Journal of Industrial Information Integration最新文献_第10页

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system EBH-IoT：基于云辅助区块链的物联网医疗系统电子健康记录的高能效安全数据收集与分发

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-10-10 DOI: 10.1016/j.jii.2024.100702

Anita Sahoo, Srichandan Sobhanayak

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

健康物联网（H-IoT）与区块链技术的整合正在许多领域得到大量开发和使用，特别是在电子医疗保健领域，以收集患者的数据，即电子健康记录（EHR）。H-IoT 设备能够提供患者的实时感知数据，并对其进行处理、分析和分发。区块链为电子病历数据提供去中心化的计算、分发和存储。因此，将 H-IoT 与区块链技术相结合，可以成为设计基于 H-IoT 的去中心化电子医疗系统的合理选择。但 H-IoT 网络存在一些内在挑战，如低计算量、能源限制、安全性、能源优化、数据存储和实时数据分析等。此外，传统的基于电子病历的系统还存在数据可能丢失、安全性不足和健康记录不可更改的共识、不同机构之间的连接分散、临床数据检索方法无效等问题。本文首先研究了区块链技术在医疗系统中的性能。其次，我们提出了一种基于哈里斯鹰优化算法（HHO）的改进型聚类机制，用于电子病历的收集和共享。我们使用基于超级账本架构的电子病历（EHR）共享系统和 Matlab 对所提出的系统进行了测试和验证。在区块链网络的延迟和吞吐量方面，拟议系统分别实现了 12% 和 7% 的增量改进。与现有技术水平相比，拟议的聚类技术在活节点、能耗、吞吐量和平均传输延迟方面分别提高了 10%、12%、14% 和 16%。

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引用次数: 0

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation 用于钻井风险评估的具有外部和内部因果关系关注的新型物理引导时空数据挖掘方法

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-10-10 DOI: 10.1016/j.jii.2024.100701

Fengtao Qu , Hualin Liao , Huajian Wang , Jiansheng Liu , Tianyu Wu , Yuqiang Xu

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

随着钻井技术的发展和钻井作业扩展到更复杂的地质环境，钻井风险评估变得越来越复杂，对传统方法的有效性提出了挑战。针对这一问题，提出了一种新颖的物理引导的时空数据挖掘方法，该方法整合了外部和内部因果关注机制，用于钻井风险评估。首先，设计了一种基于时空序列聚类的风险校准方法。该方法通过挖掘钻井过程中标志数据的微妙变化，动态校准钻井风险。其次，建立了基于钻井风险标志相关性和模糊推理系统的专家经验提取方法。Kendall's tau 用于量化钻井风险标志之间的相关性。采用模糊推理系统将模糊且难以量化的专家经验转化为可计算、可解释的规则。为了提高模糊推理系统的灵活性和适应性，还构建了专家经验规则库。随后，构建了一个整合外部和内部因果注意机制的时空数据挖掘模型（STMIEICAM）。外部因果关注机制（ECAM）量化了迹象与风险之间的相关性。内部因果注意机制（ICAM）提高了模型捕捉和量化时空序列特征的能力。最后，将来自模糊推理系统和井场的物理知识嵌入 STMIEICAM 模型，形成了一个物理引导的时空数据挖掘模型（PG-STMIEICAM），该模型集成了外部和内部因果关注机制，可对钻井风险进行分级评估。为了验证所提方法的有效性，将其应用于油田溢流风险评估。结果表明，该方法不仅能揭示数据中的隐藏关系，还能整合专家知识，实现对钻井风险的准确评估。

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引用次数: 0

Towards autonomous supply chains: Definition, characteristics, conceptual framework, and autonomy levels 实现自主供应链：定义、特点、概念框架和自主水平

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-10-05 DOI: 10.1016/j.jii.2024.100698

Liming Xu, Stephen Mak, Yaniv Proselkov, Alexandra Brintrup

Recent global disruptions, such as the COVID-19 pandemic and the ongoing geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Among various solution offerings, Autonomous supply chains (ASCs) have emerged as key enablers of increased integration and visibility, enhancing flexibility and resilience in turbulent trade environments through the widespread automation of low level decision making. Although ASC solutions have been discussed and trialled over several years, they still lack well-established theoretical foundations. This paper addresses this research gap by presenting a formal definition of ASC along with its defining characteristics and auxiliary concepts. We propose a layered conceptual framework, called the MIISI model. An illustrative case study focusing on the meat supply chain demonstrates an initial ASC implementation based on this conceptual model. Furthermore, we introduce a seven-level supply chain autonomy reference model, delineating a trajectory towards achieving full supply chain autonomy. Recognising that this work represents an initial endeavour, we emphasise the need for continued exploration in this emerging domain. This work is designed to stimulate further research, both theoretical and technical, and contribute to the continual evolution of ASCs.

