Dynamic Evaluation Model Research Articles

Dynamic economic evaluation of hundred megawatt-scale electrochemical energy storage for auxiliary peak shaving

With the rapid development of wind power, the pressure on peak regulation of the power grid is increased. Electrochemical energy storage is used on a large scale because of its high efficiency and good peak shaving and valley filling ability. The economic benefit evaluation of participating in power system auxiliary services has become the focus of attention since the development of grid-connected hundred megawatt-scale electrochemical energy storage systems (ESS). Based on the relationship between power and capacity in the process of peak shaving and valley filling, a dynamic economic benefit evaluation model of peak shaving assisted by hundred megawatt-scale electrochemical ESS considering the equivalent life of the battery is proposed. The model considers the investment cost of energy storage, power efficiency, and operation and maintenance costs, and analyzes the dynamic economic benefits of different energy storage technologies participating in the whole life cycle of the power grid. Then, according to the current ESS market environment, the auxiliary service compensation price, peak-valley price difference and energy storage cost unit price required to make the energy storage technology achieve the balance of payments are calculated, and the economic balance points of different energy storage types are clarified. Finally, based on the measured data of different provincial power grids, the economies of six energy storage types applied to three provincial power grids are compared and analyzed, and the rationality and effectiveness of the relevant models proposed are verified. The work has theoretical guiding significance for the economic benefit evaluation of hundred megawatt-scale electrochemical energy storage.

Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions.

(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection.

A Fracture Model for Dynamic Sliding Safety Evaluation of a Concrete Dam Subjected to Seismic Excitation

The Sliding Safety Factor (SSF) is a crucial criterion for the sliding stability evaluation of concrete dam structures. A concrete gravity dam subjected to strong earthquakes undergoes progressive fractures, in addition to pre-existing fractures, at the dam–foundation interface, which causes a reduction in the shear strength against sliding. In this study, a new SSF is suggested to take account of the progressive fractured area at the dam–foundation interface. A contact and sliding model for the dam–foundation system is also suggested to compute the dynamically varying normal forces and sliding motions for the suggested SSF. To investigate the effect of the progressively fractured area on the sliding safety evaluation, the conventional, improved, and newly suggested SSFs are compared using the dynamic seismic analysis results of a concrete gravity dam. The conventional formulation of the SSF, in which the fractured area is not represented, yields extremely overestimated sliding safety judgements when a dam is subjected to strong earthquakes. On the other hand, the newly suggested SSF with the proposed contact–sliding model provides more realistic and conservative sliding safety evaluation results than the others.

Risk assessment for yachting tourism in China using dynamic Bayesian networks.

Scientific evaluation of yachting tourism safety risks (YTSRs) is crucial to reducing accidents in this sector. This paper is based on the data of 115 yachting tourism accidents in China's coastal areas from 2008 to 2021. A fishbone diagram and the analytic hierarchy process (AHP) were used to identify the risk factors of yachting tourism from four aspects human, yachting, environmental, and management risk and to construct an evaluation index system. To perform dynamic evaluation, a dynamic evaluation model of YTSRs was built using dynamic Bayesian networks (DBN). The results indicate that human factors, such as the unsafe behavior of yachtsmen and tourists, are the primary risk factors; the risk is higher in summer than in winter, and the Pearl River Delta region has a greater risk of yachting tourism. It is suggested to improve the normal safety risk prevention and control system of yachting tourism, to advocate for multi-subject coordination and co-governance, and to improve the insurance service system so as to provide a guarantee for the safe and healthy development of yachting tourism in China. The findings provide theoretical and practical guidance for marine and coastal tourism safety management, as well as the prevention and control of YTSRs.

The promotion strategies and dynamic evaluation model of exhibition-driven sustainable tourism based on previous/prospective tourist satisfaction after COVID-19

After COVID-19, all art exhibitions in 2020 were forced to close and postponed until 2021 in Japan. The current paper studies the dynamic evaluation model and exhibition-driven event tourism promotion strategies based on previous/prospective tourist satisfaction surveys. Echigo-Tsumari Art Triennale (ETAT) was selected for the evaluation case. A new methodology, prospective-importance previous-performance analysis (PIPPA), was used for the previous tourist satisfaction questionnaire survey (by term frequency-inverse document frequency (TF-IDF)) and prospective tourist satisfaction questionnaire survey for the restart. It is an attempt not only for ETAT 2021 but also a sample for the others (hundreds of art exhibitions) in Japan and other countries.

