The parameterized level set method for structural topology optimization with shape sensitivity constraint factor

In recent years, the parameterized level set method (PLSM) has attracted widespread attention for its good stability, high efficiency and the smooth result of topology optimization compared with the conventional level set method. In the PLSM, the radial basis functions (RBFs) are often used to perform interpolation fitting for the conventional level set equation, thereby transforming the iteratively updating partial differential equation (PDE) into ordinary differential equations (ODEs). Hence, the RBFs play a key role in improving efficiency, accuracy and stability of the numerical computation in the PLSM for structural topology optimization, which can describe the structural topology and its change in the optimization process. In particular, the compactly supported radial basis function (CS-RBF) has been widely used in the PLSM for structural topology optimization because it enjoys considerable advantages. In this work, based on the CS-RBF, we propose a PLSM for structural topology optimization by adding the shape sensitivity constraint factor to control the step length in the iterations while updating the design variables with the method of moving asymptote (MMA). With the shape sensitivity constraint factor, the updating step length is changeable and controllable in the iterative process of MMA algorithm so as to increase the optimization speed. Therefore, the efficiency and stability of structural topology optimization can be improved by this method. The feasibility and effectiveness of this method are demonstrated by several typical numerical examples involving topology optimization of single-material and multi-material structures.

     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

OWL2Vec*: embedding of OWL ontologies

Machine Learning - Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing...     

Texture compensation with multi-scale dilated residual blocks for image denoising

Neural Computing and Applications - Deep convolutional neural networks have achieved great success for image denoising recently. However, increasing the depth of the neural network cannot...     

基于MAX16802B的水下相机控制系统设计

水下相机对海洋生物科学研究有重要意义,其控制系统的设计关键在于光源控制方案以及电源效率的提高;提出并实现了一种基于低功耗微控制器、高亮LED光源以及LED控制驱动器MAX16802B的水下相机控制系统,详细介绍了系统组成,并给出了基于低功耗策略的硬件和软件优化设计方案;实验结果表明,该设计方案电路结构简单,稳定性好,电源效率达75%以上,能够满足长时间水下拍摄工作;在实际海下测试中,按20分钟的拍摄间隔需求,水下相机可连续工作2个月以上。     

基于无线通信技术的FFU风机群控系统

针对大型洁净工程实现自动化控制的需要,利用数字无线通信技术,设计了一种新型网络化的多风机过滤器单元(FFU)群控系统,详细介绍了系统的组成实现原理和软硬件设计方法;系统间采用基于射频收发芯片nRF2401实现数据的无线收发,有效地减少了系统布线和设计成本,提高了系统的组网灵活性和适用性;对设计开发的系统进行了测试实验,实验结果表明该系统数据通信正确可靠,系统具有结构简单、组网灵活、监控效果好和可扩展性强等优点。     

基于ARM的QNMV指纹识别系统的研究

目前指纹识别技术正向着嵌入式和小型化的方向发展,搭建了基于ARM的指纹识别系统的硬件平台并对硬件进行了优化,研究了指纹识别的软件开发环境,给出了指纹识别系统的软件实现方框图和详细的指纹识别算法流程图;详细论述了全新的基于QNMV结构向量的指纹匹配算法,给出了算法优化后的一些测试数据,将指纹匹配算法与其他常见的算法进行了数据比对后,发现系统具有较好的性能指标:具有较低的误识率(FAR)和拒识率(FRR)、识别率(MRI)有了较大的提高、有效地缩短了匹配耗费时间(Time-Match);系统具有安全可靠、易于扩展、性价比高等优点。     

Timing analysis of MSC specifications with asynchronous concatenation

Message Sequence Chart (MSC) is a graphical and textual language for describing the interactions between system components, and MSC specifications (MSSs) are a combination of a set of basic MSCs (bMSCs) and a High-level MSC that describes potentially iterating and branching system behavior by specifying the compositions of basic MSCs, which offer an intuitive and visual way of specifying design requirements. With concurrent, timing, and asynchronous properties, MSSs are amenable to errors, and their analysis is important and difficult. This paper deals with timing analysis of MSC specifications with asynchronous concatenation. For an MSC specification, we require that for any loop, its first node be flexible in execution time and its any associated external timing constraint be enforced on the entire loop. Such an MSC specification is called a flexible loop-closed MSC specification (FLMSS). We show that for FLMSSs, the reachability analysis and bounded delay analysis problems can be solved efficiently by linear programming. The solutions have been implemented into our tool TASS and evaluated by experiments.     

Handling multiple objectives with biogeography-based optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability effciently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.     

改进的遮挡条件下瞳孔检测方法

受到眼睑、睫毛、反光等干扰,提取的瞳孔往往无法近似成标准的椭圆,给瞳孔精确检测带来了难度。针对遮挡问题,提出了一种改进的遮挡瞳孔精确检测方法,该方法首先获取瞳孔图像的感兴趣区域,在对该区域进行阈值分割的基础上,通过滤波去噪和扫描线法去除光源反光的影响;根据沿遮挡瞳孔的分界线进行水平旋转,应用内接平行四边形法确定瞳孔的中心;根据圆形检测理论,提出一种中心修正算法,将求得的两个圆的交点作为眼睑与瞳孔的交点,计算出旋转角度,从而精化瞳孔中心的定位;最后利用五点法计算得到的瞳孔椭圆参数作为初始值,通过最小化拟合椭圆与边缘点之间的欧氏距离,使得该椭圆在非线性最小二乘意义下是最优的。实验结果表明,该方法具有良好的鲁棒性和准确性,在遮挡不超过瞳孔面积一半的情况下,能得到较为精确的瞳孔位置和边界。