Performance Evaluation Index Research Articles

Adaptive virtual leader–leader–follower based formation switching for multiple autonomous tracked mobile robots in unknown obstacle environments

This article investigates the formation switching problem of a group of autonomous tracked mobile robots (ATMRs) traveling in unknown obstacle environments. Firstly, a novel formation structure model of virtual leader–leader–follower is designed, where the leader tracks the virtual leader determined by a reference trajectory, while the followers track their desired positions dictated by the virtual leader. Subsequently, a dynamic optimization formation switching strategy is developed, in which the leader calculates a formation adjustment factor in real-time to dynamically adjust the formation parameters according to the environmental constraints. Furthermore, a comprehensive performance evaluation index system is presented to assess formation switching efficiency, leveraging the inherent simplicity of the ATMR’s kinematics. Finally, extensive numerical simulations and physical experiments are carried out to validate the effectiveness of the proposed strategies.

Understanding front-rear track effect of seabed mining vehicle and traction performance optimization

The dual-tracked deep-sea mining vehicles used for sea trials generally cannot meet the demands of large-scale manganese nodules production due to their inadequate load bearing and traction performance. Various seabed mining organizations have gradually started four-tracked mining vehicles as seabed traveling ore collection machines to meet the demand for enhanced tractive performance. A notable challenge encountered is the significant front-rear track effect observed when these vehicles navigate soft-cohesive sediments. Addressing this, our study introduces a novel design concept that involves setting the grouser heights with a deviation between the front and rear tracks, coupled with the development of a traction force-slip rate model inspired by this innovative approach. This model facilitates an in-depth analysis of the front-rear track effect, including phenomena such as deep sinkage and an increase in pitch angle experienced by vehicles on soft-cohesive terrains. It is calculated that the front and rear grouser deviation height of only 2 cm can improve the traction force by 21%. Utilizing the multi-body dynamics (MBD) method, we validated the effectiveness of this grouser height adjustment in mitigating front-rear track effects, demonstrating its potential to reduce sinkage by at least 30 mm and pitch angle by at least 0.3 degrees, thereby guiding the optimization strategy. Finally, through optimization simulation, we present optimized track structural parameter sets suitable for three types of seabed pavement, concluding that for soft silty soil, saturated high cohesive soil, and saturated soft sandy soil, the grouser deviation heights should be approximately 5.3, 4.1, and 2.0 cm, respectively. After applying the optimized track structural parameter sets, traction performance evaluation indexes, such as traction force and the traction force-disturbing area ratio, improved by at least 23.4% and 3.3%, respectively. Meanwhile, static sinkage was reduced by at least 21.4% across the three types of pavements.

Enhancing river flow predictions: Comparative analysis of machine learning approaches in modeling stage-discharge relationship

Streamflow, a pivotal variable in water resources management, holds profound significance in shaping the decision-making processes of hydrologic projects. This paper tries to delve into the exploration of the stage-discharge relationship using three machine learning methods (MLMs) namely multi-layer neural networks (MLNN), radial basis neural networks (RBNN), and neuro-fuzzy systems (ANFIS) to predict and simulate mean daily stage-discharge data derived from two monitoring stations, Bulakbasi and Karaozü, Kizilirmak River, Turkey. Root mean square error (RMSE), Mean absolute percentage error (MAPE), coefficient of determination (R2), and the Developed Discrepancy Ratio (DDR) metrics were utilized to MLMs' performance assessment. The performance evaluation indices (RMSE, MAEP, R2, DDR) for the preeminent MLNN model applied to Bulakhbashi and Karasu stations were determined as (0.29, 1.57, 0.9998, 17.62) and (1.71, 6.56, 0.9980, 6.65), respectively. The MLNN model contributed to a notable enhancement in the RMSE performance index for the aforementioned stations, exhibiting improvements of 87% and 56%, respectively. These results affirm the MLNN's proficiency in accurately capturing the stage-discharge at both monitoring stations.

