Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 628-637.DOI: 10.11772/j.issn.1001-9081.2023020196
Special Issue: 前沿与综合应用
• Frontier and comprehensive applications • Previous Articles Next Articles
Rui ZHANG1(), Siqi SONG1, Jing HU1, Yongmei ZHANG2, Yanfeng CHAI1
Received:
2023-02-28
Revised:
2023-05-11
Accepted:
2023-05-15
Online:
2024-02-22
Published:
2024-02-10
Contact:
Rui ZHANG
About author:
SONG Siqi, born in 1998, M. S. candidate. Her research interests include intelligent information processing.Supported by:
通讯作者:
张睿
作者简介:
宋思琪(1998—),女,山西太原人,硕士研究生,主要研究方向:智能信息处理基金资助:
CLC Number:
Rui ZHANG, Siqi SONG, Jing HU, Yongmei ZHANG, Yanfeng CHAI. Performance evaluation of industry-university-research based on statistics and adaptive ParNet[J]. Journal of Computer Applications, 2024, 44(2): 628-637.
张睿, 宋思琪, 胡静, 张永梅, 柴艳峰. 基于统计和自适应ParNet的产学研绩效评价[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 628-637.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023020196
目标层 (一级指标) | 权重 | 准则层 (二级指标) | 权重 | 指标层(三级指标) | 权重 |
---|---|---|---|---|---|
1.各主体 贡献评价 | 0.336 6 | 1.1学校贡献 | 0.181 0 | 1.1.1配套高校导师数 | 0.042 9 |
1.1.2专业教师在企业挂职数 | 0.013 0 | ||||
1.1.3校企合作期间在培学生人数 | 0.035 9 | ||||
1.1.4校方投入专项经费(万元) | 0.089 2 | ||||
1.2各主体对 学校评价 | 0.014 9 | 1.2.1学生对学校评价 | 0.006 8 | ||
1.2.2企业对学校评价 | 0.008 1 | ||||
1.3企业贡献 | 0.123 6 | 1.3.1配套企业导师数 | 0.034 1 | ||
1.3.2企业研发经费实际投入(万元) | 0.089 5 | ||||
1.4各主体对 企业评价 | 0.017 1 | 1.4.1学生对企业评价 | 0.004 9 | ||
1.4.2学校对企业评价 | 0.012 2 | ||||
2.各主体 协同培养 质量评价 | 0.663 4 | 2.1团队建设 及获奖类 | 0.054 3 | 2.1.1校企共建研究基地、研究中心、研究平台数 | 0.008 4 |
2.1.2获省级及以上科技奖项团队数 | 0.011 7 | ||||
2.1.3获省级及以上科技奖项个人数 | 0.015 6 | ||||
2.1.4合作期间企业与学校共同获省级及以上奖项数 | 0.018 5 | ||||
2.2科研项目 立项类 | 0.264 3 | 2.2.1合作期间企业与学校共同申报国家项目累计经费(万元) | 0.113 5 | ||
2.2.2合作期间企业与学校共同申报省级项目累计经费(万元) | 0.072 5 | ||||
2.2.3合作期间企业与学校共同申报横向及其他科研项目累计经费(万元) | 0.078 2 | ||||
2.3知识产权 成果类 | 0.156 9 | 2.3.1合作期间企业与学校共同获取发明专利数 | 0.023 0 | ||
2.3.2合作期间企业与学校共同获取实用新型、外观专利等其他专利数 | 0.028 7 | ||||
2.3.3合作期间企业与学校共同制定国家标准、行业标准数 | 0.020 2 | ||||
2.3.4合作期间企业与学校共同发表高质量论文数 | 0.085 1 | ||||
2.4学科建设 情况 | 0.070 3 | 2.4.1共建课程数 | 0.016 7 | ||
2.4.2共建教材数 | 0.016 2 | ||||
2.4.3合作期间企业与学校共同申报研究生教学改革项目累计经费(万元) | 0.037 4 | ||||
2.5研究生 能力培养 | 0.110 1 | 2.5.1参与项目情况 | 0.016 9 | ||
2.5.2毕业生获得职业技能证书数 | 0.032 6 | ||||
2.5.3学生参加本领域国内外重要赛事情况 | 0.019 0 | ||||
