Collaborative Optimization of a Matrix Manufacturing System Based on Overall Equipment Effectiveness

When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.

Seismic effectiveness evaluation and optimized design of tie up method for securing museum collections

To quantify the seismic effectiveness of the most commonly used fishing line tie up method for securing museum collections and optimize fixed strategies for exhibitions,shaking table tests of the seismic systems used for typical museum collection replicas have been carried out.The influence of body shape and fixed measure parameters on the seismic responses of replicas and the interaction behavior between replicas and fixed measures have been explored.Based on the results,seismic effectiveness evaluation indexes of the tie up method are proposed.Reasonable suggestions for fixed strategies are given,which provide a basis for the exhibition of delicate museum collections considering the principle of minimizing seismic responses and intervention.The analysis results show that a larger ratio of height of mass center to bottom diameter led to more intense rocking responses.Increasing the initial pretension of fishing lines was conducive to reducing the seismic responses and stress variation of the lines.Through comprehensive consideration of the interaction forces and effective securement,it is recommended to apply 20%of breaking stress as the initial pretension.For specific museum collections that cannot be effectively protected by the independent tie up method,an optimized strategy of a combination of fishing lines and fasteners is recommended.

Key indexes identifying approach of weapon equipment system-of-systems effectiveness integrating Bayes method and dynamic grey incidence analysis model

Aiming at the characteristics of multi-stage and(extremely)small samples of the identification problem of key effectiveness indexes of weapon equipment system-of-systems(WESoS),a Bayesian intelligent identification and inference model for system effectiveness assessment indexes based on dynamic grey incidence is proposed.The method uses multi-layer Bayesian techniques,makes full use of historical statistics and empirical information,and determines the Bayesian estima-tion of the incidence degree of indexes,which effectively solves the difficulties of small sample size of effectiveness indexes and difficulty in obtaining incidence rules between indexes.Sec-ondly,The method quantifies the incidence relationship between evaluation indexes and combat effectiveness based on Bayesian posterior grey incidence,and then identifies key system effec-tiveness evaluation indexes.Finally,the proposed method is applied to a case of screening key effectiveness indexes of a missile defensive system,and the analysis results show that the proposed method can fuse multi-moment information and extract multi-stage key indexes,and has good data extraction capability in the case of small samples.

An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning

Land use structure optimization(LUSO) is an important issue for land use planning. In order for land use planning to have reasonable flexibility, uncertain optimization should be applied for LUSO. In this paper, the researcher first expounded the uncertainties of LUSO. Based on this, an interval programming model was developed, of which interval variables were to hold land use uncertainties. To solve the model, a heuristics based on Genetic Algorithm was designed according to Pareto Optimum principle with a confidence interval under given significance level to represent LUSO result. Proposed method was applied to a real case of Yangzhou, an eastern city in China. The following conclusions were reached. 1) Different forms of uncertainties ranged from certainty to indeterminacy lay in the five steps of LUSO, indicating necessary need of comprehensive approach to quantify them. 2) With regards to trade-offs of conflicted objectives and preferences to uncertainties, our proposed model displayed good ability of making planning decision process transparent, therefore providing an effective tool for flexible land use planning compiling. 3) Under uncertain conditions, land use planning effectiveness can be primarily enhanced by flexible management with reserved space to percept and hold uncertainties in advance.

Types and Optimization Paths Between Poverty Alleviation Effectiveness and Rural Revitalization:A Case Study of Hunan Province,China

Exploring the synergy types and optimization paths between Poverty Alleviation Effectiveness and Rural Revitalization is necessary for achieving the two centenary goals.Taking poverty alleviation counties in Hunan Province,China as an example,our study proposed an indicator to measure the synergistic development between Poverty Alleviation Effectiveness and Rural Revitalization using the multi-index integrated evaluation method.Then,the coupling types were classified based on both the proposed indicator and regional characteristics.Besides,the corresponding optimization path for each coupling type was proposed to promote the synergistic development of Poverty Alleviation and Rural Revitalization.Results are as follows:1)Lower synergy focused on the southwestern Hunan,while low synergy is widely distributed(such as the west,southwest,northwest,and midland).Moderate synergy is in the midland,such as Huaihua and Chenzhou cities.High synergy is distributed in Yongzhou,Huaihua,Xiangxi cities,etc.Besides,only Hecheng City belongs to the higher synergy.2)This paper proposes corresponding development paths for different development characteristics and main problems from multiple perspectives of the protection system,industrial planning,and rural market.Continuously consolidate and enhance the effectiveness of Poverty Alleviation and Rural Revitalization to achieve coupled and synergistic development of the two systems.Our research results can provide theoretical support for implementing Poverty Alleviation and Rural Revitalization in Hunan Province,China.

