Current Issue : July - September Volume : 2020 Issue Number : 3 Articles : 5 Articles
Electric vehicles (EVs) are the development direction of new energy vehicles in the future. \nAs an important part of the Internet of things (IOT) communication network, the charging pile is \nalso facing severe challenges in information security. At present, most detection methods need a lot \nof prophetic data and too much human intervention, so they cannot do anything about unknown \nattacks...............................
In the area of recognition and classification of children activities, numerous works have been proposed that make use of different\ndata sources. In most of them, sensors embedded in childrenâ??s garments are used. In this work, the use of environmental sound\ndata is proposed to generate a recognition and classification of children activities model through automatic learning techniques,\noptimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing\nthe original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the\nevaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC),\nartificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the\nmodels obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best\nperformance in the development of a model that allows the identification of children activity based on audio signals. According to\nthe results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92,\nwhich allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with\nsignificant accuracy....
The computer security has become a major challenge. Tools and mechanisms\nhave been developed to ensure a level of compliance. These include the Intrusion\nDetection Systems (IDS). The principle of conventional IDS is to detect\nattempts to attack a network and to identify abnormal activities and behaviors.\nThe reasons, including the uncertainty in searching for types of attacks\nand the increasing complexity of advanced cyber-attacks, IDS calls for the need\nfor integration of methods such as Deep Neuron Networks (DNN) and Recurring\nNeuron Networks (RNN) more precisely long-term memory (LSTM).\nIn this submission, DNN and LSTM were used to predict attacks against the\nNetwork Intrusion Detection System (NIDS). In this memory, we used four\nhidden layers for all deep learning algorithms, forty-one layers of inputs and\ntwo layers of outputs and with 100 iterations. In fact, learning is kept constant\nat 0.01 while the other parameters are optimized. After that for DNN, the number\nof neurons of the first hidden layer was further increased to 1280 but did\nnot give any appreciable increase in accuracy. Therefore, the number of neurons\nhas been set to 1024 and the LSTM we set the number of neurons of all\nhidden layers to 32. The results were compared and concluded that a three-layer\nLSTM performs better than all other conventional machine learning and deep\nlearning algorithms....
When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to\nmonitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the\nactual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate\nis low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of\natmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer-Bouguer-Lambert law,\nparallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation\ntransmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous\nequations are constructed on the basis of multiband satellite remote sensing data through pixel information,\nand the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The\nquantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The\nexperimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high\nmonitoring accuracy....
Copper mining activity is going through big changes due to increasing technological development in the area and the influence of\nindustry 4.0. These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming\nvisible in Chilean mining. New regulations from the Chilean government and changes in the copper mining industry (such as a\ntrend to underground mining) are fostering the search for better results in typical processes such as leaching. This paper\ndescribes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for\ncopper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years. Two models, one\nof them with actual operational data and another one with data generated in a controlled environment (piling) are presented.\nWell-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained. The methodology devised\nfor the study can be transferred to piling columns or piles with other characteristics, though the operation must focus on copper\nleaching. It can even be transferred to other leaching processes using another type of mineral, with proper adjustments....
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