Search Results (12)

In the quest for greener alternatives to conventional organic solvents, Deep Eutectic Solvents (DESs) have gained significant attention due to their sustainability, biodegradability, and tunability. The use of water as an active and genuine component has recently led to the emergence of water-based DESs (wb-DESs). Here, a careful experimental characterization was performed on choline acetate (ChAc)/water mixtures across a range of water:ChAc molar ratios (n = 2–6). Differential Scanning Calorimetry (DSC) revealed glass transitions between 150 and 180 K, with no first-order transitions, leading to a classification of these mixtures as Low Transition-Temperature Mixtures (LTTMs). Physicochemical measurements, including density, viscosity, electrical conductivity, and refractive index, were conducted over a broad temperature range. NMR analyses provided insights into dynamics and solvation environments, with ¹H T1_slow relaxation times reaching their lowest value at n = 2, consistent with the formation of a strong hydrogen-bonding network. The n = 2 mixture was further investigated using Small and Wide-Angle X-ray Scattering (S-WAXS) and ab initio molecular dynamics (AIMD). These studies, jointly with ¹H NMR choline diffusion coefficient, directly challenge previous claims of the existence of aggregation phenomena in wb-DES. The simulation revealed a well-organized solvation structure, where acetate and water synergistically stabilize the choline cation through a cooperative hydrogen-bonding network. These findings highlight the impact and significance of an integrated physicochemical study in guiding the rational development of new sustainable systems, such as wb-DESs. Full article

The cooperative patrol control of multiple unmanned surface vehicles (Multi-USVs) in dynamic aquatic environments presents significant challenges in global coverage efficiency and system robustness. The study proposes a cooperative patrol control algorithm for multiple unmanned surface vehicles (Multi-USVs) based on a hybrid embedded task state information model and reward reshaping techniques, addressing global coverage challenges in dynamic aquatic environments. By integrating patrol, collaboration, and obstacle information graphs, the algorithm generates kinematically feasible control actions in real time and optimizes the exploration-cooperation trade-off through a dense reward structure. Simulation results demonstrate that the algorithm achieves 99.75% coverage in a 1 km × 1 km task area, reducing completion time by 23% and 74% compared to anti-flocking and partition scanning algorithms, respectively, while maintaining collision rates between agents (CRBAA) and obstacles (CRBAO) below 0.15% and 0.5%. Compared to DDPG, SAC, and PPO frameworks, the proposed training framework (TFMUSV) achieves 28% higher rewards with 40% smaller fluctuations in later training stages. This study provides an efficient and reliable solution for autonomous monitoring and search-rescue missions in complex aquatic environments. Full article

Compared with traditional active detection radar, non-cooperative bistatic radar has a series of advantages, such as a low cost and low detectability. However, in real-life scenarios, it is limited by the non-cooperation of the radiation source and the bistatic geometric model, resulting in a low target signal-to-noise ratio (SNR) and unstable detection between frames in the radar scanning cycle. The traditional detect-before-track (DBT) method fails to exploit adequately the target information and is incapable of achieving consistent and effective tracking. Therefore, in this paper, we propose a two-stage track-before-detect (TBD) method based on deep learning. This method employs a low-threshold detection network to identify the target initially, followed by utilizing the model method to ascertain potential tracks. Subsequently, a diverse range of network structures are employed to extract and integrate position information, innovation score, and target structural information from the track in order to obtain the target track. Experimental results demonstrate the method’s ability to achieve multi-target tracking in highly cluttered environments, where the higher the number of frames processed, the better the target tracking effect. Moreover, the method exhibits real-time processing capabilities. Hence, this method provides an effective solution for target tracking in non-cooperative bistatic radar systems. Full article

