基于弱变异准则的测试用例约简方法

针对为数较多的测试用例增加了回归测试成本的问题，提出一种基于弱变异准则的测试用例约简方法。首先，基于弱变异准则获得测试用例和变异分支关系矩阵；然后，重复约简4种无效测试需求和子集测试用例；最后，结合人工鱼群算法选择当前最优测试用例，并且交替执行简化和测试用例选择操作直至覆盖所有测试需求。该方法针对6个经典程序与贪心算法和HGS算法相比，基于弱变异准则并且不改变或稍微改变变异评分的条件下，约简率分别提高了73.4%和8.2%，且耗时分别降低了25.3%和56.1%。实验结果表明，所提方法在回归测试中可有效约简测试用例，降低测试代价。     

以“互联网+”促进学前教育信息化建设

云计算、大数据、人工智能等技术的快速发展，有力地促进了“互联网+教育”的普及和应用。学前教育作为“互联网+”的重要应用领域，可以充分地利用人工智能、物联网、大数据等技术，实现学前教育硬件设备的互联互通，利用大数据搜索与推荐学前教育资源，基于人工智能开发可视化、虚拟化的学前教育课程，培养儿童的学习积极性和主动性，让学生养成一种良好的学习习惯，具有重要作用和意义。     

上游泵送机械密封多目标多工况优化研究

为了获得具有较强适应工况变化能力的上游泵送机械密封,以微间隙内流场的CFD计算为基础,建立涉及多工况的多目标优化数学模型,综合分析相关研究成果和密封特性确定了优化变量及其取值范围、工况参数取值范围和优化目标。应用模糊集理论确定不同工况下的液膜刚度和泄漏量的权重因子,并研究人工神经网络模型对优化结果的影响。基于穷尽搜索法和遗传算法对密封进行优化设计,对比分析2种优化方法得到的优化结果,并对优化前后的密封润滑膜流场特性进行BVF诊断分析。优化研究表明:人工神经网络模型很好地拟合了液膜刚度、泄漏量随槽型参数变化的函数关系;2种优化方法都能提高密封性能,但遗传算法优化比穷尽搜索法优化能获得更好的优化结果。     

A Colorimetric Artificial Olfactory System for Airborne Improvised Explosive Identification

The detection of ultralow or nonvolatile target analytes remains a significant challenge for artificial olfactory systems even after decades of development, which severely limits their widespread application. To overcome this challenge, an artificial olfactory system based on a colorimetric hydrogel array is constructed for the first time as a universal representative. As an effective extension of conventional artificial olfactory systems that integrates the merits of its predecessors, the proposed system accurately mimics olfactory mucosa and specific odorant binding proteins using hydrogels endowed with specific colorimetric reagents for the detection of hypochlorite, chlorate, perchlorate, urea, and nitrate. Therefore, the proposed system is capable of detecting and discriminating between these five airborne improvised explosive microparticulates with a detection limit as low as 39.4 pg. Additionally, the system demonstrates good reusability over ten cycles, rapid response time of ≈0.2 s, and excellent discrimination properties, despite significant variation. This proof-of-concept study on colorimetric artificial olfactory systems yields a novel strategy for the direct and discriminative detection of nonvolatile airborne microparticulates.     

Can Tolerant Diatom Taxa be Used for Effective Assessments of Human Pressure?

Because lately the low bioindicative value of tolerant species from different taxonomic groups is often questioned, in this study, we hypothesized that tolerant diatoms may be used for effective temporal assessments of human pressure. We tested this on the lowland Bzura River in central Poland, on 156 diatom samples (DSs) from two study periods of 1972 with extremely severe point‐source water pollution and of 2002–2004, where the complex wastewater management in the river catchment improved water quality considerably. Out of the total 295 diatom species recorded in the samples, only 133 tolerant ones were included in the study. Patterns in their abundance were recognized with a Kohonen artificial neural network (self‐organizing map, SOM), whereas the species significantly associated with each SOM cluster of diatom samples were identified with the indicator value (IndVal) index and the Monte Carlo test. If the hypothesis tested was not supported, the assignation of DSs to the SOM clusters would be random. However, the separation of DSs from 1972 and 2002–2004 was almost perfect because the number of exceptions was as low as 4.5%. In addition, the relatively high number (64 out of 133) of tolerant species significantly associated with any SOM cluster (and respective environmental conditions at sites from which samples assigned to it come) supports the hypothesis tested. This means that almost half of the studied species are not evenly distributed in particular clusters as might be expected for highly tolerant species. The study proves that the abundances of many diatom species, currently classified as tolerant, carry quite precise information on the differences in the quality of the environment, and indicates those tolerant diatom species whose bioindicative potential may be highest. Copyright © 2014 John Wiley & Sons, Ltd.     

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

综采长壁工作面推进速度对优势瓦斯通道的诱导与控制作用

基于采动裂隙椭抛带理论，确定了采动优势瓦斯通道带的上、下边界，构建了采动优势瓦斯通道带的时空形态理论模型。以淮北祁南矿34下采区为原型，开展了上覆岩采动瓦斯通道扩展数值仿真研究，发现优势瓦斯通道在上覆岩的空间位置随推进速度的增加而降低，发育高度、宽度和范围随推进速度的增加而减小等特征。以此为依据，对综采工作面推进速度加快后的高位钻孔参数进行了相应调整，并在祁南矿34下2工作面开展了卸压瓦斯抽采人工导流试验，现场试验结果验证了高位钻孔参数依据推进速度而进行的优化是合理的，从而也验证了本文提出的推进速度对优势瓦斯通道的诱导与控制规律是正确的。     

Particle Swarm Optimization inspired by starling flock behavior

Swarm intelligence is a meta-heuristic algorithm which is widely used nowadays for efficient solution of optimization problems. Particle Swarm Optimization (PSO) is one of the most popular types of swarm intelligence algorithm. This paper proposes a new Particle Swarm Optimization algorithm called Starling PSO based on the collective response of starlings. Although PSO performs well in many problems, algorithms in this category lack mechanisms which add diversity to exploration in the search process. Our proposed algorithm introduces a new mechanism into PSO to add diversity, a mechanism which is inspired by the collective response behavior of starlings. This mechanism consists of three major steps: initialization, which prepares alternative populations for the next steps; identifying seven nearest neighbors; and orientation change which adjusts velocity and position of particles based on those neighbors and selects the best alternative. Because of this collective response mechanism, the Starling PSO explores a wider area of the search space and thus avoids suboptimal solutions. We tested the algorithm with commonly used numerical benchmarking functions as well as applying it to a real world application involving data clustering. In these evaluations, we compared Starling PSO with a variety of state of the art algorithms. The results show that Starling PSO improves the performance of the original PSO and yields the optimal solution in many numerical benchmarking experiments. It also gives the best results in almost all clustering experiments.