Linear Weighted Method Research Articles

Multicriteria Evaluation of Labor Stability

The job stability of workers represents an indicator for people's quality of life. Evaluating job stability from a legal point of view would allow quantifying its impact on workers from a legal perspective. The present investigation proposes a method for the legal evaluation of the labor stability of the workers. It bases its operation on the Neutrosophic Multi-criteria Linear Weighting method. A Case study is developed on three Cantons of Los Rios province to demonstrate the applicability of the proposed method. Paper ends with conclusion, recommendation and the projections for future works.

Safety risk assessment of heritage buildings in metro construction based on SPA theory: a case study in Zhengzhou, China

While the urban rail transit systems have been developed rapidly, the nearby heritage buildings and historic sites are facing possible threats of unexpected damages. In particular, a rail project has a potential impact on numerous heritage buildings and sites in an old city. Thus, to better protect these heritage buildings and sites, it is crucial for decision-makers to be able to rapidly and accurately evaluate these threats. Based on Set Pair Analysis (SPA) theory, this study developed a risk assessment model to assess the safety of heritage buildings adjacent to metro construction. In this model, the risk levels of adjacent heritage buildings are ranked. Moreover, this study establishes an assessment index system comprising 16 individual indexes among four categories related to heritage building, metro, soil, and management. The index system determines the interval values for each evaluation factor. To improve the reliability of the index weight, a linear weighting method was adopted. In this study, both subjective weights calculated via the Analytic Hierarchy Process (AHP) and objective weights calculated by the Clustering Weight Method (CWM) are analyzed comprehensively. Moreover, a case study was conducted by applying the proposed assessment model into assessing the structural safety risk of a heritage building adjacent to the Zhengzhou Metro Line Three. It was found the results of the proposed model were consistent with the Matter-Element (ME) method. The proposed model can be used to provide decision-making support for controlling risk on similar projects.

A General 3D Space-Time-Frequency Non-Stationary Model for 6G Channels

In this paper, a general three-dimensional (3D) space-time-frequency non-stationary model is proposed for sixth generation (6G) channels. From the proposed model, a novel method, so-called correlated cluster based birth-death (BD) process method, is developed to efficiently and jointly mimic the 3D channel space-time-frequency non-stationarity. In this developed method, the frequency non-stationarity is properly captured by correlated clusters, which are obtained via an unsupervised learning algorithm in machine learning, i.e., K-Means clustering algorithm. Additionally, the developed method involves the cluster based space-time non-stationary modeling. Based on the correlation coefficient of clusters, the BD probabilities on the array and time axes are reasonably modified by the linear weight method and matrix iteration algorithm. Therefore, interactions among the space, time, and frequency non-stationary modeling are sufficiently considered. Important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the channel non-stationarity in space-time-frequency domains can be sufficiently characterized. Finally, the excellent agreement between the simulation results and measurements further verifies the accuracy of the proposed model.

Comprehensive Evaluation and its Application in Mathematical Modeling

This paper first introduces the comprehensive evaluation and its purpose, five elements of comprehensive evaluation, and gives the establishment methods of the comprehensive evaluation model, including linear weighting method, nonlinear weighting method and approximate ideal point method. Secondly, the general steps of comprehensive evaluation are summarized, and the treatment methods of evaluation indexes are given: using minimizing – maximizing, centering – maximizing and interval - maximizing to achieve the consistency of index types; using vector normalization, extreme difference transformation, standard sample transformation and linear proportional transformation to standardize index types; using expert consultation and relative comparison to determine the weight of index. Finally, a comprehensive evaluation model of aircraft type is established.

