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2021, 41(3): .

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2021, 41(3): .

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Design of Low-Voltage Drive Solid-State Safety & Arming Device Based on Weak Environmental Force Miniature Fuze

FENG Hengzhen, LOU Wenzhong, SUN Yi, ZHAO Yuecen

2021, 41(3): 231-236. doi:10.15918/j.tbit1001-0645.2020.028

Abstract(929) PDF(335)

Abstract:
In order to solve the problem that the traditional electromechanical fuze cannot complete the insurance release under the weak environmental forces and the traditional MEMS solid state switch cannot achieve the low-voltage driving capacity (5~35 V). A MEMS solid-state fuze was proposed based on silicon-based MEMS processing technology and corona discharge effect. A micrometer-scale design idea was used to achieve energy and information communication between the solid-state fuze and the fuze control system. First, establishing a mathematical model of Corona discharge and using finite element analysis methods such as COMSOL, the solid-state insurance control layer based on low-voltage driving was completed, and the average driving voltage (33.1 V) of the control layer was achieved in laboratory static testing. Second, the capacitor discharge mode was combined with electrical and Joule thermal simulations, using the capacitor energy storage index (10 V, 10 μF). Finally, the structural design of the execution layer under different current intensity environments was completed. Based on the MEMS processing technology, the production of solid-state safety fuze was finally achieved.

Study on 3D Model Pretreatment Method for MCNP Calculation

ZHAO Yingfeng, LIU Jianhua, MA Jiangtao, WU Linlin, CHENG Xun

2021, 41(3): 237-244. doi:10.15918/j.tbit1001-0645.2020.050

Abstract(837) PDF(322)

Abstract:
A 3D model pretreatment method was proposed for MCNP calculation program, including model recognition and simplification algorithm, the cavity generation algorithm, and the efficient conversion algorithm from CAD models to half space CSG models. And a 3D model pretreatment system was developed for MCNP calculation program to make the CAD model be transformed into half space CSG model rapidly. Finally a practical example was arranged to validate the developed system. The results show that, the proposed method can quickly transform complex CAD models into half space CSG models, providing an efficient 3D model pretreatment system for MCNP calculation program. It can improve the efficiency of the MCNP input file construction.

Driving Style Recognition Based on the Score Coefficient Under the Following Condition

JIN Hui, LÜ Ming

2021, 41(3): 245-250. doi:10.15918/j.tbit1001-0645.2020.095

Abstract(760) PDF(340)

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Based on NGSIM database, THW and ITTC were selected as parameters to evaluate the collision risk level. And a rapid recognition metric, called the score coefficient of objectivity (SCO), was proposed to measure the driving radicalness in the sampling period with a standard value between 0 and 1. Furthermore, the decision boundary of SCO was analyzed to avoid miscarriage of justice. The accuracy of the score coefficient of objectivity classification was evaluated based on K-Means clustering algorithm. The results show that, compared with traditional classification algorithms, the new method can provide a better veracity and real-time performance. The overall accuracy rate can reach up to 95.54% and the boundary miscarriage of justice can reduce to 4.46%. When the model parameters and evaluation methods are applied to new condition, the 94% overall accuracy rate can also be obtained. Based on the new method, a real-time and convenient driving style recognition system can be developed to achieve a cooperating and individuation control for the advanced driving.

Characteristics and Failure Mechanism of Unbalanced Bonded-Riveted Hybrid Joints

CHEN Xiaokai, GUO Ziyu, JIN Jiawei, SUN Lingyu

2021, 41(3): 251-257. doi:10.15918/j.tbit1001-0645.2000.041

Abstract(811) PDF(325)

Abstract:
To solve the problem of multi-material connection in lightweight automotive bodies, the mechanical properties and failure mechanism of carbon fiber reinforced polymer (CFRP) and aluminum alloy unbalanced four-rivets bonded-riveted hybrid joints were studied. Based on re-import and predefined field, a nonlinear finite element analysis model of CFRP/Al unbalanced hybrid joint was established. The process of riveting, springback and progressive failure mechanism under tensile load of the hybrid joint were simulated. And comparative analysis of mechanical properties and failure modes between bonded joint, riveted joint and hybrid joint was carried out. It is shown that the modeling method proposed can take into consideration the impact of the riveting process and the process sequence on the characteristics of the hybrid joint simultaneously. The error value of the peak load and energy absorption predicted by the model is of 2.3% and 5.2% respectively with high prediction accuracy. The riveting can provide a strengthening effect on the bonding at the early stage of failure. The failure takes place usually in the form, the adhesive layer fails firstly and after that the aluminum plate fails. The simulation and test results are consistent relatively, verifying the correctness of the modeling method proposed.

