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Nested Wave Function Collapse Enables Large-Scale Content Generation

IEEE TRANSACTIONS ON GAMES 2024年第4期16卷 892-902页

作者： Nie, Yuhe Zheng, Shaoming Zhuang, Zhan Togelius, Julian NYU Dept Comp Sci Game Innovat Lab Brooklyn NY 11201 USA Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

The Wave Function Collapse (WFC) algorithm is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and related optimized algorithms based on it lack the ability to generate commercial-scale or infinite content due to constraint conflicts and high time complexity. This article proposes the Nested WFC algorithm framework to reduce time complexity. To avoid conflict and backtracking problems, we offer a complete and subcomplete tileset preparation strategy, which requires only a small number of tiles to generate infinite, aperiodic, and deterministic content. We use Mario and Carcassonne as two game examples to describe their application and discuss potential research value. Our contribution addresses WFC's challenge in massive content generation and provides a theoretical basis for implementing concrete games.

关键词： Games Wave functions Backtracking Time complexity Three-dimensional displays Task analysis Indexes algorithm optimization constraint satisfaction problem procedural content generation (PCG) tessellation Wave Function Collapse (WFC)

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Improved Quantum algorithm: A Crucial Stepping Stone in Quantum-Powered Drug Discovery

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JOURNAL OF ELECTRONIC MATERIALS 2024年第5期54卷 3434-3443页

作者： Bikku, Thulasi Malligunta, Kiran Kumar Thota, Srinivasarao Surapaneni, Phani Praveen Amrita Vishwa Vidyapeetham Amrita Sch Comp Amaravati Dept Comp Sci & Engn Amaravati 522503 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Elect & Elect Engn Vaddeswaram 522302 AP India Amrita Vishwa Vidyapeetham Amrita Sch Phys Sci Dept Math Amaravati 522503 Andhra Pradesh India PVP Siddhartha Inst Technol Dept Comp Sci & Engn Vijayawada 520007 Andhra Pradesh India

Quantum algorithms which were derived from quantum mechanics principles are further revolutionizing this process with their potential for detailed simulation of molecular interactions. Highly notable observations can be made by delving into quantum chemistry and molecular dynamics, which facilitates the identification of suitable candidates for drug synthesis. This capability enables the identification of potential drug candidates with greater efficiency and accuracy. The critical challenges that must be addressed to realize the potential of quantum computing in drug discovery are suppression of noise due to the susceptibility of quantum structures, enhancing the scalability of tests, and design optimization for complex algorithms. These requirements necessitate the design and implementation of improved quantum algorithms with guaranteed positive computational consistency. This will empower precise simulations of molecular interactions, establishing the groundwork for more sophisticated, world-class, and selective drug discovery methodologies. The proposed quantum algorithm model was tested with PubChem, BindingDB, Tox21, and Maximum Unbiased Validation (MUV) datasets. The performance was compared to existing machine learning algorithms in terms of accuracy, precision, recall, F1 score, qubit fidelity, quantum volume, measurement time, error rate, success rate, scalability, variational quantum eigensolver convergence, entanglement entropy, resource requirements, chemical accuracy, and parallelism, which improved outcomes in most of the parameters. The experimental study shows the transformative potential of integrating quantum algorithms in the pharmaceutical industry, paving the way for the development of more effective and targeted therapeutic solutions.

关键词： Quantum computing drug discovery noise mitigation scalability algorithm optimization

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Steady-state and dynamic control of hydrocracking tail oil distillation process for high-valued products

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2024年第4期19卷 e3079-e3079页

作者： Li, Min Liu, Wenzhi Gao, Yan Huang, Zhuocheng Li, Gaoyang Pan, Hui Ling, Hao East China Univ Sci & Technol State Key Lab Chem Engn Sch Chem Engn Shanghai 200237 Peoples R China Shanghai Univ Elect Power Coll Environm & Chem Engn Shanghai Key Lab Mat Protect & Adv Mat Elect Power Shanghai 200090 Peoples R China

