CT reconstruction with priori MRI images through multi-group datasets expansion

Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.     

Attitudes,mode switching behavior,and the built environment: A longitudinal study in the Puget Sound Region

Carpooling in the US has a storied history. After experiencing a peak 20% mode share in 1980, the current share of carpooling for work trips is about 10% and the majority of these carpooling trips are made by intra-household members. Casting the choice between SOV and carpool as a social dilemma in which SOV is a noncooperative choice and carpool is a cooperative one, we propose to test two hypotheses. First, the switch from SOV to carpool and the reverse choice are attributed to different factors—structural factors, or those factors altering the objective features of a decision scenario such as travel time and travel cost, play a dominant role in the switch from carpool to SOV while psychosocial factors (attitudes and beliefs) play a critical role in the switch from SOV to carpool. Second, the two choices are underlay by different behavioral mechanisms. In particular, we expect self-justification by carpool-to-SOV switchers—after they switch from carpool to SOV, they adjusted their attitudes toward carpool accordingly to match their behavior. The analysis of the first three waves of the Puget Sound Transportation Panel supports these two hypotheses. Our study results recommend developing programs and policies that aim at influencing people’s subjective assessments of carpooling, in addition to the existing ones that mostly focus on incentivizing carpooling, and differentiating between programs seeking to encourage SOV users to switch to carpool and those aiming to maintain existing carpoolers.     

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions北大核心CSCD

［Objective ］ To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. ［Method］On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. ［Results］The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters. ［Conclusions］The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems. © 2022 Journal of Clinical Hepatology. All rights reserved.     

Personalization method of e-catalog based on user interesting degree

The user interesting degree evaluation index is designed to fulfill the users’ real needs, which includes the user’ attention degree of commodity, hot commodity and preferential commodity. User interesting degree model (UIDM) is constructed to justify the value of user interesting degree; the personalization approach is presented; operations of add and delete nodes (branches) are covered in this paper. The improved e-catalog is more satisfied to users’ needs and wants than the former e-catalog which stands for enterprises, and the improved one can complete the recommendation of related products of enterprises.     

Estimation of delay variability at signalized intersections for urban arterial performance evaluation

Urban arterial performance evaluation has been broadly studied, with the major focus on average travel time estimation. However, in view of the stochastic nature of interrupted flow, the ability to capture the characteristics of travel time variability has become a critical step in determining arterial level of service (LOS). This article first presents a stochastic approach that integrates classic cumulative curves and probability theories in order to investigate delay variability at signalized intersections, as a dominant part of the link travel time variability. This serves as a basis for arterial travel time estimation, which can be obtained through a convolution of individual link travel time distributions. The proposed approach is then applied in the estimation of travel time along one arterial in Shanghai, China, with abundant automatic vehicle identification (AVI) data sources. The travel time variability is evaluated thoroughly at 30-min intervals, with promising results achieved in comparison to the field measurements. In addition, the estimated travel time distributions are utilized to illustrate the probability of multiple LOS ranges, namely, reliability LOS. The results provide insights into how we might achieve a more reliable and informative understanding of arterial performance.     

A proposed vision and vehicle-to-infrastructure communication-based vehicle positioning approach

It is essential to obtain accurate location of vehicles for new applications of Intelligent Transportation Systems. To remedy the defects of present Global Positioning System and vehicle-to-infrastructure (V-I) positioning technology, a new positioning approach based on vision and V-I communication is proposed. This approach aims at lane-level positioning with lower cost than conventional ones. In this approach, the position of the vehicle is represented by its lateral position (the lane number) and longitudinal position (the distance from entrance of the road) in a course coordinate system along the road; the specific lane the vehicle is occupying (the lane number) can be judged using the information of lane lines detected by vision systems; then the distance to the vehicle is obtained by a Road Side Unit (RSU) during the V-I communication; and the longitudinal position is calculated. The error of the approach on typical operating conditions is analyzed, indicating that the new approach can achieve the accuracy of less than 0.31 m for straight road and 0.58 m for typical arc road with ultra-wideband communication and ranging technologies and rational arrangement of RSUs. The feasibility of this approach is presented.     

Robust control for four-wheel-independent-steering electric vehicle with steer-by-wire system

A four-wheel-independent-steering (4WIS) electric vehicle (EV) with steer-by-wire (SBW) system is proposed in this paper. The fast terminal sliding mode controller (FTSMC) is designed for the SBW system to suppress external disturbances. Taking unstructured and structured uncertainties into consideration, a robust controller is designed for the 4WIS EV utilizing μ synthesis approach and the controller order reduction is implemented based on Hankel-Norm approximation. Since sideslip angle is the feedback signal of robust controller and it is hard to measure, the extended Kalman filter (EKF) is employed to estimate sideslip angle. To evaluate the vehicle performance with the designed control system, step and sinusoidal steering maneuvers are simulated and analyzed. Simulation results show that the designed control system have good tracking ability, strong robust stability and good robust performance to improve vehicle stability and handing performance.     

短线匹配法预制拼装技术及其工程应用

短线匹配法节段预制拼装技术是将整孔箱梁设计成若干个标准节段,工厂内逐节段匹配预制,架桥机现场拼装的施工技术。该项技术1965年最早出现在法国,在国外得到了广泛应用。2002年自苏通大桥开始,我国逐步实现了短线匹配法的大规模应用以及设计、施工、装备、材料等技术的全面国产化。结合我国已建成的几座代表性的短线匹配法桥梁工程,对短线匹配法节段预制拼装的接缝质量控制、线形控制及长期性能保证等关键要点进行了论述,对短线匹配法施工技术应用中存在的问题及未来发展趋势进行了初步分析。     

小型直喷式柴油机燃用生物质能燃料的性能研究

文章主要研究了生物质能混合燃料在柴油机上的应用,通过在一台小型直喷式柴油机上进行不同组分柴油-生物柴油-乙醇混合燃料的燃烧、油耗和排放性能对比试验,分析了乙醇含量的改变对混合燃料的发动机燃烧压力、滞燃期、放热规律、比油耗和排放的影响．     

基于ARENA的双车道超车视距设计风险评价

通过计算机仿真软件ARENA模拟双向两车道路段上不同的超车视距长度下的超车行为,并建立风险指标。风险指标主要根据超车动作结束时,超越车与对向车辆间的最短时间距离确定。一共确定了六个风险等级,用于评价不同设计时速下的各种PSD长度的风险。利用此方法可以很直接的评价超车视距设计的优劣。