Autonomous Intelligent Systems

Abstract As the core competitiveness of the national industry, large-scale equipment such as ships, high-speed rail and nuclear power equipment, their production process involves in-depth personalization. It includes complex processes and long manufacturing cycles. In addition, the equipment’s supply chain management is extremely complex. Therefore, the development of a supply chain management knowledge graph is of significant strategic significance. It not only enhances the synergistic effect of the supply chain management but also upgrades the level of intelligent management. This paper proposes a distant supervision knowledge extraction and knowledge graph construction method in the supply chain management of large equipment manufacturing, which achieves digital and structured management and efficient use of supply chain management knowledge in the industry. This paper presents an approach to extract entity-relation knowledge using limited samples. We achieve this by establishing a distant supervision model. Furthermore, we introduce a fusion gate mechanism and integrate ontology information, thereby enhancing the model’s capability to effectively discern sentence-level semantics. Subsequently, we promptly modify the weights of input features using the gate mechanism to strengthen the model’s resilience and address the issue of vector noise diffusion. Finally, an inter-bag sentence attention mechanism is introduced to integrate different sentence bag information at the sentence bag level, which achieves more accurate entity-relation knowledge extraction. The experimental results prove that compared with the latest distant supervision method, the accuracy of relation extraction is improved by 2.8%, and the AUC value is increased by 3.9%, effectively improving the quality of knowledge graph in supply chain management.

Feiyue Huang

Lianglun Cheng

Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain

供应链管理领域远监督知识抽取与知识图谱构建方法

Abstract As an essential part of modern smart manufacturing, road transport with large and heavy trucks has in-creased dramatically. Due to the inside wheel difference in the process of turning, there is a considerable safety hazard in the blind area of the inside wheel difference. In this paper, multiple cameras combined with deep learning algorithms are introduced to detect pedestrians in the blind area of wheel error. A scheme of vehicle-pedestrian safety alarm detection system is developed via the integration of YOLOv5 and an improved binocular distance measurement method. The system accurately measures the distance between the truck and nearby pedestrians by utilizing multiple cameras and PP Human recognition, providing real-time safety alerts. The experimental results show that this method significantly reduces distance measurement errors, improves the reliability of pedestrian detection, achieves high accuracy and real-time performance, and thus enhances the safety of trucks in complex traffic environments.

Tingting Bao

Ding Lin

Xumei Zhang

Zhiguo Zhou

Kejia Wang

Pedestrian safety alarm system based on binocular distance measurement for trucks using recognition feature analysis

基于双目测距的卡车行人安全报警系统：识别特征分析

Abstract Combining safety objectives with stability objectives is crucial for safety-critical systems. Existing studies generally unified these two objectives by constructing Lyapunov-type barrier functions. However, insufficient analysis of key set relationships within the system may render the proposed safety and stability conditions conservative, and these studies also did not provide how to use such conditions to design safety-stability control strategies. This paper proposed a feasible and constructive design to achieve stabilization of safety-critical systems by a relaxed converse Lyapunov-barrier approach. By analyzing the relationships between a series of sets associated with the safety-critical system, the stability and safety conditions can be appropriately relaxed. Then, with the help of relaxed converse control Lyapunov-barrier functions (RCCLBFs), a theoretical result was obtained for the stability of affine nonlinear systems with safety constraints. Subsequently, a constructive method was developed for a second-order strict-feedback system to transform the process of solving RCCLBFs into a Lyapunov-like stabilization problem. Finally, the proposed safety-stability control method is exerted on a robotic system and demonstrated by simulations.

Haoqi Li

Jiangping Hu

Xiaoming Hu

Bijoy K. Ghosh

Stabilization of nonlinear safety-critical systems by relaxed converse Lyapunov-barrier approach and its applications in robotic systems