Compressive strength resistance coefficient of sustainable concrete in sulfate environments: Hybrid machine learning model and experimental verification

The compressive strength corrosion resistance coefficient of sustainable concrete (SC-SCRC) is a crucial indicator for evaluating the mechanical performance degradation of SC after erosion. Therefore, this study collected 326 sets of SC-SCRC data from 20 literature sources and established an optimized machine learning algorithm-based SC-SCRC prediction model, evaluating its performance. To validate the model's accuracy, thirteen different SC mix ratios were prepared for SC-SCRC testing. The results demonstrate that the grey wolf optimization supported vector regression (GWO-SVR) model provides predictions that better align with the actual values. The model exhibits lower standard deviation and mean of model residuals. The performance evaluation indicators of the GWO-SVR model (R=0.972; MSE=1.46e-3; MAE=2.9e-2; MAPE=3.1e-2; RMSE=3.9e-2) are also outstanding. Furthermore, Shapley Additive Explanations reveal that the dry-wet cycle count and the water-binder ratio are the two most critical influencing factors on SC-SCRC, with both showing negative correlations. A graphical user interface (GUI) based on the GWO-SVR model has been developed to enable efficient predictions of SC-SCRC. Test results indicate that the error between the experimental values and the predicted values is less than 6%, demonstrating the model's high accuracy and generalizability. This work lays the foundation for optimizing the performance of SC and predicting SC-SCRC. The findings provide valuable guidance for engineering projects in areas with high concentrations of sulfate ions.

Research on Running Performance Optimization of Four-Wheel-Driving Ackerman Chassis by the Combining Method of Quantitative Experiment with Dynamic Simulation

The wheeled chassis, which is the carrying device of the existing handling robot, is mostly only suitable for flat indoor environments and does not have the ability to work on outdoor rugged terrain, greatly limiting the development of chassis driven handling robots. On this basis, this paper innovatively designs a four-wheel-driving Ackerman chassis with strong vibration absorption and obstacle surmounting capabilities and conducts performance research and optimization on it through quantitative experiments and dynamic simulation. Firstly, based on the introduction of the working principle and structure of the four-wheel-driving Ackerman carrier chassis, a multi-sensor distributed dynamic performance test system is constructed through the analysis of the chassis performance evaluation index. Then, according to the quantitative operation experiment of the chassis, the vibration and acceleration characteristics of the chassis at different positions of the chassis, the amount of slip and straightness of the chassis under different running distance, and the operating characteristics of the chassis under different road conditions and different damping springs conditions were analyzed respectively, which verified the rationality of the chassis design. Finally, by constructing the chassis dynamics simulation model; the influence law of chassis structure; and performance parameters such as chassis wheelbase, guide rod structure, and parameters, wheel friction coefficient and assembly error on the dynamic characteristics of the chassis is studied, and the optimal structure of the four-wheel-driving Ackerman chassis is determined while it is verified based on the simulation results. The research shows that the four-wheel-driving Ackerman chassis has good vibration performance and stability and has strong adaptability to different roads. After optimization, the vibration performance, stability, amount of slip, and straightness of the chassis structure are significantly improved, and the straightness is reduced to 0.399%, which is suitable for precise carriage applications on the chassis. The research has important guiding significance for promoting the development and application of wheeled chassis.

Analysis of TBM tunneling performance based on mass fractal dimension of rock chips

The size of the rock chips and its particle size distribution can fully reflect the rock-breaking efficiency of the TBM, but the index that can comprehensively characterize the particle size distribution of rock chips has not yet been found to quantitatively evaluate the TBM tunneling efficiency. Based on the fractal theory, this study uses the mass fractal dimension to quantitatively analyze the particle size distribution of rock chips, and explores its relationship with TBM tunneling performance index. Field sieving tests were carried out on rock chips from two tunnel projects. According to the sieving test results, the mass fractal dimension of the rock chips is calculated as well as the correlation between it and common TBM performance evaluation indexes, average tunneling thrust is discussed. The research results show that the mass distribution of TBM rock chips has significant fractal and self-similar characteristics, the higher the content of large-size rock chips, the lower the content of small-size rock chips, the smaller the fractal dimension value. Simultaneously, under the same surrounding rock conditions, the mass fractal dimension of rock chips is positively correlated with the logarithm of specific energy and negatively correlated with the logarithm of coarseness index, which indicates that the smaller the fractal dimension, the higher the efficiency of rock breaking. It is demonstrated that the interval of TBM thrust force corresponding to smaller specific energy, larger coarseness index and smaller fractal dimension is the same, which can be determined as the optimal thrust interval.

