虚拟肝脏的规则和非规则手术切除技术

为了提高肝癌手术治疗计划的可靠性,在利用腹部CT图像数据进行肝脏三维重建的基础上,实现了任意角度对虚拟肝脏进行平面切割和切割面结构的显示;通过构造可以调节大小、位置的球体模型,实现了球体切除;通过在肝脏三维模型表面上勾画不规则封闭区域,实现了任意曲面肝脏切除;并依据肝内血管结构进行肝段划分,实现了解剖性肝段切除.临床实验结果表明,文中所述的技术能够满足虚拟手术的需求,有利于医生制定安全可靠的术前计划,提高手术成功率.     

医院排队叫号系统应用现状综述

为了减少患者在医院的滞留量和高效服务于患者,很多医院都采用了排队叫号系统。医院排队叫号系统产品繁多,医院的实施机制也各不相同,因此分析了目前医院排队叫号系统的应用现状,以及硬、软件实施情况,以求发现系统的主要组成部分和可更新、扩展部分。结合挂号方式、就诊流程优化和物联网技术分析了医院排队叫号系统的发展趋势,这对今后其他领域排队叫号系统的研究有参考意义。     

Authority updating: An expert authority evaluation algorithm considering post‐evaluation and power indices in social networks

In group assessment, the focus is on finding high‐authority experts to improve the reliability of assessment results. In this study, we propose an authority updating algorithm while considering the power and judgement reliability of an expert on the basis of social networks and post‐evaluations. A network power index is established and used to reflect the power of an expert while considering social networks. The measurement of the judgement reliability of an expert considers the post‐evaluation of the objects selected by experts, thereby more scientifically reflecting the reliability of experts. The analysis shows the following: although the social‐network structure influences the authority of experts, the influence weakens when the assessment group is a highly or even fully connected group; the network effect may increase the authority of some experts and reduce that of others, and it will weaken as the network connectivity increases; moreover, the judgement reliability and authority of an expert while considering post‐evaluation can encourage him/her to make fair assessments and strive to reduce his/her motivation and cognitive biases.     

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.     

Protect privacy of deep classification networks by exploiting their generative power

Machine Learning - Research showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a...     

Image classification method on class imbalance datasets using multi-scale CNN and two-stage transfer learning

Neural Computing and Applications - Image classification tasks widely exist in many actual scenarios, including medicine, security, manufacture and finance. A major problem that hurts algorithm...     

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Neural Computing and Applications - RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is...     

基于软件无线电的基带调制系统设计

针对通信系统中基带调制的多模式的需求,基于软件无线电原理,用FPGA设计了一种可实现2ASK、2FSK、BPSK三种调制模块,在Quartus II环境中利用VHDL语言完成了芯片程序的设计,并对其进行了仿真和功能测试,实验结果验证了用FPGA可快速实现2ASK、2FSK和BPSK基带调制功能,调制信号稳定可靠,可以用软件来控制调制方式,系统方案正确可行,而且具有良好的稳定性和灵活性.     

基于FPGA和ARM的弹上相位自动微调控制器设计与实现

为了提高相位自动微调控制器的控制性能,针对某型导弹,提出并实现了以ARM+ FPGA双微处理器为核心的硬件设计方案,采用了模糊PID控制算法,通过优化步进电机控制模式,提高了相位自动微调控制器的控制精度,将步进电机的调整时间由800 ms缩短到100 ms以内;该方案已成功应用于某型弹上系统,实践证明,该设计方案成本低、可扩展性强,能够满足系统控制需求,具有较高的实用性,大大提高了装备维护效率.     

自洁式超声波在线过滤器的研制

我国地表水浊度高,极大地影响基于光度法的在线分析仪器正常运行;以自主创新的在线采水过滤头为核心,提出一种新型的自洁式超声波在线过滤器,由超声波电源、采水和反冲洗模块、嵌入式控制模块等组成,具备定时定速过滤采水、超声波反冲清洗等功能;实验结果表明,过滤器能够连续地自动滤除水中的悬浮物质,并且不影响目标物的化学形态,对同一水样的浊度去除率维持在85%左右。