我国中小体育用品生产企业产业集群集聚研究——以福建晋江运动鞋产业集群为实证

入世后,我国中小体育用品业面临着国外知名品牌企业的竞争威胁,引起了我国业内人士的高度重视;产业集聚战略已成为提升区域竞争力的重要措施之一。以晋江运动鞋行业为例证,分析了我国中小体育用品生产企业产业集群集聚竞争优势和形成条件

厦门后溪水质与流域景观特征沿城乡梯度的关系分析

国家重点研发计划“全球变化及应对”重点专项（２０１７ＹＦＡ０６０５２０３）；国家自然科学基金项目（３１３７０４７１）；中国科学院城市环境与健康重点实验室跨组合作项目（ＫＬＵＥＨ?Ｃ?２０１８０１

Self-adaptive coding for spiking neural network

脉冲神经网络(SNN)采用脉冲序列表征和传递信息，与传统人工神经网络相比，更具有生物可解释性。但典型SNN的特征提取能力受到其结构限制，对于图像数据等多分类任务的识别准确率不高，不能与卷积神经网络(CNN)相媲美。针对该问题，提出了一种新型的自适应编码脉冲神经网络(SCSNN)，将CNN的特征提取能力和SNN的生物可解释性结合起来，采用生物神经元动态脉冲触发特性构建网络结构，并设计了一种新的替代梯度反向传播方法直接训练网络参数。所提出的SCSNN网络分别在MNIST数据集和Fashion-MNIST数据集做了验证，取得较好的识别结果，在MNIST数据集上准确率达到了99.62%，在Fashion-MNIST数据集上准确率达到了93.52%，验证了本模型的有效性。</p

A prey-predator model for efficient robot tracking

Tracking is a common topic in various areas of robotics research. Motivated by the hunting behavior of predators in nature, we propose a prey-predator model for efficient robot tracking. The head direction and speed of the pursuer is automatically adjusted according to the position and velocity of the prey. Under the situation with perception uncertainty, where the actual location of the prey is not observable, the pursuer predicts the location of the prey according to simple inference, an online adaptive autoregressive model, or an online adaptive echo state network. Simulation results demonstrate that the proposed prey-predator model is able to control the pursuer and to track the prey efficiently, even under perception uncertainty. Simple inference gives better results when the motion of the target is piecewise linear, while echo state network is more suitable when the dynamics of the target are more complex. The proposed prey-predator model thus provides an efficient method tracking targets with various statistical nature of trajectories for applications such as underwater robot tracking, human tracking and team formation

A transfer weighted extreme learning machine for imbalanced classification

Previous class imbalance learning methods are mostly grounded on the assumption that all training data have been labeled, however, is impractical in many real-world applications. The limited amount of labeled instances may produce a classifier with poor generalization. To address the issue, a transfer weighted extreme learning machine (TWELM) classifier is proposed, with the purpose of extracting knowledge from other domains to improve the classification performance of a classifier in a limited labeled target domain. To be specific, a well-tuned weighted extreme learning machine classifier is first learned from source data that has been completely labeled. Subsequently, another extreme learning machine classifier is obtained from the limited labeled target domain data to preserve the target domain structural knowledge and the decision boundary information. Finally, the target classifier is optimized by minimizing the outputs of the two classifiers on unlabeled target data. Experimental results on real-world data sets show that TWELM outperforms existing algorithms on classification accuracy and computation cost.</p

Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback-Leibler divergence

Due to its advantages in size and energy consumption, mechanical scanning imaging sonar (MSIS) has been widely used in portable and economic underwater robots to observe the turbid and noisy underwater environment. However, handicapped by the coarseness in spatial and temporal resolution, it is difficult to stitch the scan pieces together into a panoramic map for global understanding. A registration method named symmetrical Kullback-Leibler divergence (SKLD)-distribution-to-distribution (D2D), which models each scan as a Gaussian mixture model (GMM) and evaluates the similarity between two GMMs in a D2D way with the measure defined by SKLD, is proposed to register the scans collected by MSIS. SKLD not only weights the difference between distributions with the prior probability but also increases the numerical stability with the symmetrical constraint in distance measure. Moreover, an approximation strategy is designed to derive a tractable solution for the KLD between two GMMs. Experimental results on the scans that were collected from the realistic underwater environment demonstrate that SKLD-D2D dramatically reduces the computational cost without compromising the estimation precision. (C) 2019 SPIE and IS&

A novel oversampling technique based on the manifold distance for class imbalance learning

Oversampling is a popular problem-solver for class imbalance learning by generating more minority samples to balance the dataset size of different classes. However, resampling in original space is ineffective for the imbalance datasets with class overlapping or small disjunction. Based on this, a novel oversampling technique based on manifold distance is proposed, in which a new minority sample is produced in terms of the distances among neighbours in manifold space, rather than the Euclidean distance among them. After mapping the original data to its manifold structure, the overlapped majority and minority samples will lie in areas easily being partitioned. In addition, the new samples are generated based on the neighbours locating nearby in manifold space, avoiding the adverse effect of the disjoint minority classes. Following that, an adaptive adjustment method is presented to determine the number of the newly generated minority samples according to the distribution density of the matched-pair data. The experimental results on 48 imbalanced datasets indicate that the proposed oversampling technique has the better classification accuracy.</p