Multiple Evaluation Indexes Research Articles

Constructing and applying a neural-network-based architectural landscape evaluation model

With the continuous improvement of living standards, people go outdoors and spend more and more time in scenic spots. The landscape architecture design that serves people in urban scenic spots attracts more and more public attention, which puts forward higher requirements for landscape architecture design that serves people in scenic spots. How to integrate better the design of all kinds of landscape architecture into nature, to serve the public better, is an urgent problem to be solved at this stage. This paper selects the evaluation indexes of urban architectural landscapes, uses the analytic hierarchy process to determine the weight of each index and quantifies six evaluation indexes to build an evaluation model of architectural landscape design. In terms of the improvement of the you only look once version 4 (YOLOv4) model, MobileNetV3 was selected as the backbone feature extraction network, and the convolution in the feature enhancement extraction network was replaced by the depth-separable volume, and an architectural landscape recognition system based on the improved YOLOv4 model was constructed. In terms of algorithm performance verification, the improved algorithm was compared with the single-shot detector, MobileNetV3, ShuffleNetV2, YOLOv3, YOLOv4 and YOLOv5s algorithms under multiple evaluation indexes. The experimental results show that the size of the model is 51.4 MB, which does not cause a large burden. The mean average precision value of the improved YOLOv4 algorithm is 93.5%, and the frames per second (FPS) value is 30 FPS, which has higher recognition accuracy and detection speed and has obvious advantages.

Enhancing Autonomy Capability in Regional Power Grids: A Strategic Planning Approach with Multiple Autonomous Evaluation Indexes

Enhancing Autonomy Capability in Regional Power Grids: A Strategic Planning Approach with Multiple Autonomous Evaluation Indexes

Channel structure design and optimization for immersion cooling system of lithium-ion batteries

The phenomenon of heat accumulation during the discharge process of lithium-ion batteries (LIBs) significantly impacts their performance, lifespan, and safety. A well-designed cooling architecture is a critical issue for solving the heat accumulation problem of the battery immersion cooling system (BICS). In this study, four cooling channel design schemes (CC-1, CC-2, CC-3, and CC-4) for the BICS were developed. The effect of various cooling channel structures on the cooling performance of BICS has been studied. Comparative analysis reveals that the CC-1 structure demonstrates the most outstanding overall performance. It effectively manages battery pack temperatures while maintaining temperature uniformity, and its pressure drop remains comparable to other designs. Applying orthogonal analysis, the CC-1 structure was further optimized. Subsequently, a linear weighting method was employed to analyze multiple evaluation indexes comprehensively. The following optimal structural parameters were obtained: battery spacing (S) of 4 mm, baffle height (H) of 42 mm, distance from the inlet to the channel bottom (L) of 14 mm, and a coolant mass flow rate (Q) of 0.04 kg/s. Compared to before optimization, the battery pack's maximum temperature and temperature difference have been diminished by 17.9 % and 20.8 %.

Design of an Integral Fuzzy Logic Controller for a Variable-Speed Wind Turbine Model

The demand for electricity is continuously growing around the world and thus the need for renewable and long-lasting sources of energy has become an essential challenge. Wind turbines are considered one of the major sources of renewable electricity generation. Therefore, there is a crucial demand for wind turbine model and control systems that are capable of precisely simulating the actual wind power systems. In this paper, an advanced fuzzy logic controller is proposed to control the speed of a wind turbine system. Initially, aero dynamical, mechanical and electrical models of two mass wind turbines models are derived. Analytical calculation of the power coefficient is adopted through a nonlinear function of six coefficients that mainly depends on pitch angle and tip speed ratio. The ultimate power output from the turbine can reach up to 50 % which is achieved at zero pitch angle with an approximately tip speed ratio of eight. This is then followed by designing a hybrid fuzzy-plus I pitch controller to regulate the speed of the wind turbine shaft. In general, fuzzy logic control strategy have the advantages over traditional control techniques especially when the system is highly non-linear and has to deal with strong disturbances such as wind turbulence. To evaluate the reliability and robustness of the controller, the response of the wind turbine system is tested under several types of disturbances including wind fluctuation, sudden disturbances on high and low speed shafts. Simulation findings reveals that the performance of fuzzy-integral control technique outweighs that of conventional fuzzy approach in terms of multiple performance evaluation indexes such as zero overshoot and steady state error, rise time and a settling time of (32.9 s) (44.7 s) respectively. The reliability and robustness of the controller is tested by applying speed and torque disturbances of 25% of their maximum ranges. Results have revealed that the controller was able to reject all disturbances efficiently with a change in pitch angle up to a maximum of 10 degrees in order to retain a constant rotor speed at 1000 rpm.