Research on behavior safety evaluation of subway construction personnel based on improved AHP-FCE method

This paper studies the behavior safety evaluation of subway construction personnel. According to expert interviews, accident case analysis and references, the behavior safety evaluation system of subway construction personnel is constructed, including 6 first-class indicators and 20 second-class indicators. The improved AHP method, entropy weight method and difference coefficient linear weighting method is used to calculate the subjective and objective combination weights of each evaluation index, and FCE method is used to evaluate the behavior safety status of the construction personnel of a subway project. The results show that: (1)The evaluation model is scientific and reasonable, and has strong operability; (2) The behavior safety status of subway construction personnel is greatly affected by knowledge and skills, safety atmosphere and management factors. Among them, safety attitude, safe operation skills, exemplary role of team leaders, safety initiative participation, safety education and training, and safety leadership are of great importance. Strengthening their management will effectively improve the level of behavior safety management and put forward new management ideas for the project management idea.

Regional Integrated Energy Site Layout Optimization Based on Improved Artificial Immune Algorithm

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers.

Adaptive decision-level fusion and complementary mining for visual object tracking with deeper networks

Multiple region proposal networks (RPNs) have been recently combined with the Siamese network with deeper backbone networks for tracking and shown excellent accuracy with high efficiency. Although the destruction of the strict translation invariance caused by network padding in the original ResNet-50 is solved by a custom sampling strategy, its impact is not eliminated from the network structure itself, and the multilayer feature fusion is insufficient. To this end, we propose an object tracking framework based on SiamRPN with the deeper backbone networks and cascaded RPN (D-CRPN). First, we exploit the cropping-inside residual units for reforming ResNet-50 to break the spatial invariance restriction and train the robust backbone networks for visual tracking. Then, the feature transfer blocks are proposed to achieve the effective integration of the outputs of multiple blocks in a specific network stage. Finally, to improve the robustness of our tracker, we present a quality measure for the synthetic response maps of RPN modules and then use it to calculate the adaptive weights for the linear weighting method. The extensive evaluation performed on OTB100, VOT2016, and VOT2018 benchmark datasets demonstrates that the proposed D-CRPN tracker outperforms most of the state-of-the-art approaches while maintaining real-time tracking speed.

An improved model to predict the water-inrush risk from an Ordovician limestone aquifer under coal seams: a case study of the Longgu coal mine in China

The mining of stratigraphically low coal seams in North China-type coalfields is subject to water inrush from the underlying Ordovician limestone aquifer. The water-inrush coefficient method that is currently used for the evaluation of the water-inrush risk has inherent shortcomings, because it takes into account only the aquifer head pressure and the aquiclude thickness. Therefore, an improved water-inrush coefficient (IWIC) model is proposed. Based on the normalized water-inrush parameter, water-resisting parameter and structural parameter, the IWIC model is established using a linear weighting method. The first-order weights of each parameter are determined by the analytic hierarchy process, and the second-order weights are determined by the trapezoidal fuzzy number technique. Contour maps of the water-inrush risk calculated with the IWIC model are then obtained. The water-inrush risk grades are classified by thresholds derived via the Jenks natural breaks technique. The IWIC model is applied to the Longgu coal mine, as a typical coal mine in China, to evaluate the water-inrush risk of the lower four coal seams (L4CS). The evaluation results show that the risk of water inrush in the L4CS can be divided into five grades: safe, slightly safe, slightly dangerous, dangerous, and extremely dangerous. Overall, the L4CS mining in the Longgu coal mine is seriously threatened by the underlying Ordovician limestone aquifer. As the depth increases, the risk of water inrush increases from the No. 151 to No. 182 coal seams. Among the L4CS, No. 17 and No. 182 have the highest grade of water-inrush risk, and it is proposed that these two coal seams should not be mined to prevent water-inrush accidents.

Analysing and forecasting the security in supply-demand management of Chinese forestry enterprises by linear weighted method and artificial neural network

ABSTRACT To better forecast the security in supply-demand management of Chinese forestry enterprises and close the supply-demand gap due to short timber supply, the linear weighted method and artificial neural network (ANN) are jointly applied to forecast the security in supply-demand of the forestry industry. The theories of supply-demand management and early-warning systems are clarified. Indexes in the early-warning system are built by the ecological evaluation pressure-state-response (PSR) model. The optimised early-warning system by linear weighted method and ANN can analyse and forecast the security in supply-demand management of Chinese forestry enterprises.

