Jipeng Xie^1,2, Guolai Yang^1,*, Liqun Wang¹, Lei Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 793-819, 2024, DOI:10.32604/cmes.2024.048174

Abstract To enhance the comprehensive performance of artillery internal ballistics—encompassing power, accuracy, and service life—this study proposed a multi-stage multidisciplinary design optimization (MS-MDO) method. First, the comprehensive artillery internal ballistic dynamics (AIBD) model, based on propellant combustion, rotation band engraving, projectile axial motion, and rifling wear models, was established and validated. This model was systematically decomposed into subsystems from a system engineering perspective. The study then detailed the MS-MDO methodology, which included Stage I (MDO stage) employing an improved collaborative optimization method for consistent design variables, and Stage II (Performance Optimization) focusing on the independent optimization of local design variables and… More >

NURUL HAYATI YUSOF^1,*, DAZYLAH DARJI¹, TAN KIM SONG¹, NGHIEM THI THUONG²

Journal of Polymer Materials, Vol.39, No.1-2, pp. 151-166, 2022, DOI:10.32381/JPM.2022.39.1-2.10

Abstract In this work, a pure gum mould made of prevulcanized natural rubber/cloisite-Na⁺ nanocomposite (PVNR/CN) was prepared and characterized for ornament application. The suitable conditions to prepare PVNR/CN latex mixtures and the properties of the resulting PVNR/CN nanocomposites were investigated. The optimum CN concentration in the latex mixture was 1.0 phr, with the properties of 60 wt% total solid content, more than 600 sec mechanical stability time, lower than 350 cP Brookfield viscosity, and pH 10. The properties of PVNR/CN nanocomposite showed high strength, moderate hardness, and good thermal stability. The morphology by TEM showed well dispersion of CN particles in… More >

Chun-Ming Wu¹, Jin Lei^1,*, Wu-Kai Liu¹, Mei-Ling Ren¹, Ling-Li Ran²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3071-3088, 2024, DOI:10.32604/cmc.2023.047062

Abstract Aiming at defects such as low contrast in infrared ship images, uneven distribution of ship size, and lack of texture details, which will lead to unmanned ship leakage misdetection and slow detection, this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm (R_YOLO). The algorithm incorporates the Efficient Multi-Scale Attention mechanism (EMA), the efficient Reparameterized Generalized-feature extraction module (CSPStage), the small target detection header, the Repulsion Loss function, and the context aggregation block (CABlock), which are designed to improve the model’s ability to detect targets at multiple scales and the speed of model inference. The algorithm… More >

M. L. Maruthi Prasad^*, R. Santhosh

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 1-13, 2024, DOI:10.32604/iasc.2023.036628

Abstract This research concentrates to model an efficient thyroid prediction approach, which is considered a baseline for significant problems faced by the women community. The major research problem is the lack of automated model to attain earlier prediction. Some existing model fails to give better prediction accuracy. Here, a novel clinical decision support system is framed to make the proper decision during a time of complexity. Multiple stages are followed in the proposed framework, which plays a substantial role in thyroid prediction. These steps include i) data acquisition, ii) outlier prediction, and iii) multi-stage weight-based ensemble learning process (MS-WEL). The weighted… More >

Jinbo Yang¹, Hai Huang¹, Lailai Yin², Jiaxing Qu³, Wanjuan Xie^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

Zaihe Yang^1,*, Shuling Wang¹, Runhang Zhu¹, Jiao Cui², Ji Su², Liling Chen³

Energy Engineering, Vol.121, No.3, pp. 807-820, 2024, DOI:10.32604/ee.2023.028167

Abstract To address the scheduling problem involving energy storage systems and uncertain energy, we propose a method based on multi-stage robust optimization. This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method, which helps overcome the limitations of traditional methods in terms of time scale. The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day. To achieve this, a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power. The generalized… More >

Zifeng Yu¹, Xianfeng Li^1,*, Lianpeng Sun², Jinjun Zhu², Jianxin Lin³

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 435-451, 2024, DOI:10.32604/cmc.2023.046685

Abstract Urban sewer pipes are a vital infrastructure in modern cities, and their defects must be detected in time to prevent potential malfunctioning. In recent years, to relieve the manual efforts by human experts, models based on deep learning have been introduced to automatically identify potential defects. However, these models are insufficient in terms of dataset complexity, model versatility and performance. Our work addresses these issues with a multi-stage defect detection architecture using a composite backbone Swin Transformer. The model based on this architecture is trained using a more comprehensive dataset containing more classes of defects. By ablation studies on the… More >

Ming Li^1,*, Rifucairen Fu¹, Tuerhong Yaxiaer¹, Yunping Zheng¹, Abiao Huang², Ronghui Liu², Shunfu Lin²

Energy Engineering, Vol.121, No.2, pp. 405-424, 2024, DOI:10.32604/ee.2023.044509

Abstract From the perspective of a community energy operator, a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads. The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system (IES) before and after; the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme, taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of… More >

Huaiyin He¹, Longqing Zou¹, Yanchao Li¹, Yixuan Wang¹, Junxiang Li¹, Huan Wen¹, Bei Chang¹, Lijun Liu^2,*

Energy Engineering, Vol.121, No.1, pp. 111-123, 2024, DOI:10.32604/ee.2023.041010

Abstract Multistage multi-cluster hydraulic fracturing has enabled the economic exploitation of shale reservoirs, but the interpretation of hydraulic fracture parameters is challenging. The pressure signals after pump shutdown are influenced by hydraulic fractures, which can reflect the geometric features of hydraulic fracture. The shutdown pressure can be used to interpret the hydraulic fracture parameters in a real-time and cost-effective manner. In this paper, a mathematical model for shutdown pressure evolution is developed considering the effects of wellbore friction, perforation friction and fluid loss in fractures. An efficient numerical simulation method is established by using the method of characteristics. Based on this… More >

Yang Yang¹, Yuhan Long¹, Yijing Lin², Zhipeng Gao¹, Lanlan Rui¹, Peng Yu^1,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250

Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the… More >