Research and Application of Multi-Link Aggregation Algorithm Based on Link Priority for Telemedicine in Complex Environments.

As mobile robots are becoming more and more widely used, it is of great significance to design an efficient path planning method for multi-robot systems (MRS) that can adapt to complex and unknown environments. In this paper, we present a deep reinforcement learning (DRL) based method to navigate the MRS with collision avoidance in unknown dynamic environments, which consists of centralized learning and decentralized executing paradigm. The proposed policy maps original laser information into robot control commands without constructing global maps. The learned policy is tested in Gazebo environments with three robot systems, which shows the effective performance in terms of success rate, extra time rate, and formation maintenance rate.

Facing the implementation problems such like low growth reward, long training time, and poor stability of the multiagent learning methods when dealing with complex environment and more agents, this paper proposes a fast optimal coordination method for multiagent in complex environment (FOC-MACE). Firstly, the environment exploration strategy is introduced into the policy network based on the MADDPG method for higher growth rewards. Then, the parallel computing technology is adopted in the critic network, in purpose to effectively reduce the training time. These tactics together are beneficial to enhance the stability of multiagent learning. Lastly, the optimal resource allocation is carried out to realize optimal coevolution of the multiagents and further improve the learning ability of the agents’ group. To verify the effectiveness of our proposal, the FOC-MACE is compared with several advanced methods at current stage in the MPE environment. Three different experiments prove that by using our method, the growth reward is increased by up to 37.1%, the training is speed up significantly, and the stability of the method, which represented by standardized variance, is also improved. In addition, this paper validated the fast optimal coordination method for multiagent systems in the context of UAV scenarios, demonstrating the practical performance of the approach. Through comprehensive experiments and scenario validations, the study successfully confirmed the effectiveness of the proposed fast optimal coordination method for multiagent systems in complex environments.

Accurately describing the electronic properties of heavy-element molecular systems in complex environments is essential for advancing technologies such as optoelectronics and solar cells. However, achieving accurate predictions remains challenging because both relativistic and electron correlation effects must be considered equally, along with interactions involving other species in the complex environment (e.g., solvent). This paper extends our real-time time-dependent Dirac-Kohn-Sham (rt-TDDKS) implementation in PyBERTHA-RT to include environmental effects using the "uncoupled" Frozen-Density-Embedding (FDE) scheme, where only the active subsystem evolves dynamically in time. This adaptation utilizes existing FDE functionality within the PyEmbed module of the PyADF scripting framework. The native Python APIs of PyBERTHA-RT and PyADF provide an ideal environment for development, enhancing readability and reusability. We demonstrate that the FDE potential maintains the numerical stability of the active subsystem's density matrix propagation. Illustrative results for lead halides (PbCl2 and PbI2) in γ-butyrolactone (GBL) solution show the effects of increasing solvent molecules on absorption spectra. This case study demonstrates the new implementation's applicability to realistic systems, offering a basis for studying electron dynamics in heavy-element molecules in complex environments under linear and nonlinear regimes, relevant to perovskite precursor chemistry.

This paper introduces a method to estimate latent traffic from its origin to destination from the link packet loss rate and traffic volume. In addition, we propose a method for the joint optimization of routing and link provisioning based on the estimated latent traffic. Observed traffic could deviate from the original traffic demand and become latent when the traffic passes through congested links because of changes in user behavioral and/or applications as a result of degraded quality of experience (QoE). The latent traffic is actualized by improving congested link capacity. When link provisioning is based on observed traffic, actual traffic might cause new congestion at other links. Thus, network providers need to estimate the origin-destination (OD) original traffic demand for network planning. Although the estimation of original traffic has been well studied, the estimation was only applicable for links. In this paper, we propose a method to estimate latent OD traffic by combining and expanding techniques. The method consists of three steps. The first step is to estimate the actual OD traffic and loss rate from the actual traffic and packet loss rate of the links. The second step is to estimate the latent traffic demand. Finally, using this estimated demand, the link capacity and routing matrix are optimized. We evaluate our method by simulation and confirm that congestion could be avoided by capacity provisioning based on estimated latent traffic, while provisioning based on observed traffic retains the congestion. The combined method can avoid congestion with an increment of 23% compared with capacity provisioning only. We also evaluated our method's adaptability, i.e., the ability to estimate the required parameter for the estimations using fewer given values, but values obtained in the environment.