最近发生的全球破坏事件，如 COVID-19 大流行病和持续的地缘政治冲突，深刻暴露了传统供应链的脆弱性，需要探索更具弹性的替代方案。在各种解决方案中，自主供应链（ASC）已成为提高集成度和可视性的关键因素，通过广泛的低层次决策自动化，增强了动荡贸易环境中的灵活性和应变能力。虽然 ASC 解决方案已经讨论和试用了数年，但仍缺乏完善的理论基础。本文针对这一研究空白，提出了 ASC 的正式定义及其定义特征和辅助概念。我们提出了一个分层概念框架，称为 MIISI 模型。一个以肉类供应链为重点的示例研究展示了基于该概念模型的 ASC 初步实施情况。此外，我们还引入了一个七级供应链自主参考模型，勾勒出实现全面供应链自主的轨迹。我们认识到这项工作是一项初步尝试，因此强调有必要在这一新兴领域继续探索。这项工作旨在促进理论和技术方面的进一步研究，为供应链自主的持续发展做出贡献。

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引用次数: 0

Novel decision making approach for sustainable renewable energy resources with cloud fuzzy numbers 利用云模糊数实现可持续可再生能源的新型决策方法

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-10-03 DOI: 10.1016/j.jii.2024.100700

Musavarah Sarwar , Muhammad Akram , Muhammet Deveci

Decision making approaches depending on the assessments of individual decision makers produce inaccurate results due to the existence of multiple uncertainties. To model intrapersonal uncertainty, interpersonal uncertainty and randomness in decision making assessments, this research study proposes a novel approach by integrating linear and non-linear type of fuzzy numbers with cloud model theory using novel technique of computing entropy of these fuzzy numbers. A novel mathematical model known as cloud fuzzy numbers is introduced using the concepts of fuzzy numbers and cloud theory. The self-evaluated relative weights of experts are computed using a non-linear optimization method which is based on maximum deviation method and Lagrange multipliers of cloud fuzzy numbers. The new cloud fuzzy numbers are then combined with CODAS (combinative distance based assessment) approach that is based on the largest Euclidean and Taxicab distances for the selection of suitable criteria. Firstly, the linguistics evaluations are converted into the fuzzy numbers and then cloud fuzzy numbers using formulae of expectation and entropy ensuring that the obtained interval cloud values follows a normal distribution. Secondly, the cloud fuzzy weighted arithmetic averaging operator is used to aggregate cloud fuzzy numbers using the self-evaluated fuzzy weights Thirdly, the assessment score is determined to rank the alternatives by computing the distance between normalized weighted matrix and the negative ideal solution. Finally, a case study is discussed for the selection of best renewable energy resource in Turkey to elaborate the significance of the proposed research. The convergence and accuracy of the proposed model is proved with certain mathematical and theoretical results.

由于存在多种不确定性，依赖决策者个人评估的决策方法会产生不准确的结果。为了模拟决策评估中的个人内部不确定性、人际间不确定性和随机性，本研究提出了一种新方法，将线性和非线性类型的模糊数与云模型理论相结合，并使用计算这些模糊数熵的新技术。利用模糊数和云理论的概念，引入了一种称为云模糊数的新型数学模型。专家自我评估的相对权重是通过非线性优化方法计算得出的，该方法基于最大偏差法和云模糊数的拉格朗日乘数。然后，将新的云模糊数与 CODAS（基于距离的组合评估）方法相结合，该方法基于最大欧氏距离和出租车距离，用于选择合适的标准。首先，使用期望和熵公式将语言学评价转换为模糊数，然后再转换为云模糊数，确保所获得的区间云值服从正态分布。第三，通过计算归一化加权矩阵与负理想解之间的距离，确定评估分数，对备选方案进行排序。最后，讨论了土耳其最佳可再生能源资源选择的案例研究，以阐述所提研究的意义。所提模型的收敛性和准确性通过一定的数学和理论结果得到了证明。

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引用次数: 0

Flotation separation of lithium–ion battery electrodes predicted by a long short-term memory network using data from physicochemical kinetic simulations and experiments 利用物理化学动力学模拟和实验数据，通过长短期记忆网络预测锂离子电池电极的浮选分离情况

