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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Atomically precise Au_(25)(GSH)_(18)nanoclusters versus plasmonic Au nanocrystals:Evaluating charge impetus in solar water oxidation

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Chinese Chemical Letters 2023年第6期34卷 195-199页

作者： Qiao-Ling Mo Xiao-Cheng Dai Yang Xiao Fang-Xing Xiao College of Materials Science and Engineering Fuzhou UniversityNew CampusMinhou 350108China Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350108China

Atomically precise metal nanoclusters(NCs)have been deemed as an emerging class of metal nanomaterials owing to fascinating size-dependent physicochemical properties,discrete energy band structure,and quantum confinement effect,which are distinct from conventional metal nanoparticles(NPs).Nevertheless,metal NCs suffer from photoinduced self-oxidative aggregation accompanied by in-situ transformation to metal NPs,markedly reducing the photosensitization of metal ***,maneuvering the generic instability of metal NCs,we perform the charge transport impetus comparison between atomically precise metal NCs and plasmonic metal NPs counterpart obtained from in-situ self-transformation of metal NCs in photoelectrochemical(PEC)water splitting *** conceptual demonstration,we proposed two quintessential heterostructures,which include TNTAs-Au_(25)heterostructure fabricated by electrostatically depositing glutathione(GSH)-protected Au_(25)(GSH)_(18)NCs on the TiO_(2)nanotube arrays(TNTAs)substrate,and TNTAs-Au heterostructure constructed by triggering self-transformation of Au_(25)(GSH)_(18)NCs to plasmonic Au NPs in TNTAs-Au_(25)via *** results indicate that photoelectrons produced over Au_(25)NCs are superior to hot electrons of plasmonic Au NPs in stimulating the interracial charge transport toward solar water *** is mainly ascribed to the significantly accelerated carrier transport kinetics,prolonged carrier lifespan,and substantial photosensitization effect of Au_(25)NCs compared with plasmonic Au NPs,resulting in the considerably enhanced PEC water splitting performance of TNTAs-Au_(25)relative to plasmonic TNTAs-Au counterpart under visible light *** work would provide important implications for rationally designing atomically precise metal NCsbased photosystems toward solar energy conversion.

关键词： Metal nanoclusters Surface Plasmon resonance Interfacial charge transfer Photoelectrochemical water splitting

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Causes of Winter Persistent Extreme Cold Events in Northeastern China

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Advances in Atmospheric sciences 2025年第4期42卷 780-793页

作者： Ming YANG Qingjiu GAO Tim LI State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing 210044China Department of Atmospheric Sciences University of Hawaii at ManoaHonoluluHI 96822USA

Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data *** extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10–40-day periodicity,indicating a close link with atmospheric intraseasonal oscillations(ISOs).The ECEs are categorized into W-and *** the former,the low-frequency cooling associated with the ISO first penetrates into the western boundary of the northeastern China domain and later occupies the entire domain at its peak *** upper-tropospheric circulation associated with this type is characterized by a northwest–southeast-oriented Rossby wave train,expanding from the Ural Mountains to the western Pacific *** the latter,the cooling invades the northern boundary first and then penetrates into the entire *** upper tropospheric precursory signal associated with this type is a zonally oriented negative geopotential height anomaly,which slowly moves southward.A downward-propagating signal is observed in the stratospheric potential vorticity field prior to the peak cooling,implying a possible stratospheric *** addition to the W-and N-types,ECEs can also occur in a localized region near either at the northern or southern boundary of the domain.

关键词： extreme cold events low-frequency ISO mode Rossby wave train composite analysis downward propagation potential vorticity

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive Control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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A security control scheme based on quantum keys for IoT terminals

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International Journal of Security and Networks 2023年第3期18卷 189-198页

作者： Zhu, Dexin Du, Xiaogang Mao, Jigui Song, Lijun College of Computer Science and Technology Changchun University Changchun130022 China College of Science Changchun University Changchun130022 China Jilin Engineering Laboratory for Quantum Information Technology Changchun130062 China

With the development of computer technology and the continuous improvement of computing power, the security control of internet of things terminals has received great attention. This paper proposes a security control scheme based on quantum keys for the internet of things terminals. In the wide area network, the control commands between Alice and Bob are transmitted by quantum key encryption. In the local area network, Bob sends the decrypted plaintext to the LED microcontroller board to execute the control instruction. This paper also designs a two-way identity authentication scheme based on a hash function, a sliding window key synchronisation scheme, and an eavesdropping experiment. Through experiments, the quantum key encoding rate of the quantum key distribution system is 7.4 kbps, the average time for Alice to send instructions is 141.6 ms, the average time for Bob to decrypt instructions is 12.4 ms, and the average time for key synchronisation in the time sliding pane is 116 ms. Even after stealing Alice’s ciphertext data, Eve still cannot recover the plaintext data. The scheme realises the security control of the internet of things terminal. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Synchronization

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On Cylinder- vs. Sphere-Based Stochastic Modeling of Space-Time Correlated UAV-to-UAV Channels 24

On Cylinder- vs. Sphere-Based Stochastic Modeling of Space-T...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Zeng, Linzhou Liao, Xuewen Zhu, Yunzhe Ma, Zhangfeng Jiang, Hao Xi'an Jiaotong University Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology School of Information and Communications Engineering Xi'an710049 China Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Shaoyang University School of Information Science and Engineering Shaoyang422000 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing210044 China

ISBN: (纸本)9798350363760

The two-cylinder model and the two-sphere model are two widely-used geometrical channel models that are both the generalizations of the two-ring model;however, a comparative study between them seems to be missing in literature. In this paper, under the background of unmanned aerial vehicle (UAV) air-to-air propagation modeling, similarities and differences between the two geometry-based stochastic models (GBSMs) are analyzed by reviewing their respective closed-form space-time correlation functions (ST-CFs). Notably, some interesting properties of the ST-CFs are analytically obtained, which reveals the behaviors of the ST-CF with respect to several UAV-related parameters, including the moving directions and the antenna orientations. © 2024 IEEE.

