The imperative need to develop guidelines to manage human versus machine intelligence

Machine intelligence is increasingly entering roles that were until recently dominated by human intelligence. As humans now depend upon machines to perform various tasks and operations, there appears to be a risk that humans are losing the necessary skills associated with producing competitively advantageous decisions. Therefore, this research explores the emerging area of human versus machine decision-making. An illustrative engineering case involving a joint machine and human decision-making system is presented to demonstrate how the outcome was not satisfactorily managed for all the parties involved. This is accompanied by a novel framework and research agenda to highlight areas of concern for engineering managers. We offer that the speed at which new human-machine interactions are being encountered by engineering managers suggests that an urgent need exists to develop a robust body of knowledge to provide sound guidance to situations where human and machine decisions conflict. Human-machine systems are becoming pervasive yet this research has revealed that current technological approaches are not adequate. The engineering insights and multi-criteria decision-making tool from this research significantly advance our understanding of this important area.     

Multi-Attribute Selection Procedures Based on Regret and Rejoice for the Decision-Maker

Feelings influence human beings’ decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people’s preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multi-criteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multi-attribute selection procedure that incorporates both regret and rejoice to select the best choice. We utilize the positional advantage operator concept to develop regret and rejoice mathematical equations, and prove them. The proposed MCDM procedure that incorporates these two emotional factors offers a decision-maker the flexibility to trade off some benefits in order to gain a state of psychological satisfaction. More specifically, regret and rejoice are presented mathematically to enable the decision-maker to determine the values of regret and rejoice, and then make the decision in which the rejoice value is higher than the regret value. To test the performance of this new procedure, we apply it to three numerical examples proposed in previous works. The results are matched with those obtained by other methods such as the regret model, VIKOR, PROMETHEE I, and PROMETHEE II, thereby proving the efficacy of the new procedure.     

Risk warning technologies and emergency response mechanisms in Sichuan–Tibet Railway construction

Safety is one of the most critical themes in any large-scale railway construction project. Recognizing the importance of safety in railway engineering, practitioners and researchers have proposed various standards and procedures to ensure safety in construction activities. In this study, we first review four critical research areas of risk warning technologies and emergency response mechanisms in railway construction, namely, (i) risk identification methods of large-scale railway construction projects, (ii) risk management of large-scale railway construction, (iii) emergency response planning and management, and (iv) emergency response and rescue mechanisms. After reviewing the existing studies, we present four corresponding research areas and recommendations on the Sichuan–Tibet Railway construction. This study aims to inject new significant theoretical elements into the decision-making process and construction of this railway project in China.     

KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme learning machine and Adaboost algorithm, which has three main advantages: (1) KPCA can extract optimal representative features by leveraging a nonlinear mapping function; (2) We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM, so as to improve the generalization ability and prediction capacity of ELM; (3) We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor, which can further improve the effect of defect prediction. To effectively evaluate the performance of KAEA, we use eleven datasets from large open source projects, and compare the KAEA with four machine learning basic classifiers, ELM and its three variants. The experimental results show that KAEA is superior to these baseline models in most cases.     

Using finite state automata (FSA) for formal modelling of affordances in human-machine cooperative manufacturing systems

Modelling complex systems poses significant challenges on how one represents the system components and interactions among them. In order to provide a systematic approach to represent human participation as a part of a dynamic system, this paper presents a formal automata model of human-machine cooperative systems that incorporates human capabilities with respect to system conditions. Specifically, we propose a control model for human-involved shop floor systems based on discrete event-based systems (DES) and an environmental concept known as an affordance. When modelling human-involved systems where a human operator is considered a crucial system component, it is necessary to analyse the model complexity that increases significantly due to a human's behavioural patterns. From the perspective of the temporal and physical state domains a human operator's behaviour is usually limited by attention and resource constraints. We investigate these limitations and map them into constrained system affordances, and then construct a formal human-machine cooperative model based on the finite state automaton (FSA) model. The proposed model can provide a framework to combine human activities into systems operations in consideration of human's effectivities and system affordances. A detailed application example is provided to illustrate that the proposed model can effectively be applied to manufacturing control systems.     

