Design and development of automotive blind spot detection radar system based on ROI pre-processing scheme

In the conventional 2D-FFT based target detection method, all range-Doppler cells are computed by FFT (Fast Fourier Transform) and scanned by CA-CFAR (Cell-Averaging Constant False Alarm Rate) detection. This results in high computational complexity and long processing time. In this paper, we developed an automotive 24 GHz BSD (Blind Spot Detection) FMCW (Frequency Modulated Continuous Wave) radar with a low complexity target detection architecture based on a ROI (Region Of Interest) pre-processing scheme. In the real BSD zone, because the number of cars to be detected is limited, the designed method only extracts their velocities corresponding to the range ROIs in which real targets exist. Moreover, the presence probability of vehicles with the same range-bin but different velocities is very low. Thus, in the designed method, some Doppler ROIs cells with a high magnitude are only applied for CA-CFAR detection. This architecture can dramatically reduce the amount of data to be processed compared to that of the conventional 2D FFT based method, resulting in enhanced processing time. We developed a 24 GHz FMCW radar system composed a transceiver, antennas, and signal processing module. The designed algorithm was implemented in a tiny micro-processor of the signal processing module. By implementing our proposed algorithm in the developed 24 GHz FMCW radar system in an anechoic chamber and a real road, we verified that the range and velocity of a car occupying the BSD zone were detected. Compared to that of the conventional method, the reduction ratio of the total processing time was measured to be 52.4 %.     

Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle

This paper presents a calibration method of a rule-based energy management strategy designed for a plug-in hybrid electric vehicle, which aims to find the optimal set of control parameters to compromise within the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economy performance is constructed by the method of radar chart. Moreover, a radial basis functions (RBFs) neural network model is proposed to establish a precise model within the control parameters and the comprehensive evaluation indicator. The best set of control parameters under offline calibration is gained by the multi-island genetic algorithm. Finally, the offline calibration results are compared with the experimental results using a chassis dynamometer. The comparison results validate the effectiveness of the proposed offline calibrating approach, which is based on the radar chart method and the RBF neural network model on vehicle performance improvement and calibrating efficiency.     

隧道-洞口滑坡平行体系受力变形模式与计算方法研究北大核心CSCD

我国目前对隧道-滑坡工程的设计尚无可供参照的行业标准,尤其是滑坡洞口段隧道缺少相应的计算理论。文章首先以平行体系中隧道-洞口滑坡为研究对象,通过归纳总结滑坡地段隧道衬砌的病害特征,构建了相应的工程地质模型;然后将剩余滑坡推力视为导致隧道变形破坏的直接原因,通过荷载传递规律得到作用于隧道结构上的附加荷载,将其与围岩压力叠加推导出了隧道外荷载的计算公式;接着采用弹性地基梁理论,推导出滑坡推力作用下的隧道内力计算方法,从而得到隧道-洞口滑坡的受力变形模式及计算理论;最后通过模型试验对其合理性进行了验证分析,结果表明该方法与实际工程相符,能够为滑坡地段洞口隧道的设计提供参考。     

通过多转子防护装置提高潮汐水轮机性能的策略（英文）

Constructive interference between tidal stream turbines in multi-rotor fence configurations arrayed normally to the flow has been shown analytically, computationally, and experimentally to enhance turbine performance. The increased resistance to bypass flow due to the presence of neighbouring turbines allows a static pressure difference to develop in the channel and entrains a greater flow rate through the rotor swept area. Exploiting the potential improvement in turbine performance requires that turbines either be operated at higher tip speed ratios or that turbines are redesigned in order to increase thrust. Recent studies have demonstrated that multi-scale flow dynamics, in which a distinction is made between device-scale and fence-scale flow events, have an important role in the physics of flow past tidal turbine fences partially spanning larger channels. Although the reduction in flow rate through the fence as the turbine thrust level increases has been previously demonstrated, the within-fence variation in turbine performance, and the consequences for overall farm performance, is less well understood. The impact of turbine design and operating conditions, on the performance of a multi-rotor tidal fence is investigated using Reynolds-Averaged Navier-Stokes embedded blade element actuator disk simulations. Fences consisting of four, six, and eight turbines are simulated, and it is demonstrated that the combination of device-and fence-scale flow effects gives rise to cross-fence thrust and power variation. These cross-fence variations are also a function of turbine thrust, and hence design conditions,although it is shown simple turbine control strategies can be adopted in order to reduce the cross-fence variations and improve overall fence performance. As the number of turbines in the fence, and hence fence length, increases, it is shown that the turbines may be designed or operated to achieve higher thrust levels than if the turbines were not deployed in a fence configuration.     

