系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (6): 1743-1754.doi: 10.12305/j.issn.1001-506X.2023.06.18
• 系统工程 • 上一篇
田晨智, 宋敏, 薛瑞君, 田继伟
收稿日期:
2022-09-27
出版日期:
2023-05-25
发布日期:
2023-06-01
通讯作者:
宋敏
作者简介:
田晨智(1995—), 男, 硕士研究生, 主要研究方向为领航筹划与作战控制Chenzhi TIAN, Min SONG, Ruijun XUE, Jiwei TIAN
Received:
2022-09-27
Online:
2023-05-25
Published:
2023-06-01
Contact:
Min SONG
摘要:
为了更加科学、精准地评估指挥控制人员的空战控制能力, 采用眼动追踪技术, 基于眼动、时间、准确率等指标建立了量化评估模型, 并利用熵权-逼近理想解排序(technique for order preference by similarity to an ideal solution, TOPSIS)法对空战控制能力进行了综合评估。该模型以眼动指标作为重要评估依据, 将客观赋权的熵权法与多属性决策TOPSIS法相结合, 解决了传统评估方式受主观影响较大的问题, 并结合实例分析验证了其有效性和可行性, 为今后指挥控制人员的训练评估提供了合理的手段。
中图分类号:
田晨智, 宋敏, 薛瑞君, 田继伟. 基于眼动指标和熵权-TOPSIS空战控制能力评估[J]. 系统工程与电子技术, 2023, 45(6): 1743-1754.
Chenzhi TIAN, Min SONG, Ruijun XUE, Jiwei TIAN. Air combat control capability evaluation based on eye movement index and entropy weighted-TOPSIS[J]. Systems Engineering and Electronics, 2023, 45(6): 1743-1754.
表1
敏捷度评估指标"
子阶段 | 指标 | 含义 |
感知子阶段S1r | V1rguide/(件/秒) | ABMs感知关键态势的敏捷度 |
m1/件 | “ABMs感知关键态势”这一事件 | |
t1rtotal/s | ABMs空战控制的感知总时间 | |
t1ra, t1rb, t1rc/s | ABMs对关键态势的发现时间、反应时间、感知时间 | |
T1r1, T1r2, T1r3, T1r4/s | 关键态势的出现时刻, ABMs对关键态势的首次注视时刻、感知起始时刻、感知完成时刻 | |
w1ra, w1rb, w1rc/% | 各时段所占权重 | |
分配子阶段S2k | V2kguide/(件/秒) | ABMs给我机分配目标的敏捷度 |
m2/件 | “ABMs给我机分配目标”这一事件 | |
t2ktotal/s | ABMs给我机分配目标的总时间 | |
t2ka, t2kb, t2kc/s | ABMs对目标的发现时间、反应时间、分配时间 | |
T2k1, T2k2, T2k3, T2k4/s | 目标的出现时刻, ABMs对目标的首次注视时刻、分配起始时刻、完成时刻 | |
w2ka, w2kb, w2kc/% | 各时段所占权重 | |
通报子阶段S3u | V3uguide/(件/秒) | ABMs对威胁目标进行通报的敏捷度 |
m3/件 | “ABMs对威胁目标进行通报”这一事件 | |
t3utotal/s | ABMs对威胁目标进行通报的总时间 | |
t3ua, t3ub, t3uc/s | ABMs对威胁目标的发现时间、反应时间、通报时间 | |
T3u1, T3u2, T3u3, T3u4/s | 威胁目标的出现时刻, ABMs对威胁目标的首次注视时刻、通报起始时刻、通报完成时刻 | |
w3ua, w3ub, w3uc/% | 各时段所占权重 | |
处置子阶段S4g | V4gguide/(件/秒) | ABMs处置特情的敏捷度 |
m4/件 | “ABMs处置特情”这一事件 | |
t4gtotal/s | ABMs处置特情的总时间 | |
t4ga, t4gb, t4gc/s | ABMs对特情的发现时间、反应时间、处置时间 | |
T4g1, T4g2, T4g3, T4g4/s | 特情的出现时刻, ABMs对特情的首次注视时刻、处置起始时刻、处置完成时刻 | |
w4ga, w4gb, w4gc/% | 各时段所占权重 |
表2
准确性评估指标"
子阶段 | 指标/% | 含义 |
感知子阶段S1r | R1raccuracy | ABMs感知关键态势的准确性 |
q1r1, q1r2, …, q1rn | ABMs感知关键态势时各信息要素的正确率 | |
w1r1, w1r2, …, w1rn | 各信息要素所占权重 | |
分配子阶段S2k | R2kaccuracy | ABMs给我机分配敌机的准确性 |
q2k1, q2k2, …, q2kn | ABMs给我机分配敌机时每种分配方案的正确率 | |
w2k1, w2k2, …, w2kn | 每种分配方案所占权重 | |
通报子阶段S3u | R3uaccuracy | ABMs通报威胁目标的准确性 |
