Constructing the ecological security pattern is imperative to stabilize ecosystem services and sustainable development coordination of the social economy and ecology. This paper focuses on the Karst region in southeastern Yunnan, which is ecologically fragile. This paper selects the main types of ecosystem services and identifies the ecological source using hot spot analysis for Guangnan County. An inclusive consideration of the regional ecologic conditions and the rocky desertification formation mechanism was made. The resistance factor index system was developed to generate the basic resistance surface modified by the ecological sensitivity index. The Ant algorithm and Kernel density analysis were used to determine ecological corridor range and ecological restoration points that constructed the ecological security pattern of Guangnan County. The results demonstrated that, firstly, there were twenty-three sources in Guangnan County, with a total area of 1292.77 km2, accounting for 16.74% of the total. The forests were the chief ecological sources distributed in the non-Karst area, where Bamei Town, Yangliujing Township and Nasa Town had the highest distribution. Secondly, the revised resistance value is similar to “Zhe (Zhetu Township)-Lian (Liancheng Town)-Yang (Yangliujing Township)-Ban (Bambang Township)”. The values were lower in the north and higher in the south, which is consistent with the regional distribution of Karst. Thirdly, the constructed ecological security pattern of the “Source-Corridor-Ecological restoration point” paradigm had twenty-three ecological corridors. The chief ecological and potential corridor areas were 804.95 km2 and 621.2 km2, respectively. There are thirty-eight ecological restoration points mainly distributed in the principal ecological corridors and play a vital role in maintaining the corridor connectivity between sources. The results provide guidance and theoretical basis for the ecological security patterns construction in Karst areas, regional ecologic security protection and sustainable development promotion.

Ecological Security Pattern Construction in Karst Area Based on Ant Algorithm.

Individualized extreme dominance (IndED): A new preference-based method for multi-objective recommender systems

Design, optimization and scale-up of a new micromixer design based on plate column for organic synthesis

https://www.researchsquare.com/article/rs-1361955/latest.pdf

A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling

The subway sliding plug door system has been opened and closed frequently for a long time under variable working conditions, and multiple failures are prone to occurring and resulting in train shutdowns and even major safety accidents. Due to the complex physical mechanism of the sliding plug door system, it is difficult for a single signal to accurately represent the failure states of the sliding plug door system. Thus, identifying the multiple failure causes of the subway sliding plug door system has become a challenging problem. Aiming at the problem, an equipment multiple failure causes intelligent identification method based on an integrated strategy is proposed for the subway sliding plug door system under variable working condition. In the proposed method, firstly, the sensitive features that can reflect the degradation state of equipment are obtained by using the random forest to measure the importance of fetatures and sort them. Secondly, feature dimensionality is reduced by using t-distributed stochastic neighbor mbedding (TSNE) to map the screened high-dimensional features to low-dimensional space. Finally, the parameters of the extreme learning machine (ELM) are optimized by using the particle swarm optimization (PSO) algorithm to build a multiple failure causes classification model. The proposed method is verified by the 1:1 benchmark test data of the subway sliding plug door system. The results show that the proposed method has higher classification accuracy, faster calculation speed, and stronger generalization ability. The proposed method is an effective integrated strategy to identify multiple failure causes in the subway sliding plug door system and guide the health management and operational maintenance of the subway sliding plug door system.

An equipment multiple failure causes intelligent identification method based on integrated strategy for subway sliding plug door system under variable working condition

An intelligent measurement method of the resonant frequency of ultrasonic scalpel transducers based on PSO-BP neural network

Microfluidic mixers are widely applied in biochemical detection experiments. In this study, based on the compact rectangular baffle structure, the structural optimization is performed from the perspectives of baffle length and angle parameters. To shorten the optimization time and reduce the cost of simulation calculation, the response surface method (RSM) is adopted. In the optimization process, different indicators of mixing effects have contradictory objectives and it is prone to generate local optimal solutions of design parameters of the baffle. Therefore, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is introduced to obtain the Pareto solution set of the optimal baffle parameters. The results of biochemical detection simulation experiments indicated that when the length of the baffle is 140μm, the degree of asymmetry of the velocity curve is the largest and the detection sensitivity is the highest. When the baffle angle is between 60∘ and 80∘, the microchannel fluid can generate obvious vortices near the baffle and reduce the pressure drop. The MOEA/D algorithm obtains a set of rectangular baffle parameters (l=140μm; θ=61.5°). The structural optimization model can improve the concentration distribution level and reduce the pressure drop and the mixed energy cost. 

A structural optimization model of a biochemical detection micromixer based on RSM and MOEA/D

An effective and controlled design change in product attributes can improve product quality, promote product innovation, and meet customer demands. During design changes, the links among product attributes are intricate. A change in the design of one attribute may lead to a series of changes in other attributes, which result in the change propagation as well as the re-planning and re-design of the propagation path. Therefore, an Expanded Petri Net (EPN) model is constructed to describe the propagation relationship of product attribute design change in this paper. Firstly, the attribute change propagation rules are established based on change propagation impact values. Secondly, to optimize the dynamic scheme of change propagation path, the ant colony path optimization algorithm is integrated into the Extended Petri Net model (Expanded Petri Ant Colony Optimization Algorithm, EP-ACOA). In addition, the constraint test factor and the upper and lower limits of pheromone values are introduced into the algorithm to ensure the whole network traversal of the change propagation path and accelerate the solving speed of the model. Finally, taking the attribute design change of Langevin piezoelectric transducer in an ultrasonic knife as an example, the Petri Net fusion ant colony algorithm was verified to be able to quickly and effectively provide the optimal solution or approximate optimal solution to the propagation path problem of product attribute design changes.

Propagation path optimization of product attribute design changes based on Petri Net fusion ant colony algorithm

Given the pattern matching problem between the knowledge in the Expert Knowledge Recommendation System (EKRS) established by the author of this article and user’s expertise knowledge, based on the full consideration of some constraint conditions such as user characteristics, task characteristics and matching eigenvalues, the user-knowledge integrated decision-making mathematical model (UKID) is established by using the crowdsourcing task allocation method and matrix decomposition algorithm. Aiming at the characteristics of slow convergence speed and inaccurate user-knowledge (task) matching of the UKID model in a high-dimensional space, the improved genetic matrix factorization algorithm (GMF) is designed. The genetic matrix algorithm is used to map the high-dimensional spatial data to the low-dimensional characteristic space which can improve the accuracy of task assignment. Through analysis to ‘hereditary property’ under high complexity condition, with the maximum preserved crossover operator and adaptive crossover mutation, GMF not only furthest retains the excellent characteristics of parents and improves the algorithm ‘premature properties’, but also builds up the quality of the simple genetic algorithm’s optimization capabilities and enhances the solution’s accuracy. Finally, the algorithm was experimentally verified with Amazon Public dataset and the real data extracted with EKRS. The experimental results showed that the UKID model and algorithm were applicable and could provide the decision-making guidance for the knowledge crowdsourcing task assignment.

Yi Gan

Papers

An intelligent measurement method of the resonant frequency of ultrasonic scalpel transducers based on PSO-BP neural network

A structural optimization model of a biochemical detection micromixer based on RSM and MOEA/D

Propagation path optimization of product attribute design changes based on Petri Net fusion ant colony algorithm

The user-knowledge crowdsourcing task allocation integrated decision model and genetic matrix factorization algorithm

A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology