Verification Method Research Articles

ПЕРСПЕКТИВИ ОНТОЛОГІЧНОГО МОДЕЛЮВАННЯ ЯК ЗАСОБУ ВЕРИФІКАЦІЇ РЕЗУЛЬТАТІВ ПСИХОЛОГІЧНОГО ДОСЛІДЖЕННЯ (НА ПРИКЛАДІ ВИВЧЕННЯ ЯВИЩ ГРИ)

Purpose. Methodological differences in the verification standards for the results of scientific knowledge between individual psychological branches represented in the domestic and global scientific space, the peculiarities of extremely complex psychological knowledge objects indicates the need to modernize the relevant standards and choose the optimal methodological platform for the verification of the results of modern psychological research. This paper discusses the feasibility of choosing such a ontological modeling platform, which is a special descriptive methodology and an alternative to the naturalistic experimental approach in verifying the results of psychological research. Ontological modeling provides the possibility for a more objective verification of the achievements for the study of intelligible objects, which have a distinct cultural and historical meaning, extremely complex structure and functionality, an ancient and diverse history of their own empirical representations. The purpose of the article is a theoretical study of ways to modernize the standards of verification of the results of modern psychological research using the example of the verification of the promising theory of game modeling of human vital activity in uncertainty conditions. Methods.The research uses the methods of analysis and systematization for psychological and interdisciplinary approaches in the study of verification patterns for the psychological knowledge results, comparison and optimization for the content of its standards on the study example of game modeling of human vital activity in uncertainty conditions. Results. According to the results of the study, the expediency of choosing ontological modeling (the names "conceptual modeling", "formal ontology", "formal ontology" and "ontological engineering" are also used synonymously) as a perspective basis for the verification of the psychological research results, a new universal methodological tool of Ukrainian psychology, has been proven using the example for game modeling of human vital activity in uncertainty. The existing scientific approaches that argue for the choice of ontological modeling as the basis for the verification of new psychological knowledge are specified. The methodological perspective of the modernization for the verification norms of psychological theories and concepts based on the ontological approach has been updated and substantiated. Modern scientific ideas are generalized and fundamental approaches to the verification of the results of knowledge of unlimited generality objects, i.e. ultra-complex simultaneously intelligible and empirical objects, are deepened. It has been established that ontological modeling has a universal scientific purpose and is able to overcome the shortcomings of the naturalistic experimental method for verification of research products, to be the optimal methodological platform for the verification of the modern psychological research results. Conclusions. The results of the study allow us to conclude about the relevance of the above issues and the expediency of choosing ontological modeling as a prospective basis for the verification of the psychological research results using the example for game modeling of human vital activity in uncertainty. A well-founded description of the standards and the process for verification of the psychological research results for game modeling of human vital activity in uncertainty conditions is considered promising.

The Influence of Leader-Member Exchange and Affective Commitment on Organizational Citizenship Behavior

This study uniquely focuses on the impact of Leader-Member Exchange (LMX) and affective commitment on Organizational Citizenship Behavior (OCB) at the Yamaha Flagship Shop, a prominent company in the automotive sector, located in the vibrant city of Bandung. OCB, which refers to the voluntary actions of employees that contribute to the business's effectiveness, is a key area of investigation. The research methodology is a quantitative technique combining descriptive and verification methods. The study collected samples from 35 respondents who were employees of the Yamaha Flagship Shop in the Bandung City branch. The data and information utilized in this study consist of primary data acquired directly from companies, as well as secondary data sourced from books, journals, and the Central Bureau of Statistics (BPS). The findings of this study are significant as they indicate a robust correlation of 0.832 between the Leader-Member Exchange variable and the Affective Commitment variable at the Bandung City branch of the Yamaha Flagship Shop. This correlation is statistically significant with a p-value of 0.000. The Leader-Member Exchange variable has a positive and statistically significant effect of 0.410 on Organizational Citizenship Behavior. The Affective Commitment variable shows a positive and statistically significant effect of 0.569 on Organizational Citizenship Behavior. The direct impact of Leader-Member Exchange and Affective Commitment on Organizational Citizenship Behavior is 16.8% and 32.4%, respectively. Each of the indirect effects accounted for 19.4% of the total, resulting in a cumulative total of 88%. These findings underscore the substantial impact of Leader-Member Exchange and Affective Commitment on the Organizational Citizenship Behavior of the Yamaha Flagship Shop in the Bandung City Branch, providing valuable insights into organizational behavior.

