On computation of real eigenvalues of matrices via the Adomian decomposition

AbstractThe problem of matrix eigenvalues is encountered in various fields of engineering endeavor. In this paper, a new approach based on the Adomian decomposition method and the Faddeev-Leverrier’s algorithm is presented for finding real eigenvalues of any desired real matrices. The method features accuracy and simplicity. In contrast to many previous techniques which merely afford one specific eigenvalue of a matrix, the method has the potential to provide all real eigenvalues. Also, the method does not require any initial guesses in its starting point unlike most of iterative techniques. For the sake of illustration, several numerical examples are included

Investigation of Nonlinear Problems of Heat Conduction in Tapered Cooling Fins Via Symbolic Programming

In this paper, symbolic programming is employed to handle a mathematical model representing conduction in heat dissipating fins with triangular profiles. As the first part of the analysis, the Modified Adomian Decomposition Method (MADM) is converted into a piece of computer code in MATLAB to seek solution for the mentioned problem with constant thermal conductivity (a linear problem). The results show that the proposed solution converges to the analytical solution rapidly. Afterwards, the code is extended to calculate Adomian polynomials and implemented to the similar, but more generalized, problem involving a power law dependence of thermal conductivity on temperature. The latter generalization imposes three different nonlinearities and extremely intensifies the complexity of the problem. The code successfully manages to provide parametric solution for this case. Finally, for the sake of exemplification, a relevant practical and real-world case study, about a silicon fin, for the complex nonlinear problem is given. It is shown that the numerical results are very close to those calculated by the classical Finite Difference Method (FDM)

Compressive Sensing for Remote Flood Monitoring

Although wireless sensor networks (WSNs) are considered as one of the prominent solutions for flood monitoring; however, the energy constraint nature of the sensors is still a technical challenge. In this paper, we tackle this problem by proposing a novel energy-efficient remote flood monitoring system, enabled by compressive sensing. The proposed approach compressively captures water level data using; i) a random block-based sampler, and ii) a gradient-based compressive sensing approach, at a very low rate, exploiting water level data variability over time. Through extensive experiments on real water-level dataset, we show that the number of packet transmissions as well as the size of packets are significantly reduced. The results also demonstrate significant energy reduction in sensing and transmission. Moreover, data reconstruction from compressed samples are of high quality with negligible degradation, compared to classic compression techniques, even at high compression rates

Effect of dispersed hydrophilic silicon dioxide nanoparticles on batch adsorption of benzoic acid from aqueous solution using modified natural vermiculite: An equilibrium study

The equilibrium adsorption of benzoic acid from an aqueous medium on a natural vermiculite-based adsorbent was studied in the presence and absence of hydrophilic silicon dioxide nanoparticles in batchwise mode. The adsorbent was prepared through grinding natural vermiculite in a laboratory vibratory disk mill and the surfactant modification of ground vermiculite by cetyltrimethylammonium bromide, subsequently. The equilibrium isotherm in the presence and absence of nanoparticles was experimentally obtained and the equilibrium data were fitted to the Langmuir, Freundlich, Dubinin–Radushkevich and Temkin models. The results indicated that the dispersion of silicon dioxide nanoparticles at optimum concentration in the liquid phase remarkably increases the removal efficiency. Furthermore, it yields a more favorable equilibrium isotherm and changes the compatibility of equilibrium data from the Langmuir and Temkin equations to just the Langmuir equation. A quadratic polynomial model predicting the equilibrium adsorbent capacity in the presence of nanoparticles as a function of the adsorbate and initial nanoparticle concentrations was successfully developed using the response surface methodology based on the rotatable central composite design. A desirability function was used in order to optimize the values of all variables, independent and dependent ones, simultaneously

Drought Forecasting for Future Periods Using LARS-WG Model (Case Study: Shiraz Station)

In this study, in order to simulate the current climate (1970-2016) for calculating the drought index in Fars Province, the data used include daily rainfall, minimum temperature, maximum temperature, and sunny hours at Shiraz station in a period of 46 years (1970-2016) as the entry for the LARS-WG statistical model. To simulate the climatic parameters at the Shiraz station basin, the data of HADCM3 model were downscaled using WG-LARS model under two scenarios A2 and A1B. The results showed that the average annual rainfall will increase under A2 scenario by about 1.5% and under A1B scenario by about 5.5%. Moreover, sunshine hours in the study period will be reduced under both scenarios. With high precision, the model could simulate maximum temperature, minimum temperature, and radiation parameters, but more error in simulating was presented in the precipitation parameter than other parameters. The highest increase, with about 80%, was due in September under scenario A2, which occurred in the upcoming period of the study period. Based on the SPI drought index, the most severe droughts occurred in 2008 in Shiraz station showing an index value of -2.89. Moreover, SPI shows that the most precipitation was recorded in 1995 with an index value of 1.91

Fuzzy Logic with Deep Learning for Detection of Skin Cancer

Melanoma is the deadliest type of cancerous cell, which is developed when melanocytes, melanin producing cell, starts its uncontrolled growth. If not detected and cured in its situ, it might decrease the chances of survival of patients. The diagnosis of a melanoma lesion is still a challenging task due to its visual similarities with benign lesions. In this paper, a fuzzy logic-based image segmentation along with a modified deep learning model is proposed for skin cancer detection. The highlight of the paper is its dermoscopic image enhancement using pre-processing techniques, infusion of mathematical logics, standard deviation methods, and the L-R fuzzy defuzzification method to enhance the results of segmentation. These pre-processing steps are developed to improve the visibility of lesion by removing artefacts such as hair follicles, dermoscopic scales, etc. Thereafter, the image is enhanced by histogram equalization method, and it is segmented by proposed method prior to performing the detection phase. The modified model employs a deep neural network algorithm, You Look Only Once (YOLO), which is established on the application of Deep convolutional neural network (DCNN) for detection of melanoma lesion from digital and dermoscopic lesion images. The YOLO model is composed of a series of DCNN layers we have added more depth by adding convolutional layer and residual connections. Moreover, we have introduced feature concatenation at different layers which combines multi-scale features. Our experimental results confirm that YOLO provides a better accuracy score and is faster than most of the pre-existing classifiers. The classifier is trained with 2000 and 8695 dermoscopic images from ISIC 2017 and ISIC 2018 datasets, whereas PH2 datasets along with both the previously mentioned datasets are used for testing the proposed algorithm

Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network.

