We investigate the problem of synthesizing dynamic masks that preserve the infinite-step opacity in the context of discrete-event systems. Dynamic mask is an information acquisition mechanism that ...controls the observability of the system's events dynamically online, e.g., by turning sensors on/off . A system equipped with a dynamic mask is said to be infinite-step opaque if an outside intruder that can access all acquired information can never infer that the system was at some secret state for any specific previous instant. Existing works on the dynamic mask synthesis problem can only preserve the current-state opacity. However, synthesizing dynamic masks for the infinite-step opacity, which is stronger than the current-state opacity, is much more challenging. The main reason is that the delayed information is involved in this problem and whether or not a current secret can be revealed depends on sensing decisions to be synthesized in the future. In this paper, a new type of information state is proposed to capture all the delayed information in the infinite-step opacity synthesis problem. An effective algorithm is then presented to solve the synthesis problem, which extends existing dynamic mask synthesis techniques from the current-state opacity to infinite-step opacity. Additionally, an information-state-reduction-based approach is proposed to further mitigate the computational complexity of the synthesis procedure. Finally, we discuss how to generalize our results to a class properties with delayed information including infinite-step <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula>-anonymity and infinite-step indistinguishability.
Data-driven methods have been regarded as effective methods for modeling in chemical processes. However, with the increasing complexity of chemical processes in spatial domain and time domain, how to ...extract meaningful features and build corresponding models are keys for accurate modeling tasks. To retain temporal features of original inputs, a Recurrent Denosing Autoencoder (RDAE) is built to extract meaningful features and reduce input dimension in the spatial domain, and Cumulative Percent Variance (CPV) is introduced to decide the number of extracted features. Considering correction and prior knowledge effects of real history outputs, a Weighted Auto Regressive Long Short Term Memory (WAR-LSTM) structure is proposed as the basic cell, then multiple WAR-LSTMs are stacked as deep WAR-LSTM to extract high level representations from multi variables. Hence, spatial and temporal information are sufficiently used both in extracting features and building model. The benchmarked Tennessee Eastman process data and real process data from a Fluid Catalytic Cracking (FCC) unit verify the effectiveness of our work.
Depression is a highly prevalent disorder, and its treatment is far from satisfactory. There is an urgent need to develop a new treatment for depression. Although still at its early stage, ...transcutaneous auricular vagus nerve stimulation (taVNS) has shown promising potential for treating depression. In this article, we first summarize the results of clinical studies on the treatment effect of taVNS on depression. Then, we re-analyze a previous study to identify the specific symptoms taVNS can relieve as indicated by subscores of the 24-item Hamilton Depression Scale in patients with depression. We found that taVNS can significantly reduce multiple symptoms of depression patients, including anxiety, psychomotor retardation, sleep disturbance, and hopelessness. Next, we pose several hypotheses on the mechanism of taVNS treatment of depression, including directly and indirectly modulating the activity and connectivity of key brain regions involved in depression and mood regulation; inhibiting neuro-inflammatory sensitization; modulating hippocampal neurogenesis; and regulating the microbiome-brain-gut axis. Finally, we outline current challenges and lay out the future directions of taVNS treatment of depression, which include (1) intensively comparing stimulation parameters and "dose effect" (treatment frequency and duration) to maximize the treatment effect of taVNS; (2) exploring the effect of taVNS on disorders comorbid with depression (such as chronic pain disorders, cardiovascular disorder, and autism) to provide new "two-for-one" treatment approaches for patients with these disorders; and (3) applying multiple scale methods to explore the underlying mechanism of taVNS.
A class of large-scale systems, which is naturally divided into many smaller interacting subsystems, is usually controlled by a distributed or decentralized control framework. In this case, how to ...improve the performance of the entire system is a problem. A novel distributed model predictive control (MPC) is proposed for improving the global performance. Each subsystem is controlled by a subsystem-based MPC. These controllers coordinate with each other through global performance optimization index, and take the interactions among subsystems into account when predicting states evolution. The stability analysis for the unconstrained distributed MPC is given for guiding the control parameters tuning. Numeric results show that the proposed architecture could guarantee satisfactory global performance under even strong interactions among subsystems.
This paper presents a multi-time hierarchical stochastic predictive control (MHSPC) scheme for an island microgrid, in which electric vehicles (EVs) can be used as mobile energy storages to improve ...power balance and realise load-frequency control (LFC) with micro-turbines (MTs). At the upper layer, a stochastic model predictive control is proposed to handle the EVs uncertainties on a long time scale, while optimising controllable power adjustment of MTs, the charge/discharge energy of the battery electric storage (BES) unit and guaranteeing EVs to be fully charged at the expected plug-out time. At the lower layer, the coordination between EVs and MTs for LFC is achieved by a standard MPC framework on a short time scale. In this way, the power balance is met, and the frequency fluctuation is inhibited. Finally, simulation results are presented to illustrate the satisfactory operation of the island microgrid.
