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Erratum to: Theoretical foundation of a decision network for urban development

Shih-kung Lai, Jhong-you Huang,lai@tongji.edu.cn,jhongyouhuang@gmail.com

《信息与电子工程前沿（英文）》 2017年第18卷第10期页码 1677-1677 doi: 10.1631/FITEE.15e0000

摘要： Erratum to: , 2017 18(8):1033-1039. doi:

关键词： Decision making Linked decisions Decision network Planning

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

《机械工程前沿（英文）》 2021年第16卷第4期页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要： The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词： bearing cross-severity fault diagnosis hierarchical fault diagnosis convolutional neural network decision tree

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城市发展决策网络的理论基础 Perspective

Shih-kung LAI, Jhong-you HUANG

《信息与电子工程前沿（英文）》 2017年第18卷第10期页码 1677-1677 doi: 10.1631/FITEE.1510000

摘要：规划问题具有挑战性和复杂性，因为它们通常涉及多重属性偏好和多个利益相关者。因此，很少有规划工具能帮助规划者解决这些问题。传统决策分析在处理规划问题时无法达到预期，因为它主要侧重于为特定决策者制定单一决策。本文介绍了一个名为“决策网络”的规划工具的理论基础，其目的是帮助规划者在面对具有多属性偏好的多个利益相关者时，作出多重且相互关联的决定。本研究提供了一个成熟科学技术的起点，将有助于处理复杂的规划问题。我们首先提出决策网络模式所欲处理的规划问题的一般化模式。然后我们针对这个规划问题提出一个有效的解决演算法，再使用数值示例来演示该演算法是如何运作。所提出的解决演算法被证明是有效的，亦使该规划工具的计算机化成为可能。同时，我们指明“决策网络”的图形表示较“决策树”的图形表示来得更为有效。因此，在处理具有挑战性和复杂性的城市规划问题时，通过决策网络模式所作出的多重的且相关的决策，将会比独立地做出这些决策产生更多的好处。

关键词：决策、关联的决策、决策网络、规划

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization

《化学科学与工程前沿（英文）》 2022年第16卷第4期页码 523-535 doi: 10.1007/s11705-021-2083-5

摘要： Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates, as it has a profound impact on the crystallization process. Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallization process efficiency, has attracted increasing attention. However, there are still many urgent challenges thus far. Herein we used seven descriptors based on understanding dissolution behavior to establish two solubility prediction models by machine learning algorithms. The solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the prediction models, which were constructed by random decision forests and artificial neural network with optimized data structure and model accuracy. Furthermore, a comparison with traditional prediction methods including the modified solubility equation and the quantitative structure-property relationships model was carried out. The highest accuracy shown by the testing set proves that the ML models have the best solubility prediction ability. Multiple linear regression and stepwise regression were used to further investigate the critical factor in determining solubility value. The results revealed that the API properties and the solute-solvent interaction both provide a nonnegligible contribution to the solubility value.

关键词： solubility prediction machine learning artificial neural network random decision forests

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

《结构与土木工程前沿（英文）》 2023年第17卷第2期页码 284-305 doi: 10.1007/s11709-022-0901-6

摘要： Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength self-compacting concrete artificial neural network decision tree CatBoost

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城市虚拟交通系统与交通发展决策支持模式研究

王炜, 赵德,华雪东,周伟

《中国工程科学》 2021年第23卷第3期页码 163-172 doi: 10.15302/J-SSCAE-2021.03.020

摘要：

本土化的城市虚拟交通系统与交通发展决策支持模式，在解决由快速城镇化引发且趋于严重的城市交通问题、克服现阶段智能交通系统存在的瓶颈方面具有重要意义。本研究依托大数据、互联网等信息技术，建立了由基础数据库、分析模型库、软件模块库、备选预案库组成的城市虚拟交通系统，涵盖其理论框架、系统功能、技术体系，据此提出覆盖城市土地开发、交通设施建设、交通运行管控、公共交通运营、交通政策制定等应用场景的交通发展决策支持模式。以重庆市为例，阐述了城市虚拟交通系统构建过程，完成重庆市新型冠状病毒肺炎疫情期间居民出行、车辆通行错峰方案的政府决策论证。结果表明，基于城市虚拟交通系统的交通发展决策支持模式在实际交通方案论证过程中具有及时性和有效性。研究建议，未来我国城市交通规划管理可注重城市虚拟交通系统的建设和运用，及时纳入新型信息技术成果以优化应用效果。

