kNN and SVM classification for EEG: a review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances

Degradation of Potassium Rock by Earthworms and Responses of Bacterial Communities in Its Gut and Surrounding Substrates after Being Fed with Mineral

BACKGROUND: Earthworms are an ecosystem's engineers, contributing to a wide range of nutrient cycling and geochemical processes in the ecosystem. Their activities can increase rates of silicate mineral weathering. Their intestinal microbes usually are thought to be one of the key drivers of mineral degradation mediated by earthworms,but the diversities of the intestinal microorganisms which were relevant with mineral weathering are unclear. METHODOLOGY/PRINCIPAL FINDINGS: In this report, we show earthworms' effect on silicate mineral weathering and the responses of bacterial communities in their gut and surrounding substrates after being fed with potassium-bearing rock powder (PBRP). Determination of water-soluble and HNO(3)-extractable elements indicated some elements such as Al, Fe and Ca were significantly released from mineral upon the digestion of earthworms. The microbial communities in earthworms' gut and the surrounding substrates were investigated by amplified ribosomal DNA restriction analysis (ARDRA) and the results showed a higher bacterial diversity in the guts of the earthworms fed with PBRP and the PBRP after being fed to earthworms. UPGMA dendrogram with unweighted UniFrac analysis, considering only taxa that are present, revealed that earthworms' gut and their surrounding substrate shared similar microbiota. UPGMA dendrogram with weighted UniFrac, considering the relative abundance of microbial lineages, showed the two samples from surrounding substrate and the two samples from earthworms' gut had similarity in microbial community, respectively. CONCLUSIONS/SIGNIFICANCE: Our results indicated earthworms can accelerate degradation of silicate mineral. Earthworms play an important role in ecosystem processe since they not only have some positive effects on soil structure, but also promote nutrient cycling of ecosystem by enhancing the weathering of minerals

Achievable tolerances in robotic feature machining operations using a low-cost hexapod

Portable robotic machine tools potentially allow feature machining processes to be brought to large parts in various industries, creating an opportunity for capital expenditure and operating cost reduction. However, robots lack the machining capability of conventional equipment, which ultimately results in dimensional errors in parts. This work showcases a low-cost hexapod-based robotic machine tool and presents experimental research conducted to investigate how the widely researched robotic machining challenges, e.g. structural dynamics and kinematics, translate to achievable tolerance ranges in real-world production to highlight currently feasible applications and provide a context for considering technology improvements. Machining trials assess the total dimensional errors in the final part over multiple geometries. A key finding is error variation which is in the sub-millimetre range, although, in some cases, upper tolerance limits < 100 μm are achieved. Practical challenges are also noted. Most significantly, it is demonstrated that dimensional machining error is mainly systematic in nature and therefore that the total error can be dramatically reduced with in situ measurement and compensation. Potential is therefore found to achieve a flexible, high-performance robotic machining capability despite complex and diverse underlying scientific challenges. Overall, the work presented highlights achievable tolerances in low-cost robotic machining and opportunities for improvement, also providing a practical benchmark useful for process selection

Profile monitoring for brain activities using fMRI data to locate the onset of seizure

Not availabl

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized 16 by a relation (profile) between a response variable and one or more explanatory variables. Such profiles 17 can be modeled using linear or nonlinear regression models. In this paper we use artificial neural net- 18 works to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial 19 neural networks are developed to monitor linear profiles. Their efficacies are assessed using average 20 run length criterion

On the estimating burr XII distribution Parameters

Burr XII distribution plays an important role in reliability modeling, risk analyzing and process capability estimation. However, estimating two parameters of the Burr XII distribution, i.e., c and k, is a complicated task and using conventional methods is not straightforward. In this paper a neural network to estimate Burr XII parameters is presented. The inputs of proposed neural network are skewness and kurtosis. The performance of proposed methods is evaluated in different simulation examples

A transformation technique to estimate the process capability index for non-normal processes

Estimating the process capability index (PCI) for non-normal processes has been discussed by many researches. There are two basic approaches to estimating the PCI for non-normal processes. The first commonly used approach is to transform the non-normal data into normal data using transformation techniques and then use a conventional normal method to estimate the PCI for transformed data. This is a straightforward approach and is easy to deploy. The alternate approach is to use non-normal percentiles to calculate the PCI. The latter approach is not easy to implement and a deviation in estimating the distribution of the process may affect the efficacy of the estimated PCI. The aim of this paper is to estimate the PCI for non-normal processes using a transformation technique called root transformation. The efficacy of the proposed technique is assessed by conducting a simulation study using gamma, Weibull, and beta distributions. The root transformation technique is used to estimate the PCI for each set of simulated data. These results are then compared with the PCI obtained using exact percentiles and the Box-Cox method. Finally, a case study based on real-world data is presented

