PLC is an indispensable technology for modern automation. The future social development will require a large number of PLC technical talents, so higher requirements are put forward for the teaching of PLC courses in colleges and universities. The intelligence and practical effect of the PLC course evaluation system are particularly important. Based on this, this article combines machine learning and image feature retrieval to construct a PLC course performance evaluation system. Moreover, this paper introduces the smoothness of the multispectral image, the smoothness of the blur function and the smoothness between the blur functions of adjacent spectral images as constraints and uses the gradient of the image blur kernel to express the smoothness of the image blur kernel itself. In addition, this article constructs the model system architecture according to the teaching requirements of the course and analyzes its realization process. Finally, in order to verify the performance of the model, this paper conducts system performance verification experiments through practical teaching methods and analyzes the results with statistical methods. The research results show that the PLC performance evaluation system constructed in this paper has a certain effect.
ZimmermanB.J. and KitsantasA., Comparing students’ self-discipline and self-regulation measures and their prediction of academic achievement, Contemporary Educational Psychology39(2) (2014), 145–155.
2.
BensonN.F., KranzlerJ.H. and FloydR.G., Examining the integrity of measurement of cognitive abilities in the prediction of achievement: Comparisons and contrasts across variables from higher-order and bifactor models, Journal of School Psychology58(6) (2016), 1–19.
3.
KlusmannU., RichterD. and LüdtkeO., Teachers’ emotional exhaustion is negatively related to students’ achievement: Evidence from a large-scale assessment study, Journal of Educational Psychology108(8) (2016), 1193–1203.
4.
MarshH.W., AbduljabbarA.S., ParkerP.D., et al., The Internal/External Frame of Reference Model of Self-Concept and Achievement Relations: Age-Cohort and Cross-Cultural Differences, American Educational Research Journal52(1) (2015), 168–202.
5.
PinxtenM., SoomC.V., PeetersC.M., et al., At-risk at the gate: prediction of study success of first-year science and engineering students in an open-admission university in Flanders—any incremental validity of study strategies?European Journal of Psychology of Education34(5) (2019), 45–66.
6.
BrehmM., ImbermanS.A. and LovenheimM.F., Achievement effects of individual performance incentives in a teacher merit pay tournament, Labour Economics44 (2017), 133–150.
7.
McgillR.J. and SpurginA.R., Assessing The Incremental Value Of Kabc-Ii Luria Model Scores In Predicting Achievement: What Do They Tell Us Beyond The Mpi?Psychology in the Schools53(7) (2016), 677–689.
8.
MohoricT. and TaksicV., Emotional understanding as a predictor of socio-emotional functioning and school achievement in adolescence, Psihologija49(4) (2016), 357–374.
9.
RabinerD.L., GodwinJ. and DodgeK.A., Predicting Achievement and Attainment: The Contribution of Early Academic Skills, Attention Difficulties, and Social Competence, School Psychology Review45(2) (2016), 250–267.
10.
BrownG.T.L. and HannaE., Swedish student perceptions of achievement practices: The role of, Intelligence69(11) (2018), 94–103.
11.
BeigiA.M. and MaroosiA., Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms, Solar Energy171(2) (2018), 435–446.
12.
KimH. and KimG., Premium and Benefit Levels in the Public Pension System: a Martingale Approach, Dynamic Systems and Applications28 (2019), 561–572.
13.
KooN. and LeeH., Variationally Stable Impulsive Integro-Differential Systems, Dynamic Systems and Applications29(2) (2020), 343–354.
14.
GotmareA., PatidarR. and GeorgeN.V., Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model, Expert Systems with Applications42(5) (2015), 2538–2546.
15.
ChenZ., YuanX., TianH., et al., Improved gravitational search algorithm for parameter identification of water turbine regulation system, Energy Conversion and Management78(12) (2014), 306–315.
16.
KellyB.S., RainfordL.A., DarcyS.P., et al., The development of expertise in radiology: in chest radiograph interpretation,“expert” search pattern may predate “expert” levels of diagnostic accuracy for pneumothorax identification, Radiology280(1) (2016), 252–260.
17.
PatwardhanA.P., PatidarR. and GeorgeN.V., On a cuckoo search optimization approach towards feedback system identification, Digital Signal Processing32 (2014), 156–163.
18.
DzengR.J., LinC.T. and FangY.C., Using eye-tracker to compare search patterns between experienced and novice workers for site hazard identification, Safety Science82(10) (2016), 56–67.
19.
ZhuX., WuB., HuangD., et al., Fast open-world person re-identification, IEEE Transactions on Image Processing27(5) (2017), 2286–2300.
20.
KoulayevS., Search for differentiated products: identification and estimation, The RAND Journal of Economics45(3) (2014), 553–575.
21.
FarkasKertesz-A., KeichU. and NobleW.S., Tandem mass spectrum identification via cascaded search, Journal of Proteome Research14(8) (2015), 3027–3038.
22.
VarleyJ.B., MiglioA., HaV.A., et al., High-throughput design of non-oxide p-type transparent conducting materials: data mining, search strategy, and identification of boron phosphide, Chemistry of Materials29(6) (2017), 2568–2573.
23.
ChenZ.L., MengJ.M., CaoY., et al., A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides, Nature Communications10(1) (2019), 1–12.
24.
AyalaH.V.H, dos SantosC.L., MarianiV.C., et al., An improved free search differential evolution algorithm: A case study on parameters identification of one diode equivalent circuit of a solar cell module, Energy93(7) (2015), 1515–1522.
25.
PaulA., AhmadA., RathoreM.M. and JabbarS., Smartbuddy: defining human behaviors using big data analytics in social internet of things, IEEE Wireless Communications23(5) (2016), 68–74.
26.
PaulA., JiangY.C., WangJ.F. and YangJ.F., Parallel reconfigurable computing-based mapping algorithm for motion estimation in advanced video coding, ACM Transactions on Embedded Computing Systems (TECS)11(S2) (2012), 1–18.
27.
RathoreM.M., PaulA., HongW.H., SeoH.C., AwanI. and SaeedS., Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data, Sustainable Cities and Society40 (2018), 600–610.
28.
PaulA., Internet of Things: A primer, R Jeyaraj Human Behavior and Emerging Technologies1(1) (2019), 37–47.
29.
PaulA., DanielA., AhmadA. and RhoS., Cooperative cognitive intelligence for internet of vehicles, IEEE Systems Journal11(3) (2017), 1249–1258.