The role of Chinese universities in enterprise–university research collaboration

In this paper the role of Chinese universities in enterprise–university research collaboration is investigated. This study focuses on a special aspect of the collaboration—co-authored articles. The two cases are analyzed: (1) research collaboration between Baosteel Group Corporation and Chinese universities; (2) research collaboration between China Petroleum & Chemical Corporation and Chinese universities. The co-authorship data over the period 1998–2007 were searched from CNKI database, the largest Chinese publication and citation database. The main findings are as follows: the number of articles co-authored by enterprise and university scientists has been increasing rapidly; the share of co-authored articles has been growing; the authors from universities are more possible to be the first authors; as a whole, enterprise–university co-authored articles tend to receive more citations and get downloaded more frequently; a mathematical orientation emerges in the enterprise–university articles. To reveal and describe such a trend the methods of keywords analysis and co-occurrence analysis are applied. The Chinese government’s policy instruments and substantial supports for pushing and improving enterprise–university research collaboration are introduced and analyzed.     

Cyber–Physical Power System (CPPS): A review on measures and optimization methods of system resilience

The Cyber–Physical Power System (CPPS) is one of the most critical infrastructure systems in a country because a stable and secure power supply is a key foundation for national and social development. In recent years, resilience has become a major topic in preventing and mitigating the risks caused by large-scale blackouts of CPPSs. Accordingly, the concept and significance of CPPS resilience are at first explained from the engineering perspective in this study. Then, a review of representative quantitative assessment measures of CPPS resilience applied in the existing literature is provided. On the basis of these assessment measures, the optimization methods of CPPS resilience are reviewed from three perspectives, which are mainly focused on the current research, namely, optimizing the recovery sequence of components, identifying and protecting critical nodes, and enhancing the coupling patterns between physical and cyber networks. The recent advances in modeling methods for cascading failures within the CPPS, which is the theoretical foundation for the resilience assessment and optimization research of CPPSs, are also presented. Lastly, the challenges and future research directions for resilience optimizing of CPPSs are discussed.     

Theoretical issues in the design of visual inspection systems

The objective of this review essay is to both chronicle and analyse literature in the area of visual inspection. Classical as well as contemporary papers are included to describe both the historical development and the state of the art of visual inspection theories and technologies. Human operators, despite well-documented problems, often perform visual inspection. While supervized machine systems obviate some of the problems associated with human inspectors, other problems still exist. In particular, accounting for a supervisor's perception of a machine's performance (as gauged, e.g. by trust) and consequent actions. The difficulties associated with these two alternatives have led to the emergence of a third alternative: collaborative human/machine or hybrid systems, which combine the advantages of both alternatives—in theory. However, in practice, how to best distribute the functions between a human and machine, in a dynamic environment in real time, is problematic. Moreover, a human's perceptions of its counterpart's performance remain an issue. These unresolved problems are subjects for future research. In the interim, the alternatives are critiqued to create a basis for establishing guidelines to select the alternative that is best suited for a given situation.     

Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision-making approach

Flexible manufacturing cells (FMC) have been used as a tool to implement flexible manufacturing processes to increase the competitiveness of manufacturing systems. In implementing an FMC, decision-makers encounter the machine selection problem including attributes, e.g. machine type, cost, number of machines, floor space and planned expenditures. This paper proposes a fuzzy multiple attribute decision-making (FMADM) model to assist the decision-maker to deal with the machine selection problem for an FMC realistically and economically. In addition, the membership functions of weights for those attributes are determined in accordance with their distinguishability and robustness when the ranking is performed.     

Robust multiattribute decision making under risk and uncertainty in engineering design

In this article, the problem of choosing from a set of design alternatives based upon multiple, conflicting, and uncertain criteria is investigated. The problem of selection over multiple attributes becomes harder when risky alternatives exist. The overlap measure method developed in this article models two sources of uncertainties—imprecise or risky attribute values provided to the decision maker and inabilities of the decision-maker to specify an exact desirable attribute value. Effects of these uncertainties are mitigated using the overlap measure metric. A subroutine to this method, called the robust alternative selection method, ensures that the winning alternative is insensitive to changes in the relative importance of the different design attributes. The overlap measure method can be used to model and handle various sources of uncertainties and can be applied to any number of multiattribute decision-making methods. In this article, it is applied to the hypothetical equivalents and inequivalents method, which is a multiattribute selection method under certainty.     

Management of traffic congestion in adaptive traffic signals using a novel classification-based approach

Traffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration (i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion.     

Production scheduling in the context of Industry 4.0: review and trends

Notwithstanding its disruptive potential, which has been the object of considerable debate, Industry4.0 (I4.0) operationalisation still needs significant study. Specifically, scheduling is a key process that should be explored from this perspective. The purpose of this study is to shed light on the issues regarding scheduling that need to be considered in the new I4.0 framework. To achieve this, a two-stage cascade literature review is performed. The review begins with an analysis regarding the opportunities and challenges brought by I4.0 to the scheduling field, outputting a set of critical scheduling areas (CSA) in which development is essential. The second-stage literature review is performed to understand which steps have been taken so far by previous research in the scheduling field to address those challenges. Thus, a first contribution of this work is to provide insight on the influence and expected changes brought by I4.0 to scheduling, while showcasing relevant research. Another contribution is to identify the most promising future lines of research in this field, in which relevant challenges such as holistic scheduling, or increased flexibility requirements are highlighted. Concurrently, CSA such as decentralised decision-making, and human–robot collaboration display large gaps between current practice and the required technological level of development.     

