Keyframe selection plays a crucial role in balancing computational efficiency and localization accuracy in Visual Simultaneous Localization and Mapping (VSLAM) systems. Existing keyframe selection methods often struggle to capture high-level semantic information in environments where multiple semantic dimensions interact. In this paper, we propose the Multi-dimensional Semantic Analysis (MSA) module based on Visual-Language Models (VLMs). By leveraging the capability of VLMs to extract rich semantic features, we compute the similarity between each image frame and a set of textual descriptions, generating a scene descriptor that quantifies the semantic distance between frames across multiple dimensions (e.g., object count, texture, and lighting). We then introduce the Scene Change Assessment (SCA) module based on Bayesian On-line Changepoint Detection (BOCD), which identifies keyframes with significant semantic information gain, thereby reducing the total number of keyframes. Extensive experiments on an open dataset demonstrate that our method not only significantly reduces the number of keyframes but also maintains high localization accuracy. Furthermore, the inference speed of the MSA module satisfies the real-time requirements of VSLAM. These results underscore the potential of our approach to enhance the efficiency of keyframe selection.

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.

Decentralized optimizations have been extensively applied in large-scale industrial cyber-physical systems to achieve control scalability. However, state-of-the-art methods heavily depend on explicit communications between participants, exposing the entire control framework to data confidentiality risks. To overcome this challenge, in this article, a privacy-preserving decentralized multi-agent cooperative optimization paradigm was developed via integrating cryptography into decentralized optimization. The proposed approach can effectively protect participants’ privacy against external eavesdroppers, honest-but-curious agents, and the system operator. Theoretical security and correctness analyses are provided. Simulations of numerical examples and experiments on a real-world platform are given to demonstrate the security, accuracy, and applicability of the proposed method.

Coordinating the charging process of a large population of electric vehicles (EV) is promising in increasing power grid flexibility from the demand side, yet requires highly scalable control protocols. In contrast to classical decentralized optimization based methods that require approximated distribution network models, this paper frames the EV charging control problem into a multi-agent reinforcement learning (MARL) framework. The MARL-based framework is trained through an actor-critic network and adopts the structure of centralized training and decentralized execution with partial observations. Comparing with model-based approaches, the developed MARL-based approach better captures the attributes of the distribution network, improves grid-level service performance, achieves better network constraints control, reduces the communication load, and achieves a faster response. The efficacy and efficiency of the developed method are verified by simulations on the IEEE 13-bus test feeder.

This paper proposes a mixed-integer program (MIP) to address a challenge issue of vehicle upgrade policy in current electric car rental market, i.e., idle luxury and high-end vehicles can be used as ordinary vehicles when the demand for ordinary vehicles is high. This model essentially is to maximize the total profit through balancing the demand and supply with a comprehensive consideration of revenue of rental operation, cost of potential demand loss, dispatching cost, energy consumption per kilometer, mileage limitation of electric vehicles (EVs), and the uncertainty of user demand for multi-type EVs. The proposed MIP model can be solved by CPLEX or Gurobi to search for the global optimal solution. Finally, real data from an electric carsharing system (ECS) with three types of EVs and 20 carsharing stations was used to validate the model. As a result, the daily increase of profit could reach 11.09% with an average of 8.08%.

A sliding mode control method based on nonlinear disturbance observer and nominal model is proposed to track the trajectory of manipulator with uncertain interference. A dynamic model of the manipulator is established, a sliding mode control law is designed, and the stability of the system is verified by the Lyapunov stability theory. A nonlinear disturbance observer is introduced to improve the performance of the control system. The results show that the control method reduces the unmodelled dynamic errors and the influence of uncertain external interference. Furthermore, simulation results based on Matlab prove that compared with the traditional PD position control method, proposed method has higher accuracy and better robustness.

While we have a good understanding of how cyber crime is perpetrated and the profits of the attackers, the harm experienced by humans is less well understood, and reducing this harm should be the ultimate goal of any security intervention. This paper presents a strategy for quantifying the harm caused by the cyber crime of typo squatting via the novel technique of intent inference. Intent inference allows us to define a new metric for quantifying harm to users, develop a new methodology for identifying typo squatting domain names, and quantify the harm caused by various typo squatting perpetrators. We find that typo squatting costs the typical user 1.3 seconds per typo squatting event over the alternative of receiving a browser error page, and legitimate sites lose approximately 5% of their mistyped traffic over the alternative of an unregistered typo. Although on average perpetrators increase the time it takes a user to find their intended site, many typo squatters actually improve the latency between a typo and its correction, calling into question the necessity of harsh penalties or legal intervention against this flavor of cyber crime.