Potato ( Solanum tuberosum L.), as the world’s fourth largest food crop, plays a crucial role in ensuring security through its disease resistance. The RPP13 gene family is known to play pivotal plant resistance responses; however, specific functions potato remain unclear. In this study, we conducted first comprehensive identification and analysis of 28 members potato, examining their structures, chromosomal locations, expression patterns, functional characteristics. Gene structure revealed...

Research on the intelligent fault diagnosis method of rolling bearing based laboratory data has made some achievements. However, due to change working conditions and lack historical same equipment in actual diagnosis, methods mostly have problems such as poor generalization. Model training verification are insufficient, engineering practice still lacks effective methods. In this paper, we propose a weighted k-nearest neighbor (WKNN) model multi-dimensional sensitive features, for bearings...

As an efficient disposal method of food waste, anaerobic digestion (AD) for biogas production is widely used. In order to understand the enhanced efficiency and stability AD by appropriate amounts ammonia volatile fatty acids (NH4 (+)/VFAs), characteristics corresponding microbial community with ammonium acetate supplement were investigated denatured gradient gel electrophoresis (DGGE) pyrosequencing analyses samples, or without NH4 (+)/VFAs.In this study, four different strategies adding...

Opioids are the main analgesic drugs used in perioperative period, but they often have various adverse effects. Recent studies shown that quadratus lumborum block (QLB) has an opioid sparing effect. The aim of this study was to further evaluate effect opioid-free anesthesia (OFA) combined with regional on quality recovery patients undergoing retroperitoneoscopic renal surgery.

Due to the importance of bearings in modern machinery, prediction remaining useful life (RUL) rolling has been widely studied. When predicting RUL engineering practice, is usually predicted based on historical data, and as data increases, results should be more accurate. However, existing methods have shortcomings low accuracy, large cumulative error failure dynamically give with increase which are not suitable for practice.To address above problems, a novel method proposed. The proposed...

Considerable studies have been carried out in recent years regarding fault diagnosis and prediction for the rotating machinery industrial plants. However, few works present use of clustering approaches applied to time series diagnose machine faults. With increasing practical requirement safety, reliability, availability maintainability running, predictive maintenance based on technologies has also received significant attention years. In study, under Cyber-physical systems (CPS) condition,...

摘要: 面对全球性的能源危机和环境污染,量大面广的挖掘机亟须在技术上寻求新的解决方案。油电混合动力技术采用发动机和电机复合驱动来改善发动机燃油经济性并可进行能量回收,是公认的节能减排最佳方案之一,已在车辆领域取得了卓有成效的进展。由于挖掘机与车辆在负载工况、能量回收途径、储能装置、操作性及可靠性等方面存在显著差异,须对挖掘机的混合动力技术开展专项研究。概述国内外、特别是浙江大学流体动力与机电系统国家重点实验室在动力复合模式与参数优化、动力系统控制、电动回转及制动能量回收、动臂势能回收及能量回收电机等油电混合动力挖掘机关键技术方面的解决方案及研究成果,并在研制的20 t混合动力挖掘机综合试验样机上得到充分验证。这些研究成果对油电混合动力技术在其他工程机械上的应用推广也具有重要意义。

Poor model generalization, missing or false alarms, and heavy dependence on expert's experience are some of the major problems which exist in traditional incipient fault detection (IFD) methods. An IFD rolling bearing application method based combination improved λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> trend filtering (L1TF) support vector data description (SVDD) is proposed. First, spectral distance index multi-scale...

Chemical sensors drift is a slow change in sensitivity that occurs time which makes it difficult to construct an appropriate sensor treatment. The main purpose of this paper study new methodology for series prediction chemical based on LSTM (long short-term memory, LSTM) recurrent neural network, including data preprocessing and partition, network training, prediction. This technique can mine the deep information signals instead manual extraction match complex nonlinearity much more exactly....

In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic bearing fault identification model including support vector machine (SVM) training module, classification knowledge base and automatic module proposed. A generalized method for faults based on refined composite multi-scale dispersion entropy (RCMDE) developed. First, order to solve problem setting value range decomposition level K empirical variational modal (VMD),...

The integration of sensors array and pattern recognition to replace large scale analytical instruments is an important feasible method for accurate rapid measurement atmospheric pollution gases. This paper proposed a quantitative detection mixed gases based on long short-term memory (LSTM) recurrent neural network, including data pre-processing, network structure design, model training prediction process. can extract the deep characteristics array's responses automatically match complex...

Due to the potential of achieving high speed and precision, linear motor driven gantry systems are widely used in industrial applications such as machine tools, semiconductor manufacturing equipments microelectronics equipments. To have large enough driving force, a H-type structure with dual parallel motors is usually adopted. Though dual-motor can deliver higher power, “pull drag” problem between two exists when they controlled separately. In this paper, synchronous control scheme proposed...

In this paper we study the effect of networks with long time delay in feedback loop a control system. A new model NCSs was proposed using buffering method and uncertain can be converted to switch some certain delays. Thus networked system is modelled as discrete-time Markov jumping linear (MJLS). Based on results for MJLS, performance measured via an H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> norm from disturbance error output...

摘要: 传统在线监测系统未能实现早期故障预警,旋转机械状态劣化评估采用固定阈值分级报警方法,存在较多的误报警和漏报警现象,难以指导企业设备预测性维修开展,设备运行安全性、可靠性、利用率难以保障。立足于工程应用,研究基于小波包分解、动态核主成分分析、T2统计分析、Beta分布预警控制限自学习等技术,构建数据驱动基于振动信号分析的旋转机械早期故障检测模型。应用辛辛那提大学智能维修系统中心滚动轴承试验数据和中国某石化公司加氢裂化装置P3409A离心泵轴承"运转到坏"的在线监测振动数据,对构建的设备早期故障检测模型进行验证,结果表明,构建的设备早期故障检测模型,相比传统固定阈值分级报警方法,能够检测滚动轴承早期故障并实现早期故障准确告警,能够有效降低错误报警率和漏报警率。构建的基于振动信号的旋转机械早期故障检测模型,只需要知道监测部件正常运行状态历史数据,无需外部专家支持,实时数据驱动即可实现早期故障检测预警。

Many researches have been carried out on incipient fault prediction technology for key machine components (such as bearings) based historical and real-time condition monitoring data. However, there is still lack of well-understood systematic methodologies detecting rotating machines. Based learning technology, this paper studies an model applying with wavelet packet decomposition dynamic kernel principal component analysis (WPD-DKPCA) to meet the needs engineering applications. The WPD-DKPCA...