2.5.4毕业生就业及升学率累计 | 0.041 5 | ||||
2.6各主体对 学生评价 | 0.007 6 | 2.6.1企业对学生评价 | 0.005 0 | ||
2.6.2学校对学生评价 | 0.002 6 |
Tab. 1 Performance evaluation system and indicators for industry-university-research
目标层 (一级指标) | 权重 | 准则层 (二级指标) | 权重 | 指标层(三级指标) | 权重 |
---|---|---|---|---|---|
1.各主体 贡献评价 | 0.336 6 | 1.1学校贡献 | 0.181 0 | 1.1.1配套高校导师数 | 0.042 9 |
1.1.2专业教师在企业挂职数 | 0.013 0 | ||||
1.1.3校企合作期间在培学生人数 | 0.035 9 | ||||
1.1.4校方投入专项经费(万元) | 0.089 2 | ||||
1.2各主体对 学校评价 | 0.014 9 | 1.2.1学生对学校评价 | 0.006 8 | ||
1.2.2企业对学校评价 | 0.008 1 | ||||
1.3企业贡献 | 0.123 6 | 1.3.1配套企业导师数 | 0.034 1 | ||
1.3.2企业研发经费实际投入(万元) | 0.089 5 | ||||
1.4各主体对 企业评价 | 0.017 1 | 1.4.1学生对企业评价 | 0.004 9 | ||
1.4.2学校对企业评价 | 0.012 2 | ||||
2.各主体 协同培养 质量评价 | 0.663 4 | 2.1团队建设 及获奖类 | 0.054 3 | 2.1.1校企共建研究基地、研究中心、研究平台数 | 0.008 4 |
2.1.2获省级及以上科技奖项团队数 | 0.011 7 | ||||
2.1.3获省级及以上科技奖项个人数 | 0.015 6 | ||||
2.1.4合作期间企业与学校共同获省级及以上奖项数 | 0.018 5 | ||||
2.2科研项目 立项类 | 0.264 3 | 2.2.1合作期间企业与学校共同申报国家项目累计经费(万元) | 0.113 5 | ||
2.2.2合作期间企业与学校共同申报省级项目累计经费(万元) | 0.072 5 | ||||
2.2.3合作期间企业与学校共同申报横向及其他科研项目累计经费(万元) | 0.078 2 | ||||
2.3知识产权 成果类 | 0.156 9 | 2.3.1合作期间企业与学校共同获取发明专利数 | 0.023 0 | ||
2.3.2合作期间企业与学校共同获取实用新型、外观专利等其他专利数 | 0.028 7 | ||||
2.3.3合作期间企业与学校共同制定国家标准、行业标准数 | 0.020 2 | ||||
2.3.4合作期间企业与学校共同发表高质量论文数 | 0.085 1 | ||||
2.4学科建设 情况 | 0.070 3 | 2.4.1共建课程数 | 0.016 7 | ||
2.4.2共建教材数 | 0.016 2 | ||||
2.4.3合作期间企业与学校共同申报研究生教学改革项目累计经费(万元) | 0.037 4 | ||||
2.5研究生 能力培养 | 0.110 1 | 2.5.1参与项目情况 | 0.016 9 | ||
2.5.2毕业生获得职业技能证书数 | 0.032 6 | ||||
2.5.3学生参加本领域国内外重要赛事情况 | 0.019 0 | ||||
2.5.4毕业生就业及升学率累计 | 0.041 5 | ||||
2.6各主体对 学生评价 | 0.007 6 | 2.6.1企业对学生评价 | 0.005 0 | ||
2.6.2学校对学生评价 | 0.002 6 |
空间域 | 卷积神经网络 | 平均值 | |||||
---|---|---|---|---|---|---|---|
RegNet | ShuffleNetV1 | ShuffleNetV2 | EfficientNetV1 | EfficientNetV2 | ParNet | ||
连续序列 | 86.4 | 72.8 | 77.1 | 80.4 | 92.9 | 93.4 | 83.8 |
MTF | 89.1 | 82.0 | 88.0 | 86.6 | 93.6 | 95.0 | 89.1 |
GADF | 92.0 | 84.9 | 89.1 | 89.7 | 94.6 | 96.3 | 91.1 |
GASF | 93.3 | 87.4 | 92.8 | 90.9 | 95.1 | 97.1 | 92.8 |
Tab. 2 Comparison of classification accuracy for multiple spatial domains under different networks
空间域 | 卷积神经网络 | 平均值 | |||||
---|---|---|---|---|---|---|---|
RegNet | ShuffleNetV1 | ShuffleNetV2 | EfficientNetV1 | EfficientNetV2 | ParNet | ||
连续序列 | 86.4 | 72.8 | 77.1 | 80.4 | 92.9 | 93.4 | 83.8 |
