scholarly journals The Accuracy of Real-Time hmF2 Estimation from Ionosondes

2020 ◽  
Vol 12 (17) ◽  
pp. 2671
Author(s):  
Carlo Scotto ◽  
Dario Sabbagh
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Root Mean Square ◽  
Magnetic Activity ◽  
Mean Square ◽  
Incoherent Scatter Radar ◽  
Incoherent Scatter ◽  
Scatter Radar ◽  
Mean Square Errors

A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST systems tend to underestimate hmF2 values with |Δ| in all cases less than 10 km. For high magnetic activity ARTIST offers better accuracy than Autoscala, as evidenced by RMSE[ARTIST] < RMSE[Autoscala], under both daytime and nighttime conditions, and considering all hours of the day. Conversely, under low and medium magnetic activity Autoscala tends to estimate hmF2 more accurately than the ARTIST system for both daytime and nighttime conditions, when RMSE[Autoscala] < RMSE[ARTIST]. However, RMSE[Autoscala] slightly exceeds RMSE[ARTIST] for the day as a whole. RMSE values are generally substantial (RMSE > 16 km in all cases), which places a limit on the results obtainable with real-time models that ingest ionosonde data.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Precipitating electron energy spectra and auroralpower estimation by incoherent scatter radar with hightemporal resolution

2021 ◽  
Author(s):  
Habtamu W. Tesfaw ◽  
Ilkka I. Virtanen ◽  
Anita T. Aikio ◽  
Amore' Elsje Nel ◽  
Michael Kosch ◽  
...  
Keyword(s):  
Electron Energy ◽  
Energy Spectra ◽  
Incoherent Scatter Radar ◽  
Incoherent Scatter ◽  
Scatter Radar

Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 24-29
Author(s):  
Adlian Jefiza ◽  
Indra Daulay ◽  
Jhon Hericson Purba
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Nearest Neighbors ◽  
Training Data ◽  
Mean Square ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

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