工业设计决策问题研究

目的 对工业设计过程的决策问题进行分析。方法 通过分析工业设计决策的特点,建立了工业设计决策的语义模型,综述了其研究现状与相关设计决策方法,指出了工业设计决策面临的问题与挑战,给出了未来可能的研究方向。结果 工业设计决策的模糊性、感性与理性并存、多阶段性、非线性与动态性等特点,决定了设计决策将面临网络化、动态性、多模态、冲突性、不确定性等挑战,使得未来研究将从多源数据融合、多阶段联合决策、共识驱动决策、智能决策及借助更多的数学理论和方法等多方面,推动工业设计决策的科学化、客观化与智能化发展。结论 工业设计决策是创新设计过程的重要组成部分之一,梳理工业设计过程的决策问题,将对工业设计方法论体系的完善起到补充与推动作用,减少工业设计过程的无效迭代。     

Reliability guarantee framework for the Sichuan–Tibet Railway

The Sichuan–Tibet Railway is facing extraordinary challenges in terms of construction, operation, and maintenance because of its extremely complicated natural environment and geological conditions. Consequently, countermeasures are necessary and urgent to ensure its safety and reliability in the whole life cycle. This study proposes a novel reliability framework to guarantee the ideal operation state of the Sichuan–Tibet Railway. Reliability application in many fields are summarized, including military equipment, rail locomotive, and railway engineering. Given the fact that the Sichuan–Tibet Railway is a complex giant system, Nine-Connotation was summarized (i.e., safety, inherent reliability, testability, maintainability, supportability, environmental adaptability, predictability, resilience, and durability) under the goal of optimizing the operational efficiency. On the basis of the concept of the Nine-Connotation and the understanding of reliability transmission mechanism, the framework of reliability for the Sichuan–Tibet Railway was established, which can facilitate a comprehensive and real-time evaluation of all situations with a clear hierarchy. The proposed framework is composed of a resilience management system, an integrated technology system, and a dynamic reliability assessment system. The pathway for its application on railway construction was developed in this study. The proposed framework can assist in well-informed decisions for the construction, as well as the operation of the Sichuan–Tibet Railway. On the basis of a top–down design concept for the first time, this study emphasizes the railway’s availability and validity to complete the assigned tasks as a whole, that is, operational efficiency. It also shows the reliability transmission and control mechanism of the railway’s giant complex system, innovating and establishing the management principle of great safety and great reliability over the life cycle.     

Intensity-modulated radiotherapy – a large scale multi-criteria programming problem

Abstract. Radiation therapy planning is often a tight rope walk between dangerous insufficient dose in the target volume and life threatening overdosing of organs at risk. Finding ideal balances between these inherently contradictory goals challenges dosimetrists and physicians in their daily practice. Todays inverse planning systems calculate treatment plans based on a single evaluation function that measures the quality of a radiation treatment plan. Unfortunately, such a one dimensional approach cannot satisfactorily map the different backgrounds of physicians and the patient dependent necessities. So, too often a time consuming iterative optimization process between evaluation of the dose distribution and redefinition of the evaluation function is needed. In this paper we propose a generic multi-criteria approach based on Pareto's solution concept. For each entity of interest – target volume or organ at risk – a structure dependent evaluation function is defined measuring deviations from ideal doses that are calculated from statistical functions. A reasonable bunch of clinically meaningful Pareto optimal solutions are stored in a data base, which can be interactively searched by physicians. The system guarantees dynamic planning as well as the discussion of tradeoffs between different entities. Mathematically, we model the inverse problem as a multi-criteria linear programming problem. Because of the large scale nature of the problem it is not possible to solve the problem in a 3D-setting without adaptive reduction by appropriate approximation schemes. Our approach is twofold: First, the discretization of the continuous problem results from an adaptive hierarchical clustering process which is used for a local refinement of constraints during the optimization procedure. Second, the set of Pareto optimal solutions is approximated by an adaptive grid of representatives that are found by a hybrid process of calculating extreme compromises and interpolation methods. Correspondence to: K.-H. Küfer     