MTF | 89.1 | 82.0 | 88.0 | 86.6 | 93.6 | 95.0 | 89.1 |
GADF | 92.0 | 84.9 | 89.1 | 89.7 | 94.6 | 96.3 | 91.1 |
GASF | 93.3 | 87.4 | 92.8 | 90.9 | 95.1 | 97.1 | 92.8 |
函数 | MPA | WOA | SSA | GWO | AOA | EAOA | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | |
F1 | 1.12E-49 | 2.32E-49 | 2.12E-147 | 1.16E-146 | 6.44E-28 | 3.43E-27 | 5.06E-59 | 8.11E-59 | 1.06E-06 | 4.57E-07 | 0.0 | 0.0 |
F2 | 9.96E-28 | 1.85E-27 | 1.85E-52 | 7.96E-104 | 1.59E-13 | 6.08E-13 | 7.85E-17 | 1.68E-34 | 6.18E-04 | 8.18E-04 | 0.0 | 0.0 |
F3 | 1.72E-12 | 4.41E-12 | 2.07E+04 | 1.12E+04 | 1.69E-15 | 6.23E-15 | 1.54E-14 | 6.82E-14 | 2.81E-04 | 2.72E-04 | 0.0 | 0.0 |
F4 | 3.04E-19 | 4.82E-19 | 3.30E+01 | 2.65E+01 | 2.23E-08 | 1.18E-07 | 1.85E-14 | 2.91E-14 | 9.84E-03 | 7.96E-03 | 0.0 | 0.0 |
F5 | 2.36E+01 | 5.56E-01 | 2.74E+01 | 5.75E-01 | 1.86E-01 | 1.11E-01 | 2.68E+01 | 6.62E-01 | 2.75E+01 | 2.82E-01 | 6.36E-03 | 3.12E-04 |
F6 | 1.83E-09 | 8.89E-10 | 6.84E-02 | 1.06E-01 | 7.11E-03 | 3.04E-03 | 6.60E-01 | 2.96E-01 | 1.72E+00 | 1.98E-01 | 1.34E-09 | 8.68E-10 |
Tab. 3 Experimental results of unimodal test functions
函数 | MPA | WOA | SSA | GWO | AOA | EAOA | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | |
F1 | 1.12E-49 | 2.32E-49 | 2.12E-147 | 1.16E-146 | 6.44E-28 | 3.43E-27 | 5.06E-59 | 8.11E-59 | 1.06E-06 | 4.57E-07 | 0.0 | 0.0 |
F2 | 9.96E-28 | 1.85E-27 | 1.85E-52 | 7.96E-104 | 1.59E-13 | 6.08E-13 | 7.85E-17 | 1.68E-34 | 6.18E-04 | 8.18E-04 | 0.0 | 0.0 |
F3 | 1.72E-12 | 4.41E-12 | 2.07E+04 | 1.12E+04 | 1.69E-15 | 6.23E-15 | 1.54E-14 | 6.82E-14 | 2.81E-04 | 2.72E-04 | 0.0 | 0.0 |
F4 | 3.04E-19 | 4.82E-19 | 3.30E+01 | 2.65E+01 | 2.23E-08 | 1.18E-07 | 1.85E-14 | 2.91E-14 | 9.84E-03 | 7.96E-03 | 0.0 | 0.0 |
F5 | 2.36E+01 | 5.56E-01 | 2.74E+01 | 5.75E-01 | 1.86E-01 | 1.11E-01 | 2.68E+01 | 6.62E-01 | 2.75E+01 | 2.82E-01 | 6.36E-03 | 3.12E-04 |
F6 | 1.83E-09 | 8.89E-10 | 6.84E-02 | 1.06E-01 | 7.11E-03 | 3.04E-03 | 6.60E-01 | 2.96E-01 | 1.72E+00 | 1.98E-01 | 1.34E-09 | 8.68E-10 |
函数 | MPA | WOA | SSA | GWO | AOA | EAOA | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | |
F7 | -9.61E+03 | 5.01E+02 | -1.11E+04 | 1.69E+03 | -1.15E+02 | 1.03E+01 | -6.02E+03 | 1.00E+03 | -5.52E+03 | 3.56E+02 | -5.14E+01 | 2.90E+00 |
F8 | 0.0 | 0.0 | 1.89E-15 | 1.04E-14 | 0.0 | 0.0 | 1.14E-14 | 2.75E-14 | 3.69E-07 | 3.96E-07 | 0.0 | 0.0 |