Hyperparameter optimization method based on dynamic Bayesian with sliding balance mechanism in neural network for cloud computing

Hyperparameter optimization (HPO) of deep neural networks plays an important role of performance and efficiency of detection networks. Especially for cloud computing, automatic HPO can greatly reduce the network deployment cost by taking advantage of the computing power. Benefiting from its global-optimal search ability and simple requirements, Bayesian optimization has become the mainstream optimization method in recent years. However, in a non-ideal environment, Bayesian method still suffers from the following shortcomings: (1) when search resource is limited, it can only achieve inferior suboptimal results; (2) the acquisition mechanism cannot effectively balance the exploration of parameter space and the exploitation of historical data in different search stages. In this paper, we focused on the limited resources and big data provided by the cloud computing platform, took the anchor boxes of target detection networks as the research object, employed search resource as a restraint condition, and designed a dynamic Bayesian HPO method based on sliding balance mechanism. The dynamism of our method is mainly reflected in two aspects: (1) A dynamic evaluation model is proposed which uses the cross-validation mechanism to evaluate the surrogate model library and select the best model in real time; (2) A sliding balance mechanism is designed based on resource constraints to seek a balance between exploration and exploitation. We firstly augment the recommended samples of probability of improvement acquisition function by using k-nearest neighbor method, then introduce Hausdorff distance to measure the exploration value and match sampling strategy with resource utilization, which makes it slide smoothly with resource consumption to establish a dynamic balance of exploration to exploitation. The provided experiments show that our method can quickly and stably obtain better results under the same resource constraints compared with mature methods like BOHB.Graphical

A Dynamic Systems Model for an Economic Evaluation of Sales Forecasting Methods

Sales forecasts are essential for a smooth workflow and cost optimization. Usually, they are assessed using statistical error measures, which might be misleading in a business context. This paper proposes a new dynamic systems model for an economic evaluation of sales forecasts. The model describes the development of the inventory level over time and derives the resulting overstock and shortage costs. It is tested on roughly 3,000 real-world time series and compared with the commonly used approach based on statistical measures. The experiments show that different statistical measures have no coherent evaluation, making their usage even less suitable for a practical economic application.

A Modified Equivalent Factor Method Evaluation Model Based on Land Use Changes in Tianfu New Area

Scientific understanding of urban ecosystem service value (ESV) is fundamental to building an urban ecological landscape pattern and improving urban environmental quality. The equivalent factor method (EFM) is widely used in evaluating ESV for natural ecosystems. In this study, using the EFM and sensitivity analysis, our research explored the space–time changes in land use and ESV during the planning and construction of Tianfu New Area from 2010 to 2020. This study selected correction factors from natural geography and social economy aspects, and established space–time correction models for standard equivalent coefficients as well as a comprehensive dynamic evaluation model for the ecosystem service value of specific urban areas. In terms of land use, the area of farmland decreased the most. The areas of construction land, grassland, and water bodies increased significantly. The reduced farmland was mainly converted into construction land, followed by grassland and water bodies. Other land use types had smaller changes. Due to the increased area of water bodies and their high-value coefficient per unit area, the urban ecosystem service value showed an increasing trend. During the study period, the conversion of about 1% of land led to about a 0.25% change in the urban ecosystem service value. Farmland in 2010 and water body in 2020 are the most sensitive land factors for ESV in Tianfu New Area. The results might have important insights for urban ecological environment protection and improving ecosystem services during the construction of newly built urban areas.

