Proposal for a Random-based Algorithm to Solve Data-fitting Problems

This preprint is a complement to a preceding article. Since error-squaring exaggerates the influence of outliers in the linear least square method, a more natural procedure to avoid mutual cancellation of errors would be taking absolute values of errors instead. However, such a choice would prevent using differential calculus. It is then suggested that calculus be replaced by an algorithm which selects the best line from a set of random-generated lines. The proposal has been tested with an experimental algorithm written in Python language and found to work well.

Shoreline Extraction Based on an Active Connection Matrix (ACM) Image Enhancement Strategy

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including the WorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images.

Analysis of dexmedetomidine on the quality of awakening during neurosurgery

Dexmedetomidine has a dose-dependent sedative and analgesic effect. To further evaluate the wake-up quality of dexmedetomidine in patients undergoing neurosurgery, a meta-analysis of dexmedetomidine in a randomized controlled trial of general anesthesia was performed. Firstly, an experimental algorithm was proposed, and then the data fusion algorithm was used to conduct randomized controlled trials. The clinical efficacy and safety of dexmedetomidine in the acupuncture of neurosurgical patients were evaluated one by one for quality evaluation and data extraction. The effect of different input variables on the depth of anesthesia was studied by using a multi-data fusion approach. The results show that the data fusion algorithm proposed can effectively connect redundant information and complementary information in multiple data, and estimate the real parameters of the measured object. In addition, data fusion brings great convenience to the design of control algorithms and controllers, and provides an effective basis for system simplification. Experiments have shown that dexmedetomidine is effective and safe in the operation of neurosurgical motor function, and the management of the recovery period is safe and effective. Based on the research, it can provide some reference for the awakening of patients undergoing neurosurgery, and promote the progress and development of medicine.

Specific yield of aquifer evaluation by means of a new experimental algorithm and its applications

A simplified method to determine specific yield (i.e., effective porosity) from hydraulic conductivity data obtained through pumping tests is proposed. This new method derives from a reprocessing of literature data and a subsequent calibration with results from pumping tests performed in different hydrogeological contexts. The use of the algorithm allows obtaining values of specific yield (Sy), which could be useful for the resolution of problems concerning the water balance and the transport of contaminants in groundwater. The proposed algorithm is applied to a large-scale area (Milan and its suburbs, northwestern Italy) to determine a map of the specific yield of a sandy-gravel aquifer and the effects on the estimation of water volumes stored in the subsoil from a hydrogeological point of view, considering about seventy years of measures. It is demonstrated that the great variation in water volumes reflects the socio-economic history of the territory.

GFIT2: an experimental algorithm for vertical profile retrieval from near-IR spectra

An algorithm for retrieval of vertical profiles from ground-based spectra in the near IR is described and tested. Known as GFIT2, the algorithm is primarily intended for CO2, and is used exclusively for CO2 in this paper. Retrieval of CO2 vertical profiles from ground-based spectra is theoretically possible, would be very beneficial for carbon cycle studies and the validation of satellite measurements, and has been the focus of much research in recent years. GFIT2 is tested by application both to synthetic spectra and to measurements at two Total Carbon Column Observing Network (TCCON) sites. We demonstrate that there are approximately 3° of freedom for the CO2 profile, and the algorithm performs as expected on synthetic spectra. We show that the accuracy of retrievals of CO2 from measurements in the 1.61μ (6220 cm−1) spectral band is limited by small uncertainties in calculation of the atmospheric spectrum. We investigate several techniques to minimize the effect of these uncertainties in calculation of the spectrum. These techniques are somewhat effective but to date have not been demonstrated to produce CO2 profile retrievals with sufficient precision for applications to carbon dynamics. We finish by discussing ongoing research which may allow CO2 profile retrievals with sufficient accuracy to significantly improve the scientific value of the measurements from that achieved with column retrievals.

GFIT2: an experimental algorithm for vertical profile retrieval from near IR spectra

An algorithm for retrieval of vertical profiles from ground-based spectra in the near IR is described and tested. Known as GFIT2, the algorithm is primarily intended for CO2, and is used exclusively for CO2 in this paper. Retrieval of CO2 vertical profiles from ground-based spectra is theoretically possible, would be very beneficial for carbon cycle studies and the validation of satellite measurements, and has been the focus of much research in recent years. GFIT2 is tested by application both to synthetic spectra, and to measurements at two TCCON sites. We demonstrate that there are approximately 3° of freedom for the CO2 profile, and the algorithm performs as expected on synthetic spectra. We show that the accuracy of retrievals of CO2 from measurements in the 1.6 μ spectral band is limited by small uncertainties in calculation of the atmospheric spectrum. We investigate several techniques to minimize the effect of these uncertainties in calculation of the spectrum. These techniques are somewhat effective, but to date have not been demonstrated to produce CO2 profile retrievals superior to existing techniques for retrieval of column abundance. We finish by discussing on-going research which may allow CO2 profile retrievals with sufficient accuracy to significantly improve on the results of column retrievals, both in total column abundance and in profile shape.

Using Particle Swarm Optimization Based Neural Network for Modeling of Thrust Force Drilling of PA-6/Nanoclay Nanocomposites

This paper presents a newly approach for modeling thrust force in drilling of PA-6/ Nanoclay Nanocomposites materials, by using Particle Swarm Optimization based Neural Network (PSONN). In this regard, advantages of statistical experimental algorithm technique, experimental measurements, particle swarm optimization and artificial neural network are exploited in an integrated manner. For this purpose, numerous experiments for PA-6 and PA-6/ Nanoclay Nanocomposites are conducted to obtain thrust force values by using drill of high speed steel with point angles and 2mm in diameter. Then, a predictive model for thrust force is created by using PSONN algorithm. Also, the training capacity of PSONN is compared to that of the conventional neural network. The results indicate that nanoclay content on PA-6 polyamide significantly decrease the thrust force. Also, the obtained results for modeling of thrust force have shown very good training capacity of the proposed PSONN algorithm with compared to that of a conventional neural network (BPNN).