关键词： Stochastic models

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Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models 31

Chain-of-Specificity: Enhancing Task-Specific Constraint Adh...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Kaiwen Zhong, Jiang Zhang, Hongzhi Zhang, Di Jin, Li Yu, Yue Zhang, Jingyuan College of Computer Science Chongqing University China Kuaishou Inc. China Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that LLMs sometimes struggle with adhering to specific constraints (e.g., in specific place or at specific time), at times even overlooking them, which leads to responses that are either too generic or not fully satisfactory. Existing approaches attempted to address this issue by decomposing, rewriting input instructions, or reflecting on prior failings, yet they fall short in adequately emphasizing specific constraints and in unlocking the underlying knowledge (e.g., programming within the context of software development). In response, this paper proposes a simple yet effective method Chain-of-Specificity (CoS). Specifically, CoS iteratively emphasizes the specific constraints in the input instructions, unlocks knowledge within LLMs, and refines responses. Experiments conducted on publicly available and self-build complex datasets demonstrate that CoS outperforms existing methods in enhancing generated content especially for the specificity. Besides, as the number of specific constraints increase, other baselines falter, while CoS still performs well. Moreover, we show that distilling responses generated by CoS effectively enhances the ability of smaller models to follow the constrained instructions. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Pressure-induced superconductivity in the topological crystalline insulator NaCd4As3

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Physical Review B 2024年第6期110卷 L060509-L060509页

作者： Haiyang Yang Yonghui Zhou Jing Wang Shuyang Wang Chao An Ying Zhou Xuliang Chen Lili Zhang Xiaoping Yang Zhaorong Yang Anhui Key Laboratory of Low-Energy Quantum Materials and Devices Science Island Branch of Graduate School Institute of Advanced Magnetic Materials College of Materials and Environmental Engineering Institutes of Physical Science and Information Technology Shanghai Advanced Research Institute

Recently, a united symmetry-protected topological crystalline insulator (TCI) NaCd4As3 has been discovered experimentally, which further features a temperature-driven transformation from TCI to TI due to the mirror symmetry broken across a rhombohedral-to-monoclinic structural transition. Here, we report the pressure tuning of structural property and emergence of superconductivity in TCI NaCd4As3 by comprehensive high-pressure experiments in diamond-anvil cell. Temperature-dependent Raman measurements show that the structural transition temperature gradually reduces upon compression and is completely suppressed above ∼4 GPa. The stable rhombohedral phase further undergoes amorphization evidenced by synchrotron x-ray-diffraction result, which sets in at 20.7 GPa and completes at 34.4 GPa. Along with the complete amorphization, the metallization and superconductivity with a critical temperature Tc∼2.5 K appear simultaneously, as observed in transport experiments. We argue that the sudden enhancement in Tc across 60.0 GPa is likely related to the pressure-driven amorphous polymorphism. Our finding unravels the fundamental structure-property relationship and highly tunable crystallographic symmetry in NaCd4As3 by external pressure, potentially sparking further investigations via alternative tuning strategies.

关键词： Phase transitions Pressure effects Superconductivity Crystalline systems Topological materials Pressure techniques

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Dynamic motion of quadrupedal robots on challenging terrain:a kinodynamic optimization approach

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Frontiers of Mechanical engineering 2024年第3期19卷 23-40页

作者： Qi LI Lei DING Xin LUO Engineering Research Center of Hubei Province for Clothing Information Wuhan 430200China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan 430200China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China

The dynamic motion of quadrupedal robots on challenging terrain generally requires elaborate spatial–temporal kinodynamic motion planning and accurate control at higher refresh rate in comparison with regular ***,conventional quadrupedal robots usually generate relatively coarse planning and employ motion replanning or reactive strategies to handle terrain *** resultant complex and computation-intensive controller may lead to nonoptimal motions or the breaking of locomotion *** this paper,a kinodynamic optimization approach is *** generate long-horizon optimal predictions of the kinematic and dynamic behavior of the quadruped robot on challenging terrain,we formulate motion planning as an optimization problem;jointly treat the foot’s locations,contact forces,and torso motions as decision variables;combine smooth motion and minimal energy consumption as the objective function;and explicitly represent feasible foothold region and friction constraints based on terrain *** track the generated motions accurately and stably,we employ a whole-body controller to compute reference position and velocity commands,which are fed forward to joint controllers of the robot’s *** verify the effectiveness of the developed approach through simulation and on a physical quadruped robot *** show that the quadruped robot can successfully traverse a 30°slope and 43% of nominal leg length high step while maintaining the rhythm of dynamic trot gait.

关键词： quadrupedal robot kinodynamic planning nonlinear optimization challenging terrain whole-body control

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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network 22

Prediction of Driving Departure of Mining Autonomous Transpo...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Lecong Yu, Guizhen Li, Han Xia, Qi Zhang, Xiangyu Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. Key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms. © 2024 IEEE.

关键词： Mine transportation

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