Perbandingan Model Characteristic Drying Rate Curve dan Reaction Engineering Approach Berdasarkan Hasil Eksperimen Pengeringan Mango Tissues

Proses pengeringan dapat dimodelkan secara umum menjadi dua model matematika, model empirikal dan mekanistik. CDRC (Characteristic Drying Rate Curve) and REA (Reaction Engineering Approach) adalah salah satu model mekanistik untuk mencari kondisi fisis di semua bagian, yang tidak bisa dilakukan oleh model empirikal. Hasil yang diperoleh dengan cara simulasi menunjukkan profil temperatur dan kadar air pada jaringan mangga yang telah divalidasi berdasarkan literatur. Mean square error dan nilai variasinya menunjukkan kemiripan antara kadar air dan temperatur. Data eksperimen untuk REA menggunakan temperatur pengeringan 338K dan nilai Y pada 0,0134kg H2O/kg udara kering. CDRC dibagi menjadi dua pendekatan model, Constant Rate Period (CRP) dan Falling Rate Period (FRP) pada temperatur 338K. Penentuan energi aktivasi relatif diperoleh sebagai jumlah kadar air yang hilang, pada temperatur 328K. Hasil yang ditunjukkan model CDRC dan REA relatif sama, dimana CDRC lebih mendekati data eksperimen. Pada kurva pengeringan, kadar air jaringan mangga menurun secara eksponensial sedangkan temperatur jaringan mangga menaik secara eksponensial.

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Effects of low temperature drying processing on longan fruit

MATEC Web of Conferences ◽

10.1051/matecconf/201819203024 ◽

2018 ◽

Vol 192 ◽

pp. 03024

Author(s):

Thadchapong Pongsuttiyakorn ◽

Pachareeporn trusphimai ◽

Pitikhate Sooraksa ◽

Pimpen Pornchaloempong

Keyword(s):

Drying Rate ◽

Suitable Model ◽

Moisture Removal ◽

Browning Index ◽

Air Temperatures ◽

Constant Rate ◽

Drying Techniques ◽

Rate Period ◽

Low Temperature Drying

In this study, the single-stage drying in tray dryer at air temperatures of 40, 50, 60, 70 and 80°C is modelled and investigated. The longan fruits, E-dor variety, are peeled and seeded before testing. The drying rate is significantly influenced by the drying techniques and temperatures. Drying rats are initialized adjustment constant rate periods at 60 70 and 80°C. The rate of moisture removal is rapidly changed drastically during the falling rate period. The Midilli model with high R2 and low χ2 and RMSE is the most suitable model for predictability of longan drying. Variation rates of quality of the water activity, the shrinkage, and the browning index are also reported.

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MATHEMATICAL MODELING OF DIFFERENT DRYING METHODS OF DRIED OKRO SLICE

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v7.i3.2019.975 ◽

2019 ◽

Vol 7 (3) ◽

pp. 302-309

Author(s):

Olajire A. S. ◽

Tunde-Akintunde T. Y. ◽

Ogunlakin G. O. ◽

Adeyanju J. A.

Keyword(s):

Mathematical Modeling ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Drying Methods ◽

Mean Square ◽

Page Model ◽

Rate Period ◽

Drying Behaviour

Drying is an essential process that is used in preserving okro sample. This study investigates the modelling of dried okro slice using different drying methods. Freshly harvested okro fruits were cut transversely into slices (0.5, 1.5 and 2.0 cm) and dried using three different drying methods; sun, solar and oven at 40, 45 and 50 ºC. Drying of okro occurred in the falling rate period. Newton, Pabis, and Page and Henderson models were used to describe the drying of okro. An appropriate model was selected based on highest R2, least values of X2, Root Mean Square Error (RMSE) and Mean Biased Error (MBE). Page model described the drying behaviour of okro slice 0.5 cm satisfactorily having the highest R2 of 0.999 and lowest value of X2 (0.152 x10-8), RMSE (4.5 x10-6) and that of MBE (-3.089 x10-4).