Fuzzy Decision-Making and Resource Management Model of Performance Evaluation Indices

The Performance Evaluation Matrix (PEM) is an excellent decision-making tool for assessment and resource management. Satisfaction Index and Importance Index are two important evaluation indicators of construction and PEM. Managers can decide whether the service item needs to be improved based on the Satisfaction Index of the service item. When resources are limited, managers can determine the priority of improving the service item based on the Importance Index. In order to avoid the risk of misjudgment caused by sample errors and meet the needs of enterprises’ rapid decision-making, this study proposed a fuzzy test built on the confidence intervals of the above two key indicators to decide whether essential service items should be improved and determine the priority of improvement. Since the fuzzy test was relatively complex, this study further came up with fuzzy evaluation values and fuzzy evaluation critical values of service items following fuzzy testing rules. Besides, evaluation rules were established to facilitate industrial applications. This approach can be completed with any common word processing software, so it is relatively convenient in application and easy to manage. Finally, an application example was presented in this paper to explain the applicability of the proposed approach.

Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model

The integration of large-scale offshore wind power into the power grid presents significant challenges for grid operation and dispatch due to the variability and intermittency of offshore wind energy. However, non-stationarities and noise in wind speed time series pose challenges. This study proposes a VMD-PE-FCGRU methodology to address this challenge of predicting offshore wind speeds. Firstly, the Variational Mode Decomposition (VMD) algorithm decomposes the original wind speed signal, considering the strong fluctuations and high noise inherent to offshore wind energy. A corresponding performance evaluation index is established to assess the effectiveness of this deconstruction process. To further enhance the model's (GRU) ability to capture time series information, a Position Encoding layer (PE) has been added, and the network is deepened through a Fully Connected Neural Network (FCNN). This allows multi-dimensional time series features to be input into the Gated Recurrent Unit (GRU) network for forecast. The accuracy of the proposed method is then verified using measured historical data from the wind tower of an offshore wind farm in Guangdong. Experimental results demonstrate that the proposed method can accurately forecast short-term wind speeds for offshore wind farms, with a Mean Absolute Error (MAE) of 0.199 and a Mean Absolute Percentage Error (MAPE) of only 2.45%. Moreover, the qualified rate (r) reaches 100%, providing an accurate reference for real-time power grid dispatching and can inform decision-making for the integration of offshore wind power into the grid.

Theoretical Modeling and Experimental Verification of Elliptical Hyperbolic Hybrid Flexure Hinges

A flexure hinge composed of elliptical and hyperbolic hybrid configurations is developed and analyzed in this paper. The analytical models of compliance, rotation accuracy, and maximum stress of the flexure hinge are established, and the correctness of the models is validated by finite element analysis and experiments. The influence of structural parameters on compliance and rotation accuracy is discussed. The concept of compliance stress ratio is proposed to assess the deformation capacity of flexure hinges when subjected to the same stress, which provides a basis for quantitatively comparing the comprehensive performance of flexure hinges. The performance of the hybrid flexure hinge is compared with that of elliptical, hyperbolic, and circular flexure hinges by taking the compliance accuracy ratio and the compliance stress ratio as the performance evaluation indexes. The results show that the hybrid flexure hinge combines the advantages of hyperbolic and elliptical hinges and has a balanced performance in compliance, rotation accuracy, and low stress. The designed hybrid flexure hinge is suitable for the support structure of fast steering mirrors, which provides a valuable reference for the engineering optimization design of flexure hinges.

Research on Performance Evaluation Index System of Rural Highway PPP Project

In recent years, China has actively and steadily expanded the effective investment in rural transportation infrastructure, actively guided social capital to participate in rural development, promoted the combination of PPP model and all aspects of rural areas, and implemented a new round of rural road construction and transformation. Rural highway construction is an important measure to promote rural economic development. Because of the wide range of rural construction in China and the huge capital investment and demand, it is necessary to build a scientific and comprehensive evaluation index system to evaluate the performance of rural highway construction projects, analyze the evaluation results, and put forward feasible suggestions to help decision makers grasp the benefits and effects of construction, and take targeted measures to guide and promote the positive development of rural highways. Taking the northern extension project of XL highway as an example, this paper constructs a performance evaluation index system, which can provide reference for the project management of XL highway and other highway projects.

