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The restricted intermittent control for high-speed train movement via the full state dependent event-triggered method

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ISA TRANSACTIONS 2025年 157卷 481-495页

作者： Yang, Meng Wang, Junguo Liu, Bin Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China Hunan Univ Technol Coll Sci Zhuzhou 412008 Hunan Peoples R China

In this paper, the full state dependent event-triggered aperiodic intermittent control (FE-AIC) strategy based on input constraints is introduced to minimize energy consumption and enhance speed tracking accuracy in the high-speed train (HST) operation. Firstly, a dynamic model based on multi-mass-point (MMP) for HST has been established, transforming the cruise control problem into an error asymptotic convergence problem. Secondly, restricted FE-AIC (RFE-AIC) controller is designed separately in the presence and absence of external disturbances to realize tracking objects. The proposed control scheme is not only based on control input constraints, but also intermittent control with full state event dependence. The RFE-AIC scheme and the conditions for determining parameters are given, which ensures the stability of the ideal tracking speed and coupler deviation at the equilibrium point. Eventually, the availability of the proposed method in cruise control is confirmed through numerical simulations. It is proved that the RFE-AIC has better performance compared with the selftriggered and guaranteed optimal cruise control methods.

关键词： The restricted event-triggered control Aperiodic intermittent control High-speed train (HST) Multi-mass-point Cruise control

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Completion of irregular emotion sequence based on users' social relationships and historical emotions

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2025年第3期40卷 263-281页

作者： Li, Hao Xie, Chunzhi Xu, Kexin Du, Yajun Li, Xianyong Li, Yanli Liu, Jia Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China

The use of multivariate time series is often impeded by the discontinuity of key missing features. In social networks, the absence of individual sentiment attributes presents challenges to sentiment-driven applications, such as sentiment prediction. Traditional missing value imputation fails in this field as it overlooks the interplay between missing and observed features. This paper introduces a novel deep learn-ing model that captures and assimilates the evolving features of users and their neighbors for effective sentiment completion. Experimental evidence shows that our model outperforms five other methods in performance and efficiency.

关键词： Sentiment analysis data completion social relationships encoding and decoding attention

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Mechanisms driving technological innovation behavior in construction and demolition waste remanufactured products via self-determination theory

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ENVIRONMENTAL technology & INNOVATION 2025年 38卷

作者： Li, Xingwei Zhang, Yi Xu, Shiqi Tan, Lili Yunnan Univ Sch Govt Kunming 650504 Peoples R China Sichuan Agr Univ Coll Architecture & Urban Rural Planning Chengdu 611830 Peoples R China

Technological innovation (TI) behavior serves as a crucial driver for enhancing the quality and safety performance of construction and demolition waste (CDW) remanufactured products. However, the complex nature of TI behavior presents challenges for CDW recyclers and remanufacturers in making strategic decisions. This study explores the evolution of TI behavior among CDW recyclers and remanufacturers through the lens of Self-Determination Theory (SDT). The key findings are as follows: (1) The growth rate of market demand positively influences the adoption of TI behavior by CDW recyclers and remanufacturers. A higher market demand for remanufactured products accelerates the convergence of these stakeholders towards a steadystate, although recyclers reach stability faster than remanufacturers, who face a higher threshold for adopting TI behavior. (2) The quality and safety awareness of different subjects has a positive impact on the evolution of TI behavior adopted by construction waste recyclers and construction waste remanufacturers. The greater the awareness, the more rapid the adoption of TI behavior. (3) Quality and safety awareness has a particularly strong impact on the evolution of TI behavior, with an increase in recyclers' awareness leading to enhanced awareness among remanufacturers, further accelerating TI behavior adoption. However, remanufacturers face constraints in their adoption of TI behavior, which can be overcome by combining market demand expansion with heightened awareness of quality and safety. This study pioneers SDT integration into CDW recycling, unveils key innovation mechanisms, highlights remanufactured product quality's impact, and provides a strategic foundation for advancing recycling technologies.

