Judgment Method Research Articles

Softskill Base Arabic Language Teaching Model in Improving Talaqul Kalam for College Student in Universitas Sains Al-Quran Jawa Tengah Wonosobo

Arabic language learning has a major problem related to its renewal. Arabic language learning is always identical with grammatical (Qawa’id) aspects which seem to be rigid, complicated, and confusing. There are two major Arabic language learning orientations: as body of knowledge and mastery. The purpose of this research is to produce an Arabic language learning model based on soft skills to effectively improve the students’ Talaqah al kalaam at Universitas Sains Al-Qur’an (UNSIQ), Wonosobo, Central Java, Indonesia. This research was considered as a field study in the form of Research and Development (R&D) involving students from each faculty of Universitas Sains Al-Qur’an (UNSIQ), Wonosobo, Central Java, Indonesia. The research data were collected through observation, in-depth interview, questionnaire, test, and documentation. The collected data were then analyzed through several stages: (a) initial-product test analysis, (b) limited data trial result analysis, (c) extensive data trial result analysis, and (d) student learning response analysis. The research results showed that the Arabic language learning model based on soft skills had fulfilled the validity elements through Expert Judgment method and been considered very effective proven by the significant improvement in post-test results. This model was also proven practical proven by the test results showing that the supporting instruments, such as Course Learning Unit, Learning Module, Semester Learning Plan, Test Instrument, and Learning Implementation Sheet, were very good.

A mixed method evaluation of knowledge, attitude and practice on dengue fever among Lalitpur Metropolitan City residents: a cross-sectional investigation.

Dengue poses a significant global public health challenge, including in Nepal. Understanding community's knowledge, attitudes, and behaviors concerning dengue fever is imperative to developing effective prevention and control strategies. This study aimed to assess the knowledge, attitude, and practices related to dengue fever among residents of Lalitpur Metropolitan City. A descriptive cross-sectional household study was conducted using a mixed-method approach, which included quantitatively studying 636 individuals and conducting 20 qualitative interviews. The data was collected between April 2023 and June 2023. The multistage cluster sampling method was applied for household selection during the quantitative study and a purposive judgmental sampling method was used to identify participants for the qualitative interviews. Face-to-face interviews were conducted using a structured questionnaire for the quantitative study and an interview guide for the qualitative study. Quantitative data were analysed using logistic regression in STATA version 13, and thematic analysis was applied to the qualitative data. The findings were validated through triangulation of results from both the qualitative and quantitative study. Regarding knowledge, 64.94% (n = 413/636) reported being informed about dengue fever. In terms of attitude, a substantial majority, 91.51% (n = 582/636), expressed a positive attitudes toward dengue fever, indicating a favorable perception and knowledge of its significance. Concerning practice, 49.84% (n = 317/636) of respondents reported actively engaging in dengue fever prevention measures. The variables gender, previous history of dengue fever and residency were the determinants of dengue fever knowledge. Additionally, gender, residency, and attitude were predictors of preventive practices concerning dengue fever. Our study revealed that while the community demonstrated good knowledge of dengue fever and positive attitudes toward prevention, their preventive practices were inconsistent, indicating a gap between knowledge and action. A positive attitude was linked to better adherence to preventive measures. To address this gap, it is crucial to promote a positive attitude toward dengue prevention through initiatives like education efforts and social mobilization programs. Implementing Social and Behavior Change Communication (SBCC) programs focused on dengue prevention and control measures can help bridge this knowledge-action gap.

A Three-Dimensional Modeling Approach for Carbon Nanotubes Filled Polymers Utilizing the Modified Nearest Neighbor Algorithm.

Carbon nanotubes (CNTs) are extensively utilized in the fabrication of high-performance composites due to their exceptional mechanical, electrical, and thermal characteristics. To investigate the mechanical properties of CNTs filled polymers accurately and effectively, a 3D modeling approach that incorporates the microstructural attributes of CNTs was introduced. Initially, a representative volume element model was constructed utilizing the modified nearest neighbor algorithm. During the modeling phase, a corresponding interference judgment method was suggested, taking into account the potential positional relationships among the CNTs. Subsequently, stress-strain curves of the model under various loading conditions were derived through finite element analysis employing the volume averaging technique. To validate the efficacy of the modeling approach, the stress within a CNT/epoxy resin composite with varying volume fractions under different axial strains was computed. The resulting stress-strain curves were in good agreement with experimental data from the existing literature. Hence, the modeling method proposed in this study provides a more precise representation of the random distribution of CNTs in the matrix. Furthermore, it is applicable to a broader range of aspect ratios, thereby enabling the CNT simulation model to more closely align with real-world models.

