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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "RLS-90 MODEL"

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Nurmutiazifah, A., and Aisyah Fitri Yuniasih. "PENERAPAN MODEL REGRESI DATA PANEL : DETERMINAN KETIMPANGAN CAPAIAN PENDIDIKAN DI KAWASAN TIMUR INDONESIA (KTI) 2015-2019." Seminar Nasional Official Statistics 2020, no. 1 (January 5, 2021): 1294–304. http://dx.doi.org/10.34123/semnasoffstat.v2020i1.705.

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Pendidikan menjadi faktor penting dalam pembentukan modal manusia. Pendidikan yang berkualitas dan merata akan meningkatkan modal manusia, memperluas kesempatan kerja dan meningkatkan kesejahteraan masyarakat. Salah satu indikator yang dapat digunakan untuk menilai kualitas pendidikan yakni rata-rata lama sekolah (RLS). Indonesia dalam Human Development Report 2019 berada di peringkat terendah di antara negara anggota ASEAN 5 untuk RLS. Jika dikaji lebih lanjut berdasarkan kawasan maka KTI memiliki RLS yang lebih rendah dari RLS KBI dan RLS nasional. Jika masalah kualitas pendidikan dan tidak meratanya pendidikan di Indonesia khususnya di KTI tidak segera diatasi, maka 90 persen masyarakat miskin yang bertempat tinggal di Indonesia (bersama Filipina) khususnya di KTI akan terus terjerat dalam lingkaran kemiskinan karena tidak mampu meningkatkan kesejahteraan hidupnya.Metode yang digunakan dalam penelitian ini adalah analisis regresi panel untuk menjelaskan determinan ketimpangan capaian pendidikan di KTI. Jenis data yang digunakan berupa data panel dengan cross-section sebanyak 17 provinsi dari tahun 2015-2019. Berdasarkan hasil regresi dengan metode estimasi FGLS/SUR diperoleh bahwa investasi, distribusi guru dan akses pendidikan berpengaruh negatif signifikan, sedangkan remaja menikah muda dan ketimpangan distribusi pendapatan berpengaruh positif signifikan terhadap ketimpangan capaian pendidikan.
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Sonaviya, Dipeshkumar R., and Bhaven N. Tandel. "Integrated road traffic noise mapping in urban Indian context." Noise Mapping 7, no. 1 (June 12, 2020): 99–113. http://dx.doi.org/10.1515/noise-2020-0009.

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Анотація:
AbstractRoad traffic noise has been recognized as a serious issue that affects the urban regions. Due to urbanization and industrialization, transportation in urban areas has increased. Traffic noise characteristics in cities belonging to a developing country like India are highly varied compared to developed nations because of its heterogeneous conditions. The objective of the research study is to assess noise pollution due to heterogeneous traffic conditions and the impact of horn honking due to un-authorized parked vehicles on the main roadside. Noise mapping has been done using the computer simulation model by taking various noise sources and noise propagation to the receiver point. Traffic volume, vehicular speed, noise levels, road geometry, un-authorized parking, and horn honking were measured on tier-II city roads in Surat, India. The study showed not so significant correlation between traffic volume, road geometry, vehicular speed and equivalent noise due to heterogeneous road traffic conditions. Further, analysis of traffic noise showed that horn honking due to un-authorized parked vehicles contributed an additional up to 11 dB (A), which is quite significant. The prediction models such as U.K’s CoRTN, U.S’s TNM, Germany’s RLS-90 and their modified versions have limited applicability for heterogeneity. Hence, the noise prediction models, which can be used for homogeneous road traffic conditions are not successfully applicable in heterogeneous road traffic conditions. In this research, a new horn honking correction factor is introduced with respect to unauthorized parked vehicles. The horn honking correction values can be integrated into noise model RLS-90, while assessing heterogeneous traffic conditions.
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Dai, Benlin, Yulong He, Jiming Xu, Ning Xu, Zhen Wu, and Yuanfang Deng. "Applying the RLS 90 to Develop an Inland Waterway Traffic Noise Prediction Model in China That Considers Water Surface Influence." Journal of Low Frequency Noise, Vibration and Active Control 34, no. 1 (March 2015): 73–85. http://dx.doi.org/10.1260/0263-0923.34.1.73.

