Prediction of the height of a water-flowing fracture zone(WFFZ)is the foundation for evaluating water bursting conditions on roof coal.By taking the Binchang mining area as the study area and conducting an in-depth st...Prediction of the height of a water-flowing fracture zone(WFFZ)is the foundation for evaluating water bursting conditions on roof coal.By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness,burial depth,working face length,and roof category on the height of a WFFZ,we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height.Based on data of WFFZ height and its influence index obtained from field observations,a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine.The reliability and superiority of the prediction model were verified by a comparative study and an engineering application.The results show that the main factors affecting WFFZ height in the study area are coal seam thickness,burial depth,working face length,and roof category.Compared with multiple-linear-regression and back-propagation neural-network approaches,the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.展开更多
Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit ...Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.展开更多
针对医疗耗材种类繁多、规格复杂,难以实现有效的信息化管理,导致库存积压、滥用以及缺货的情况,提出一套完整的医院医疗耗材信息化管理系统。该系统采用云原生微服务与事件驱动混合架构,通过硬件层、网络层和云平台的三级协同设计,实...针对医疗耗材种类繁多、规格复杂,难以实现有效的信息化管理,导致库存积压、滥用以及缺货的情况,提出一套完整的医院医疗耗材信息化管理系统。该系统采用云原生微服务与事件驱动混合架构,通过硬件层、网络层和云平台的三级协同设计,实现医疗耗材全生命周期的智能化管理。在硬件层面,系统集成工业级个人数字助理(Personal Digital Assistant,PDA)设备、优化的LoRa无线通信网络和云端服务平台,构建了高可靠性的物联网基础设施;在软件层面,开发了基于支持向量回归(Support Vector Regression,SVR)的智能采购预测模型、实时库存跟踪系统和改进型贝叶斯分类处置算法三大核心功能模块。通过特征空间重构、动态权重调整和增量学习等技术,显著提升了医疗耗材管理的精准度和适应性。结果表明,该系统在事务处理能力、分类准确性和资源利用效率等方面具有显著优势,为医院提供了集智能预测、精准管控和合规处置于一体的综合管理平台,有效解决了传统医疗耗材管理中的资源浪费、响应迟滞和监管困难等关键问题。展开更多
在锂离子电池正常使用过程中,受内部化学反应和外部环境等因素影响,锂离子电池容量会在短期内出现再生现象。针对未考虑锂离子电池容量再生现象导致电池健康状态SOH(state-of-health)估计不精确等问题,提出基于层数的最优变分模态分解策...在锂离子电池正常使用过程中,受内部化学反应和外部环境等因素影响,锂离子电池容量会在短期内出现再生现象。针对未考虑锂离子电池容量再生现象导致电池健康状态SOH(state-of-health)估计不精确等问题,提出基于层数的最优变分模态分解策略OVMD(optimal variational mode decomposition),聚类容量反映整体老化趋势的低频平稳序列和反映局部容量再生的高频非平稳序列。考虑容量高频和低频分量,引入加权向量平均INFO(weighted mean of vectors)方法改进支持向量回归SVR(support vector regression),建立锂离子电池短期SOH估计模型。选取NASA电池老化数据集,设计基于反向传播BP(back propagation)神经网络、SVR、INFO-SVR、OVMD-INFO-SVR的短期锂离子电池SOH估计实验对比方案。结果表明,在减少容量训练集的情况下,基于高频和低频分量的OVMD-INFO-SVR模型在锂离子电池短期SOH的估计上具有更高的准确性。展开更多
文摘Prediction of the height of a water-flowing fracture zone(WFFZ)is the foundation for evaluating water bursting conditions on roof coal.By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness,burial depth,working face length,and roof category on the height of a WFFZ,we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height.Based on data of WFFZ height and its influence index obtained from field observations,a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine.The reliability and superiority of the prediction model were verified by a comparative study and an engineering application.The results show that the main factors affecting WFFZ height in the study area are coal seam thickness,burial depth,working face length,and roof category.Compared with multiple-linear-regression and back-propagation neural-network approaches,the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.
文摘Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.
文摘针对医疗耗材种类繁多、规格复杂,难以实现有效的信息化管理,导致库存积压、滥用以及缺货的情况,提出一套完整的医院医疗耗材信息化管理系统。该系统采用云原生微服务与事件驱动混合架构,通过硬件层、网络层和云平台的三级协同设计,实现医疗耗材全生命周期的智能化管理。在硬件层面,系统集成工业级个人数字助理(Personal Digital Assistant,PDA)设备、优化的LoRa无线通信网络和云端服务平台,构建了高可靠性的物联网基础设施;在软件层面,开发了基于支持向量回归(Support Vector Regression,SVR)的智能采购预测模型、实时库存跟踪系统和改进型贝叶斯分类处置算法三大核心功能模块。通过特征空间重构、动态权重调整和增量学习等技术,显著提升了医疗耗材管理的精准度和适应性。结果表明,该系统在事务处理能力、分类准确性和资源利用效率等方面具有显著优势,为医院提供了集智能预测、精准管控和合规处置于一体的综合管理平台,有效解决了传统医疗耗材管理中的资源浪费、响应迟滞和监管困难等关键问题。
文摘在锂离子电池正常使用过程中,受内部化学反应和外部环境等因素影响,锂离子电池容量会在短期内出现再生现象。针对未考虑锂离子电池容量再生现象导致电池健康状态SOH(state-of-health)估计不精确等问题,提出基于层数的最优变分模态分解策略OVMD(optimal variational mode decomposition),聚类容量反映整体老化趋势的低频平稳序列和反映局部容量再生的高频非平稳序列。考虑容量高频和低频分量,引入加权向量平均INFO(weighted mean of vectors)方法改进支持向量回归SVR(support vector regression),建立锂离子电池短期SOH估计模型。选取NASA电池老化数据集,设计基于反向传播BP(back propagation)神经网络、SVR、INFO-SVR、OVMD-INFO-SVR的短期锂离子电池SOH估计实验对比方案。结果表明,在减少容量训练集的情况下,基于高频和低频分量的OVMD-INFO-SVR模型在锂离子电池短期SOH的估计上具有更高的准确性。
