Accuracy Of Evaluation Results Research Articles

Research on digital-visual evaluation method of down fibers odor

Down is often used as a natural filler for warm clothing, but the odor of down is one of the issues that affects the performance of warm clothing applications. At present, the evaluation of down odor mostly uses the human olfactory evaluation method, which has low reproducibility and accuracy. This article first uses the method of activated carbon gas extraction/gas chromatography–mass spectrometry to identify five down odor compounds that can be detected in a stable way, as representative compounds of down odor. Then, a simple direct impregnate extraction/gas chromatography–mass spectrometry method was used to determine the exact concentration of odor compounds in down fibers. Finally, an odor level radar chart was constructed by analyzing the concentration of odor representative compounds of levels 1–5 in down. The digital visual evaluation method was composed of direct impregnate extraction combined with radar chart to judge the down odor level and compared to the human olfactory method. The results indicate that the radar chart evaluation has relatively accurate evaluation results and is more objective and accurate than the human olfactory method. This method for evaluating the odor level of down is simple and reliable, and has certain promotion and application prospects in the down production industry.

Impact and flexibility support capability estimation model of urban electric vehicles based on the integration of urban traffic network and Urban Distribution Network

Electric Vehicle (EV) is regarded as the bridge between Urban Road Network (URN) and Urban Distribution Network (UDN). It can not only make negative impact on power grid, but can also provide flexibility support capability to power grid. Whether the benefit is negative or positive depends on the dynamical behavior of EVs. So with the rapid development of EVs in the urban area, accurate evaluation of both negative impact and flexibility support capability of dispersed EVs in the urban area is very important for V2G application. In order to get the accurate evaluation results, the interaction between transportation system and electric power system should be taken into consideration. In this paper, an estimation model of the impact and flexibility support capability of urban dispersed EVs considering the interaction between URN and UDN is proposed. In order to improve the accuracy of the estimation model, the interaction between URN and UDN is modeled as an integration model based on EV trip chain. It takes EVs’ temporal and spatial dynamic stochastic characteristics into consideration simultaneously and can be used to describe the energy interaction between URN and UDN. An indices set consist of multi-index is proposed to evaluate the impact and flexibility support capability of urban EVs based on the integration model. Three numerical cases are conducted to verify the validity of the proposed model. The results show that the proposed integration model of URN and UDN based on EV trip chain can not only be used to describe the energy interaction between URN and UDN, but also to estimate the impact and flexibility support capability of urban dispersed electric vehicles.

Safety resilience evaluation of hydrogen refueling stations based on improved TOPSIS approach

The traditional use of TOPSIS evaluation for assessing the resilience of hydrogen refueling stations results in overly one-sided outcomes and fails to identify the indicators that have a significant impact on the resilience of the stations. Therefore, this paper proposes an improved method for evaluating the safety resilience of hydrogen refueling stations based on CRITIC-DEMATEL and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The CRITIC-DEMATEL combination weighting method is employed to determine weights, thus improving the TOPSIS method. This method is then applied to assess the resilience level of hydrogen refueling stations. Then, introducing a Conditional Logistic Regression model identifies indicators with significant impact on resilience. The improved TOPSIS method enhances the reliability and accuracy of evaluation results. The Conditional Logistic Regression model provides a more accurate delineation of the impact size of indicators on the safety resilience of hydrogen refueling stations compared to traditional methods.

Application of video behavior fast detection based on wearable motion sensor devices in sports training

The sensors can collect the athletes' movement data in real time, providing more accurate and objective feedback for the training process. This study aims to explore the application of video behavior rapid detection based on sports wearable sensor devices in sports training. By integrating sensor data and video information, more comprehensive and accurate behavioral analysis is provided to help coaches better understand athlete performance and progress. The study uses an approach based on motion wearable sensor devices and video technology, in which athletes wear sensor devices and exercise during training. Sensors automatically record movement data, and cameras record video of the training process. Then, computer vision algorithms and machine learning techniques are used, combined with sensor data and video information, for rapid behavior detection. The experimental results show that the video behavior detection based on sports wearable sensor devices has high accuracy and efficiency in sports training. Through the fusion of sensor data and video information, the movement, posture and technical details of the athlete can be analyzed in real time, and accurate evaluation results can be obtained.

