Chao Zhang¹, Shang-Xi Lai¹, Hua-Ping Wang^1,2,*

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 25-54, 2025, DOI:10.32604/sdhm.2024.053662 - 15 November 2024

Abstract Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure. Therefore, it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring (SHM) system, so as to provide a scientific basis for structural damage identification and dynamic model modification. In view of this, this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters. The paper primarily introduces data-driven modal parameter recognition methods… More >

Huayi Zhang¹, Maobin Song², Lei Shen^1,*, Nizar Faisal Alkayem¹, Maosen Cao³

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 55-75, 2025, DOI:10.32604/sdhm.2024.052869 - 15 November 2024

Abstract The concrete panel of earth-rock dams in cold regions tends to crack due to the combination effect of non-uniform foundation settlement, ice expansion loads, and freeze-thaw damage. In this work, simulations are designed to investigate the effects of freeze-thaw damage degrees on the fracture behavior caused by the partial detachment and ice expansion loads on concrete panels. Results show that the range of detached panels and freeze-thaw damage degree are the dominant factors that affect the overall load-bearing capacity of the panel and the failure cracking modes, whereas the panel slope is a secondary factor. More >

Li Wang^1,*, Xiaoyong Wang²

Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

Guowei Li^1,*, Shujuan Bu², Xinle Yang², Kaijie Liang¹, Zhengri Shao¹, Xiaobei Song¹, Yitian Tang³, Dejing Zong⁴

Energy Engineering, Vol.121, No.12, pp. 3843-3874, 2024, DOI:10.32604/ee.2024.056195 - 22 November 2024

Abstract A novel dual-pressure organic Rankine cycle system (DPORC) with a dual-stage ejector (DE-DPORC) is proposed. The system incorporates a dual-stage ejector that utilizes a small amount of extraction steam from the high-pressure expander to pressurize a large quantity of exhaust gas to perform work for the low-pressure expander. This innovative approach addresses condensing pressure limitations, reduces power consumption during pressurization, minimizes heat loss, and enhances the utilization efficiency of waste heat steam. A thermodynamic model is developed with net output work, thermal efficiency, and exergy efficiency (W_net, η_t, η_ex) as evaluation criteria, an economic model is established… More >

Weiru Wang¹, Yulong Cao^1,*, Yanxu Wang¹, Jiale You¹, Guangnan Zhang¹, Yu Xiao²

Energy Engineering, Vol.121, No.12, pp. 3801-3823, 2024, DOI:10.32604/ee.2024.055398 - 22 November 2024

Abstract To ensure frequency stability in power systems with high wind penetration, the doubly-fed induction generator (DFIG) is often used with the frequency fast response control (FFRC) to participate in frequency response. However, a certain output power suppression amount (OPSA) is generated during frequency support, resulting in the frequency modulation (FM) capability of DFIG not being fully utilised, and the system’s unbalanced power will be increased during speed recovery, resulting in a second frequency drop (SFD) in the system. Firstly, the frequency response characteristics of the power system with DFIG containing FFRC are analysed. Then, based… More >

Xiaohui Ye^1,*, Fucheng Tan¹, Xinli Song², Hanyang Dai², Xia Li², Shixia Mu², Shaohang Hao²

Energy Engineering, Vol.121, No.12, pp. 3689-3710, 2024, DOI:10.32604/ee.2024.055200 - 22 November 2024

Abstract Energy storage batteries can smooth the volatility of renewable energy sources. The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system (BESS). However, the current modeling of grid-connected BESS is overly simplistic, typically only considering state of charge (SOC) and power constraints. Detailed lithium (Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions. Additionally, there is a lack of real-time batteries risk assessment frameworks. To address these issues, in this… More >

Inna Bilous¹, Dmytro Biriukov¹, Dmytro Karpenko², Tatiana Eutukhova², Oleksandr Novoseltsev^2,*, Volodymyr Voloshchuk¹

Energy Engineering, Vol.121, No.12, pp. 3617-3634, 2024, DOI:10.32604/ee.2024.051684 - 22 November 2024

Abstract This article focuses on the challenges of modeling energy supply systems for buildings, encompassing both methods and tools for simulating thermal regimes and engineering systems within buildings. Enhancing the comfort of living or working in buildings often necessitates increased consumption of energy and material, such as for thermal upgrades, which consequently incurs additional economic costs. It is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions, considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in… More > Graphic Abstract

Conghao Niu^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

Sagiraju Srinadhraju^*, Samaresh Mishra, Suresh Chandra Satapathy

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1641-1668, 2024, DOI:10.32604/csse.2024.055612 - 22 November 2024

Abstract This paper introduces the integration of the Social Group Optimization (SGO) algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model (COCOMO). COCOMO’s fixed coefficients often limit its adaptability, as they don’t account for variations across organizations. By fine-tuning these parameters with SGO, we aim to improve estimation accuracy. We train and validate our SGO-enhanced model using historical project data, evaluating its performance with metrics like the mean magnitude of relative error (MMRE) and Manhattan distance (MD). Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost More >

Stephen Afrifa^1,2,*, Vijayakumar Varadarajan^3,4,5,*, Peter Appiahene², Tao Zhang¹, Richmond Afrifa⁶

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1625-1639, 2024, DOI:10.32604/csse.2024.055084 - 22 November 2024

Abstract Users of social networks can readily express their thoughts on websites like Twitter (now X), Facebook, and Instagram. The volume of textual data flowing from users has greatly increased with the advent of social media in comparison to traditional media. For instance, using natural language processing (NLP) methods, social media can be leveraged to obtain crucial information on the present situation during disasters. In this work, tweets on the Uttarakhand flash flood are analyzed using a hybrid NLP model. This investigation employed sentiment analysis (SA) to determine the people’s expressed negative attitudes regarding the disaster. More >