A dynamic evaluation model of data price based on game theory

A dynamic evaluation model of data price based on game theory

The Safety Risk Assessment of Mine Metro Tunnel Construction Based on Fuzzy Bayesian Network

With the acceleration of urbanization, the construction of urban subway tunnel networks is advancing towards deeper, denser, and larger subterranean forms. Currently, there is a lack of systematic identification and dynamic reasoning analysis of factors throughout the entire process of subway tunnel construction using the mining method. To reduce the probability of accidents and improve safety risk management in the whole process of subway tunnel construction using the mining method, we propose a dynamic safety evaluation method based on Fuzzy Set Theory (FST) and Bayesian Network (BN). Firstly, based on the identification of main stages of the construction process using the Work Breakdown Structure, a safety risk evaluation index system for subway tunnel construction using the mining method was constructed according to the Risk Breakdown Structure. Secondly, by combining Fuzzy Set Theory, the Analytic Hierarchy Process, and the Bayesian Network, we established a dynamic safety risk evaluation model for subway tunnel construction using the mining method, based on FBN. Lastly, taking a large-section tunnel project using the mining method as an example, the effectiveness and accuracy of this model were verified. The results showed: (1) Causal reasoning analysis indicated that, under the condition of known prior probability, if the case reasoning result is greater than 5%, there is a significant possibility of a safety risk incident. The evaluation results of the model are basically consistent with the actual situation. (2) Diagnostic reasoning analysis revealed that factors such as the tunnel excavation method, the quality of the main waterproof construction, the quality of the detailed construction waterproofing, the design of the monitoring and measurement plan, and the results of the monitoring and measurements, are the main influences on the safety of subway tunnel construction using the mining method. (3) Secondary diagnostic reasoning demonstrated that repeated diagnostic reasoning for the main influencing factors, leading to an investigation path dominated by critical risk factors, can effectively reduce the overall project risk. This research is expected to provide useful insights for the scientific management of safety risks in the construction of subway tunnels using the mining method.

Synergetic Development Measure of Airport Groups Composite System and Its Influencing Factors Analysis: Some Evidence from China

Whether airport groups develop synergistically has become a concern for the aviation industry. Existing studies have neglected the dynamic comprehensive evaluation and analysis of influencing factors of synergy degree. Taking the synergistic development of airport groups as the research perspective, we integrate the dynamic comprehensive evaluation model into the traditional synergy model. From two dimensions of airport development and regional economy, we measured the static synergy degree of the Chengdu-Chongqing airport group and the dynamic synergetic development degree on the overall time series. Further, using a spatial econometric model, we construct a distance economy-nested spatial weight matrix so as to explore the influence of each indicator on the synergy degree of the composite system. The results show that the overall synergy of the Chengdu-Chongqing airport group is above 0.5 in terms of static synergy degree. The clustering spectrum shows that the Chengdu-Chongqing airport group can be divided into three categories and basically forms the characteristic of “two main and multiple auxiliary airports.” In terms of dynamic synergetic development degree and comprehensive evaluation value, the change speed of synergy degree is relatively smooth in the early stage and fluctuates in the later stage, and the overall dynamic evaluation value of the Chongqing regional airports is high. The effects of the explanatory variables of the airport group composite system on the synergy degree show more direct effects than spillover effects, and the direct effects are all positive feedback effects, while the negative spillover effects of the explanatory variables cannot be ignored.

Ergonomic investigations on novel dynamic postural estimator using blaze pose and transfer learning

The aim is to develop a computer-based assessment model for novel dynamic postural evaluation using RULA. The present study proposed a camera-based, three-dimensional (3D) dynamic human pose estimation model using ‘BlazePose’ with a data set of 50,000 action-level-based images. The model was investigated using the Deep Neural Network (DNN) and Transfer Learning (TL) approach. The model has been trained to evaluate the posture with high accuracy, precision, and recall for each output prediction class. The model can quickly analyse the ergonomics of dynamic posture online and offline with a promising accuracy of 94.12%. A novel dynamic postural estimator using blaze pose and transfer learning is proposed and assessed for accuracy. The model is subjected to a constant muscle loading factor and foot support score that could evaluate one person with good image clarity at a time. Practitioner summary: A detailed investigation of dynamic work postures is largely missing in the literature. Experimental analysis has been performed using transfer learning, BlazePose, and RULA action levels. An overall accuracy of 94.12% is achieved for dynamic postural assessment.