Power quality interval grading evaluation method of AC/DC distribution network considering source-load weighting and voltage coupling

In the context of the ‘dual carbon’ goal, the integration of renewable energy sources has a significant impact on distribution systems, while the substantial integration of DC loads and photovoltaic power has led to a progressive transition of conventional distribution networks towards hybrid AC-DC distribution networks in the field of electrical engineering. However, this shift has brought about more formidable power quality challenges within the power industry. To address these emerging power quality issues, we propose a novel power quality interval grading evaluation method for AC/DC distribution networks considering source-load weighting and voltage coupling. Firstly, the voltage acquisition data is meticulously decoupled to disentangle the interdependencies. Secondly, the power quality evaluation system of the AC/DC distribution network is established. Considering the influence of different access source-load types on each node, the subjective weight is determined by source load weighted two-tuple linguistic analytic hierarchy process (AHP), the objective weight is determined by the entropy weight method, and the idea of using game equilibrium algorithm to obtain comprehensive weight to balance the error of single weight on evaluation result is put forward. Then, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) of interval number improvement is used to classify and evaluate the power quality index to make up for the shortcomings of the single-point sampling method. Finally, a more accurate and comprehensive power quality evaluation result is obtained based on a practical example.

On the imaging depth limit of photoacoustic tomography in the visible and first near-infrared windows.

It is well known that photoacoustic tomography (PAT) can circumvent the photon scattering problem in optical imaging and achieve high-contrast and high-resolution imaging at centimeter depths. However, after two decades of development, the long-standing question of the imaging depth limit of PAT in biological tissues remains unclear. Here we propose a numerical framework for evaluating the imaging depth limit of PAT in the visible and the first near-infrared windows. The established framework simulates the physical process of PAT and consists of seven modules, including tissue modelling, photon transportation, photon to ultrasound conversion, sound field propagation, signal reception, image reconstruction, and imaging depth evaluation. The framework can simulate the imaging depth limits in general tissues, such as the human breast, the human abdomen-liver tissues, and the rodent whole body and provide accurate evaluation results. The study elucidates the fundamental imaging depth limit of PAT in biological tissues and can provide useful guidance for practical experiments.

Grey comprehensive evaluation of development performance of provinces in China based on spatiotemporal probability function and variable weight strategy

In the new stage of promoting high-quality economic development, the effect of the transformation of development momentum and the ability of sustainable development has become the key factors for the competitiveness of provinces in China. Especially in the context of the impact of COVID-19 and the obstacles of world trade protectionism, the sustainability of development performance is increasingly important. In the past, when evaluating the development performance of various provinces in China, a single index weight was usually used. In view of evaluation criteria, the lack of consideration of regional differentiation factors would result in the evaluation results deviating from reality. This paper introduces the entropy weight method to determine the weight of regional indicators of differentiated development. Based on the space-time probability function, a grey clustering evaluation model of regional development performance is constructed to conduct a comprehensive grey evaluation of the development performance of various provinces in China from 2009 to 2019. It is found that the new evaluation model can correct the deficiencies of similar probability functions and single index weight and obtain more accurate evaluation results. It’s found that the development performance evaluation results of each province are always in a dynamic adjustment process, which needs to be verified with the help of subsequent expansion analysis.

Evaluation of mine water quality based on the PCA–PSO–BP model

Abstract To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry.

Application research on digital twins of urban earthquake disasters

The digital twin of an earthquake disaster city constructed using digital twin technology can reflect the data of different stages of the earthquake disaster in the virtual space and can ensure high synchronisation and simulation of the two to effectively improve the safety of emergency rescue personnel and the scientific decision-making ability of decision makers. Oblique photogrammetry matched the Light Detection and Ranging (LiDAR) data to complete a high-precision three-dimensional real-scene model. The urban digital twin model was established as a module with the three-dimensional base model generated by the two-dimensional frame data with a fast update speed. Based on the shortest-fault-distance intensity-attenuation model, the seismic intensity was calculated using a known active fault and aftershock sequence, and the applicability of this method was verified. The elastic-plastic time history analysis method was used to calculate the seismic response of the building, and the seismic damage level was visualised using a three-dimensional model. The dynamic changes of earthquake disasters are monitored by sensors in real time and analysed in a digital twin model to provide an optimal scheme for earthquake emergency rescue and accurate evaluation results of earthquake disasters.

