Search

Uranium is considered as a very important nuclear energy material because of the huge amount of energy it releases. As the main product of the spontaneous decay of uranium, it is difficult for helium to react with uranium because of its chemical inertness. Therefore, bubbles will be formed inside uranium, which could greatly reduce the performance of uranium or cause safety problems. Additionally, nuclear materials are usually operated in an environment of high-temperature and high-pressure, so it is necessary to figure out the exact state of helium inside uranium under extreme conditions. Here, we explored the structural stability of the U–He system under high pressure and high temperature by using density functional theory calculations. Two metastable phases are found between 50 and 400 GPa: U4He with space group Fmmm and U6He with space group P1¯. Both are metallic and adopt layered structures. Electron localization function calculation combined with charge density difference analysis indicates that there are covalent bonds between U and U atoms in both Fmmm-U4He and P1¯-U6He. Regarding the elastic modulus of α-U, the addition of helium has certain influence on the mechanical properties of uranium. Besides, first-principles molecular dynamics simulations were carried out to study the dynamical behavior of Fmmm-U4He and P1¯-U6He at high-temperature. It was found that Fmmm-U4He and P1¯-U6He undergo one-dimensional superionic phase transitions at 150 GPa. Our study revealed the exotic structure of U–He compounds beyond the formation of bubbles under high-pressure and high-temperature, which might be relevant to the performance and safety issues of nuclear materials under extreme conditions. [ABSTRACT FROM AUTHOR]

Geomagnetic matching navigation is extensively utilized for localization and navigation of autonomous robots and vehicles owing to its advantages such as low cost, wide-area coverage, and no cumulative errors. However, due to the influence of magnetometer measurement noise, geomagnetic localization algorithms based on single-point particle filters may encounter mismatches during continuous operation, consequently limiting their long-range localization performance. To address this issue, this paper proposes a real-time sequential particle filter-based geomagnetic localization method. Firstly, this method mitigates the impact of noise during continuous operation while ensuring real-time performance by performing real-time sequential particle filtering. Then, it enhances the long-range positioning accuracy of the method by rectifying the trajectory shape of the odometry through odometry calibration parameters. Finally, by performing secondary matching on the preliminary matching results via the MAGCOM algorithm, the positioning error of the method is further minimized. Experimental results show that the proposed method has higher positioning accuracy compared to related algorithms, resulting in reductions of over 28.58%, 37.11%, and 0.77% in RMSE, max error, and error at the end, respectively. [ABSTRACT FROM AUTHOR]

As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes' coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means clustering to effectively remove the positioning results with significant errors in this paper, which makes full use of the position and distance information of the anchor nodes in the area. In this method, after analyzing the factors affecting the optimization of the trilateration and selecting optimal parameters, we carry out experiments to verify the effectiveness and feasibility of the proposed algorithm. We also compare the positioning accuracy and positioning efficiency of the proposed algorithm with those of other algorithms in different environments. According to the comparison of the least-squares method, the maximum likelihood method, the classical trilateration and the proposed trilateration, the results of the experiments show that the proposed trilateration algorithm performs well in the positioning accuracy and efficiency in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. Then, we test our approach in three realistic environments, i.e., indoor, outdoor and hall. The experimental results show that when there are few available anchor nodes, the proposed localization method reduces the mean distance error compared with the classical trilateration, the least-squares method, and the maximum likelihood. [ABSTRACT FROM AUTHOR]

ABSTRACT The cooperative localization technology of multi‐AUV has been a research hotspot in the area of underwater localization in recent years. Aiming at the situation that the localization accuracy is reduced and diverging due to uncertain information such as outliers and unknown time‐varying noise in the measurement information in the dynamic topology environment, a new measurement filtering module is designed by using factor graphs. First, the K‐means algorithm is introduced based on the cooperative localization algorithm based on factor graphs in the dynamic environment, and an outlier filtering module is designed to filter measurement outliers. After outlier correction, the EM algorithm is introduced to resolve the issue of unknown measurement noise variance caused by unknown time‐varying noise and design an adaptive filtering module. Finally, to address the issue of degraded real‐time performance due to a large number of system communication nodes and diverse quality, the paper introduces the CRLB algorithm, distance evaluation factor, and measurement smoothing factor to design a node optimization module. Finally, these modules are integrated into the cooperative localization system through factor graphs. Through experimental analysis, the proposed algorithm effectively reduces outliers and unknown time‐varying noise in measurement information, as well as the impact of a large number of system nodes on localization accuracy and real‐time performance. In addition, the proposed algorithm also shows strong robustness in the face of extreme underwater environments. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, due to the significance of the location information of mobile nodes for many applications, location services are the basis of many application scenarios. However, node state and communication uncertainty affect the distance estimation and position calculation of the range-based localization method, which makes it difficult to guarantee the localization accuracy and the system robustness of the distributed localization system. In this paper, we propose a distributed localization method based on anchor nodes selection and particle filter optimization. In this method, we first analyze the uncertainty of error propagation to the least-squares localization method. According to the proportional relation between localization error and uncertainty propagation, anchor nodes are selected optimally in real-time during the movement of mobile nodes. Then we use the ranging and position of the optimally selected anchor nodes to obtain the location information of the mobile nodes. Finally, the particle filter (PF) algorithm is utilized to gain the optimal estimation of the localization results. The experimental evaluation results verified that the proposed method effectively improves the localization accuracy and the robustness of the distributed system. [ABSTRACT FROM AUTHOR]

