Nugroho Agung Pambudi^1,2,4,*, Andrew Chapman, Alfan Sarifudin^1,3, Desita Kamila Ulfa⁴, Iksan Riva Nanda⁵

Energy Engineering, Vol.121, No.5, pp. 1143-1159, 2024, DOI:10.32604/ee.2024.047555

Abstract One of the impacts of the Fukushima disaster was the shutdown of all nuclear power plants in Japan, reaching zero production in 2015. In response, the country started importing more fossil energy including coal, oil, and natural gas to fill the energy gap. However, this led to a significant increase in carbon emissions, hindering the efforts to reduce its carbon footprint. In the current situation, Japan is actively working to balance its energy requirements with environmental considerations, including the utilization of hydrogen fuel. Therefore, this paper aims to explore the feasibility and implications of using hydrogen power plants as a… More >

Xiang Lin^1,*, Jian Fang¹, Ming Zhang¹, Kuang Yin¹, Yan Tian¹, Yingfei Guo², Qianggang Wang²

Energy Engineering, Vol.121, No.5, pp. 1127-1141, 2024, DOI:10.32604/ee.2024.046861

Abstract Efforts to protect electric power systems from faults have commonly relied on the use of ultra-high frequency (UHF) antennas for detecting partial discharge (PD) as a common precursor to faults. However, the effectiveness of existing UHF antennas suffers from a number of challenges such as limited bandwidth, relatively large physical size, and low detection sensitivity. The present study addresses these issues by proposing a compact microstrip patch antenna with fixed dimensions of 100 mm × 100 mm × 1.6 mm. The results of computations yield an optimized antenna design consisting of 2nd-order Hilbert fractal units positioned within a four-layer serpentine… More > Graphic Abstract

Dong Pan¹, Xu Gui¹, Jiayin Xu¹, Yuming Shen¹, Haoran Xu², Yinghao Ma^2,*

Energy Engineering, Vol.121, No.5, pp. 1309-1328, 2024, DOI:10.32604/ee.2024.046464

Abstract With the increasing urgency of the carbon emission reduction task, the generation expansion planning process needs to add carbon emission risk constraints, in addition to considering the level of power adequacy. However, methods for quantifying and assessing carbon emissions and operational risks are lacking. It results in excessive carbon emissions and frequent load-shedding on some days, although meeting annual carbon emission reduction targets. First, in response to the above problems, carbon emission and power balance risk assessment indicators and assessment methods, were proposed to quantify electricity abundance and carbon emission risk level of power planning scenarios, considering power supply regulation… More >

Yu Zhang^*, Lianmin Li, Zhongxiang Liu, Yuhu Wu

Energy Engineering, Vol.121, No.5, pp. 1209-1221, 2024, DOI:10.32604/ee.2024.046141

Abstract With the development of renewable energy technologies such as photovoltaics and wind power, it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage. To solve the problem of the interests of different subjects in the operation of the energy storage power stations (ESS) and the integrated energy multi-microgrid alliance (IEMA), this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game. In the upper layer, ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.… More > Graphic Abstract

Yuancong Chai, Yuefei Zhu^*, Wei Lin, Ding Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1223-1243, 2024, DOI:10.32604/cmc.2024.049904

Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission. Faced with these challenges, identifying… More >

Jinlin Xu^1,2, Wansu Pan^1,*, Longle Cheng^1,2, Haibo Tan^1,2, Munan Yuan^1,*, Xiaofeng Li^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1399-1418, 2024, DOI:10.32604/cmc.2024.049622

Abstract The existing multipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is a lack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptation of service requirements and network resources. To address these issues, we propose a multi-constraint path optimization scheme based on information fusion in SDN. The proposed scheme collects network topology and network state information on the network side and computes disjoint paths between end hosts. It uses the Fuzzy Analytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters and constructs a composite quality… More >

Deming Lei, Surui Duan, Mingbo Li^*, Jing Wang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 47-63, 2024, DOI:10.32604/cmc.2024.049481

Abstract Bottleneck stage and reentrance often exist in real-life manufacturing processes; however, the previous research rarely addresses these two processing conditions in a scheduling problem. In this study, a reentrant hybrid flow shop scheduling problem (RHFSP) with a bottleneck stage is considered, and an elite-class teaching-learning-based optimization (ETLBO) algorithm is proposed to minimize maximum completion time. To produce high-quality solutions, teachers are divided into formal ones and substitute ones, and multiple classes are formed. The teacher phase is composed of teacher competition and teacher teaching. The learner phase is replaced with a reinforcement search of the elite class. Adaptive adjustment on… More >

Dingping Chen¹, Zhiheng Zhu², Jinyang Fu^1,3, Jilin He^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1679-1703, 2024, DOI:10.32604/cmc.2024.049048

Abstract The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safety and performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of road tunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combined with a deep neural network model is an effective means to realize the localization and identification of crack defects on the surface of road tunnels. We propose a complete set of automatic inspection methods for identifying cracks on the walls of road tunnels as a… More >

Aodi Liu, Na Wang^*, Xuehui Du, Dibin Shan, Xiangyu Wu, Wenjuan Wang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1705-1726, 2024, DOI:10.32604/cmc.2024.049011

Abstract Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access control mechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policy management efficiency and difficulty in accurately describing the access control policy. To overcome these problems, this paper proposes a big data access control mechanism based on a two-layer permission decision structure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes are introduced in the ABAC model as business constraints between entities. The proposed mechanism implements a two-layer permission decision structure composed of the inherent attributes of… More >

Khaled Soliman^1,*, Mohamed Sobh², Ayman M. Bahaa-Eldin²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883

Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse sources to ensure the efficiency… More >