Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

A Hybrid Handover Forecasting Mechanism Based on Fuzzy Forecasting Model in Cellular Networks

As the increasing demand for mobile communications and the shrinking of the coverage of cells, handover mechanism will play an important role in future wireless networks to provide users with seamless mobile communication services. In order to guarantee the user experience, the handover decision should be made timely and reasonably. To achieve this goal, this paper presents a hybrid handover forecasting mechanism, which contains long-term and short-term forecasting models. The proposed mechanism could cooperate with the standard mechanisms, and improve the performance of standard handover decision mechanisms. Since most of the parameters involved are imprecise, fuzzy forecasting model is applied for dealing with predictions of them. The numerical results indicate that the mechanism could significantly decrease the rate of ping-pong handover and the rate of handover failure.

The Anomaly Field of Subsurface Fluids and Its Formation and Evolution Mechanism Before Three Strong Earthquakes in the Northern North China Area

The data of pre-seismic subsurface fluid anomalies of such earthquakes as Datong-YanggaoM_s6.1 event on Oct.19,1989,western Baotou M_s6.4 event on May 3,1996 and Zhangbei-Shangyi M_s6.2 event on Jan.10,1998 are systematically collected and arranged.Then thefeatures of patterns,spatial distribution,time variation and time-spatial evolution of theseanomalies are compared and comprehensively analyzed.Then the formation and evolutionmechanism of medium-and short-term anomaly field of subsurface fluids in the northernNorth China area is proposed.The results show that the medium-term anomaly field is causedby regional tectonic activities,which further strengthen the local tectonic activities andpromote the formation and evolution of the seismic source body.The enhancement of loealtectonic activities causes the formation of anomaly field of short-term subsurface fluids,andthe evolution of source body engenders the source-precursor anomalies of subsurface fluids inthe epicenters at imminent stage.

Short-term train arrival delay prediction:a data-driven approach

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.

Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

An Analysis of China's Physician Salary Payment System

Physician payment system （PPS） is a principal incentive system to motivate doctors to provide excellent care for patients. During the past decade, physician remuneration in China has not been in proportional to physician＇s average work load and massive responsibilities. This paper reviewed the constitution of the PPS in China, and further discussed the problems and issues to be addressed with respect to pay for performance. Our study indicated that the lower basic salary and bonus distribution tied to ＂profits＂ was the major contributor to the physician＇s profit-driven incentive and the potential cause for the speedy growth of health expenditures. We recommend that government funding to hospitals should be increased to fully cover physicians＇ basic salary, a flexible human resource and talent management mechanism needs to be established that severs personal interest between physicians and hospitals, and modern performance assessment and multiplexed payment systems should be piloted to encourage physicians to get the more legitimate compensation.

Multi-head attention-based long short-term memory model for speech emotion recognition

To fully make use of information from different representation subspaces,a multi-head attention-based long short-term memory(LSTM)model is proposed in this study for speech emotion recognition(SER).The proposed model uses frame-level features and takes the temporal information of emotion speech as the input of the LSTM layer.Here,a multi-head time-dimension attention(MHTA)layer was employed to linearly project the output of the LSTM layer into different subspaces for the reduced-dimension context vectors.To provide relative vital information from other dimensions,the output of MHTA,the output of feature-dimension attention,and the last time-step output of LSTM were utilized to form multiple context vectors as the input of the fully connected layer.To improve the performance of multiple vectors,feature-dimension attention was employed for the all-time output of the first LSTM layer.The proposed model was evaluated on the eNTERFACE and GEMEP corpora,respectively.The results indicate that the proposed model outperforms LSTM by 14.6%and 10.5%for eNTERFACE and GEMEP,respectively,proving the effectiveness of the proposed model in SER tasks.

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems.The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays.Relied on learning an amount of information from given data,the long short-term memory(LSTM)method and multi-layer neural networks(MNN)method are applied to predict solutions.Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio,single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale.The predicted results by deep learning algorithms are well-agreed with experimental data.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

可再生能源投资的政企随机演化博弈研究——基于动态碳价视角

针对传统确定性演化博弈模型的不足,引入几何布朗运动模型模拟动态的碳价参数,构建了具有随机支付矩阵的演化博弈模型,以全国碳市场背景下的发电企业为例,研究了政企双方的演化过程和策略选择,探究了不同因素对演化均衡和政企决策的影响.结果显示:碳价是影响政企决策的重要因素,碳价较低时,政企的最优决策分别是选择作为策略即采取奖惩措施和不投资可再生能源,碳价较高时最优决策转变为不作为和投资.火力发电的成本、收益和碳排放系数以及可再生能源发电的成本、收益和建设成本是影响政府和发电企业策略选择的关键因素.发电企业投资意愿与政府的奖惩力度正相关,政府作为意愿与奖惩力度负相关,短期内提高政府的奖惩力度可激励发电企业的投资行为,但会缩短政府作为时长.