The Use of "Seven" as an English Learning Medium for 7th Grade Junior High School Students

This article focuses on the development of mobile learning media using Android-based applications to enhance the teaching and learning process, specifically for English language education in seventh-grade classrooms. The study follows the research and development (R&D) model proposed by Lee and Owens (2004) and was conducted in two private schools in the Malang district. The development process consists of five phases: assessment/analysis, design, development, implementation, and evaluation. The "Seven" application, developed using the Andromo platform, aims to improve the four basic skills of English language learning: reading, speaking, writing, and listening. The effectiveness of the application was evaluated through material verification and media verification, using qualitative and quantitative data analysis methods. The findings indicate that the "Seven" application was well-received by students, with positive feedback regarding its user-friendly interface and the engaging learning experience it provided. The study demonstrates the potential of Android-based mobile learning as an effective and practical tool for enhancing English language education in seventh-grade classrooms.

Taming the frame problem: an automated approach for robust UML class diagram specification and verification

It is vital to have precise specifications and verification of UML class diagrams to ensure the correctness of complex software systems. However, current specification and verification methods often face a challenge known as the frame problem. This problem occurs due to incomplete operation specifications that can lead to unintended system behavior. To tackle this issue, we have developed an automated solution to autonomously identify and define frame conditions, effectively minimizing the frame problem’s impact on class diagram verification. Frame conditions are explicit contracts that meticulously outline the permissible effects of operations within the system. Our approach carefully analyzes the behavioral blueprint of a class diagram and extracts crucial information to create these conditions. Through rigorous evaluations encompassing diverse UML diagrams and simulated execution scenarios, we have demonstrated the effectiveness of our approach in preventing unintended system behavior caused by the frame problem. We have integrated the approach into the Temporal Property Validator tool, empowering practitioners to leverage its benefits for practical class diagram specification and verification.

Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method

 Despite their vulnerability to competent forgers, signatures are one of the most widely used user verification methods. Recent research has revealed that EEG signals are harder to reproduce and give superior biometric information. This study aims to improve the effectiveness of person authentication by using deep learning techniques on electroencephalogram (EEG) signals. The broad implementation of EEG-based authentication systems has been hindered by problems such as noise, variability, and inter-subject variances despite the potential distinctiveness of EEG signals. We propose a multiscale convolutional neural network (CNN) and a Bidirectional LSTM (BiLSTM) model called CNN-BiLSTM to extract features and classify raw EEG data. This methodology involves acquiring raw EEG data, preprocessing for noise reduction, standardization, normalization, and employing deep learning techniques for feature extraction and classification. Experimental results exhibit a notable improvement in accuracy and reliability compared to existing EEG authentication methods such as LOF, CNN, FCN, EfficientNet-B0, and BiLSTM. The results showcase the performance of the proposed deep learning model utilizing established metrics such as precision, sensitivity, specificity, and accuracy. The proposed methodology outperforms existing methods and achieves a training and validation accuracy of 98.9% and 92.2%, respectively. The findings of the research demonstrate that the proposed approach has been successful in achieving highly effective results by using EEG signals for the purpose of resolving issues related to person identification. 

Мodern methods of diagnosis of endometrial carcinoma

The main methods of histological diagnosis of endometrial carcinoma are dilatation and curettage, fractional curettage, and diagnostic hysteroscopy with endometrial biopsy. This retrospective study aims to compare the preoperative histological results obtained by different diagnostic methods with the postoperative ones in patients with endometrial carcinoma and determine the most accurate and safest method for diagnosing the disease. From 06.2020 to 09.2023, a retrospective study was conducted on 80 patients with histologically proven carcinoma of the endometrium, treated at University Hospital Saint Marina-Pleven. The mean age of the patients was 58.04 ± 10.46, ranging from 28 to 78. The comparative analysis of the discrepancies of the histological verification methods in the evaluation of the preoperative and postoperative histological result showed that a statistically significantly higher percentage of discrepancies (19.5%) had the dilatation and curettage compared to the zero discrepancy of the hysteroscopy (0.0%). The relative proportion of discrepancies of the fractional curettage is 14.3%. The cytological result was negative for tumor cells in 97.5% and positive in 2.5%. In all patients with a positive cytological result for tumor cells, the method of histological verification was dilatation and curettage. Performing diagnostic hysteroscopy and observing safety principles such as creating low intrauterine pressure and short operative time is a safe and reliable method for diagnosing endometrial carcinoma.

