Search results for: mineral potential classification

Authors: Danutė Jablonskytė-Raščė, Audronė MankevičIenė, Laura Masilionytė

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During the period 2009–2015, in Joniškėlis Experimental Station of the Lithuanian Research Centre for Agriculture and Forestry, the effect of ecologic fertilizers Ekoplant, bio-activators Biokal 01 and Terra Sorb Foliar and their combinations on the formation of the productivity elements, grain yield and quality of winter wheat, spelt (Triticum spelta L.), and common wheat (Triticum aestivum L.) was analysed in ecological agro-system. The soil under FAO classification – Endocalcari-Endo-hypogleyic-Cambisol. In a clay loam soil, ecological fertilizer produced from sunflower hull ash and this fertilizer in combination with plant extracts and bio-humus exerted an influence on the grain yield of spelt and common wheat and their mixture (increased the grain yield by 10.0%, compared with the unfertilized crops). Spelt grain yield was by on average 16.9% lower than that of common wheat and by 11.7% lower than that of the mixture, but the role of spelt in organic production systems is important because with no mineral fertilization it produced grains with a higher (by 4%) gluten content and exhibited a greater ability to suppress weeds (by on average 61.9% lower weed weight) compared with the grain yield and weed suppressive ability of common wheat and mixture. Spelt cultivation in a mixture with common wheat significantly improved quality indicators of the mixture (its grain contained by 2.0% higher protein content and by 4.0% higher gluten content than common wheat grain), reduced disease incidence (by 2-8%), and weed infestation level (by 34-81%).

Keywords: common and spelt-wheat, ecological fertilizers, bio-activators, productivity elements, yield, quality

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Julija Konstantinavičienė, Vlada Vitunskienė

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The role of sustainable biomass, including wood biomass, is becoming more important because of European Green Deal. The New EU Forest strategy is a flagship element of the European Green Deal and a key action on the EU biodiversity strategy for 2030. The first measure of this strategy is promoting sustainable forest management, including encouraging the sustainable use of wood-based resources. The first aim of this research was to develop and present a new approach to the concept of forest wood biomass potential in terms of sustainable development, distinguishing theoretical, technical and sustainable potential and detailing its constraints. The second aim was to prepare the methodology outline of sustainable forest wood biomass potential assessment and empirically check this methodology, considering economic, social and ecological constraints. The basic methodologies of the research: the review of research (with a combination of semi-systematic and integrative review methodologies), rapid assessment method and statistical data analysis. The developed methodology of assessment of forest wood potential in terms of sustainable development can be used in Lithuania and in other countries and will let us compare this potential a different time and spatial levels. The application of the methodology will be able to serve the development of new national strategies for the wood sector.

Keywords: assessment, constraints, forest wood biomass, methodology, potential, sustainability

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Authors: Ana Paula Almeida Castaldelli Maciel, Gabriela Medeiros, Amanda de Souza Machado, Maria Clara Pilatti, Ralpho Rinaldo dos Reis, Silvio Cesar Sampaio

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Sustainable agricultural systems are crucial to ensuring global food security and the long-term production of nutritious food. Comprehensive soil and water management practices, including nutrient management, balanced fertilizer use, and appropriate waste management, are essential for sustainable agriculture. Swine wastewater (SWW) treatment has become a significant focus due to environmental concerns related to heavy metals, antibiotics, resistant pathogens, and nutrients. In South America, small farms use soil to dispose of animal waste, a practice that is expected to increase with global pork production. The potential of SWW as a nutrient source is promising, contributing to global food security, nutrient cycling, and mineral fertilizer reduction. Short- and long-term studies evaluated the effects of SWW on soil and plant parameters, such as nutrients, heavy metals, organic matter (OM), cation exchange capacity (CEC), and pH. Although promising results have been observed in short- and medium-term applications, long-term applications require more attention due to heavy metal concentrations. Organic soil amendment strategies, due to their economic and ecological benefits, are commonly used to reduce the bioavailability of heavy metals. However, the rate of degradation and initial levels of OM must be monitored to avoid changes in soil pH and release of metals. The study aimed to evaluate the long-term effects of SWW application on soil fertility parameters, focusing on calcium (Ca), magnesium (Mg), and potassium (K), in addition to CEC and OM. Experiments were conducted at the Universidade Estadual do Oeste do Paraná, Brazil, using 24 drainage lysimeters for nine years, with different application rates of SWW and mineral fertilization. Principal Component Analysis (PCA) was then conducted to summarize the composite variables, known as principal components (PC), and limit the dimensionality to be evaluated. The retained PCs were then correlated with the original variables to identify the level of association between each variable and each PC. Data were interpreted using Analysis of Variance - ANOVA for general linear models (GLM). As OM was not measured in the 2007 soybean experiment, it was assessed separately from PCA to avoid loss of information. PCA and ANOVA indicated that crop type, SWW, and mineral fertilization significantly influenced soil nutrient levels. Soybeans presented higher concentrations of Ca, Mg, and CEC. The application of SWW influenced K levels, with higher concentrations observed in SWW from biodigesters and higher doses of swine manure. Variability in nutrient concentrations in SWW due to factors such as animal age and feed composition makes standard recommendations challenging. OM levels increased in SWW-treated soils, improving soil fertility and structure. In conclusion, the application of SWW can increase soil fertility and crop productivity, reducing environmental risks. However, careful management and long-term monitoring are essential to optimize benefits and minimize adverse effects.