Research on optimal configuration of park-level multi-energy complementary system with multiple evaluation indexes

At present, energy shortages are becoming increasingly severe, and the concept of park level multi energy complementary systems (MECS) has provided direction for sustainable energy development. In recent years, how to improve the economy and reliability of multi energy complementary systems has become a research hotspot in this field. In this paper, a two-layer optimal scheduling strategy is proposed to allocate the capacity of various energy equipment in the park, considering the comprehensive energy self-sufficiency rate, comprehensive energy utilization rate and energy shortage expectation. The proposed capacity allocation scheme can effectively improve the economy of MECS in the park. Finally, the effectiveness and practicability of the algorithm are verified by simulation analysis.

Optimization of Automatic Well Pattern Deployment in High Water-Cut Oilfield

Abstract In view of the problems such as a plurality of dominant water flow channels formed by flushing the reservoir and inferior development effect in the water injection oilfields, reconstructing the current well pattern and providing well pattern evaluation methods are important ways to enhance oil recovery by improving the injection–production relation and increasing the swept area of water flooding. However, the reservoir engineering methods, the simulation methods, and the artificial intelligence algorithms with few objectives enable comprehensive evaluation of the well pattern. In this article, considering multiple evaluation indexes in oilfield development by the glowworm swarm optimization algorithm and niche technology, automatic well pattern optimization is carried out. The glowworm swarm optimization algorithm has the advantage of efficient global search and simpler algorithm flow, which can speed up the convergence and reduce the parameter adjustment. The niche technology can better maintain the diversity of the solutions and solve the multimodal optimization problems more efficiently, accurately, and reliably. The new method was used to optimize the well pattern of one block in a water-flooding oilfield with high water-cut in a certain oilfield. The optimal well pattern is obtained by multiple iterations to maximize the control degree of the well pattern to the sand body. The results indicate that the injection production correspondence ratio and the reserves control degree of the well pattern to the sand body are improved by 4.48% and 7.94%, respectively.

Multi-Scale Orthogonal Model CNN–Transformer for Medical Image Segmentation

Because of the limitations of convolution kernel, the traditional image segmentation network is not sufficient to obtain the context information, but the image segmentation task is very dependent on the context information. Transformer’s linear input can just get enough context information. In this paper, we propose a transformer segmentation network hyperfusion transformer based on a pyramid structure. First, the model divides the single-scale coding form into several-different-scale coding forms, and then fuses the decoding results. Second, in order to ensure the specificity of the output characteristics of each branch, we orthogonalize the results of a variety of different scales. By orthogonalizing in pairs, we can ensure that the results obtained by different branches are not completely similar to a certain extent, and reduce the redundancy of branch information. On the two datasets, the method in this paper surpasses a variety of classical models under multiple evaluation indexes, confirming that it is an effective segmentation method.

STUDIES ON THE PERFORMANCE OF MULTIVOLTINE BREEDS OF SILKWORM, BOMBYX MORI L.

With the objective of identifying potential parental breeds for breeding, a total of 81 multivoltine silkworm germplasm stocks conserved at Central Sericultural Germplasm Resources Centre, Hosur were evaluated in three replications by rearing in five crops during different seasons of the year. Data on the performance was collected and assessed for 12 quantitative traits of economic importance by employing multiple trait evaluation index method. Significant variations among the multivoltine breeds were observed and based on higher Evaluation Index values for economic parameters, the better performing breeds viz., BMI-023, BMI-080 and BMI-081 with higher values in more traits have been identified as promising breeds. The results of the study lay emphasis on utilization of these breeds expressing higher E. I. values in more number of quantitative traits for further breeding and large scale trials. 