Application of an improved EEMD method in bearing fault diagnosis of induction motors北大核心CSCD

[Objective]In order to overcome the disadvantages of the traditional ensemble empirical mode decomposition (EEMD) method in selecting parameters (integration time and white noise amplitude coefficient) based on experience, and reduce the cost of calculation time, a fast ensemble empirical mode decomposition (FEEMD) method is used to extract the characteristic frequency. [Method]By changing the distribution density of the added white noise, different signal envelopes can be obtained. Furthermore, we can identify the optimal envelope by finding the optimal search window width of the moving mean filter, thereby avoiding the defect of EEMD selecting parameters by experience. At the same time, after the abnormal component in the signal is decomposed, the residual component can be decomposed by EMD to further save the calculation time cost. Finally, the method is combined with Hilbert envelope demodulation technology and applied to the fault characteristic frequency diagnosis of the bearing inner ring of an asynchronous motor. [Results]As the results show, compared with the traditional EEMD method, FEEMD can extract the fault frequency more efficiently. [Conclusion]FEEMD overcomes the disadvantages of the traditional EEMD method in selecting parameters based on experience and shortens the calculation time. As such, it can be effectively applied in bearing fault frequency extraction experiments. © The Author(s) 2022.     

Fault diagnosis of steam power system based on convolutional neural network北大核心CSCD

[Objectives]In order to improve the fault diagnosis level of marine power systems, this paper studies the real-time fault diagnosis of a marine supercharged boiler based on a convolutional neural network (CNN).[Methods]First, the simulation program of the marine supercharged boiler is developed based on the GSE platform, and the simulation fault data is obtained. The fault diagnosis model of the boiler is then established using the CNN method. Next, through the change trends of temperature, flow and other parameters, combined with a priori knowledge and the machine learning method, fault identification is carried out. Lastly, the performance of the method is evaluated against criteria such as confusion matrix and accuracy. [Results]According to the comparison results between the feature extracted dataset and the original dataset, the stability of the model output results and the generalization ability of the model are optimized and improved, with an overall fault classification accuracy reaching 99.53%.[Conclusion]The results of this study can provide valuable references for the intelligent monitoring of marine power systems. © 2022 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.     

贵阳岩溶地层地铁盾构隧道工程中新型球状碳化钨刀具的研究与应用北大核心CSCD

在贵阳轨道交通3号线盾构隧道施工中,采用普通刀具易出现滚刀多边形磨损、偏磨、刀刃崩裂等异常磨损和刀圈脱落现象。为改进滚刀刀圈的耐磨、耐冲击性能,提高刀具的综合寿命,提出采用球状碳化钨刀具替换普通刀具,并介绍了激光熔覆焊工艺以及宏观磨损检测工艺。经过施工现场的实际应用,发现球状碳化钨刀具具有更好的耐磨性,新型球状碳化钨刀具平均磨损量为0.00255 mm/延米,相邻普通刀具平均磨损量为0.01655 mm/延米。在第338~736环掘进中,新型球状碳化钨刀具最大磨损量为0.015 mm/延米,刀具无崩刃现象,滚刀轴承、密封等均无异常,有效解决了普通刀具易磨损、磨损不均的问题,为喀斯特地质条件下盾构刀具的选型提供参考。     

A dynamic approach to road freight flows modeling in Spain

This paper presents an innovative approach to analyzing road vehicle freight traffic that uses a dynamic panel data specification derived from a gravity model. This dynamic approach, which has recently been employed in international goods trade models in lieu of the traditional static specification, is applied to the case of Spain using data for the countrys 15 NUTS-3 regions between 1999 and 2009. Using the system general method of moments approach, we obtained significant evidence that the flow of vehicles carrying commodities by road has a strong persistence effect when controlling for unobserved heterogeneity. We also found that the quality of road transport infrastructure has a significant impact on vehicle trips. According to our findings, we suggest that this type of specification be employed in distribution models in which fixed effects and lags of the dependent variable are included to account for unobserved heterogeneity and persistence effects, respectively.     

Open-type ferry safety system design for using LNG fuel

In this feasibility study, we investigate the viability of using Liquefied Natural Gas (LNG) fuel in an open type Ro-Ro passenger ferry and the associated potential challenges with regard to the vessel safety systems. We recommend an appropriate methodology for converting existing ships to run on LNG fuel, discuss all the necessary modifications to the ship’s safety systems, and also evaluate the relevant ship evacuation procedures. We outline the basic requirements with which the ship already complies for each safety system and analyze the additional restrictions that must be taken into consideration for the use of LNG fuel. Appropriate actions are recommended. Furthermore, we carry out a hazard identification study. Overall, we clearly demonstrate the technical feasibility of the investigated scenario. Minimal modifications to the ship’s safety systems are required to comply with existing safety rules for this specific type of ship.     

Decision fusion of a multi-sensing embedded system for occupant safety measures

The need for an embedded system that can fuse incomplete, inconsistent, and imprecise decisions from several sensing systems is a crucial step in achieving an effective decision for occupant safety measures. This paper deals with the decision fusion strategies of a multi-sensing embedded system to achieve significant enhancement in the reliability of occupant safety through the fused decisions. Multi-sensing approaches to determine weight, vision, and crash sensing are developed for occupant detection, classification, position calculation, and crash detection. A rule-based decision fusion algorithm is then developed to fuse the multi-sensing decisions. The developed sensing systems are incorporated into an embedded device. To execute the embedded system, a system interface between the software and hardware is developed using Lab Window/CVI with the C programming language. The experimental results demonstrated that the real time operation of the embedded system validate the effectiveness of the decision fusion algorithm, characterize the safety measures and monitor the decision application. Several events were tested that prove the performance of the embedded system is robust towards occupant safety measures.