Boron Adsorption on Muscovite Mineral as a Function of pH, Ionic strength of Solution and Kinds of Cation

Introduction: Boron is one of the eight essential micronutrients required for plant growth and development. The optimal concentration range (between deficiency and phytotoxicity) for boron is narrower than for other plant essential nutrients. Generally, irrigating water containing concentrations of B greater than 1 mg L-1 would be detrimental for most plants. Although, there are a large number of different studies on the removal of B ions from aqueous solutions using different adsorbents, every special adsorbent material requires individual research. Information about the chemical behavior of muscovite for boron is very limited. Therefore, the objective of this study was to investigate boron adsorption on muscovite as a function of solution pH, ionic strength of the background electrolyte, kinds of cation, and initial boron concentration. Materials and Methods: The muscovite sample was obtained from a mine near Hamadan city in western Iran. It was powdered in a mortar and sieved before sorption experiment. Boron adsorption experiments were performed in batch systems using 15 mL polyethylene (PE) bottles in 0.01 M Ca(NO3)2 electrolyte solution at a adsorbent concentrations of 10 g L-1, and at room temperature (23±2 ◦C). All samples were prepared in duplicate. Blank samples (without adsorbent) were prepared for all experiments. For pH dependent B adsorption, aliquots of B stock solution (1000 mg L−1) were added to obtain initial B concentrations of 5 and 15 mg L-1. The pH of the solutions were adjusted to values of 6.8, 7.7 and 8.8 by adding negligible predetermined volumes of 0.03M NaOH or 0.03M HNO3 solution. To study the effects of kinds of cation on boron adsorption, samples of adsorbent (0.1 g) were mixed with 10 mL background electrolyte solutions (0.01M Ca(NO3)2, Mg(NO3)2 and NaNO3) in 15 mL centrifuge tubes. Then, predetermined amount of B were added to the centrifuge tubes to obtain final concentrations of 5 mg L-1 B. For determination of boron adsorption isotherm, after 10 ml 0.01 M of Ca(NO3)2 was transferred into 15 ml centrifuge tubes, 0.1 g sample of muscovite was added to obtain adsorbent concentration of 10 g L-1. Then a predetermined amount of boron from the stock solution was added to give final concentration range between 1 and 15 mg B per liter. Initial pH of the solution was adjusted to 8.2 ± 0.1 by predetermined amount of 0.03 M NaOH solution. Suspensions were then shaken for 24h. At the end of equilibrium time, final pH was measured in the suspensions and the tubes were then centrifuged for 10 min at 5000 g. Half of the supernatant volume (5 mL) was pipetted out from each tube and then B in the supernatants were measured using the colorimetric Azomethin-H method. The amount of B adsorbed on the adsorbent was calculated as the difference between the B concentration in the blanks and the concentration in the solution after equilibration. Chemical species in the solutions were also predicted using Visual MINTEQ, a chemical speciation program developed to simulate equilibrium processes in aqueous systems. Results and Discussion: The effect of pH on the amount of B retained depended on the initial B concentration. The amount of boron adsorption increased with increasing equilibrium pH. Boron adsorption on muscovite increased with increasing ionic strength. Greater adsorption was observed in the presence of Mg2+ as compared with Ca2+ at the same ionic strength. Calculations using Vminteq showed that the concentration of Mg-borate ion pairs (MgH2BO3+) were higher than the concentration of Ca and Na-borate ion pairs (CaH2BO3+ and NaH2BO3°). It thus seems that the much greater loss of B from solution observed in the Mg system was caused by Mg-borate ion pair adsorption. Sorption isotherm of B were well described by the Freundlich, Langmuir and Sips models but the Sips sorption model describes the interaction between B and the mineral better than the Langmuir model. On the basis of n value of Freundlich model, adsorption isotherm of boron on muscovite was classified as L-type (n≤ 1). This kind of adsorption behavior could be explained by the high affinity of the adsorbent for the adsorptive at low concentrations, which then decreases as concentration increases. Maximum sorption capacity (qmax) was obtained to be 13.98 mmol kg-1 for muscovite. Conclusion: The experimental data showed that less than 5% of initial boron concentration was adsorbed by muscovite, thus this mineral has not a reasonable adsorption capacity for B. Keywords: Boron, Adsorption, Muscovite, Speciation