Asynchronous control for two‐dimensional hidden Markovian jump systems with partly known mode observation conditional probabilities

This study focuses on the asynchronous control problem for two‐dimensional discrete‐time hidden Markovian jump systems where the mode observation conditional probability matrix is partly known. Considering the original system modes are invisible, the observed modes emitted from an observer serve as an alternative for stability analysis and controller design where a mode observation conditional probability matrix is constructed to characterize the emission between system modes and observed modes. Specially, only partly known information of the mode observation conditional probability matrix is accessible. With the introduction of the free‐connection weighting matrices, the asymptotic mean square stability criterion is firstly derived based on Lyapunov method. This introduction provides a further degree of relaxation and less conservatism is therefore achieved. Secondly, we present synthesis conditions for asynchronous state feedback controller design given in terms of a set of interconnected linear matrix inequalities. Moreover, cluster concept based on the partitions of observed modes is adopted which helps to decrease the number of controllers and simplify the design complexity. A numerical example, regarding the cases with and without clustering of the observed modes, is presented to illustrate the effectiveness of the proposed method.     

A hidden Markov model to detect regime changes in cryptoasset markets

The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well-described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.     

基于信息熵—集对分析的煤矿隐患排查治理风险评估模型

以煤矿安全隐患排查治理风险为研究对象，分析隐患排查闭环管理全过程，并基于此建立了隐患排查治理能力纵向评估指标体系，融合熵值法和集对分析理论，建立了煤矿安全隐患排查治理风险纵向评估模型，为企业更好地了解自身隐患排查治理能力及变化发展趋势提供参考，最后进行了实例分析。     

水产品及养殖水中丁香酚类化合物风险研究

目的 检测水产品及养殖水中丁香酚类化合物。方法 应用食品补充检验方法BJS 201908《水产品及水中丁香酚类化合物的测定》，以海水鱼、淡水鱼、海水虾、淡水虾及养殖水作为基质，测定6种丁香酚类化合物的含量。结果 通过对实际样品测定后发现，在水产品中检出了丁香酚类化合物，主要为丁香酚，检出率为28.9%，其中海水鱼相对突出。结论 本研究为丁香酚作为渔用麻醉剂提供了方法论，为丁香酚类化合物在水产运输的合理使用提供理论依据。     

隐蔽老火区立体式综合治理研究

对2320隐蔽老火区首先通过灌注泥浆对火区进行治理,同时对部分裂隙通道进行堵漏,然后对泥浆不能到达的区域注入高倍数三相泡沫灭火材料泡沫,瞬间将火源处覆盖,火源因隔绝氧气而窒息灭火。三相泡沫具有良好的堆积性和流动性,对煤体具有保水保湿,惰化遗煤氧化空间,并能够对松散煤体进行化学阻化,从本质上降低遗煤自然氧化速率,消灭了隐蔽老火区的火灾隐患,具有较高的推广价值。     

Energy-efficient operation of indirect adiabatic data center cooling systems via Newton-like phasor extremum seeking control

This paper considers the problem of optimizing the operation of Indirect Adiabatic Cooling (IAC) systems with application to data centers. Optimal operation is achieved when the required cooling demand is satisfied at the minimum energy cost. For this purpose, we design a supervisory control system, where the higher layer determines the optimal set-points for the local controllers by employing an Extremum Seeking Control (ESC) scheme. In particular, we consider a Newton-like phasor ESC, which augments the derivative estimator underlying the phasor approach to capture also the Hessian of the plant index and then it uses these estimates to steer the system along a Newton-like direction. The effectiveness of the considered approach is tested in simulation by exploiting a Matlab-based simulation environment including an IAC system and a computer room.     

基于广义隐马尔可夫模型的PM2.5浓度预测

近年来，以PM_2.5为主要污染物的重霾污染事件频频发生，给我国国民经济和居民健康造成了严重损失。在空气质量尚未得到根本性改善的情况下，对重霾污染的准确预警不仅能使公众合理回避污染危害，还能为政府实施应急管理提供时间裕量。针对影响PM_2.5浓度的前体物及气象因素的非高斯分布特点以及传统隐马尔可夫模型（hidden Markov model，HMM）必须已知隐含状态个数的缺点，采用广义隐马尔可夫模型（generalized hidden Markov model，GHMM）对北京市除去定陵外的11个国控站点2013年1月～2017年1月的PM_2.5浓度进行了预测。结果表明：GHMM对严重污染及以上PM_2.5样本浓度预测准确率显著高于传统连续HMM，但针对中度污染及以下PM_2.5样本浓度的预测准确率接近传统HMM。     

张家峁矿构建双重预防机制的实践

为了将风险控制在隐患形成之前、把隐患消灭在事故前面,以风险管控为核心,通过“基于风险”的PDCA全方位安全管理模式,构建了风险分级管控和隐患排查治理双重预防机制。以张家峁煤矿建立煤矿企业安全生产的长效机制为例,结合该矿在构建双重预防性工作机制方面的创新做法,分析了双重预防性工作机制的形成背景、建设过程及实施方法。分析认为,完善安全生产管理理论体系,能有效预防和遏制重特大事故的发生,对企业安全生产管理起到积极的推动作用。     

红石岩煤矿隐蔽致灾因素分析

为保障煤矿安全开采,有针对性地制定预防和治理措施,对红石岩煤矿隐蔽致灾因素进行探究。首先梳理了红石岩煤矿已揭露的地质情况和现有的钻探、物探和水质化验成果,瓦斯鉴定等资料,结合煤矿隐蔽致灾因素具有隐蔽性、时变性、突发性、难探测和难预防的特点,对其地面和井田内的采空区、废弃老窑,钻孔、地质构造、地下含水体、瓦斯和煤层自燃及煤层顶底板等因素进行了分析。总结出该矿主副井筒周边采空区水、因采动影响产生的顶底板导水裂隙带等主要隐蔽致灾因素,以及断层的活化和滞后出水及带出的有害气体等次要隐蔽致灾因素,并提出了相应的预防和治理措施。