The maximum utilization of hydrocracking tail oil becomes increasingly important for petrochemical industry. The aim of this work is to develop optimized distillation processes to achieve various high-valued qualified oil products from hydrocracking tail oil. Six different oil products is produced and the steady-state distillation process, which aims to fractionate six qualified narrow distillates is established. The algorithm method incorporating divided-wall column (DWC) configuration was introduced into the steady-state design. Compared with traditional separation sequences, the DWC configuration leads to an energy-saving potential up to 11.17%. Furthermore, effective dynamic control strategies were proposed, demonstrating precise and efficient control performance. In the presence of a 15% feed disturbance, the dynamic control structure is capable of maintaining the product distillation range near the set value. This comprehensive study provides a thorough investigation into the efficient utilization of hydrocracking tail oil, establishing a robust theoretical foundation for its industrial application.

关键词： algorithm optimization dynamic simulation hydrocracking tail oil steady-state simulation

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A Reconfigurable Metasurface Enhancing Signal Coverage for Wireless Communication Using Reduced Numbers of p-i-n Diodes

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2024年第3期72卷 1964-1978页

作者： Xue, Hao Lu, Zeqing Ma, Xiangjin Wang, Zhiding Zhu, Lihao Yue, Shuyao Han, Jiaqi Liu, Haixia Li, Long Xidian Univ Sch Elect Engn Key Lab High Speed Circuit Design & EMC Minist Educ Xian 710071 Peoples R China

This article proposes a reflective reconfigurable metasurface design that can reduce the number of p-i-n diodes required by up to 60% for enhancing wireless communication signal coverage. The metasurface is also optimized from two aspects of element and array and can achieve enhanced signal coverage in a variety of regions, which proposes a low-cost general-purpose scheme and device. The reconfigurable element with a relative bandwidth of 8.2% is designed in a "subarray" form to reduce the p-i-n diode loading by half, and the array is sparsely designed, resulting in a further reduction of 20%. The genetic algorithm (GA) optimization method suitable for the digital coding metasurface is proposed to optimize the element's phase discretization scheme and the array's sparse method. The proposed metasurface is loaded in familiar T-shaped corridor models, and the coverage enhancement is verified based on the co-simulation of different software, showing the device can be applied in different scenes. For the first time, systematic measurement in a realistic environment using a reconfigurable metasurface with a reduced number of p-i-n diodes is performed, which proves that it can achieve signal coverage improvement in the corridor and in the actual 5G communication environment. The results indicate that this design greatly reduces system complexity and cost, which can be compatible with existing communication optimization methods, providing a practical, scalable scheme and device for enhancing signal coverage in various scenes.

关键词： & nbsp algorithm optimization low diode loading ratio low-cost general-purpose scheme reflective reconfigurable meta-surface signal coverage wireless propagation measurements

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Analysis of Network Coverage optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm algorithm

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IEEE ACCESS 2020年 8卷 42864-42876页

作者： Feng, Yingying Zhao, Shasha Liu, Hui Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China

There is a certain energy loss in the process of wireless sensor network information collection. Moreover, the current network protocols and network coverage methods are not sufficient to effectively reduce system energy consumption. In order to improve the operating efficiency and service life of wireless sensor networks, this study analyzes the classic LEACH protocol, summarizes the advantages and disadvantages, and proposes a targeted clustering method based on the K-means algorithm. At the same time, in order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a wireless sensor network coverage optimization method based on an improved artificial fish swarm algorithm was proposed. In addition, a controlled experiment is designed to analyze the effectiveness and practical effects of the proposed algorithm. The experimental results show that the method proposed in this paper has certain advantages over traditional methods and can provide theoretical references for subsequent related research.

