Evaluation Of Proposed Method Research Articles

Disentangled variational auto-encoder for multimodal fusion performance analysis in multimodal sentiment analysis

Multimodal Sentiment Analysis (MSA) holds extensive applicability owing to its capacity to analyze and interpret users' emotions, feelings, and perspectives by integrating complementary information from multiple modalities. However, inefficient and unbalanced cross-modal information fusion substantially undermines the accuracy and reliability of MSA models. Consequently, a critical challenge in the field now lies in effectively assessing the information integration capabilities of these models to ensure balanced and equitable processing of multimodal data. In this paper, a Disentanglement-based Variable Auto-Encoder (DVAE) is proposed for systematically assessing fusion performance and investigating the factors that facilitate multimodal fusion. Specifically, a distribution constraint module is presented to decouple the fusion matrices and generate multiple low-dimensional and trustworthy disentangled latent vectors that adhere to the authentic unimodal input distribution. In addition, a combined loss term is modified to effectively balance inductive bias, signal reconstruction, and distribution constraint items to facilitate the optimization of neural network weights and parameters. Utilizing the proposed evaluation method, we can evaluate the fusion performance of multimodal models by contrasting the classification degradation ratio derived from disentangled hidden representations and joint representations. Experiments conducted with eight state-of-the-art multimodal fusion methods on the CMU-MOSEI and CMU-MOSEI benchmark datasets demonstrate that DVAE is capable of effectively evaluating the effects of multimodal fusion. Moreover, the comparative experimental results indicate that the equalizing effect among various advanced mechanisms in multimodal sentiment analysis, as well as the single-peak characteristic of the ground label distribution, both contribute significantly to multimodal data fusion.

Multi-modal evaluation of respiratory diaphragm motion in chronic obstructive pulmonary disease using MRI series and CT images.

Chronic obstructive pulmonary disease (COPD), characterized by airflow limitation and breathing difficulty, is usually caused by prolonged inhalation of toxic substances or long-term smoking habits. Some abnormal features of COPD can be observed using medical imaging methods, such as magnetic resonance imaging (MRI) and computed tomography (CT). This study aimed to conduct a multi-modal analysis of COPD, focusing on assessing respiratory diaphragm motion using MRI series in conjunction with low attenuation volume (LAV) data derived from CT images. This study utilized MRI series from 10 normal subjects and 24 COPD patients, along with thoracic CT images from the same patients. Diaphragm profiles in the sagittal thoracic MRI series were extracted using field segmentation, and diaphragm motion trajectories were generated from estimated diaphragm displacements via registration. Re-sliced sagittal CT images were used to calculate regional LAVs for four distinct lung regions. The similarities among diaphragm motion trajectories at various positions were assessed, and their correlations with regional LAVs were analyzed. Compared with the normal subjects, patients with COPD typically exhibited fewer similarities in diaphragm motion, as indicated by the mean normalized correlation coefficient of the vertical motion component (0.96 for normal subjects vs. 0.76 for severity COPD patients). This reduction was significantly correlated with the LAV% in the two lower lung regions with a regression coefficient of 0.81. Our proposed evaluation method may assist in the diagnosis and therapy planning for patients with COPD.

Evaluation on Configuration Stiffness of Overconstrained 2R1T Parallel Mechanisms

Currently, two rotations and one translation (2R1T) three-degree-of-freedom (DOF) parallel mechanisms (PMs) are widely applied in five-DOF hybrid machining robots. However, there is a lack of an effective method to evaluate the configuration stiffness of mechanisms during the mechanism design stage. It is a challenge to select appropriate 2R1T PMs with excellent stiffness performance during the design stage. Considering the operational status of 2R1T PMs, the bending and torsional stiffness are considered as indices to evaluate PMs' configuration stiffness. Subsequently, a specific method is proposed to calculate these stiffness indices. Initially, the various types of structural and driving stiffness for each branch are assessed and their specific values defined. Subsequently, a rigid-flexible coupled force model for the over-constrained 2R1T PM is established, and the proposed evaluation method is used to analyze the configuration stiffness of the five 2R1T PMs in the entire workspace. Finally, the driving force and constraint force of each branch in the whole working space are calculated to further elucidate the stiffness evaluating results by using the proposed method above. The obtained results demonstrate that the bending and torsional stiffness of the 2RPU/UPR/RPR mechanism along the x and y-directions are larger than the other four mechanisms.