Formal Verification of Code Conversion: A Comprehensive Survey

Code conversion, encompassing translation, optimization, and generation, is becoming increasingly critical in information systems and the software industry. Traditional validation methods, such as test cases and code coverage metrics, often fail to ensure the correctness, completeness, and equivalence of converted code to its original form. Formal verification emerges as a crucial methodology to address these limitations. Although numerous surveys have explored formal verification in various contexts, a significant research gap exists in pinpointing appropriate formal verification approaches to code conversion tasks. This paper provides a detailed survey of formal verification techniques applicable to code conversion. This survey identifies the strengths and limitations of contemporary adopted approaches while outlining a trajectory for future research, emphasizing the need for automated and scalable verification tools. The novel categorization of formal verification methods provided in this paper serves as a foundational guide for researchers seeking to enhance the reliability of code conversion processes.

Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure

The research presented in this paper takes another step towards developing methods for automatic condition verification to detect structural damage to vehicle wheel rims. This study presents the utilisation of vibration spectra via Fast Fourier Transform (FFT) and a neural network’s learning capabilities for evaluating structural damage. Amplitude and time cycles of acceleration were analyzed as the structural response. These cycles underwent FFT analysis, leading to the identification of four diagnostic symptoms described by 20 features of the diagnostic signal, which in turn defined a condition vector. In the subsequent stage, the amplitude and frequency cycles served as input data for the neural network, and based on them, self-organizing maps (SOM) were generated. From these maps, a condition vector was defined for each of the four positions of the rim. Therefore, the technical condition of the wheel rim was determined based on the variance in condition parameter features, using reference frequencies of vibration spectra and SOM visualisations. The outcome of this work is a unique synergetic diagnostic system with innovative features, identifying the condition of a wheel rim through vibration and acoustic analysis along with neural network techniques in the form of Kohonen maps.

Consistency Verification Method of Chinese and Russian Traditional Culture Translation Text Corpus Based on CNN-BiGRU

China and Russia have completely different cultural backgrounds, historical traditions and ways of thinking, which makes it difficult to directly correspond or accurately convey many culturally unique concepts, customs and expressions during the translation process. For example, some idioms, sayings or cultural symbols may have completely different meanings or no equivalents in the two cultures. Therefore, there are problems such as poor verification resulting in the consistency verification of Chinese and Russian traditional cultural external publicity translation text corpus. In order to solve this problem, this paper designs a consistency verification method for Chinese and Russian traditional cultural external publicity translation text corpus based on CNN-BiGRU. Through the part-of-speech correspondence of the Chinese and Russian traditional cultural external publicity translation text corpus, it is represented by vectors, the feature weights of the Chinese and Russian traditional cultural external publicity translation text corpus are calculated, the collocation information between the feature items is determined, and the maximum likelihood of the features is calculated using data smoothing technology. The rules are adjusted to extract the characteristics of the Chinese and Russian traditional cultural external publicity translation text corpus. Preprocess the key feature source documents of each Chinese and Russian traditional culture external publicity translation text corpus, determine the source documents and their corresponding key features of the traditional culture external publicity translation text corpus, and use TF-IDF and IDF to calculate the key feature sources of the corpus. Based on the rarity of document data, the BERT model is introduced as an encoder to determine the key features of the Chinese and Russian traditional cultural external publicity translation text corpus, integrating the CNN-BiGRU algorithm to design a consistency verification algorithm for Chinese and Russian traditional cultural external publicity translation text corpus. Set up the convolution layer, weight sharing layer, pooling layer, and BiGRU verification layer to build a CNN-BiGRU model to achieve consistency verification of the Chinese and Russian traditional cultural external publicity translation text corpus. Experimental results show that this method can improve the consistency verification effect of Chinese and Russian traditional cultural external publicity translation text corpus.

Method for verifying solutions of sparse linear systems with general coefficients

This paper proposes a verification method for sparse linear systems Ax=b with general and nonsingular coefficient matrices. A verification method produces the error bound for a given approximate solution. Practical methods use one of two approaches. One approach is to verify the computed solution of the normal equation ATAx=ATb by exploiting symmetric and positive definiteness; however, the condition number of ATA is the square of that for A. The other approach applies an approximate inverse of A; however, the approximate inverse of A may be dense even if A is sparse. Additionally, several other methods have been proposed; however, they are considered impractical due to various issues. Here, this paper provides a computing method for verified error bounds using the previous verification method and the latest equilibration. The proposed method can reduce the fill-in and is applicable to many problems. Moreover, we will show the efficiency of an iterative refinement method to obtain accurate solutions.