关键词：城市交通系统道路网络交通分析模型虚拟交通系统决策支持模式

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The research on structural damage identification using rough set and integrated neural network

Juelong LI, Hairui LI, Jianchun XING, Qiliang YANG

《机械工程前沿（英文）》 2013年第8卷第3期页码 305-310 doi: 10.1007/s11465-013-0259-5

摘要：

A huge amount of information and identification accuracy in large civil engineering structural damage identification has not been addressed yet. To efficiently solve this problem, a new damage identification method based on rough set and integrated neural network is first proposed. In brief, rough set was used to reduce attributes so as to decrease spatial dimensions of data and extract effective features. And then the reduced attributes will be put into the sub-neural network. The sub-neural network can give the preliminary diagnosis from different aspects of damage. The decision fusion network will give the final damage identification results. The identification examples show that this method can simplify the redundant information to reduce the neural network model, making full use of the range of information to effectively improve the accuracy of structural damage identification.

关键词： rough set integrated neural network damage identification decision making fusion

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李浩然：基于同伴效应与网络效应的“气代煤〞改造决策研究（2020年7月11日）

2022年05月19日

关键词：能源

Convergence to real-time decision making

James M. TIEN

《工程管理前沿（英文）》 2020年第7卷第2期页码 204-222 doi: 10.1007/s42524-019-0040-5

摘要： Real-time decision making reflects the convergence of several digital technologies, including those concerned with the promulgation of artificial intelligence and other advanced technologies that underpin real-time actions. More specifically, real-time decision making can be depicted in terms of three converging dimensions: Internet of Things, decision making, and real-time. The Internet of Things include tangible goods, intangible services, ServGoods, and connected ServGoods. Decision making includes model-based analytics (since before 1990), information-based Big Data (since 1990), and training-based artificial intelligence (since 2000), and it is bolstered by the evolving real-time technologies of sensing (i.e., capturing streaming data), processing (i.e., applying real-time analytics), reacting (i.e., making decisions in real-time), and learning (i.e., employing deep neural networks). Real-time includes mobile networks, autonomous vehicles, and artificial general intelligence. Central to decision making, especially real-time decision making, is the ServGood concept, which the author introduced in an earlier paper (2012). It is a physical product or good encased by a services layer that renders the good more adaptable and smarter for a specific purpose or use. Addition of another communication sensors layer could further enhance its smartness and adaptiveness. Such connected ServGoods constitute a solid foundation for the advanced products of tomorrow which can further display their growing intelligence through real-time decisions.

关键词： real-time decision making services goods ServGoods Big Data Internet of Things artificial intelligence wireless communications

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Current applications of artificial intelligence for intraoperative decision support in surgery

Allison J. Navarrete-Welton, Daniel A. Hashimoto

《医学前沿（英文）》 2020年第14卷第4期页码 369-381 doi: 10.1007/s11684-020-0784-7

摘要： Research into medical artificial intelligence (AI) has made significant advances in recent years, including surgical applications. This scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical specialties. Within the twenty-one ( =21) included papers, three main categories of motivations were identified for developing such technologies: (1) augmenting the information available to surgeons, (2) accelerating intraoperative pathology, and (3) recommending surgical steps. While many of the proposals hold promise for improving patient outcomes, important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance statistics. Despite limitations, the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.