q3u1, q3u2, …, q3un | ABMs通报威胁目标时各信息要素的正确率 | |
w3u1, w3u2, …, w3un | 各信息要素所占权重 | |
处置子阶段S4g | R4gaccuracy | ABMs处置特情的准确性 |
q4g1, q4g2, …, q4gn | ABMs处置特情时各信息要素的正确率 | |
w4g1, w4g2, …, w4gn | 各信息要素所占权重 |
表3
关注度评估指标"
子阶段 | 指标 | 含义 |
感知子阶段S1r | E1rattention/% | ABMs对关键态势的关注度 |
t1rfixation, t1rsum/s | ABMs对关键态势的注视时间, 关键态势出现的总时间 | |
T1r5, T1r6/s | 关键态势的出现时刻、结束时刻 | |
分配子阶段S2k | E2kattention/% | ABMs给我机分配敌机的关注度 |
t2kfixation, t2ksum/s | ABMs对敌机的注视时间、分配总时间 | |
T2k5, T2k6/s | ABMs给我机分配敌机的首次注视时刻、完成时刻 | |
通报子阶段S3u | E3uattention/% | ABMs对威胁目标进行通报的关注度 |
t3ufixation, t3usum/s | ABMs对威胁目标的注视时间、通报总时间 | |
T3u5, T3u6/s | ABMs对威胁目标进行通报的起始时刻、完成时刻 | |
处置子阶段S4g | E4gattention/% | ABMs处置特情的关注度 |
t4gfixation, t4gsum/s | ABMs对特情的注视时间、处置总时间 | |
T4g5, T4g6/s | 特情的出现时刻, ABMs处置特情的完成时刻 |
表4
认知负荷评估指标"
子阶段 | 指标 | 含义 |
感知子阶段S1r | L1rload/(个/秒·毫米) | 关键态势给ABMs带来的认知负荷 |
t1rfixation, t1rsum/s | ABMs对关键态势的注视时间, 关键态势出现的总时间 | |
C1r/(个/秒) | 单位时间注视点数 | |
D1rpupil-work, Dpupil-relax/mm | ABMs在工作状态和放松状态下的左眼瞳孔直径 | |
分配子阶段S2k | L2kload/(个/秒·毫米) | ABMs给我机分配敌机产生的认知负荷 |
t2kfixation, t2ksum/s | ABMs对敌机的注视时间、分配总时间 | |
C2k/(个/秒) | 单位时间注视点数 | |
D2kpupil-work, Dpupil-relax/mm | ABMs在工作状态和放松状态下的左眼瞳孔直径 | |
通报子阶段S3u | L3uload/(个/秒·毫米) | ABMs对威胁目标进行通报产生的认知负荷 |
t3ufixation, t3usum/s | ABMs对威胁目标的注视时间、通报总时间 | |
C3u/(个/秒) | 单位时间注视点数 | |
D3upupil-work, Dpupil-relax/mm | ABMs在工作状态和放松状态下的左眼瞳孔直径 | |
处置子阶段S4g | L4gload/(个/秒·毫米) | ABMs处置特情产生的认知负荷 |
t4gfixation, t4gsum/s | ABMs对特情的注视时间、处置总时间 | |
C4g/(个/秒) | 单位时间注视点数 | |
D4gpupil-work, Dpupil-relax/mm | ABMs在工作状态和放松状态下的左眼瞳孔直径 |
表6
主要眼动指标"
序号 | 指标 | 含义 |
1 | 注视时间/s | 构成一个注视点的首个采样点与最后一个采样点之间的持续时间。表现为被试者视觉焦点在被观察物体表面停留, 停留时间至少持续100~200 ms |
2 | 瞳孔直径/mm | 瞳孔指眼睛虹膜中央直径2.5~4 mm左右的圆孔, 瞳孔直径的变化可体现被试的疲劳程度以及认知负荷 |
3 | 注视点热区 | 将界面的各部分用表示热度的不同颜色进行标注, 以此来展现被试的注意力分配情况, 通常颜色越深,表示关注度越高 |
4 | 注视点序列 | 记录被试的注视点按一定时间次序进行转换, 是度量被试注意力分配的一个标志, 通常注视点半径越大, 表示注视时间越长 |
5 | 首次注视时刻/s | 兴趣区内首个注视点的注视时刻, 能够作为度量感知速度的重要指标 |
6 | 单位时间注视点数/个 | 一段时间内, 兴趣区注视点总数与总时间之比, 可用来度量被试的认知负荷 |
表7
空战控制的认知负荷"
认知负荷 | ABMs | |||||||||
X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | |
L11load | 1.988 | 1.945 | 1.971 | 1.959 | 1.918 | 2.159 | 1.927 | 1.924 | 1.913 | 1.942 |
L12load | 1.933 | 1.955 | 1.892 | 1.920 | 1.904 | 1.900 | 1.912 | 1.891 | 1.894 | 1.965 |
L13load | 1.891 | 1.917 | 1.891 | 1.891 | 1.894 | 1.891 | 1.897 | 1.891 | 1.894 | 1.892 |