关键词： Construction and demolition waste Remanufactured products Quality and safety awareness Technological innovation behavior Evolutionary game Self-determination theory

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Downhole Microseismic Detection using Fiber Optic Distributed Acoustic Sensing Based on Segmentation Model and Connected Domain Algorithm

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IEEE Sensors Journal 2025年第9期25卷 15116-15129页

作者： Yang, Xike Wang, Honghui Wang, Xiang Liu, Tong Wu, Wei Shui, Qianfeng Ren, Jizhou State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu610059 China Dep. of Artificial Intelligence Chengdu University of Technology Chengdu610059 China Key Laboratory of Earth Exploration and Information Technology of Ministry of Education Chengdu University of Technology Chengdu610059 China

The widespread adoption of fiber optic distributed acoustic sensing (DAS) technology in oil and gas production, the timely and precise identification of microseismic events within DAS datasets holds importance for enhancing both the efficacy and safety of mining operations. The current DAS microseismic detection methods, including template matching technology and CNN-based approaches, predominantly face challenges such as high computational complexity, slow detection speed and low detection accuracy. In response, we introduce SegDetection deep learning model, a semantic segmentation model that integrates dynamic snake convolution with MobileNetV3 to enhance feature extraction capabilities. The model employs LRASPP as its segmentation head network. Subsequently, a two-stage connected domain algorithm is utilized to produce prediction boxes and confidence scores. To enhance the segmentation accuracy of our model, we implement a segmentation correction strategy. In the microseismic detection task using the downhole DAS microseismic dataset in Utah, USA, our proposed method achieved an F1 score of 0.902. After applying the error segmentation correction strategy, the F1 score improved to 0.951. The experimental results indicate that the method proposed in this paper exhibits commendable performance in downhole DAS microseismic detection. Additionally, the error segmentation and correction strategy introduced significantly enhances the model's detection accuracy, suggesting its broad applicability to various downhole DAS microseismic detection tasks. © 2025 IEEE.

关键词： Semantic Segmentation

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Multiagent DRL-Based Consensus Mechanism for Blockchain-Based Collaborative Computing in UAV-Assisted 6G Networks

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 4331-4348页

作者： Abishu, Hayla Nahom Sun, Guolin Yacob, Yasin Habtamu Boateng, Gordon Owusu Ayepah-Mensah, Daniel Liu, Guisong Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Intelligent Terminal Key Lab Sichuan Prov Yibin 644005 Peoples R China Trusted Cloud Comp & Big Data Key Lab Sichuan Prov Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Southwestern Univ Finance & Econ Sch Comp & Artificial Intelligence Chengdu 611130 Peoples R China

Sixth generation (6G) networks deploy unmanned aerial vehicles and mobile edge computing to provide collaborative computing and reliable connectivity for resource-limited mobile devices (MDs). However, due to the untrusted and broadcast nature of wireless transmission among communicating MDs and computing resource providers, ensuring the security of resource transactions will be challenging. Blockchain-based resource-sharing systems have been proposed to address security issues. However, these systems use existing consensus mechanisms like Proof-of-Work that consume massive amounts of system resources. In addressing this, some studies attempted to use single-agent deep reinforcement learning (DRL) in leader selection. Nevertheless, these solutions overlooked the intelligence and flexibility of blockchain configuration, and a single-point of failure can cause the system to fail. We propose a multiagent distributed deep deterministic policy gradient (MAD3PG)-assisted consensus mechanism for blockchain-based collaborative resource sharing to address these issues. First, we propose a stochastic game-based incentive-mechanism to encourage consensus nodes to participate in transaction validation. Then, we formulate the optimization problem of node selection and blockchain configuration as a Markov decision process and solve it with the MAD3PG algorithm. With MAD3PG, the agents select consensus nodes based on their experience and available resources and dynamically adjust blockchain settings. The simulation results show that MAD3PG outperforms the benchmarks in maximizing throughput and incentive while minimizing block production latency.

