Linear Weighted Method Research Articles

A Practical Study of Computer-Assisted College English Intelligent Teaching Course Based on OBE Theory

Abstract The development of wisdom teaching has brought great impact and examination to the traditional English education mode. Based on the educational platform of Rain Classroom, this paper constructs a university English wisdom teaching classroom, designs the teaching activities of wisdom teaching classroom, and constructs a diversified evaluation index system for wisdom teaching mode based on the OBE concept. The objective weights obtained by the entropy weight method, CRITIC method and coefficient of variation method are combined by linear weighting method to solve the comprehensive weights of each evaluation index. In order to verify the effectiveness of the relevant methods in this paper, the verification and analysis of the data were carried out with the actual situation of the students in the School of Foreign Languages of Zhuhai Institute of Science and Technology. The results show that the highest weights of teachers’ classroom teaching organization and students’ classroom communication and cooperation under the wisdom teaching mode are 0.3761 and 0.3649, respectively, and the correlation of each evaluation index exceeds 0.5, showing a strong correlation. The fuzzy comprehensive evaluation score of university English teaching quality in the School of Foreign Languages of Zhuhai College of Science and Technology is 75.975, indicating that the overall teaching quality of university English smart classrooms is good to excellent. The English smart teaching course helps cultivate students’ independent learning abilities and innovative thinking and promotes the deep integration of language proficiency and intercultural communication skills.

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 %.

Integration of Rural Revitalization Strategy and Modernized Agricultural Governance Based on Intelligent Big Data Analysis

Abstract This paper explores the integration mechanism of rural revitalization strategy and agricultural modernization governance. The relationship between the two is complementary and closely linked, and rural revitalization can bring new impetus to agricultural modernization. The logistic development model is proposed to carry out a stage-by-stage study on the integration development of agricultural modernization governance and rural revitalization, and the subsystem orderliness and composite system synergy models are established by using the geometric mean method and the linear weighting method, respectively, and the degree of integration development is judged by measuring the integration status. The model constructed in this paper, as well as the governance level of rural revitalization strategies and agricultural modernization in place B, are measured through empirical research. The experiment shows that the two variables in this paper and their logarithmic means are 100, and the means are 0.3436, 0.3463, -1.3217, and -0.9874, respectively. In the stability test, the number of scatters in the region is 92, and the truthfulness and predictive intervals are in [-1,1], which makes the model stability and credibility good. After the implementation of the rural revitalization strategy for 11 years, the level of the comprehensive development index of agricultural modernization in the rural areas of place B in 2020 is 0.3189, 0.2473, 0.2434, 0.2645, respectively, which is in the stage of steady growth.

Multi-objective optimal sizing for grid-connected LVDC system with consideration of demand response of electric vehicles

Grid-connected low-voltage direct current (LVDC) systems offer a promising solution for achieving low-carbon buildings. However, a new challenge arises in sizing grid-connected LVDC systems, which is to fully utilize distributed renewable energy, energy storage, and load demand response to minimize both the economy and carbon emissions. To address this gap, this paper studies the sizing of grid-connected LVDC systems, considering the grid-connected converter, photovoltaic (PV), battery, and demand response of electric vehicles (EVs). Firstly, two objective functions are proposed, including the economy and carbon emissions. Secondly, constraints for sizing and operation are presented, with particular consideration given to the discharging of EVs and the corresponding cost of cycle-life loss. Finally, a minimum fuzzy carbon emission method (MFCEM) is proposed to solve the model, which can adaptively and objectively transform the multi-objective into a single objective. According to the case study, the proposed model comprehensively considers the cost and carbon emissions of grid-connected LVDC systems, and its feasibility and validity are verified. Compared with the linear weighting method, MFCEM is a useful method for finding a point on the Pareto front that balances cost and carbon emissions.