Comprehensive safety risk evaluation of fireworks production enterprises using the frequency-based ANP and BPNN

The fireworks industry has long struggled with the problem of safety. Scientific, reasonable, and operable evaluation models are prerequisites of reducing risk. Based on the data from over 100 fireworks production safety accidents in China from 2010 to 2022, two evaluation models were established from the perspective of safety risk definition. Firstly, a weight calculation derivative method, the frequency-based analytic network process (ANP), was proposed creatively. This method optimized the importance ranking index calculation process in the ANP by considering the causal frequency of risk factors in the historical accident samples, thus determining how much each indicator affects the likelihood of accidents. Secondly, utilizing the historical accident samples as the dataset, a back propagation neural network (BPNN) model was developed to extract the mathematical relationship between each risk factor and the severity of accident consequence. Finally, the frequency-based ANP and BPNN models were combined to determine the safety risk level of the fireworks production enterprises. Meanwhile, the safety evaluation research samples were used as the comparison set for empirical study with historical accident samples, involving 100 fireworks production enterprises in China evaluated from 2017 to 2020. The significance result of zero shows that there is a statistically significant difference between the likelihood evaluation results of the accident and non-accident companies. Additionally, the severity evaluation model exhibits an excellent result, revealing a classification accuracy of 98.21 %, a mean square error of 8.97 × 10−4, a percent bias of 1.24 %, and a correlation coefficient and Nash-Sutcliffe efficiency coefficient both of 0.96. The frequency-based ANP and BPNN models integrate self-learning, self-adaptive, and fuzzy information processing, obtaining more accurate and objective evaluation results. This work provides a new strategy for the promotion and application of artificial intelligence in the field of safety risk evaluation, thus offering real-time safety risk evaluation and decision support of the safety management for the enterprises.

A novel method for quantitative evaluation of oil content and mobility in shale oil reservoirs by NMR logging: a case study of the inter-salt shale oil in the Jianghan Basin

The evaluation of oil content and mobility in shale oil reservoirs is the key to the optimization of the "sweet spot" of shale reservoirs and the evaluation of available resources. In order to address the challenge of quantitative evaluation of oil content and mobility in shale oil reservoirs, a combined multi-temperature pyrolysis and NMR experimental platform and experimental procedure are innovatively designed and implemented in this paper. The experiments reveal the NMR T2 distribution characteristics of hydrocarbon components in different reserve states. Accordingly, the NMR T2 cutoff values of different hydrocarbon components are determined, and the quantitative evaluation method based on NMR logging for total oil content and movable hydrocarbon content is established. The method has been applied to the inter-salt shale oil BYY2 well in Jianghan Oilfield, and the results show that the total porosity of the shale reservoir in the Qian 4 sub-Member is similar, but the total hydrocarbon content (0.04 g/kg∼16.0 g/kg) and the movable oil content (0.02 g/kg∼4.9 g/kg) are significantly different. Compared with the preferred scheme of reservoir based on porosity, accurate quantitative evaluation results of oil content and mobility based on logging provide a more straightforward indication of the resource potential of the reservoir that is available for exploitation. The experimental scheme of multi-temperature pyrolysis-NMR and the quantitative evaluation method for oil content and motility based on NMR logging will provide an important basis for resource evaluation, optimal formation selection and efficient development of shale oil reservoirs.

Research on river water quality evaluation based on the GA-PP and improved fuzzy model.

To ensure the water quality of rivers, it is crucial to scientifically evaluate their water quality status. This study takes a river in Jiangsu, China, as an example to establish six targeted main indicators for river water quality evaluation and uses a projection pursuit model optimized by the genetic algorithm to determine weights. Applying the improved fuzzy evaluation model to the final evaluation of water quality, the results indicate that this article adopts a weight calculation model that reduces dimensionality without losing data features, and the comprehensive evaluation model is also more complete, resulting in more accurate evaluation results. According to model analysis, the summer water quality is good and peaks from June to July. This article proposes corresponding measures and suggestions in response to the reasons behind this seasonal change. The evaluation model used in this article is superior to other models in terms of accuracy and portability, making it an excellent choice for river water quality evaluation. It can provide valuable technical guidance for similar river water quality evaluations.