F9 | 4.20E-15 | 9.01E-16 | 4.20E-15 | 2.27E-15 | 1.01E-07 | 5.14E-07 | 1.62E-14 | 2.66E-15 | 1.82E-04 | 1.15E-04 | 0.0 | 0.0 |
F10 | 7.11E-03 | 9.52E-03 | 1.69E-03 | 9.27E-03 | 5.01E-01 | 3.31E-01 | 3.65E-03 | 7.34E-03 | 6.57E-06 | 2.80E-06 | 4.44E-16 | 0.0 |
Tab. 4 Experimental results of multimodal test functions
函数 | MPA | WOA | SSA | GWO | AOA | EAOA | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std | |
F7 | -9.61E+03 | 5.01E+02 | -1.11E+04 | 1.69E+03 | -1.15E+02 | 1.03E+01 | -6.02E+03 | 1.00E+03 | -5.52E+03 | 3.56E+02 | -5.14E+01 | 2.90E+00 |
F8 | 0.0 | 0.0 | 1.89E-15 | 1.04E-14 | 0.0 | 0.0 | 1.14E-14 | 2.75E-14 | 3.69E-07 | 3.96E-07 | 0.0 | 0.0 |
F9 | 4.20E-15 | 9.01E-16 | 4.20E-15 | 2.27E-15 | 1.01E-07 | 5.14E-07 | 1.62E-14 | 2.66E-15 | 1.82E-04 | 1.15E-04 | 0.0 | 0.0 |
F10 | 7.11E-03 | 9.52E-03 | 1.69E-03 | 9.27E-03 | 5.01E-01 | 3.31E-01 | 3.65E-03 | 7.34E-03 | 6.57E-06 | 2.80E-06 | 4.44E-16 | 0.0 |
待优化超参数 | 搜索范围 |
---|---|
学习率 | [1E-9,1E-3] |
批量大小 | [ |
激活函数 | Sigmoid、Tanh、ReLU、ReLU6、LReLU、SiLU |
优化器 | Adam、Adamax、AdamW、SGD、ASGD、RMSprop |
模型流结构_1中 RepVGG-SSE模块数 | [ |
模型流结构_2中 RepVGG-SSE模块数 | [ |
模型流结构_3中 RepVGG-SSE模块数 | [ |
Tab. 5 Parameters to be optimized and search ranges of ParNet model
待优化超参数 | 搜索范围 |
---|---|
学习率 | [1E-9,1E-3] |
批量大小 | [ |
激活函数 | Sigmoid、Tanh、ReLU、ReLU6、LReLU、SiLU |
优化器 | Adam、Adamax、AdamW、SGD、ASGD、RMSprop |
模型流结构_1中 RepVGG-SSE模块数 | [ |
模型流结构_2中 RepVGG-SSE模块数 | [ |
模型流结构_3中 RepVGG-SSE模块数 | [ |
超参数及待优化模型组件 | EAOA寻优后的最优取值 |
---|---|
学习率 | 1E-3 |
批量大小 | 26 |
激活函数 | ReLU |
优化器 | Adam |
模型流结构_1中RepVGG-SSE模块数 | 3 |
模型流结构_2中RepVGG-SSE模块数 | 4 |
模型流结构_3中RepVGG-SSE模块数 | 4 |
Tab. 6 ParNet optimal model components optimized by EAOA
超参数及待优化模型组件 | EAOA寻优后的最优取值 |
---|---|
学习率 | 1E-3 |
批量大小 | 26 |
激活函数 | ReLU |
优化器 | Adam |
模型流结构_1中RepVGG-SSE模块数 | 3 |
模型流结构_2中RepVGG-SSE模块数 | 4 |
模型流结构_3中RepVGG-SSE模块数 | 4 |
网络模型 | 模型深度 | 参数/M | 单幅图像测试时间/ms | 准确率/% |
---|---|---|---|---|
ParNet | 12 | 19.71 | 21.0 | 97.1 |
AParNet | 11 | 17.58 | 13.7 | 98.6 |
Tab. 7 Comparison experiment results of network model performance
网络模型 | 模型深度 | 参数/M | 单幅图像测试时间/ms | 准确率/% |
---|---|---|---|---|
ParNet | 12 | 19.71 | 21.0 | 97.1 |
AParNet | 11 | 17.58 | 13.7 | 98.6 |
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