L14load | 1.892 | 1.897 | 1.892 | 1.891 | 1.893 | 1.891 | 1.897 | 1.891 | 1.891 | 1.891 |
L15load | 1.893 | 1.926 | 1.913 | 1.902 | 1.891 | 1.894 | 1.894 | 1.895 | 1.909 | 1.942 |
L16load | 1.891 | 1.895 | 1.896 | 1.895 | 1.906 | 1.891 | 1.891 | 1.892 | 1.893 | 1.891 |
L17load | 1.895 | 1.947 | 1.899 | 1.917 | 1.907 | 1.891 | 1.910 | 1.902 | 1.891 | 1.891 |
L21load | 1.913 | 2.003 | 1.910 | 1.901 | 2.003 | 1.892 | 2.111 | 1.902 | 1.963 | 2.456 |
L22load | 1.897 | 2.031 | 1.911 | 1.909 | 1.983 | 1.904 | 2.025 | 1.960 | 2.002 | 1.891 |
L23load | 1.995 | 2.063 | 1.898 | 1.901 | 1.941 | 1.895 | 2.011 | 1.915 | 1.972 | 1.981 |
L24load | 1.945 | 1.969 | 1.891 | 1.900 | 1.891 | 1.899 | 1.891 | 2.083 | 1.981 | 2.018 |
L25load | 1.897 | 1.891 | 1.891 | 1.939 | 1.891 | 1.909 | 1.891 | 1.923 | 2.099 | 2.056 |
L31load | 1.898 | 1.917 | 1.896 | 1.913 | 2.004 | 1.894 | 1.906 | 1.907 | 1.897 | 1.950 |
L32load | 1.896 | 1.891 | 1.892 | 1.914 | 1.998 | 1.891 | 1.891 | 1.895 | 1.905 | 1.936 |
L33load | 1.905 | 1.939 | 1.891 | 1.893 | 1.896 | 1.891 | 1.931 | 1.897 | 1.902 | 1.941 |
L34load | 1.891 | 1.892 | 1.892 | 1.905 | 2.029 | 1.894 | 1.905 | 1.892 | 1.893 | 1.928 |
L35load | 1.894 | 1.891 | 1.891 | 1.975 | 1.891 | 1.891 | 1.919 | 1.891 | 1.931 | 1.991 |
L36load | 1.903 | 2.070 | 1.891 | 1.994 | 1.978 | 1.891 | 1.916 | 1.900 | 1.933 | 1.954 |
L41load | 1.923 | 2.006 | 1.924 | 1.899 | 1.967 | 1.965 | 2.019 | 1.939 | 1.935 | 2.072 |
L42load | 1.891 | 2.155 | 1.891 | 1.905 | 1.891 | 1.895 | 1.907 | 1.891 | 1.891 | 1.891 |
L43load | 1.909 | 1.957 | 1.923 | 1.892 | 1.957 | 1.904 | 1.927 | 1.910 | 1.897 | 1.947 |
L44load | 1.901 | 1.891 | 1.892 | 1.921 | 1.891 | 1.891 | 1.913 | 1.906 | 1.924 | 1.897 |
表8
综合评估指标体系标准化评价矩阵"
指标 | ABMs | |||||||||
X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | |
V1guide | 0.387 | 0.082 | 0.183 | 0.555 | 1.000 | 0.098 | 0.000 | 0.510 | 0.402 | 0.007 |
V2guide | 0.570 | 0.287 | 0.000 | 1.000 | 0.072 | 0.463 | 0.140 | 0.604 | 0.581 | 0.473 |
V3guide | 0.617 | 0.027 | 0.134 | 0.191 | 0.000 | 0.325 | 0.883 | 1.000 | 0.640 | 0.734 |
V4guide | 0.740 | 0.448 | 0.268 | 0.553 | 0.000 | 0.479 | 0.435 | 0.302 | 0.073 | 1.000 |
R1accuracy | 0.564 | 1.000 | 0.782 | 0.437 | 0.220 | 0.904 | 0.863 | 0.672 | 0.625 | 0.000 |
R2accuracy | 0.623 | 0.275 | 0.000 | 0.000 | 0.652 | 1.000 | 0.152 | 0.779 | 0.340 | 0.499 |