Rapid advances in energy applications require new theory and computational models to provide guidance for interpretation of experimental results and mechanistic understanding. New theory development is necessary to treat systems of increasing complexity, size, and relevance to real applications. Functional transition metal compounds, including molecules, clusters, nanoparticles, surfaces, and solids, provide particular promise for magnetic, optical, and catalytic applications. However, such systems can be exceptionally challenging to model. To make progress in understanding and designing transition metal compounds for energy applications, theory must be able to simulate such systems in complex environments, as well as simulate the spectra of such complex systems to provide direct connections with experiment. Leveraging the independent expertise of the team’s members, this project aimed to make inroads to the theoretical and computational challenges associated with studying transition metal compounds, their reaction chemistry, photophysics and photochemistry, and response to spectroscopic interrogation.

This chapter examines good practices in implementing effective Monitoring, Evaluation, and Learning (MEL) systems within complex international development Democracy, Human Rights, and Governance (DRG) programs, which are characterized by challenges of non‐linearity, limited evidence of theories of change, and contextual and politically contingent nature of outcomes. The chapter presents three cases of MEL systems in complex projects implemented by Pact across distinct and diverse operating contexts – Zimbabwe, Cambodia, and Somalia – to illustrate those projects’ MEL approaches that enabled continuous adaptation. The authors analyze the cases to respond to two questions: (1) What are the key elements of effective adaptive management‐focused MEL systems in complex environments? (2) What is practical guidance for designing and enabling complexity‐responsive and effective adaptive management‐focused MEL systems? The case studies illustrate three key elements: (1) Information gathering that closely links context, research, and performance data; (2) Systems for reflection that offer scheduled learning moments of varying frequency and intensity, as well as multiple feedback mechanisms; and (3) Enabling structures that promote adaptive mindsets and attitudes within project teams.

In order to quantify and evaluate the technical economy of power system operation plan in complex environment, a multi-period panoramic operation simulation method of new energy power system is proposed. Based on the complexity analysis of multi-period coordinated panoramic operation simulation, the time series load modeling is carried out and the new energy output model is constructed. After calculating the waste power of new energy, the frame of multi-period panoramic operation simulation is constructed and the timing operation simulation is completed. Finally, a practical example is given to demonstrate the effectiveness and advancement of the panoramic simulation scheme.

Robots are being used in the operating room to aid in surgery, prompting changes to workflow and adaptive behavior by the users. This case study presents a methodology for examining human-robot team interaction in a complex environment, along with the results of its application in a study of the effects of experience and workplace culture, for human-robot team interaction in the operating room. The analysis of verbal and non-verbal events in robotic surgery in two different surgical teams (one in the US and one in France) revealed differences in workflow, timeline, roles, and communication patterns as a function of experience and workplace culture. Longer preparation times and more verbal exchanges related to uncertainty in use of the robotic equipment were found for the French team, who also happened to be less experienced. This study offers an effective method for studying human-robot team interaction and has implications for the future design and training of teamwork with robotic systems in other complex work environments.

This paper proposes an uncalibrated adaptive visual servoing (VS) control framework based on dual-camera fusion to address key technical challenges in robotic visual servoing systems, including real-time state estimation, multi-space coordination, and dynamic target tracking. By combining the complementary advantages of “eye-in-hand” and “eye-to-hand” camera configurations, an adaptive switching mechanism is designed to achieve coordinated control between image space and Cartesian space, addressing convergence problems of traditional methods during target occlusion or field-of-view loss. Key features of the framework include: an uncalibrated control method based on the image Jacobian matrix; adaptive parameter adjustment based on Kalman filtering (KF); and a dual-camera fusion switching strategy. Experiments show the method achieves a positioning accuracy of 1.197 mm and an orientation accuracy of 0.149° in a representative static positioning task; demonstrates effective performance in scenarios involving out-of-view target acquisition and occlusion recovery; and reduces tracking errors by 13%–28% while shortening convergence time by 5%–32% in dynamic tracking tasks. This framework provides a practical technical approach for visual servoing systems in complex environments, showing potential for broad industrial applications.