Risk and benefits of expanded donor screening: A viewpoint from Germany.

This review describes the risks and benefits of expanding screening for transmissible pathogens in deceased organ donors. The focus is on the experience and procedure in Germany to make a decision on how to proceed with a possible donor. Three issues are of interest in how screening policies impact the process with the aim of mitigating unexpected transmission risks: (1) Should we add universal or targeted nucleic acid testing to serological tests for common blood-borne viruses (BBVs; HIV, HBV, and HCV)? (2) Which tests should be added for screening in a geographically restricted region beyond testing for these BBVs? (3) Being faced with changes (e.g., climate and population) in the own geographically restricted region, what strategies are needed before implementing new tests, and which considerations apply for proper indication to do this? Testing may only be effective when during donor characterization the appropriate conclusions are drawn from the existing findings and screening tests are initiated. This statement overlaps the need to implement universal screening for a pathogen or targeted screening based on the risk that the donor has acquired the transmissible pathogen or is not as possible to identify by current methods of clinical judgment and/or specific tests.

Research on signal denoising algorithm based on ICEEMDAN eddy current detection

This study addresses the challenges of non-stationarity and significant background noise interference in eddy current detection signals by proposing a noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise (ICEEMDAN). The process commences with the signal being decomposed using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise into a finite number of Intrinsic Mode Functions (IMFs). Each Intrinsic Mode Function is then evaluated for the presence of high-frequency noise using a Power Spectral Density (PSD) analysis. The high-frequency noise present in the Intrinsic Mode Functions is then reduced using Normalized Least Mean Squares (NLMS) before being reconstructed with the remaining Intrinsic Mode Functions. Subsequently, the reconstructed signals are subjected to another round of decomposition using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise. The Pearson Product Moment Correlation Coefficient (PPMCC) is utilised to calculate the correlation between the Intrinsic Mode Functions within each layer, retaining those with a strong correlation to further attenuate noise. Ultimately, the local maxima judgement method selectively amplifies defect signals by assessing changes in peak and valley degrees, thereby improving the signal-to-noise ratio of the eddy current detection signal. The experimental results demonstrate that, in comparison to the use of only the conventional Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise and Normalized Least Mean Squares denoising methods, the proposed method increases the Signal-to-Noise Ratio (SNR) by 1.08 dB and 2.31 dB, respectively, and decreases the Mean Square Error (MSE) by 106.9 and 223.9, respectively. The false alarm rate for stainless steel welded tubes with defects is 1.4%, while the false alarm rate for stainless steel welded tubes without defects is 0.4%.

A research on resistance spot welding quality judgment of stainless steel sheets based on revised quantum genetic algorithm and hidden markov model

In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment.Firstly, by RSW test to obtain the main influencing factors of quality, and combining with expert experience to realize the preliminary judgment of it, on this basis, HMM was established. Next, to address the susceptibility of HMM to initial model, quantum genetic algorithm (QGA) was utilized to search for the optimal initial model. Moreover, in view of the limitations of QGA (“coding length disaster” and fixed rotation angle), a corresponding improvement was also presented. At last, the established model was applied to the RSW process of stainless steel sheets railway carriage roof. Compared with the other five models, it took the least time to complete parameter training of HMM in four quality states, less than 15s; when judging the quality, its log-likelihood probability value was −47.8418, which was close to the maximum value of this state; during the classification test, the average classification accuracy of the four quality states were 90.8 %, 92.2 %, 90.8 % and 92.0 %, respectively, which was higher than the other five models. Consequently, the model established in this paper was effective.

Large language model (LLM) comparison between GPT-3 and PaLM-2 to produce Indonesian cultural content

