Home appliances classification based on multi-feature using ELM

Liu, Qi; Chen, Fangpeng; Chen, Fenghua; Wu, Zhengyang; Liu, Xiaodong; Linge, Nigel

doi:10.1504/ijsnet.2018.094710

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The common scoring functions are dot product operations, scaled dot product operations, and bilinear operations, respectively, as shown in formula (12), formula (13), and formula (14):…”

Section: • the Information Extraction Module Of Gcnmentioning

confidence: 99%

“…The forecast objects include user load electricity consumption, annual and monthly electricity consumption and daily electricity load change process. In addition, the power data after statistical analysis can also provide a basis for the intelligent configuration and planning of the state grid, and ensure the safety and reliability of the power grid operation 14,15 . In the process of load data collection, due to the influence of communication quality, sensor performance, network delay and other factors, there are sometimes abnormal jump values in the load data.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the power data after statistical analysis can also provide a basis for the intelligent configuration and planning of the state grid, and ensure the safety and reliability of the power grid operation. 14,15 In the process of load data collection, due to the influence of communication quality, sensor performance, network delay and other factors, there are sometimes abnormal jump values in the load data. This kind of data that deviates from the normal value generated by the detection equipment or the transmission process is called "wild value".…”

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confidence: 99%

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A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

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SummaryAs the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

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“…The common scoring functions are dot product operations, scaled dot product operations, and bilinear operations, respectively, as shown in formula (12), formula (13), and formula (14):…”

Section: • the Information Extraction Module Of Gcnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

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“…First, some attacks, e.g., Distributed Denial-Of-Service attacks are non-differentiable from normal activities, which makes it a challenge to design effective intrusion detection mechanisms to counter them [11], [12]. Second, preventative countermeasures requires often beforehand knowing what constitutes an attack, which is difficult for zero-day attacks [13], [14]. Preventative countermeasures often either tempt loose access control in favor of functionality and features, lower expenses, or convenience of using software or systems, or invite over-stringent access control in favor of policy compliance or out of paranoia, in particular, when users' voices are unheard.…”

Section: Introductionmentioning

confidence: 99%

Prototyping Flow-Net Logging for Accountability Management in Linux Operating Systems

et al. 2019

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Accountability in conjunction with preventative countermeasures is necessary to satisfy the needs of real-world computer security. A common method to achieve accountability is via logging and auditing. To achieve better accountability, a logging system should be capable of capturing activities as well as the relationships among the activities in a computer system or network. Existing logging techniques record activity events in isolation and rely on attributes and time stamps of the logged events to establish their relationships, and this approach leads to probable loss of event relationships among large and complex logs and a confusion during auditing. Prior works have indicated that flow-net is effective in addressing this problem by organizing events in a direct acyclic graph and preserving event relationships during logging. In this work, we provide a prototypical design and implementation of a flow-net accountable logging framework in the Linux operating system. Particularly, it can be applied to Internet of Things (IoTs) with Android Operating Systems. We measure the performance overhead introduced by the flow-net logging prototype via experiments in Linux. The results indicate that the flow-net prototype only introduces a small overhead when compared with existing logging methods. In addition, we show by examples enforcement of accountability policies in the flow-net logging framework and its performance overhead. This work thus constitutes a further step to advance flow-net in addressing accountability in computer systems and networks.

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Near-Data Prediction Based Speculative Optimization in a Distribution Environment

Sun

Jin

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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ApacheHadoop is an open source software framework that supports data-intensive distributed applications and is distributed under the Apache 2.0 licensing agreement, where consumers will no longer deal with complex configuration of software and hardware but only pay for cloud services on demand. So how to make the performance of the cloud platform become more important in a consumer-centric environment. There exists imbalance between in some distribution of slow tasks, which results in straggling tasks will have a great influence on the Hadoop framework. By monitoring those tasks in real-time progress and copying the potential Stragglers to a different node, the speculative execution (SE) realizes to improve the probability of finishing those backup tasks before the original ones. The Speculative execution (SE) applies this principle and thus proposed a solution to handle the Straggling tasks. At present, the performance of the Hadoop system is unsatisfying because of the erroneous judgement and inappropriate selection for the backup nodes in the current SE policy. This paper proposes an SE optimized strategy which can be used in prediction of near data. In this strategy, the first step is gathering the real-time task execution information and the remaining runtime required for the task is predicted by a local prediction method. Then it chooses a proper backup node according to the near data and actual demand in the second step. On the other side, this model also includes a cost-effective model in order to make the performance of SE to the peak. The results show that using this strategy in Hadoop effectively improves the accuracy of alternative tasks and effects better in heterogeneous Hadoop environments in various situations, which is beneficial to consumers and cloud platform.

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Home appliances classification based on multi-feature using ELM

Cited by 5 publications

References 8 publications

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Prototyping Flow-Net Logging for Accountability Management in Linux Operating Systems

Near-Data Prediction Based Speculative Optimization in a Distribution Environment

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