Keywords: contamination, water research, biodigester, nutrients

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Authors: Muthana A. M. Jamel Al-Gburi

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Elimination of carbon dioxide (CO2) gas from our atmosphere is very important but complicated, and since there is always an increase in the gas amounts of the other greenhouse ones in our atmosphere, causes by both some of the human activities and the burning of the fossil fuels, which leads to the Global Warming phenomena i.e., increasing the earth temperature to a higher level, creates desertification, tornadoes and storms. In our present research project, we constructed our own system to extract carbon dioxide directly from the atmospheric air at the room conditions and investigated how to convert the gas into a useful mineral or Nano scale fibers made of carbon by using several chemical processes and chemical reactions leading to a valuable building material and also to mitigate the environmental negative change. In the present water pool system (Carbone Dioxide Domestic Extractor), the ocean-sea water was used to dissolve the CO2 gas from the room and converted into carbonate minerals by using a number of additives like shampoo, clay and MgO. Note that the atmospheric air includes CO2 gas has circulated within the sea water by air pump connected to a perforated tubes fixed deep on the pool base. Those chemical agents were mixed with the ocean-sea water to convert the formed acid from the water-CO2 reaction into a useful mineral. After we successfully constructed the system, we did intense experiments and investigations on the CO2 gas reduction level and found which is the optimum active chemical agent to work in the atmospheric conditions.

Keywords: global warming, CO₂ gas, ocean-sea water, additives, solubility level

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

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This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

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Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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Authors: Sherrene Bogle, Verlia Bogle, Tyrone Anderson

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In recent years, organizations have become increasingly interested in the possibility of analyzing social media as a means of gaining meaningful feedback about their products and services. The aspect based sentiment analysis approach is used to predict the sentiment for Twitter datasets for Digicel and Lime, the main mobile companies in Jamaica, using supervised learning classification techniques. The results indicate an average of 82.2 percent accuracy in classifying tweets when comparing three separate classification algorithms against the purported baseline of 70 percent and an average root mean squared error of 0.31. These results indicate that the analysis of sentiment on social media in order to gain customer feedback can be a viable solution for mobile companies looking to improve business performance.

Keywords: machine learning, sentiment analysis, social media, supervised learning

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Zahid Ahmad, Nauman Ali

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This study has estimated the trade between USA and individual members of European Union using Gravity Model of Trade as The USA has a complex trade relationship with the European countries consist of a large number of consumers, which make USA dependent on EU for major of its total world trade. However, among the member of EU, the trade potential of USA with individual members of EU is not known. Panel data techniques e.g. Random Effect, Fixed Effect and Pooled Panel have been applied to secondary quantitative data to analyze the Trade between USA and EU. Trade Potential of USA with individual members of EU has been obtained using the ratio of Actual trade of USA with EU members and the trade as predicted by Gravity Model. The Study concluded that the USA has greater trade potential with 16 members of EU, including Croatia, Portugal and United Kingdom on top. On the other hand, Finland, Ireland, and France are the top countries with which the USA has exhaustive trade potential.

Keywords: analytical technique, economic, gravity, international trade, significant

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

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The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: Elizabeth Malebogo Mosepele

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Road transport has become increasingly common in the world; people rely on road networks for transportation purpose on a daily basis. However, environmental impact of roads on surrounding landscapes extends their potential effects even further. This study investigates the impact of road network on natural vegetation. The study will provide baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. The general hypothesis of this study is that the amount and condition of road side vegetation could be explained by road network conditions. Remote sensing techniques were used to analyze vegetation conditions. Landsat 8 OLI image was used to assess vegetation cover condition. NDVI image was generated and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface, and water. The classification of the image was achieved using the supervised classification technique. Road networks were digitized from Google Earth. For observed data, transect based quadrats of 50*50 m were conducted next to road segments for vegetation assessment. Vegetation condition was related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that 'there is no variation in vegetation condition as we move away from the road.' Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation the distance increases away from the road. The conclusion is that the road network plays an important role in the condition of vegetation.

