Generative Upper-Level Policy Imitation Learning With Pareto-Improvement for Energy-Efficient Advanced Machining Systems.

The potential intelligence behind advanced machining systems (AMSs) offers positive contributions toward process improvement. Imitation learning (IL) offers an appealing approach to accessing this intelligence by observing demonstrations from skilled technologists. However, existing IL algorithms that implement single policy strategies have yet to consider realistic scenarios for complex AMS tasks, where the available demonstrations may have come from various experts. Moreover, most IL assumes that the expert's policy is optimal, preventing the learning from fulfilling the previously ignored green missions. This article introduces a novel three-phase policy search algorithm based on IL, enabling the learning of heterogeneous expert policies while balancing energy preferences. The first phase equips the agent with machining basics through upper-level policy learning, generating an imitation policy distribution with various decision-making principles. The second phase enhances energy conservation capabilities by employing Pareto-improvement learning and fine-tuning the agent's policies to a Pareto-policy manifold. The third phase produces outcomes and amplifies the efficacy of human feedback by utilizing ensemble policies. The experimental results indicate that the proposed method outperforms meta-heuristics, exhibiting superior solution quality and faster computation times compared to four diverse baseline methods, each with diverse samples.

Partial Consistency for Stabilizing Undiscounted Reinforcement Learning.

Undiscounted return is an important setup in reinforcement learning (RL) and characterizes many real-world problems. However, optimizing an undiscounted return often causes training instability. The causes of this instability problem have not been analyzed in-depth by existing studies. In this article, this problem is analyzed from the perspective of value estimation. The analysis result indicates that the instability originates from transient traps that are caused by inconsistently selected actions. However, selecting one consistent action in the same state limits exploration. For balancing exploration effectiveness and training stability, a novel sampling method called last-visit sampling (LVS) is proposed to ensure that a part of actions is selected consistently in the same state. The LVS method decomposes the state-action value into two parts, i.e., the last-visit (LV) value and the revisit value. The decomposition ensures that the LV value is determined by consistently selected actions. We prove that the LVS method can eliminate transient traps while preserving optimality. Also, we empirically show that the method can stabilize the training processes of five typical tasks, including vision-based navigation and manipulation tasks.

Distributed Fusion Estimation for Stochastic Uncertain Systems With Network-Induced Complexity and Multiple Noise.

This article investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event trigger is developed to handle network-induced packet dropouts, as well as packet disorders resulting from random transmission delays, where the H2/H∞ performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complex network-induced problem, which may deteriorate system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.

A novel linear active disturbance rejection controller for main steam temperature control based on the simultaneous heat transfer search.

The main steam temperature of boiler outlet has been deemed as a significant parameter of the safety and economic performances in the thermal power plant operation. The complex working status of the thermal generation endures highly uncertain factors and remarkable disturbance, which call for effective controlling approaches in the corresponding temperature management. The linear active disturbance rejection controller (LADRC) is a conducive and powerful controlling method, whereas strong correlation between LADRC parameters leads to difficulties in optimally determining the controller parameters. Aiming at eliminating the negative effects on main steam temperature control caused by uncertainties factors and disturbances, a high performance LADRC based on a novel parameters optimization strategy, the simultaneous heat transfer search (SHTS) algorithm, is designed to deliver a stability, rapidity, and precision of control process. In the presented SHTS algorithm, all the three phases of heat transfer are randomly and parallel operated, providing a significant improvement towards the optimization performance. The proposed algorithm is first verified on various benchmark functions contrasted to state-of-the-art counterparts in performance validating, and then adopted in the parameter selection of LADRC in the main steam temperature control system. The excellent control performance, strong robustness and disturbance rejection ability of the designed approach are illustrated through the simulation results on main steam temperature control system.

[Clinical application of minimally invasive core decompression combined with impaction bone grafting to the treatment of femoral head necrosis].

OBJECTIVE: To explore a simple,effective threrapeutic method for the treatment of ischemia necrosis of femoral head. METHODS: From March 2003 to April 2008, 61 hips of 55 patients, including 37 males and 18 females, aged from 12 to 55 years old (averaged 39.8), with ischemia necrosis of femoral head were treated by three methods combination of partial synovectomy, minimally invasive core decompression and impaction bone grafting. The course of diseace was from 8 months to 16 years. The therapeutic effects were evaluated according to the preoperative and postoperative X-ray and Harris scoring for hip funtion. RESULTS: These 55 patients (61 hips) were followed up for from 6 mouths to 5 years (means 2.2 years). X-ray results showed that collapse or aggrevation occurenced in 39 hips,the aggravation of collapse no more than 2 mm in 11 hips,the collapse from 2 to 4 mm in 9 hips, the collapse surpassed 4 mm in 2 hips. Harris scores increased obviously from preoperative (59.74 +/- 11.56) points to postoperative (89.75 +/- 9.58) points (t = 2.3461, P < 0.05). The results were excellent in 31 hips, good in 22 hips, fair in 6 hips and poor in 2 hips. CONCLUSION: Minimally invasive core decompression combined with impaction bone grafting can reduce the stress load of femoral head, stabilized the environment of femoral head,promote osteonecrotic bone rapairing and prevent effectively the femoral head form collapsing. This method can be applied to femoral head necrosis at Ficat II, III stage, especially for young and middle-age patients.