关键词： Feedback K-means clustering artificial fish swarm algorithm network coverage algorithm optimization

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A review of welding simulation methods for large components

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Progress in Natural Science:Materials International 2023年第5期33卷 551-568页

作者： Shuai Liu Zhiqiang Wu Wenhao Zhou Honggang Zhou Keke Zhang Danqing Yin Yongping Lei Yongfeng Qiu Engineering Research Center of Mining Development Technology and Equipment for Deep Sea and Earth Ministry of Education Hunan University of Science and Technology Hunan Valin Xiangtan Iron & Steel Group Co. Ltd Research Institute of Technology Jianglu Electromechanical Science & Technology Co. Ltd.Xiangtan School of Materials Science and Engineering Henan University of Science and Technology School of Materials Science and Engineering Beijing University of Technology Guiyang Aluminum Magnesium Design & Research Institute Co. Ltd

The challenges posed by cross-scale effects, large computational requirements, high nonlinearity, and complex interactions of multiple physical phenomena constitute major obstacles to the effectiveness and accuracy of welding simulations for large structural components. Therefore, achieving accurate welding simulation to predict and effectively control welding deformation of substantial engineering structural parts is essential to ensure welding quality and improve production efficiency. This paper reviews the current research status of welding numerical simulation of large engineering components, including the establishment of the finite element model,optimization of the heat source model, selection of appropriate finite element calculation method, and the control of welding deformation through optimization algorithm. In addition, the strategies to improve the precision and efficiency of welding numerical simulation and control welding deformation of engineering components are analyzed and discussed. The future development of welding numerical simulation will focus on balancing simulation accuracy and simulation efficiency and optimizing welding parameters to control welding deformation efficiently.

关键词： Large welded components algorithm optimization Welding deformation Simulation accuracy and efficiency

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Fast-OverlaPIM: A Fast Overlap-Driven Mapping Framework for Processing In-Memory Neural Network Acceleration

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 130-143页

作者： Wang, Xuan Zhou, Minxuan Rosing, Tajana Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

Processing in-memory (PIM) is promising to accelerate neural networks (NNs) because it minimizes data movement and provides large computational parallelism. Similar to machine learning accelerators, application mapping, which determines the operation scheduling and data layout, plays a critical role in the NN acceleration on PIM. The mapping optimization of the previous NN accelerators focused on optimizing the latency of sequential execution. However, PIM accelerators feature a distinct design space of application mapping from conventional NN accelerators, due to the spatial execution of NN layers across different memory locations. This enables opportunities for overlapping execution of consecutive NN layers to improve the latency, where the succeeding layer can start execution before the preceding layer fully completes the computation. In this article, we propose Fast-OverlaPIM framework that incorporates computational overlapping optimization into the deep neural network mapping exploration process on PIM architectures. Fast-OverlaPIM includes analytical algorithms for fast and accurate overlap analysis. Furthermore, it proposes a novel mapping search strategy and a transformation mechanism to enable efficient design space exploration on the overlap-based mapping for the whole network. Our framework demonstrates a significant improvement in runtime performance from 3.4x to 323.1x compared to the previous state-of-the-art overlap-based framework. Our experiments show that Fast-OverlaPIM can efficiently produce mappings that are 4.6x to 18.1x faster than the state-of-the-art mapping optimization framework under the same architecture constraints.

关键词： optimization Computer architecture Artificial neural networks Design automation Random access memory Parallel processing Vectors algorithm optimization deep neural networks (DNNs) processing in-memory (PIM) software-hardware co-design

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Optimizing time-delay estimation for underwater acoustic positioning in tank inspection robots to mitigate reflection interference

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APPLIED OCEAN RESEARCH 2025年 154卷

作者： Xu, Zhenyu Chen, Zhangwei Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China