Evaluation method for health state of highway tunnel structure based on adaptive comprehensive weighting

Accurately evaluating the health state of tunnel structure is of great significance for ensuring the operation safety of tunnel. Based on engineering investigation, fuzzy mathematics and statistical analysis, this paper proposes a new method to evaluate the health state of highway tunnel structure, which considers the multi-disease characteristics and adaptive comprehensive weighting theory. Firstly, by investigating the health state of 20 operation highway tunnels in China, a tunnel disease database with 539 samples was constructed. Considering some new tunnel diseases such as floor heave and lining spalling, a health evaluation index system of highway tunnel was established further. Secondly, utilizing the subjective-objective weighting theory and the principle of variable weight, a new adaptive comprehensive weighting method was proposed to calculate the indexes’ weights. This method overcomes the defect that the constant weight cannot be dynamically updated according to the actual situation of disease development. Then, according to the established four-level classification standard suitable for health evaluation of highway tunnels, the normal distribution membership functions of second-level indexes were determined. Combining with the fuzzy theory, the quantitative evaluation model of tunnel health state was constructed further, which was applied to a case study of the Nanyangshan Tunnel of Kanglin Expressway in China. Furthermore, a comparative analysis with the traditional subjective-objective combination weighting method was conducted, demonstrating the advantages of the proposed evaluation method in terms of engineering applicability and result accuracy. The research findings can provide a decision-making basis for the health maintenance and scientific management of highway tunnels in China.

A Benchmark for Morphological Segmentation in Uyghur and Kazakh

Morphological segmentation and stemming are foundational tasks in natural language processing. They have become effective ways to alleviate data sparsity in agglutinative languages because of the nature of agglutinative language word formation. Uyghur and Kazakh, as typical agglutinative languages, have made significant progress in morphological segmentation and stemming in recent years. However, the evaluation metrics used in previous work are character-level based, which may not comprehensively reflect the performance of models in morphological segmentation or stemming. Moreover, existing methods avoid manual feature extraction, but the model’s ability to learn features is inadequate in complex scenarios, and the correlation between different features has not been considered. Consequently, these models lack representation in complex contexts, affecting their effective generalization in practical scenarios. To address these issues, this paper redefines the morphological-level evaluation metrics: F1-score and accuracy (ACC) for morphological segmentation and stemming tasks. In addition, two models are proposed for morpheme segmentation and stem extraction tasks: supervised model and unsupervised model. The supervised model learns character and contextual features simultaneously, then feature embeddings are input into a Transformer encoder to study the correlation between character and context embeddings. The last layer of the model uses a CRF or softmax layer to determine morphological boundaries. In unsupervised learning, an encoder–decoder structure introduces n-gram correlation assumptions and masked attention mechanisms, enhancing the correlation between characters within n-grams and reducing the impact of characters outside n-grams on boundaries. Finally, comprehensive comparative analyses of the performance of different models are conducted from various points of view. Experimental results demonstrate that: (1) The proposed evaluation method effectively reflects the differences in morphological segmentation and stemming for Uyghur and Kazakh; (2) Learning different features and their correlation can enhance the model’s generalization ability in complex contexts. The proposed models achieve state-of-the-art performance on Uyghur and Kazakh datasets.

