scholarly journals Aplikasi Jaringan Saraf Tiruan dengan Metode Fungsi Tan-Sigmoid dalam Memprediksi Kinerja Struktur Bangunan Gedung

2021 ◽  
Vol 9 (1) ◽  
pp. 34-40
Author(s):  
Muhammad Gala Garcya ◽  
Zulfikar Djauhari ◽  
Reni Suryanita
Keyword(s):  
Finite Element ◽  
Transfer Function ◽  
Mean Square Error ◽  
Displacement Velocity ◽  
Mean Square ◽  
Data Input ◽  
Structural Engineer ◽  
Fema 356

Gempa bumi merupakan salah satu ancaman terbesar terhadap gedung, sehingga perlu untuk mendesain gedung dengan memperhitungkan pembebanan gempa bumi yang terjadi. Dengan bantuan software finite element dapat diperoleh respons struktur berupa displacement, velocity, dan acceleration yang terjadi akibat gempa bumi. Jaringan Saraf Tiruan (JST) merupakan salah satu metode yang dapat memprediksi kerusakan bangunan dengan memanfaatkan data respons struktur dengan waktu analisis yang relatif lebih singkat dibandingkan menganalisis struktur satu per satu. Penelitian ini bertujuan untuk menganalisis data gempa dengan magnitude intensitas tinggi yang berbeda-beda. Data input dan output diperoleh melalui software Finite Element untuk menghasilkan jumlah data yang diperlukan JST yaitu sebanyak 4489 data. Pada penelitian ini, komposisi yang digunakan untuk training, testing, dan validating adalah 60%, 25%, dan 15% masing – masingnya. Data input yang digunakan yaitu waktu, acceleration arah x dan y, velocity arah x dan y, serta displacement arah x dan y. Sedangkan untuk data target yang digunakan yaitu kinerja struktur yang ditentukan oleh FEMA 356 dan simpangan antar lantai arah x dan y. Hasil pengujian menunjukkan analisis oleh JST yang menggunakan transfer function Tan-Sigmoid menunjukkan nilai R2 sebesar 97,542% dan Mean Square Error (MSE) yang dihasilkan yaitu sebesar 1,2449.E-07. Hal ini menunjukkan analisis JST dengan transfer function Tan-Sigmoid dapat digunakan untuk memprediksi kinerja dari struktur dengan cepat dan akurat. Dengan demikian metode ini diharapkan dapat direkomendasikan untuk Structural Engineer dan perencana gedung dalam mendesain bangunan gedung bertingkat tahan gempa.

Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study

2020 ◽  
Vol 24 (3) ◽  
pp. 243-251
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Mean Square Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coefficient Of Determination ◽  
Hydraulic Structures ◽  
Mean Square ◽  
Feed Forward Back Propagation

Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

scholarly journals An experimental and computational investigation of the effects of volumetric boundary conditions on the compressive mechanics of passive skeletal muscle

2019 ◽  
Author(s):  
Benjamin Wheatley
Keyword(s):  
Skeletal Muscle ◽  
Finite Element ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Material Properties ◽  
Mean Square ◽  
Unconfined Compression ◽  
Confined Compression ◽  
Passive Muscle

Computational modeling, such as finite element analysis, is employed in a range of biomechanics specialties, including impact biomechanics and surgical planning. These models rely on accurate material properties for skeletal muscle, which comprises roughly 40% of the human body. Due to surrounding tissues, compressed skeletal muscle in vivo likely experiences a semi-confined state. Nearly all previous studies investigating passively compressed muscle at the tissue level have focused on muscle in unconfined compression. The goals of this study were to (1) examine the stiffness and time-dependent material properties of skeletal muscle subjected to both confined and unconfined compression (2) develop a model that captures passive muscle mechanics under both conditions and (3) determine the extent to which different assumptions of volumetric behavior affect model results. Muscle in confined compression exhibited stiffer behavior, agreeing with previous assumptions of near-incompressibility. Stress relaxation was found to be faster under unconfined compression, suggesting there may be different mechanisms that support load these two conditions. Finite element calibration was achieved through nonlinear optimization (normalized root mean square error <6%) and model validation was strong (normalized root mean square error <17%). Comparisons to commonly employed assumptions of bulk behavior showed that a simple one parameter approach does not accurately simulate confined compression. We thus recommend the use of a properly calibrated, nonlinear bulk constitutive model for modeling of skeletal muscle in vivo. Future work to determine mechanisms of passive muscle stiffness would enhance the efforts presented here.