Robot inspection of large storage tanks has become the primary method for addressing hidden risks in the petrochemical industry. Accurate positioning of Tank Inspection Robots (TIR) is crucial in this process. However, the unique characteristics of the liquid medium and tank walls introduce challenges such as noise, reverberations, and multipath effects, which frequently complicate the acoustic positioning of robots. These issues are further complicated by numerous interface reflections. To address the problems caused by reflections, this paper focuses on time-delay estimation for underwater acoustic positioning. It analyzes the impact of reflections on various acoustic positioning methods and simplifies the time-delay estimation process within the Time Difference of Arrival (TDOA) localization algorithm in reverberant environments. To this end, the paper introduces the AMCOCEP algorithm, which integrates Generalized Cross-Correlation, the cepstrum, and the Average Magnitude Difference Function (AMDF). The effectiveness of the proposed positioning model and method is validated by positioning a robot within a storage tank and comparing positioning accuracy before and after algorithm optimization. According to the experimental results, the AMCOCEP algorithm reduces the average error by 1.07 cm and improves the average accuracy by 30.7 %.

关键词： Tank inspection robot Underwater acoustic positioning Time-delay estimation Reflection interference algorithm optimization

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A sensitive wavelength-enhanced reconstruction algorithm for track quality assessment based on measured data and vehicle dynamics

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MEASUREMENT 2025年 240卷

作者： Chen, Zelin Cai, Xiaopei Tang, Xueyang Wang, Meng Liu, Weibin Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China State Key Lab Track Technol High Speed Railway Beijing 100081 Peoples R China

High-speed railways are extensively utilized worldwide. However, with prolonged operation, track irregularities increasingly pose significant challenges, adversely affecting the safety and stability of train operations. This paper proposes a sensitive wavelength-enhanced reconstruction algorithm based on the cmplete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to address this issue. This method evaluates the influence of reconstructed track irregularities on vehicle dynamic response, considering sensitive wavelengths and amplification factors. The algorithm effectively enhances these sensitive wavelengths while maintaining the integrity of the input signal, utilizing measured track irregularities. After applying the algorithm, the maximum vertical body acceleration rises by 206.09%. The study validates a coupled train model and analyzes the mapping relationship between single-wave irregularities and vertical body acceleration. Compared to random irregularities, the maximum vertical acceleration can amplify up to 122.80% under various operating conditions. The periodic sensitive wavelengths of track irregularities significantly impact the train's dynamic response.

关键词： High-speed railway Track irregularity Vehicle dynamics simulations algorithm optimization

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Integrating knowledge-data-driven method to predict load-displacement curve on a trapdoor

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COMPUTERS AND GEOTECHNICS 2025年 178卷

作者： Zeng, Hui Lin, Xing-Tao Wang, Deng Chen, Xiangsheng Su, Dong Chen, Ruidi Liu, Wei Shenzhen Univ Key Lab Resilient Infrastruct Coastal Cities MOE Shenzhen 518060 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518061 Peoples R China State Key Lab Intelligent Geotech & Tunnelling Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Underground Polis Acad Shenzhen 518060 Peoples R China

Accurately predicting the overlying earth pressure of underground structures is a key point in the design and construction of underground structures. On the basis of the existing composite function model describing the load-displacement curve (LDC) and experimental data, a intergrating knowledge-data-driven method to predict LDC is proposed in this paper. The buried depth (H), trapdoor width (B), soil weight (gamma) and internal friction angle (phi) are selected as multi-characteristic input variables. The initial modulus of arching a, the minimum soil arching ratio rho min, the minimum soil arching ratio displacement xi min and the ultimate soil arching ratio rho ult are output variables. Two swarm intelligence optimization algorithms (i.e., sparrow search algorithm (SSA) and particle swarm optimization (PSO)) are used to optimize the parameters of the established generalized regression neural network (GRNN), and the knowledge-data cooperatively driven prediction of the LDC is realized. The results show that the GRNN model optimized by swarm intelligence algorithm has better prediction performance than the GRNN model. The LDC obtained from the output parameters of three GRNN models are compared with the results of the trapdoor experiments. The comparison results show that the LDC obtained by the GRNN model optimized by swarm intelligence algorithm are more consistent with the experimental results than that those obtained by GRNN model, and the prediction performance of the SSA-GRNN is slightly better than that of the PSO-GRNN.

关键词： Soil arching effect Load-displacement curve (LDC) Composite function model Generalized regression neural network algorithm optimization

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