Screening of sustainable supply chain performance evaluation indicators based on the ill-conditioned index cycle method.

The establishment of an evaluation indicator system that can accurately assess the sustainability of a supply chain while further enhancing its performance is vital and relevant. Based on the connotation of sustainable supply chains and triple bottom line theory, indicators are initially proposed from economic, environmental, and social dimensions. To increase the explanatory power of the indicator system and decrease information redundancy, the coefficient of variation is applied to identify the indicators with weak interpretation intensity, the ill-conditioned index cycle method is utilized to filter out indicators with redundant information, and data on 100 Chinese listed companies from 2019 to 2021 are used as samples. A performance evaluation indicator system of sustainable supply chains with 16 indicators is ultimately established. The information interpretation strength index and cumulative information contribution rate verify the rationality of the final indicator system. The outcome demonstrates that this screening method can strengthen the representativeness of the indicator system and rapidly reduce redundancy, leading to the better discrimination of the evaluation results. The findings of this study provide an indicator system and a methodological reference for both companies and policymakers and can aid in the transformation of supply chains toward sustainability.

Oversampling method via adaptive double weights and Gaussian kernel function for the transformation of unbalanced data in risk assessment of cardiovascular disease

In risk assessment of cardiovascular disease (CVD), the classification error caused by unbalanced data is a significant challenge, which has sparked widespread concern and research upsurge in the field of data mining. Therefore, in view of the imbalance of CVD data sets, an oversampling method via adaptive double weights and Gaussian kernel function (ADWGKFO) is proposed, which converts the unbalanced data sets into balanced data sets. Firstly, clustering algorithm is utilized to cluster minority samples, boundary samples are identified by Borderline-Synthetic Minority Over-sampling Technique (Borderline-SMOTE), K nearest neighbor and support vector machine algorithms, and the number of samples synthesized in each group is calculated based on the double weights of boundary points and majority distribution. Secondly, in order to clearly define the classification boundary, the mutual class potential of new samples in each cluster is calculated by Gaussian kernel function, and new samples are filtered according to the mutual class potential until the data set is balanced. Finally, taking the data sets from Kaggle platform as the research samples, the proposed method is empirically analyzed. In order to validate the efficacy and universality of the proposed method, this paper selects CatBoost that is a new integrated algorithm to test the effect of the ADWGKFO method, and compares it with different sampling methods and different classifiers using performance evaluation indexes such as accuracy, F1-score and area under the curve (AUC). Compared with the combinations of other methods, the accuracy, F1-score and AUC are significantly improved. It is concluded that the ADWGKFO method described in this paper can successfully improve the data quality, and increases the reliability of CVD risk assessment.

Prediction and optimization of adverse responses for a highway tunnel after blasting excavation using a novel hybrid multi-objective intelligent model

It is one of the main challenges of modern traffic engineering to monitor and control the adverse response of highway tunnels after blasting excavation. To that end, this paper proposes a novel hybrid multi-objective intelligent model to predict and optimize four adverse responses including tunnel roof settlement (TRS), linear overbreak (LO), underbreak area (UA), and the maximum block size (MBS) based on a case highway tunnel. This model is assembled by the support vector regression (SVR) and a multi-objective golden jackal optimization (MOGJO) algorithm. 95 monitored data after tunnel blasting containing 11 features of four categories are used to train and test the models. Other five classical intelligence models are also developed to compare the predictive performance of the proposed model. The results of model development indicate that the proposed model has the optimal predictive performance among all models when the ratio of training set to test set is 8: 2. The calculation results of performance evaluation indices (determination coefficient (R2) and root mean square error (RMSE)) demonstrate that the proposed MOGJO-SVR model is the best model for predicting four tunnel adverse responses among all models, i.e., TRS (R2: 0.9837; RMSE: 0.5591), LO (R2: 0.9711; RMSE: 4.5022), UA (R2: 0.9800; RMSE: 0.3803), and MBS (R2: 0.9535; RMSE: 5.9248). Besides, the model interpretation results based on the Shapley additive explanations (SHAP) method show that the contribution of blasting and explosive parameters to the adverse responses is significant. Then, a visualization procedure is generated to optimize the adverse responses for different tunnel excavation designs in real time. This work guides the prediction and control of adverse responses in highway tunnel construction.

