微乳液法制备碳酸铅

以醋酸铅和碳酸钠为原料,选择优化的工艺条件,用微乳液法制备得到碳酸铅.结果表明,在S-60为分散剂的微乳液体系中制备得到超细碳酸铅,粒度分布均匀.采用正交实验法得到最佳实验条件:稀释5倍的S-60为分散剂,碳酸铅溶液pH值为2.0,碳酸铅浓度为0.01 mol/L,搅拌速度为1200 rpm.     

汽车牌照识别系统的设计与实现

基于图像处理技术设计与实现了汽车牌照识别系统.     

A quantitative solution to Constraint Satisfaction Problem (CSP)

Constraint Satisfaction Problem (CSP) is an important problem in artificial intelligence and operations research. Many practical problems can be formulated as CSP, i.e., finding a consistent value assignment to variables subject to a set of constraints. In this paper, we give a quantitative approach to solve the CSPs which deals uniformly with binary constraints as well as high order,k-ary (k ≥ 2) constraints. In this quantitative approach, using variable transformation and constraint transformation, a CSP is transformed into a satisfiability (SAT) problem. The SAT problem is then solved within a continuous search space. We will evaluate the performance of this method based on randomly generated SAT problem instances and regularly generatedk-ary (k ≥ 2) CSP problem instances.     

实现“清洁生产”是防治工业污染的根本途径

本文论述了实现“清洁生产”是从根本上消除工业污染,促进环保与经济协调发展的有效途径;指出过去因忽略了生产全过程的控制和工艺进步等关键性环节,造成“先污染,后治理”的事倍功半的后果;提高了实现“清洁生产”的途径和实现“清洁生产”的必要性和紧迫性。     

Neural network-based robust finite-time attitude stabilization for rigid spacecraft under angular velocity constraint

Neural Computing and Applications - In this paper, the problem of attitude stabilization control of spacecraft under angular velocity constraint is investigated. A state-constrained finite-time...     

Single image super-resolution using global enhanced upscale network

Current works on super-resolution have obtained satisfactory results since the advance of the convolution neural network. Nevertheless, most previous works use one network for one integer scale factor so ignore the super-resolution of the arbitrary scale factor. In this work, we propose a novel approach called Global Enhanced Upscale Network (GEUN) to tackle super-resolution with a single model adapting the arbitrary scale factor. In our GEUN, we propose the Global Enhanced Upscale module to replace the conventional upscale module. Our GEUN can upscale low-resolution images with an arbitrary scale factor through only one model. Extensive experimental results demonstrate the superiority of our GEUN.

     

Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network

Computational Economics - The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory...     

Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy

Computational Economics - Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other...