R3accuracy | 0.895 | 0.212 | 0.506 | 0.163 | 0.000 | 0.416 | 0.451 | 0.826 | 1.000 | 0.349 |
R4accuracy | 1.000 | 0.441 | 0.828 | 0.314 | 0.000 | 0.726 | 0.882 | 0.813 | 0.397 | 0.443 |
E1attention | 0.468 | 1.000 | 0.134 | 0.498 | 0.000 | 0.199 | 0.426 | 0.217 | 0.121 | 0.032 |
E2attention | 0.322 | 0.649 | 0.000 | 0.383 | 0.111 | 0.181 | 0.415 | 0.902 | 1.000 | 0.616 |
E3attention | 0.280 | 0.641 | 0.000 | 1.000 | 0.484 | 0.049 | 0.751 | 0.551 | 0.589 | 0.615 |
E4attention | 0.216 | 1.000 | 0.000 | 0.573 | 0.026 | 0.162 | 0.587 | 0.364 | 0.385 | 0.108 |
L1load | 0.516 | 0.000 | 0.713 | 0.562 | 0.859 | 0.103 | 0.765 | 1.000 | 0.984 | 0.247 |
L2load | 0.804 | 0.263 | 0.997 | 0.928 | 0.666 | 1.000 | 0.351 | 0.562 | 0.212 | 0.000 |
L3load | 0.885 | 0.274 | 0.999 | 0.353 | 0.021 | 1.000 | 0.616 | 0.920 | 0.683 | 0.000 |
L4load | 0.942 | 0.000 | 0.899 | 1.000 | 0.598 | 0.883 | 0.499 | 0.872 | 0.886 | 0.333 |
1 | 刘彦茂, 高茂林, 贺中敏, 等. 指挥引导学[M]. 西安: 西北工业出版社, 2021. |
LIU Y M , GAO M L , HE Z M , et al. Command and guidance[M]. Xi'an: Press of Northwest Industry, 2021. | |
2 | FOWLEY J W. Undergraduate air battle manager training: prepared to achieve combat mission ready[R]. Montgomery: Air Command and Staff College, 2016. |
3 | LUPPO A , RUDNENKO V . Competence assessment of air tra-ffic control personnel[J]. Proсeedings of the National Aviation University, 2012, 51 (2): 47- 50. |
4 | PICANO J J, ROLAND R R, WILLIAMS T J, et al. Assessment of elite operational personnel[M]//STEPHEN V B, PAUL T B. Handbook of military psychology. Cham: Springer, 2017: 277-289. |
5 | 邱玮, 张增磊, 田文祥, 等. 基于层次分析法和模糊综合评判的装备保障人员能力评估[J]. 兵器装备工程学报, 2018, 39 (4): 108- 113. |
QIU W , ZHANG Z L , TIAN W X , et al. Ability evaluation of equipment support staff based on analytic hierarchy process and fuzzy synthentic[J]. Journal of Weapon Equipment Engineering, 2018, 39 (4): 108- 113. | |
6 | 宋宜平, 董健, 董振旗, 等. 基于灰色熵权法的装备保障人员能力评估模型研究[J]. 价值工程, 2013, 32 (31): 110- 112. |
SONG Y P , DONG J , DONG Z Q , et al. Study on the capability assessment model for equipment support personnel based on grey entropy weight method[J]. Value Engineering, 2013, 32 (31): 110- 112. | |
7 | 柴志君, 欧阳中辉. 着舰回收引导任务指挥人员能力评估[J]. 兵工自动化, 2020, 39 (12): 1- 4.1-4, 11 |
