In this paper, we propose the simple method to optimize the datasets noise under the uncertainty applied to many applications in industry. Specifically, we use firstly the deep learning module at transfer learning based on using the mask-rcnn to detect the objects and segmentation effectively, then return the contours only. After that we address the shortest path for reduce the noise in order to increasing the highspeed in industrial applications. We illustrate adaptive many applications web applications such as mobile application where power computer is limited a source

A dynamical system is a mathematical model described by a high dimensional ordinary differential equation for a wide variety of real world phenomena, which can be as simple as a clock pendulum or as complex as a chaotic Lorenz system. Stability is an important topic in the studies of the dynamical system. A major challenge is that the analytical solution of a time-varying nonlinear dynamical system is in general not known. Lyapunov's direct method is a classical approach used for many decades to study stability without explicitly solving the dynamical system, and has been successfully employed in numerous applications ranging from aerospace guidance systems, chaos theory, to traffic assignment. Roughly speaking, an equilibrium is stable if an energy function monotonically decreases along the trajectory of the dynamical system. This paper extends Lyapunov's direct method by allowing the energy function to follow a rich set of dynamics. More precisely, the paper proves two theorems, one on globally uniformly asymptotic stability and the other on stability in the sense of Lyapunov, where stability is guaranteed provided that the evolution of the energy function satisfies an inequality of a non-negative Hurwitz polynomial differential operator, which uses not only the first-order but also high-order time derivatives of the energy function. The classical Lyapunov theorems are special cases of the extended theorems. the paper provides an example in which the new theorem successfully determines stability while the classical Lyapunov's direct method fails.

A document by Rogério Negri . Click on the document to view its contents.

Ever since the invention of Bitcoin by the pseudonymous Satashi Nakamoto, cryptocurrency has provoked debate in banking and finance sectors, and is sometimes considered a potential successor to fiat currency. Blockchain, the new technology underpinning decentralised and immutable databases, has seen much discussion as a potentially game-changing development. Although many industries are exploring its value, the technology has thus far made only minor impacts. A rapidly expanding base of research has emerged on blockchain's role as a potential disruptor in the electrical energy industry. However, it may be difficult to distinguish hype from more imminently plausible impacts. This paper attempts to serve as a guide for engineering management wishing to make sense of blockchain's potential in electricity. This is accomplished by formulating a novel blockchain industry disruption framework, which exists across three tiers. These tiers extend from ideas with the least effect on an industry to total revolutionary concepts that could completely transform an industry. This taxonomy is constructed by examining existing research into disruption hierarchies and blockchain classification methods. Through the lens of this taxonomy, a literature review is performed on blockchain's role in energy to draw out themes and ideas characterising each tier. The potential likelihood of real-world application of various ideas are discussed, giving consideration to how established industries may be affected or disrupted. The authors provide some conjecture here. Finally, courses of action are suggested for those whose sector may be affected by blockchain.

As home energy management systems (HEMSs) are implemented in homes as ways of reducing customer costs and providing demand response (DR) to the electric utility, homeowner’s privacy can be compromised. As part of the HEMS framework, homeowners are required to send load forecasts to the distribution system operator (DSO) for power balancing purposes. Submitting forecasts allows a platform for attackers to gain knowledge on user patterns based on the load information provided. The attacker could, for example, enter the home to steal valuable possessions when the homeowner is away. In this paper, we propose a framework using a smart contract within a private blockchain to keep customer information private when communicating with the DSO. The results show the HEMS users’ privacy is maintained, while the benefits of data sharing are obtained. Blockchain and its associated smart contracts may be a viable solution to security concerns in DR applications where load forecasts are sent to a DSO.

A document by vinayakumar R . Click on the document to view its contents.

We report a photonic radio frequency (RF) fractional differentiator based on an integrated Kerr micro-comb source. The micro-comb source has a free spectral range (FSR) of 49 GHz, generating a large number of comb lines that serve as a high-performance multi-wavelength source for the differentiator. By programming and shaping the comb lines according to calculated tap weights, arbitrary fractional orders ranging from 0.15 to 0.90 are achieved over a broad RF operation bandwidth of 15.49 GHz. We experimentally characterize the frequency-domain RF amplitude and phase response as well as the temporal response with a Gaussian pulse input. The experimental results show good agreement with theory, confirming the effectiveness of our approach towards high-performance fractional differentiators featuring broad processing bandwidth, high reconfigurability, and potentially reduced sized and cost.

As submitted to IEEE EnergyCon 2020 Abstract: This paper proposes new tools for predicting and visualising the plausible near term shifts in branch loading that may arise due to output fluctuations from renewable generators. These tools are proposed to enhance situational awareness for control room operators, by providing early warnings of where bottlenecks may manifest in a transmission system. For predicting plausible branch loading shifts, a linear optimal power flow formulation is presented which uses a novel objective function to characterise the maximum loading a branch could be exposed to in the short term. This analysis therefore identifies which branches could become overloaded due to shifts in output from volatile generators. Equivalently, these branches can be seen as congestion bottlenecks which may cause curtailment of renewable generation. To allow the system operator to maintain awareness of such potentialities, these congestable branches are highlighted on a system diagram which is drawn to explicitly portray the electrical distance between components in the network.