It is difficult to determine the accurate pose of non-cooperative space targets in on-orbit servicing (OOS). The visual camera is easily affected by the extreme light environment in space, and the scanning lidar will have motion distortion when the target moves at high speed. Therefore, we proposed a non-cooperative target pose-estimation system combining a registration and a mapping algorithm using a TOF camera. We first introduce the projection model of the TOF camera and proposed a new calibration method. Then, we introduce the three modules of the proposed method: the TOF data preprocessing module, the registration module and the model mapping module. We assembled the experimental platform to conduct semi-physical experiments; the results showed that the proposed method has the smallest translation error 8 mm and Euler angle error 1° compared with other classical methods. The total time consumption is about 100 ms, and the pose tracking frequency can reach 10 Hz. We can conclude that the proposed pose-estimation scheme can achieve the high-precision pose estimation of non-cooperative targets and meet the requirements necessary for aerospace applications. Full article

Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected according to the generated 3D points of the transmission line corridor. Tests on both corresponding datasets and practical 220-kV transmission line corridors are conducted. The experimental results of different modules reveal that our proposed system can be applied in practical inspection environments and has good performance. Full article

The human casualties from simulated nuclear detonation scenarios in New Delhi, India are analyzed, with a focus on the distribution of casualties in urban environments and the theoretical application of a nuclear-specific triage system with significant innovation in interdisciplinary disaster management applicable generally to urban nuclear detonation medical response. Model estimates of nuclear war casualties employed ESRI’s ArcGIS 9.3, blast and prompt radiation were calculated using the Defense Nuclear Agency’s WE program, and fallout radiation was calculated using the Defense Threat Reduction Agency’s (DTRA’s) Hazard Prediction and Assessment Capability (HPAC) V404SP4, as well as custom GIS and database software applications. ESRI ArcGISTM programs were used to calculate affected populations from the Oak Ridge National Laboratory’s LandScan^TM 2007 Global Population Dataset for areas affected by thermal, blast and radiation data. Trauma, thermal burn, and radiation casualties were thus estimated on a geographic basis for New Delhi, India for single and multiple (six) 25 kt detonations and a single 1 mt (1000 kt) detonation. Major issues related to the emergency management of a nuclear incident are discussed with specific recommendations for improvement. The consequences for health management of thermal burn and radiation patients is the worst, as burn patients require enormous resources to treat, and there will be little to no familiarity with the treatment of radiation victims. Of particular importance is the interdisciplinary cooperation necessary for such a large-scale emergency response event, which would be exemplified by efforts such as the application of a Nuclear Global Health Workforce. Full article

OpenSWAP is a class of innovative open architecture, low cost autonomous vehicles for geological/geophysical studies of shallow water environments. Although they can host different types of sensors, these vehicles were specifically designed for geophysical surveys, i.e., for the acquisition of bathymetric and stratigraphic data through single- and multibeam echosounders, side-scan sonars, and seismic-reflection systems. The main characteristic of the OpenSWAP vehicles is their ability of following pre-defined routes with high accuracy under acceptable weather and sea conditions. This would open the door to 4D (repeated) surveys, which constitute a powerful tool to analyze morphological and stratigraphic changes of the sediment/water interface and of the shallow substratum eventually caused by sediment dynamics (erosion vs. deposition), slumps and gravitative failures, earthquakes (slip along seismogenic faults and secondary effects of shaking), tsunamis, etc. The low cost and the open hardware/software architectures of these systems, which can be modified by the end users, lead for planning and execution of cooperative and adaptive surveys with different instruments not yet implemented or tested. Together with a technical description of the vehicles, we provide different case studies where they were successfully employed, carried out in environments not, or very difficultly accessed through conventional systems. Full article