Abstract Background Depression presents a significant burden to both patients and society. One treatment that has emerged is vagus nerve stimulation (VNS), an FDA-approved physical treatment for ...depressive disorders. However, the application of this intervention has been limited by the involvement of surgery and potential side effects. The aim of this study is to explore the effectiveness of stimulating the superficial branches of the vagus nerve as a solo treatment for MDD. Methods This is a nonrandomized, controlled study. The first cohort of patients (n=91) only received transcutaneous auricular VNS (taVNS) for 12 weeks. In the second cohort (n=69), patients first received 4 weeks of sham taVNS followed by 8 weeks of taVNS. All treatments were self-administered by the patients at home after they received training from the hospitals. The primary outcome measurement was the 24-item Hamilton Depression Rating Scale measured at weeks 0, 4, 8, and 12. Data analysis included a timelag analysis comparing (1) real and sham taVNS groups at week 4; (2) the real taVNS group at week 4 vs the sham taVNS group at week 8 (fourth week of real taVNS following 4 weeks of sham); and (3) the real taVNS group at week 8 vs the sham taVNS group at week 12 (eighth week of real taVNS following sham). Results After four weeks of treatment, MDD patients in the taVNS group showed greater improvement than patients in the sham taVNS group as indicated by Hamilton score changes as well as response and remission rates at week four. In addition, we also found that the clinical improvements continued until week 12 during taVNS. Limitations Patients were not randomized in this study. Conclusions Our results suggest that taVNS is a promising, safe, and cost-effective therapeutic method for mild and moderate MDD.
Traditional control system design to complex networks is generally implemented by integrated structural analysis aiming at a global network. However, such a global method may be inefficient, in ...particular, when a massive network with a huge number of nodes and associations is considered. In this paper, motivated by the idea of “dividing and dealing”, we propose a block-based approach to the issue of minimum input design for structural controllability (MIDSC) of complex networks that potentially incurs in higher efficiency. Specifically, we consider a large-scale networked system that consists of several local blocks. The main challenge for control configuration design of this class of systems is how to find the minimum inputs of global network according to the local block information while maintaining system’s structural controllability. To this end, two block-based graphical algorithms are developed to meet the conditions required for achieving structural controllability, and meanwhile determine an optimal solution for addressing the MIDSC problem. The complexity of the proposed method is analyzed, which is also compared with existing algorithms designed mainly based on monolithic model. In particular, we show that, under some mild conditions on blocking structure, the complexity of the proposed algorithm is strictly lower than that of existing algorithms to the MIDSC problem.
In this paper, the control problem of multi‐robot systems under temporal logic tasks with limited time and logic constraints is studied, where each robot is required to reach the specified region in ...a given time and avoid collision all the time. Since the cooperative collision avoidance task of one robot depends on other robots' behaviors, the satisfaction of all the tasks may be conflicting. In this work, a distributed model predictive control (DMPC) strategy is proposed for conflicting temporal logic tasks. First, signal temporal logic (STL) is adopted to formally describe the temporal logic tasks. Based on robust semantics of STL formulas, a reference trajectory for the satisfaction degree of the task is designed to determine the short‐term task in the optimization horizon. In the DMPC optimization problem, the compatibility constraints are introduced to redesign the collision avoidance constraints, such that the collision avoidance tasks can be fulfilled using neighbouring robots' predicted information of the last time instant. Then, the terminal constraint is designed by the short‐term motion task which enforces each robot to move towards the goal region within the specified time interval. For conflicting tasks, a slack parameter is introduced in the terminal set to relax the motion task of each robot. The recursive feasibility of the DMPC algorithm is guaranteed, and the relaxed temporal logic tasks are fulfilled. In the proposed method, the discrete events including limited time and logic requirements are incorporated into the DMPC optimization problem, such that the motion task can be satisfied as much as possible and safety requirements are fulfilled. The effectiveness of the algorithm is illustrated by simulation and experiment results.
Ti-bearing blast-furnace slag is a metallurgical waste but a promising Ti resource. In this study, Ti and Al–Si alloys can be prepared directly by separating a Ti–Si–Al alloy that was prepared by ...reducing Ti and Si from Ti-bearing blast-furnace slag with Al. The technology of directional solidification with electromagnetic force was employed to carry out the separation of the Ti–Si–Al alloy. When additions of CaO and Al were 24 wt% and 40 wt%, respectively, the composition of the obtained Ti–Si–Al alloy was 31.8 wt% Ti, 22.7 wt% Si, 28.5 wt% Al, and 10.2 wt% Fe. Ti5Si4, TiSi, Al–Si–Ti (τ2), Al–Si–Fe (τ4), and Al–Si alloys were prepared in sequence after separating the Ti–Si–Al alloy using directional solidification with electromagnetic force. However, when the composition of the Ti–Si–Al alloy was changed to 21.2 wt% Ti, 48.5 wt% Si, 19 wt% Al, and 6.8 wt% Fe, the TiSi2, Si, Al–Si–Fe and Al–Si alloys were prepared in sequence. This study will propose an approach to employ the metallurgical waste, titanium-bearing blast-furnace slag, to prepare Ti and Al–Si alloys.
•An approach to employ Ti-bearing blast-furnace slag to prepare Ti and Al–Si alloys.•Ti and Al–Si alloys were prepared directly by separating a Ti–Si–Al alloy.•Electromagnetic directional solidification was employed to separate Ti–Si–Al alloy.
In this paper, a new neural network enhanced synchronized control approach is proposed for multiple robotic manipulators systems (MRMS) based on the leader–follower network communication topology. ...The justification of introducing two adaptive Radial Basis Function Neural Networks (RBF NN), also called neuro-agents, is to facilitate the whole control system design and analysis. Otherwise such design is impossible with classical analytical procedure. The first agent is the neuro-compensator to accommodate uncertainty associated with the follower manipulators, and the second agent is the neuro-estimator to obtain acceleration of the leader manipulator. Correspondingly the stability analysis of the designed control system is formulated with Lyapunov method. Finally numerical bench tests under various critical conditions are conducted to validate the effectiveness of the proposed approach.
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