scholarly journals PEMODELAN MIKE21 DALAM KEJADIAN BANJIR ROB MENJELANG GERHANA BULAN DI PESISIR SEMARANG

2019 ◽  
Vol 5 (3) ◽  
pp. 46-52
Author(s):  
Ratna Cintya Dewi ◽  
Oky Sukma Hakim ◽  
Ejha Larasati Siadari
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Data Input ◽  
Mike 21

Pesisir Semarang merupakan wilayah yang rentan terkena dampak bencana banjir rob. Hampir setiap bulan wilayah tersebut tergenang akibat pasang air laut. Peristiwa banjir rob yang paling diingat publik adalah saat terjadi menjelang gerhana bulan tanggal 31 Januari 2018. Peringatan dini informasi banjir rob perlu dibuat dengan tingkat akurasi tinggi dan dalam bentuk yang mudah dipahami masyarakat untuk kepentingan mitigasi. Tipe pasang surut (pasut) di Pesisir Semarang perlu diidentifikasi untuk mengetahui kecenderungan waktu kejadian banjir rob. Model Hidrodinamika Mike 21 digunakan untuk mengolah data pengamatan pasut sepanjang tahun 2017 menjadi informasi prakiraan pasut sepanjang bulan Januari 2018. Nilai akurasi prakiraan pasut Mike 21 diukur dengan Root Mean Square Error (RMSE), serta dibandingkan dengan keakurasian prakiraan pasut dari Pushidrosal (Pusat Hidrografi dan Oseanografi TNI Angkatan Laut) dan BIG (Badan Informasi Geospasial). Hasil prakiraan pasut terbaik selanjutnya diolah pada aplikasi ArcGIS, dengan mengambil nilai prakiraan pasang tertinggi pada hari kejadian sebagai data input untuk pembuatan peta luas genangan banjir rob. Puncak kejadian banjir rob cenderung terjadi pada pukul 06.00 WIB dan 17.00 WIB, yang mana berdasarkan hasil identifikasi di Pesisir Semarang termasuk tipe pasang surut harian ganda. Prakiraan pasut dari Mike 21 memiliki nilai RMSE terbaik. Nilai RMSE prakiraan pasut Mike 21, Pushidrosal dan BIG berturut-turut bernilai 10 cm, 30 cm dan 22 cm. Nilai pasut tertinggi baik model Mike 21 maupun hasil pengamatan terjadi pukul 20.00 WIB. Pemodelan nilai pasut tertinggi dari Mike21 sebesar 122 cm menghasilkan peta luas genangan banjir rob sebesar 1753.35 ha, sedangkan nilai pasang tertinggi hasil pengamatan sebesar 133 cm menghasilkan 185.31 ha lebih luas.

Komparasi Gaussian Plume Model dan Cellular Automata pada Simulasi Distribusi Gas Polutan Cerobong Pembuangan Pembangkit Listrik

POSITRON ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 14
Author(s):  
Haris Kawarizmi ◽  
Apriansyah Apriansyah
Keyword(s):  
Cellular Automata ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Data Input ◽  
Gaussian Plume Model ◽  
Plume Model ◽  
Gaussian Plume

Telah dilakukan simulasi distribusi gas polutan cerobong pembuangan pembangkit listrik menggunakan Gaussian Plume Model (GPM) dan Cellular Automata (CA). GPM adalah sebuah metode analitik untuk mempelajari pola sebaran polutan gas di udara akibat proses adveksi dan difusi yang disebabkan oleh angin. Sementara CA merupakan model sederhana yang berbasiskan sel diskrit dalam suatu kisi dengan seperangkat aturan interaksi antar sel tetangga. Sehingga, CA dapat digunakan untuk mensimulasikan dinamika spasial yang disebabkan oleh proses adveksi dan difusi. Polutan yang dipilih sebagai data input adalah Sulfur Dioksida (SO2). Simulasi sebaran polutan di udara dilakukan dengan menerapkan model difusi dan adveksi gas berdasarkan metode CA dan GPM. Simulasi dilakukan pada domain berukuran 100  100 meter dengan dua skenario, yakni skenario satu sumber dan skenario dua sumber identik. Hasil simulasi dari kedua model yang digunakan, menghasilkan pola sebaran polutan yang saling bersesuaian. Hal ini ditunjukkan dengan nilai Root Mean Square Error (RMSE) pada skenario 1 sumber sebesar 0,01229 untuk penampang sumbu-x (y = 50) dan 0,004195 untuk penampang sumbu-y  (x = 35). Sedangkan pada skenario 2 sumber, nilai RMSE sebesar 0,02032 untuk penampang sumbu-x (y = 60) dan 0,004814 untuk penampang sumbu-y (x = 50).

The Bordeaux Automatic Meridian Circle

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°.

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

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.

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

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).

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

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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