Risk factors and nomogram predictive models for postsurgical progression/hyperprogression recurrence in hepatocellular carcinoma with macroscopic vascular invasion

PurposeThis study aimed to develop postsurgical progression/hyperprogression recurrence (type III-IV recurrence) prediction models for hepatocellular carcinoma (HCC) patients with macroscopic vascular invasion (MaVI) and to guide treatment strategies in the accurate healthcare era.Patients and methods393 HCC patients with MaVI from two central hospitals made up the entire study population. In developmental (290 patients) and validation (103 patients) cohorts, all patients were randomized into one or the other. Two prediction models for type III-IV recurrence were developed, based on the findings of univariate and multivariate analysis in the development cohort, and multidimensional verification was carried out in both cohorts.ResultsThe postoperative recurrence rate of type III-IV in 393 HCC patients with MaVI was 70.9%. Young age, large tumor size (≥ 10 cm), node number, incomplete tumor capsule, postoperative complications, and high Ki67 index were the independent risk factors for relapse of type III-IV. In the development cohort, two nomograms (pre- and postoperative) had the Area Under the ROC curve (AUC) of 0.827 and 0.891, respectively. The two nomograms performed well, according to multidimensional verification methods such as clinical impact curves, decision curve analysis (DCA), and calibration curves. The validation cohort saw similar encouraging results. Both nomograms could separate patients into two distinct prognosis subgroups with ideal cutoff values of 170.3 presurgery and 175.0 postsurgery (both P < 0.05).ConclusionWe constructed two novel and potentially clinically valuable models for predicting type III-IV recurrence. These two models can develop strategies for treating those suffering from HCC with MaVI owing to their strong prediction performance and availability.

LIDeepDet: Deepfake Detection via Image Decomposition and Advanced Lighting Information Analysis

The proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods threatens the integrity of legal processes. In response, researchers have explored deep forgery detection, proposing various forensic techniques. However, the swift evolution of deep forgery creation and the limited generalizability of current detection methods impede practical application. We introduce a new deep forgery detection method that utilizes image decomposition and lighting inconsistency. By exploiting inherent discrepancies in imaging environments between genuine and fabricated images, this method extracts robust lighting cues and mitigates disturbances from environmental factors, revealing deeper-level alterations. A crucial element is the lighting information feature extractor, designed according to color constancy principles, to identify inconsistencies in lighting conditions. To address lighting variations, we employ a face material feature extractor using Pattern of Local Gravitational Force (PLGF), which selectively processes image patterns with defined convolutional masks to isolate and focus on reflectance coefficients, rich in textural details essential for forgery detection. Utilizing the Lambertian lighting model, we generate lighting direction vectors across frames to provide temporal context for detection. This framework processes RGB images, face reflectance maps, lighting features, and lighting direction vectors as multi-channel inputs, applying a cross-attention mechanism at the feature level to enhance detection accuracy and adaptability. Experimental results show that our proposed method performs exceptionally well and is widely applicable across multiple datasets, underscoring its importance in advancing deep forgery detection.

Neutron detectors in proton therapy: Calibration, operational in situ verification, and comparison with Monte Carlo simulation

The number of proton therapy centers is continuing to increase and the use of suitable neutron detectors is essential in the field of radiation protection to ensure a safe working environment. The neutrons present in proton therapy centers range from thermal neutrons to neutrons with the maximum energy of the irradiated proton beam. Therefore, the neutron detectors must be of extended-range. The first aim of the study is to develop a robust and easy-to-implement method to operationally verify the neutron detectors in a proton therapy facility. The neutron field generated in the facility under specific conditions was used as the neutron reference source against which the neutron detectors were evaluated. For this purpose, an analysis of the detectors' uncertainties and the establishment of indicators that report the correct status of the detectors was performed. In addition, the experiment was characterized in TOPAS (a Monte Carlo toolkit based on Geant4) to validate the experimentally measured data and vice versa. The WENDI-II and LUPIN-II responses were determined in TOPAS. Both detectors were calibrated in an accredited laboratory and the simulation of this calibration in Monte Carlo was performed. An estimated uncertainty of 13.1% (k = 2) was found for the results of the operational verification of the WENDI-II and LUPIN-II detectors. Thus, for the neutron detectors, the investigation interval is the range between 13.1% and 17.2% (k = 3) while the intervention level is set at 17.2%. An overall agreement (within 61%) was obtained among neutron H∗(10) values calculated with TOPAS and H∗(10) measured for the operational verification of the detectors. The calibration factor for WENDI-II coincides with that indicated by the manufacturer, although a discrepancy of 19% was found for LUPIN-II. The verification method for neutron detectors, studied for two different types of irradiations (a single energy and an energy sweep irradiation), is sufficiently conservative to ensure the correct operation of the equipment, fully complies with international recommendations (i.e. IEC 61005:2014) and can be applied in other proton therapy facilities.