Bi-objective optimization of post-combustion CO2 capture using methyldiethanolamine

Process simulation and analyzes based on multiple evaluation indexes are crucial for accelerating the practical use of the post-combustion CO2 capture process. This study presents a bi-objective optimization of the post-combustion CO2 absorption process using methyldiethanolamine (MDEA) via machine-learning and genetic algorithm to evaluate CO2 emissions from the absorption process using life cycle assessment and cost from operating and capital expenditures. An initial dataset was generated by changing eight design variables, and machine-learning models were built using random forest classifier and Gaussian process regression. Pareto solutions were predicted using a genetic algorithm (NSGA-II) with the constraints of purity, recovery, and temperature, and were verified via process simulation. Verified data were added to the dataset, and model building, prediction, and verification were repeated. Eventually, 56 Pareto solutions were obtained after 11 iterations. In the final Pareto solutions, CO2 emissions increased from 0.56 to 0.6 t-CO2/t-CO2 with a decrease in cost from 74 to 66 USD/t-CO2. The trends and composition of each objective variable were examined, and the optimal structure of the equipment and operation conditions was clarified. The approach of bi-objective optimization in this study is promising for evaluating the CO2 capture process and individual processes of carbon capture, utilization, and storage.

A Modified FMEA Approach to Predict Job Shop Disturbance

Failure modes and effects analysis (FMEA) is a systematic approach that focuses on evaluating critical disturbances in a system. However, traditional FMEA has its own drawbacks, such as invalid computations and ambiguous priority definitions, which lead to many constraints in the application of complex production processes, especially in job shops with various resources. Therefore, this paper proposes an analytic disturbance prediction method for job shop with multiple resources and multiple evaluation indexes, which combines the vector computing techniques, FMEA, and fuzzy analytic hierarchy process (FAHP). In contrast to other work, this paper focuses on the establishment of FMEA mathematical model to improve the readability of multi-resource disturbance risk results. To this end, the projection of the disturbance vector is visualized to reduce repeated calculation results, triangles and trapezoids are used as membership functions to improve the accuracy of weight, and the differentiation index is used to reduce the ambiguity of priorities. The proposed method can effectively discover the critical disturbances and enable managers to undertake more assertive decisions.

Gaussian cloud and TOPSIS based power supply quality evaluation method

The accurate evaluation of power supply quality in the distribution network is regarded as an important way to improve the power quality of the user side and guide the power quality management of the power grid. Thus, a comprehensive evaluation method of the power supply quality based on the Gaussian cloud (GC) and the technique for order preference by similarity to ideal solution (TOPSIS) is proposed in this work. First, multiple evaluation indexes of the power supply quality are customized based on the measurement data of the distribution network and the power supply work order. On this basis, an index conversion method based on Gaussian cloud is adopted to convert the above indexes into Gaussian cloud distribution. Then, a comprehensive evaluation method of power supply quality based on TOPSIS is proposed for converting the similarity between Gaussian clouds into comprehensive evaluation results. Case study on power supply quality in Zhejiang province, China is employed to demonstrate the effectiveness and rationality of the proposed method. The result shows that the power supply quality can be evaluated by proposed method in an objective and reasonable manner.

State of charge estimation for lithium-ion batteries based on improved barnacle mating optimizer and support vector machine

Precise state of charge (SOC) estimation is essential for battery management systems. The improved barnacle mating optimizer-support vector machine (IBMO-SVM) model is proposed and used for SOC estimation of lithium-ion batteries. (1) The cubic chaotic mapping, hyperbolic sinusoidal conditioning factor, and Gauss-Cosey variation are introduced to improve the barnacle mating optimizer (BMO) to obtain the improved barnacle mating optimizer (IBMO); (2) The convergence performance of IBMO is verified by comparing with other five intelligent optimization algorithms under several test functions; (3) IBMO-SVM is created by using IBMO to optimize the search for support vector machine (SVM) parameters; and (4) IBMO-SVM is used for SOC estimation of lithium-ion batteries and the estimation results are analyzed by multiple evaluation indexes. The proposed model's root mean squared error (RMSE) and mean absolute percentage error (MAPE) are 0.0042 and 0.61 %, and its R-square (R2) is 0.9994, outperforming the four comparison models. The SOC estimation methodology proposed in this study is highly accurate and reliable, and it provides advantages for improving the battery management system.