The effective removal of uranium from an aqueous solution is a highly valuable process for the environment and health. In this study, we developed a facile and rapid method to synthesize hierarchically porous Cu-BTC (RT-Cu-BTC) using a cooperative template strategy. The as-synthesized RT-Cu-BTC exhibited hierarchically porous structure and excellent thermostability, as revealed by X-ray powder diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis. Compared with conventional metal–organic frameworks (MOFs) and zeolites, the obtained RT-Cu-BTC exhibited enhanced adsorption capacity (839.7 mg·g⁻¹) and high removal efficiency (99.8%) in the capture of uranium (VI) from aqueous solutions. Furthermore, the conditions such as adsorbent dose, contact time, and temperature in adsorption of uranium (VI) by RT-Cu-BTC were investigated in detail. The thermodynamics data demonstrated the spontaneous and endothermic nature of the uranium (VI) adsorption process. The Langmuir isotherm and pseudo-second-order models could better reflect the adsorption process of uranium (VI) onto RT-Cu-BTC. In addition, the as-synthesized RT-Cu-BTC showed excellent stability in removing uranium (VI) from an aqueous solution. This work provides a facile and rapid approach for fabricating hierarchically porous MOFs to realize a highly efficient removal of uranium (VI) from aqueous systems. Full article

Intelligent vehicles and connected vehicles have garnered more and more attention recently, and both require accurate positions of the vehicles in their operation, which relies on navigation sensors such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and so on. GNSS is the key sensor to obtain high accuracy positions in the navigation system, because GNSS Real Time Kinematic (RTK) with correct ambiguity resolution (AR) can provide centimeter-level absolute position. But AR may fail in the urban occlusion environment because of the limited satellite visibility for single vehicles. The navigation data from multiconnected vehicles can improve the satellite geometry significantly, which is able to help improve the AR, especially in occlusion environment. In this work, the GNSS, INS, and LiDAR data from multiconnected vehicles are jointly processed together to improve the GNSS RTK AR, and to obtain high accuracy positioning results, using a scan-to-map matching algorithm based on an occupancy likelihood map (OLM) for the relative position between the connected vehicles, a Damped Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method with least-squares for a relative AR between the connected vehicles, and a joint RTK algorithm for solving the absolute positioning for the vehicles by involving the relative position and relative ambiguity constraints. The experimental results show that the proposed approach can improve the AR for the connected vehicles with higher ratio values, success rates, and fixed rates, and achieve high-precision cooperative absolute positions compared with traditional GNSS RTK methods, especially in occlusion environments such as below a viaduct. Full article

Modern robotic exploratory strategies assume multi-agent cooperation that raises a need for an effective exchange of acquired scans of the environment with the absence of a reliable global positioning system. In such situations, agents compare the scans of the outside world to determine if they overlap in some region, and if they do so, they determine the right matching between them. The process of matching multiple point-cloud scans is called point-cloud registration. Using the existing point-cloud registration approaches, a good match between any two-point-clouds is achieved if and only if there exists a large overlap between them, however, this limits the advantage of using multiple robots, for instance, for time-effective 3D mapping. Hence, a point-cloud registration approach is highly desirable if it can work with low overlapping scans. This work proposes a novel solution for the point-cloud registration problem with a very low overlapping area between the two scans. In doing so, no initial relative positions of the point-clouds are assumed. Most of the state-of-the-art point-cloud registration approaches iteratively match keypoints in the scans, which is computationally expensive. In contrast to the traditional approaches, a more efficient line-features-based point-cloud registration approach is proposed in this work. This approach, besides reducing the computational cost, avoids the problem of high false-positive rate of existing keypoint detection algorithms, which becomes especially significant in low overlapping point-cloud registration. The effectiveness of the proposed approach is demonstrated with the help of experiments. Full article

This paper proposes a cooperative environment scan system (CESS) using multiple robots, where each robot has low-cost range finders and low processing power. To organize and maintain the CESS, a base robot monitors the positions of the child robots, controls them, and builds a map of the unknown environment, while the child robots with low performance range finders provide obstacle information. Even though each child robot provides approximated and limited information of the obstacles, CESS replaces the single LRF, which has a high cost, because much of the information is acquired and accumulated by a number of the child robots. Moreover, the proposed CESS extends the measurement boundaries and detects obstacles hidden behind others. To show the performance of the proposed system and compare this with the numerical models of the commercialized 2D and 3D laser scanners, simulation results are included. Full article

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system. Full article