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-09-28 DOI: 10.1016/j.jii.2024.100697

Allan Gomez-Flores , Hyunsu Park , Gilsang Hong , Hyojeong Nam , Juan Gomez-Flores , Seungmin Kang , Graeme W. Heyes , Laurindo de S. Leal Filho , Hyunjung Kim , Jung Mi Lee , Junseop Lee

Anode and cathode active materials from spent lithium–ion batteries may be recovered and potentially used in new batteries to promote recycling and resource circulation. Froth flotation was applied to pristine active materials and the black mass obtained from pretreated spent batteries. Flotation kinetics was simulated with the use of computational fluid dynamics and surface chemistry. Bubble surface coverage and entrainment in the flotation kinetics model were selected and optimized by systematic fitting to experimental data. Entrainment influences the recovery and grade of the active materials. The optimized flotation kinetics model was used for generating additional data that, along with the fitted data, were used to train a deep learning neural network. The trained network was validated using anode–cathode and black mass flotation experiments, and its predictions showed a maximum residual error of 0.18 ± 0.11 recovery. The simulation framework was developed into a desktop application that predicts the flotation behavior of active materials. It provides information for estimating results following operational and physicochemical changes and for optimizing flotation processes. Finally, recovered anode active materials from black mass were selected for coin cell tests. The coulombic efficiency of these coin cells was initially lower (86.8 %) than that of cells made with pristine graphite particles (98.4 %).

废旧锂离子电池中的正极和负极活性材料可以回收，并有可能用于新电池，以促进回收和资源循环。对原始活性材料和从预处理废电池中获得的黑色物质进行了浮选。利用计算流体动力学和表面化学模拟了浮选动力学。通过对实验数据进行系统拟合，选择并优化了浮选动力学模型中的气泡表面覆盖率和夹带率。夹带影响活性物质的回收率和品位。优化后的浮选动力学模型用于生成更多数据，这些数据与拟合数据一起用于训练深度学习神经网络。利用阳极-阴极和黑质浮选实验对训练后的网络进行了验证，其预测结果显示最大残余误差为 0.18 ± 0.11 回收率。模拟框架被开发成一个桌面应用程序，用于预测活性材料的浮选行为。它为估计操作和物理化学变化后的结果以及优化浮选工艺提供了信息。最后，从黑泥中回收的阳极活性材料被选中用于硬币池测试。这些硬币电池的库仑效率（86.8%）最初低于使用原始石墨颗粒制造的电池（98.4%）。

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引用次数: 0

Full-progress crop management and harvesting scheme with integrated space information: A case of jujube orchard 综合空间信息的作物全程管理和收获方案：以枣园为例

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-09-28 DOI: 10.1016/j.jii.2024.100699

Jing Nie , Yichen Yuan , Yang Li , Jingbin Li , Achyut Shankar , Bilal Abu-Salih , Joel J.P.C. Rodrigues

Space information integration can better obtain the environmental information, crop information, climate information and other key factors of the farmland, which is more helpful for crop management and harvesting. In the traditional crop management and harvesting process, crop management and harvesting are two relatively independent processes, lacking a complete full-process scheme. In this regard, this paper proposes a full-process crop management and harvesting scheme with integrated space information, which takes the space information of the crop planting area as the core, fully associates and closely integrates the crop management process and harvesting process, and carries out scientific field management, accurate yield estimation, and reasonable harvesting path planning for crops, to improve the intelligence and precision of the crop management and harvesting process. By using the jujube orchard as a case study, it was verified that the full-process crop management and harvesting scheme with integrated space information can improve the management level and economic benefits of the jujube orchard.

空间信息集成可以更好地获取农田的环境信息、作物信息、气候信息等关键因素，更有助于作物管理和收获。在传统的作物管理与收获过程中，作物管理与收获是两个相对独立的过程，缺乏完整的全过程方案。为此，本文提出了一种集成空间信息的全过程作物管理与收获方案，该方案以作物种植区域的空间信息为核心，将作物管理过程与收获过程充分关联、紧密结合，对作物进行科学的田间管理、准确的产量估算、合理的收获路径规划，提高作物管理与收获过程的智能化与精准化。以枣园为例，验证了集成空间信息的作物全过程管理与收获方案能够提高枣园的管理水平和经济效益。

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引用次数: 0

Low-altitude intelligent transportation: System architecture, infrastructure, and key technologies 低空智能交通：系统架构、基础设施和关键技术

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-09-18 DOI: 10.1016/j.jii.2024.100694