Performance study of organic photovoltaic/thermal system with synergistic effect of photocatalytic and thermal catalytic technology

Organic photovoltaic cells are considered to be an effective solution for maintaining excellent electrical energy output in high temperature environment. However, the application of OPV alone has a low utilization rate of the solar spectrum. The combined application of OPV and other technologies is explored to improve the utilization efficiency of the solar spectrum. Based on the above gaps, this study proposes an organic photovoltaic/thermal system with synergistic effect of photocatalytic and thermal catalytic (PC/TC-OPV/T). By integrating organic photovoltaic cells, photocatalytic technology, thermal catalysis technology and photothermal components, the utilization efficiency of the solar spectrum is improved, and the triple functions of formaldehyde removal, electrical energy and hot water are realized. Therefore, this study proposes a new air purification-thermal-electrical model, establishes the heat transfer balance equation inside the system and the air purification and mass transfer balance equation in the air duct, proposes the performance evaluation index of the system, and uses Ansys Fluent software to predict the working characteristics of the system under different operating conditions, and draw the following conclusions: (1) Under the synergistic action of photocatalytic and thermal catalytic technologies, the formaldehyde removal efficiency is 84.4%, which is 30% higher than that of photocatalytic alone. (2) The electrical efficiency of the new system benefits from high solar irradiance and environment temperature, enabling it to achieve the efficiency of 17.84% within the research scope. (3) Under the research conditions, the instantaneous water flow heating efficiency of the system is up to 69.6%.

Evaluating the energy-exergy-economy-environment performance of the biomass-photovoltaic-hydrogen integrated energy system based on hybrid multi-criterion decision-making model

Integrating biomass, photovoltaic, and other renewable energy sources for hydrogen production can form a biomass-photovoltaic-hydrogen integrated energy system (BPH-IES). The system features multi-energy storage and joint supply, as well as cascaded utilization, is a promising co-generation way to meet the system's electricity, heat and hydrogen needs, and has significant energy-exergy-economy-environment (4E) performance. Therefore, scientific and effective evaluation of the BPH-IES is an important prerequisite for promoting its development. Firstly, by analyzing the structure of the BPH-IES system, this paper constructs a comprehensive performance evaluation index system of the system from the 4E dimensions. Secondly, a hybrid multi-criterion decision making (MCDM) framework for the 4E comprehensive performance evaluation of the BPH-IES is constructed, in which the anti-entropy weighting method is used to determine the objective weights, the subjective weights are determined by the decision making trial and evaluation laboratory method improved by intuitionistic fuzzy sets, and the final combined weighting results are obtained based on a game-theoretic method that the objective and subjective weights' heterogeneity can be fully minimized. The grey-relation-analysis-based measurement alternatives and ranking according to the compromise solution method is proposed to determine the ranking of alternatives. This paper selected 8 typical BPH-IES cases for 4E evaluation analysis. The results showed that energy performance is the core dimension that reflects the 4E performance of the system, and system's economy indicators such as energy supply income and energy supply cost will also significantly affect the system's 4E performance. Therefore, the 4E performance of BPH-IES can be effectively improved by expanding the system's demand and supply scale and increasing the system's installed proportion of clean energy and hydrogen penetration. Finally, the ranking consistency test, Leave-One-Out analysis and sample separation test demonstrate that the developed hybrid MCDM can ensure robustness meanwhile improve the decision efficiency.

Dynamic Performance Optimization of Monorail Rapid Transit Formation Vehicles Based on Improved Artificial Potential Field Algorithm

This paper presents a new type of monorail rapid transit vehicle, which can operate in formation. In order to optimize the dynamic performance of the formation vehic-les when passing through the small radius curve, this paper first analyzes the structure of the new monorail rapid transit vehicle, and establishes the vehicle dynamic model and multi-body dynamics simulation model. Then the improved artificial potential field algorithm is used to build the formation vehicle operation controller. Finally, the dynamic performance evaluation index of the new mono-rail rapid transit formation vehicle is formulated, and the formation vehicle passing through the small radius curve scene is simulated. The simulation results show that the controller of the formation vehicle with dynamic optimi-zation control can effectively optimize the load transfer coefficient of running wheels, unbalanced centrifugal acce-leration and roll angle of vehicle body when passing thro-ugh the curve. Through this control method, the dynamic performance of formation vehicles passing through small radius curve lines can be optimized.

