A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

六面顶金刚石压机液压系统压力及超高压优化控制

针对六面顶人造金刚石压机的复杂生产流程要求,设计开发了六面顶人造金刚石压机液压系统的计算机控制装置.其中液压系统压力控制部分采用外置模拟式PI调节器,可根据生产工序的要求自动优化控制器设定值;超高压控制部分采用智能预测与纯比例控制相结合的控制策略,明显提高了人造金刚石压机的液压控制精度.该控制装置已投入实际运行,在改善产品质量品级方面取得了显著的经济效益.     

水泥回转窑的智能控制

文章介绍了PIT Navigator对水泥熟料生产自动化控制的实现,基于神经网络的自学习、自适应控制元件,将相关的过程数据记录下来,随后借助数据关联性进行分析,在预测和自学习过程模型(模型预测控制)中处理这些数据,并计算过程参数的实时预测,使用这些工具,可以实现更稳定的窑况,同时提高产量和降低能耗。     

Human tracking system based on adaptive multi-feature mean-shift for robot under the double-layer locating mechanism

Human tracking has been a challenging task for robot in the past decades. In this paper, to realize the human following in a cluttered environment, a human tracking system based on adaptive multi-feature mean-shift (AMF-MS) under the double-layer locating mechanism (DLLM) is proposed to solve the problem of distinguishing target, occlusion, and quick turning. The DLLM, considering the course location processing and fine location processing, is designed to estimate the person’s position using the fusion of heterogeneous data. As an ID tag attached on target can be detected by RF antennas, the course locating method can track the target easily and quickly. The Bayes rule is introduced to calculate the probability where the tag exists due to the instability of RF signals. In the fine locating step, the AMF-MS is proposed because it can reduce computational load and represent target by multi-feature histogram function. Meanwhile, we combine extended Kalman filter and AMF-MS to overcome MS’s inability of occlusion. To control the robot following the target person precisely, an intelligent gear shift strategy based on fuzzy control is implemented by analyzing the robot structure. Experiments demonstrate that the proposed approach is robust to handle complex tracking conditions, and show the system has an optimum performance.     

基于通用即插即用的服务监控机器人在智能家居中的应用

针对智能家居系统中多种异构电子设备难以与其他设备集成的问题,采用一种通用即插即用(UPnP)方式将服务机器人集成到智能家居系统中,着重介绍了集成后衍生的新服务.首先系统地介绍了UPnP标准和服务机器人Rovio;然后开发一个PC应用程序作为服务机器人和家庭智能网络之间连接的虚拟桥,形成服务机器人为智能家居网络提供服务的控制回路.最后通过一些实际操作来验证UPnP Rovio虚拟桥的性能,旨在探索服务机器人集成到智能家居的可能性和一些衍生的先进服务.通过路径跟踪、垃圾检测等一些先进服务的实际演示验证了通过UPnP方式将服务机器人集成到智能家居网络中的可行性,并展示了智能家居互通后的巨大潜在利益.     

智能电动车辆横纵向协调与重构控制

针对具有高度非线性、强耦合和冗余特性的智能电动车辆运动控制问题,提出了一种由协调控制律和控制分配律组成的横纵向综合控制新方法.首先,建立准确表征智能电动车辆行为机理的动力学模型;其次,采用非奇异滑模控制技术,引入非线性滑动模态切换面,设计有效克服非线性及不确定特性的协调控制律,保证系统状态在有限时间内收敛至平衡点;在此基础上,考虑到轮胎存在冗余和耦合特性,提出基于内点法的控制分配算法来完成期望广义力/力矩的优化分配,实现轮胎横纵向力的协调与重构.仿真结果表明了该方法的有效性.     

基于信息资源聚合的农民专业合作社信息系统

针对农民专业合作社信息获取与信息发布难、成本高的问题,提出基于XM L Schema的农民专业合作社信息聚合元数据表示规范,设计数据传输流程和信息定制与智能推送机制,建设两层部署体系的农民专业合作社信息系统。系统信息服务中心与农业搜索引擎（AgSoso）相结合,为远端用户系统提供最新的信息资源;系统客户端实现合作社生产经营信息的本地化管理,并向信息服务中心同步最新的农产品等信息。应用结果表明,该方案投入成本、维护成本低,资源利用效率高,具有较高的实用应用价值,为农民专业合作社提供了一套基于多来源数据聚合服务的技术解决方案。     

基于工作流和智能手机的城市综合管理平台

为适应现代城市综合管理的需要,提高城市管理水平,提出利用GIS技术的二三维一体化联动提高管理平台可视化;基于工作流的管理使事件处理更加自动化;利用智能手机的方便使管理人员与系统进行动态交互;利用模糊数学提高任务完成质量评价的科学性。整个平台在Windows7下进行开发,基于B/S(浏览器/服务器)和C/S(客户端/服务器)结合的架构,运用JavaEE、工作流、模糊数学、无线通信、视频融合等技术,实现平台对交通灯、人员、车辆、消防、事件和任务的管理。     

基于FPGA的多路矿用气体传感器智能调校系统

矿用气体传感器因煤矿安全被大量使用,国家规定必须定期进行调校,目前的传感器调校系统可同时对5-8台传感器进行调校,针对大中型煤矿生产过程中需要调校的矿用气体传感器数量大、调校耗时长的现状,提出了一种基于单片机与现场可编程门阵列（FPGA）的64路气体传感器调校系统,介绍了系统的整体设计并详细阐述了该系统的气路控制模块和传感器输出信号采集模块,使用Verilog语言在QuartusⅡ工具中实现FPGA的逻辑设计、仿真和综合。结果证明：该系统能有效地完成对64路气体传感器同时调校的目的,很大程度节省了人力消耗,提高了调校效率,节约了调校时间。     

粮情智能测控系统中的数据融合技术研究

粮情智能测控系统是保证粮食储藏安全的关键措施之一。针对目前粮情智能测控系统中单一传感器存在测量不足、粮情安全状态难以判断且无法直接检测等问题,研究了一种基于粒子群优化最小二乘支持向量机的粮情安全状态数据融合方法。以粮情数据为研究对象,以粮情安全状态检测为目的,构建基于最小二乘支持向量机的数据融合模型,并采用粒子群优化算法对最小二乘支持向量机的关键参数进行寻优,获取最佳的数据融合结果。根据广西某粮库提供的数据进行实例分析,数据融合结果与实际值误差较小,均方误差为0.06,结果表明,该方法在粮情安全状态的检测上表现出优越的性能,具有科学性和可行性,提高了粮情智能测控系统的准确性和可靠性。