RCM 3 Methodology Application to Armored Military Vehicle Cooling System

The ultimate goal of developing the future of Reliability Centered Maintenance is to introduce the RCM3 methodology, applied in this article to the cooling system of high-performance military armored vehicles fleet, used in current operation theaters. This methodology is not only more advanced and aligned with the international standards for physical asset management and risk management, but also allows users to fully understand and quantify the associated risks, focused on the reliability of the systems. The case study aims to obtain a proposed maintenance plan to the vehicle’s cooling system. Methods such as the distribution of Weibull applied to reliability and Right Censored Data, were used for the calculation of MTBF (Mean Time Between Failures). The results of the study confirm the possibility of using the proposed methodology to evaluate the operational reliability of the high-performance military armored vehicles fleet in any armed forces. The maintenance plan obtained with RCM3 proves to be more suitable and capable of reducing the risk associated with the system failure modes.

Oxygen Sensors for Industrial Application

The present work aimed to study a family of solid ceramic electrolytes based on magnesium oxide doped zirconium oxide, usually identified as Mg-PSZ (zirconia partially stabilized with magnesia), used in the manufacture of oxygen sensors for molten metals. A set of electrolytes was prepared by mechanical (milling) and thermal (sintering) processing, varying the composition in magnesia and the cooling rate from the sintering temperature. These two parameters are essential in terms of phase composition and microstructure of Mg-PSZ, determining the behavior of these materials. The structural and microstructural characterization was done by means of X-ray diffraction (XRD). The electrical properties were analyzed by impedance spectroscopy in air. In general, the results obtained from various concentrations of dopant, different cooling rates and the same sintering step condition showed an increased conductivity for samples with predominance of high temperature stable phases (tetragonal and cubic).

Design of Low Voltage Quasi-floating Self Cascode Current Mirror

In this paper, a modified structure of self-cascode structure is proposed. In the proposed structure, the MOSFET working in saturation mode is replaced by a Quasi-floating gate MOSFET by which the threshold voltage can be scaled, resulting in an increase in the drain-to-source voltage of other MOSFET operating in the linear region. The increased drain-to-source voltage results in a change in the operating region, which here is from linear to saturation regime. To exploit the performance of the proposed structure, the design of the current mirror circuit is shown in this paper. The proposed architecture when compared with its conventional design showed improvement in performance without affecting the other parameters. The complete design is done using MOSFET models of 180nm technology using Spice at supply dual supply of 0.5V.

Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.

Design and Implementation of High Frequency and Low-Power Phase-locked Loop

Phase-locked loop (PLL) operates at a high frequency and due to the increased switching rate of the circuits, the power consumption is high. Designing a PLL which consumes less power without compromising the frequency of operation is essential. The sub-components of PLL such as the phase frequency detector, charge pump, loop filter, voltage-controlled oscillator, and the frequency divider have to be designed for reduced power consumption. The proposed PLL along with its sub-components have been designed using the CMOS 180nm technology library in the Cadence Virtuoso and simulated using Cadence Spectre with a supply voltage of 1.8V resulting in a 20% reduction in power with a higher frequency of operation compared to the reference PLL architecture. The capture range and lock range of the proposed PLL are 2.09 to 2.14 GHz and 1 to 3.5GHz, respectively. The designed PLL consumes less power and operates at a higher frequency.

PyTorch Operations Based Approach for Computing Local Binary Patterns

Advances in machine learning frameworks like PyTorch provides users with various machine learning algorithms together with general purpose operations. PyTorch framework provides Numpy like functions and makes it practical to use computational resources for accelerating computations. Also users may define their custom layers or operations for feature extraction algorithms based on the tensor operations. In this paper, Local Binary Patterns (LBP) which is one of the important feature extraction approaches in computer vision were realized using tensor operations of PyTorch framework. The algorithm was written both using Python code with standard libraries and tensor operations of PyTorch in Python. According to experimental measurements which were realized for various batches of images, the algorithm based on tensor operations considerably reduced the computation time and provides significant accelerations over Python implementation with standard libraries.

Development of a Management Plan for the Municipal Ecological Structure

In a world increasingly urbanized, management of cities and spatial planning take an important place in political and technical concerns, and it is in this perspective that the Municipal Ecological Structure (MES) arises in Portugal’s urban planning system. However, this instrument still struggles with some delimitation, regulation and management issues, that challenge its implementation. In order to overcome these problems, this article wants to explore the designing hypothesis of a Management Plan for the Municipal Ecological Structure (MPMES). To support the plan, this study explores the role of ecosystem services and their potential to provide a vision of the value of Municipal Ecological Structure to the territories, to the people, and as an impulse for local sustainable economic growth. In order to gather insights on the contribution of the management plan for the Municipal Ecological Structure implementation, an approach was made, based on interviews, confronting visions and discourses, by planning experts’ contrasts with the stakeholders. Therefore, it was possible to identify, characterize and value the functions performed by the Municipal Ecological Structure of Sesimbra. Ultimately, the objectives, contents, development, approval and articulation with other territorial management instruments were identified as requirements for the Management Plan for the Municipal Ecological Structure development.

Stochastic Diffusion Process-based Multi-Level Monte Carlo for Predictive Reliability Assessment of Distribution System

Reliability assessment of electrical distribution systems is an important criterion to determine system performance in terms of interruptions. Probabilistic assessment methods are usually used in reliability analysis to deal with uncertainties. These techniques require a longer execution time in order to account for uncertainty. Multi-Level Monte Carlo (MLMC) is an advanced Monte Carlo Simulation (MCS) approach to improve accuracy and reduce the execution time. This paper provides a systematic approach to model the static and dynamic uncertainties of Time to Failure (TTF) and Time to Repair (TTR) of power distribution components using a Stochastic Diffusion Process. Further, the Stochastic Diffusion Process is integrated into MLMC to estimate the impacts of uncertainties on reliability indices. The Euler Maruyama path discretization applied to evaluate the solution of the Stochastic Diffusion Process. The proposed Stochastic Diffusion Process-based MLMC method is integrated into a systematic failure identification technique to evaluate the distribution system reliability. The proposed method is validated with analytical and Sequential MCS methods for IEEE Roy Billinton Test Systems. Finally, the numerical results show the accuracy and fast convergence rates to handle uncertainties compared to Sequential MCS method.

Overview of the Leather Industry and Pollution Impact

The growing awareness of the human and environmental vulnerability, to the pollution resulting from industrial activity, highlights the urgent need for control and mitigate the degradation of the world as we know it. The leather industry, considered as one of the industries with a significant environmental impact, applies several chemicals, some of them considered as hazardous chemicals, such as chromium, in leather production. The restricted EU environmental regulations have driven the search for a process that ensures regulatory compliance and a final product that fulfills society’s requirements. The present review describes alternative options, applied in the leather tanning process in the last five years, to overcome some of the industry barriers, without compromising the final characteristics of leather.

Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.