Changqing Huang , Shifeng Fang , Hua Wu , Yong Wang , Yichen Yang

In the context of the burgeoning low-altitude economy, low-altitude intelligent transportation (LAIT) has emerged as the focal point of research. This study comprehensively explores the current state, challenges, and future development prospects of LAIT from three key aspects: system architecture, infrastructure, and critical technologies. First, we propose a future LAIT system framework based on a cyber-physical system (CPS) layered architecture to provide a potential solution for urban air transport. Second, we introduce a framework for the entire lifecycle and management chain of the LAIT system, offering an in-depth analysis of each stage from design, construction, and operation to management, with the aim of realizing intelligent management and operation of low-altitude transportation. Finally, this study discusses the technical, security, and social challenges that future LAIT will face in the context of Industry 4.0, while envisioning the pathways of technological innovation. It summarizes the need for advanced infrastructure and breakthroughs in key technologies such as the Internet of Things (IoT), 6G, artificial intelligence (AI), and low-altitude geographic information system. This study provides a systematic framework and technical guidelines for the future development of low-altitude intelligent transportation, supporting continuous innovation, and upgrading the low-altitude economy.

在低空经济蓬勃发展的背景下，低空智能交通（LAIT）已成为研究的焦点。本研究从系统架构、基础设施和关键技术三个方面全面探讨了低空智能交通（LAIT）的现状、挑战和未来发展前景。首先，我们提出了基于网络物理系统（CPS）分层架构的未来 LAIT 系统框架，为城市航空运输提供了一个潜在的解决方案。其次，我们介绍了 LAIT 系统的全生命周期和管理链框架，深入分析了从设计、建设、运营到管理的各个阶段，旨在实现低空运输的智能化管理和运营。最后，本研究探讨了工业 4.0 背景下未来 LAIT 将面临的技术、安全和社会挑战，同时展望了技术创新的途径。本研究总结了先进基础设施的需求以及物联网（IoT）、6G、人工智能（AI）和低空地理信息系统等关键技术的突破。本研究为低空智能交通的未来发展提供了系统框架和技术指南，支持持续创新，提升低空经济。

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引用次数: 0

E-CARGO-based dynamic weight offload strategy with resource contention mitigation for edge networks 基于 E-CARGO 的动态权重卸载策略，缓解边缘网络的资源争用问题

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-09-18 DOI: 10.1016/j.jii.2024.100695

Wenyi Mao , Jinjing Tan , Wenan Tan , Ruiling Gao , Weijia Zhuang , Jin Zhang , Shengchun Sun , Kevin Hu

With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

随着移动边缘计算（MEC）在工业物联网（IIoT）和 5G 网络的智能制造系统中得到广泛应用，如何高效地卸载计算任务已成为一个热门研究领域。本文介绍了基于角色的协作（RBC）环境--类、代理、角色、组和对象（E-CARGO）模型，以全面管理边缘网络环境中的 MEC 服务器和用户计算任务，从而提高智能制造系统中任务卸载的效率和性能。首先，延迟和能耗是评估卸载效果的重要指标。本文提出了一种基于用户延迟容忍度的预分配算法，动态调整延迟-能耗权重系数，优化系统资源分配，实时调整卸载决策。其次，扩展了基于 E-CARGO 的代理角色冲突的群角色分配（GRACAR）模型，以及 GRACAR 的动态加权（GRACAR-DW）模型和形式建模。通过引入资源争用约束，主动缓解了因任务数据过度卸载到同一 MEC 服务器而造成的资源争用问题。最后，开发了基于混合整数线性规划（MILP）的 Gurobi 解决方案，以帮助验证和综合所提出的模型。仿真结果表明，该策略大大提高了 MEC 系统在延迟和能耗方面的整体性能，同时还为边缘网络中的卸载决策提供了新思路和技术支持。

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引用次数: 0

Collaborative human and computer controls of smart machines – A proposed hybrid control 智能机器的人机协同控制--混合控制建议

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-09-12 DOI: 10.1016/j.jii.2024.100684