Evaluation of the performance characteristics of a new chemical sensor, device, or method

For those of us involved in the design of novel sensors and methods, understanding the analytical requirements based upon their intended use is essential. In the case of a medical sensor, we must know how the results of a test, performed with the novel sensor, will increase the probability of a good diagnostic or disease management decisions by a physician. So, to assess whether the new sensor meets the expectations, one must design and execute experiments and analyze the results by appropriate statistical methods. As a last step of the evaluation of a novel sensor, one can measure the agreement between the results provided by an established method and the new sensor in a method comparison study. However, the correct statistical approach in method comparison studies is not obvious. In this paper a new evaluation method for determining the unbiased parameters of the association between the compared methods is discussed. It is based on the relationship between the slope and intercept of the regression line in the scatter plot and the parameters of the regression line in the Bland and Altman plot of the differences, in which the differences between the values measured by the compared methods in the same samples are plotted as a function of the average of the measured values. Moreover, we suggest a graphical representation of the data for assessing both the association and the agreement between the compared methods and show the limits of the proposed evaluation method theoretically and using computer-generated data and real-world examples.

An improved method for evaluating fracture density using pulsed neutron capture logging

Fracture density is a critical fracture evaluation parameter for the optimization and production prediction of hydraulic fracturing models. Non-radioactive tracer techniques (NRT) have successfully used a quantitative fracture density method based on neutron-induced gamma ray from formation elements. Furthermore, fracture density can be calculated using the formation capture cross-section before and after hydraulic fracturing based on pulsed neutron capture logging. However, the response sensitivity of the macroscopic scattering cross section to the fractures will decrease when the fracture density is high due to the neutron self-shielding phenomenon. Therefore, an improved fracture density evaluation method is applied, which combines the macroscopic capture cross section and the neutron self-shielding correction factor. In the new method, the peak area of titanium from the captured gamma spectrum was used to obtain a neutron self-shielding correction factor, in order to improve the sensitivity of fracture density determination. Furthermore, the response of capture cross-section variation to fracture density at various tagged proppant concentrations and formation backgrounds was investigated. The findings indicate that the tagged proppant concentration influences the detection limit of fracture density and the sensitivity of fracture density identification. The accurate calculation range of fracture density using the new method has been extended from 5% to 10% under the condition that the tagged proppant concentration is 0.2%. Meanwhile, water salinity significantly impacts capture cross-section variation, while the effects of porosity, lithology, and fluid type on capture cross-section variation are negligible. A simulated fracturing example demonstrates the method's applicability in various measuring environments. The results show that fracture density and height are consistent with the model settings after correcting for water salinity, and the fracture density calculation error is less than 3%. Therefore, our proposed evaluation method for fracture density corrected for the neutron self-shielding effect improves response sensitivity and fracture density calculation accuracy.

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains

The online channel has affected many facets of an individual's identity, commercial, social policy, and culture, among others. It implies that discovering the topics on which these brief writings are focused, as well as examining the qualities of these short texts is critical. Another key issue that has been identified is the evaluation of newly discovered topics in terms of topic quality, which includes topic separation and coherence. A topic modeling method has been shown to be an outstanding aid in the linguistic interpretation of quite tiny texts. Based on the underlying strategy, topic models are divided into two categories: probabilistic methods and non-probabilistic methods. In this research, short texts are analyzed using topic models, including latent Dirichlet allocation (LDA) for probabilistic topic modeling and non-negative matrix factorization (NMF) for non-probabilistic topic modeling. A novel approach for topic evaluation is used, such as clustering methods and silhouette analysis on both models, to investigate performance in terms of quality. The experiment results indicate that the proposed evaluation method outperforms on both LDA and NMF.

Functional-Combination-Based Comprehensive Benefit Evaluation of Energy Storage Projects under Source-Grid-Load Scenarios via Super-Efficiency DEA