Analysis on Blade Characteristics of Low Specific Speed Electric-Compressors

ZHANG Hong, CHEN Yi, WANG Zhuo

2021, 41(3): 258-265. doi:10.15918/j.tbit1001-0645.2019.290

Abstract(709) PDF(306)

Abstract:
In this paper, the performance requirements and design characteristics of the electric-compressor were analyzed to conceive a conceptual design for the blade of electric-compressor, running with low specific speed and high efficiency. The performance of the centrifugal compressor impeller J90 used in the ordinary vehicle turbocharger and the compressor impeller JE90 used in the low specific speed were analyzed respectively. Comparing and analyzing the differences in the meridional channel, blade angle distribution and blade load distribution of the two impellers in different applications, the design geometry rule and strategy of the electric-compressor impeller were obtained. The research results show that, optimizing the shape of the meridional channel can improve the flow and reduce the secondary flow loss, the split blade with back-swept can increase the stall margin and the operation margin, choosing a smaller exit blade angle for distribution can improve pressure gap between the suction surface and the pressure surface to enhance the function for the air, the maximum of the main blade thickness should not be too large and the thickness distribution in the middle span should be uniform.

Numerical Investigation on Hydrodynamic Performance and Structural Response of Composite Propeller

LIU Ying, ZHANG Jing, WU Qin, ZHANG Hanzhe, HUANG Biao

2021, 41(3): 266-273. doi:10.15918/j.tbit1001-0645.2019.263

Abstract(772) PDF(356)

Abstract:
In this paper, the hydrodynamic load of the composite propeller was calculated based on computational fluid dynamics (CFD),FEM was used to calculate structural response of composite blades. Based on the bidirectional coupling algorithm, the fluid-structure interaction simulation of the composite propeller under uniform flow was carried out. The hydrodynamic performance and structural response of the composite propeller with different advance coefficients and different ply angles were studied. The results show that, the propulsive efficiency of the composite propeller is higher than that of rigid propeller when advance coefficientJ≤ 0.8. With the increase of advance coefficient, the propulsive efficiency of the composite propeller increases first and then decreases, the maximum value can be obtained when advance coefficientJ=0.8. The distribution of total deformation and equivalent stress of the blades are great related with ply angles. Compared with the metallic propeller, the pitch angle of the composite propeller is smaller. When the reduced pitch angle matches the change of the attack angle, the propulsive efficiency of the propeller can be improved adaptively.

A Method for Design of Silo Ejector Concerning with Wall Friction

QUAN Hui, XIE Jian, XIE Zheng, LI Liang

2021, 41(3): 274-285. doi:10.15918/j.tbit1001-0645.2000.029

Abstract(689) PDF(385)

Abstract:
Aiming at the problem of silo design, a quasi-one dimensional method concerning with wall friction was proposed based on the ejector function method. A mathematical model of wall friction was proposed and ejector functions and static pressure matching functions concerning with wall friction were established. The analysis results prove that the velocity at the stagnation critical point is less than sonic speed, the stagnation critical point of ejector can be achieved and the third critical point is impossible to achieve. And then, the characteristics were analyzed with practical examples, considering the engine total pressure, the cross-sectional area of the silo and the outlet pressure of the mixing chamber changed respectively, and the characteristic curves were drawn. The flow patterns of supersonic jet ejectors were analyzed by CFD method, considering respectively the engine total pressure, the cross-sectional area of the silo and the outlet pressure of the mixing chamber changed. At the same time, the inlet pressures of ejectors obtained by CFD were compared with that obtained by the quasi-one dimensional method. The results show that the errors between the CFD method and the quasi-one dimensional method are little on characteristic curves and their largest value is 4.31% when the backpressure changes, which verified the quasi-one dimensional method.