Mathematical Methodology in the Seismic Resilience Evaluation of the Water Supply System

Abstract This paper has collected more than one hundred papers about resilience at home and abroad, summarised and analysed the meaning and evaluation methods of resilience and then summarised the key issues. Through research on these papers, we found that the current evaluation method for the water supply system seismic resilience is divided into two aspects: on the one hand, it’s based on the seismic performance of water supply system, where the system itself evaluates the resilience absorbs the earthquake resistance level. On the other hand, the assessment is based on the post-earthquake recovery ability of the water supply system, namely the recovery time, recovery degree, recovery path and the resilience level of resources input. Based on past seismic safety of the water supply system and research on post-earthquake recovery ability, the meaning of water supply system in seismic resilience should include two aspects of seismic safety and post-earthquake recovery ability. From using the seismic safety evaluation method also and post-earthquake recovery ability in two angles, this paper gives a comprehensive multiple evaluation index framework of the evaluation model and points out that water system earthquake state resilience evaluative criteria and the requirements of the current seismic code should match.

A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines

Accurate prediction of remaining useful life (RUL) is necessary to ensure stable and safe operations for rocket engines. The paper proposed a multi-head attention network coupled with adaptive meta-transfer learning for RUL prediction. By combining the convolution-based branch with an attention-based branch, the multi-head attention network is proposed for accurate RUL prediction of cryogenic bearings in rocket engines under the steady stage. In addition, an adaptive model-agnostic meta-transfer learning strategy is developed to further improve the performance under small sample circumstances with adaptive hyper-parameters. To demonstrate the superiority, the proposed method is compared with typical benchmark algorithms using real monitoring data from a high-precision cryogenic rocket engine experiment platform. Results indicate that the proposed method achieves better performance compared with existing models under multiple evaluation indexes.

Study on the Characteristics of Surrounding Rock and Design of Backfill Material Parameters for Tunnels Passing through Giant Caverns and Underground Rivers

The influence on tunnel construction of karst and underground rivers is always an important problem in tunnel engineering. In order to demonstrate the rationality of backfill parameters and the effectiveness of supports under the influence of groundwater when a tunnel passes through a large karst cave, the finite element software FLAC3D was used for numerical analysis. Converting the mean values and standard deviations of mechanical response results of the surrounding rock and the supports on multiple sections into the ECULID distance from the origin point on a two-dimensional plane as evaluation indexes, the influence of the reinforcement parameters on the mechanical response of the surrounding rock and the supports was analyzed by orthogonal experiments. Based on fuzzy decision theory, by regarding the ECULID distance between the simulated result of each group and the global optimal value of the multiple evaluation index as the comprehensive evaluation index, a backfill parameter design method was proposed. By comparing the results which used optimal and worst parameters in the FLAC3D, 10 times and 2.5 times differences in longitudinal and horizontal displacement were observed, respectively. Then, the optimal backfill parameters were applied to the actual project for verification. The field monitoring results showed that the optimal backfill parameters can effectively reduce the displacement around the tunnel. After constructing a diversion for the underground river, the water flow in the karst cave did not rise during a rainstorm. This study provides a reference for the design and construction of other projects in the future.

Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy

As the physical subject, determining vehicle operating modes is a prerequisite for implementing global optimization energy management. To avoid the case study of different vehicle configurations, a “kinetic/potential energy & onboard energy” conservation framework is proposed to determine vehicle working modes. Firstly, typical topologies and existing work modes for hybrid vehicles with different architectures are summarized. As a numerical method, the state space is meshed, which is restricted by introducing trip information. Then, a “kinetic/potential energy & onboard energy” conservation framework is established to determine the work mode between any reachable state points. By combining external factors, internal factors and additional factors reasonably and feasibly, various trigger conditions are generated to realize the one-to-one mapping between work mode and driving condition, which standardizes the DP optimizing process. Correspondingly, the stage cost and control are determined to achieve the optimal energy distribution. Finally, regarding DP strategy as a benchmark, multiple evaluation indexes are proposed to evaluate the utilization ratio of a control strategy to global trip information. An example is given to evaluate the optimal rule-based strategy. The higher the index is, the higher the similarity with the DP strategy is, and the higher the economic performance of the vehicle is.