A Stigmergy-Based Island Model for Dynamic Evaluation of Constraint-Handling Techniques and Differential Evolution Algorithms

A Stigmergy-Based Island Model for Dynamic Evaluation of Constraint-Handling Techniques and Differential Evolution Algorithms

Analyzing dynamic coupling and coordination of modern service and advanced manufacturing industries in Guangdong–Hong Kong–Macao Greater Bay Area

Integrating the modern service industry with the advanced manufacturing industry is an important way to cultivate a modern industrial system and achieve high-quality development of economy. This study aims to enhance the supporting and leading role of the Greater Bay Area of Guangdong, Hong Kong, and Macao in China's national economic development and opening up by promoting the in-depth integration of these two industries. A new monitoring system is developed to simulate the level of integration of the modern service and advanced manufacturing industries in the Guangdong–Hong Kong–Macao Greater Bay Area. The dynamic comprehensive evaluation model of stock increment was used to simulate the coupling coordination degree of these two industries. Empirical results reveal that the development of these industries has uneven stock and incremental resource advantages, and their development has not been balanced. The coupling coordination degree of these industries in some areas has become maladjusted, while in other areas, it has fluctuated or developed from coordination to disorderly leap-forward. These findings demonstrate that the study methods and results are significant for analyzing industrial integration processes and promoting in-depth integration development in the Guangdong–Hong Kong–Macao Greater Bay Area. In conclusion, building a monitoring system using the dynamic comprehensive evaluation model of stock increment is an effective way to evaluate the level of integration of these two industries and promote their in-depth integration in the Greater Bay Area.

Dynamic evaluation of regional economic resilience under major public emergencies: based on an improved dynamic evaluation model of grey incidence projection-fuzzy matter element

The sudden outbreak of COVID-19 in 2020 causes great impact on the economic development of all countries and even the whole world. Under the background of major public emergencies, a timely dynamic evaluation of regional economic resilience can provide an objective basis for economic regulation and control behavior. Based on the existing evaluation model, an improved dynamic evaluation model of grey incidence projection- fuzzy matter element is proposed in this study. The improved model is a universal evaluation model that can be used in different contexts. This model method can, not only limited to analyzing economic resilience, but also be applied to other different contexts. The evaluation indexes are selected (from the market, industry, investment, foreign trade and finance) to construct an evaluation index system of regional economic resilience under major public emergencies. The improved dynamic evaluation model of grey incidence projection- fuzzy matter element is applied to evaluate economic resilience of Hubei province (with its neighboring areas) in context of COVID-19. At the same time, the relative validity of the model is tested based on the empirical evaluation results.

A Dynamic Product Evaluation Model Based on Online Customer Reviews from the Perspective of the Elaboration Likelihood Model

Plenty of online customer reviews (OCRs) provide consumers with an abundant source of word-of-mouth (WOM). It makes potential customers evaluate alternative products or services more conveniently. Thus, relative studies have been arising. Considering that existing research is almost deployed from the perspective of reviewers, the study proposes an evaluation model based on the OCRs from the view of potential customers. In the model, the OCRs in text form are translated into probabilistic linguistic term sets from the perspective of the cognitive process model, namely, the elaboration likelihood model, for further evaluation. Meanwhile, the influence of the OCRs’ published time is also taken into consideration, and then, the model is transformed into a dynamic evaluation model. In general, we propose a dynamic product evaluation model to simulate the cognitive process of the potential consumers disposing of OCRs. In addition, an evaluation of 6 hotels in Chengdu, Sichuan Province, is developed based on the proposed dynamic product evaluation model, and some discussions, as well as conclusions, are also carried out.

Study on the spatial–temporal patterns and evolution characteristics of ecological security based on dynamic evaluation in the Three Gorges Reservoir Area