Quantitative Evaluation and Prediction Analysis of the Healthy and Sustainable Development of China’s Sports Industry

In this paper, to explore the current situation and future trend of the healthy development of China’s sports industry, the health evaluation index system of the sports industry was established by deconstructing the health dimension of the sports industry. The health evaluation model of the sports industry was constructed by using the entropy method and the multi-objective linear weighting method. The model was used to empirically analyze the characteristics and laws of the health of China’s sports industry from 2008 to 2017 and predict the health status of China’s sports industry in the next 10 years with the combination with the GM (1.1) model. The results show that the potential health degree of China’s sports industry exhibits a fluctuating growth trend, the actual health degree of the sports industry, and the health degree of the supporting environment in the sports industry show a steady growth trend on the whole. In the next 10 years, the health degree of China’s sports industry will maintain a healthy state in which the actual health degree and potential health degree of the sports industry will enter a healthy state in 2021 and 2024, respectively. In contrast, the health degree of the supporting environment in the sports industry is always in a very unhealthy state.

Dual-objective Programming Model for Equipment Resource Allocation and Its Algorithm Implementation

This paper in view of the allocation of equipment resources in enterprise groups is studied. Firstly, it introduces the 0-1 decision variable, and takes the order group self-made processing period, equipment resource condition and self-made parts processing status as constraints, emphasizing the processing quality control of the self-made parts required for key important features. A dual-objective programming model of equipment resource allocation based on quality and cost is constructed, and the dual-objective function is transformed into a single objective function by linear weighting method, and then, the improved honey evolutionary genetic algorithm is used to solve it. Finally has carried on the simulation analysis, the simulation analysis results show that the resource allocation dual-objective programming model and the algorithm are effective.

Sand and dust storm sources identification: A remote sensing approach

Abstract In recent years, the sand and dust storm (SDS) events have become one of the most critical environmental challenges around the world. Identification of the sources not only helps to monitor and predict the processes of dust storms, but also it contributes to the reduction of its negative impacts and management of this phenomenon in a better manner. Identifying the sources based on field observation is impossible due to the vast extent of these lands, as well as the limitation of access to most of their areas, so remote sensing data can be used as a reliable alternative. The primary objective of this study is developing a comprehensive approach for sand and dust storm source identification and surveying their changing trend during a specific period via remotely sensed data. For this purpose, to generate wind erosion sensitivity maps based on the vegetation cover, soil moisture and land cover, Landsat8 data from 2013 to 2015 were acquired. After undergoing pre-processing on Landsat data with the help of spectral vegetation indexes and classification, wind erosion sensitivity maps were obtained for vegetation, soil moisture, and land use. To obtain a potential dust source map, the previously generated maps were integrated with geology and soil roughness information through multi-criteria evaluation to produce a wind erosion sensitivity map. Then, the synoptic data and air-quality information data were collected and using statistical analysis and MODIS data, local dust and sand events were identified, which were then validated based on HYSPLIT air flow simulation model to ensure that airflow trajectory and erodible lands are in physical contact. Based on the contact areas between airflow and the terrain besides applying non-erodible masks on them, the wind erosion risk was mapped. Next, the wind erosion risk map and the wind erosion sensitivity map via fuzzy multi-criteria assessment through linear weighted method were combined, then based on stratified-random sampling, probable SDS sources were identified. To validate the identified SDS sources and survey their trend, time series and synoptic data were utilized and vegetation cover, soil moisture and land surface temperature (LST) trend in specified areas for 15 years were monitored. Validation results showed high accuracy in identifying the areas; moreover, they confirmed the significant decrease in the vegetation cover, soil moisture and LST in the SDS sources during the study period (the last 15 years). Our results showed the high capability of time series analysis of remotely sensed data. LST as a climatic parameter has a crucial role to identify and validate the SDS sources. For instance, in areas with high SDS frequency, a significant decrease in LST is observable and confirmed by AOD analysis results. In this research, we integrated the most critical data that can be effective in identifying dust sources. To validate these sources, we used time series satellite data to measure the power of these data. Finally, we proposed the whole procedure as a complete process. Hence, the applied method in this research can be used as a comprehensive approach for future studies in SDS source identification by remotely sensed data.