通过松弛逆李雅普诺夫-障碍方法稳定非线性安全关键系统及其在机器人系统中的应用

Abstract Feasible, smooth, and time-jerk optimal trajectory is essential for manipulators utilized in manufacturing process. A novel technique to generate trajectories in the joint space for robotic manipulators based on quintic B-spline and constrained multi-objective student psychology based optimization (CMOSPBO) is proposed in this paper. In order to obtain the optimal trajectories, two objective functions including the total travelling time and the integral of the squared jerk along the whole trajectories are considered. The whole trajectories are interpolated by quintic B-spline and then optimized by CMOSPBO, while taking into account kinematic constraints of velocity, acceleration, and jerk. CMOSPBO mainly includes improved student psychology based optimization, archive management, and an adaptive ε -constraint handling method. Lévy flights and differential mutation are adopted to enhance the global exploration capacity of the improved SPBO. The ε value is varied with iterations and feasible solutions to prevent the premature convergence of CMOSPBO. Solution density estimation corresponding to the solution distribution in decision space and objective space is proposed to increase the diversity of solutions. The experimental results show that CMOSPBO outperforms than SQP, and NSGA-II in terms of the motion efficiency and jerk. The comparison results demonstrate the effectiveness of the proposed method to generate time-jerk optimal and jerk-continuous trajectories for manipulators.

Zhijun Wu

Jianliang Chen

Multi-objective optimal trajectory planning for manipulators based on CMOSPBO

基于CMOSPBO的多目标机械臂最优轨迹规划

Abstract Customer maintenance is of vital importance to the enterprise management. Valuable assessment and efficient prediction for customer ordering behavior can offer better decision-making and reduce business costs significantly. According to existing studies about customer behavior regularity segment and demand prediction most focus on e-commerce and other fields with large amount of data, making them not suitable for small enterprises and data features like sparsity and outliers are not mined when doing regularity quantification. Additionally, more and more complex network structures for demand prediction are proposed, which builds on the assumption that all the samples have predictive value, ignoring the fine-grained analysis of different time series regularity with high cost. To deal with the above issues, a multi-step regularity assessment and joint prediction system for ordering time series is proposed. For extracting features, comprehensive assessment of customer regularity based on entropy weight method with the result of predictability quantification using K-Means clustering algorithm, real entropy, LZW algorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to ‘how high the regularity of customers is’, filling the gap in the field of regularity quantification, but also provides a theoretical basis for demand prediction models selection. Prediction models: Random Forest regression, XGBoost, CNN and LSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of the proposed regularity quantitation method. Moreover, a merged CNN-BiLSTM neural network model is established for predicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms, which performs better on the data set compared to others. Random Forest is still used for prediction of customers with high regularity due to its high training efficiency. Finally, the results of prediction, regularity quantification, and classification are output from the intelligent system, which is capable of providing scientific basis for corporate strategy decision and has highly extendibility in other enterprises and fields for follow-up research.

Yichen Zhou

Wenhe Han

Heng Zhou

A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

基于熵和深度学习的多步规则性评估与联合预测系统用于排序时间序列

Abstract Metal powder contributes to the environmental burdens of additive manufacturing (AM) substantially. Current life cycle assessments (LCAs) of metal powders present considerable variations of lifecycle environmental inventory due to process divergence, spatial heterogeneity, or temporal fluctuation. Most importantly, the amounts of LCA studies on metal powder are limited and primarily confined to partial material types. To this end, based on the data surveyed from a metal powder supplier, this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization, respectively. Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’ physical properties, we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process. This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available. With the predicted energy use information of specific powder, the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages. Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO 2 equivalency. The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models. This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.

Haibo Xiao

Baoyun Gao

Shoukang Yu

Bin Liu

Sheng Cao

Shitong Peng

Life cycle assessment of metal powder production: a Bayesian stochastic Kriging model-based autonomous estimation

金属粉末生产生命周期评估：基于贝叶斯随机克里金模型的自主估算

涉及主题	artificial intelligencemachine learningintelligent roboticsunmanned systemsautonomous agent systems
出版信息	出版商: Springer，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2021，原创研究文献占比: ，自引率:， Gold OA占比:
平均审稿周期	21 weeks

Autonomous Intelligent Systems

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投稿经验分享

Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain

Pedestrian safety alarm system based on binocular distance measurement for trucks using recognition feature analysis

Stabilization of nonlinear safety-critical systems by relaxed converse Lyapunov-barrier approach and its applications in robotic systems

Multi-objective optimal trajectory planning for manipulators based on CMOSPBO

A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

Stabilization of nonlinear safety-critical systems by relaxed converse Lyapunov-barrier approach and its applications in robotic systems

Pedestrian safety alarm system based on binocular distance measurement for trucks using recognition feature analysis

Multi-objective optimal trajectory planning for manipulators based on CMOSPBO

A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

Life cycle assessment of metal powder production: a Bayesian stochastic Kriging model-based autonomous estimation

ISSN	2730-616X
h-index	8