Using deep learning to detecting abnormal behavior in internet of things

<span lang="EN-US">The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.</span>

Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUs

Dense SLAM is an important application on an embedded environment. However, embedded platforms usually fail to provide enough computation resources for high-accuracy real-time dense SLAM, even with high-parallelism architecture such as GPUs. To tackle this problem, one solution is to design proper approximation techniques for dense SLAM on embedded GPUs. In this work, we propose two novel approximation techniques, critical data identification and redundant branch elimination. We also analyze the error characteristics of the other two techniques—loop skipping and thread approximation. Then, we propose SLaPP, an online adaptive approximation controller, which aims to control the error to be under an acceptable threshold. The evaluation shows SLaPP can achieve 2.0× performance speedup and 30% energy saving on average compared to the case without approximation.

Enhancement of Sensorless Control for Non-Sinusoidal Multiphase Drives-Part I: Operation in Medium and High-Speed Range

This two-part study proposes a new sensorless control strategy for non-sinusoidal multiphase permanent magnet synchronous machines (PMSMs), especially integrated motor drives (IMDs). Based on the Sliding Mode Observer (SMO), the proposed sensorless control strategy uses the signals (currents and voltages) of all fictitious machines of the multiphase PMSMs. It can estimate the high-accuracy rotor positions that are required in vector control. This proposed strategy is compared to the conventional sensorless control strategy that applies only current and voltage signals of the main fictitious machine, including the fundamental component of back electromotive force (back EMF) of non-sinusoidal multiphase PMSMs. Therefore, in order to choose an appropriate sensorless control strategy for the non-sinusoidal multiphase PMSMs, these two sensorless control strategies will be highlighted in terms of precision with respect to rotor position and speed estimation. Simulations and the experimental results obtained with a non-sinusoidal seven-phase PMSM will be shown to verify and compare the two sensorless control strategies. In this part of the study (part I), only sensorless control in the medium and high-speed range is considered. Sensorless control at the zero and low-speed range will be treated in the second part of this study (part II).

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.