VERIFICATION OF THE PROPERTIES OF AN INDUSTRIAL ROBOT FOR THE NUCLEAR INDUSTRY

The article deals with the issue of verification of selected geometric characteristics of a special robot intended for the nuclear industry. The design of the robot is specific in that the robot works in both semi-automatic and manual mode. This leads to frequent collisions of some parts of the robot with fragmented equipment. Therefore, it was necessary to ensure the accuracy and repeatability of the robot after its production and during its operation. Verification of the parameters of the robot after one year of operation was carried out in the controlled zone of the nuclear power plant, which was specific due to the presence of ionizing radiation. The methodology, technique and method of verification had to be adapted to this.

SAL Method Applied in Grid Forecasting Product Verification with Three-Source Fusion Product

Quantitative precipitation forecast (QPF) verification stands out as one of the most formidable endeavors in the realm of forecast verification. Traditional verification methods are not suitable for high-resolution forecasting products in some cases. Therefore, the SAL (structure, amplitude and location) method was proposed as a method of object-based spatial verification that studies precipitation verification in a certain range, which is combined with factors including structure, amplitude and location of the targets. However, the setting of the precipitation threshold would affect the result of the verification. This paper presented an improved method for determining the precipitation threshold using the QPF from ECMWF, which is an ensemble forecast model and three-source fusion product that was used in China from 1 July to 31 August 2020, and then the results obtained with this method were compared with the other two traditional methods. Furthermore, the SAL and the traditional verification methods were carried out for geometric, simulated and real cases, respectively. The results showed the following: (1) The proposed method in this paper for determining the threshold was more accurate at identifying the precipitation objects. (2) The verification area size was critical for SAL calculation. If the area selected was too large, the calculated SAL value had little significance. (3) ME (Mean Error) could not identify the displacement between prediction and observation, while HSS (Heidke Skill Score) was sensitive to the displacement of the prediction field. (4) Compared with the traditional verification methods, the SAL method was more straight forward and simple, and it could give a better representation of prediction ability. Therefore, forecasters can better understand the model prediction effect and what needs to be improved.

Proton bunch monitors for the clinical translation of prompt gamma-ray timing

Objective. Prompt gamma-ray timing is an emerging technology in the field of particle therapy treatment verification. This system measures the arrival times of gamma rays produced in the patient body and uses the cyclotron radio frequency signal as time reference for the beam micro-bunches. Its translation into clinical practice is currently hindered by observed instabilities in the phase relation between the cyclotron radio frequency and the measured arrival time of prompt gamma rays. To counteract this, two proton bunch monitors are presented, integrated into the prompt gamma-ray timing workflow and evaluated.Approach. The two monitors are (a) a diamond detector placed at the beam energy degrader, and (b) a cyclotron monitor signal measuring the phase difference between dee current and voltage. First, the two proton bunch monitors as well as their mutual correlation were characterized. Then, a prompt gamma-ray timing measurement was performed aiming to quantify the present magnitude of the phase instabilities and to evaluate the ability of the proton bunch monitors to correct for these instabilities.Main results. It was found that the two new monitors showed a very high correlation for intermediate proton energies after the first second of irradiation, and that they were able to reduce fluctuations in the detected phase of prompt gamma rays. Furthermore, the amplitude of the phase instabilities had intrinsically decreased from about 700 ps to below 100 ps due to cyclotron upgrades.Significance. The uncertainty of the prompt gamma-ray timing method for proton treatment verification was reduced. For routine clinical application, challenges remain in accounting for detector load effects, temperature drifts and throughput limitations.