Application of cosmic‐ray neutron probes for measuring soil moisture in rocky areas of the Taihang Mountains, North China

AbstractThe cosmic‐ray neutron probe (CRNP) is a mesoscale and noninvasive method for measuring soil moisture and has been widely studied and applied. However, studies of its applicability in rocky mountainous areas are still challenging in complex topography and high gravel content. In this study, a field experiment was carried out to assess the applicability of the CRNP for measuring soil moisture in rocky areas of Taihang Mountains of North China. The results showed that the Pearson correlation coefficient and the root mean square error between the soil moisture from CRNP and the drying method are 0.911 and 0.025 m3 m−3, respectively, indicating that the CRNP can estimate the average soil moisture well in the study area. Compared with the capacitive sensor, the CRNP overestimated soil moisture when small rainfall events occurred, which was caused by the interception of canopy and litter. The nonlinear weighting method performed better than the linear weighting method in representing average soil moisture within the CRNP footprint. The high gravel content that contained high lattice water content reduced the penetration depth of CRNP. Biomass reduces the accuracy of the CRNP by affecting the neutron intensity. In summary, CRNP can measure soil moisture accurately in rocky areas of the Taihang Mountains, especially in dry environments with low biomass.

The Meal Delivery Routing Problem in E-commerce Platforms under the Shared Logistics Mode

The meal delivery routing problem faced by the e-commerce platform is challenging. In order to reduce the logistics cost and meet the needs of customers, a multi-objective meal delivery routing problem is studied under the shared logistics services mode (SLSM). In the meal delivery problem, the third-party logistics providers need to arrange vehicles in the multi-depot to pick up meals from multiple food providers and deliver them to customers, so as to realize the sharing of logistics services between food providers and compare them with the traditional logistics service mode (TLSM). While realizing sharing, it is also necessary to meet customer time requirements as much as possible. In this case, a multi-objective mathematical model to minimize customer dissatisfaction and delivery cost is established, and the linear weighting method is employed to transform the model. An improved Ant Lion Optimizer (IALO) is proposed to solve the problem, and compared with other algorithms. The experiments verified the effectiveness of the proposed algorithm and show that SLSM can save logistics costs and meet customer needs more than TLSM, and provide certain reference opinions for the e-commerce platform.

Research on the path of high-quality development of tourism in Hainan based on international comparison

Abstract This paper analyzes the current situation of the development of tourism in Hainan and provides a new reference for the high-quality development of Hainan tourism. Firstly, the evaluation model and evaluation indexes of Hainan tourism high quality development based on a comprehensive evaluation method are constructed, the comprehensive index method is used to dimensionless process the data, and the entropy value method is used to conduct a comprehensive analysis of the quantitative relationship of selected indexes and the degree of variation to determine the index weights. Then the multi-objective linear weighting method is used to calculate the comprehensive level of Hainan tourism quality development. Then the regional difference characteristics of Hainan tourism quality development and the trend of tourism development time sequence evolution were analyzed, and finally, the path of Hainan tourism quality development was analyzed from multiple factors. On the regional difference characteristics, the northern and southern regions of Hainan have obvious advantages in terms of quality tourism development, with a comprehensive development index of 0.498 and 0.482 respectively, while the central region is relatively backward, with a development index of 0.364. The dynamics of industrial development in Hainan are relatively low, with slow and unstable growth, rising only from 0.0048 in 2013 to 0.0059 in 2018 and also showing a small downward trend in 2015 and 2016. This study has important implications for the high-quality development of tourism in Hainan.

Eco-geological environment quality assessment in a mining city: a case study of Jiangxia District, Wuhan City

This study investigates the eco-geological environmental challenges faced by Jiangxia District, a mining region in Wuhan City. By combining field surveys, literature analysis and multi-source data such as remote sensing images and on-site monitoring data, an eco-geological environment quality (EGEQ) assessment system was developed. The system consists of six indicators representing biological abundance, vegetation cover, land erosion, water abundance, surface deformation and socio-economic characteristics of the assessed area. The grey relational analysis method was utilized to calculate the weights of each indicator, and the eco-geological environment quality index (EGQI) value was obtained through the linear weighted method. The assessment results indicated a moderate grade of overall eco-geological condition in Jiangxia District between 2017 and 2021, with an 8.56% increase in the EGQI during that period. This suggests a stable and favorable eco-geo logical environment situation in the district during the specified timeframe.