关键词： Blockchains Consensus protocol 6G mobile communication Artificial intelligence Heuristic algorithms Security Quality of service Resource management Production Collaboration Blockchain consensus mechanisms deep reinforcement learning (DRL) resource sharing sixth-generation (6G) networks

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Frequency-domain diffusion adaptation over networks with missing input data

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SIGNAL PROCESSING 2025年 226卷

作者： Peng, Yishu Zhang, Sheng Zhou, Zhengchun Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China

Recently, a modified adapt-then-combine diffusion (mATC) strategy has been developed to handle distributed estimation problem with missing regressions (inputs). However, the mATC algorithm only considers the white input scenario and suffers from high complexity for long model filter lengths. To overcome these shortcomings, this paper proposes novel regularization-based frequency-domain diffusion algorithms for networks with missing input data. First, bias-eliminating cost function based on regularization is established by using the frequency-domain diagonal approximation. Then, with stochastic gradient descent, periodic update, and power normalization schemes, we design the regularization-based frequency-domain least mean square (R-FDLMS) algorithm as well as its normalized variant (R-FDNLMS). The latter converges faster than the former under colored inputs. The stability and steady-state behavior of the R-FDNLMS algorithm are also analyzed. Moreover, two effective power estimation methods are presented for both situations without and with the power ratio between the input signal and perturbation noise, along with a reset mechanism in the first case to enhance tracking performance. Finally, simulations are conducted to illustrate the superiority of the proposed algorithms and the validity of theoretical findings.

关键词： Adaptive networks Diffusion strategy Frequency domain Missing input data Stochastic performance

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Regulation of Zinc Hydroxide Sulfate Growth Environment for Stable Zinc Anode/Electrolyte Interfaces

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LANGMUIR 2025年第4期41卷 2735-2743页

作者： Huang, Kaixin Zeng, Xianguang Zhang, Dan Wen, Chengyan Lu, Chao Wu, Wenjuan Guo, Yi Xing, Jie Chengdu Univ Informat Technol Coll Optoelect Engn Chengdu 610225 Peoples R China Sichuan Univ Sci & Engn Coll Mat Sci & Engn Zigong 643000 Peoples R China Chengdu Univ Sch Mech Engn Chengdu 610106 Peoples R China Sichuan Univ Coll Mat Sci & Engn Chengdu 610064 Peoples R China

Metallic Zn is a promising anode for high-safety, low-cost, and large-scale energy storage systems. However, it is strongly hindered by unstable electrode/electrolyte interface issues, including zinc dendrite, corrosion, passivation, and hydrogen evolution reactions. In this work, an in situ interface protection strategy is established by turning the corrosion/passivation byproducts (zinc hydroxide sulfates, ZHSs) into a stable hybrid protection layer. The hydrolysis of the diglycolamine buffer layer on the zinc anode provides a homogeneous basic electrolyte environment for the generation of small-sized ZHS, thereby leading to the formation of a ZHS-based hybrid layer. Benefiting from this hybrid layer, uniform zinc ion flux and high anticorrosion ability can be achieved. As a result, the decorated symmetric cell presents a long cycling lifespan of over 1500 h at a current density of 1 mA cm-2 and an area capacity of 1 mAh cm-2. It also contributes to the appealing cycling and rate performance of Zn|NH4V4O10 full cells. This work provides insight into regulating and reusing interfacial byproducts for high-performance zinc metal batteries.

关键词： Anodes

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Multispectral methods combined with computer simulation to explore the binding mechanism of the azo pigment allure red to hemoglobin and myoglobin

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LWT-FOOD science AND technology 2025年 221卷

作者： Pang, Jingtao Song, Yali Chen, Lijuan Li, Yilin Geng, Fang Gan, Na Wang, Xinhui Wang, Qingling Zeng, Tingting Wu, Di Chengdu Univ Sch Food & Biol Engn Meat Proc Key Lab Sichuan Prov 2025 Chengluo Ave Chengdu 610106 Peoples R China Sichuan Univ West China Hosp Dept Lab Med Chengdu 610100 Peoples R China Shihezi Univ Food Coll Shihezi 832061 Xinjiang Autono Peoples R China

Allure Red (AR) is a standard food-grade azo pigment and is used in meat products, but the mode of action between allure red and meat-derived proteins is rarely reported. This paper selected two essential proteins in meat, hemoglobin (Hb) and myoglobin (Mb), as protein models to investigate their binding mechanisms with AR. The binding mechanism of AR to Hb/Mb and its conformational changes were investigated using multispectral and computer simulation experiments. The results show that the AR-Hb/Mb system is promoted to bind by hydrogen bonding and van der Waals force;the two systems have a static quenching mechanism at a single binding site. The binding constants (Ka) of the AR-Hb/Mb system are 2.59 x 10-4 L/mol and 1.05 x 10-4 L/mol at 277 K. The addition of AR has less effect on the secondary structure of Hb/Mb;the two systems still maintained the structure dominated by alpha-helix. Computer simulations also showed that the protein system remains stable with the addition of AR, but the Solvent Accessible Surface Area (SASA) increased;the trend was small and did not affect the overall stability of the protein. The study's results help provide theoretical references for the application of AR in related meat products.

