Mobile edge computing (MEC) emerges as a promising paradigm that aims to extend the computing capabilities of mobile devices. This enhancement is achieved by offloading heavy computational tasks to nearby edge servers, where relevant services, such as programs, databases, and libraries, are stored to support the execution of these tasks. Given the restricted storage capacity and computing resources of edge servers, it is crucial to carefully consider which tasks to offload and which services to cache. However, the highly dynamic system and closely coupled decisions between offloading and caching pose considerable challenges to developing an effective strategy. In this article, we investigate both computation offloading and service caching in an end-edge–cloud collaborative system under time-varying wireless channels and task requests. We formulate an optimization problem aimed at minimizing the system cost, including time and energy consumption, and model it further as a Markov decision process (MDP). To address this, we propose a deep reinforcement learning (DRL)-based approach with dynamic action masking to concurrently optimize offloading and caching decisions. This approach renormalizes the valid action probability distribution at every step, enabling the agent to explore within the space of valid action combinations and thereby learn the optimal policy. Extensive experimental results demonstrate that the proposed approach greatly reduces the system cost compared to other representative benchmark schemes.

Open Information Extraction (Open IE) provides a method to extract triplets from text, which has become a research frontier and hot topic in recent years. However, the traditional open information extraction approaches have low precision and significant costs, which heavily rely on the artificially defined extraction paradigm. In this paper, we propose ZoIE, a zero-shot open information extraction model. First, we set positive and negative samples and use the pretraining method based on contrastive learning to train a language model. Then, we use the self-attention weight matrix to perform triplet extraction by using the method of nucleus sampling beam search. Finally, we construct a triplet fine-sorting strategy based on the loss function and select the optimal triplet from the candidate triplets. Experimental results on serveral datasets show that our proposed approach achieves better performance than other baselines.

Multiaccess edge computing has emerged as a powerful paradigm for increasing the computation performance of mobile devices (MDs). Applying nonorthogonal multiple access (NOMA) to MEC can further improve the spectrum efficiency and reduce offloading delays caused by the upload congestion. In this article, we examine the joint computation offloading and resource allocation problem in the NOMA–MEC system, which benefits from the combination of NOMA and MEC. Our optimization objective is to minimize the computational overhead (the weighted sum of the execution delay and the energy consumption) in dynamic environments with time-varying wireless fading channels. The optimization problem is formulated as a mixed-integer programming (MIP), which involves jointly optimizing the task offloading decisions, channel assignment, and transmit power allocation. To solve such an optimization problem, we formalize the task offloading and the resource allocation as a Markov decision process (MDP). Then, we propose a deep reinforcement learning (DRL)-based approach, which combines multiple deep neural networks (DNNs) to directly approximate different statistical models for continuous and discrete control. The simulation results demonstrate that the proposed approach can rapidly converge and efficiently decrease the total computational overhead compared to other baseline approaches in different scenarios.

Mobile edge computing (MEC) has been regarded as a promising paradigm for increasing the computing capacity of mobile devices (MDs) by offloading tasks to edge servers. Non-orthogonal multiple access (NOMA) is a critical multiple access technique that allows many MDs to transmit on the same resource block simultaneously. Attracted by the enormous benefits of combining NOMA and MEC, we investigate the dynamic computation offloading in a multi-device multi-server NOMA-MEC system. We consider the partial offloading policy such that MDs can offload a portion of the task to the edge server for execution. To minimize the overall computation delay and energy consumption, we formulate a mixed integer programming (MIP) problem to jointly optimize edge server selection and offloading task ratio. Solving the optimization problem with a discrete-continuous hybrid action space is not straightforward since most existing deep reinforcement learning (DRL) algorithms are only applicable to discrete or continuous action spaces. We present the hybrid advantage actor-critic (HA2C) approach, which employs an actor-critic architecture consisting of two par-allel actor networks and a critic network, to tackle this problem. Specifically, the discrete actor and continuous actor networks based on deep neural networks (DNNs) determine MEC server selection and offloading ratio, respectively. The critic network evaluates the current state value, and the advantage function is computed for the discrete and continuous network parameter updates. Experimental results show that the proposed algorithm is superior to conventional DRL algorithms that convert the hybrid action space into a unified homogeneous action space.

A control algorithm for a 3-RPS parallel platform driven by pneumatic cylinders is discussed. All cylinders are controlled by proportional directional valves while the kinematic and dynamic properties of the system are modeled. The method of adaptive robust control is applied to the controller using a back-stepping approach and online parameter estimation. To compensate for the uncertainty and the influence caused by estimations, a fast dynamic compensator is integrated in the controller design. To prevent any influence caused by the load applied to the moving platform changing in a practical working situation, the identification of parameters is taken as the initialization of unknown parameters in the controller, which can improve the adaptability of the algorithm. Using these methods, the response rate of the parameter estimation and control performance were improved significantly. The adverse effects of load and restriction forces were eliminated by the initialization and online estimation. Experiments under different situations illustrated the effectiveness of the adaptive robust controller with parameter initialization, approaching average tracking errors of less than 1%.

The non-dominated sorting genetic algorithm II (NSGA-II) is applied to the concurrent generation scheduling and load management of the isolated microgrid: the joint generation — voyage scheduling of an all-electric ship (AES). The AES uses integrated power generators and an energy storage system (ESS) to match its propulsion and service loads, thus forming an isolated microgrid. The propulsion load is the variable load that determines th ship voyage; hence, the schedulings of the generator, the propulsion load and the ESS have to be processed concurrently, for the optimal operation of the AES. NSGA-II outperforms the existing methods for the AES scheduling problem by modelling the schedulings in a one-stage optimisation. Another advantage of NSGA-II applied here is its multi-objective optimisation. Existing work on ship scheduling has hitherto set the minimisation of the AES operational cost as the single optimisation objective, with the reduction of the greenhouse gas (GHG) emission merely treated as a constraint of the optimisation. The proposed work emphasises the environmental concerns and makes GHG mitigation a separate objective, thus expanding the optimisation into multi-objective. The simulation demonstrate that optimising the ship voyage jointly with the generation scheduling results in the reduction of both operational cost and GHG emission, compared to the fixed-voyage generation scheduling. Integrating the ESS dispatch into the generation scheduling further enhances the benefits.