A dynamic load balancing algorithm for CFD–DEM simulation with CPU–GPU heterogeneous computing

Computational fluid dynamics combined with discrete element method (CFD–DEM) is widely adopted to study particle–fluid multiphase systems. However, due to the global dynamical evolution and the naturally non-uniform distribution of particles, the computational load in parallel computing varies dynamically in each process, leading to significant load imbalance. Thus, a dynamic load balancing (DLB) algorithm is proposed for parallel CFD–DEM simulations in the CPU–GPU heterogeneous architecture. A type of computing load analyzing grid (AG) is developed which covers the entire simulation region. Then Kalman filtering and linear weighted method is used to solve the weight of fluid grids and particles. The domain decomposition scheme is updated according to the weights of AGs dynamically. This DLB method is tested for gas–solid fluidized beds and periodic gas–solid systems of different scales. Results show that the DLB algorithm can significantly speed up CFD–DEM simulation and the highest performance is 2.28 times faster with DLB.

Assessment of the tradeoff between energy efficiency and transfer opportunities in an urban rail transit network

Urban rail transit (URT) in metropolitan areas consumes huge energy. Energy-efficient timetabling (EET) of URT is an essential measurement of URT management and technologies toward carbon neutralization initiatives. However, the majority EET studies focus on single URT lines ignoring passenger transfer and path choice in the entire URT network. As passenger path choice and timetabling are interdependent in a URT network, the ignorance of passenger transfers potentially results in irrelevant energy efficiency of a URT network. This paper proposes a bi-objective EET model incorporating the minimization of passenger transfer times as an objective in addition to energy efficiency. The timetabling objectives and constraints are linearized, and the bi-objective is transformed into a single objective by a linear weighting method. Utilizing the passenger demand and speed profile data of URT in the City of Xi’an (China), a case study is performed to demonstrate the effectiveness of the proposed EET model. The numerical results show that an optimized timetable solution can reduce 25.1% energy consumption and save 3.3% passenger transfer time.

Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning

In order to achieve accurate segmentation of each grape image per berry, we construct a dataset composed of red globe grape samples and use a two-stage “localization–segmentation” framework-based mask region convolutional neural network (Mask R-CNN) and one-stage “pixel classification without localization” framework-based You Only Look At CoefficienTs (YOLACT) and segmenting objects by locations (SOLO) models in the grape segmentation experiments. The optimal performance of the model Mask R-CNN was applied for further study. To address the problem of overlapping and occlusion causing inaccurate fruit detection in this model, the postprocessing algorithm of the Mask R-CNN model was improved by using the linear weighting method, and the experimental results were significantly improved. The model average precision (AP)0.50, AP0.75, the mean average precision (mAP), and the mean intersection of union (mIoU) improved by 1.98%, 2.72%, 4.30%, and 3.55%, respectively. The correlation coefficient was improved from 93.59% to 96.13% by using the improved Mask R-CNN to count the number of red globe grape berries, which also further illustrates that the fruit detection problem was well solved. Using the generalized method on untrained images of different grape varieties in different scenes also achieved good segmentation results. In this study, we provide a method for segmenting and counting grape berries that is useful for automating the grape industry.

Joint dynamic routing and resource allocation in satellite-terrestrial integrated networks

The Satellite-Terrestrial Integrated Networks (STINs) is considered as a reliable and agile next-generation communication network scheme, because it is flexible to deploy and robust to disasters (e.g., earthquakes, floods, and volcanic eruptions). The routing and resource allocation directly affect the power consumption of satellites in the STINs. However, most of existing research works study these two issues separately, and neglect the impact of path selection on the satellite power consumption. This paper investigates the joint optimization problem of routing, bandwidth allocation, user association, channel allocation, and power allocation of the STINs. We aim at minimizing satellites’ power consumption while satisfying user's Quality of Service (QoS). Because the power consumption of satellites for routing in the space segment is involved with the data rate through satellite gateways in the user segment, this optimization problem is a Multi-Objective Optimization (MOO) problem. To this end, we convert the MOO problem to a Single-Objective Optimization (SOO) problem by linear weighted method. Considering that the SOO problem is a Mixed Integer Nonlinear Programming (MINLP) problem, we decompose it into the resource allocation sub-problem in the user segment and the routing optimization sub-problem in the space segment. And we solve these two sub-problems iteratively. Specifically, we convert the first sub-problem into a convex optimization problem and obtain the current user association and channel allocation. Then we formulate the second sub-problem into a multi-commodity flow problem and solve it based on the obtained user association and channel allocation. The SOO problem is solved by iteratively optimizing these two sub-problems. Experimental results demonstrate that our proposed algorithm is able to get an approximate optimal solution quickly and effectively reduce the power consumption of satellites compared with benchmark algorithms in the STINs.