As an important support for power systems with high penetration of sustainable energy, the energy storage system (ESS) has changed the traditional model of simultaneous implementation of electricity production and consumption. Its installed capacity under the source-grid-load scenario is rising year by year, contributing to sustainable development, but it faces the problems of insufficient utilization and benefits. This study analyzes the functional combination of ESS under source-grid-load scenarios. A comprehensive benefit evaluation method of energy storage projects (ESPs), based on a fuzzy decision-making trial and evaluation laboratory (DEMATEL) and super-efficiency data envelopment analysis (DEA), is proposed. Firstly, the functional requirements of energy storage in source-grid-load scenarios are explored, and the characteristics of various functions are analyzed to form eight functional combination schemes. Secondly, index modeling is carried out from three aspects—the whole life cycle cost, functional combination benefits, and social and environmental benefits—and a comprehensive benefit evaluation index system of ESP is proposed. Then, the intuitionistic trapezoidal fuzzy number (ITFN) is combined with DEMATEL to form an effective analysis method for the input–output relationship of the indices, and the comprehensive evaluation is realized based on the SE-DEA model. Compared with other methods, this model can ensure the objectivity and stability of the evaluation results in ESP evaluation. Finally, the effectiveness of the proposed evaluation method and the rationality of the functional combination are verified under source-grid-load scenarios. The calculation results show that in the application scenario of source-grid-load, after adopting the functional combination scheme formulated in this article, the comprehensive investment benefits of ESPs have been improved. Moreover, the source side effect is at its best, with an efficiency value of 2.209.

Rams-based comprehensive evaluation method for EMU components

EMU (Electric Multiple Units) components were evaluated in multiple dimensions, i.e. reliability, availability, maintainability, and safety. A parameter estimation method, which is based on the data on the mileage between failures and quantities monitored in the actual operation, was put forward for the Weibull proportional hazard model to determine their operational reliability. A multi-index component maintainability evaluation model, which is based on empirical distribution functions, and a maintainability cluster evaluation model, which is based on whitened weight functions, were built, and the entropy weight method was introduced to determine the estimates of the indexes. A component safety evaluation method, which has the safety risk factor as its evaluation basis, was built using both the fuzzy theory and the analytic hierarchy process. A RAMS comprehensive evaluation model based on the linear weighted synthesis method was proposed. All of the models and methods are aimed at meeting the needs of this multidimensional component evaluation. This evaluation method was verified using actual gearbox operation parameters. The reliability and availability evaluation curves based on the Weibull proportional hazard model, whose parameters were determined according to the failure data, show that the EMU component reliability and availability decrease as the EMU mileage increases, thus verifying that this evaluation method is reasonable. The calculated EMU gearbox degree of safety is 0.8659, at an excellent level. The evaluated EMU gearbox maintainability is at the moderate level of III. The proposed evaluation method can reflect the relations of the component reliability and availability to the quantities monitored in operation more accurately and more comprehensively and the actual component maintainability and safety levels in a more objective way. It can provide a theoretical basis for managers and design departments to improve and evaluate the performance of the components, determine their maintenance intervals, and develop scientific preventive maintenance strategies in order to reduce their overhaul cost and increase their maintenance efficiency.

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

Time-Dependent Fuzzy Reliability Analysis of Concrete Slab during the Initial Water Storage Period of CFRD: A Case Study

The concrete-faced rockfill dam (CFRD) has been widely constructed worldwide, and the reliability of the concrete panels, the most important containment structure, is critical to the safety of the CFRDs and downstream communities. Several practical projects show that concrete slab cracking usually occurs during the water storage period, which can be attributed to the rapid increase of hydrostatic pressure and uneven settlement of the dam body. In this paper, a time-dependent reliability analysis method considering the fuzziness of the failure criterion is presented to assess the slab cracking risk of the Houziyan CFRD during the water storage period. Based on the observed deformation, the material parameters are calibrated to ensure the fidelity of the numerical simulation. Based on the Drucker–Prager yield criterion and tensile strength criterion, the response surface method is utilized to construct the performance functions at different moments, and then the time-dependent reliability analysis considering the fuzzy failure criteria is implemented for the concrete slab. The case study of the Houziyan CFRD shows that, during the initial period of water storage, the reliability index of the concrete slab decreases with the rise of the water level. With a stable water level, the probability of cracking of the concrete slab slowly decreases as the deformation of the dam body and slab tend to be coordinated. Especially, considering the fuzziness of the failure criteria, the reliability index decreased by about 2%, indicating that the proposed evaluation method is biased toward safety. The method proposed in this paper can reflect the evolution law of concrete slab reliability with the operating environment and provides a new approach to evaluate the performance of the concrete slab during the water storage period.