Large language models can help to compile content with a cultural theme. However, any information generated by large language models needs to be evaluated to see the truth/fact of the information generated. With many studies discussing the comparison of the capabilities of large language models, there is not much research that directly discusses the comparison of the performance of large language models in producing Indonesian cultural content. This research compares the correctness of the information generated by the large language model using the expert judgment method when creating Indonesian cultural content and its fine-tuning capabilities evaluated using BERTScore. The evaluation method was successfully applied and the results show that in this case, PaLM-2 included less misinformation while GPT-3 excelled in fine-tuning. Using the combination of expert judgment and BERTScore makes it possible to evaluate large language models and obtain additional valid training data to correct deficiencies. The results showed that PaLM-2 produced more valid content with a score of 27 points, while GPT-3 scored 8 points. For training on new datasets/fine-tuning, it was found that the GPT-3 language model was able to learn the dataset more quickly, with a time of 50 minutes and a cost of IDR 27,000, while PaLM-2 took 2 hours 10 minutes and a cost of IDR 1,377,204. For the training dataset evaluation results, GPT-3 is superior with an average of all scores reaching 0.85205. Meanwhile, the PaLM-2 Tuned Model got an average overall score of 0.78942. In this case, the GPT-3 Tuned Model is superior by 8 %. In practice, this method can be used if the assessment is descriptive and requires direct assessment from experts

Investigations of sequential excavation and reinforcement in tunnels based on the extended three-dimensional discontinuous deformation analysis method

The unloading effect of excavation is the key factor leading to rock mass instability, and bolt support is the key means of controlling the stability. However, the original three-dimensional discontinuous deformation analysis method (3D DDA) can only simulate rock mass instability and bolt support after the tunnel is excavated, which makes it difficult to study the effect of tunnel excavation and bolt support on rock mass stability. Therefore, an algorithm to confirm the excavated blocks and reinforced blocks based on the volume judgment method (VJM) was proposed in this paper. Sequential excavation and reinforcement in tunnels can be simulated by introducing the time variables of block excavation and bolt deployment into the 3D DDA method. By embedding these two algorithms in the 3D DDA method, an extended 3D DDA method for sequential excavation and reinforcement in tunnels was developed, named the 3D DDA_ES method. Based on the 3D DDA_ES method, the collapse that occurred during the construction of the Huangjiazhuang Tunnel was simulated, and the displacement of key blocks and the axial force of the bolts were analyzed. According to the simulation results, the axial force on the bolts is strongly related to the position of the key blocks, and the axial force on the bolts at the tunnel roof is much greater than that at the tunnel arch shoulder. In this regard, these bolts with axial forces less than 1 kN are removed during the simulation, and the key blocks remain stable with a maximum displacement of 12 cm. The research results can provide a theoretical basis for tunnel construction and reinforcement parameter optimization for similar projects.

Structural integrity assessment of CANDU pressure tubes using Sobol indices for global sensitivity analysis

CANDU11CANDU is a registered trademark of Atomic Energy of Canada Limited. reactors are a channel-type design where up to 480 zirconium alloy pressure tubes (PTs) act as the reactor pressure boundary. Fitness-for-service standards such as CSA N285.8 (CSA Group, 2015) have been established to prescribe the requirements of pressure tube evaluations to maintain their integrity throughout their lifetime. Probabilistic leak-before-break (LBB) assessments can be used in evaluations to demonstrate that the likelihood of a flaw growing past the stability point while undetected (or before a safe shutdown can be achieved) is below regulatory limits. The assessments typically require a ranking of model parameters based on their influence. The ranking process traditionally uses expert judgment and local sensitivity analysis methods. There has been recent interest in implementing global methods for LBB assessments as they can manage large numbers of variables and extensive sampling spaces. However, their adoption is hindered by the computational cost of the models involved.The current study proposes a method for performing global sensitivity parameter rankings during probabilistic LBB assessments. The study is the continuation of the CANDU nuclear power plant (NPP) pressure tube (PT) case study in El Bouzidi et al., (2024) where a probabilistic approach was implemented in RAVEN (Rabiti and Alfonsi, 2019) to estimate whether the probability of unstable crack growth in PTs is within acceptable limits. This follow-up work proposes surrogate modeling using a high-dimensional model representation to derive Sobol indices and establish a parameter influence ranking. The methodology is demonstrated in a Monte Carlo simulation campaign of a pressure tube inlet rolled joint. The implemented approach successfully identified and quantified critical parameters of the model while efficiently ranking and quantifying the interactions between input variables.

