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Ina??season potato yield prediction with active optical sensors
摘要： Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha?1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha?1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.
关键词： sensor technology,petiole sampling,potato,prediction models,multiple regression analysis,Yield prediction,nitrogen loss
更新于2025-09-23 15:21:01
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Multi-Objective Optimization of Cutting Parameters during Laser Cutting of Titanium Alloy Sheet using Hybrid approach of Genetic Algorithm and Multiple Regression Analysis
摘要： Now a day’s advance engineering materials are playing a key role in the field of aeronautics, defense and medical. Titanium and its alloys are one of the advanced engineering materials that have great demand in the various fields of industries due to its greater behavior and better mechanical properties. These applications require precise and better quality cuts which may not be obtained by conventional machining processes due to the unfavourable properties of Ti and its alloys. This problem may be minimized by using laser cutting process. In this study, the Advanced 300 W Nd:YAG Laser cutting system has been applied for the cutting of titanium alloy sheet. The point of current research is to optimize kerf width and kerf deviation, simultaneously during the laser cutting of Titanium alloy sheet (Grade 5).The multiple regression analysis has been applied for developing the second order regression models of kerf width and kerf deviation. The optimization technique genetic algorithm has been applied for the multi-objective optimization of the developed models. The comparison results show an improvement of 29.78% for kerf width and 95% for kerf deviation, respectively. The overall percentage improvement of 27.39% has been found by considering the equal importance of both quality characteristics. The parametric effects on quality characteristic have been also discussed.
关键词： Multiple Regression Analysis,Kerf Deviation,Laser cutting,Hybrid Approach,Kerf Width,Genetic Algorithm
更新于2025-09-16 10:30:52
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Description of short circuit current of outdoor photovoltaic modules by multiple regression analysis under various solar irradiance levels
摘要： Short-circuit current (ISC) values of test photovoltaic (PV) modules, i.e., multi-crystalline silicon, heterostructure-with-intrinsic-thin-layer, single-crystalline silicon back-contact, CuInSe2 (CIS), and CdTe modules, are descripted using multiple regression analysis based on environmental factors (solar irradiance, average photon energy (APE), and module temperature (Tmod)) under several solar irradiance levels. The APE is an index of the solar spectral irradiance distribution. PV module irradiance sensor is used to investigate simultaneous solar irradiance (PVMS), single-crystalline silicon PV module, (IrrTPVMS). It is disclosed that ISC is primarily determined by IrrTPVMS. Error between the estimated ISC and measured ISC of test PV modules is investigated. Consequently, precise ISC description (low error) is obtained when IrrTPVMS is utilized. The more precise description of the ISC for CIS and CdTe PV modules, having the bandgap (Eg) different from PVMS, is realized when adding APE environment factor even under low IrrTPVMS ((cid:1)0 kW/m2), accumulated on both sunny day and cloudy day suggesting the enhancement of investigation opportunity. This is because APE minimizes spectral mismatch error caused by Eg difference between PVMS and test PV module. Moreover, the precision of ISC description is further increased under enhanced IrrTPVMS of (cid:1)0.5 kW/m2 (on sunny day) due to stable solar irradiance.
关键词： Multiple regression analysis,Solar irradiance levels,PV module irradiance sensor,Short-circuit current
更新于2025-09-12 10:27:22
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - The Social-Economic Impact of Fiber Broadband: A Hype or a Reality?
摘要： The social-economic impact of broadband has been studied extensively. The consensus is that basic broadband (from none or upgraded from 256 kbps to the order of 2 – 6 Mbps) indeed has positive impact on economic growth and performance in terms of e.g. GDP and employment. Nevertheless, studies on the social-economic impact of high-speed broadband access (20-30 Mbps and beyond) are far more controversial showing a mixed picture. In this paper, we investigated fiber broadband access impact on the employment rate, population evolution, reduced driving distance per capita and new company registrations at the municipality level in Sweden using multiple regression analysis. Based on the availability of historical fiber network deployment data, analyses can be carried out by regressing current values of social-economic indicators under study on the fiber penetration levels 4 years earlier, hence reverse causality in the regression analyses can be tackled effectively. Our results show that, given all the other significant factors remain the same and with at least 90% confidence interval, 10% increase of fiber broadband penetration would result in 0.17% population increase, 0.32% employment rate enhancement, 28.7 km reduced driving distance per capita per year, and one new company among 17857 inhabitants.
关键词： social-economic impact,causality,social-economic indicators,multiple regression analysis,municipality,fiber broadband penetration
更新于2025-09-09 09:28:46