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| 2. |
- A., Trubetskaya, et al.
(författare)
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The Effect of Wood Composition and Supercritical CO2 Extraction on the Charcoal Production
- 2019
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Ingår i: 2019 AIChE Annual Meeting proceedings. - : American Institute of Chemical Engineers.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This work demonstrated that the coupling of supercritical carbon dioxide extraction with slow pyrolysis is effective to remove over half of extractives from low quality wood and to generate biochar from remaining solid wood fractions. The high yields of extractives from supercritical carbon dioxide extraction illustrates the potential utilizing of low quality wood as an alternative feedstock for the sustainable production of value-added chemicals. Results showed that supercritical carbon dioxide extraction has neither a strong impact on the physical properties of original wood nor on the yield of solid biochar. These results are promising as they show that the biochar obtained for this renewable feedstock could be used as an alternative to fossil-based coke in applications including ferroalloy industries. Moreover, the heat treatment temperature and supercritical carbon dioxide extraction had a significant impact on the tar yields, leading to the increase in naphthalene, polycyclic aromatic hydrocarbons, aromatic and phenolic fractions with the greater temperature. The differences in gasification reactivity and dielectric properties of solid biochars, composition and yields of liquid products of non-treated pinewood and extracted wood fraction emphasize the impact of supercritical carbon dioxide extraction on the pyrolysis process.
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| 3. |
- Aalipour, Mojgan, et al.
(författare)
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Identification of Factors affecting Human performance in Mining Maintenance tasks
- 2014
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Ingår i: Proceedings of the 3rd international workshop and congress on eMaintenance. - Luleå : Luleå tekniska universitet. - 9789174399721 - 9789174399738 ; , s. 71-76
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the factors affecting humanperformance in maintenance task in mining sector. Theobjective is identify various factors and to classify them asdriving (strong driving power and weak dependence) anddependent factors (weak driving power and strongdependence). The factors were identified through literaturesurvey and are ranked using mean score of data questionnaire.The reliability of measures is pretested by applyingCronbach’s alpha coefficient to responses to a questionnairegiven to maintenance personnel. The interrelationshipsbetween human factors have been recognized by interpretivestructural modeling (ISM). Further, these factors have beenclassified using matrice d'impacts croises-multiplicationappliqué à un classement (MICMAC) analysing. This casestudy will figure out the factors affecting human performancefor deriving maintenance management insights to improveproductivity in the mining sector. Further, this understandingmay be helpful in framing the policies and strategies formining industry. Temperature, lighting, documentation,communication and fitness are driving factors. Moreover,Work layout, tools availability, complex tasks, time pressure,safety, boss decisions, training, fatigue and motivation havestrong driving power as well as high dependencies and itcomes under the category of linkage factors.
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| 4. |
- Aalipour, Mojgan, et al.
(författare)
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Work place factors effect on maintainability in challenging operating conditions
- 2015
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Ingår i: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). - Piscataway, NJ : IEEE Communications Society. - 9781467380669 - 9781467380669 ; , s. 767-771
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Konferensbidrag (refereegranskat)abstract
- Some industries such as mining industry create complex and challenging work place for maintenance crews. For example in an underground mine, for some machines, heavy maintenance tasks must be performed on site in a limited workspace in a harsh environment, including dust and improper illumination. Such operating conditions can increase the health, safety, and environment (HSE) risk, reduce the availability of the machines and increase the life cycle cost of equipment. A review of current mining equipment design and maintenance procedure confirms that considerable reduction in HSE risk, as well as substantial cost savings, can be achieved by considering human factors. This study discusses the effect of workplace factors on the maintainability of mining equipment. It presents the results from questionnaires on the effect of work place factors on maintainability performance given to maintenance staff at two mines, one in northern Sweden and the other in Iran.
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| 7. |
- Abadei, S., et al.
(författare)
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Microwave properties of tunable capacitors basee on magnetron sputtered ferroelectric Na0.5K0.5NbO3 film on low and high resistivity silicon substrates
- 2001
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Ingår i: Integrated Ferroelectrics. - : Informa UK Limited. - 1058-4587 .- 1607-8489. ; 39:1-4, s. 359-366
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this work, small signal DC voltage dependent dielectric permittivity, loss tangent, and tuneability of magnetron sputtered epitaxial Na0.5K0.5NO3 films are studied experimentally. (100)-oriented Na0.5K0.5NbO3 films are deposited onto SiO2-buffered CMOS grade low resistivity (p = 10-20 cm) and high resistivity (p = 15-45 kcm) silicon substrates. Planar capacitors with 2 or 4 m gaps between electrodes have been fabricated on top of ferroelectric films. These devices have been characterized in the frequency range 1.0 MHz to 50 GHz at temperatures 30 - 300K. Na0.5K0.5NbO3/SiO2/Si structures on high resistivity silicon substrate exhibit C-V performances typical for Metal-Insulator- Semiconductor (MIS) capacitors. At low frequencies, f 1.0 GHz, the large tuneability and large losses are associated with the MIS structure, while at higher microwave frequencies the tuneability is mainly associated with the ferroelectric, film. At 1.0 MHz and room temperature, the tuneability of Na0.5K0.5NbO3/SiO2/Si structures more than 90%, reducing to 10-15 % at 50 GHz. The losses decrease with increasing the DC bias and frequency. A Q-factor more than 15 at 50 GHz is observed. The dielectric permittivity of the Na0.5K0.5NbO3 film is in the range 50-150 at frequencies 0.045-50 GHz. On low resistivity substrate the performance of Na0.5K0.5NbO3 films is completely screened by the high losses in silicon, and the tuneability is negligible. © 2001 Taylor and Francis.
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| 8. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
- 2016
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Ingår i: Building Sustainable Health Ecosystems. - Cham : Springer International Publishing. - 9783319446714 - 9783319446721 ; , s. 151-160
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Konferensbidrag (refereegranskat)abstract
- The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.
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| 9. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Design and implementation of a computer-aided diagnosis system for brain tumor classification
- 2017
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Ingår i: 2016 28th International Conference on Microelectronics (ICM). - 9781509057214 ; , s. 73-76
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Konferensbidrag (refereegranskat)abstract
- Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.
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| 10. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images
- 2019
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Ingår i: IEEE-ICM 2019 CAIRO-EGYPT. - : IEEE. ; , s. 304-307
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.
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