Numerical Study on Effects of Flow Channel Length on Solid Oxide Fuel Cell-Integrated System Performances

The structural dimensions of the SOFC have an important influence on the solid oxide fuel cell (SOFC)-integrated system performance. The paper focuses on analyzing the effect of the flow channel length on the integrated system. The system model includes a 3-D SOFC model, established using COMSOL 6.1, and a 1-D model of the SOFC-integrated system established, using Aspen Plus V11. This analysis was conducted within an operating voltage range from 0.4 V to 0.9 V and flow channel length range from 6 cm to 18 cm for the SOFC-integrated system model. Performance evaluation indicators for integrated systems are conducted, focusing on three aspects: net electrical power, net electrical efficiency, and thermoelectric efficiency. The purpose of the paper is to explore the optimal flow channel length of SOFC in the integrated system. The results indicate that there is inevitably an optimal length in the integrated system at which both the net electrical power and net electrical efficiency reach their maximum values. When considering the heat recycling in the system, the integrated system with a flow channel length of 16 cm achieves the highest thermoelectric efficiency of 65.68% at 0.7 V. Therefore, there is a flow channel length that allows the system to achieve the highest thermoelectric efficiency. This study provides optimization ideas for the production and manufacturing of SOFCs from the perspective of practical engineering applications.

Radiation effects on photo diode modeling in space environment for laser inter-satellite links

In this paper the effect of space radiation environment on a silicon photo diode is studied. This detector is considered as a part of the optical receiver chain of the satellite and exposed to the radiation and vacuum environment of the space. The hardness of this detector during its confrontation to space environment is an important index for link performance evaluation. This effect is analyzed and simulated with Geant4, and then the simulation results are verified by irradiating protons to the PIN diode detectors.

Semantic Segmentation of Surface Cracks in Urban Comprehensive Pipe Galleries Based on Global Attention

Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall segmentation and have a large perceptual range. However, for urban underground comprehensive pipe gallery crack segmentation tasks, it is difficult to pay attention to the detailed features of local edges to obtain accurate segmentation results. A Global Attention Segmentation Network (GA-SegNet) is proposed in this paper. The GA-SegNet is designed to perform semantic segmentation by incorporating global attention mechanisms. In order to perform precise pixel classification in the image, a residual separable convolution attention model is employed in an encoder to extract features at multiple scales. A global attention upsample model (GAM) is utilized in a decoder to enhance the connection between shallow-level features and deep abstract features, which could increase the attention of the network towards small cracks. By employing a balanced loss function, the contribution of crack pixels is increased while reducing the focus on background pixels in the overall loss. This approach aims to improve the segmentation accuracy of cracks. The comparative experimental results with other classic models show that the GA SegNet model proposed in this study has better segmentation performance and multiple evaluation indicators, and has advantages in segmentation accuracy and efficiency.

Design and Development of Intelligent Learning System for University Innovation and Entrepreneurship Based on Knowledge Visualisation

To build a comprehensive learning resource system covering learning process, learning users, learning resources and other aspects, an intelligent learning system for innovation and entrepreneurship in universities was constructed and designed combined with knowledge visualisation theory. The system includes student information record module, system recommendation calculation module, learning material module, and knowledge visualisation module. The research focuses on the system recommendation calculation module, including the User-based Collaborative Filtering Algorithm (UCF) and the Joint Recommendation (JR) algorithm of the application content recommendation algorithm. The best neighbour number of JR is 20, and the best transmission path length is 2. The recommendation effect is optimal under this setting. The convergence iterations of the training and test set of the joint recommendation algorithm are 80 times and 100 times, respectively. For the training set and test set, the accuracy, precision, sensitivity, recall, running time, error, and other performance evaluation indicators of the joint recommendation algorithm are better than the corresponding values of other recommendation algorithms. The system has a good design effect and experience effect in the application of the online learning platform.