STUDY OF HTA DISTRIBUTION NETWORK RELIABILITYIN NIAMEYCITY

With the growth of demand for electrical energy, electrical networks are nowadays subjected to very high loads, sometimes beyond their capacity. This leads to their malfunction and their inability to meet expectations. But given the importance of electricity in our lives, this disruption cannot be tolerated for long, which leads distribution operators to implement maintenance programs to ensure continuity of service. However, these measures, although extremely important, are time consuming, costly and sometimes do not provide complete satisfaction, resulting in the absence of electricity to consumers. The study of reliability of medium voltage distribution network in Niamey allowed us to analyze the performance indices of latter. Indeed, we based ourselves on the number of incidents (opening of HV outlets) and their duration while taking into account the origin of the associated disturbances. In the light of this analysis, the reliability of lines is close to 95% only for a period of one (1) hour (h) and is almost zero beyond 100 hours of operation. As for maintainability, it is only guaranteed within a period of 10 hours after the occurrence of incident on network.

Physiology and bromatology of Oryctolagus cunicullus L. 1758 fed browse legume with enzyme levels

The objective of this experiment was to determine the effects of feeding rabbits with Maxigrain® (M) enzyme supplemented Gliricidia sepium leaf meal (GLM) on their physiology, performance characteristics, and nutrients digestibility. Twenty weaned rabbits of mixed sexes, 5-6 weeks old, were randomly allotted to five dietary treatments including 0 g M which was soybean without M (control) and GLM supplemented with M at 50, 100, 150 and 200 g M per kilogramme of GLM. There were four rabbits per treatment and one rabbit as replicate in a completely randomized design. There were no significant (p > 0.05) treatment effects in all physiological and performance indices as well as those of crude fat, fibre and NFE digestibilities. There were variations (p < 0.05) due to the treatment effects on dry matter, crude protein and ash digestiblities. This implies that the feeding of soft faeces directly from the caecum called coprophagy mechanism in rabbits has its concomitant nutritional benefits. This advantageous benefit can enable rabbit to effectively and efficiently utilize forage or forage-based diet with or without enzyme supplementation.

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonlinear kernel functions of GPR including squared exponential (SE), rational quadratic (RQ) and Matern5/2 was investigated, and it was found by comparing with the experimental data that the SE kernel function is more suitable to capture the relationship between impeller parameters and performance indices because of the highest R square and the lowest values of max absolute relative error (MARE), mean absolute proportional error (MAPE), and root mean square error (RMSE). In addition, the results predicted by GPR with SE kernel function were compared with the results given by other three machine learning models. The comparison shows that the GPR with SE kernel function is more accurate and robust than other models in centrifugal pump performance prediction, and its prediction errors and uncertainties are both acceptable in terms of engineering applications. The GPR method is less costly in the performance prediction of centrifugal pump with sufficient accuracy, which can be further used to effectively assist the design and manufacture of centrifugal pump and to speed up the optimization design process of impeller coupled with stochastic optimization methods.

Performance Analysis of Diode Clamped Five-Level Inverter integrated PV Grid system using Soft computing based MPPT Scheme

This paper focuses on diode clamped five-level inverter for the application of PV system. Carrier based phase disposition method is adopted for control of inverter switches. The five-level diode clamped integrated to grid connected PV system is considered for the performance evaluation. The PI controller is adopted for the regulation of DC voltage. The Fuzzy Logic Control (FLC) based MPPT scheme is used to track the maximum power from the PV panel. The performance indices including PV current, DC link voltage and %THD of diode clamped inverter based grid connected PV system is compared with conventional two-level Inverter. The proposed work is carried out in the environment of Matlab/Simulink. Keywords: Diode clamped five-level inverter, FLC MPPT, Two-level voltage source inverter, PWM, THD, PV, Grid.

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients

BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved. OBJECTIVE: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients. METHODS: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models. RESULTS: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds. CONCLUSION: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.

Over-Actuated Underwater Robots: Configuration Matrix Design and Perspectives

In general, for the configuration designs of underwater robots, the positions and directions of actuators (i.e., thrusters) are given and installed in conventional ways (known points, vertically, horizontally). This yields limitations for the capability of robots and does not optimize the robot’s resources such as energy, reactivity, and versatility, especially when the robots operate in confined environments. In order to optimize the configuration designs in the underwater robot field focusing on over-actuated systems, in the paper, performance indices (manipulability, energetic, reactive, and robustness indices) are introduced. The multi-objective optimization problem was formulated and analyzed. To deal with different objectives with different units, the goal-attainment method, which can avoid the difficulty of choosing a weighting vector to obtain a good balance among these objectives, was selected to solve the problem. A solution design procedure is proposed and discussed. The efficiency of the proposed method was proven by simulations and experimental results.

Driving liberal change? Global performance indices as a system of normative stratification in liberal international order

The existing literature on Global Performance Indices (GPIs) is mostly dominated by unit-level analyses focused on specifying the relevant properties of the GPIs and the motivations of state actors in being influenced by GPIs. This article advances a systemic approach, which conceives of GPIs as collectively constituting a system of normative stratification in International Relations (IR). By bringing together the literature on GPIs with the relevant IR literatures on international hierarchies and status-seeking, we identify the structural attributes of the GPI-based system of stratification, how these structural attributes shape the distribution of normative status positions among states, and how this distribution is likely to condition the pursuit of status by states. In particular, we argue that the disaggregated structure and relative ranking of states, respectively, generate status ambiguity and immobility, which both dissuade states from seeking higher moral status through improving their scores in the existing indices. We illustrate the patterns of status ambiguity and immobility present in the GPI-based system of stratification through an empirical analysis of the scores and rank positions of the United States, European Union (EU) members, and “rising powers” in five different indices in the past decade.