关键词： Hemoglobin Myoglobin Allure red Binding mechanism Computer simulation

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Enhanced landslide susceptibility mapping in data-scarce regions via unsupervised few-shot learning

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GONDWANA RESEARCH 2025年 138卷 31-46页

作者： Kong, Linghao Feng, Wenkai Yi, Xiaoyu Xue, Zhenghai Bai, Luyao Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China Pipechina Res Inst Tianjin 300450 Peoples R China

Given the critical need to assess landslide hazards, producing landslide susceptibility map (LSM) in regions with scarce historical landslide inventories poses significant challenges. This study introduces a novel landslide susceptibility assessment framework that combines unsupervised learning strategies with few-shot learning methods to increase the accuracy of LSM in these areas. The framework has been practically validated in a representative geological disaster-prone area along the West-East Gas Pipeline in Shaanxi Province, China. We employed three advanced few-shot learning models: a support vector machine, meta-learning, and transfer learning. These models implement feature representation learning for weakly correlated influencing factors through an unsupervised approach, thereby constructing an effective landslide susceptibility assessment model. We compared traditional learning methods and used the receiver operating characteristic (ROC) curve and SHAP values to quantify the effectiveness of the models. The results indicate that the meta-learning algorithm outperforms both the SVM and transfer learning in areas with limited landslide data. The integration of unsupervised strategies significantly improves performance, achieving area under the curve (AUC) values of 0.9385 and 0.9861, respectively. Compared with using meta-learning alone, incorporating unsupervised learning strategies increased the AUC by 4.76%, enhancing both the predictive power of the model and the interpretability of the features. Meta-learning under unsupervised conditions effectively mitigates the evaluation difficulties caused by insufficient landslide records, providing a viable path and empirical evidence for performance improvement in similar data- scarce regions worldwide.

关键词： Landslide susceptibility mapping Few-shot learning Meta-learning Unsupervised learning Data scarcity

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Multi-objective optimization of counter-flow dew-point evaporative coolers for multi-scenario applications using non-dominated sorting genetic algorithm II

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APPLIED THERMAL ENGINEERING 2025年 262卷

作者： Li, Yongcai Jing, Chengjun Sichuan Univ Coll Architecture & Environm Chengdu 610065 Peoples R China

As a green and low-carbon cooling technology, the improvement of evaporative cooling performance has always been the focus of attention. However, traditional research often lacks concurrent consideration of trade-offs in design parameters and multi-objective contradictions, focusing on unilateral decision-making and localized optimization in fixed environments. Therefore, this study innovatively couples non-dominated sorting genetic algorithm II with mathematical modeling. Based on MATLAB programming, a complex multi-objective optimization model of counter-flow dew-point evaporative cooler capable of parameter prediction, multi-scenario application and multi-dimensional optimization is developed. To reveal the driving mechanism of multivariable on performance parameters, comparative studies of single-objective optimization under three decision modes for two typical environments are reported. The results indicate that the better optimization is achieved by adopting five decision variables with a dew-point efficiency of 98.25 %. Compared with the original working condition, the cooling capacity and dew-point efficiency could be unilaterally increased by 128.73 % and 121.28 %, respectively, and the corresponding increment of space utilization rate reaches up to 56.39 % and 56.11 %. Subsequently, setting the cooling capacity, dew-point efficiency and fan energy consumption as the synergistic objective function, a trade-off optimization with multi-decision variables is performed for four different latitudes. The obtained Pareto frontier could flexibly invert the most potential structural and operation parameter recommendations. Especially in dry regions, a cooling capacity of 4800 W and a dew-point efficiency of 91.2 % could be realized. The method realizes the controllability of performance parameters and adjustability of energy-saving effect, which could provide a solution for the efficient design of cooling equipment.

关键词： Counter-flow dew-point evaporative cooling Model construction Multi-objective optimization Non-dominated sorting genetic algorithm II Performance improvement

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