The ultra-short baseline underwater positioning is one of the most widely applied methods in underwater positioning and navigation due to its simplicity, efficiency, low cost, and accuracy. However, there exists environmental noise, which has negative impacts on the positioning accuracy during the ultra-short baseline (USBL) positioning process, which results in a large positioning error. The positioning result may lead to wrong decision-making in the latter processing. So, it is necessary to consider the error sources, and take effective measurements to minimize the negative impact of the noise. In our work, we propose a USBL positioning system with Kalman filtering to improve the positioning accuracy. In this system, we first explore a new kind of element array to accurately capture the acoustic signals from the object. We then organically combine the Kalman filters with the array elements to filter the acoustic signals, using the minimum mean-square error rule to obtain accurate acoustic signals. We got the high-precision phase difference information based on the non-equidistant quaternary original array and the phase difference acquisition mechanism. Finally, on account of the obtained accurate phase difference information and position calculation, we determined the coordinates of the underwater target. Comprehensive evaluation results demonstrate that our proposed USBL positioning method based on the Kalman filter algorithm can effectively enhance the positioning accuracy. [ABSTRACT FROM AUTHOR]

Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results. In the integrated positioning system, an organic combination of a filtering gain compensation adaptive filter and a filtering gain compensation strong tracking filter is explored to fuse position information to obtain higher accuracy and a more stable positioning result. Firstly, the system selects the indirect filtering method and uses the integrated positioning error to model the navigation parameters of the system. Then, a filtering gain compensation adaptive filtering method is developed by using the filtering gain compensation algorithm based on the error statistics of the positioning parameters. The positioning parameters of the system are filtered and information on errors in the navigation parameters is obtained. Finally, integrated with the positioning parameter error information, the positioning parameters of the system are solved, and high-precision positioning results are obtained to accurately position autonomous underwater vehicles (AUVs). The simulation results show that the SINS/DVL integrated positioning method, based on the filtering gain compensation adaptive filtering technology, can effectively enhance the positioning accuracy. [ABSTRACT FROM AUTHOR]

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value. [ABSTRACT FROM AUTHOR]

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries.

The ultra-short baseline underwater positioning is one of the most widely applied methods in underwater positioning and navigation due to its simplicity, efficiency, low cost, and accuracy. However, there exists environmental noise, which has negative impacts on the positioning accuracy during the ultra-short baseline (USBL) positioning process, which results in a large positioning error. The positioning result may lead to wrong decision-making in the latter processing. So, it is necessary to consider the error sources, and take effective measurements to minimize the negative impact of the noise. In our work, we propose a USBL positioning system with Kalman filtering to improve the positioning accuracy. In this system, we first explore a new kind of element array to accurately capture the acoustic signals from the object. We then organically combine the Kalman filters with the array elements to filter the acoustic signals, using the minimum mean-square error rule to obtain accurate acoustic signals. We got the high-precision phase difference information based on the non-equidistant quaternary original array and the phase difference acquisition mechanism. Finally, on account of the obtained accurate phase difference information and position calculation, we determined the coordinates of the underwater target. Comprehensive evaluation results demonstrate that our proposed USBL positioning method based on the Kalman filter algorithm can effectively enhance the positioning accuracy.

Hydrogen-rich compounds are important for understanding the dissociation of dense molecular hydrogen, as well as searching for room temperature Bardeen-Cooper-Schrieffer (BCS) superconductors. A recent high pressure experiment reported the successful synthesis of novel insulating lithium polyhydrides above 130 GPa. However, the results are in sharp contrast to a previous theoretical prediction by the PBE functional that around this pressure range all lithium polyhydrides (LiH n (n = 2-8)) should be metallic. In order to address this discrepancy, we perform an unbiased structure search with first-principles calculation by including the van der Waals interaction that was ignored in the previous prediction to predict the high pressure stable structures of LiH n (n = 2-11, 13) up to 200 GPa. We reproduce the previously predicted structures, and further find novel compositions that adopt more stable structures. The van der Waals functional (vdW-DF) significantly alters the relative stability of lithium polyhydrides, and predicts that the stable stoichiometries for the ground state should be LiH 2 and LiH 9 at 130-170 GPa, and LiH 2 , LiH 8 , and LiH 10 at 180-200 GPa. Accurate electronic structure calculation with GW approximation indicates that LiH, LiH 2 , LiH 7 , and LiH 9 are insulative up to at least 208 GPa, and all other lithium polyhydrides are metallic. The calculated vibron frequencies of these insulating phases are also in accordance with the experimental infrared (IR) data. This reconciliation with the experimental observation suggests that LiH 2 , LiH 7 , and LiH 9 are the possible candidates for lithium polyhydrides synthesized in that experiment. Our results reinstate the credibility of density functional theory in the description of H-rich compounds, and demonstrate the importance of considering van der Waals interaction in this class of materials.

Application of high pressure can substantially enhance the chemical reactivity of xenon and has recently extended the Xe-compounds to unexpected elements such as Fe and H. Using unbiased structure searching techniques combined with first-principles calculations, we predict novel compounds of stable XeH2 and XeH4, and metastable XeH, XeH3, XeH5, XeH6, XeH7, and XeH8 under high pressure. Rather than van der Waals complexes, these are weakly covalent or ionic compounds stabilized by a pressure-induced increase in charge transfer from Xe to H atoms. The calculated electronic structures with hybrid exchange-correlation functionals reveal that only XeH and XeH2 are metalized under 300 GPa. For the metallic XeH and XeH2 at certain pressures, the superconducting critical temperatures are finally studied, by using Allen-Dynes modified McMillan equation combined with the calculated electron-phonon coupling parameter.