Deep Knowledge-Enhanced Network for News Recommendation

LIU Qiongxin, SONG Xiang, QIN Mingshuai

2021, 41(3): 286-294. doi:10.15918/j.tbit1001-0645.2019.273

Abstract(809) PDF(236)

Abstract:
In the news recommendation scenario, the traditional text-based feature recommendation model only considers the co-occurrence relationship of words, and cannot capture the implicit meaning and associated knowledge of words.The recommendation model based on deep learning only considers the information of the entity in the process of merging the knowledge graph information, ignoring the connection between the distant entities, resulting in the lack of related information and deep semantic relations between entities.A model named deep knowledge-enhanced network (DKEN) was proposed to solve the problem.Firstly, a long-short-term memory network was used to extract the entity path features from the knowledge graph.And then, path features were added to the attention network and the user feature was built dynamically based on the candidate news.Finally, some experiments were carried out.The results show that the entity path features can improve the model's effect and increase by about 1% on theF₁indicator.

Double-Channel GAN with Multi-Level Semantic Correlation for Event Detection

PAN Limin, LI Xiaoya, LUO Senlin, WU Zhouting

2021, 41(3): 295-302. doi:10.15918/j.tbit1001-0645.2019.177

Abstract(891) PDF(293)

Abstract:
Event detection is an important task of information extraction. In recent years, it has been widely used in the fields of knowledge graph construction, information retrieval and intelligence research. For current event detection methods, events within one sentence are often identified as independent individuals, while the correlation among the events within one sentence or document is ignored. Besides, some triggers may trigger different events in different contexts, and the word vectors training in multiple contexts can introduce noise that is not semantically related to the current context. To solve the problems, a double-channel GAN with multi-level semantic correlation was proposed for event detection. Firstly, a multi-level gated attention mechanism was utilized to capture the semantic correlation among sentence-level events and document-level events. And then, a double-channel GAN with self-regulation learning was used to reduce noise and improve accuracy of the representation of event. Finally, some experiments on ACE2005 English corpus were carried out. The results show that,F₁score can achieve 77%, and the method can effectively obtain semantic correlation among multi-level events, and improve accuracy of context determination.

Robust Boundary-Enhanced GMM-SMOTE Software Defect Detection Method

LUO Senlin, SU Xia, PAN Limin

2021, 41(3): 303-310. doi:10.15918/j.tbit1001-0645.2019.312

Abstract(704) PDF(279)

Abstract:
Software defects are bugs that can disrupt the normal operation of the system or software, the cost of detection and positioning for software defects is high. Automatic defect detection model based on software data have become an important tool for defect discovery. Defective samples that are accurately labeled is rare, and the rate of missing labels and mislabeling is high, which leads the existing data balance optimization methods to exacerbate noise and blur boundaries of classification. To solve this problem, a robust boundary-enhanced GMM-SMOTE software defect detection method was proposed. This method was arranged to use Gaussian mixture clustering to divide the software data set into multiple clusters, to make reliable sample selection based on intra-cluster category ratio, and to implement boundary recognition based on posterior probability, to guide the completion of the weighted data balance, and finally to build a software defect detection model using balanced optimization data. Experimental results on multiple NASA public data sets show that GMM-SMOTE can achieve data balance of noise suppression and boundary enhancement, effectively improve the effect of software defect detection, possessing great practical value.