工业系统的智能交互模式及人因工效研究综述

随着物联网、CPS、大数据等技术的出现和发展,生产制造、航空驾驶、安全监控等工业系统已进入第四次工业革命的智能转型升级。目的 工业系统的智能交互模式与人因工效是人机协同共生的关键核心问题。方法 从工业制造的智能化转型、航空航天的人机协同、核电安全智能监控等多个重大行业背景出发,分析人—信息—物理系统智能交互的发展趋势;剖析工业智能背景下国内外人因工效测评技术、评价模型及多源指标关联性研究进展;梳理智能化工业系统的信息表征发展趋势及多通道交互研究方法。结论 从国内外研究综述表明,需要从多学科交叉融通的角度构建智能化工业系统的人机交互研究体系,这将极大地改善系统中的人（任务执行者）获取信息、知识推理、判断决策的认知绩效,达成智能交互的人机物闭环,实现人（自然人、机器人）、信息系统、物理系统的充分感知融合,即人机协同共生模式。     

New technology foresight method based on intelligent knowledge management

The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and technologies. However, qualitative and quantitative methods have shortcomings. The former involve high costs and many limitations, while the latter lack expert experience. Intelligent knowledge management emphasizes human–machine integration, which combines the advantages of expert experience and data mining. Thus, we proposed a new technology foresight method based on intelligent knowledge management. This method constructs a technological online platform to increase the number of participating experts. A secondary mining is performed on the results of patent analysis and bibliometrics. Thus, forward-looking, innovative, and disruptive areas and relevant experts must be discovered through the following comprehensive process: Topic acquisition → topic delivery → topic monitoring → topic guidance → topic reclamation → topic sorting → topic evolution → topic conforming → expert recommendation.     

From Brain Science to Artificial Intelligence

Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such as deep learning. At present, although the developmental trend in AI and its applications has surpassed expectations, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI research, including a link from brain science to AI, and a connection from knowing the brain to simulating the brain. The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology; to establish a dynamic connection diagram of the brain; and to integrate neuroscience experiments with theory, models, and statistics. Based on these steps, a new generation of AI theory and methods can be studied, and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established. This article discusses the opportunities and challenges of adapting brain science to AI.     

Interface projection techniques for fluid–structure interaction modeling with moving-mesh methods

The stabilized space–time fluid–structure interaction (SSTFSI) technique developed by the Team for Advanced Flow Simulation and Modeling (T★AFSM) was applied to a number of 3D examples, including arterial fluid mechanics and parachute aerodynamics. Here we focus on the interface projection techniques that were developed as supplementary methods targeting the computational challenges associated with the geometric complexities of the fluid–structure interface. Although these supplementary techniques were developed in conjunction with the SSTFSI method and in the context of air–fabric interactions, they can also be used in conjunction with other moving-mesh methods, such as the Arbitrary Lagrangian–Eulerian (ALE) method, and in the context of other classes of FSI applications. The supplementary techniques currently consist of using split nodal values for pressure at the edges of the fabric and incompatible meshes at the air–fabric interfaces, the FSI Geometric Smoothing Technique (FSI-GST), and the Homogenized Modeling of Geometric Porosity (HMGP). Using split nodal values for pressure at the edges and incompatible meshes at the interfaces stabilizes the structural response at the edges of the membrane used in modeling the fabric. With the FSI-GST, the fluid mechanics mesh is sheltered from the consequences of the geometric complexity of the structure. With the HMGP, we bypass the intractable complexities of the geometric porosity by approximating it with an “equivalent”, locally-varying fabric porosity. As test cases demonstrating how the interface projection techniques work, we compute the air–fabric interactions of windsocks, sails and ringsail parachutes.     

Robust energy-efficient train speed profile optimization in a scenario-based position–time–speed network

Train speed profile optimization is an efficient approach to reducing energy consumption in urban rail transit systems. Different from most existing studies that assume deterministic parameters as model inputs, this paper proposes a robust energy-efficient train speed profile optimization approach by considering the uncertainty of train modeling parameters. Specifically, we first construct a scenario-based position–time–speed (PTS) network by considering resistance parameters as discrete scenario-based random variables. Then, a percentile reliability model is proposed to generate a robust train speed profile, by which the scenario-based energy consumption is less than the model objective value at \alpha confidence level. To solve the model efficiently, we present several algorithms to eliminate the infeasible nodes and arcs in the PTS network and propose a model reformulation strategy to transform the original model into an equivalent linear programming model. Lastly, on the basis of our field test data collected in Beijing metro Yizhuang line, a series of experiments are conducted to verify the effectiveness of the model and analyze the influences of parameter uncertainties on the generated train speed profile.