Development of a Smart Lock System using QR Code Technology

A smart lock system refers to a modern security solution that enables users to remotely control access to their homes or businesses using a smart device such as smartphone, tablet, or computer. This paper presents the development of a smart lock system using Quick Response (QR) code technology to address the problem of insecurity, inconvenience, and inflexibility in access control. The proposed system eliminates the need for physical keys or complex authentication and verification methods. The system integrates an ESP8266 microcontroller, barcode scanner, 16x2 liquid-crystal display (LCD), solenoid lock, relay, and a 12V power supply. The system allows authorised users to generate unique QR codes representing their access credentials through a user-friendly interface. These QR codes are scanned by the system, which processes the codes, verifies access permissions via a central server and provides real-time feedback through the LCD. Upon successful verification, the system triggers a relay to control the solenoid lock, to grant or deny access. The implementation and testing of the smart lock system demonstrated successful operation. The performance indices include authentication speed, system reliability, accuracy and response time, which were evaluated and found to be satisfactory. The obtained results showed that the proposed system outperformed traditional methods such as keys, and fingerprint authentication, with an access speed of 2.00 seconds, which is significantly faster than the alternatives. Comparative analysis with existing access control techniques demonstrated the developed system's enhanced security features and user-friendly operation, making it applicable for residential, commercial, and industrial use. Additionally, the system's scalability and adaptability allow it to be customised for various environments, offering a versatile solution to diverse security challenges. This work contributes to the field of security technology by providing an efficient and scalable smart lock system with novel algorithms, which enhances the convenience, robustness of access control and seamless user experience.

Evaluating inverse modeling methods for measurement and verification of chiller energy efficiency measures

A commonly budget and time constrained yet crucial measurement and verification process for chiller energy efficiency measures often requires rarely trended chiller performance data for the pre-retrofit or post-retrofit chiller necessitating chiller modeling for year-round performance prediction. This study evaluates chiller inverse modeling methods for measurement and verification applications. Variable speed drive-controlled centrifugal chillers operating in a hot and humid climate are used as case studies. Two scenarios are explored: one where full-range metered chiller data are available and another with limited data that requires a short-term metering process. The biquadratic black-box and Gordon-Ng with a variable entropy term models perform exceptionally well when full-range chiller performance data is available, displaying a coefficient of variation of approximately 5 % for both training and test datasets. However, in situations that use short-term metered data not covering the full range, the fundamental and Foliaco reformulated Gordon-Ng models outperform other models. These models show minimal degradation in average statistical performance when trained with one-month metering data instead of four-month data, indicating their potential to capture the year-round chiller performance variation with short-term metered data. Furthermore, the optimal metering period for an accurate modeling process is identified as one containing data from at least one shoulder month like April, May, or October for a hot and humid climate. These findings provide valuable guidance for practitioners involved in chiller efficiency measure assessments, emphasizing the significance of proper model selection and an appropriate metering period for a reliable measurement and verification process.

Study on the analytical verification method of geometric error models

Abstract Geometric error compensation is a widely used and effective approach for enhancing the accuracy of Coordinate Measuring Machines (CMMs) and Computer Numerical Control (CNC) machines. The research in this field primarily focuses on the challenging task of developing geometric error modelling techniques. Currently, there are several well-established methods for modelling geometric errors. However, there is scarce availability of methods that can validate the established geometric error models. Therefore, this article proposes an analytical validation method for geometric error models based on the error transformation equation. The principle of the method is elaborately explained, and its correctness is verified through specific model verification example. The method proposed in this article enables rapid and accurate model correction and iteration, effectively reducing the economic and time costs of model validation in the current geometric error compensation process. Therefore, the method provides guidance for standardising the modelling process and could be widely applied to various structural forms of measurement and machining systems.

Research on assessment method of maximum distributed generation hosting capacity in distribution system with high shares of renewables and power electronics.

With the rapid development of distributed generation (DG) within the framework of modern power systems, accurately assessing the maximum DG hosting capacity in distribution networks is crucial for ensuring the safe and stable operation of the power grid. This paper first introduces an assessment model of maximum DG hosting capacity in distribution network based on optimal power flow (OPF). Then, a two-step method that combines the linearization method and the recursive method is proposed, which consists of two parts: firstly, using linearization method to quickly calculate the preliminary assessment value of maximum DG hosting capacity, and then using a recursive method to accurately correct the preliminary assessment value. Additionally, the proposed improved comprehensive sensitivity index and safety constraint verification method can enhance the computational efficiency and accuracy of the recursive algorithm. Finally, the proposed methods were simulated and validated on the IEEE 33-bus system.