关键词： artificial intelligence decision support clinical decision support systems intraoperative deep learning computer vision machine learning surgery

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Challenges of human–machine collaboration in risky decision-making

《工程管理前沿（英文）》 2022年第9卷第1期页码 89-103 doi: 10.1007/s42524-021-0182-0

摘要： The purpose of this paper is to delineate the research challenges of human–machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human–machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well. We first summarize features of risky decision-making and possible biases of human decision-makers therein. Then, we argue the necessity and urgency of advancing human–machine collaboration in risky decision-making. Afterward, we review the literature on human–machine collaboration in a general decision context, from the perspectives of human–machine organization, relationship, and collaboration. Lastly, we propose challenges of enhancing human–machine communication and teamwork in risky decision-making, followed by future research avenues.

关键词： human–machine collaboration risky decision-making human–machine team and interaction task allocation human–machine relationship

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CORRECTION to: Special issue: Decision, risk analytics and data intelligence

Xiaozhe ZHAO, Desheng Wu

《工程管理前沿（英文）》页码 697-697 doi: 10.1007/s42524-020-0139-8

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An overview on the applications of the hesitant fuzzy sets in group decision-making: Theory, support

Zeshui XU, Shen ZHANG

《工程管理前沿（英文）》 2019年第6卷第2期页码 163-182 doi: 10.1007/s42524-019-0017-4

摘要： Due to the characteristics of hesitant fuzzy sets (HFSs), one hesitant fuzzy element (HFE), which is the basic component of HFSs, can express the evaluation values of multiple decision makers (DMs) on the same alternative under a certain attribute. Thus, the HFS has its unique advantages in group decision making (GDM). Based on which, many scholars have conducted in-depth research on the applications of HFSs in GDM. We have viewed lots of relevant literature and divided the existing studies into three categories: theory, support and methods. In this paper, we elaborate on hesitant fuzzy GDM from these three aspects. The first aspect is mainly about the introduction of HFSs, HFPRs and some hesitant fuzzy aggregation operators. The second aspect describes the consensus process under hesitant fuzzy environment, which is an important support for a complete decision-making process. In the third aspect, we introduce seven hesitant fuzzy GDM approaches, which can be applied in GDM under different decision-making conditions. Finally, we summarize the research status of hesitant fuzzy GDM and put forward some directions of future research.

关键词： hesitant fuzzy set hesitant fuzzy preference relation group decision-making

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Clinical decision-making by the emergency department resident physicians for critically ill patients

null

《医学前沿（英文）》 2012年第6卷第1期页码 89-93 doi: 10.1007/s11684-012-0183-9

摘要：

The application of main methodologies for clinical decision-making by residents in emergency medical practice was assessed, and issues in this area were investigated. The treatments provided to 2 611 critical patients by the Peking Union Medical College Hospital of were analyzed by independent investigators who evaluated the main clinical decision-making processes applied by the hospital residents. The application of decision-making strategies by PG1 and PG3 groups, which means the residents in first year and the third year, were compared. The patients were treated according to pattern recognition (43.0%), hypothetico-deductive reasoning (23.4%), event-driven models (19.3%), and rule-using algorithms (5.9%). A significant difference was found between PG1 and PG3 groups (χ²=498.01, P<0.001). Pattern recognition and hypothetic-deductive methods were the most common techniques applied by emergency physicians in evaluating critically ill patients. The decision-making processes applied by junior and senior residents were significantly different, although neither group adequately applied rule-using algorithms. Inclusion of clinical decision-making in medical curricula is needed to improve decision-making in critical care.

关键词： clinical decision-making emergency medicine critically ill patient resident methodology

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双枝模糊决策与决策识别问题

史开泉,李歧强

《中国工程科学》 2001年第3卷第1期页码 71-77

摘要：

文章提出具有中性域X^*(X^*≠{x})X的上的双枝模糊决策的概念和决策优化分析模型、决策判定定理、决策识别定理、决策去余定理和决策因素域X上的挖洞原理。双枝模糊决策具有下列特征：决策结论的双向依赖特性，决策结论的叠加合成特性，决策结论的枝-分离特性，决策结论的枝-退化特性，决策结论的非失误特性。研究结果得到了应用。

关键词：双枝模糊决策决策模型决策判定定理决策识别定理决策去余定理挖洞原理