Electrochemical Impedance Spectroscopy (EIS) has gained traction as a technique apt for condition monitoring of batteries. The drawback of EIS is that it is only applicable when the system is offline (i.e. it must be disconnected from the load), takes a long time to complete and requires an expensive equipment for measurement. This work aims to adapt the EIS to serve as an in-situ measurement technique, that can be utilized for online condition monitoring of two unique battery chemistries – lead acid and lithium NCM. This work develops in twofold – firstly, the Chirp broadband signal is proposed amongst a variety of other broadband signals to significantly shorten the time required for EIS measurement. Subsequently, a power converter that is typically used to interface a battery with the load for current and voltage regulation functions, is utilized for online condition monitoring of both batteries through closed loop control of the power converter and duty-cycle perturbation. This combined approach presents a novel low-cost technique for online condition monitoring of batteries, with the ability to complete battery characterization in a very short time. In this regard, EIS measurement is completed for a lead acid battery (with lowest EIS characterization frequency of 0.1Hz) in 5 seconds and lithium NCM battery (with lowest EIS characterization frequency of 20mHz) in 25 seconds.

A document by Alexander Wiebel . Click on the document to view its contents.

In this paper, we propose an enhanced Huffman coded orthogonal frequency-division multiplexing with index modulation (EHC-OFDM-IM) scheme. The proposed scheme is capable of utilizing all legitimate subcarrier activation patterns (SAPs) and adapting the bijective mapping relation between SAPs and leaves on a given Huffman tree according to channel state information (CSI). As a result, a dynamic codebook update mechanism is obtained, which can provide more reliable transmissions. We take the average block error rate (BLER) as the performance evaluation metric and approximate it in closed form when the transmit power allocated to each subcarrier is independent of channel states. Also, we propose two CSI-based power allocation schemes with different requirements for computational complexity to further improve the error performance. Subsequently, we carry out numerical simulations to corroborate the error performance analysis and the proposed dynamic power allocation schemes. By studying the numerical results, we find that the depth of the Huffman tree has a significant impact on the error performance when the SAP-to-leaf mapping relation is optimized based on CSI. Meanwhile, through numerical results, we also discuss the trade-off between error performance and data transmission rate and investigate the impacts of imperfect CSI on the error performance of EHC-OFDM-IM.

The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What’s more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%.

Virtual machine consolidation techniques provide ways to save energy and cost in cloud data centers. However, aggressive packing of virtual machines can cause performance degradation. Therefore, it is essential to strike a trade-off between energy and performance in data centers. Achieving this trade-off has been an active research area in recent years. In this paper, a host underload detection algorithm and a new VM selection and VM placement techniques are proposed to consolidate Virtual machines based on the growth potential of VMs. Growth potential is calculated based on the utilization history of VMs. The interdependence of VM selection and VM placement techniques are also studied in the proposed model. The proposed algorithms are evaluated on real- world PlanetLab workload on Cloudsim. The experimental evaluation shows that our proposed technique reduces Service Level Agreement Violation (SLAV) and energy consumption compared to the existing algorithms.

The recent advances in vehicle industry and vehicle-to-everything communications are creating a huge potential market of intelligent vehicle applications, and exploiting vehicle mobility is of great importance in this field. Hence, this paper proposes a novel vehicle mobility prediction algorithm to support intelligent vehicle applications. First, a theoretical analysis is given to quantitatively reveal the predictability of vehicle mobility. Based on the knowledge earned from theoretical analysis, a deep recurrent neural network (RNN)-based algorithm called DeepVM is proposed to predict vehicle mobility in a future period of several or tens of minutes. Comprehensive evaluations have been carried out based on the real taxi mobility data in Tokyo, Japan. The results have not only proved the correctness of our theoretical analysis, but also validated that DeepVM can significantly improve the quality of vehicle mobility prediction compared with other state-of-art algorithms.

This work aims to review the state-of-the-art deep learning architectures in Cyber Security applications by highlighting the contributions and challenges from various recent research papers.

Relay node placement is one of the critical need of wireless sensor networks when connectivity, lifetime, fault tolerance like factors are desired in the network. In this study, we review the present relay node placement techniques and provide an overall view of this study by summarizing previous achievements. We categorize the placement strategies into four broad categories, namely, approximation, algorithms, heuristics, meta-heuristics based techniques. A taxonomy is presented to enlist the present techniques. The paper also emphasizes on the research challenges and gives an idea of potential future scope in this research domain.

A document by Runnan He . Click on the document to view its contents.

The data used in this paper are given in the paper.