A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.

Evaluation of the effectiveness of preventive nursing measures for pressure injuries in patients in the neurology intensive care unit

BACKGROUND Patients in neurology intensive care units(ICU)are prone to pressure injuries(PU)due to factors such as severe illness,long-term bed rest,and physiological dysfunction.PU not only causes pain and complications to patients,but also increases medical burden,prolongs hospitalization time,and affects the recovery process.AIM To evaluate and optimize the effectiveness of pressure injury prevention nursing measures in neurology ICU patients.METHODS A retrospective study was conducted,and 60 patients who were admitted to the ICU of the Department of Neurology were selected and divided into an observation group and a control group according to the order of admission,with 30 people in each group.The observation group implemented pressure injury prevention and nursing measures,while the control group adopted routine care.RESULTS Comparison between observation and control groups following pressure injury prevention nursing intervention revealed significantly lower incidence rates in the observation group compared to the control group at 48 h(8.3%vs 26.7%),7 d(16.7%vs 43.3%),and 14 d(20.0%vs 50.0%).This suggests a substantial reduction in pressure injury incidence in the observation group,with the gap widening over time.Additionally,patients in the observation group exhibited quicker recovery,with a shorter average time to get out of bed(48 h vs 72 h)and a shorter average length of stay(12 d vs 15 d)compared to the control group.Furthermore,post-intervention,patients in the observation group reported significantly improved quality of life scores,including higher scores in body satisfaction,feeling and function,and comfort(both psychological and physiological),indicating enhanced overall well-being and comfort following the implementation of pressure injury prevention nursing measures.CONCLUSION Implementing pressure injury preventive care measures for neurology ICU patients will have better results.

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Modeling Anthrax with Optimal Control and Cost Effectiveness Analysis

Anthrax is an infection caused by bacteria and it affects both human and animal populations. The disease can be categorized under zoonotic diseases and humans can contract infections through contact with infected animals, ingest contaminated dairy and animal products. In this paper, we developed a mathematical model for anthrax transmission dynamics in both human and animal populations with optimal control. The qualitative solution of the model behaviour was analyzed by determining Rhv, equilibrium points and sensitivity analysis. A vaccination class was incorporated into the model with waning immunity. Local and global stability of the model’s equilibria was found to be locally asymptotically stable whenever Rhv Rhv. It was revealed that reducing animal and human interaction rate, would decrease Rhv. We extended the model to optimal control in order to find the best control strategy in reducing anthrax infections. It showed that the effective strategy in combating the anthrax epidemics is vaccination of animals and prevention of humans.

Structural optimization of a reusable seismic frame active link

Circular design encompasses the use of inventive construction methodologies that possess the capability to be readily dismantled,repurposed,or recycled upon reaching the conclusion of their functional lifespan.This work specifically examines the creation of a reusable design case-study idea for seismic frame design,which is commonly employed in steel-frame constructions in New Zealand.A reusable optimized design for the full seismic frame was proposed in the research.Optimizing the dimensions of welded structures,whether in terms of weight or cost,leads to a decrease in the weight of the steel utilized.The decrease in weight is directly associated with a decrease in environ-mental impact,as the environmental impact is directly proportional to the mass of the construction.The environmental consequences associated with welding technique are contingent upon the dimensions of the weld,hence exerting an indirect influence on the overall mass of the structure.Given the presence of mass dependence in all three areas,albeit in distinct manners,this work employed a multi-objective function optimization strategy to simultaneously address these areas while also partially evaluating them separately.On this way substantial reductions can be achieved both at structural mass and environmental effects.

Optimization Model of the Effects of Transplanting Density, Nitrogen and Potassium Fertilization on Yield and Quality of an Aromatic Hybrid Rice "Luyoumingzhan"

Effects of transplanting density, nitrogen （N） application quantity and potassium （K） application quantity on hybrid rice ＂Luyoumingzhan＂ were studied by optimal design. Regression models between yield, quality and the three cultivation measures were built to study the effects of the three cultivation measures on rice yield and quality. The results showed that the yield and quality were influenced in various degrees. An optimization measure for high yield and low chalky ratio was simulated by computer,which was 214500 clumps per ha, 140.2 kg N per ha and N 136.6 kg per ha.

RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.

The Optimization Study of General Education Practice Effect in Our Country

General education has become more and more attention.The culture has a more long-term and lasting contribution,so more and more colleges and universities have adopted a series of reform measures.Effect of the practice of general education,however,still cannot satisfactory.In this paper,main factors affecting was analyzed on the basis of extensive research and questionnaire data analysis and targeted put forward the optimization effect of general education.It is important to make every effort to make the general education in China to achieve"Phoenix Nirvana".

Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.

Multi-Objective Bacterial Foraging Optimization Algorithm Based on Effective Area in Cognitive Emergency Communication Networks

Cognitive emergency communication net-works can meet the requirements of large capac-ity,high density and low delay in emergency com-munications.This paper analyzes the properties of emergency users in cognitive emergency communi-cation networks,designs a multi-objective optimiza-tion and proposes a novel multi-objective bacterial foraging optimization algorithm based on effective area(MOBFO-EA)to maximize the transmission rate while maximizing the lifecycle of the network.In the algorithm,the effective area is proposed to prevent the algorithm from falling into a local optimum,and the diversity and uniformity of the Pareto-optimal solu-tions distributed in the effective area are used to eval-uate the optimization algorithm.Then,the dynamic preservation is used to enhance the competitiveness of excellent individuals and the uniformity and diversity of the Pareto-optimal solutions in the effective area.Finally,the adaptive step size,adaptive moving direc-tion and inertial weight are used to shorten the search time of bacteria and accelerate the optimization con-vergence.The simulation results show that the pro-posed MOBFO-EA algorithm improves the efficiency of the Pareto-optimal solutions by approximately 55%compared with the MOPSO algorithm and by approx-imately 60%compared with the MOBFO algorithm and has the fastest and smoothest convergence.

Studies on the Fertilization Effect and Optimal Fertilizing Amount of Brazil Banana Based on "3414" Field Trials

Fertilizer effect model such as ternary quadratic, unary quadratic, straight line and platform model was respectively used to analyze the two-year ＂3414＂ test data collected from banana garden in Fushan Town of Hainan Province. The results showed that the optimal fertilizing amount of ternary quadratic model simulation was0.374 kg/plant of N, 0.289 kg/plant of P2O5 and 0.891 kg/plant of K2 O. According to the yield trend characteristic, the optimal fertilizing amount of unary quadratic model was 0.400kg/plant of N, 0.214 kg/plant of P2O5 and 0.901kg/plant of K2 O. Thus it can be seen that only partial indices of the optimal fertilizing amount of ternary quadratic model simulation were higher than that of unary quadratic model. Considering the results, the optimal fertilizing amount of Brazil banana was 0.374-0.400kg/plant of N, 0.214-0.289 kg/plant of P2O5 and 0.891-0.901 kg/plant of K2 O.

System optimization-oriented spare parts dynamic configuration model for multi-echelon multi-indenture system

In order to optimize the spares configuration project at different stages during the life cycle, the factor of time is considered to relax the assumption of the spares steady demand in multi-echelon technique for recoverable item control (METRIC) theory. According to the method of systems analysis, the dynamic palm theorem is introduced to establish the prediction model of the spares demand rate, and its main influence factors are analyzed, based on which, the spares support effectiveness evaluation index system is studied, and the system optimization-oriented spares dynamic configuration method for multi-echelon multi-indenture system is proposed. Through the analysis of the optimization algorithm, the layered marginal algorithm is designed to improve the model calculation efficiency. In a given example, the multi-stage spares configuration project during its life cycle is gotten, the research result conforms to the actual status, and it can provide a new way for the spares dynamic optimization.

Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

Optimization of reluctance accelerator efficiency by an improved discharging circuit

In this paper,an improved discharging circuit was proposed to quicken the decay of the current in the drive coil in a reluctance accelerator when the armature reaches the center of the coil.The aim of this is to prevent the suck-back effect caused by the residual current in drive coil.The method is adding a reverse charging branch with a small capacitor in the traditional pulsed discharging circuit.The results under the traditional circuit and the improved circuit were compared in a simulation.The experiment then verified the simulations and they had good agreement.Simulation and experiment both demonstrated the improved circuit can effectively prevent the suck-back effect and increase the efficiency.At the voltage of 800 V,an efficiency increase of 36.34% was obtained.

Conceptual Design Optimization of Fighter Trainer Aircraft with Double-delta Wing Configuration

Compared with a delta wing aircraft, the double delta wing configuration has better aerodynamic performance at high angles of attack. An operational analysis was introduced as a method for evaluating training effectiveness of trainer aircraft. Approaches to the engineering estimation of aerodynamic characteristics for aircraft with a double delta wing configuration were studied, and the procedures for determining aircraft performance indices formulated. Taking training effectiveness as the objective function and geometric parameters of the wing platform as design variables, through a numerical multivariate optimization arithmetic, the conceptual design optimization for a certain fighter trainer aircraft with double delta wing configuration was carried out under the constraints of tactical and technical requirements and interrelated geometry. Agreement of a calculation example with engineering practice indicates that the optimal design has higher training effectiveness than the baseline design, and in addition, improves the structural force bearing conditions.