Scheduling deteriorating jobs to minimize makespan

We consider a single-machine problem of scheduling n independent jobs to minimize makespan, in which the processing time of job J_j grows by w_j with each time unit its start is delayed beyond a given common critical date d. This processing time is p_j if J_j starts by d. We show that this problem is NP-hard, give a pseudopolynomial algorithm that runs in time and O(nd) space, and develop a branch-and-bound algorithm that solves instances with up to 100 jobs in a reasonable amount of time. We also introduce the case of bounded deterioration, where the processing time of a job grows no further if the job starts after a common maximum deterioration date D > d. For this case, we give two pseudopolynomial time algorithms: one runs in O(n²d(D − d) time and O(nd(D − d)) space, the other runs in p_j)²) time and p_j) space. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 511–523, 1998     

不确定失效阈值影响下考虑设备剩余寿命预测信息的最优替换策略

为了进一步提升设备维修决策的科学性,通过建立综合设备剩余寿命预测数据与不确定失效阈值的最优维修决策模型,实现了不可维修设备的最优替换策略.构建基于非线性Wiener过程的设备性能退化模型,并采用极大似然法估计退化模型参数;提出一种基于期望最大(Expectation Maximization,EM)算法的不确定失效阈值...     

捷联惯导位置更新中的多子样算法

以地理坐标系作为导航坐标系,推导了捷联惯导位置更新中的位置增量算法;提出了基于曲线拟合的多子样(二、三、四子样)涡卷补偿算法和位置旋转补偿算法.仿真结果表明,二、三、四子样涡卷补偿算法和位置旋转补偿算法都能有效地计算出涡卷补偿和位置旋转补偿值,并且二、三、四子样涡卷补偿算法和位置旋转补偿算法的精度依次提高.     

逐步Ⅰ型混合截尾试验下Burr部件的可靠性分析

在逐步Ⅰ型混合截尾试验下,研究了Burr部件寿命参数及可靠性指标的极大似然估计和Bayes估计.利用简单迭代方法,给出了寿命参数和可靠性指标的极大似然估计的数值解.然后利用Lindely Bayes近似算法得到了平方损失下寿命参数以及可靠性指标的Bayes估计.最后,运用Monte-Carlo方法对各估计结果作了模拟比较,结果表明Bayes估计较极大似然估计的误差小.     

系统仿真技术初探

为了对系统仿真技术有一个总体认识,采用归纳和分析的方法,对系统仿真技术的体系进行了初步探讨.     

基于卷积神经网络的卫星网络协调态势评估方法

为充分发掘利用海量卫星网络数据,提高决策效率,加强空间频轨资源获取与储备的分析手段,尤其是对地球静止轨道资源的协调获取问题,提出基于机器学习算法的卫星网络态势评估策略。通过对卫星网络协调因素进行特征分析,选择卷积神经网络(Convolution Neural Network, CNN)为目标算法模型,并建立算法模型的训练数据集及Label规则,采用分裂信息增益度量方法对数据进行降维处理,建立CNN评估模型,并进行了验证分析。结果表明,CNN模型对卫星网络协调态势评估问题测试的正确率高达80%以上,具有较高的评估效能。随着数据量的增多,CNN评估效果逐步提升,是一种在卫星网络协调态势分析、资源储备的有效评估方法。     

基于改进遗传算法的舰载机保障调度方法

舰载机保障作业过程受到多资源约束,同时可能存在着突发事件的干扰;针对此特点,分析并制定干扰情况下的模型修正策略。在遗传算法中引入禁忌搜索算子改进传统遗传算法的变异操作,并通过具体实例进行仿真验证,其结果证明改进后的遗传算法在优化含干扰事件的多机保障问题时效率更高,并通过甘特图直观地反映重调度方案。为真实情况下有效处理舰载机保障过程中的干扰事件奠定理论基础。     

Gabor-CNN for object detection based on small samples

Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of military objects, as in these instances, a large number of samples is hard to obtain. In order to solve this problem, this paper proposes the use of Gabor-CNN for object detection based on a small number of samples. First of all, a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed, and the optimal Gabor convolution kernel group is obtained by means of training and screening, which is convolved with the input image to obtain feature information of objects with strong auxiliary function. Then, the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes, which improves the quality of the regional proposals. We call this regional proposal process the Gabor-assisted Region Proposal Network (Gabor-assisted RPN). Finally, the Deeply-Utilized Feature Pyramid Network (DU-FPN) method is proposed to strengthen the feature expression of objects in the image. A bottom-up and a top-down feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales. Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate, and thus has a strong application prospect.     

A novel facial emotion recognition scheme based on graph mining

Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. To reduce the number of generated sub-graphs, overlap ratio metric is utilized for this purpose. After encoding the final selected sub-graphs, binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification. Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the final system accuracy was 90.00%. The results show significant accuracy improvements (about 2%) by the proposed system in comparison to current published works in SAVEE database.     

提高近场精度的海洋声学快速场改进模型

为了提高海洋声学快速场模型在近场区域的计算精度,分析了影响经典快速场模型精度的因素,主要包括Bessel函数近似、忽略内行波项以及在水平距离最远处波数采样率过低,这些因素导致快速场模型近场误差较大、远场水平距离最远处结果不正确(计算结束后需要去除水平距离后段的声场)。提出能够提高经典快速场模型近场计算精度的改进模型,改进部分主要是采用保留内行波项的近似Bessel函数,再将近场上下两个基于声源点与对称轴的三角形区域用波数积分解(使用精确Bessel函数)覆盖。算例测试结果表明:与经典快速场模型相比,改进模型可在绝对时间增加较少的情况下,显著提高近场计算精度,综合性能更优;与波数积分法相比,改进模型在误差为同量级的情况下,积分时间大幅降低,实际应用价值更高。