This paper investigates the design of sampling strategies for linear networked control systems (NCSs) under dual-channel denial-of-service (DoS) attacks. We focus on enhancing system stability and communication efficiency by combining periodic and event-triggered sampling strategies. First, an ACK-based method is used to detect DoS attacks, and a mixed sampling strategy is proposed to conserve communication resources and improve DoS resilience. A dynamic quantizer module is then incorporated into the system, with suitable dynamic quantizer designed for scenarios involving quantization error. Global asymptotic stability(GAS) conditions are derived for both scenarios, and MATLAB simulations are conducted to validate the proposed methods.

This paper investigates the mean square stability (MSS) of quantized Markov jump linear systems (MJLSs) based on networked control, where the control loop is formed in a digital feedback path. Taking channel capacity constraints into account, we mainly research on the effects of finite constant bits quantization on the MJLSs’ stability. The original system is stabilized by pre-processing coordination transformation of the system matrices and constructing a corresponding strategy of quantization and control. Sufficient and necessary bit rate conditions required to mean square stabilize the system are finally obtained, while these two conditions define the range that the minimum bit rate is located in. The gap between the conditions is narrowed by the method proposed by this paper compared to existed results. We also extend the bit rate conditions to the case that system state cannot be directly accessed through constructing a mode-dependent state observer. Numerical example is used to verify the achieved theoretical results.

In this paper, we consider a linear time-invariant discrete system with quantized state feedback. In the networked control system we consider, the next sampling time is given by a self-triggering mechanism based on feedback packets received. For unstable or marginally stable systems, we develop a quantization strategy and self-triggered sampling mechanism to guarantee system stability. And the bit rate we consume can arbitrarily approximate the bit rate lower bound in periodic sampling case. Simulation results are presented to validate the effectiveness of our approach.

This paper investigates the consensus problem of networked multi-agent systems employing event-triggering strategies. Agents establish communication among nodes via channels characterized by Markovian packet loss, where distributed packet losses occur independently on each communication channel. Due to communication bandwidth limitations, we introduce an event-triggering strategy based on consensus error and the state of the agent to design the state-feedback consensus protocol. We investigate the sufficiency conditions that ensure the system reaches mean-square consensus under Markovian packet loss. Furthermore, we do not consider the packet loss on each channel separately but model the overall communication topology of the multi-agent system using only one Markov process. Also, this paper takes into account the inevitable noise within the communication channel, recognizing its potential impact on system performance. Simulation experiments verify the effectiveness of the proposed control strategies.

A nickel-catalyzed direct hydromonofluoromethylation of unactivated olefins with industrial raw fluoroiodomethane is developed, furnishing various primary alkyl fluorides in a step-economic manner. The key factor to success is the use of pyridine-oxazoline as ligand and（MeO） 2 MeSiH as the hydrogen source.This transformation demonstrates high efficiency, mild conditions, good functional-group compatibility and great potential in the drug discovery.

This paper mainly investigates the consensus problem of multi-agent Markovian jump systems (MJSs) under event triggering strategies. To reduce the frequency of system information transmission, information transmission will only occur if the trigger conditions are met. Due to the information transfer between MJSs and overall performance considerations involved in multi-agent systems, it poses difficulties and challenges to the design of event triggering strategies. Our control strategy ensures that the multi-agent system is consensus for a limited period of time when the corresponding conditions are satisfied. Finally, the effectiveness of these consensus conditions is proved by simulation experimental results.

As one of the most clean energy sources, solar energy is playing an increasingly important role in energy generation, thus driving the rapid development of photovoltaic (PV) power plants. In one PV power plant, there may be hundreds of thousands PV modules. Fault diagnosis of such a huge number of PV modules is critical and challenging. Recently Unmanned Aerial Vehicles (UAVs) equipped with infrared cameras are taken to execute this fault diagnosis by taking photos and analyzing these photos for faults. To improve the fault diagnosis accuracy and efficiency of PV modules, this paper proposes an automatic PV module fault diagnosis algorithm based on deep learning for infrared images, which is made up of two steps, including localization and classification. In the localization step, we first design a lightweight convolution neural network (CNN) to detect edges of PV modules in infrared images; then a region extraction method is proposed to segment PV modules. In the classification step, a lightweight classifier is used to detect faulty PV modules. Moreover, we introduce an Out-Of-Distribution (OOD) detection algorithm to identify and eliminate false PV modules, which actually come from the background and are wrongly segmented in the localization step. The effectiveness of the proposed PV module fault diagnosis algorithm has been successfully verified on real images.