Development of a comprehensive framework for quantifying the collective and individual influence of climate change and human activities on hydrological regimes

Previous research rarely identified the synergistic effects of climate change and human activities on hydrological regimes, thus leading to an incomplete attribution of hydrological regime changes. To address this issue, this paper presents a complete approach for accurately quantifying these impacts. The framework integrates various methodologies, including the runoff abrupt change point judgment method for distinguishing pre- and post-impact periods, The Soil and Water Assessment Tool (SWAT) is used to isolate runoff processes influenced solely by climate change, the linear separation method for identifying runoff processes affected solely by human activities, and the Indicator of Hydrologic Alteration (IHA) for quantifying changes in hydrological regimes. Application of this framework to the Ganjiang River Basin (GRB) unveils significant alterations in hydrological regimes. This has resulted in a significant decline in aquatic organism species compared to the prior-impact period levels. Notably, climate change and human activities exert opposing effects on minimum flow indicators and flow pulse indicators in the GRB. Moreover, these factors exhibit a substantial synergistic effect on numerous indicators, resulting in hydrological regimes combined influenced by climate change and human activities being considerably different with the cumulative impact on hydrological regimes solely influenced by climate change and those solely influenced by human activities. Consequently, in future water management, it is crucial to recognize the positive role of prudent human activities in mitigating the adverse impacts of climate change.

Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches

BackgroundChildhood stunting is a major indicator of child malnutrition and a focus area of Global Nutrition Targets for 2025 and Sustainable Development Goals. Risk factors for childhood stunting are well studied and well known and could be used in a risk prediction model for assessing whether a child is stunted or not. However, the selection of child stunting predictor variables is a critical step in the development and performance of any such prediction model. This paper compares the performance of child stunting diagnostic predictive models based on predictor variables selected using a set of variable selection methods.MethodsFirstly, we conducted a subjective review of the literature to identify determinants of child stunting in Sub-Saharan Africa. Secondly, a multivariate logistic regression model of child stunting was fitted using the identified predictors on stunting data among children aged 0–59 months in the Malawi Demographic Health Survey (MDHS 2015–16) data. Thirdly, several reduced multivariable logistic regression models were fitted depending on the predictor variables selected using seven variable selection algorithms, namely backward, forward, stepwise, random forest, Least Absolute Shrinkage and Selection Operator (LASSO), and judgmental. Lastly, for each reduced model, a diagnostic predictive model for the childhood stunting risk score, defined as the child propensity score based on derived coefficients, was calculated for each child. The prediction risk models were assessed using discrimination measures, including area under-receiver operator curve (AUROC), sensitivity and specificity.ResultsThe review identified 68 predictor variables of child stunting, of which 27 were available in the MDHS 2016–16 data. The common risk factors selected by all the variable selection models include household wealth index, age of the child, household size, type of birth (singleton/multiple births), and birth weight. The best cut-off point on the child stunting risk prediction model was 0.37 based on risk factors determined by the judgmental variable selection method. The model’s accuracy was estimated with an AUROC value of 64% (95% CI: 60%-67%) in the test data. For children residing in urban areas, the corresponding AUROC was AUC = 67% (95% CI: 58–76%), as opposed to those in rural areas, AUC = 63% (95% CI: 59–67%).ConclusionThe derived child stunting diagnostic prediction model could be useful as a first screening tool to identify children more likely to be stunted. The identified children could then receive necessary nutritional interventions.

The Clinical Assessment of Prosocial Emotions (CAPE): Initial tests of reliability and validity in a clinic-referred sample of children and adolescents.

Recent changes to diagnostic criteria for serious conduct problems in children and adolescents have included the presence of elevated callous-unemotional traits to define etiologically and clinically important subgroups of youth with a conduct problem diagnosis. The Clinical Assessment of Prosocial Emotions (CAPE) is an intensive assessment of the symptoms of this limited prosocial emotions specifier that uses a structured professional judgment method of scoring, which may make it useful in clinical settings when diagnoses may require more information than that provided by behavior rating scales. The present study adds to the limited tests of the CAPE's reliability and validity, using a sample of clinic-referred children ages 6-17 years of age, who were all administered the CAPE by trained clinicians. The mean age of the sample was 10.13 years (SD = 2.64); 54% of the sample identified as male and 46% identified as female; and 67% of participants identified as White, 29% identified as Black, and 52% identified as another race/ethnicity (i.e., Asian, Hispanic/Latinx, or other). The findings indicated that CAPE scores demonstrated strong interrater reliability. The scores also were associated with measures of conduct problems and aggression, even when controlling for behavior ratings of callous-unemotional traits. Further, when children with conduct problem diagnoses were divided into groups based on the presence of the limited prosocial emotions specifier from the CAPE, the subgroup with the specifier showed more severe conduct problems and aggression. The results support cautious clinical use of the CAPE, its further development and testing, and research into ways to make its use feasible in many clinical settings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Pilot Study Approach to Assessing the Reliability and Validity of Relevancy and Efficacy Survey Scale