A Multi-Dimensional Network Security Metrics Model Based on TOPSIS

ZHAO Xiaolin, ZENG Chonghan, XUE Jingfeng, LIN Qingyu, GUO Jiong

2021, 41(3): 311-321. doi:10.15918/j.tbit1001-0645.2019.269

Abstract(648) PDF(261)

Abstract:
In order to measure network security comprehensively, technique for order preference by similarity to an ideal solution (TOPSIS) was chosen as a comprehensive evaluation method of network security metrics to quickly detect network attacks and measure their risks, and the network security was divided into three dimensions by analytic hierarchy process, namely, environmental safety, reliability security and vulnerability security. According to the three dimensions, the network security was divided, and the metrics of each dimension were extracted and quantified. In the dimension of environment security, the evaluation value of the network security was presented based on the measurement of the network infrastructure and basic data. In the dimension of reliability security, the network was abstracted as a graph, and the reliability safety index was calculated based on the complex network theory and graph theory. In the dimension of vulnerability security, the vulnerabilities in the network were scanned with tools, and the vulnerability security index was calculated. The experimental results show that the model can improve accuracy and real-time performance in network security metrics. It is important to locate the network security risks and enhance the safety of network in time and accurately.

A Demosaicking Algorithm Based on the Gradient Direction

ZHONG Shun'an, GUO Yuhao, MA Yue, REN Shiwei, WANG Weijiang

2021, 41(3): 322-326. doi:10.15918/j.tbit1001-0645.2019.325

Abstract(788) PDF(302)

Abstract:
At present, many problems exist in the anti-mosaic algorithm used in digital image acquisition system, such as serious interpolation pseudo-image and large computation. In order to solve the above problems, an anti-mosaic algorithm based on gradient direction was proposed to improve the demosaicking performance in practical application. Firstly, the horizontal and vertical gradients of the current pixel position were computed, and theGchannel was interpolated accordingly. Then, theRchannel andBchannel were interpolated based on bilinear algorithm with the calibration ofGchannel. And this algorithm was compared with other typical algorithms from the perspective of composite peak signal noise ratio (CPSNR) and computation. The comparison results show that the proposed algorithm can consume lower computation amount and achieve excellent performance of demosaicking processing. In future, with its higher cost-effective feature, it will be used in real-time imaging and other high-tech fields.

P300 Waveform Extraction and Target Classification Algorithm Based on Temporal-Delayed and Spatio-Temporal Filtering

LIN Yanfei, LU Zhiqiang, LI Bowen, LIU Zhiwen, GAO Xiaorong

2021, 41(3): 327-333. doi:10.15918/j.tbit1001-0645.2019.293

Abstract(790) PDF(427)

Abstract:
In this study, an algorithm of P300 waveform extraction and target classification was proposed based on temporal-delayed and spatio-temporal filtering. Firstly, the multi-channel electroencephalogram (EEG) signal was delayed in temporal domain. And a cost function was constructed based on the least square method. The alternately optimizing was conducted to estimate the spatio-temporal filter and the desired signal until the cost function was converged. At last, the spatio-temporal filter could be obtained to separate the components in the spatial domain and extract the P300 waveform in the temporal domain. And then, simulation analysis was carried out to verify the waveform extraction performance of the algorithm with P300 data. The results show that the algorithm is better than the correlative algorithm for P300 waveform recovery. Finally, the obtained spatio-temporal filter was utilized to extract P300 components as classification features from real EEG data. A Fisher linear discriminant analysis was trained with the P300 components got from training dataset and utilized to classify the EEG signals. The results indicated that the P300 waveform extraction performance, classification accuracy rate and area under curve (AUC) value of the proposed algorithm are significantly better than the correlative algorithm. Therefore, the proposed algorithm can extract P300 waveform and classify target effectively.

A SAR Image Recognition Method Based on Distance Metric Learning

GAO Fei, ZHAO Jieqiong, LIN Chong, CHEN Haoran

2021, 41(3): 334-340. doi:10.15918/j.tbit1001-0645.2019.301

Abstract(788) PDF(322)

Abstract:
Due to synthetic aperture radar (SAR) image sample data is insufficient,and the similarity of intra-class images,which causes difficulty in recognition. A distance metric learning method was proposed for SAR image recognition. The method was arranged to use CNN networks to obtain the feature distribution of the image,and use the LSTM networks to strengthen the correlation between images. Based on the cosine similarity distance measurement method,the matching degree between images was calculated,and the results were classified based on the attention mechanism. Combined with the training method of few-shot learning,the training experiments were carried out with pre-training strategies and using the public MSTAR data set to perform SAR image recognition. The results show that the recognition rate of the method can reach up to 99.3%,be 2.5% higher than SVM.

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