Keywords: Chi squared, geographic information system, multinomial logistic regression, remote sensing, road side vegetation

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Authors: Canga Mimoza, Irene Malagnino, Giulia Malagnino, Vito Malagnino

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Aim: This study aims at evaluating the impact of total dust (LGS) and mineral dust (PM10), in the cardio vascular and respiratory systems. Also proving that these air polluters are the cause of several diseases, such as bronchopneumonia, pneumonia, bronchitis, angina pectoris and cardiac insufficiency. Material and Method: The study is concentrated in the cities of Fier and Vlora. This is a clinic-epidemiological study conducted during the time period 2014-2019. Some of the data of LGS and PM10 were obtained from the database of the Institute of Public Health. The formula to measure the mean value of LGS and PM10 is ∆X=X (mean)-Xᵢ. Results: Based on the calculations made, we noticed that: The mean value of LGS in the city of Fieri was 227,33, while the mean value of LGS in the city of Vlora was 177,4. Whereas, the mean value of PM10 in the city of Fieri was 105.5 and the mean value of PM10 in the city of Vlore was 77.5. According to, our statistics the values of LGS were 1.2 times higher in Fier than in Vlora and the PM10 values were 1.36 times higher in Fier than in Vlora. Based on the data, in the city of Fier, the incidence of the bronchopneumonia was 56.53 sick patients/1000 inhabitants, but in Vlora, it was 22 sick patients/1000 inhabitants, so the number of the sick patients was 2.5 times higher in the city of Fieri compared with Vlora city, (P=0.001). The number of the patients with bronchitis, in the city of Fier, was 18 patients/1000 inhabitants, whereas, in Vlora, it was 9 patients/1000 inhabitants, (P=0.005). Based on the data, 8 patients/1000 inhabitants in the city of Fier, suffered from the pneumonia disease, while in Vlora city, were 4 patients/1000 inhabitants, (P=0.005). Another disease taken in consideration was angina pectoris. This study can claim that in the city of Fier, 9.5 patients/1000 inhabitants suffered from this disease, while in Vlora city, were only 4 patients /1000 inhabitants, (P=0.001). Findings of the present study proved that 3.7 patients/1000 inhabitants in the city of Fieri, had cardiac insufficiency, whereas in the city of Vlora, were 1.8 patients/1000 inhabitants, (P=0.05). Conclusions: LGS and PM10 have an influential impact on the cardio vascular and respiratory system; that’s why their levels should be kept under control. The pollution levels are 1.2 and 1.4 times higher in Fier than in Vlora; also the incidences of the diseases are 2 times higher in Fier than in Vlora. Recommendations: In order to prevent the cardio vascular and respiratory diseases, we should avoid places where pollution is higher than the norm. This can be achieved by frequenting places where the air pollution is lower, such as parks, gardens, top floors, etc.

Keywords: impact of total dust, LGS, mineral dust, PM 10, cardio vascular pathologies, respiratory disease

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Authors: Vishwas Goel, Kapil Surve, Somnath Basu

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In-situ alloying in steel during the electro-slag remelting (ESR) process has already been achieved by the addition of necessary ferroalloys into the electro-slag remelting mold. However, the use of commercially available ferroalloys during ESR processing is often found to be financially less favorable, in comparison with the conventional alloying techniques. However, a process of alloying steel with elements like chromium and manganese using the electro-slag remelting route is under development without any ferrochrome addition. The process utilizes in-situ reduction of refined mineral chromite (Cr₂O₃) and resultant enrichment of chromium in the steel ingot produced. It was established in course of this work that this process can become more advantageous over conventional alloying techniques, both economically and environmentally, for applications which inherently demand the use of the electro-slag remelting process, such as manufacturing of superalloys. A key advantage is the lower overall CO₂ footprint of this process relative to the conventional route of production, storage, and the addition of ferrochrome. In addition to experimentally validating the feasibility of the envisaged reactions, a mathematical model to simulate the reduction of chromium (III) oxide and transfer to chromium to the molten steel droplets was also developed as part of the current work. The developed model helps to correlate the amount of chromite input and the magnitude of chromium alloying that can be achieved through this process. Experiments are in progress to validate the predictions made by this model and to fine-tune its parameters.