Two-sided matching for hesitant fuzzy numbers in smart intelligent technique transfer

Hesitant fuzzy set (HFS), as an extension of fuzzy set, is deemed as a very useful mean to exhibit hesitant information in handling real-life problems. To enlarge the range of applicability, this paper combines HFS with two-sided matching (TsM). Firstly, this paper retrospects the definitions of hesitant fuzzy Numbers (HFNs) and TsMs, where a new score function of HFN is provided. After that, the TsM decision problem based on HFNs is demonstrated. To solve it, a TsM decision model on the basis of HFNs is built. Through using linear weighted method, the TsM model is transformed into a two-goal TsM model. Moreover, the two-goal TsM model with HFN can be changed to a single-goal TsM model with scores through using the introduced score function. The “soundest” alternative of TsM could be acquired through model solution. Lastly, an example in smart intelligent technique transfer is used to state the feasibility and validity of the presented method.

Research on intelligent scheduling strategy of elevator group under the big data platform

The traditional elevator dispatching system has a long response time, resulting in poor scheduling performance. To this end, an elevator group intelligent dispatching system is designed under the large data platform. The overall structure of the system is given, and the elevator data is fused under the big data platform. The objective function is determined based on the results of the big data processing. The comprehensive evaluation function of the elevator group intelligent dispatching system is established by linear weighting method. The traffic mode is judged by the elevator group, the constraint rules are given, and the ant colony algorithm is applied to the elevator group intelligent scheduling to obtain the optimal scheduling scheme. The experimental results show that the system can respond to the time period and the elevator runs efficiently. This paper designs the elevator group intelligent dispatching system to have good overall performance.

Intelligent RGV Dynamic Scheduling Optimization Model under Possible Failure

This paper analyses the dynamic RGV dynamic scheduling under the condition of possible failure. Firstly, by introducing 0-1 decision variables, the RGV position matrix and the RGV-CNC moving time distance matrix are constructed to meet the requirements of each link in the scheduling process. The time is the constraint condition, and the maximum number of processed materials and the shortest idle time of the machine tool are the objective function. The dual-objective programming model of RGV dynamic scheduling is established, and the double-objective programming problem is transformed into the single-objective programming problem by linear weighted method. For the possible fault conditions during the processing, the Poisson distribution is used to randomly generate the serial number of the CNC fault and the time of the uniform distribution to simulate the fault elimination. Then the improved genetic algorithm of the model is given, and finally the national mathematics model of 2018 is adopted. The contest B is an example and the example are verified. The test results are good.

A hybrid deep learning model for short-term PV power forecasting

The integration of PV power brings great economic and environmental benefits. However, the high penetration of PV power may challenge the planning and operation of the existing power system owing to the intermittence and randomicity of PV power generation. Achieving accurate forecasting for PV power generation is important for providing high quality electric energy for end-consumers and for enhancing the reliability of power system operation. Motivated by recent advancements in deep learning methods and their satisfactory performance in the energy sector, a hybrid deep learning model combining wavelet packet decomposition (WPD) and long short-term memory (LSTM) networks is proposed in this study. The hybrid deep learning model is utilized for one-hour-ahead PV power forecasting with five-minute intervals. WPD is first used to decompose the original PV power series into sub-series. Next, four independent LSTM networks are developed for these sub-series. Finally, the results predicted by each LSTM network are reconstructed and a linear weighting method is employed to obtain the final forecasting results. The performance of the proposed method is demonstrated with a case study using an actual dataset collected from Alice Springs, Australia. Comparisons with individual LSTM, recurrent neural network (RNN), gated recurrent (GRU), and multi-layer perceptron (MLP) models are also presented. The values of three performance evaluation indicators, MBE, MAPE, and RMSE, show that the proposed hybrid deep learning model exhibits superior performance in both forecasting accuracy and stability.