Monitoring ecological security in the Three Gorges Reservoir Area (TGRA) is the prime objective of the ecosystem research on the Yangtze River basin. The comprehensive dynamic evaluation model of ecological security based on the “Pressure-State-Benefit” and four dimensions of “time–space” was designed in this study. Furthermore, comparative analysis and coefficient of sensitivity (CS) were used to verify the rationality and scientificity of this model. This study interpreted and determined the land use data of the TGRA from 2000 to 2020. The coupling coordination degree model, the spatial correlation model and the cluster analysis model assisted in determining the spatial pattern distribution and dynamic interaction characteristics of ecological security at the grid-county-watershed scales in the TGRA. The analytical results demonstrated that the ecological security index (ESI) in the TGRA was generally not very high due to resource constraints and background environmental conditions. The ESI was significantly clustered in space. The initial impoundment of the TGRA significantly impacts ecological security. Implementing dot-shaped ecological restoration projects has greatly improved unsafe and low-security state areas. However, the non-significant growth in the high-security state level region can be attributed to the lack of overall planning and implementation scale. The ecological pressure caused by economic and social development in the main urban area has led to a substantial decline in regional ecological benefit. Therefore, it is necessary to balance the coordinated development relationship between economic-social and ecological benefit in the region. These findings will provide references and supportive data for environmental protection and ecological restoration in the TGRA.

A Methodology to Evaluate the Vulnerability of the Natural Gas Supply Chain Based on Set Pair Analysis and Markov Chain

In this study, a methodology to evaluate and predict the vulnerability of the natural gas supply chain is proposed, and both the dynamics and stochasticity of the natural gas supply chain are considered. In the methodology, the evaluation system of the vulnerability is established firstly based on the definition of the vulnerability of the natural gas supply chain, and the evaluation system is composed of 12 evaluation indicators that cover the upstream, midstream, and downstream of the natural gas supply chain. Then, a combination weighting method based on the analytic hierarchy process method and entropy weighting method is employed to determine the weight of each indicator. Finally, the dynamic evaluation and prediction models of the vulnerability of the natural gas supply chain are developed based on the set pair analysis and Markov chain. Furthermore, the methodology is applied to evaluate and predict the vulnerability of the natural gas supply chain in China. According to the evaluation results, the vulnerability level in the year of 2021 is relatively vulnerable, and the natural gas supply chain in China was relatively stable in the last 10 years, but it is developing toward a more vulnerable trend. Moreover, some suggestions to reduce the vulnerability of China’s natural gas supply chain are proposed as well.

Dynamic Evaluation of Energy Carbon Efficiency in the Logistics Industry Based on Catastrophe Progression

The logistics industry has an irreplaceable role in promoting Chinese economic development, and its carbon emissions have become a hot topic of academic research. However, more research needs to be conducted on this. This study is based on establishing an evaluation index system for the efficiency of energy carbon emissions in the Chinese logistics industry. The catastrophe progression method was used to evaluate this statically. A dynamic evaluation model was also established based on the characteristics of fuzzy rewards and punishments. The results showed that the static values in the southeastern provinces of China were always between 0.9 and 1, and there was a significant increase in the dynamic values under the fuzzy reward and punishment scenario. Provinces in the southwest fluctuated between 0.8 and 0.95, while the dynamic values did not increase much. In the northern provinces, the static assessment values were consistently between 0.7 and 0.9, while the dynamic values were decreasing. It is therefore important to reward provinces with high static assessment values and penalize those with low static assessment values. The perspective of the characteristics of fuzzy rewards and punishments is also essential for fair and equitable management, reward and punishment in the different provinces in the study.

Safety Analysis of Offshore Wells Plugging and Abandonment Process with Riserless Well Intervention System Using a DBN based Comprehensive Method

The Riserless Well Intervention (RLWI) system that performs complex offshore oil well Plugging and Abandonment (P&A) operations is a typical Multi-Mission Phased-Mission System (MM-PMS), which requires multiple missions to be completed within a phase. P&A processes involve complex operations and equipment that can contaminate local marine ecosystems if they fail. Therefore, it is necessary to evaluate the reliability of the RLWI system. This paper proposes a dynamic reliability evaluation model for analyzing the RLWI MM-PMS. The GO model of the phase operation process and the Fault Tree (FT) model used to analyze the failure of each mission were established, and a Dynamic Bayesian Network (DBN) model based on the GO model and the FT model was developed for reliability evaluation. The established model can analyze the changes in the reliability of the RLWI MM-PMS more comprehensively, and can also clarify the importance of different missions and different system components. In addition, considering the impact of the marine environment on operators, the Standardized Plant Analysis Risk-Human (SPAR-H) reliability analysis is used for quantification. These findings can guide the improvement of the reliability of the RLWI system and the success rate of P&A operations.