As the number of ships docking at ports and using onshore power grows, there is a pressing need for efficient economic dispatch within the port microgrid (PMG). The computing power network (CPN) leverages ubiquitous computing resources, presenting new opportunities for handling complex tasks, such as the economic dispatch problem (EDP) more efficiently. However, integrating CPNs into PMGs raises significant concerns about data security and privacy. To address these issues, this article proposes a distributed prescribed-time optimization (PPDPTO) algorithm specifically designed for the EDP in the integrated PMG and CPN. This algorithm ensures both high computation efficiency and enhanced privacy security. Specifically, this algorithm combines the distributed prescribed-time optimization theory and the privacy-preserving technique, which can protect the sensitive energy information of the berthing ships while ensuring fast convergence. Moreover, based on the proposed mask function protecting the power demand information of the berthing ships, the optimal solution of the EDP can be obtained with limited decoding resources. A smooth piecewise mask function is proposed to promote the nonvulnerability of the system and the bias in the optimal steady states of the proposed algorithm is reduced. Furthermore, the prescribed-time convergence of the PPDPTO algorithm is proven; meanwhile, the supply and demand balance constraints of the EDP can be guaranteed under the mask function. Finally, simulations undermine the effectiveness of the PPDPTO algorithm.

Spatial frequency domain imaging (SFDI) is a wide-field diffuse optical imaging modality that has attracted considerable interest in recent years. Typically, diffuse reflectance measurements of spatially modulated light are used to quantify the optical absorption and reduced scattering coefficients of tissue, and with these, chromophore concentrations are extracted. However, uncertainties in estimated absorption and reduced scattering coefficients are rarely reported, and we know of no method capable of providing these when look-up table (LUT) algorithms are used to recover the optical properties. We present a method to generate optical property uncertainty estimates from knowledge of diffuse reflectance measurement errors. By employing the Cramér-Rao bound, we can quickly and efficiently explore theoretical SFDI performance as a function of spatial frequencies and sample optical properties, allowing us to optimize spatial frequency selection for a given application. In practice, we can also obtain useful uncertainty estimates for optical properties recovered with a two-frequency LUT algorithm, as we demonstrate with tissue-simulating phantom and in vivo experiments. Finally, we illustrate how absorption coefficient uncertainties can be propagated forward to yield uncertainties for chromophore concentrations, which could significantly impact the interpretation of experimental results.

This article presents an adaptive prescribed-time dynamic surface control approach for a differential-driven unmanned surface vehicle (USV) with fully unknown parameters, which is a cascaded control system consisting of kinematic, kinetic, and motor-driven propeller layers. First, the control design of the kinematic layer determines the virtual surge and yaw velocities, which are filtered as the pseudocommands for the kinetic layer. By imposing a performance function for the position error of the USV, it can be driven into the preset precision region within the prescribed time. Second, the virtual force control laws designed in the kinetic layer give the desired motor angular speed commands, which are filtered as the pseudocommands for the propeller layer. Then, in the control design of the propeller layer, the actual command duty cycles are proposed to make the actual motor angular speed follow these pseudospeed commands of motors to apply in practice directly. By employing the fuzzy logic system to approximate the nonlinearities, we can establish a simpler control structure than the existing disturbance-observer-based controller due to the avoidance of virtual control laws and the adaptive compensations of modeling uncertainties. Finally, experimental results show that the proposed method makes the tacking task successful with user-defined performance.

Accurate performance and low energy consumption are essential for trajectory tracking control of an underactuated surface vessel (USV) to satisfy marine operation requirements. This paper studies the user-specified optimal tracking control problem for the USV in the presence of modeling uncertainties. A fuzzy weights-based adaptive reinforcement learning (RL) control scheme is developed to achieve the tracking control with the predefined convergence time and steady-state accuracy while minimizing the performance index function. The constructed performance index function includes the surge velocity control law and the constrained position tracking error. Following that, an optimal backstepping design frame is proposed to derive the controllers with unknown gradient terms by constructing the error subsystems at the kinematic and kinetic levels of the USV. Then, a fuzzy reinforcement learning algorithm (FRLA) is presented to derive the optimal control solutions of corresponding error subsystems. Since the fuzzy basis function vectors derived from measured states of the USV system may be zero vectors, the persistence excitation condition may not always be satisfied. Thus, in the design of fuzzy RL updating laws of critics and actors, a positive parameter compensation technique is proposed to make the matrixes consist of fuzzy basis functions strictly positive definite. It releases the need for assumption of persistence excitation, and the convergence of the critic weight estimate and the boundedness of closed-loop systems can be guaranteed. Finally, the effectiveness of the presented method is demonstrated by numerical simulations and real-world experiments.

With increasing awareness of environmentally friendly, the ship-integrated energy system (S-IES) combined with power and heating networks has become an upcoming trend in the shipbuilding industry. It decreases the consumption of fuel-based resources by integrating renewable energy resources to replace traditional fossil-based generators partly. Meanwhile, with many intelligent devices, the S-IES performs partitioned and autonomous features because of the unique operational structure. Therefore, the normal operation of S-IES is highly vulnerable, which will be bothered by the infeasibility-driven attack for shipboard devices and influenced by the uncertainties of renewable energy resources. To this end, the energy management problem under cyber attacks for S-IES is focused on ensuring secure and green sailing in this paper. To balance the number of nods and decrease the communication resources among energy devices, a partitioning algorithm is designed to construct the energy router (ER) based energy hub (EH) for S-IES. Meanwhile, considering the trade-off relationship between the economy and carbon emission, a multi-objective energy management model with feasible sailing constraints is designed. Then, to ensure the information exchange among EHs using ERs, a distributed resilient energy management strategy with a detection mechanism is proposed to acquire the optimal solutions under cyber attack. Finally, theoretical analysis verifies the effectiveness of the designed distributed resilient strategy. Simulation results of different scenarios prove the great performance on ensuring secure and green sailing for ships by the constructed energy management model and its distributed resilient strategy.