This paper investigates the stabilization of switched linear systems under denial-of-service (DoS) attacks with event-triggered strategies and finite bit rate quantization. Unlike previous research that only considers switching when DoS attacks are inactive, we investigate a general case where unknown switches are allowed during DoS activation, as well as asynchronous communication between controller modes and subsystem modes. We assume that switching instants follow the average dwell time and that DoS attacks with limited energy are described by restricted frequency and duration. Firstly, event-triggered strategies and finite bit rate quantized policy are designed to estimate the bounds of the state estimation error under unknown switches and DoS attacks, and achieve the quantized state feedback under finite bandwidth. Additionally, by using multiple Lyapunov functions, we establish a joint constraint of switching signals and DoS attacks energy, which implicates the influence of multiple switches in DoS attacks and quantization error on system stability. Based on the above discussions, the global asymptotic stability of the closed-loop system is established. Finally, simulations are conducted to confirm the obtained results.

Lane detection is one of the fundamental yet important tasks in autonomous driving, which provides further clues for drivable regions detection and lane departure decision. Nowadays, Convolutional Neural Network(CNN) based lane detection methods have achieved a great success due to its strong contextual representation learning ability. Nevertheless, these methods still suffer dramatic performance degradation under challenging driving scenarios where lane occlusion and various extreme light conditions may occur. In fact, the performance of a lane detection model is closely related to the quality of the data representation. Normally, lanes are spatially continuous and appear on the ground. Therefore, we believe proper utilization of these structure prior in the data augmentation should lead to better detection precision under challenging scenarios. To this end, this paper explores the effect of a series of random structural data augmentation methods when applied to a row anchor based lane detection network. The experimental results confirm that structural data augmentations like ’Extending’ and ’Cutout’ can help network focus on the structural clues and improve lane detection performance by a large margin especially in challenging scenarios lacking visual clues. We also elaborate intuitions behind these methods and they can be easily applied to many other lane detection algorithms without effort. The achieved results have already been implemented in Kaizhou District, Chongqing.

近年来深度强化学习在一系列顺序决策问题中取得了巨大的成功,使其为复杂高维的多智能体系统提供有效优化的决策策略成为可能.然而在复杂的多智能体场景中,现有的多智能体深度强化学习算法不仅收敛速度慢,而且算法的稳定性无法保证.本文提出了基于值分布的多智能体分布式深度确定性策略梯度算法(multi-agent distributed distributional deep deterministic policy gradient,MA-D4PG),将值分布的思想引入到多智能体场景中,保留预期回报完整的分布信息,使智能体能够获得更加稳定有效的学习信号;引入多步回报,提高算法的稳定性;引入了分布式数据生成框架将经验数据生成和网络更新解耦,从而可以充分利用计算资源,加快算法的收敛.实验证明,本文提出的算法在多个连续/离散控制的多智能体场景中均具有更好的稳定性和收敛速度,并且智能体的决策能力也得到了明显的增强.

人群行为识别在公共安全等领域具有重要的应用价值.现有研究分别考虑了人群情绪、人群类型、人群密度以及人群社会文化环境等因素对于人群行为的影响,但少有综合考虑这些因素的模型,导致模型性能受限.本文综合考虑人群的物理特征、社交特征、情绪人格特征和文化背景特征之间的相关性,以及相结合之后对人群行为的影响,提出一种融合多特征与时间序列的人群行为识别模型.模型采用两个并行的网络层分别处理多特征相关性和时间序列依赖性对于人群行为的影响,同时为提高模型可解释性,网络层采用融合结构因果模型(SCM)与图神经网络(GNN)的因果图网络(CGN).通过在运动情感数据集(MED)上进行实验并与其他方法模型进行对比,证明了本文方法能够成功识别人群行为,并且优于目前最先进的方法.

人群模型评估是虚拟人群仿真研究的关键问题,现有的研究多通过个体仿真轨迹与真实轨迹之间的误差来评估人群模型.然而人群行为本质上是复杂的随机系统,简单的轨迹对比并不能有效反映模型能力.本文应用熵度量的模型评估方法,通过估计真实人群状态与仿真人群状态的误差分布实现了精确的人群仿真定量评估.同时引入失真情况的判断和处理规则,使得评估方法在仿真失真情况下能够保持准确性.实验结果表明,本文提出的算法及规则能有效地实现人群仿真模型的定量评估并给出模型参数选择的指导.

This paper focuses on a scalar nonlinear system, whose feedback packets are transmitted through an unreliable digital communication network. The network is band-limited and suffers from external disturbance, bounded transmission delay and i.i.d. feedback dropouts. To save communication resource, a periodic event-triggered strategy is proposed to maintain the mean square stability of the concerned system. In comparison with periodic sampling, our strategy can stabilize the system at a lower bit rate. Note that the obtained bit rate condition is up to the Lipschitz parameter, the bound of network delay, the dropout rate, the sampling period and the number of quantization bits. Some simulations are provided to verify the effectiveness of our proposed method.