我国不同付费模式信用评级校验机制研究

我国债券市场刚性兑付被打破后,不断出现高评级债券违约事件。为此,监管部门积极发展多种评级,以期提高债券评级质量。通过理论建模与实证分析,研究了不同付费模式信用评级如何综合发挥校验作用。基于博弈模型的理论分析发现,不同付费模式评级发挥校验作用的机制在于投资者的反应:当投资者付费和发行人付费两种模式评级差值增大,投资者不以单一评级结果进行债券定价,而是根据评级差值调整债券价格。基于2011—2020年企业债评级数据的经验证据显示,随着评级差值增大,发行人付费评级机构会降低后续评级,且评级差值对单一信用评级与债券信用利差之间的负向关系具有抑制作用。进一步检验发现,发行人付费评级机构提供的偏正面的私有信息会降低债券信用利差,而以中债资信为代表的投资者付费评级机构提供的私有信息则不会显著影响债券信用利差,均体现出单一评级模式在定价方面的弊端。因此,监管部门应继续鼓励发展投资者付费评级模式,并鼓励机构投资者发展内部评级,以充分发挥多种评级模式的交叉校验作用。

价值支付视角下门诊按人头付费的理论内涵和改革建议

按人头付费是医保门诊统筹改革的核心任务之一。价值支付视角下按人头付费应具有控制医疗成本、提升服务质量、改善患者健康结果的价值目标,其核心要素包括:以整合型医疗卫生服务组织和基层家庭医生签约团队为供方主体,为特定疾病人群或签约人群提供连续性医疗服务;支付标的为固定时间范围内发生的医疗费用总额,通过合理设定人头基础费率和风险调整因子,建立以质量和健康结果为核心的绩效支付和结余分享、风险分担机制等,建立起支付方、供方、患者三方价值均衡且相容的激励约束机制。

Payments for Ecosystem Services: Market Mechanism or Diversified Modes?

Payment for ecosystem services is a concept of environmental protection and method of environmental management that has ＂purchasing conservation＂ as a major feature and has grown around the world since the 1990 s. It is stressed by the school of environmental economics that as a voluntary mechanism of exchange between ecological service providers and demanders, payments for ecosystem services can help to increase inputs and improve efficiency. Ecological economics holds that the ecological system and the complexity of the policy environment restrict the functional space of market mechanisms. The negative influence of the objective of giving priority to efficiency on environmental protection and social fairness cannot be neglected; therefore, the exchange mechanism is just one type of eco-compensation models. Here, we posit that payments for ecosystem services is a good tool for environmental protection and increases inputs and efficiency. Although payment for ecosystem services is confronted with challenges in application, it is playing an increasingly important role in the field of ecological services with a relatively high degree of commodification. Payments for ecosystem services can also increase the cost effectiveness of publicly managed environmental projects with the cooperation of other policy tools.

农村人居环境整治农民付费制度研究——以河南省为例

建设和运营资金保障不充分是影响农村人居环境整治效果的重要因素。农民作为农村人居环境整治的主要参与者和直接受益人,在农村人居环境整治中处于主体地位。探索农民付费制度是破解农村人居环境整治资金投入不足难题、健全长效管护机制的重要途径。本研究以河南省为例,基于典型案例调研和农户问卷调查,以农村改厕、农村污水处理、农村垃圾处理三大领域为重点,深入研究农村人居环境整治农民付费制度的可行性、实现方式及效果,系统总结农村人居环境整治农民付费制度的经验与问题,为完善农村人居环境整治农民付费制度、健全农村人居环境整治可持续性长效投入机制提供针对性建议。

DRG支付方式带给中国医疗服务体系发展的机遇与挑战分析

DRG支付方式是中国医疗服务体系高质量发展的机遇,体现在DRG是促进医疗管理和绩效管理升级的有效工具,DRG支付方式改革将促进医院病案质量的提升、促进合理用药和临床药学的发展、推动医用耗材的精细化管理、促进检验与临床诊疗的更好结合及医疗服务效率的提高、医疗服务体系的重塑。同时,DRG的科学设置与执行能力、门诊统筹的建立对医保支付管理的要求、基层医务人员的素质对建立基层首诊与转诊制度的阻碍、门诊共济制度下的基层与医院门诊服务支付方式等问题,是对支付方式改革和中国医疗服务体系的挑战。中国医疗服务提供体系未来的变化趋势为三级医疗机构规模的压缩和基层社区守门人制度的建立,影响改革的重要因素之一是医疗服务价格机制改革。

