基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

改进Elman神经网络在短期热负荷预测中的应用

热电厂的短期热负荷预测在城市集中供暖中起着至关重要的作用,直接影响热电厂的经济效益和热能利用率。电厂的短期热负荷一般采用神经网络预测模型进行预测,而BP神经网络应用最为广泛。Elman神经网络算法在BP神经网络基础上加入了承接层,作为一步延时算子,实现记忆能力,使系统具备适应时变能力,增强系统全局稳定性。但Elman神经网络算法模型的构造依然需要大量样本的支撑,而且输入层的变量多,导致预测时间依然很长,收敛速度慢。该文在Elman神经网络预测前,进行了相关系数预处理和对样本中异常值的平均化预处理,通过数据归一化运算,使Elman神经网络输入层变量大幅减少。仿真实验表明,改进的Elman神经网络算法使预测模型快速寻优,减少预测时间的同时明显提高预测精度。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Deformation-Strength Relationships in Short-Term Tension of Heat-Resistant Materials

The analysis of 124 curves obtained in short-term tensile tests demonstrate that they can be described by varying strain hardening and softening characteristics. Different stress–strain curves can be produced at invariable yield strength and ultimate strength and interrelated proportional variations of the above characteristics. To determine some specific stress–strain curve, it is necessary to take account of yield strength and ultimate strength as well as strain corresponding to the latter. The relations between yield strength, ultimate strength and hardening and their practically complete absence between these parameters and softening were statistically established.     

Treatment of depressive symptoms during short-term rehabilitation: An attempted replication of the DOUR project.

Objective: To replicate and extend P. A. Lichtenberg and colleagues' (1996) cross-disciplinary intervention to improve physical and mental health among older adults. Participants: 14 depressed older adults (6 treatment, 8 control). Setting: The short-term rehabilitation unit of an urban nursing home. Intervention: Occupational therapists were trained to treat depression using pleasant events and cognitive-behavioral therapies. Outcome Measures: Geriatric Depression Scale, the Short Form-12, and the Multi-Level Assessment Instrument: Activities of Daily Living. Results: No significant group differences were found in physical or mental health. However, more control group members (75%) than treatment group members (33%) were depressed at study completion. Conclusions: The treatment of depressive symptoms can be integrated with a nonmental health treatment modality. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

风电典型机型的闪变严重度分析

风力发电机组的并网运行对当地电网的电能质量有不良影响。为此．根据定速机型、变速机型引起电压波动和闪变的原理及并网风电机组电能质量的国际电工标准IEC61400—21，讨论了测量风电机组输出闪变的方法，给出了两种典型机型的短时闪变严重度与风速的关系实验曲线，并对不同风速段的闪变成因进行了分析。结果表明，变速风电机组在高风速区域综合应用桨叶节距角和变流器控制实现输出功率的稳定能够减小短时闪变严重度，在低风速区域追求最佳风能利用系数将使闪变严重度值增高。     

The Benefits of a "Sidelong" Approach to Self-Esteem Need Satisfaction: Comment on Crocker and Park (2004).

J. Crocker and L. E. Park (2004) have achieved an admirable integration of the self-esteem literature with their claim that self-esteem is better conceived of as a dynamic human striving, rather than as a passive state or personality characteristic. However, the costs of self-esteem striving may be overstated--these costs may arise only in certain constrained cases. Also, although Crocker and Park suggested that self-esteem is not a true psychological need, there is evidence that humans in all cultures need to feel a positive sense of self-worth (K. M. Sheldon, in press). Problems may arise only when people strive too directly for this feeling, rather than deriving it as a natural concomitant of non-self-focused goals. A "sidelong" approach to self-esteem need satisfaction is advocated in this commentary. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Discrete-time adaptive sliding mode power system stabilizer with only input/output measurements

In this paper a discrete-time adaptive sliding mode control method is newly developed and applied to the power system stabilization problem. A controllable canonical form of state space realization is constructed using the parameters identified by the on-line recursive least squares method and the system state is estimated from the input/output measurements and the simple state transformations. The identified parameters and the estimated state are then used by the discrete-time sliding mode control, which is suitable for the digital equipment. The most important advantage of the proposed power system stabilizer (PSS) is that it is able to maintain its regulating performance with a slower sampling period than that of the conventional sliding mode PSS because it is developed in a pure discrete-time domain. Another advantage of the proposed PSS is that it needs neither a mathematical model of the power system nor the full-state measurements because they are identified through on-line identifications. Several computer simulations for the linear power system are performed to verify the performance of the proposed PSS. In the computer simulations for various circumstances which are probable in a power system are considered, such as transitions of the active and reactive powers, change of parameters of the synchronous machine, line-to-ground faults and measurement noise. As a result, a new power system stabilizer which can operate in a wide range of operating conditions and can overcome various disturbances and measurement noises is proposed.     

澜沧江流域水电开发融资创新与实践

云南华能澜沧江水电有限公司运用企业资产证券化、短期融资券、商业票据融资等融资手段，拓宽了项目融资渠道，优化了融资结构，降低了融资成本和工程造价。实践表明，“华能澜沧江水电收益专项资产管理计划”及“澜电收益凭证”成功实施与发行效果显著．商业票据融资潜力巨大。     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.