enabled.