scholarly journals An Innovative Thinking-Based Intelligent Information Fusion Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Huimin Lu ◽  
Liang Hu ◽  
Gang Liu ◽  
Jin Zhou
Keyword(s):  
Information Fusion ◽  
Evaluation System ◽  
Divergent Thinking ◽  
Cognitive Theory ◽  
Thinking Skills ◽  
Memory Storage ◽  
Problem Solution ◽  
Fusion Algorithm ◽  
Knowledge Based ◽  
Innovative Thinking

This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.

Measurement Invariance of Divergent Thinking Across Gender, Age, and School Forms

2009 ◽  
Vol 25 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Heinz Holling
Keyword(s):  
Measurement Invariance ◽  
Divergent Thinking ◽  
Factor Model ◽  
Age Groups ◽  
Thinking Skills ◽  
Specific Model ◽  
Confirmatory Factor Analyses ◽  
Domain Specific ◽  
Gender And Age ◽  
Confirmatory Factor

The present study explores the factorial structure and the degree of measurement invariance of 12 divergent thinking tests. In a large sample of German students (N = 1328), a three-factor model representing verbal, figural, and numerical divergent thinking was supported. Multigroup confirmatory factor analyses revealed that partial strong measurement invariance was tenable across gender and age groups as well as school forms. Latent mean comparisons resulted in significantly higher divergent thinking skills for females and students in schools with higher mean IQ. Older students exhibited higher latent means on the verbal and figural factor, but not on the numerical factor. These results suggest that a domain-specific model of divergent thinking may be assumed, although further research is needed to elucidate the sources that negatively affect measurement invariance.

An Information Fusion Algorithm Based on Kalman Filtering

2013 ◽  
Vol 444-445 ◽  
pp. 1072-1076
Author(s):  
Xiu Hu Tan
Keyword(s):  
Mathematical Model ◽  
Information Fusion ◽  
Minimum Mean Square Error ◽  
Minimum Variance ◽  
Probability Method ◽  
Fusion Algorithm ◽  
Multisensor Systems ◽  
Optimal Information ◽  
Noise Statistics ◽  
The Mathematical Model

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

scholarly journals Multi-sensor Information Fusion Method Based on BP Neural Network

2016 ◽  
Vol 12 (05) ◽  
pp. 53 ◽  
Author(s):  
Lin Liandong
Keyword(s):  
Neural Network ◽  
Bayesian Inference ◽  
Information Fusion ◽  
System Development ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fusion Algorithm ◽  
Temperature Detection ◽  
Posterior Expectation ◽  
Sensor Information

This study aims to solve the problem of multi-sensor information fusion, which is a key issue in the multi-sensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

scholarly journals Pengaruh Kemampuan Berpikir Divergen dan Kemandirian Belajar terhadap Hasil Belajar Matematika Siswa

2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Jumardi Rauf ◽  
Siti Nur Humaira Halim ◽  
Randy Saputra Mahmud
Keyword(s):  
Standard Deviation ◽  
Divergent Thinking ◽  
Mathematics Learning ◽  
Descriptive Analysis ◽  
Thinking Skills ◽  
Average Score ◽  
Self Reliance ◽  
Ex Post ◽  
Thinking Ability ◽  
Ex Post Facto

The aim of this study to know the influence of divergent thinking ability and self reliance learning towards students learning results. This is an ex-post facto research, the samples was 102 students from ninth class SMPN 24 Makassar. The instruments was divergent thinking skills test, koesioner self reliance learning, and test of student’s mathematical learning results. The results of a descriptive analysis showed that the ability to think divergent students in middle category with average score 55.91, standard deviation 10.623 of the 100 ideal score with percentage 42.42%. The self reliance of learning students in the high category with average score 60.03, standard deviation 6.528 of the 80 ideal score with percentage 74.2%. The results of students learning in the good categorized with average score 80.77, standard deviation 6.416 of the 100 ideal score with percentage 53%. The result of inferential analysis shows that the divergent thinking ability positively and significantly affects the learning results of 0.183. Self reliance learning also positively and significantly affect the learning results of 0.101. The results of inferential show that divergent thinking ability and self reliance learning simultaneously influence the results of mathematics learning students with regression equation Y=70,119+0,009X1+0,181X2. AbstrakTujuan dari penelitian ini yaitu untuk mengetahui ada tidaknya pengaruh kemampuan berpikir divergen dan kemandirian belajar terhadap hasil belajar matematika siswa. Jenis penelitian yang digunakan dalam penelitian ini adalah jenis penelitian ex-post facto, dengan mengambil sampel dari siswa kelas IX SMPN 24 Makassar sebanyak 102 orang. Instrumen yang digunakan dalam penelitian ini adalah tes kemampuan berpikir divergen, kuesioner kemandirian belajar dan tes hasil belajar matematika. Hasil analisis statistik deskriptif menunjukan bahwa kemampuan berpikir divergen siswa dikategorikan sedang dengan skor rata-rata 55,91 dan standar deviasi 10,623 dari skor ideal 100 dengan persentase 42,42%. Kemandirian belajar siswa berada pada kategori tinggi dengan rata-rata 60,03 dan standar deviasi 6,528 dari skor ideal 80 dengan persentase 74,2%. Sedangkan hasil belajar siswa dikategorikan baik dengan skor rata-rata 80,77 dan standar deviasi 6,416 dari skor ideal 100 dengan persentase 53%. Hasil analisis inferensial menunjukkan bahwa terdapat pengaruh positif dan signifikan antara kemampuan berpikir divergen terhadap hasil belajar matematika sebesar 0,183, terdapat pengaruh positif dan signifikan antara kemandirian belajar terhadap hasil belajar matematika sebesar 0,101, serta terdapat pengaruh positif dan signifikan secara simultan antara kemampuan berpikir divergen dan kemandirian belajar terhadap hasil belajar matematika persamaan regresi Y = 70,119 + 0,009X1 + 0,181X2 

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