Hussein Bilal, Zhuming Bi, Nashwan Younis, Hosni Abu-Mulaweh

Human-Machine Interaction (HMI) and Brain-Computer Interface (BCI) are evolving technologies that show the great potentials to extract and utilize humans’ intents in controlling smart machines. However, existing HMI and BCI technologies are limited in terms of (1) the number of Degrees- of-Freedom (DoF) to be controlled and (2) the ways the performance of BCI-enabled control systems are verified and validated. This study aimed to explore the solutions to addree both of above concerns; we proposed a hybrid control system that is capable of training, detecting, and interpreting humans’ intents, and utilizing humans’ intents in real-time controls of smart machines. More specifically, the system acquired brain signals in the form of Electroencephalography (EEG) by an Emotiv Epoc X and processed these signals to detect and extract humans’ intents in real-time machine controls. To cope with the frequency difference of humans’ thinking and machine motion controls, we developed a hybrid control module to fuse humans’ and machine's intelligence so that low-frequency humans’ intents could be used in real-time machine controls. The system was prototyped and verified experimentally. The system was verified to achieve the accuracy of over 90 % in recognizing humans’ intents and controlling a robot by the operator's intents with a satisfactory responding time and accuracy.

人机交互（HMI）和脑机接口（BCI）是不断发展的技术，在提取和利用人类意图控制智能机器方面显示出巨大潜力。然而，现有的人机交互（HMI）和脑机接口（BCI）技术在以下方面受到限制：（1）可控制的自由度（DoF）数量；（2）验证和确认脑机接口控制系统性能的方法。本研究旨在探索解决上述两个问题的方法；我们提出了一种混合控制系统，该系统能够训练、检测和解释人类意图，并在智能机器的实时控制中利用人类意图。更具体地说，该系统通过 Emotiv Epoc X 获取脑电图（EEG）形式的大脑信号，并对这些信号进行处理，从而在实时机器控制中检测和提取人类意图。为了应对人类思维和机器运动控制的频率差异，我们开发了一个混合控制模块，以融合人类和机器的智能，从而在实时机器控制中使用低频人类意图。我们对该系统进行了原型设计和实验验证。经过验证，该系统对人类意图的识别准确率超过 90%，并能根据操作员的意图控制机器人，而且响应时间和准确率都令人满意。

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引用次数: 0

HVPS-DFN-DL: Intelligent capture and characterization of geological fracture outcrops based on a hybrid vision-photogrammetric system and discrete fracture network HVPS-DFN-DL：基于混合视觉-摄影测量系统和离散断裂网络的地质断裂露头的智能捕捉和特征描述

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2024-09-08 DOI: 10.1016/j.jii.2024.100685

Mingyang Wang , Congcong Wang , Enzhi Wang, Xiaoli Liu, Yuhang Lu

The main objective of this article is to provide a framework for intelligent capture-acquisition analysis of geometric information from geological outcrops. By combining deep learning methods with photogrammetric data from unmanned aerial vehicles (UAVs), FPV drones, and terrestrial cameras acquired by a hybrid vision-photogrammetric system (HVPS), intelligent fracture detection and geometric information segmentation of multiscale field geological outcrops were achieved. The extraction results were subsequently used to generate a three-dimensional discrete fracture network (DFN) of real rock masses for studying the influence of the spatial connectivity of discontinuity structural planes on the mechanical and hydrodynamic characteristics of rock masses. By testing data collected in situ from a variety of field rock masses in several regions of China, this framework was shown to be a very efficient method for geostatistical work, exhibiting very low measurement errors. Furthermore, this framework is extremely safe for geologists and applicable to a wide range of site geological environments. It is also suitable for field geological surveys, geometry acquisition of outcropping lithologies, obtaining tunnel face and surrounding fissure statistics, and geological stability assessment of unstable rock masses. This framework can also provide a method for unmanned topographic-geological exploration. Furthermore, the fracture network realism and the data acquisition efficiency have been greatly improved, and the difficulty of developing field measurements and validating the DFN model has been overcome.

本文的主要目的是提供一个对地质露头的几何信息进行智能捕获-获取分析的框架。通过将深度学习方法与无人飞行器（UAV）、FPV 无人机以及混合视觉-摄影测量系统（HVPS）获取的地面相机的摄影测量数据相结合，实现了多尺度野外地质露头的智能断裂检测和几何信息分割。提取结果随后用于生成真实岩体的三维离散断裂网络（DFN），以研究不连续结构面的空间连通性对岩体力学和流体力学特征的影响。通过测试在中国多个地区采集的各种野外岩体数据，证明该框架是一种非常有效的地质统计方法，测量误差非常小。此外，该框架对地质学家非常安全，适用于各种现场地质环境。它还适用于野外地质勘测、出露岩性的几何采集、隧道工作面和周边裂隙统计以及不稳定岩体的地质稳定性评估。该框架还可为无人地形地质勘探提供一种方法。此外，该框架还大大提高了裂隙网络的真实性和数据采集效率，并克服了实地测量和验证 DFN 模型的困难。

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