Uncertain interval programming model for multi-objective multi-item fixed charge solid transportation problem with budget constraint and safety measure

This paper presents uncertain interval programming models for multi-objective multi-item fixed charge solid transportation problem with budget constraint and safety measure (MOMIFCSTPBCSM). The human languages usually involve imperfect or unknown information and are in the lack of certainty, and often, it is impossible to exactly describe an existing state or a future outcome. In using the probability theory, we must have enough historical information to estimate the probability distributions and in the case of fuzzy theory, we must have a trustworthy membership function, which is not easy to do. Thus, we often estimate the degree of belief with some hesitation that each condition may occur. To deal with such a situation, the uncertain interval theory may be very useful. Based on these facts, the parameters of the formulated problem are chosen as uncertain intervals. We consider unit transportation costs, fixed charges, transportation times, deterioration of items, supplies at origins, demands at destinations, conveyance capacities, budget at each destination, selling prices and purchasing costs, and we assume the safety factor and the desired safety measure are interval uncertain parameters. To formulate the proposed MOMIFCSTPBCSM, we use interval theory and uncertain programming techniques to develop two different models: an Expected Value Model and a Chance-Constrained Model. The equivalent deterministic models are formulated and solved using a linear weighted method, a fuzzy programming method and the goal programming method.

Multiobjective gearshift optimization with Legendre pseudospectral method for seamless two-speed transmission

Most researches on transmission gearshift optimization have converted multiobjective problems into single-objective problems using the linear weighted method. A new framework for the multiobjective optimization of the gearshift, in the form of a Pareto solution set, is proposed in this study. Shift duration, friction work, and continuous jerk are selected as the gearshift optimization objectives for a seamless two-speed transmission, utilized in electric vehicles. For this multiobjective optimization, numerous iterations were performed using the Legendre pseudospectral method, which includes advantages such as faster convergence and larger radius of convergence. The numerical results presented as a Pareto solution set provide the optimal gearshift trajectories for different performance orientations. By comparing the selected samples, the inherent relationships among the gearshift objectives were investigated in detail and revealed. This framework for solving multiobjective problems can also be applied to other optimization problems with different complex constraints and objective functions.

Ecological resilience assessment of an arid coal mining area using index of entropy and linear weighted analysis: A case study of Shendong Coalfield, China

Abstract The exploitation of coal resources will bring about a series of environmental problems, which will not only affect the surface ecological environment of the region, seriously threatening underground resources, but will also affect the normal production and living activities of human beings. The impacted ecological system is combined by both overground and underground features. In this paper, Shendong coal field, one of the high-strength coal mining areas in western China is used as a research area. An O-U (Overground-Underground) analysis framework and model was established to quantitatively assess the degree of mining impact on the eco-system and its resilience. The entropy method is used to calculate the weight of each index, and the comprehensive value of each grid is calculated by the linear weighted method. The comprehensive value of each grid’s indicators is divided into four ecological resilience (ER) partitions by natural breakpoint method. Finally, Pearson coefficients is used to analyze the main influencing factors of the four sub-areas, and reasonable suggestions are given. By zoning the ER of the study area, it can provide reference for the mining method selection and land reclamation measures, and also provide direction for the ecological environment management in other similar areas.

Identifying miRNA‐disease association based on integrating miRNA topological similarity and functional similarity

BackgroundMicroRNAs (miRNAs) are a significant type of non‐coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA‐disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA‐disease interactions.MethodsHere, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA‐disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity.ResultsTo evaluate the performance of different methods, we conduct ten‐fold cross validation and de novo test in experiment and compare MDVSI with two the‐state‐of‐art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods.ConclusionsIn summary, we propose a computational framework (MDSVI) for miRNA‐disease interaction prediction. The experiment results demonstrate that it outperforms other the‐state‐of‐the‐art methods. Case study shows that it can effectively identify potential miRNA‐disease interactions.