Feasibility Analysis of a Direct-drive PV Heat Pump System Based on Phase Change Heat Storage

A direct-drive PV pump system based on phase change heat storage is proposed to address the current building energy consumption problem in heating/cooling. The system can solve the problems of unstable solar energy, high battery cost, and short battery service life. Taking a 45m 2 office in Shanghai as the research object, the annual dynamic simulation model of the system is built through TSNSYS. The linear weighted evaluation function method is used for multi-objective optimization analysis of system performance and cost. The results show that the selection scheme with a PV area of 26 m2, a phase change volume of 0.14 m3, and a valley electricity storage ratio of 1 has the best-combined performance and cost of the system.

Chemical compositions prediction of glass artifacts based on logistic regression and linear weighting

Chemical composition analysis is of great significance for studying the category, origin and production age of the ancient glass. In order to consider the influence of weathering on chemical composition, this paper proposes a prediction method of glass chemical composition based on logistic regression and linear weighting. First, the chi-square test shows that whether the glass surface is differentiated is related to the type of glass. Secondly, the chemical composition of glass relics is divided into two categories by binary logistic regression: significantly affected by weathering and not significantly affected by weathering. Then, for the chemical components not significantly affected by weathering, combined with descriptive statistical results, the mean value was used as the chemical composition content before weathering. For the chemical components significantly affected by weathering, the linear weighting method was used to predict the content of chemical components before weathering. The final results show that this method can better reflect the influence of weathering factors on the chemical composition of glass, which is of great significance for the prediction of the glass composition.

An integrated index developed for measuring precipitation complexity: a case study of Jinsha River basin, China

Accurate quantification of precipitation complexity is vital for assessing the impact of changing environments on precipitation processes and guiding precipitation forecasting. However, previous research mostly quantified precipitation complexity from different perspectives, resulting in differences in complexity results. In this study, the multifractal detrended fluctuation analysis (MF-DFA; derived from fractal), Lyapunov exponent (derived from Chao), and sample entropy (derived from entropy) were used for investigating the complexity of regional precipitation. Then, the integrated complexity index was established by using the intercriteria correlation (CRITIC) method and the simple linear weighting (SWA) method. Finally, the proposed method is applied to China's Jinsha River basin (JRB). The research indicates that (1) the discriminability of the integrated complexity index is higher than that of MF-DFA, Lyapunov exponent, and sample entropy, which can better distinguish the precipitation complexity in the Jinsha River basin; (2) the higher complexity of monthly precipitation was mainly concentrated in the southeast, and the lower complexity was mainly located in the northwest; moreover, the monthly precipitation complexity for the selected study area is the highest at 0.854 at Weixi station and the lowest at 0.152 at Batang station; (3) the superimposed effects of the southwest monsoon, terrain, and reservoir construction have become the main factors that influence the spatial variation of complexity for precipitation. This study provides a new idea for developing an integrated complexity index, and the results are of great significance for regional precipitation disaster prevention and water resources management.

Inkjet OLED printing planning based on deep reinforcement learning and reward-based TLO

With the increasing use of OLED screens in electronic products, there is a focus on how to print OLED screens more efficiently. Deep reinforcement learning is being used to solve the OLED printing planning problem because it effectively uses historical data and is also real-time. This paper uses a modified TLO method to combine deep reinforcement learning for multi-objective optimization of OLED printing. Through experiments, the method is compared with the linear weighting method commonly used in multi-objective reinforcement learning, which is more effective in optimization.

Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint

As an important support of the e-commerce industry, the express delivery industry is particularly important in national development. Low loading rates caused by numerous types of containers and cost increases caused by low loading and unloading efficiency are still remaining issues in the process of goods delivery and packing. This study introduced the pallet with telescopic support height as the middle to address these issues and proposed a distribution scheme based on the constraints of three-dimensional pallet loading with a time window. First, combining the path optimization of the time window and cargo loading, a solution model was established to solve the existing express delivery problem with the lowest total delivery cost and the highest average vehicle loading rate. In addition, the multi-objective problem was transformed through the multi-objective linear weighting method. Second, we cluster the customer nodes. In order to solve the large number of gaps generated by the hierarchy theory, we adopt the descending order of cargo volume as the initial sequence and design the coding and decoding for path optimization and pallet loading, solving the problem through the simulated anneal-genetic algorithm. Finally, the effectiveness of the algorithm is obtained through the comparison with other algorithms and the simple three-dimensional loading and distribution scheme by using examples. It is proved that the optimization of three-dimensional packing for express delivery using pallets as carriers can not only meet the high loading rate but also improve the loading and unloading speed, reduce the time penalty cost, and improve the operability of loading. This paper provides decision reference and method support for path optimization under three-dimensional loading constraints.

A novel method of key meta-action unit integrated identification for CNC machine tool reliability

There are many parts in CNC (computer numerical control) machine tool. Considering the time and economy, it is impossible to analyse them one by one. In order to reduce the blindness of researchers in choosing analysis objects, it is necessary to identify the key parts of CNC machine tool. MAU (meta-action unit) is the smallest structure that can reflect the function and performance of CNC machine tool, so it is taken as the research object. A new method for key MAU identification of CNC machine tool is proposed, as follows. Firstly, all the meta-action units were obtained by the FMA (function-motion-action) decomposition method, and the harmfulness was selected to analyze the influence of MAU on the reliability of CNC machine tool. Secondly, fuzzy theory is integrated into RPN (risk priority number) to obtain the harmfulness of MAU. Thirdly, the subjective weight of the harmfulness about the MAU was obtained by the combination of MAHP (modified analytic hierarchy process) and optimal sequence diagram method, and the objective weight was obtained by CRITIC (criteria importance though intercrieria correlation) method. Fourthly, the subjective and objective weights were integrated by a linear weighting method, and the result were taken as the comprehensive weight of the harmfulness of MAU. Finally, the harmfulness of the MAU could be obtained by its evaluation value and weight, and the MAU with greater impact on the reliability of CNC machine tool could be obtained by Pareto principle, which is the key MAU. A CNC machine tool made in China is analysised, and the effectiveness of the proposed method is verified by the comparison of various methods and experimental analysis.

Optimal credit strategy for MSMEs

We address two main issues: one is to quantify the credit risk of enterprises and establish a complete credit risk system, and the other is to give the optimal credit risk strategy for banks. We first analyze and pre-process the data. From the data, we extracted a series of indicators such as the total amount of input and output, and the length of operation. We analyze the credit risk in three directions: strength, stability of supply and demand, and creditworthiness, and establish a credit risk quantification system for the enterprise. Then we quantify the credit risk of the enterprise by entropy method and TOPSIS. Second, a function is fitted to the bank’s customer churn rate and the bank’s lending rate. Using a planning-type model, the credit decisions of the firms are required to be given. We follow the principle of maximizing benefits and minimizing risks to build a multi-objective planning model. We base on the scores of each firm that have been solved, for the classification of firms, and follow the principle of low credit risk, low lending rate, and set the corresponding lending rate for each type of firm. The model is solved by the through-order solution method, and the linear weighting method is used to test the comparison. The credit decision for each enterprise is given.

Research on the evaluation method of steam power system operation status based on the theory of deterioration degree and health value.

The evaluation of the steam power system is very important for the operator to understand the operating status of the system, but the lack of consideration of the fuzziness of the complex system and the impact of the indicator parameters on the whole system makes the evaluation difficult. In this paper, an indicator system for evaluating the operation status of the experimental supercharged boiler is established. After discussing several methods of parameter standardization and weight correction, a comprehensive evaluation method based on the deterioration degree and health value is proposed while considering the deviation of the indicator and the fuzziness of the system. The comprehensive evaluation method, the linear weighting method and the fuzzy comprehensive evaluation method are respectively used to evaluate the experimental supercharged boiler. The comparison of the three methods shows that the comprehensive evaluation method is more sensitive to minor anomalies and faults and can draw quantitative health assessment conclusions.