On data collection time by an electronic nose

We use electronic nose data of odor measurements to build machine learning classification models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classification is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classification performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shuffling cross-validation approach for determining the reported models’ average accuracy and standard deviation

Weld Joints Inspection Using Multisource Data and Image Fusion

The problem of inspecting weld joints is very complex, especially in critical parts of machines and vehicles. The welded joint is typically inspected visually, chemically or using radiography imaging. The flaw detection is a task for specialized personnel who analyze all the data on each stage of the inspection process separately. The inspection is prone to human error, and is labor intensive. In the stages of weld joint visual control geometrical measurements are performed, joint alignment, straightness, deformation, as well as the weld\u27s uniformity. Coloration my show the heat impact zone, and melted parts of the base material. Also during this stage the unwanted cracks, pores and other surface defects can be spotted. On the other side during the X-ray inspection other flaws can be discovered. Pores, cracks, lack of penetration and slag inclusions can be observed. The author’s goal was to develop a multisource data system of easier flaw detection, and possibly inspection process automation. The methods consisted of three image sources: X-ray, laser profilometer, and imaging camera. The proposed approach consists combining spatial information in the acquired data from all sources. A novel approach of data mixing is proposed to benefit from all the information. The signal form the profilometer enables geometrical information extraction. Deformation and alignment error assessment. The radiogram provides information about the hidden flaws. The color image gives information about texture and color of the surface as well as helps in combining multiple sources

A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing

Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students' works or even providing directions to the teachers on how to enhance the lectures. We follow this research path, and in this work, we explore how an academic lecture can be assessed automatically by quantitative features. First, we prepare a set of qualitative features based on teaching practices and then annotate the dataset of academic lecture videos collected for this purpose. We then show how these features could be detected automatically using machine learning and computer vision techniques. Our results show the potential usefulness of our work.Comment: 10 pages, 9 figure

Confocal laser scanning microscopy as a valuable tool in Diptera larval morphology studies

Larval morphology of flies is traditionally studied using light microscopy, yet in the case of fine structures compound light microscopy is limited due to problems of resolution, illumination and depth of field, not allowing for precise recognition of sclerites’ edges and interactions. Using larval instars of cyclorrhaphan Diptera, we show the usefulness of confocal laser scanning microscopy (CLSM) for studying the morphological characters of immature stages by taking advantage of the autofluorescent properties of cephaloskeleton structures. We compare data obtained from killed but unprepared larvae with those from larvae prepared by clearing according to two commonly used methods, either with potassium hydroxide or with Hoyer’s medium. We also evaluated the CLSM application for examining already slide-mounted larvae stored in museum collections and those freshly prepared. Our results indicate that CLSM and 3D reconstruction are excellent for visualizing small, compound structures of cylrorrhaphan larvae cephaloskeleton, if appropriate clearing techniques, i.e. the application of KOH, are used. Maximum intensity projection of confocal data sets obtained from material freshly prepared and that stored in museum collection does not differ. Because of this and the fact that KOH is commonly used as a clearing method to examine the cephaloskeleton of Diptera larvae, it is possible, and highly recommended, to use slides already prepared with this method for re-examination by CLSM. We conclude that CLSM application can be an invaluable source of data for studies of larval morphology of Cyclorrhapha by way of taxonomic diagnoses, character identification and improvement in characters homologization.This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited

The Impact of 32 Days' Exposure to Hypobaric Hypoxia on Physiological Cost of Sub-Maximal Work Performed at the Sea Level

Abstrac

Hybrid forecasting of PM2.5 using SOFM and ELM

The article presents a new approach to atmospheric PM2.5 dust prediction using an Extreme Learning Machine (ELM) neural network with clusterization done by the Self Organizing Feature Map (SOFM). This work is concerned with the calculation of the average level of air particulate matters PM2,5 in Warsaw's Ursynow one day ahead. The brief description of the hazards posed by air pollution is included. The work presents a short description of the SOFM and ELM networks, and their hybridized system used as a prediction tool. The analysis of the obtained results was presented and discussed

Hybrid forecasting of PM2.5 using SOFM and ELM

The article presents a new approach to atmospheric PM2.5 dust prediction using an Extreme Learning Machine (ELM) neural network with clusterization done by the Self Organizing Feature Map (SOFM). This work is concerned with the calculation of the average level of air particulate matters PM2,5 in Warsaw's Ursynow one day ahead. The brief description of the hazards posed by air pollution is included. The work presents a short description of the SOFM and ELM networks, and their hybridized system used as a prediction tool. The analysis of the obtained results was presented and discussed