This paper focuses on the trajectory planning and tracking control problems of multi-unmanned aerial vehicle (UAV) systems in complex environments. It combines the improved A* algorithm with the robust control method to achieve efficient trajectory planning and precise tracking control of multi-UAV systems. First, the improved A* algorithm is utilized to plan the trajectories of UAVs. Then, the trajectory expressions in analytical form are obtained through the UAV trajectory plotting algorithm. Next, a robust control method with uncertainties based on the Udwadia–Kalaba (UK) theory is proposed. Through the stability analysis of the Lyapunov function, the uniform boundedness and uniform ultimate boundedness of the system states are proven. Finally, simulation experiments on multi-UAV systems are carried out to verify the effectiveness of the proposed methods. The experimental results show that the trajectories planned based on the improved A* algorithm can achieve collision avoidance between UAVs. Meanwhile, the trajectory tracking error based on the proposed control method is superior to that of the traditional sliding mode control.

The vibration of rotor systems is significantly affected by solid particle contaminants, leading to reduced system performance and, in some cases, failure. In this paper, the influence of lubricant viscosity index and solid particle contaminants on the vibration characteristics of a dual-disc flexible rotor was examined. A dual-disc flexible rotor test rig was established. Extensive experiments were carried out by mixing various concentrations of contaminants with four types of lubricants, each with a different viscosity index. The results indicated that nonlinear vibration phenomena were induced by the presence of contaminants. Significant changes in the rotor’s vibration characteristics were observed when contaminants were introduced into lubricants of varying viscosity indices, altering the system’s maximum amplitude and critical speed. These alterations included abrupt amplitude shifts and periodic variations in critical speed. This study provides important experimental evidence for the operation of dual-disc flexible rotor systems in complex environments and supports the further optimization of rotor design and safe operational strategies under contamination conditions.

Idlers are key load-bearing components in belt conveyors, and their proper functioning is essential for ensuring the continuous and efficient operation of bulk material transportation systems. Traditional methods for monitoring idler health are typically based on a single modality, such as sound or vibration, which has limitations in monitoring range and accuracy. This study presents a novel method for accurately locating faulty idlers by fusing image and audio modalities. This approach achieves pixel-level segmentation of faulty idlers guided by audio signals, addressing the limitations of image-only methods, which cannot detect sound sources, and sound-only methods, which struggle with accurate localization. Specifically, this work proposes a module for converting one-dimensional audio features into two-dimensional image features, a module for correlating and spatially aligning audio and image features, and a pixel segmentation module. These innovations enhance both the monitoring range and accuracy of fault detection. Experimental results demonstrate that the proposed method achieves high detection accuracy and strong anti-interference capability, enabling precise localization of a single sounding idler in a large field of view. With the mean intersection over union (MIOU) as the evaluation metric, the proposed algorithm achieves 73.8% accuracy on a custom dataset and shows robust performance even under image and audio noise interference. This method plays a significant role in advancing the effective integration of multisource heterogeneous information and improving the intelligent health management of belt conveyor systems in complex environments. Our source code is available at https://github.com/jisi123/Belt-conveyor .

Nonlinear modeling and multi-scale influence characteristics analysis of organic Rankine cycle (ORC) system considering variable driving cycles

Conference lecture rooms are considered as an indoor environment for research with indoor localization that faces different challenges. In this paper, we design an indoor localization system in such a complex environment focus on machine learning algorithms with a large scale data. In this system, users can get information where a specified people locate in. Our system has two main sections: system design and algorithm approach. We use Bluetooth Low Energy devices to achieve communication and collect the information consist of people's name and corresponding RSSI data. To illustrate the advantages of machine learning algorithms using in this situation, a traditional algorithm will be proposed as a comparison. We also design and implement a Nine-Rectangle-Grid localization system in a practical laboratory environment. Experiments on real-world environment and simulations show our system can live up to locate people with low cost, high accuracy and short computing time.

In the recent decade, robotic exploration is emerging as a vast research platform as it prolongs to be a challenging task, especially in unknown dynamic environments. Exploration plays a vital role in collecting data from the areas inaccessible to the human. The operation of manual exploration systems in complex environments is cumbersome due to ineffective noise filtering, poor visibility and shuffling of map characteristics. In order to overcome such drawbacks, our approach focuses on the development of autonomous robotic exploration by designing a robust SLAM-based system, incorporating tri-layered mapping technique for automatic map generation and a new geometrical boundary-based path-planning technique to improve the mobility of the robot during navigation. The proposed path-planning algorithm yields better trajectory planning and obstacle avoidance using the pose, odometry and orientation parameters of the robot. The performance of the developed robot is evaluated through real-time experimentation, and it is evident from the results that, the proposed approach is preferable for autonomous exploration applications.