中断与质量多重风险下的供应链动态采购策略研究

新冠病毒疫情的暴发对全球供应链稳定造成了较大冲击,导致多种风险凸显。本文建立马尔科夫决策模型,研究由两个供应商、一个制造商与两类零售商组成的供应链在应对供需中断与质量多重风险下的多期动态采购决策问题,并比较了检查与延期付款两种质量控制机制对采购决策的影响。研究表明,供应中断风险不会影响延期付款机制下的采购决策,但在检查机制下,供应中断风险越高,越倾向于采购高质量产品;区域需求中断风险与供应中断风险对采购决策的影响是一致的,不同的是区域需求中断会降低整体的采购量。同时,在高质量产品库存较低时,提高检查精度或延长延期付款期,会使得制造商向低质量的供应商采购;而在高质量产品库存较大时,延期付款机制会使制造商向高质量供应商采购,但检查机制下制造商不会改变采购决策。此外,比较检查与延期付款机制可知,在次品率较低时,制造商应选择检查机制,反之则应选延期付款机制,但这种选择会随着中断风险而改变,在中断风险越高时,增加检查精度并不能提高制造商的收益,但延长延期付款期可以提高制造商的收益。

我国DRG/DIP付费下创新医疗技术支付机制政策分析

目的:梳理我国DRG/DIP付费下创新医疗技术支付机制实践进展,并提出政策建议。方法:收集2024年4月前各省市DRG/DIP付费改革政策文件做内容分析,并对部分城市医疗保障部门负责人展开访谈。结果:45个国家试点和19个省级扩面城市的文件中包含创新医疗技术支付机制。DRG付费城市划分为调整分组权重/机构系数、单独支付等8类做法,DIP付费城市划分为专家评议确定加成分值、采用不同计算规则确定分值等6类做法。上海、杭州、南京较有特色,分别为支持产业发展向新技术倾斜、设置退坡激励和多维调节机制下精准补偿。结论:需要在国家或省级层面统一界定新技术的范围,完善准入和退出机制;利用退坡激励促进短期支付向长期支付过渡;坚持总额控制,强化对新技术的监管。

区块链技术下的智能安全可靠支付系统设计

为了增强支付系统的安全性和可靠性,设计了一种区块链技术下的智能支付系统。这个系统利用区块链的去中心化和分布式账本特性,提供了一种更加安全和透明的支付解决方案。系统将区块链技术、国密算法与支付系统相结合,给传统互联网支付系统提供一个更安全的保障。利用区块链技术的点对点特点,解决支付效率和数据安全等方面的问题。借助互联网大数据技术,对用户进行信用评级,提供信贷服务。结合跨链技术可以对不同区块链的数据进行共享,更好地解决资金流动性。相比比特币和以太坊等电子货币系统,该系统在安全性、交易吞吐率和交易时延等方面做到了权衡发展,使得各方面的性能有了一定程度的均衡提升,更能满足当前环境下的应用需要。

国家药品集中采购接续政策与医保支付标准的衔接研究

国家药品集中采购(集采)接续政策始终强调推动实施药品医保支付标准,做好集采药品中选价格与医保支付标准的协同。当前,集采药品的医保支付标准主要依靠集采推动价格形成,按通用名形成医保支付标准仍存在一定阻力。对此,应进一步厘清国家集采接续政策与医保支付标准之间的衔接关系,依据省际间中选品种价差、原研企业是否中选等因素,分级分类推进省级接续工作,科学制定医保支付标准,推动国家集采接续政策有效落地。

日韩医保支付标准调整方式对我国谈判药品简易续约规则的启示

目的 借鉴日本和韩国医保支付标准调整的实践经验,为我国简易续约规则的完善提供参考。方法 检索中国知网的相关文献、日本和韩国政府官网的相关政策文件等,从调整条件和调整公式两个方面归纳总结目前日韩调整医保支付标准的做法,并与我国现行简易续约规则进行对比,明晰我国简易续约规则可优化之处,提出政策建议。结果与结论 在调整方式上日韩与我国相似,对于超量药品根据超量情况计算药品降幅并实施调整;但具体的调整条件和调整公式又有所不同,日韩针对目前超量较大的药品采取线性降价的方式,而我国针对目前和预期均超量较大的药品采取梯度降价的方式。对比分析结果显示,我国已初步建立了符合国情和医保实际的简易续约规则,并采取了一些创新举措,包括考量药品目前与预期的超量情况和在调整公式中引入降幅减半机制;但同时也存在一定不足,如调整条件的指标设置较为单一、调整公式的梯度降价区间过于宽泛,未能充分体现“以量换价”的市场化机制。建议我国医保部门在简易续约时增加对药品基金支出大小的考量、细化调整公式的梯度降价区间、加大对特殊品类药品在新增适应证时的政策倾斜,进一步完善简易续约规则。