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Muslim, Muhammad Aziz, Goegoes Dwi Nusantoro, and Dion Putra Pribadi. "Identifikasi Motor DC dengan Metode Recursive Least Square." Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) 15, no. 2 (August 31, 2022): 73–78. http://dx.doi.org/10.21776/jeeccis.v15i2.1547.

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Motor DC Minertia tipe UGTMEM-03STC25 merupakan salah satu alat di Laboratorium Sistem Kontrol Universitas Brawijaya Malang. Dengan menggunakan metode RLS motor DC Minertia tipe UGTMEM-03STC25 diperoleh model terbaik adalah orde 4 dengan parameter a1 = -0.405, a2 = -0.01, a3 = -0.0123, a4 = -0.0138, b1 = 0.0095, b2 = 0.0169, b3 = 0.9036, dan b4 = -0.3521. Setelah dilakukan uji validitas data percobaan dengan whiteness test, akaike's FPE, dan Fitness test Motor DC Minertia tipe UGTMEM-03STC25 didapatkan hasil nilai PRBS dengan batas bawah 50 dan batas atas 70 mendapatkan nilai Best Fits sebesar 83.3973% dan nilai FPE 0.1041, dan ketika nilai PRBS dengan batas bawah 60 dan batas atas 90 mendapatkan nilai Best Fits 90.4838% dan nilai FPE 0.1891 dan nilai PRBS dengan batas bawah 50 dan batas atas 90 mendapatkan nilai Best Fits 92.2456% dan nilai FPE 0.1065. Angka keakurasian ini dinyatakan dalam persentase, dimana semakin besar nilainya (maksimal 100%) dapat diartikan keluaran model sudah mendekati keluaran sistem yang sesungguhnya. dan nilai FPE pada data-data tersebut sudah sangat mendekati nol semakin kecil nilai FPE yang didapat, maka model tersebut semakin mewakili system yang telah dimodelkan.
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Yoon, Jewon, Chulhwan Kim, Woongyong Lee, Hyejin Kang, and Jooweon Lee. "Parameter analysis and Reliability Evaluation of Road Traffic Noise Prediction Model for Highway Traffic Noise Evaluation." Journal of Korean Society of Environmental Engineers 44, no. 8 (August 31, 2022): 267–75. http://dx.doi.org/10.4491/ksee.2022.44.8.267.

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The purpose of this study is to evaluate the reliability of prediction models(KHTN, RLS-90, CRTN, NMPB-08) that are widely used in road traffic noise analysis. For this purpose, the accuracy and difference values of the prediction model were analyzed by comparing the measurement values performed at the total of 21 highway sites, reflecting various conditions such as road structure, road pavement type, and noise barrier installation. In addition, the correlation between commercial programs(SoundPlan, CadnaA) was compared and reviewed for each of the same prediction models. First of all, as a result of analyzing the accuracy of each prediction model, KHTN is rated as 92.8% the most accurate based on ±3 dB error range. And CRTN is rated as 74.0~76.8% the most accurate among prediction models inherent in commercial programs. And, as a result of analyzing the correlation between commercial programs for prediction models, CRTN is 100% highly correlated and NMPB has the lowest correlation by 69.6%.
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Hustim, Muralia, Muhammad Isran Ramli, Rasdiana Zakaria, and Zulfiani AR. "The Effect of Speed Factors and Horn Sound to The RLS 90 Model Reliability on The Visum Program in Predicting Noise of Heterogeneous Traffic." International Journal of Integrated Engineering 10, no. 2 (August 1, 2018): 77–81. http://dx.doi.org/10.30880/ijie.2018.10.02.015.