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The Bordeaux Automatic Meridian Circle

International Astronomical Union Colloquium ◽

10.1017/s0252921100074170 ◽

1978 ◽

Vol 48 ◽

pp. 227-228

Author(s):

Y. Requième

Keyword(s):

Mean Square Error ◽

Mean Square ◽

Meridian Circle ◽

The Mean ◽

Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

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MEAN SQUARE ERROR FOR UNIFORM–KERNEL ESTIMATE AND NEAREST NEIGHBOR ESTIMATE OF NONPARAMETRIC REGRESSION FUNCTIONS

Acta Mathematica Scientia ◽

10.1016/s0252-9602(18)30700-8 ◽

1985 ◽

Vol 5 (2) ◽

pp. 175-185

Author(s):

Dongchu Sun

Keyword(s):

Mean Square Error ◽

Nonparametric Regression ◽

Nearest Neighbor ◽

Mean Square ◽

Kernel Estimate ◽

Regression Functions

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Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2005.10.4.15113 ◽

2005 ◽

Vol 10 (4) ◽

pp. 333-342

Author(s):

V. Chadyšas ◽

D. Krapavickaitė

Keyword(s):

Mean Square Error ◽

Taylor Series ◽

Random Sampling ◽

Finite Population ◽

Taylor Series Expansion ◽

Simple Random Sampling ◽

Population Parameter ◽

Mean Square ◽

Parameter Ratio ◽

Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

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Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

Vietnam Journal of Mechanics ◽

10.15625/0866-7136/12015 ◽

2019 ◽

Author(s):

Nguyen Cao Thang ◽

Luu Xuan Hung

Keyword(s):

Performance Analysis ◽

Mean Square Error ◽

Nonlinear Oscillators ◽

Degree Of Freedom ◽

Equivalent Linearization ◽

Mean Square ◽

Error Criterion ◽

Stochastic Linearization ◽

Local Mean ◽

Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

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Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

Engineering and Technology Journal ◽

10.30684/etj.v38i2a.319 ◽

2020 ◽

Vol 38 (2A) ◽

pp. 255-264

Author(s):

Hanan A. R. Akkar ◽

Sameem A. Salman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Square Error ◽

Average Rate ◽

Mean Square ◽

Performance Measurements ◽

Artificial Intelligent ◽

Average Mean Square Error ◽

Artificial Neural ◽

Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

Journal of Engineering ◽

10.31026/j.eng.2018.05.05 ◽

2018 ◽

Vol 24 (5) ◽

pp. 66

Author(s):

Thamer M. Jamel ◽

Faez Fawzi Hammood

Keyword(s):

Mean Square Error ◽

Return Loss ◽

Performance Enhancement ◽

Variable Length ◽

Echo Cancellation ◽

Convergence Time ◽

Length Variation ◽

Mean Square ◽

Step Size ◽

Echo Return Loss Enhancement

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.

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Prediksi Daya Panel Surya Kapasitas 50 Wp Menggunakan Model Regresi Linier Majemuk

Jurnal Teknologi Bahan dan Barang Teknik ◽

10.37209/jtbbt.v10i2.166 ◽

2020 ◽

Vol 10 (2) ◽

Author(s):

Radian Indra Mukromin ◽

Muhammad Khamim Asy'ari

Keyword(s):

Mean Square Error ◽

Mean Square

Sistem monitoring daya listrik pada panel surya penting dilakukan. Hal ini disebabkan daya listrik panel surya dapat mempengaruhi performansi pengisian baterai dan keandalan dari panel surya. Sifat stokastik dari temperatur panel surya dan iradiasi surya mengakibatkan fluktuasi daya listrik, sehingga diperlukan sistem prediksi daya panel surya. Sistem prediksi dapat dirancang untuk mendapatkan model prediksi daya panel surya secara matematik menggunakan model regresi linier majemuk. Model dibangun untuk sistem prediksi dengan menggunakan data latih dari keluaran panel surya. Variasi yang diberikan adalah jenis variabel masukan untuk membangun model. Variabel masukan model terdiri dari temperatur panel surya, iradiasi surya, dan kombinasi dari keduanya. Pengujian data dilakukan dengan menggunakan uji korelasi majemuk, uji signifikasi regresi linier majemuk, dan uji signifikasi koefisien regresi. Hasil perancangan sistem prediksi terbaik adalah kombinasi temperatur panel surya dan iradiasi surya sebagai variabel masukan. Nilai MSE(mean square error) terkecil sebesar 9,83 untuk data latih dan 22,73 untuk data uji.

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