CHAI Z J , OUYANG Z H . Capability assessment of aircraft landing command personnel[J]. Ordnance Industry Automation, 2020, 39 (12): 1- 4.1-4, 11 | |
8 |
MODI N , SINGH J . Role of eye tracking inhuman computer interaction[J]. ECS Transactions, 2022, 107 (1): 8211- 8218.
doi: 10.1149/10701.8211ecst |
9 |
HAREZLAK K , KASPROWSKI P . Application of eye tracking in medicine: a survey, researchissues and challenges[J]. Computerized Medical Imaging and Graphics, 2018, 65, 176- 190.
doi: 10.1016/j.compmedimag.2017.04.006 |
10 |
FRIEDL K E . Military applications of soldier physiological monitoring[J]. Journal of Science and Medicine in Sport, 2018, 21 (11): 1147- 1153.
doi: 10.1016/j.jsams.2018.06.004 |
11 | WETZEL P A, ANDERSON K, GRETCHEN M, et al. Instructor use of eye position based feedback for pilot training[C]//Proc. of the Human Factors and Ergonomics Society Annual Meeting, 1998, 42(20): 1388-1392. |
12 | LI W C, JAKUBOWSKI J, BRAITHWAITE G, et al. Did you see what your trainee pilot is seeing? Integrated eye tracker in the simulator to improve instructors' monitoring performance[C]//Proc. of the International Workshop on Eye-Tracking in Avi-ation, 2020: 39-46. |
13 | 牛四芳, 娄振山, 卢天娇. Su-30飞行员在模拟飞行任务中的眼动指标分析[J]. 现代生物医学进展, 2013, 13 (34): 6776- 6780. |
NIU S F , LOU Z S , LU T J . Analysis on eye movement indices based on simulated flight task of fighter pilots[J]. Progress in Modern Biomedicine, 2013, 13 (34): 6776- 6780. | |
14 | YOUNG L R , SHEENA D . Survey of eye movement recording methods[J]. Behavior Research Methods & Instrumentation, 1975, 7 (5): 397- 429. |
15 | JANA C , PAL M . A dynamical hybrid method to design decision making process based on GRA approach for multiple attributes problem[J]. Engineering Applications of Artificial Intelligence, 2021, 100 (82): 104203. |
16 | ZHANG H , HE X Q , MITRI H . Fuzzy comprehensive evalu-ation of virtual reality mine safety training system[J]. Safety Science, 2019, 120, 341- 351. |
17 | LINSTONE H A. The delphi technique[M]//VINCENT T C, JERYL L M, PIETER J M S, et al. Environmental impact assessment, technology assessment, and risk analysis. Berlin: Springer, 1985: 621-649. |
18 | BEHZADIAN M , OTAGHSARA S K , YAZDANI M , et al. A state-of-the-art survey of TOPSIS applications[J]. Expert Systems with Applications, 2012, 39 (17): 13051- 13069. |