This paper is based on the pilot study that aims to assess the reliability and validity of the attitude measurement scale towards the relevance of mathematical knowledge of real analysis for secondary-level mathematics instruction. Research tool validation and reliability estimation are challenging due to the complexity of its procedure. This study establishes procedural guidance for piloting and tool validation. The survey method was used to conduct the pilot study. Thirty participants filled in the 45 indicators of relevance measurement and 10 mathematical efficacy measurement indicators. The reliability was assessed with Cronbach's Alpha formula while the validity of the questionnaire was examined through item-total correlation with each item by using Pearson's formula. The content validity index was also calculated based on the expert judgment method. The reliability indicator was found to be more than 0.70 and the validity indicator falls in the acceptable range. Therefore, developed scale is valid and reliable to measure the teachers' attitudes on the relevancy of mathematical knowledge of real analysis of school's mathematics instruction based on pilot study results. Furthermore, this study guides the pilot study procedure for survey research and especially benefits those looking to validate their Likert-type survey scale in any research.

Research on the Initial Arrival Recognition and Judgment Method of Microseismic Signals Based on PELT

Research on the Initial Arrival Recognition and Judgment Method of Microseismic Signals Based on PELT

Learning analytics dashboard design: Workplace learner preferences for reference frames in immersive training in practice

AbstractBackgroundLearning analytics dashboards are increasingly being used to communicate feedback to learners. However, little is known about learner preferences for dashboard designs and how they differ depending on the self‐regulated learning (SRL) phases the dashboards are presented (i.e., forethought, performance, and self‐reflection phases) and SRL skills. Insight into design preferences for dashboards with different reference frames (i.e., progress, social, internal achievement and external achievement) is important because the effectiveness of feedback can depend upon how a learner perceives it.ObjectiveThis study examines workplace learner preferences for four dashboard designs for each SRL phase and how SRL skills relate to these preferences.MethodsSeventy participants enrolled in a chemical process apprenticeship program took part in the study. Preferences were determined using a method of adaptive comparative judgement and SRL skills were measured using a questionnaire. Preferences were tested on four dashboard designs informed by social and temporal comparison theory and goal setting theory. Multinomial logistic regressions were used to examine the relationship between dashboard preferences and SRL.Results and ConclusionsResults show that the progress reference frame is more preferred before and after task performance, and the social reference frame is less preferred before and after task performance. It was found that the higher the SRL skill score the higher the probability a learner preferred the progress reference frame compared to having no preference before task performance. The results are consistent with other findings, which suggest caution when using social comparison in designing dashboards which provide feedback.

Research on the Aging Judgment Method of Electrical Performance of C4F7N/CO2 Mixed Gases

Abstract Perfluoroisobutyronitrile (C4F7N) gas exhibits favorable environmental characteristics and excellent electrical performance, which renders it a prominent subject of research in the development of new environmentally friendly insulation materials as a substitute for SF6 gas. In this study, the decomposition products of a gas mixture comprising 15% C4F7N and 85% CO2 were investigated within a 12kV ring main unit. A total of 140 arc tests were conducted on the gas mixture, allowing for the identification of the relationship between the generation of decomposition products and the number of arc tests. Through the determination of the proportional relationship between the different types and quantities of products, a novel method for assessing the extent of electrical performance aging in C4F7N/CO2 gas was developed, thus serving as a valuable reference for gas aging assessment. This study is of great significance for promoting the application of C4F7N/CO2 mixed gas in power systems.

Oscillation Times in Water Hammer Signatures: New Insights for the Evaluation of Diversion Effectiveness in Field Cases