Keywords: alloying element, chromite, electro-slag remelting, ferrochrome

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Authors: Lydia Novozhilova, Vladimir Urazhdin

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An optimization problem for reinforced thin-walled vessels under uniform external pressure is considered. The conventional approaches to optimization generally start with pre-defined geometric parameters of the vessels, and then employ analytic or numeric calculations and/or experimental testing to verify functionality, such as stability under the projected conditions. The proposed approach consists of two steps. First, the feasibility domain will be identified in the multidimensional parameter space. Every point in the feasibility domain defines a design satisfying both geometric and functional constraints. Second, an objective function defined in this domain is formulated and optimized. The broader applicability of the suggested methodology is maximized by implementing the Support Vector Machines (SVM) classification algorithm of machine learning for identification of the feasible design region. Training data for SVM classifier is obtained using the Simulation package of SOLIDWORKS®. Based on the data, the SVM algorithm produces a curvilinear boundary separating admissible and not admissible sets of design parameters with maximal margins. Then optimization of the vessel parameters in the feasibility domain is performed using the standard algorithms for the constrained optimization. As an example, optimization of a ring-stiffened closed cylindrical thin-walled vessel with semi-spherical caps under high external pressure is implemented. As a functional constraint, von Mises stress criterion is used but any other stability constraint admitting mathematical formulation can be incorporated into the proposed approach. Suggested methodology has a good potential for reducing design time for finding optimal parameters of thin-walled vessels under uniform external pressure.

Keywords: design parameters, feasibility domain, von Mises stress criterion, Support Vector Machine (SVM) classifier

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Authors: Waseem Shahzad, Ayesha Tahir Khan, Hamid Hussain Awan

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Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced.

Keywords: ant colony optimization, parallel Ant-MinerPB, vertical partitioning, classification rule discovery

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Authors: Fadimatou Ngounouno Yamgouota, Isaac Bertrand Gbambié Mbowoub, Ismaila Ngounounob

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Barium and titanium bearing biotite from alkaline trachyte of Upper Benue valley, Northern Cameroon is studied. The iron enrichment index of mica (average I.E.=0.40) is intermediate between annite and phlogopite. The biotite phenocrysts contain up to 6.2 wt. % BaO and 9.8 wt. % TiO2. The BaO content of electron-microprobe mica is positively correlated with the Al2O3, TiO2, and FeO contents, and negatively correlated with the SiO2, K2O, and MgO contents. Ba and Ti rich micas are generally found in in SiO2 deficient rocks, whereas Ba and Ti bearing mica in this study occur in silica-saturated rocks. Most of the phenocrysts analysed have deficiencies in their octahedral and interlayer sites. Deficiencies in the octahedral sites may arise from the Ti vacancy and partly the Ti tschermakite substitution. On the other hand, deficiencies in the interlayer-site are due to the replacement of K by Ba. The substitution mechanism in the Upper Benue valley mica is characterized by Ba + 2Ti + 3Al =(K + Na + Ca) + 3(Mg + Fe + Mn) + 3Si, with an excellent correlation coefficient. Biotite compositions from the Upper Benue valley area fall between the quartz-fayalite-magnetite (QFM) and nickel-nickel-oxide (NNO) oxygen fugacity buffers. All these show that Upper Benue valley mica with high Ba and Ti contents may be formed from magmas rich in these elements.

Keywords: Benue valley, trachyte, biotite, mineral chemistry, enrichment

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Ki-seok Kwon, Dong-woo Ryu, Heung-Youl Kim

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Although high-strength concrete has many advantages over generic concrete at normal temperatures (around 20℃), it undergoes spalling at high temperatures, which constitutes its structurally fatal drawback. In this study, fire resistance tests were conducted for 3 hours in accordance with ASTM E119 on bearing wall specimens which were 3,000mm x 3,000mm x 300mm in dimensions to investigate the influence the type of admixtures would exert on the fire resistance performance of high-strength concrete. Portland cement, blast furnace slag, fly ash and silica fume were used as admixtures, among which 2 or 3 components were combined to make 7 types of mixtures. In 56MPa specimens, the severity of spalling was in order of SF5 > F25 > S65SF5 > S50. Specimen S50 where an admixture consisting of 2 components was added did not undergo spalling. In 70MPa specimens, the severity of spalling was in order of SF5 > F25SF5 > S45SF5 and the result was similar to that observed in 56MPa specimens. Acknowledgements— This study was conducted by the support of the project, “Development of performance-based fire safety design of the building and improvement of fire safety” (18AUDP-B100356-04) which is under the management of Korea Agency for Infrastructure Technology Advancement as part of the urban architecture research project for the Ministry of Land, Infrastructure and Transport, for which we extend our deep thanks.

