Reeya Agrawal^1,*, Anjan Kumar¹, Salman A. AlQahtani², Mashael Maashi³, Osamah Ibrahim Khalaf⁴, Theyazn H. H. Aldhyani⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2313-2331, 2022, DOI:10.32604/cmc.2022.029019 - 16 June 2022

Abstract Most modern microprocessors have one or two levels of on-chip caches to make things run faster, but this is not always the case. Most of the time, these caches are made of static random access memory cells. They take up a lot of space on the chip and use a lot of electricity. A lot of the time, low power is more important than several aspects. This is true for phones and tablets. Cache memory design for single bit architecture consists of six transistors static random access memory cell, a circuit of write driver, and… More >

Sarayut Yaemprayoon, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3667-3684, 2022, DOI:10.32604/cmc.2022.028358 - 16 June 2022

Abstract Non-Intrusive Load Monitoring (NILM) has gradually become a research focus in recent years to measure the power consumption in households for energy conservation. Most of the existing algorithms on NILM models independently measure when the total current load of appliances occurs, and NILM usually undergoes the problem of signatures of the appliance. This paper presents a distingue NILM design to measure and classify the appliances by investigating the inrush current pattern when the alliances begin. The proposed method is implemented while the five appliances operate simultaneously. The high sampling rate of field-programmable gate array (FPGA)… More >

Sakorn Mekruksavanich¹, Anuchit Jitpattanakul^2,*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1907-1925, 2022, DOI:10.32604/iasc.2022.027233 - 25 May 2022

Abstract Numerous learning-based techniques for effective human activity recognition (HAR) have recently been developed. Wearable inertial sensors are critical for HAR studies to characterize sport-related activities. Smart wearables are now ubiquitous and can benefit people of all ages. HAR investigations typically involve sensor-based evaluation. Sport-related activities are unpredictable and have historically been classified as complex, with conventional machine learning (ML) algorithms applied to resolve HAR issues. The efficiency of machine learning techniques in categorizing data is limited by the human-crafted feature extraction procedure. A deep learning model named MBiGRU (multimodal bidirectional gated recurrent unit) neural network More >

S. Hariprasad^1,*, T. Deepa¹, N. Bharathiraja²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1425-1440, 2022, DOI:10.32604/iasc.2022.022856 - 25 May 2022

Abstract The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning… More >

Bin Zhang, Yanjun Jin^*

Energy Engineering, Vol.119, No.4, pp. 1467-1482, 2022, DOI:10.32604/ee.2022.018233 - 23 May 2022

Abstract Under complex grid conditions, the grid voltage usually has an imbalance, low order harmonics, and a small of DC bias. When the grid voltage contains low order harmonics and a small amount of DC bias component, the inverter's output current cannot meet the grid connection requirements, and there is a three-phase current imbalance in the control strategy of common VSG under unbalanced voltage. A theoretical analysis of non-ideal power grids is carried out, and a VSG control strategy under complex operating conditions is proposed. Firstly, the third-order generalized integrator (TOGI) is used to eliminate the… More >

Shun Wang¹, Lin Qiao², Wei Fang³, Guodong Jing⁴, Victor S. Sheng⁵, Yong Zhang^1,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411 - 18 May 2022

Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the… More >

Pranav Pushkar¹, Christo Ananth², Preeti Nagrath¹, Jehad F. Al-Amri⁵, Vividha¹, Anand Nayyar^3,4,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1601-1619, 2022, DOI:10.32604/cmc.2022.026205 - 18 May 2022

Abstract The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARS-Cov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have… More >

Jaeyong Kang¹, Chul-Su Kim², Jeong Won Kang³, Jeonghwan Gwak^1,4,5,6,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1-14, 2022, DOI:10.32604/cmc.2022.023641 - 18 May 2022

Abstract The anomaly detection of the brake operating unit (BOU) in the brake systems on metro vehicle is critical for the safety and reliability of the trains. On the other hand, current periodic inspection and maintenance are unable to detect anomalies in an early stage. Also, building an accurate and stable system for detecting anomalies is extremely difficult. Therefore, we present an efficient model that use an ensemble of recurrent autoencoders to accurately detect the BOU abnormalities of metro trains. This is the first proposal to employ an ensemble deep learning technique to detect BOU abnormalities… More >

Nasebah Almufadi¹, Ali Mustafa Qamar^1,2,*

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1255-1270, 2022, DOI:10.32604/csse.2022.025029 - 09 May 2022

Abstract Currently, mobile communication is one of the widely used means of communication. Nevertheless, it is quite challenging for a telecommunication company to attract new customers. The recent concept of mobile number portability has also aggravated the problem of customer churn. Companies need to identify beforehand the customers, who could potentially churn out to the competitors. In the telecommunication industry, such identification could be done based on call detail records. This research presents an extensive experimental study based on various deep learning models, such as the 1D convolutional neural network (CNN) model along with the recurrent More >

Yingnan Zhao^1,*, Peiyuan Ji¹, Fei Chen¹, Guanlan Ji¹, Sunil Kumar Jha²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710 - 03 May 2022

Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of More >