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Melo, Andrélia Maciel, Nilton Vivacqua-Gomes, Ricardo Affonso Bernardes, Rodrigo Ricci Vivan, Marco Antônio Húngaro Duarte, and Bruno Carvalho de Vasconcelos. "Influence of Different Coronal Preflaring Protocols on Electronic Foramen Locators Precision." Brazilian Dental Journal 31, no. 4 (August 2020): 404–8. http://dx.doi.org/10.1590/0103-6440202003282.

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Abstract: The aim of this study was to evaluate the influence of different coronal preflaring protocols (absent, conservative and conventional) on the accuracy of Root ZX II, Raypex 6, and RomiApex A-15 electronic foramen locators (EFLs). Twenty mandibular molars with Vertucci’s type IV mesial roots were subjected to endodontic exploration and foraminal patency confirmation. Under 16x magnification, its real lengths (RL) were measured and registered (RL1). The canals were then irrigated with 2.5% sodium hypochlorite and electronically measured (EM1) employing the alginate model; all measurements were performed in triplicate by a blind operator using adjusted endodontic hand-files introduced until the apex foramen. Coronal preflaring procedures were sequentially performed with #25/.06 (conservative) and #25/.12 (conventional) instruments; new RLs extents were performed after each coronal preparation protocol (RL2/RL3), as same as electronic measurements (EM2/EM3). The devices error (mm) was evaluated considering the difference between RLs and EMs at each preparation stage; their precision was stablished adopting ±0.5 mm as tolerance margin. The EFLs error significantly reduced after conventional coronal preflaring protocol (p<0.05), which not occur after the conservative one. The best precisions values were noted after conventional preparation as 90% (Root ZX II), 97.5% (Raypex 6), and 92.5% (RomiApex A-15). No significant differences were found in EFLs comparisons, regardless of the coronal protocol tested (p>0.05). Under the conditions tested it can be concluded that the EFLs evaluated were precise. Moreover, the preflaring protocols influences its accuracy’s, where the less conservative one produced the best results.
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AR, Zulfiani. "Prediksi Kebisingan Lalu Lintas Heterogen Menggunakan Aplikasi Visum." Jurnal Teknik Sipil MACCA 6, no. 2 (June 30, 2021): 126–34. http://dx.doi.org/10.33096/jtsm.v6i2.337.

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Анотація:
Kebisingan merupakan salah satu permasalahan yang berpengaruh pada keselamatan lalu lintas. Kebisingan yang terjadi berasal dari berbagai macam aktivitas lalu lintas di jalan, baik yang bersumber dari kendaraan umum maupun kendaraan pribadi. Indonesia sebagai negara berkembang juga tak lepas dari masalah kebisingan lalu lintas. Kendaraan yang bergerak di jalan akan mengeluarkan suara baik itu mesin maupun klakson kendaraan. Penelitian ini bertujuan memprediksi kebisingan yang dihasilkan lalu lintas heterogen menggunakan program Visum. Titik pengamatan dilakukan pada 37 titik pada ruas jalan Kota Makassar. Waktu pengamatan dilakukan pada pukul 06.00 – 18.00 dan 06.00 – 21.00 dengan objek penelitian sepeda motor (Motorcycle), kendaraan ringan (Light Vehicle) dan kendaraan berat (Heavy Vehicle). Data yang diamati adalah volume lalu lintas, kecepatan kendaraan, jumlah klakson dan kebisingan dengan menggunakan alat Sound Level Meter Tenmars TM-103. Hasil menunjukkan bahwa tingkat kebisingan hasil prediksi menggunakan program Visum dengan model prediksi RLS 90 menghasilkan nilai tingkat kebisingan rata-rata 69,3 dB dengan korelasi pearson dan RMSE sebesar 0,71 dan 9,97.
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Isasi, Iraia, Unai Irusta, Elisabete Aramendi, Trygve Eftestøl, Jo Kramer-Johansen, and Lars Wik. "Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks." Entropy 22, no. 6 (May 27, 2020): 595. http://dx.doi.org/10.3390/e22060595.