Keywords: high strength concrete, mineral admixture, fire resistance, social disaster

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Authors: Abhishek Dixit, Sandip S. Sathe, Chandan Mahanta

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The Siang river originates in Angsi glacier in southern Tibet (there known as the Yarlung Tsangpo). After traveling through Indus-Tsangpo suture zone and deep gorges near Namcha Barwa peak, it takes a south-ward turn and enters India, where it is known as Siang river and becomes a major tributary of the Brahmaputra in Assam plains. In this study, we have analyzed the bed sediment of the Siang river at two locations (one at extreme upstream near the India-China border and one downstream before Siang Brahmaputra confluence). We have also sampled bed sediment at the remote location of Yammeng river, an eastern tributary of Siang. The magnetic hysteresis properties show the combination of paramagnetic and weak ferromagnetic behavior with a multidomain state. Moreover, curie temperature analysis shows titanomagnetite solid solution series, which is causing the weak ferromagnetic signature. Given that the magnetic mineral was in a multidomain state, the presence of Ti, Fe carrying heave mineral, may be inferred. The Chemical index of alteration shows less weathered sediment. However, the Yammeng river sample being close to source shows fresh grains subjected to physical weathering and least chemically alteration. Enriched Ca and K and depleted Na and Mg with respect to upper continental crust concentration also points toward the less intense chemical weathering along with the dominance of calcite weathering.

Keywords: bed sediment, magnetic properties, Siang, weathering

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

Procedia PDF Downloads 537

Authors: N. Ghatwary, A. Ahmed, H. Jalab

Abstract:

In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

Procedia PDF Downloads 342

Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

Abstract:

In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: accelerometer, activity recognition, directiona cosine matrix filter, gyroscope, Kalman filter, magnetometer

Procedia PDF Downloads 316

Authors: Mohammed Qasim, Kyoung-Dae Kim

Abstract:

In this paper, we present the design of the super-ellipsoidal potential function (SEPF), that can be used for autonomous collision avoidance of an unmanned aerial vehicle (UAV) in a 3-dimensional space. In the design of SEPF, we have the full control over the shape and size of the potential function. In particular, we can adjust the length, width, height, and the amount of flattening at the tips of the potential function so that the collision avoidance motion vector generated from the potential function can be adjusted accordingly. Based on the idea of the SEPF, we also propose an approach for the local autonomy of a UAV for its collision avoidance when the UAV is teleoperated by a human operator. In our proposed approach, a teleoperated UAV can not only avoid collision autonomously with other surrounding objects but also track the operator’s control input as closely as possible. As a result, an operator can always be in control of the UAV for his/her high-level guidance and navigation task without worrying too much about the UAVs collision avoidance while it is being teleoperated. The effectiveness of the proposed approach is demonstrated through a human-in-the-loop simulation of quadrotor UAV teleoperation using virtual robot experimentation platform (v-rep) and Matlab programs.

Keywords: artificial potential function, autonomous collision avoidance, teleoperation, quadrotor

Procedia PDF Downloads 383

Authors: Zhao Haoyue

Abstract:

Due to the combined effects of climate change, urban expansion, and population growth, various environmental issues, such as urban heat islands and pollution, arise. Therefore, reliable information on urban environmental climate is needed to address and mitigate the negative effects. The emergence of urban climate maps provides a practical basis for urban climate regulation and improvement. This article takes Shapingba District, Chongqing City, as an example to study the construction method of urban thermal environment climate maps based on GIS spatial analysis technology. The thermal load, ventilation potential analysis map, and thermal environment comprehensive analysis map were obtained. Based on the classification criteria obtained from the climate map, corresponding protection and planning mitigation measures have been proposed.

Keywords: urban climate, GIS, heat island analysis, urban thermal environment

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Authors: B. Groß, P. Fronia, P. Nyhuis

Abstract:

In addition to the production, which is already frequently optimized, improving the distribution logistics also opens up tremendous potential for increasing an enterprise’s competitiveness. Here too though, numerous interactions need to be taken into account, enterprises thus need to be able to identify and weigh between different potentials for economically efficient optimizations. In order to be able to assess potentials, enterprises require a suitable method. This paper first briefly presents the need for this research before introducing the procedure that will be used to develop an appropriate method that not only considers interactions but is also quickly and easily implemented.

Keywords: distribution logistics, evaluation of potential, methods, model

Procedia PDF Downloads 486

Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

Abstract:

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

Procedia PDF Downloads 173

Authors: T. Garba, Y. Y. Babanyara, T. O. Quddus, A. K. Mukatari

Abstract:

Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique.

Keywords: environmental phenomena, change detection, monitor, techniques

Procedia PDF Downloads 262