Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems. Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level, utilizing their knowledge and expertise. However, this process is cumbersome, labor-intensive, and cannot be applied on a large network scale. Recent studies have begun to explore the applicability of recommendation system for urban traffic control, which offer increased control efficiency and scalability. Such a decision recommendation system is complex, with various interdependent components, but a systematic literature review has not yet been conducted. In this work, we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control, demonstrates the utility and efficacy of such a system in the real world using data and knowledge-driven approaches, and discusses the current challenges and potential future directions of this field.

It is interesting yet nontrivial to achieve given control precision within user-assignable time for uncertain nonlinear systems. The underlying problem becomes even more challenging if the transient behavior also needs to be accommodated and only system output is available for feedback. Several key design innovations are proposed to circumvent the aforementioned technical difficulties, including the employment of state estimation filters with event-triggered mechanism, the construction of a novel performance scaling function and an error transformation. In contrast to most existing performance based works where the stability is contingent on initial conditions and the maximum allowable steady-state tracking precision can only be guaranteed at some unknown (theoretically infinite) time, in this work the output of the system is ensured to synchronize with the desired trajectory with arbitrarily pre-assignable convergence rate and arbitrarily pre-specified precision within prescribed time, using output only with lower cost of sensing and communication. In addition, all the closed-loop signals are ensured to be globally uniformly bounded under the proposed control method. The merits of the designed control scheme are confirmed by numerical simulation on a ship model.

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades, and the vibration acceleration data collected by contact accelerometers have been widely investigated. In many industrial scenarios, contactless sensors are more preferred. The event camera is an emerging bio-inspired technology for vision sensing, which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency. It offers a promising tool for contactless machine vibration sensing and fault diagnosis. However, the dynamic vision-based methods suffer from variations of practical factors such as camera position, machine operating condition, etc. Furthermore, as a new sensing technology, the labeled dynamic vision data are limited, which generally cannot cover a wide range of machine fault modes. Aiming at these challenges, a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper. It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance. A cross-modality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer. An event erasing method is further proposed for improving model robustness against variations. The proposed method can effectively identify unseen fault mode with dynamic vision data. Experiments on two rotating machine monitoring datasets are carried out for validations, and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Linear temporal logic (LTL) is an intuitive and expressive language to specify complex control tasks, and how to design an efficient control strategy for LTL specification is still a challenge. In this paper, we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL specifications. Based on the regions of interest involved in the LTL formula, an accepting path is derived first to provide a high-level solution for the controller synthesis problem. Second, we develop a dynamic quantization based approach to verify the realization of the accepting path. The realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller design. Third, the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control strategy. Both abstraction construction and controller design are local and dynamic, thereby resulting in the potential reduction of the computational complexity. Since each quantization region can be considered locally and individually, the proposed hierarchical mechanism is more efficient and can solve much larger problems than many existing methods. Finally, the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots.

Contract Bridge, a four-player imperfect information game, comprises two phases: bidding and playing. While computer programs excel at playing, bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents. In this work, we introduce a Bridge bidding agent that combines supervised learning, deep reinforcement learning via self-play, and a test-time search approach. Our experiments demonstrate that our agent outperforms WBridge5, a highly regarded computer Bridge software that has won multiple world championships, by a performance of 0.98 IMPs (international match points) per deal over

deals, with a much cost-effective approach. The performance significantly surpasses previous state-of-the-art (0.85 IMPs per deal). Note 0.1 IMPs per deal is a significant improvement in Bridge bidding.

N6-methyladenosine (m6A) is an important RNA methylation modification involved in regulating diverse biological processes across multiple species. Hence, the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level. Although a variety of identification algorithms have been proposed recently, most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences, while ignoring the structural dependencies of nucleotides in their three-dimensional structures. To overcome this issue, we propose a cross-species end-to-end deep learning model, namely CR-NSSD, which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification. Specifically, CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory. It then constructs a cross-domain reconstruction encoder to learn the sequential and structural dependencies between nucleotides. By minimizing the reconstruction and binary cross-entropy losses, CR-NSSD is trained to complete the task of m6A site identification. Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms. Moreover, the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species, thus improving the accuracy of cross-species identification.

Ingredient optimization plays a pivotal role in the copper industry, for which it is closely related to the concentrate utilization rate, stability of furnace conditions, and the quality of copper production. To acquire a practical ingredient plan, which should exhibit long duration time with sufficient utilization and feeding stability for real applications, an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace conditions. To address the complex challenges posed by this integer programming model, including multiple coupling feeding stages, intricate constraints, and significant non-linearity, a multi-stage differential-multifactorial evolution algorithm is developed. In the proposed algorithm, the differential evolutionary (DE) algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed model. First, unlike traditional time-consuming serial approaches, the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm  caused by the feeding stability in a parallel manner. Second, a repair algorithm is employed to adjust infeasible ingredient lists in a timely manner. In addition, a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary algorithm. Finally, the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted, which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed approaches. It is practically helpful for reducing material cost as well as increasing production profit for the copper industry.

Battery energy storage systems (BESSs) are widely used in smart grids. However, power consumed by inner impedance and the capacity degradation of each battery unit become particularly severe, which has resulted in an increase in operating costs. The general economic dispatch (ED) algorithm based on marginal cost (MC) consensus is usually a proportional (P) controller, which encounters the defects of slow convergence speed and low control accuracy. In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance, we attempt to design a proportional integral (PI) controller with a reset mechanism (PI+R) to asymptotically promote MC consensus and total power mismatch towards 0 in this paper. To be frank, the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing, which accelerates convergence, improves control accuracy, and avoids overshoot. The eigenvalues of the system under a PI+R controller is well analyzed, ensuring the regularity of the system and enabling the reset mechanism. To ensure supply and demand balance within the isolated BESSs, a centralized reset mechanism is introduced, so that the controller is distributed in a flow set and centralized in a jump set. To cope with Zeno behavior and input delay, a dwell time that the system resides in a flow set is given. Based on this, the system with input delays can be reduced to a time-delay free system. Considering the capacity limitation of the battery, a modified MC scheme with PI+R controller is designed. The correctness of the designed scheme is verified through relevant simulations.

For the n-qubit stochastic open quantum systems, based on the Lyapunov stability theorem and LaSalle’s invariant set principle, a pure state switching control based on on-line estimated state feedback (short for OQST-SFC) is proposed to realize the state transition the pure state of the target state including eigenstate and superposition state. The proposed switching control consists of a constant control and a control law designed based on the Lyapunov method, in which the Lyapunov function is the state distance of the system. The constant control is used to drive the system state from an initial state to the convergence domain only containing the target state, and a Lyapunov-based control is used to make the state enter the convergence domain and then continue to converge to the target state. At the same time, the continuous weak measurement of quantum system and the quantum state tomography method based on the on-line alternating direction multiplier (QST-OADM) are used to obtain the system information and estimate the quantum state which is used as the input of the quantum system controller. Then, the pure state feedback switching control method based on the on-line estimated state feedback is realized in an n-qubit stochastic open quantum system. The complete derivation process of n-qubit QST-OADM algorithm is given; Through strict theoretical proof and analysis, the convergence conditions to ensure any initial state of the quantum system to converge the target pure state are given. The proposed control method is applied to a 2-qubit stochastic open quantum system for numerical simulation experiments. Four possible different position cases between the initial estimated state and that of the controlled system are studied and discussed, and the performances of the state transition under the corresponding cases are analyzed.