An image-processing method based on regional separation-parameter coupling for the stability analysis of biodiesel flame

Flame stability is vital to obtain good combustion performance. Therefore, the development of flame stabilization technologies and stability evaluation methods has always received considerable research attention. However, the existing evaluation methods have low precision, which makes it difficult to accurately assess the flame stability. To address this issue, we proposed an effective flame state evaluation method in this study. We conducted relevant combustion experiments, and applied the proposed method to evaluate the flame stability of biodiesel. Furthermore, we established a numerical evaluator of the flame state based on a regional separation-parameter coupling image-processing method. This numerical evaluator combines the statistical characteristics of six flame parameters, which we derived from the flame images. The experimental results suggested that the proposed method can be used to determine some characteristic parameters that cannot be obtained by traditional methods, such as oscillation frequency and propagation velocity. Notably, these characteristic parameters are important for the evaluation of flame stability. At an oxygen concentration of 24 % and fuel pressure of 0.5 MPa, we subdivided the combustion state of biodiesel into stable, relatively stable, unstable, and extinguished states. We observed that by decreasing the airflow rate from 0 to 12.50 m3/h, the flame color changed from orange to green, then blue, and finally to yellow-green. We also observed, contraction and spreading in the flame highlight region. The color space analysis revealed that the larger the blue-to-red ratio, the more complete the combustion, which led to a blue flame. Overall, the proposed evaluation method provides a useful guide for regulating the combustion of biodiesel. Furthermore, these result can be generalized to assess the combustion states of other fuels.

Методичні особливості оцінки рівня компетентності експертів у групі при визначенні ефективності ракетно-космічних комплексів

Expert examination methods greatly facilitate the solution of difficult-to-formalize problems. However, in this case the solution is affected by a subjective factor. The decision-making theory has a number of methodological techniques that diminish its effect on the decision made. This paper presents a method of quantitative evaluation of experts’ competence from the results of an expert examination of the efficiency determination of unique, technically complex systems of special and dual purpose, in particular space-rocket complexes. In an expert examination of projects of such systems, it is suggested that the experts’ competence be quantitatively evaluated in two stages: a preliminary evaluation of the experts’ competence from their factual data and a refined evaluation of the experts’ competence just before the calculation of the expected indices of target efficiency using the results of expert examinations made by the procedure developed. The proposed method of quantitative evaluation of experts’ competence is based on evaluating the qualification of the experts involved in the target efficiency determination of a complex engineering system. A rank matrix constructed on the basis of partial criteria of technical efficiency and additional factors of indirect control is proposed as a tool to eliminate cases where at a high level of expert evaluation consistency the most accurate expert evaluations may be considered anomalous in the expert evaluation of the technical and target efficiency of space-rocket systems. The presented mathematical model of quantitative evaluation of experts’ competence includes parameters that adjust the mathematical model to specific conditions of the expert evaluation (expert evaluation methods employed, measurement scales, specific limitations, etc.). The mathematical model is constructed around the axiom that the “true” estimates of the significance of the objects under evaluation lie within the expert evaluation domain. The paper also presents an enlarged algorithm for adjustment parameter calculation from the results of expert estimate preprocessing. The presented mathematical model and algorithm make it possible to develop a computer program for determining experts’ competence from expert evaluation results.

A Time–Frequency Image Quality Evaluation Method Based on Improved LIME

Deep learning has the characteristics of high efficiency, high accuracy and low knowledge dependence, and has become a hot method in the research field of rotating machinery fault diagnosis. The time–frequency transform can show both the time and frequency characteristics of the vibration signal, so the time–frequency image is often used as the input of deep learning networks. At present, there are many time–frequency transform methods, and how to choose one is a problem worth discussing. This paper proposed a time–frequency image quality evaluation method based on improved local interpretable model-agnostic explanations (LIME). With the input of deep learning networks as the application background, this method evaluates the time–frequency image quality of rotating machinery vibration signals from two aspects: the accuracy of the diagnosis results and the consistency of the interpretation results with prior knowledge. The feasibility of the proposed evaluation method is verified by experiments on the measured data set, and engineers’ trust in the deep learning model is improved.