19 | VAVREK R . Evaluation of the impact of selected weighting methods on the results of the TOPSIS technique[J]. International Journal of Information Technology and Decision Making, 2019, 18 (6): 1821- 1843. |
20 | LI Z , LUO Z J , WANG Y , et al. Suitability evaluation system for the shallow geothermal energy implementation in region by entropy weight method and TOPSIS method[J]. Renewable Energy, 2022, 184, 564- 576. |
21 | 宋敏, 李全胜, 李昉, 等. 空中作战指挥控制[M]. 西安: 西北工业出版社, 2021. |
SONG M , LI Q S , LI F , et al. Air combat command and control[M]. Xi'an: Press of Northwest Industry, 2021. | |
22 | 李全胜, 唐佳, 宋敏, 等. 空中作战指挥控制模拟训练手册[M]. 西安: 西北工业出版社, 2021. |
LI Q S , TANG J , SONG M , et al. Air combat command and control simulation training manual[M]. Xi'an: Press of Nor-thwest Industry, 2021. | |
23 | SWELLER J . Cognitive load during problem solving: effects on learning[J]. Cognitive Science, 1988, 12 (2): 257- 285. |
24 | PRIVITERA C M , STARK L W . Algorithms for defining visual regions-of-interest: comparison with eye fixations[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2000, 22 (9): 970- 982. |
25 | HENDERSON J M , FERREIRA F . Effects of foveal processing difficulty on the perceptual span in reading: implications for attentionand eye movement control[J]. Journal of Experimental Psychology: Learning, Memory, and Cognition, 1990, 16 (3): 417- 429. |
26 | HESS E H , POLT J M . Pupil size in relation to mental activity during simple problem solving[J]. Science, 1964, 143 (3611): 1190- 1192. |
27 | 薛耀锋, 李卓玮. 基于眼动追踪技术的在线学习认知负荷量化模型研究[J]. 现代教育技术, 2019, 29 (7): 59- 65. |
XUE Y F , LI Z W . Research on online learning cognitive load quantitative model based on eye tracking technology[J]. Mo-dern Educational Technology, 2019, 29 (7): 59- 65. | |
28 | LIU X F , ZHOU X X , ZHU B Z , et al. Measuring the maturity of carbon market in China: an entropy-based TOPSIS approach[J]. Journal of Cleaner Production, 2019, 229, 94- 103. |
29 | CHEN P Y . Effects of the entropy weight on TOPSIS[J]. Expert Systems with Applications, 2021, 168, 114186. |
30 | WU H W , LI E , SUN Y , et al. Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method[J]. Journal of Rail Transport Planning & Management, 2021, 20, 100262. |
31 | NASRABADI H R, ALONSO J M. Modular streaming pipeline of eye/head tracking data using tobii pro glasses 3[EB/OL]. [2022-09-10]. https://www.biorxiv.org/content/10.1101/2022.09.02.506255v1. |
