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High-flow hydraulic servo systems are extensively employed in contemporary industrial applications due to their considerable flow capacity and cost-effectiveness. Nonetheless, hydraulic servo systems frequently encounter unpredictable internal and external disturbances, and high-flow proportional directional valves always have unsatisfactory hydraulic characteristics, compromising the precision and robustness of high-flow hydraulic servo systems. This study proposes a novel control strategy integrating the Soft Actor-Critic (SAC) reinforcement learning algorithm with Adaptive Robust Control (ARC) to enhance system performance. This approach features a two-tiered controller: the upper controller utilizes the SAC algorithm to learn and adapt to the dynamics of the hydraulic servo system, iteratively refining the lower controller’s hyperparameters. Meanwhile, grounded in the ARC strategy, the lower controller executes real-time control of the hydraulic servo system. The simulation and experimental results demonstrate that the proposed control strategy can effectively adjust the control hyperparameters according to the learned system dynamic and tracking errors. Consequently, this approach enhances control precision amidst varying external and internal disturbances. Moreover, this control strategy is anticipated to realize high-flow, high-precision, and high-robust hydraulic servo systems, which can be used in various fields such as marine and offshore engineering.

To solve the problem that weak signals are difficult to detect accurately in low signal-to-noise ratios, this paper presents a method to achieve effective detection of weak signals, applying the method of stochastic resonance to bistable systems. The principle of the method is that by transferring part of the noise energy to the signal energy, enabling the detection of weak signals at low signal-to-noise ratios. It makes it easier to extract the signal at the receiving end. This model designs a parametrized conditioning system based on the factors influencing the output power spectrum and output SNR of a stochastic resonant system. Based on the experimental results, the influence of parameters a and b on the model can be analysed, and the optimal noise intensity range of the system can be found. At the receiving end of the system, the constellation diagram and BER are used as a measure of system performance. Simulation experiments show that stochastic resonance can effectively enhance the energy of weak signals under low signal-to-noise conditions, and the demodulation performance of the system is significantly better than that of the system without the use of stochastic resonance.

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.

As speed of communication data path is drastically improved in this decade due to the high data rate, evolutional technology is demanded to address the fast communication implementation. In this paper, we focus on data compression technology to speed up the communication data path. We have proposed a stream-based data compression called ASE coding. It compresses data stream based on the instantaneous data entropy without buffering and stalling for the compression processes. It is also suitable for hardware implementation. However, the stream-based data compression works heuristically with sensitive parameters that affect to the data compression ratio. If the parameters are statically configured, it does not follow the dynamic data entropy, and thus, the data compression performance becomes unstable. In this paper, we will disseminate the parameters, discuss the behaviors of those parameters and propose its autonomous adjustment methods. We will also propose adjustment algorithms for those parameters that follow the data entropy of the input data stream autonomously. Through experimental evaluations applying the algorithms, we will confirm the parameters are adjusted with depending on the data entropy in the data stream. And then, the compression ratio becomes stable as the compressor exploits the minimal entropy adaptively.

The scene dynamics can provide useful statistical information for adjusting parameters of Gaussian mixture models (GMMs) in video surveillance. The contributions of this paper are twofold. First, an adaptive scene dynamics estimation approach is proposed. Second, we propose a scene-dynamics based method to adjust two types of GMMs' parameters, i.e., the learning rates and number of Gaussian components. For the learning rates, the scene dynamics are integrated into different kinds of pixel-type feedback schemes to control different kinds of learning rates. Experimental results demonstrate that the proposed method can effectively improve the performance of GMMs in surveillance scenes with complex dynamic backgrounds. [ABSTRACT FROM AUTHOR]

The chief characteristic that differs markedly between parallel simulation techniques is how they manage process interaction. The linear control models for online configuration of the simulation presented in this article are different from traditional control theory. The reason is because data sampling and parameter adjustment are intrusive; these operations contend for processor cycles that can be utilized for useful computation. In addition, this control process is by necessity imprecise. First, the changes of the local logical process (LP) configurations will have secondary and tertiary effects that are reflected back to the LP. These effects are difficult to quantify. Second, because data sampling and parameter adjustment are intrusive, it is usually not feasible to implement the most accurate control system if it is too complex. The online configuration model was used to adapt the behavior of different facets of the simulation. Because of the imprecise nature of the control systems, it is nearly impossible to find the optimal settings for them. We relied on empirical data to guide the design of the control systems. In particular, control strategies for dynamic adjustment of the checkpoint interval and the cancellation strategy (lazy or aggressive) are presented. Performance results for the control strategies demonstrate that dynamic schemes can considerably improve the performance of a time warp-based parallel discrete-event simulator. In experiments conducted on several application domains, the performance of the time warp simulator was improved by dynamically limiting the overheads of the time warp algorithm [ABSTRACT FROM PUBLISHER]