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.

Evaluation of surface quality and error compensation for optical aspherical surface grinding

Optical aspherical lenses are widely used in the aerospace, electronics, and optical communication industries. Grinding is the primary processing method used to ensure their precision. It is difficult for traditional evaluation indexes to fully reflect the optical properties of optical surfaces after grinding. This paper presents a novel method for characterizing surface quality of optical aspherical surface oriented to the application of optical properties. The proposed evaluation indicators include the profile error, surface roughness, slope error, and projection-area scale-deviation value, which can be used to comprehensively evaluate the surface quality. Subsequently, a novel grinding compensation method for the optical aspherical surface is proposed, and its compensation precision is evaluated using the abovementioned evaluation indicators. The results show that the maximum surface-shape error decreases to less than 150 nm; additionally, the central cusp point is restrained. The precision and efficiency of error compensation for the optical aspherical surface are significantly better than those of the conventional method. The proposed evaluation indicators can be applied to reflect the features of optical aspheric surface directly. The high efficiency and precision compensation of optical aspherical surface can be achieved with the proposed evaluation method and modified error compensation algorithm.

Multi-attribute decision making based on VIKOR with probabilistic linguistic term sets: An application to the risk evaluation of foreign direct investment.

The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes' uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation.

A comprehensive resilience evaluation method for power distribution systems based on weighted Page-Rank algorithm

Resilience evaluation plays a critical role in the decarbonized development of modern power distribution systems since it objectively assesses how the distribution system performs in the face of stochastic renewable generation, increasing load demand, and promising to cut carbon emissions. This study proposed a comprehensive resilience evaluation method for regional distribution systems. The proposed resilience evaluation system comprises three sub-groups: performance, economy, and connections. To improve the objectivity of the proposed evaluation method, the weighting factors are calculated based on the Analytic Hierarchy Process (AHP) and weighted Page-ranking algorithm, respectively. Specifically, AHP is adopted to calculate the weighting factors for different metrics. At the same time, the original Page-ranking algorithm is improved according to the importance of the buses and then used to calculate the weighting factors for different buses within the same metric. The proposed method is applied to a real-world distribution network in China. Numerical results show that the proposed method can objectively evaluate the overall resilience level of the distribution network, including the source side, the network, and the load side. The evaluation results can help the decision-makers to make resilience enhancement in an economical and targeted way.

Dynamic Assessment of Photovoltaic-Storage Integrated Energy Stations Health Incorporating Subjective and Objective Characteristics

Photovoltaic-storage integrated systems, which combine distributed photovoltaics with energy storage, play a crucial role in distributed energy systems. Evaluating the health status of photovoltaic-storage integrated energy stations in a reasonable manner is essential for enhancing their safety and stability. To achieve an accurate and continuous assessment of the health status of photovoltaic-storage integrated energy stations, a dynamic evaluation method is proposed in this study. This method integrates both subjective and objective characteristics. Initially, considering the evaluation needs of low-carbon operation and health status for photovoltaic-storage integrated energy stations, a comprehensive health status evaluation system is developed. The significance of each indicator is subjectively analyzed, while also considering objective characteristics and sensitivity of indicators. The integration of subjective and objective characteristics is achieved using principles from game theory. Subsequently, through the establishment of the Grey-TOPSIS evaluation model, both positive and negative correlations of the health status of photovoltaic-storage integrated energy stations are determined, resulting in the derivation of a health status vector. Furthermore, the introduction of time-weight vectors and the incorporation of a time dimension enable dynamic evaluation and the comprehensive observation of health status. Finally, the scientific validity and effectiveness of the proposed evaluation method are demonstrated through practical examples, with comparisons made to traditional evaluation methods. The results clearly indicate that this method offers higher sensitivity when evaluating the health of photovoltaic-storage integrated energy stations.