Diversion is a crucial technique for effectively improving shale reservoir production by creating more complex fracture networks. Evaluating diversion effectiveness is necessary to optimize the parameters in hydraulic fracturing. Water hammer diagnostics, an emerging fracturing diagnosis technique, evaluate diversion effectiveness by analyzing water hammer signals. The water hammer attenuation, as indicated by the oscillation time, correlates with the complexity of fracture networks. However, it remains unclear whether the oscillation time is associated with diversion effectiveness. This paper elucidates the relationship between the water hammer oscillation time and diversion effectiveness by taking the probability of diversion and the treating pressure response as the evaluation criteria. Initially, a high-frequency pressure sensor was installed at the wellhead to sample the water hammer signals. Next, the oscillation times were determined using the feature extraction method. Simultaneously, the probability of diversion and the treating pressure response were calculated using the cepstrum error function and treating pressure curve, respectively. Then, the relationship between the oscillation time and diversion effectiveness was analyzed. Finally, a rapid judgment method for evaluating diversion effectiveness based on the water hammer oscillation time was proposed. The results indicated a negative correlation between the probability of diversion and the oscillation time, with higher probabilities resulting in lower oscillation times. The oscillation times exhibited a negative correlation with the treating pressure response, including the treating pressure increases and diversion pressure spikes, wherein a greater pressure differential led to lower oscillation times. Drawing from the statistics of a shale gas horizontal well in Sichuan, a better diversion effectiveness is associated with fewer oscillations, demonstrating a negative correlation between the diversion effectiveness and the oscillation time in water hammer signatures. Finally, a rapid judgment method for evaluating diversion effectiveness was proposed, utilizing the 95% confidence interval of the mean oscillation time. This paper offers useful insights into evaluating diversion performance in field cases.

Translation techniques of the positive politeness utterances in the Pay It Forward movie

This descriptive qualitative research employed the methods of observation and auto-expert judgment for the data collection and applied Brown & Levinson’s politeness theory (1987) and Molina & Albir’s translation theory (2002) for the data analysis. This research focused on finding the positive politeness strategies and the translation techniques of the utterances obtained from Pay It Forward movie as the data source. Based on the findings of the research, there were 40 utterances with positive politeness strategies as the data under analysis. The research findings, among others, showed that the strategies of Intensify interest to hearer (17.50%) and Exaggerate (17.50%) were dominantly used in the Pay It Forward movie, while the translation strategy of Established equivalent (40.00%) was mostly used in the translation of positive politeness utterances in the movie. It can be concluded here that the translator translates the positive politeness utterances of the source language (English) into the target language (Indonesian) appropriately through the Established equivalent strategy without making any significant changes to maintain the meaning relatively the same in the Indonesian translation subtitles.

Addressing multi-attribute large-scale group decision making with overlapping subgroups: A bidirectional adjustment consensus reaching method for heterogeneous decision makers

Due to the rapid development of internet technology, multi-attribute large-scale group decision making (MALSGDM) becomes a research hotspot of multi-attribute group decision making. An interesting phenomenon is that decision makers (DMs) may assume multiple roles across various interest groups, thereby naturally forming overlapping subgroups. Aiming at the MALSGDM problem with overlapping subgroups, this paper proposes an overlapping subgroup-based consensus reaching method considering heterogeneous DMs. Firstly, a novel overlapping clustering method is proposed to decompose the large-scale group into several overlapping subgroups characterized by low evaluation conflict and stable trust relationship, thereby identify ordinary and key DMs. Secondly, a two-criteria consensus reaching judgment method is devised as the termination condition for the consensus reaching process. Thirdly, a novel bidirectional adjustment method considering heterogeneous DMs is constructed, which focuses on key DMs belonging to multiple subgroups simultaneously. The adjusted evaluation values are obtained through linear uncertain minimum adjustment consensus models under two different cases. Finally, a numerical example is provided to demonstrate the feasibility of the proposed method, and the comparative and sensitivity analyses are carried out to verify the superiority of the proposed method.

Real-Time Space Trajectory Judgment for Industrial Robots in Welding Tasks

In welding tasks, the repeated positioning precision of robots can generally reach the micron level, but the data of each axis during each operation may vary. There may even be out-of-control situations where the robot does not run according to the set welding trajectory, which may cause the robot and equipment to collide and be damaged. Therefore, a real-time judgment method for the welding robot trajectory is proposed. Firstly, multiple sets of axis data are obtained by running the welding robot, and the phase of the data is aligned by using a proposed algorithm, and then the Kendall correlation coefficient is used to identify and remove weak axis data. Secondly, the mean of multiple sets of axis data with strong correlation is calculated as the standard trajectory, and the trajectory threshold of the robot is set using the μ ± nσ method based on the trajectory deviation judgment sensitivity. Finally, the absolute difference between the real-time axis trajectory and the standard trajectory is used to determine the deviation of the running trajectory. When the deviation reaches the threshold, a forewarning starts. When the deviation exceeds the threshold + σ, the robot is stopped. Take the six-axis welding robot as an example, by collecting the axis data of the robot running multiple times under the same conditions, it is proved that the proposed method can accurately warn the deviation of the running trajectory. The research results have important practical value for the prevention of welding robot accidents in industrial production.