32 | NIEHORSTER D C , ANDERSSON R , NYSTRÖM M . Titta: a toolbox for creating psych toolbox and psychopy experiments with Tobii eye trackers[J]. Behavior Research Methods, 2020, 52 (5): 1970- 1979. |
33 | STEINDORF L , RUMMEL J . Do your eyes giveyou away? A validation study of eye-movement measures used as indicators for mindless reading[J]. Behavior Research Methods, 2020, 52 (1): 162- 176. |
34 | POMPLUN M, SUNKARA S. Pupil dilation as an indicator of cognitive workload in human-computer interaction[C]//Proc. of the International Conference on HCI, 2003: 542-546. |
35 | POOLE A, BALL L J. Eye tracking in human-computer interaction and usability research: current status and future prospects, 2005[D]. Lancaster: Lancaster University, 2005. |
36 | NOUDOOST B , CHANG M H , STEINMETZ N A , et al. Top-down control of visual attention[J]. Current Opinion in Neurobiology, 2010, 20 (2): 183- 190. |
[1] | 杨倩倩, 贺成艳, 王鹏博, 韩子彬. 传输通道相位失真对非恒包络信号质量的影响分析[J]. 系统工程与电子技术, 2023, 45(6): 1597-1605. |
[2] | 李昕泽, 周文雅, 刘凯, 王博. 可达区域内最佳着陆场的筛选方法[J]. 系统工程与电子技术, 2023, 45(6): 1712-1721. |
[3] | 秦长江, 吴克宇, 成清, 黄金才. 基于杀伤网贡献率的动态体系节点重要度评估[J]. 系统工程与电子技术, 2023, 45(6): 1732-1742. |
[4] | 茹鑫鑫, 高晓光, 王阳阳. 基于模糊约束的贝叶斯网络参数学习[J]. 系统工程与电子技术, 2023, 45(2): 444-452. |
[5] | 李军亮, 祝华远, 王正, 王利明, 张鑫磊. 基于混合Gamma分布的机载产品可靠性建模[J]. 系统工程与电子技术, 2023, 45(2): 614-620. |
[6] | 徐任杰, 宫琳, 谢剑, 刘欣, 杨克巍. 基于装备体系韧性的作战网络链路重要度评估及恢复策略[J]. 系统工程与电子技术, 2023, 45(1): 139-147. |
[7] | 张堃, 张振冲, 刘泽坤, 李珂, 刘培培. 基于FD-TODIM的混杂空战多目标动态威胁评估[J]. 系统工程与电子技术, 2023, 45(1): 148-154. |
[8] | 周文明, 崔德康, 周婧怡, 张明明, 朱安石. 储供基地支援保障能力评估混合算法[J]. 系统工程与电子技术, 2022, 44(9): 2832-2839. |
[9] | 王雯琦, 江登英. 基于可能度的语言直觉模糊PROMETHEE多属性群决策[J]. 系统工程与电子技术, 2022, 44(8): 2581-2592. |
[10] | 浣顺启, 方哲梅, 王剑波. 基于功能依赖网的体系效能评估方法[J]. 系统工程与电子技术, 2022, 44(7): 2191-2200. |
[11] | 姜江, 金前程, 徐雪明, 侯帅, 李际超. 智能化时代国防科技体系工程初探[J]. 系统工程与电子技术, 2022, 44(6): 1880-1888. |
[12] | 曾守桢, 胡英杰. 基于高度残缺信息的分布式语言信任网络群决策方法[J]. 系统工程与电子技术, 2022, 44(6): 1907-1919. |
[13] | 赵长啸, 李二帅, 何锋, 王鹏. TSN时间敏感流量带宽分配与优化[J]. 系统工程与电子技术, 2022, 44(6): 2027-2034. |
[14] | 石旭东, 成博源, 黄琨, 杨占刚. 基于模糊TOPSIS-FMEA的飞机IDG风险评价[J]. 系统工程与电子技术, 2022, 44(6): 2060-2064. |
[15] | 徐鑫宇, 万路军, 陈平, 戴江斌, 高志周. 防御性制空截击中警戒巡逻空域规划建模[J]. 系统工程与电子技术, 2022, 44(5): 1589-1599. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||