Recent years evidenced an increase in the total number of skin cancer cases, and it is projected to grow exponentially. This paper proposes a computer-aided diagnosis system for the classification of a malignant lesion, where the acquired image is primarily pre-processed using novel methods. Digital artifacts such as hair follicles and blood vessels are removed, and thereafter, the image is enhanced using a novel method of histogram equalization. Henceforth, the pre-processed image undergoes the segmentation phase, where the suspected lesion is segmented using the Neutrosophic technique. The segmentation method employs a thresholding-based method along with a pentagonal neutrosophic structure to form a segmentation mask of the suspected skin lesion. The paper proposes a deep neural network base on Inception and residual blocks with softmax block after each residual block which makes the layer wider and easier to learn the key features more quickly. The proposed classifier was trained, tested, and validated over PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets. The proposed segmentation model yields an accuracy mark of 99.50%, 99.33%, 98.56% and 98.04% for these datasets, respectively. These datasets are augmented to form a total of 103,554 images for training, which make the classifier produce enhanced classification results. Our experimental results confirm that the proposed classifier yields an accuracy score of 99.50%, 99.33%, 98.56%, and 98.04% for PH2, ISIC 2017, 2018, and 2019, respectively, which is better than most of the pre-existing classifiers

Prioritizacija i procjena ključa sigurnosti pokazatelja uspjeha u automobilskoj industriji

The performance of any management system needs to be monitored with adequate and proper indicators. This study aimed to identify, set priorities and assess key indicators for implementing an effective performance evaluation system. This descriptive-analytical study was carried out in three phase. In first phase, a semi-structured interview as well as a review of the company\u27s documentation and studies carried out, then a set of key indicators were collected and selected. The validity of the indicators were determined by experts (N = 11) and indicators were prioritized using Analytic Hierarchy Process (AHP) according to SMART (Specific, Measurable, Achievable, Relevant, and Time- bound) criteria. Following the study framework, a primary set of 60 Key Performance Indicators (KPIs) were collected. The results of the validity assessment showed 23 indicators had acceptable validity. The results of examining the relationships between the indicators showed that the percentage of corrected non- compliance and the number of risk assessments had a significant relationships with the total number of work-related lost time injuries as a lagging indicator. According to the results, the four the most important key performance indicators to assess the safety performance in the automotive industry were as follows: the number of risk assessments conducted, the percentage of corrected non- compliance, the percentage of safety educational programs implemented for workers, and Frequency Severity Index (FSI) index.Učinkovitost bilo kojeg sustava upravljanja treba pratiti odgovarajućim i ispravnim pokazateljima. Cilj ove studije bio je identificirati, odrediti prioritete i procijeniti ključne pokazatelje za primjenu učinkovitog sustava vrednovanja učinka. Ovo opisno-analitičko istraživanje provedeno je u tri faze. U prvoj fazi, polustrukturirani intervju, kao i pregled provedene dokumentacije i studija tvrtke, zatim je prikupljen i odabran skup ključnih pokazatelja. Valjanost pokazatelja odredili su stručnjaci (N = 11), a pokazatelji su odredili prioritete pomoću Analitičkog postupka hijerarhije (AHP) prema SMART (Specifični, mjerljivi, dostižni, relevantni i vremenski ograničeni) kriteriji. Slijedom okvira studije, prikupljen je primarni skup od 60 KPI. Rezultati procjene valjanosti pokazali su da 23 pokazatelja imaju prihvatljivu valjanost. Rezultati ispitivanja odnosa između pokazatelja pokazali su da je postotak ispravljene neusaglašenosti i broj procjena rizika u značajnoj vezi s ukupnim brojem ozljeda izgubljenog na radu kao pokazatelj zaostajanja. Prema rezultatima, četiri najvažnija ključna pokazatelja uspješnosti za procjenu sigurnosnih performansi u automobilskoj industriji bila su sljedeća: broj provedenih procjena rizika, postotak ispravljenih nesukladnosti, postotak provedenih obrazovnih programa o sigurnosti za radnike i indeks FSI

ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved

High-Voltage Pulse Generators for Electroporation Applications: A Systematic Review

In recent years, the use of electroporation process has attracted much attention, due to its application in various industrial and medical fields. Electroporation is a microbiology technique which creates tiny holes in the cell membrane by the applied electric field. The electroporation process needs high-voltage pulses to provide the required electric field. To generate high-voltage pulses, a pulse generator device must be used. High-voltage pulse generators can be mainly divided into two major groups: Classical pulse generators and power electronics-based pulse generators. As their name suggests, the first group is associated with the primary and elementary pulse generators like Marx generators, and the second group is associated with the pulse generators that have been updated with the advancement of power electronics like Modular Multilevel Converters. These two major groups are also divided into several subgroups which are reviewed in detail in this paper. This study reviews the literature presented in the field of pulse power and pulse generators proper for the electroporation process and addresses their strengths and weaknesses. Several tables are provided to highlight and discuss the characteristics of each subgroup. Finally, a comparative study among different groups of pulse generators is performed which is followed by a classification performance analysis