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Chest compressions during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may provoque inaccurate rhythm classification by the algorithm of the defibrillator. The objective of this study was to design an algorithm to produce reliable shock/no-shock decisions during CPR using convolutional neural networks (CNN). A total of 3319 ECG segments of 9 s extracted during chest compressions were used, whereof 586 were shockable and 2733 nonshockable. Chest compression artifacts were removed using a Recursive Least Squares (RLS) filter, and the filtered ECG was fed to a CNN classifier with three convolutional blocks and two fully connected layers for the shock/no-shock classification. A 5-fold cross validation architecture was adopted to train/test the algorithm, and the proccess was repeated 100 times to statistically characterize the performance. The proposed architecture was compared to the most accurate algorithms that include handcrafted ECG features and a random forest classifier (baseline model). The median (90% confidence interval) sensitivity, specificity, accuracy and balanced accuracy of the method were 95.8% (94.6–96.8), 96.1% (95.8–96.5), 96.1% (95.7–96.4) and 96.0% (95.5–96.5), respectively. The proposed algorithm outperformed the baseline model by 0.6-points in accuracy. This new approach shows the potential of deep learning methods to provide reliable diagnosis of the cardiac rhythm without interrupting chest compression therapy.
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"Prediction and Evaluation of the Road Traffic Noise according to the Conditions of Road-side Building Using RLS-90 and CRTN Model." Transactions of the Korean Society for Noise and Vibration Engineering 19, no. 4 (April 20, 2009): 425–32. http://dx.doi.org/10.5050/ksnvn.2009.19.4.425.

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Дисертації з теми "RLS-90 MODEL"

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SINGH, AMIT. "MONITORING AND PREDICTION OF NOISE LEVEL IN URBAN AREA USING RLS-90 MODEL." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16056.

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Noise pollution is the major environmental problem in developing countries as well as in developed country. Rapid industrialization, vehicle growth, urbanization and change in lifestyle of people has increased the noise level above the prescribed standard level. Traffic noise contribution in the total noise pollution is approximately 75%. Delhi is the fourth noisiest city in the world and third noisiest city of the India. During this study, by keeping in mind the significance of the noise pollution in Delhi, ten locations have been selected to monitor the ambient noise level. These locations have been identified on the basis of different kind of land use pattern like residential, commercial and silence zone. The monitoring has been conducted during morning peak hour, off peak hour and evening peak hour. Sound level meter (Cesva SC 260) type two instrument has been used to monitor the ambient noise level at selected location. Worldwide, various traffic noise model like FHWA, CORTN, Stop and Go and RLS -90 are available for the prediction of traffic noise in different condition. In this study RLS-90 Model has been used to predict the noise level at all the selected location. At the same time, this model used to forecast the traffic noise for the year of 2022, 2027 and 2032. After data analysis, it was observed that the measured ambient noise level at all the locations were violated the permissible limits prescribed by CPCB. During monitoring, highest traffic noise level was observed at Ashram Chowk. This may be due to higher traffic volume as well as high traffic congestion at the particular location. The parentage error between monitored traffic noise level and predicted traffic noise level was found within the range of 0.5 % to 5.75% which indicates the suitability and applicability of the model in city like Delhi.
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Тези доповідей конференцій з теми "RLS-90 MODEL"

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Alam, Pervez, Kafeel Ahmad, S. S. Afsar, and Nasim Akhtar. "Validation of the Road Traffic Noise Prediction Model RLS-90 in an Urban Area." In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE). IEEE, 2020. http://dx.doi.org/10.1109/icetce48199.2020.9091759.

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