自动化技术在钢铁企业的应用与展望

简述了钢铁工业自动化在智能化技术、分布式控制系统、开放式控制系统、CIMs及现场总线技术等方面的应用．及在钢铁工业中的发展趋势。     

Donor‐Induced Performance Tuning of Amorphous SrTiO3 Memristive Nanodevices: Multistate Resistive Switching and Mechanical Tunability

Metal–oxide valence‐change memristive devices are the key contenders for the development of multilevel nonvolatile analog memories and neuromorphic computing architectures. Reliable low energy performance and tunability of nonlinear resistive switching dynamics are essential to streamline the high‐density circuit level integration of these devices. Here, manipulation of room temperature‐synthesized defect chemistry is employed to enhance and tune the switching characteristics of high‐performance amorphous SrTiO₃ (a‐STO) memristors. Substitutional donor (Nb) doping with low concentrations in the a‐STO oxide structure allows extensive improvements in energy requirements, stability, and controllability of the memristive performance, as well as field‐dependent multistate resistive switching. Evidence is presented that room temperature donor doping results in a modified insulator oxide where dislocation sites act as charge carrier modulators for low energy and multilevel operation. Finally, the performance of donor‐doped a‐STO‐based memristive nanodevices is showcased, with the possibility of mechanical modulation of the nonlinear memristive characteristics of these devices demonstrated. These results highlight the potential of donor‐doped a‐STO nanodevices for high‐density integration as analog memories and multifunctional alternative logic elements.     

"弱P型"和低迁移率n型碲镉汞体材料的迁移率谱研究

详细分析了两类碲镉汞(HgCdTe)材料的磁输运特性,并以此寻找两类材料的有效筛选方法.窄禁带HgCdTe是一种电子和空穴混合导电的多载流子体系材料.特别是"弱p型"材料,由于电子的迁移率比空穴的大两个数量级(b=μ/hμ≈102),更容易受到少数载流子(电子)的干扰,因此通过单一磁场的霍尔测试无法区分性能很差的低迁移...     

Learning active fusion of multiple experts' decisions: an attention-based approach

In this letter, we propose a learning system, active decision fusion learning (ADFL), for active fusion of decisions. Each decision maker, referred to as a local decision maker, provides its suggestion in the form of a probability distribution over all possible decisions. The goal of the system is to learn the active sequential selection of the local decision makers in order to consult with and thus learn the final decision based on the consultations. These two learning tasks are formulated as learning a single sequential decision-making problem in the form of a Markov decision process (MDP), and a continuous reinforcement learning method is employed to solve it. The states of this MDP are decisions of the attended local decision makers, and the actions are either attending to a local decision maker or declaring final decisions. The learning system is punished for each consultation and wrong final decision and rewarded for correct final decisions. This results in minimizing the consultation and decision-making costs through learning a sequential consultation policy where the most informative local decision makers are consulted and the least informative, misleading, and redundant ones are left unattended. An important property of this policy is that it acts locally. This means that the system handles any nonuniformity in the local decision maker's expertise over the state space. This property has been exploited in the design of local experts. ADFL is tested on a set of classification tasks, where it outperforms two well-known classification methods, Adaboost and bagging, as well as three benchmark fusion algorithms: OWA, Borda count, and majority voting. In addition, the effect of local experts design strategy on the performance of ADFL is studied, and some guidelines for the design of local experts are provided. Moreover, evaluating ADFL in some special cases proves that it is able to derive the maximum benefit from the informative local decision makers and to minimize attending to redundant ones.