Monofluorinated alkyl compounds are of great importance in pharmaceuticals, agrochemicals and materials. Herein, we describe a direct nickel-catalyzed monofluoromethylation of unactivated alkyl halides using a low-cost industrial raw material, bromofluoromethane, by demonstrating a general and efficient reductive cross-coupling of two alkyl halides. Results with 1-bromo-1-fluoroalkane also demonstrate the viability of monofluoroalkylation, which further established the first example of reductive C(sp 3 )-C(sp 3 ) cross-coupling fluoroalkylation. These transformations demonstrate high efficiency, mild conditions, and excellent functional-group compatibility, especially for a range of pharmaceuticals and biologically active compounds. Mechanistic studies support a radical pathway. Kinetic studies reveal that the reaction is first-order dependent on catalyst and alkyl bromide whereas the generation of monofluoroalkyl radical is not involved in the rate-determining step. This strategy provides a general and efficient method for the synthesis of aliphatic fluorides.

The first example of copper-catalyzed ring-opening, enantioselective arylation of cyclic ketoxime esters to access ω,ω-diaryl alkyl nitriles has been developed in high yield (up to 92% yield) with excellent enantioselectivity (up to 91% ee). Side-arm bis(oxazoline) ligand plays a significant role in this asymmetric catalytic transformation, which provides an efficient route to construct diverse chiral ω,ω-diaryl alkyl nitriles. Synthetic utility has also been demonstrated in the further derivatization of the ω,ω-diaryl alkyl nitrile to the corresponding amide.

A combinatorial nickel-catalyzed monofluoroalkylation of aryl bromides with the industrial raw regent ethyl chlorofluoroacetate has been developed. The two key factors to successful conversion are the combination of nickel with readily available nitrogen and phosphine ligands and the using of a mixture of different solvents. Mechanistic investigations indicated a new zinc regent might generated in situ and be involved in the reaction process.

This paper considers the problem of exponential practical stabilization of a continuous-time linear time-invariant system in the presence of bounded network delay and process noise. We utilize a model-based periodic event-triggered control strategy which can extract the information contained in the sampling time instants to save quantization bits. Sufficient estimation error conditions to guarantee the prescribed convergence rate are investigated, and appropriate event-triggered control strategies that ensure the desired performance are implemented in this paper. Compared with the time-driven strategies, our method can break the conventional lower bound of the bit rate condition. Simulations are provided to illustrate the proposed strategies.

The trifluoromethyl group represents one of the most functional and widely used fluoroalkyl groups in drug design and screening, while the drug candidates containing chiral trifluoromethyl-bearing carbons are still few due to the lack of efficient methods for the asymmetric introduction of trifluoromethyl group into organic molecules. Herein, we described a nickel-catalyzed asymmetric trifluoroalkylation of aryl iodides, for the first time, by utilizing reductive cross-coupling in enantioselective fluoroalkylation. This novel method has demonstrated high efficiency, mild conditions, and excellent functional group tolerance, especially for substrates containing diverse pharmaceutical and bioactive molecules moieties. This strategy provided an efficient and facile way for diversity-oriented synthesis of chiral trifluoromethylated alkanes.

A facile and efficient approach for the synthesis of the CF3-containing dioxodibenzothiazepines has been developed via copper-catalyzed trifluoromethylation/cyclization of alkynes utilizing a radical relay strategy. This method has demonstrated low catalyst loading, high regiocontrol, and broad scope under mild conditions. Good compatibility for the N-protecting group, gram-scale experiment, and further derivation of product prove the versatility of this transformation.

Intelligent agent design has increasingly enjoyed the great advancements in real-world applications but most agents are also required to possess the capacities of learning and adapt to complicated environments. In this work, we investigate a general and extendable model of mixed behavior tree (MDRL-BT) upon the option framework where the hierarchical architecture simultaneously involves different deep reinforcement learning nodes and normal BT nodes. The emphasis of this improved model lies in the combination of neural network learning and restrictive behavior framework without conflicts. Moreover, the collaborative nature of two aspects can bring the benefits of expected intelligence, scalable behaviors and flexible strategies for agents. Afterwards, we enable the execution of the model and search for the general construction pattern by focusing on popular deep RL algorithms, PPO and SAC. Experimental performances in both Unity 2D and 3D environments demonstrate the feasibility and practicality of MDRL-BT by comparison with the-state-of-art models. Furthermore, we embed the curiosity mechanism into the MDRL-BT to facilitate the extensions.