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  • Open Access

    ARTICLE

    Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

    Huan Ma1, Linlin Ma2, Zengwei Wang3,*, Zhendong Li3, Yuanzhen Zhu1, Yutian Liu3

    Energy Engineering, Vol.121, No.11, pp. 3133-3160, 2024, DOI:10.32604/ee.2024.055051 - 21 October 2024

    Abstract With the increasing penetration of renewable energy in power system, renewable energy power ramp events (REPREs), dominated by wind power and photovoltaic power, pose significant threats to the secure and stable operation of power systems. This paper presents an early warning method for REPREs based on long short-term memory (LSTM) network and fuzzy logic. First, the warning levels of REPREs are defined by assessing the control costs of various power control measures. Then, the next 4-h power support capability of external grid is estimated by a tie line power prediction model, which is constructed based More > Graphic Abstract

    Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

  • Open Access

    ARTICLE

    Sports Events Recognition Using Multi Features and Deep Belief Network

    Bayan Alabdullah1, Muhammad Tayyab2, Yahay AlQahtani3, Naif Al Mudawi4, Asaad Algarni5, Ahmad Jalal2, Jeongmin Park6,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 309-326, 2024, DOI:10.32604/cmc.2024.053538 - 15 October 2024

    Abstract In the modern era of a growing population, it is arduous for humans to monitor every aspect of sports, events occurring around us, and scenarios or conditions. This recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial intelligence. This research focuses on detecting and recognizing events in sequential photos characterized by several factors, including the size, location, and position of people’s body parts in those pictures, and the influence around those people. Common approaches utilized, here are feature descriptors such as MSER (Maximally Stable Extremal Regions),… More >

  • Open Access

    ARTICLE

    Do Public Health Events Promote the Prevalence of Adjustment Disorder in College Students? An Example from the COVID-19 Pandemic

    Rong Fu*, Luze Xie

    International Journal of Mental Health Promotion, Vol.26, No.1, pp. 21-30, 2024, DOI:10.32604/ijmhp.2023.041730 - 05 February 2024

    Abstract COVID-19, as one of the most serious sudden public health problems in this century, is a serious threat to people’s mental health. College students, as a vulnerable group, are more likely to develop mental health problems. When the body is unable to adapt to new changes in the environment, the main mental health problem that arises is adjustment disorder. The aim of this study was to assess the prevalence and influencing factors of adjustment disorder among college students during the COVID-19 outbreak in China. Cross-sectional data collected by web-based questionnaires were obtained through convenience sampling… More >

  • Open Access

    PROCEEDINGS

    Field Observation and Numerical Simulation of Extreme Met-Ocean Conditions: A Case Study of Typhoon Events in South China Sea

    Chen Gu1,*, Caiyu Wang1, Mengjiao Du2, Kan Yi2, Bihong Zhu1, Hao Wang2, Shu Dai1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.09776

    Abstract Site measurement is essential to the meteorological and oceanographic parameters of offshore wind farms. A floating lidar measurement buoy was deployed at a Qingzhou VI wind farm where is 45-80 km away from Guangdong coast. The field observation including wind and wave data start from March, 2021.The lidar wind data is compared and calibrated with the fixed wind tower data for three months, the accuracy meets the standard of stadge3 carbon trust. In this study, all these data are used to recalibrate for the met-ocean model to relies extreme conditions, such as Typhoon Kompasu(2118) and More >

  • Open Access

    ARTICLE

    Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1423-1438, 2023, DOI:10.32604/cmc.2023.035741 - 30 August 2023

    Abstract Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it… More >

  • Open Access

    ARTICLE

    Anomalous Situations Recognition in Surveillance Images Using Deep Learning

    Qurat-ul-Ain Arshad1, Mudassar Raza1, Wazir Zada Khan2, Ayesha Siddiqa2, Abdul Muiz2, Muhammad Attique Khan3,*, Usman Tariq4, Taerang Kim5, Jae-Hyuk Cha5,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752 - 08 June 2023

    Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the… More >

  • Open Access

    ARTICLE

    An Efficient Way to Parse Logs Automatically for Multiline Events

    Mingguang Yu1,2, Xia Zhang1,2,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2975-2994, 2023, DOI:10.32604/csse.2023.037505 - 03 April 2023

    Abstract

    In order to obtain information or discover knowledge from system logs, the first step is to perform log parsing, whereby unstructured raw logs can be transformed into a sequence of structured events. Although comprehensive studies on log parsing have been conducted in recent years, most assume that one event object corresponds to a single-line message. However, in a growing number of scenarios, one event object spans multiple lines in the log, for which parsing methods toward single-line events are not applicable. In order to address this problem, this paper proposes an automated log parsing method for

    More >

  • Open Access

    ARTICLE

    Visualization Techniques via MLBS for Personnel Management in Major Events

    Yu Su1,2,3, Lingjuan Hou2,3,*, Sinan Li1, Zhaochang Jiang1, Haoran Peng4

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 521-536, 2023, DOI:10.32604/csse.2022.028606 - 20 January 2023

    Abstract Mobile location-based services (MLBS) refer to services around geographic location data. Mobile terminals use wireless communication networks (or satellite positioning systems) to obtain users’ geographic location coordinate information based on spatial databases and integrate with other information to provide users with required location-related services. The development of systems based on MLBS has significance and practical value. In this paper a visualization management information system for personnel in major events based on microservices, namely MEPMIS, is designed and implemented by using MLBS. The system consists of a server and a client app, and it has some… More >

  • Open Access

    REVIEW

    The Relationship between T-Wave Alternans and Adverse Cardiac Events in Patients with Congenital Long QT Syndrome: A Systematic Review and Meta-Analysis

    Ying Yang1,#, Tingting Lv2,#, Siyuan Li1, Ping Zhang1,2,*

    Congenital Heart Disease, Vol.17, No.5, pp. 557-567, 2022, DOI:10.32604/CHD.2021.017292 - 06 September 2022

    Abstract Background: T-wave alternans (TWA) is a risk factor of ventricular arrhythmias or sudden cardiac death (SCD) in patients with ischemic cardiomyopathy. Nevertheless, the relationship between TWA and adverse cardiac events (ACE) in patients with congenital long QT syndrome (LQT) remains controversial. Methods: A systematic electronic search of PubMed, Embase and the Cochrane Library was conducted from database inception dates to 28 April 2021 and assessed the relationship between TWA and ACE in patients with LQTS. Sub-group analysis evaluated the association between microvolt TWA (MTWA) and ACE in different monitoring models and ECGlead numbers. Results: A pooled analysis… More >

  • Open Access

    ARTICLE

    Chinese Herbal Prescription QYSL Prevents Progression of Lung Cancer by Targeting Tumor Microenvironment

    Yang Chen1,#, Huan Wu2,#, Annan Jiao3, Jiabing Tong4, Jie Zhu5, Mei Zhang1, Zegeng Li4,*, Ping Li1,*

    Oncologie, Vol.24, No.2, pp. 295-307, 2022, DOI:10.32604/oncologie.2022.022116 - 29 June 2022

    Abstract Objectives: Lung cancer is a common and malignant tumor in adults and ranks first in the incidence and mortality of the top five malignant tumors in China. Our previous studies have shown that QYSL prescription can balance lung cancer mice Th1/Th2 and inhibit tumor cell immune escape. Here, we examined the effects of QYSL on lung cancer associated macrophage and the potential associated mechanism. Methods: C57BL/6 mice were injected with Lewis lung cancer cells and treated with QYSL. FACS, RT-PCR, and western blot were used to examined the effect of QYSL on tumor immune microenvironment. Results: We… More >

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