Libo Han^1,2, Sha Tao^1,2, Wen Xia³, Weixin Sun³, Li Yan³, Wanlin Gao^1,2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2431-2449, 2025, DOI:10.32604/cmc.2024.057833 - 17 February 2025

Abstract At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature… More >

Dianlang Wang¹, Qi Yin¹, Haifeng Wang¹, Jing Chen¹, Hong Miao², Yihan Chen^2,*

Energy Engineering, Vol.122, No.2, pp. 651-666, 2025, DOI:10.32604/ee.2025.057670 - 31 January 2025

Abstract As the penetration rate of distributed energy increases, the transient power angle stability problem of the virtual synchronous generator (VSG) has gradually become prominent. In view of the situation that the grid impedance ratio (R/X) is high and affects the transient power angle stability of VSG, this paper proposes a VSG transient power angle stability control strategy based on the combination of frequency difference feedback and virtual impedance. To improve the transient power angle stability of the VSG, a virtual impedance is adopted in the voltage loop to adjust the impedance ratio R/X; and the… More >

Jiawei Tian¹, Yu Zhou¹, Lirong Yin^2,*, Salman A. AlQahtani³, Minyi Tang⁴, Siyu Lu⁴, Ruiyang Wang⁴, Wenfeng Zheng^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 973-1019, 2025, DOI:10.32604/cmes.2024.057261 - 27 January 2025

Abstract Force feedback bilateral teleoperation represents a pivotal advancement in control technology, finding widespread application in hazardous material transportation, perilous environments, space and deep-sea exploration, and healthcare domains. This paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current complexity. It elucidates the fundamental principles underpinning interaction forces and tactile exchanges, with a specific emphasis on the crucial role of tactile devices. In this review, a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted, utilizing published journal article data as the… More >

Jianwei Zhang^1,2,*, Zhishang Zhao³, Zengyu Cai³, Yuan Feng⁴, Liang Zhu³, Yahui Sun³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 977-996, 2025, DOI:10.32604/cmc.2024.058438 - 03 January 2025

Abstract The rapid development of short video platforms poses new challenges for traditional recommendation systems. Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles: explicit feedback (interactive behavior), which significantly influences users’ short-term interests, and implicit feedback (viewing time), which substantially affects their long-term interests. However, the previous model fails to distinguish between these two feedback methods, leading it to predict only the overall preferences of users based on extensive historical behavior sequences. Consequently, it cannot differentiate between users’ long-term and short-term interests, resulting in low accuracy in describing… More >

Xianju Gao^*, Huachun Zhou, Weilin Wang, Jingfu Yan

Computer Systems Science and Engineering, Vol.49, pp. 19-48, 2025, DOI:10.32604/csse.2024.058327 - 03 January 2025

Abstract The network security knowledge base standardizes and integrates network security data, providing a reliable foundation for real-time network security protection solutions. However, current research on network security knowledge bases mainly focuses on their construction, while the potential to optimize intelligent security services for real-time network security protection requires further exploration. Therefore, how to effectively utilize the vast amount of historical knowledge in the field of network security and establish a feedback mechanism to update it in real time, thereby enhancing the detection capability of security services against malicious traffic, has become an important issue. Our… More >

Musulmon Lolaev, Anand Paul^*, Jeonghong Kim

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4707-4726, 2024, DOI:10.32604/cmc.2024.058675 - 19 December 2024

Abstract Evaluating artificial intelligence (AI) systems is crucial for their successful deployment and safe operation in real-world applications. The assessor meta-learning model has been recently introduced to assess AI system behaviors developed from emergent characteristics of AI systems and their responses on a test set. The original approach lacks covering continuous ranges, for example, regression problems, and it produces only the probability of success. In this work, to address existing limitations and enhance practical applicability, we propose an assessor feedback mechanism designed to identify and learn from AI system errors, enabling the system to perform the More >

Xiangkui Jiang^*, Zihao Wang, Chao Dong

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4303-4323, 2024, DOI:10.32604/cmc.2024.054640 - 12 September 2024

Abstract

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree (RRT) algorithm, a feedback-biased sampling RRT, called FS-RRT, is proposed based on RRT. Firstly, to improve the sampling efficiency of RRT to shorten the search time, the search area of the random tree is restricted to improve the sampling efficiency. Secondly, to obtain better information about obstacles to shorten the path length, a feedback-biased sampling strategy is used instead of the traditional random sampling, the collision of the expanding node with an obstacle generates feedback information so that the next

… More >

Liqing Wang^*, Yiheng Xiao

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 619-631, 2024, DOI:10.32604/iasc.2024.052971 - 06 September 2024

Abstract Neural Machine Translation is one of the key research directions in Natural Language Processing. However, limited by the scale and quality of parallel corpus, the translation quality of low-resource Neural Machine Translation has always been unsatisfactory. When Reinforcement Learning from Human Feedback (RLHF) is applied to low-resource machine translation, commonly encountered issues of substandard preference data quality and the higher cost associated with manual feedback data. Therefore, a more cost-effective method for obtaining feedback data is proposed. At first, optimizing the quality of preference data through the prompt engineering of the Large Language Model (LLM), More >

Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897 - 15 May 2024

Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis.… More >

Xuechuan Wang¹, Wei He^1,*, Haoyang Feng¹, Satya N. Atluri²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1263-1294, 2024, DOI:10.32604/cmes.2023.043068 - 29 January 2024

Abstract Although predictor-corrector methods have been extensively applied, they might not meet the requirements of practical applications and engineering tasks, particularly when high accuracy and efficiency are necessary. A novel class of correctors based on feedback-accelerated Picard iteration (FAPI) is proposed to further enhance computational performance. With optimal feedback terms that do not require inversion of matrices, significantly faster convergence speed and higher numerical accuracy are achieved by these correctors compared with their counterparts; however, the computational complexities are comparably low. These advantages enable nonlinear engineering problems to be solved quickly and accurately, even with rough… More > Graphic Abstract