Generative Adversarial Network-Based Anomaly Detection and Forecasting with Unlabeled Data for 5G Vertical Applications

With the development of 5G vertical applications, a huge amount of unlabeled network data can be collected, which can be employed for evaluating the user experience and network operation status, such as the identifications and predictions of network anomalies. However, it is challenging to achieve highly accurate evaluation results using the conventional statistical methods due to the limitations of data quality. In this paper, generative adversarial network (GAN)-based anomaly detection and forecasting are studied for 5G vertical applications, which can provide considerable detection and prediction results with unlabeled network data samples. First, the paradigm and deployment of the deep-learning-based anomaly detection and forecasting scheme are designed. Second, the network structure and the training strategy are introduced to fully explore the potential of the GAN model. Finally, the experimental results of our proposed GAN model are provided based on the practical unlabeled network operation data in various 5G vertical scenarios, which show that our proposed scheme can achieve significant performance gains for network anomaly detection and forecasting.

Multifaceted Pathways of Carbon Emission Reduction Policies: A Study Based on the Decoupling Effect and fsQCA Method in the Yangtze River Delta Urban Agglomeration

The urban carbon administration discussion is becoming dominant in the carbon emission reduction landscape. China, the world’s paramount carbon emitter, has instituted a series of policies geared towards carbon emissions reduction. The co-dependent nature of these policies becomes paramount in outlining accurate and comprehensive evaluation results. This research scrutinizes the effect of various policy mixtures on curbing urban carbon emissions, utilizing the Tapio decoupling elasticity coefficient and fuzzy set qualitative comparative analysis (fsQCA) techniques. The consistencies surpass the widely accepted theoretical threshold of 0.8, and each configuration can explain 14.3% to 19.8% of the sample. The findings reveal that industrial and energy policies drive the decoupling of urban carbon emissions in three of four configurations. Management-based policies merely play an auxiliary role, while social and economic policies have yet to exhibit significant implications. The outcomes of this research offer a framework for augmenting the carbon emission reduction policy system and fostering the advancement of carbon reduction initiatives.

The Swallowing Intelligent Assessment System Based on Tongue Strength and Surface EMG

Dysphagia is a very important issue in modern society, and it is common in stroke patients and the elderly. Many studies have shown that tongue strength can be used as an evaluation criterion for swallowing function. This work has implemented a methodology to assess and improvise the swallowing function of dysphagia patients. To execute the same, a tongue pressure instrument was used as a tool to assess tongue strength, and a surface electromyography instrument was used to collect electrical data on larynx muscles. In addition to this, an assessment task has been carried out that is combined with interesting games to increase users’ willingness. After completing the task, the system collects tongue pressure and muscle electrical data. We use the Scoring Function calculation to quantify the user’s swallowing performance quality. In addition to calculating the quality score of the motion, we also extract features from the collected data, build a variety of machine learning models to compare each model’s classification effectiveness and select the best model to correctly predict the level of the user’s swallowing function. Through this evaluation system, we hope to provide fast and accurate evaluation results, so that medical personnel can have more convenient and effective tools for dysphagia diagnosis and rehabilitation training.

Application Research of the Project Bid Evaluation Model Based on MATLAB

Bid evaluation, an important part of bidding activities, is the most critical part to determine the quality of bidding. For large volume and complex technology projects, the comprehensive evaluation method is usually used in China. It is known that the selection of bid evaluation method has a great influence on the quality of bidding. In the face of the basic situation of China, although the comprehensive evaluation method is basically competent for the bidding work, it still has the defects of human factors in the evaluation of the bid. Therefore, it is essential to improve the bidding evaluation method. This paper mainly evaluates the bid evaluation activities of construction projects based on the comprehensive weighted evaluation method. Through the accumulation of relevant theoretical and practical research results, the author sorts out the advantages and disadvantages of various evaluation methods and analyzes the scientificity, fairness, and practicability of the established project bidding as well as the evaluation model in the evaluation of bidders through the actual project bidding and evaluation case. Combined with the mathematical model and case analysis, the bid evaluation activity is solved by the computer program, and the conventional bid evaluation results and the mathematical model evaluation results are compared to get more accurate bid evaluation results.