Make the Rocket Intelligent at IoT Edge: Stepwise GAN for Anomaly Detection of LRE With Multisource Fusion

Anomaly detection (AD) for liquid rocket engine (LRE) is essential to improve the reliability and safety of space launch missions. However, it is difficult for existing methods to implement fast and precise detection with single-source information and absent anomalous samples. The Internet of Things (IoT) enables intelligent LRE AD via edge computing using multisource data. This article proposed an edge-based detection algorithm named stepwise generative adversarial network (StepGAN) for AD of LRE with multisource fusion at the Edge of IoT. StepGAN incorporates deep autoencoder and relativistic generative adversarial network. Through multistage stepwise training with unlabeled normal data, an encoder-generator-discriminator network is obtained to identify LRE anomalies at the edge. In addition, by fusing multisource information at feature level and aggregating neighboring information at decision level during real-time detection, the performance of StepGAN is further improved. To demonstrate its ability for IoT edge-based AD, the proposed method is compared with typical methods, using real static ignition data of LRE as cloud and edge data, and achieves the best result under multiple evaluation indexes. To evaluate the proposed fusing approach, a channel-varying case is carried out and extensive experimental results indicate its effectiveness.

Intelligent Frequency Control Strategy Based on Reinforcement Learning of Multi-Objective Collaborative Reward Function

Large scale wind power integration into the power grid will pose a serious threat to the frequency control of power system. If only Control Performance Standard (CPS) index is used as the evaluation standard of frequency quality, it will easily lead to short-term centralized frequency crossing, which will affect the effect of intelligent Automatic Generation Control (AGC) on frequency quality. In order to solve this problem, a multi-objective collaborative reward function is constructed by introducing a collaborative evaluation mechanism with multiple evaluation indexes. In addition, Negotiated W-Learning strategy is proposed to globally optimize the solution of the objective function from multi dimensions, it avoids the poor learning efficiency of the traditional Greedy strategy. The AGC control model simulation of standard two area interconnected power grid shows that the proposed intelligent strategy can effectively improve the frequency control performance and improve the frequency quality of the system in the whole-time scale.

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

The strong volatility and randomness of wind power impact the grid and reduce the voltage quality of the grid when wind power is connected to the grid in large scale. The power sector takes the wind abandonment measures to ensure the grid voltage stability. The enhanced crow search algorithm optimization-extreme learning machine (ENCSA-ELM) model is proposed to accurately forecast short-term wind power to improve the utilization efficiency of clean energy. (1) The enhanced crow search algorithm (ENCSA) is proposed and applied to short-term wind power forecast. The convergence performance test revealed that the local development and global exploration capabilities of the ENCSA were enhanced, and the test result of the proposed ENCSA algorithm outperformed other well-known nature inspired algorithms and state-of-the-art CSA variants; (2) The output and input of the forecasting models were determined by analysis of the wind power impact factors and the wind power samples in autumn, winter and spring were forecasted by the ENCSA-ELM model; and (3) The forecast results were analyzed by multiple evaluation indexes. The simulation experiments revealed that the error interval and evaluation indexes of the ENCSA-ELM model outperformed the state-of-the-art wind power forecast methods, traditional machine learning models and ELM optimized by other algorithms. The RMSE value and MAPE value of the proposed model were controlled below 20% and 4%. Accurate wind power prediction maintains the voltage stability of power grid and increases the utilization efficiency of clean energy.

Development a multicellular model to investigate the intestinal-vascular transport barrier of drug

Multicellular transport models for drug have received more attention and research due to a closer physiological structure. A Caco-2/EA.hy926 cell co-culture model was constructed to simulate intestinal-vascular barrier. The morphology of microvilli-like and desmosomes structure indicated the tight connection of Caco-2 cells. The apparent permeability coefficient value of phenol red (≤1 × 10−6 cm/s), trans-epithellal electric resistance value (≥300 Ω cm2) and the alkaline phosphatase activity ratio of AP to BL side presented a confluent and integral cell monolayer with well-established differentiation. The microscopic images and vascular endothelial cadherin expression demonstrated the adhesion junction between EA.hy926 cells. The constructed multicellular model differentiated drug transport behavior. The transport potential of EA.hy926 and Caco-2 cells was similar for daidzein and different for daidzin. Combined with multiple evaluation indexes, it could track the multicellular transport process of nanoparticles. The new multicellular model exhibited more in vivo-like characteristics and architecture from intestine to blood, and a potential improvement of in vitro absorption and transport models for drug.