Multidisciplinary Digital Publishing Institute
About: Inventions is an academic journal published by Multidisciplinary Digital Publishing Institute. The journal publishes majorly in the area(s): Computer science & Materials science. It has an ISSN identifier of 2411-5134. It is also open access. Over the lifetime, 204 publications have been published receiving 308 citations.
TL;DR: In this paper , a review of recent applications of machine learning methods for wildfire management decision support is provided, with a classification according to the case study type, machine learning method, case study location, and performance metrics.
Abstract: Wildfires threaten and kill people, destroy urban and rural property, degrade air quality, ravage forest ecosystems, and contribute to global warming. Wildfire management decision support models are thus important for avoiding or mitigating the effects of these events. In this context, this paper aims at providing a review of recent applications of machine learning methods for wildfire management decision support. The emphasis is on providing a summary of these applications with a classification according to the case study type, machine learning method, case study location, and performance metrics. The review considers documents published in the last four years, using a sample of 135 documents (review articles and research articles). It is concluded that the adoption of machine learning methods may contribute to enhancing support in different fire management phases.
TL;DR: In this paper , the social dimensions of the barriers to nuclear power generation in the Philippines are discussed and discussed in terms of politics, policy, infrastructure, technical capacities, environment and information.
Abstract: This paper offers a discussion on the social dimensions of the barriers to nuclear power generation in the country. The aim of this paper is to contribute to the literature by identifying the barriers to nuclear power generation in the Philippines and offering perspectives on the social relevance of potentially adding nuclear sources to the country’s energy mix. Given the contemporary relevance of the energy transitions globally, this work builds on the available sources over the past decade concerning nuclear energy technology in the Philippines and provides further discussions on the diverse barriers to the country’s energy transition pathway. Findings present barriers related to politics, policy, infrastructure, technical capacities, environment and information. The differences in priorities and values concerning nuclear energy reflect that the barriers to nuclear energy generation in the Philippines are social as much as technical. Based on the findings and descriptions of the current discussions on Philippine energy generation, this work provides some key points for consideration in order to deploy nuclear power plants in the country. These recommendations, however, are not definitive measures and are still subject to local conditions that may arise. This study hopes to be instructive to other countries in terms of further reflecting on the social dimensions of the barriers to nuclear energy generation.
TL;DR: In this article , a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) was used to discover the optimal scheduling of home appliances.
Abstract: Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).
TL;DR: In this article , a new method for assessing the parametric reliability of products based on a small number of tests is proposed, and the determination of the parameters and double logistic distribution based on the test results is considered.
Abstract: The paper provides an overview of methods for determining reliability indicators and, on the basis of the analysis, proposes a new method for assessing the parametric reliability of products based on a small number of tests. The determination of the parameters and double logistic distribution based on the test results is considered, a statistical experiment was carried out, which was based on the method of statistical modeling of Monte Carlo. An example of evaluating parametric reliability by a new method is also given, on the basis of which an engineering technique is proposed. In the conclusion, remarks are made regarding the advantages of the novel method.
TL;DR: The proposed algorithm for fingerprint classification using a CNN (convolutional neural network) model and making use of full images belonging to four digital databases achieved a very good performance despite the fact that it used raw data and it does not perform any handcrafted feature extraction operations.
Abstract: This study presents an algorithm for fingerprint classification using a CNN (convolutional neural network) model and making use of full images belonging to four digital databases. The main challenge that we face in fingerprint classification is dealing with the low quality of fingerprints, which can impede the identification process. To overcome these restrictions, the proposed model consists of the following steps: a preprocessing stage which deals with edge enhancement operations, data resizing, data augmentation, and finally a post-processing stage devoted to classification tasks. Primarily, the fingerprint images are enhanced using Prewitt and Laplacian of Gaussian filters. This investigation used the fingerprint verification competition with four databases (FVC2004, DB1, DB2, DB3, and DB4) which contain 240 real fingerprint images and 80 synthetic fingerprint images. The real images were collected using various sensors. The innovation of the model is in the manner in which the number of epochs is selected, which improves the performance of the classification. The number of epochs is defined as a hyper-parameter which can influence the performance of the deep learning model. The number of epochs was set to 10, 20, 30, and 50 in order to keep the training time at an acceptable value of 1.8 s/epoch, on average. Our results indicate the overfitting of the model starting with the seventh epoch. The accuracy varies from 67.6% to 98.7% for the validation set, and between 70.2% and 75.6% for the test set. The proposed method achieved a very good performance compared to the traditional hand-crafted features despite the fact that it used raw data and it does not perform any handcrafted feature extraction operations.