A Large-Scale Group Decision-Making Approach to Assess Water Resource Sustainability with Double-Level Linguistic Preference Relation

With the rapid development of science and technology and the continuous progress of society, water resource sustainability has attracted much attention. The assessment process of water resource sustainability has become a hot topic. Because professional models cannot ensure the accuracy of evaluation results, expert judgment techniques are used to perform the assessment process. Meanwhile, to eliminate the biases and consider people’s cognitive processes in complex decision making, this study utilizes a large group of experts to evaluate the sustainability of water resources, which is called a large-scale group water resource sustainability assessment (LGWRSA). This study proposes the double-level linguistic preference relation (DLLPR) to enable experts to present more reasonable and intuitive linguistic opinions. Based on the DLLPR, this study presents a clustering method, a weighting method, and a comprehensive adjustment factor determination method. To prevent minority opinions and non-cooperative behaviors from interfering with the decision-making process and to make the decision-making process develop in an accurate and objective direction, a consensus model is developed to modify minority opinions and suggestions and manage non-cooperative behaviors. After a sufficient number of experts reach a consensus through the method proposed in this article, a more accurate evaluation result can be obtained. Finally, an actual LGWRSA problem is established to derive water resource sustainability expectations for four provinces, which validates the effectiveness of the proposed method. A comparative analysis is performed to illustrate the benefits of these methods and present their shortcomings.

Application of a Modified BPS Neural Network Based on Three-Way Decision Theory in an Effectiveness Evaluation for a Remote Sensing Satellite Cluster

The remote sensing satellite cluster system, as an important component of the next generation of space architecture in the United States, has important application prospects in the military field. In order to improve the effects of time, with regard to the effectiveness evaluation of the remote sensing satellite cluster system, neural network methods are generally used to satisfy the requirements of real-time decision-making assistance in the military field. However, there are two problems that emerge when applying the existing neural network methods to an effectiveness evaluation of the remote sensing satellite cluster. On the one hand, the neural network model architecture needs to be designed specifically for the remote sensing satellite cluster system. On the other hand, there is still a lack of hyperparameter optimization methods that consume less time and have good optimization effects for the established neural network model. In this regard, two main modifications were made to the back-propagation neural network, to which an effectiveness evaluation was applied. The first comprised a new architecture named BPS, which was designed for the back-propagation neural network so as to improve its prediction accuracy. In BP architecture, one back-propagation neural network is established for each indicator involved in the effectiveness evaluation indicator system of the remote sensing satellite cluster; the output of each back-propagation neural network model is modified to the residual value between the corresponding indicator value and the value that is predicted through a multiple linear regression analysis of the corresponding indicator. The second modification involved the multi-round traversal method, which is based on the three-way decision theory, and it was proposed in order to significantly improve the model’s training time, which is a new type of hyperparameter optimization method. The results show that compared with the traditional simulation model, the modified back-propagation neural network model based on three-way decision theory can quickly and effectively provide stable and accurate evaluation results; this can assist with and meet the requirements for real-time decision-making in the military field.

The discrimination of college music teaching level based on wave equation

Abstract In order to improve the teaching level of college music teachers, in this essay the discrimination of college music teaching level based on the wave equation is put forward. In this study, the advantages of pattern recognition are utilized to establish a college music teaching level evaluation model based on wave equation. The wave equation is adopted to replace the complex function representation in the traditional statistical method, so as to reduce the influence of subjective factors on teaching level evaluation and explore the potential factor correlation in the evaluation process. The linear fuzzy indivisible samples are mapped to high dimensional space to make them linearly divisible, which avoids the traditional process from induction to deduction in essence and simplifies the classification process of nonlinear problems. Structural risk minimization theory ensures global optimization of segmentation and reduces expected risk. The results of this study can be combined with existing research methods to enhance the objectivity of evaluation methods and the accuracy of evaluation results, which has certain reference significance for improving teachers’ teaching level and promoting the improvement of teaching quality.

GAEP: a comprehensive genome assembly evaluating pipeline

With the rapid development of sequencing technologies, especially the maturity of third-generation sequencing technologies, there has been a significant increase in the number and quality of published genome assemblies. The emergence of these high-quality genomes has raised higher requirements for genome evaluation. Although numerous computational methods have been developed to evaluate assembly quality from various perspectives, the selective use of these evaluation methods can be arbitrary and inconvenient for fairly comparing the assembly quality. To address this issue, we have developed the Genome Assembly Evaluating Pipeline (GAEP), which provides a comprehensive assessment pipeline for evaluating genome quality from multiple perspectives, including continuity, completeness, and correctness. Additionally, GAEP includes new functions for detecting misassemblies and evaluating the assembly redundancy, which performs well in our testing. GAEP is publicly available at https://github.com/zy-optimistic/GAEP under the GPL3.0 License. With GAEP, users can quickly obtain accurate and reliable evaluation results, facilitating the comparison and selection of high-quality genome assemblies.