Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and newgeneration intelligent manufacturing. New-generation intelligent manufacturing represents an indepth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for ‘‘parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China.
The grand challenges of climate change demand a new paradigm of urban design that takes the performance of urban systems into account, such as energy and water efficiency. Traditional urban design methods focus on the form-making process and lack performance dimensions. Geodesign is an emerging approach that emphasizes the links between systems thinking, digital technology, and geographic context. This paper presents the research results of the first phase of a larger research collaboration and proposes an extended geodesign method for a district-scale urban design to integrate systems of renewable energy production, energy consumption, and storm water management, as well as a measurement of human experiences in cities. The method incorporates geographic information system (GIS), parametric modeling techniques, and multidisciplinary design optimization (MDO) tools that enable collaborative design decision-making. The method is tested and refined in a test case with the objective of designing a near-zero-energy urban district. Our final method has three characteristics.①Integrated geodesign and parametric design: It uses a parametric design approach to generate focal-scale district prototypes by means of a custom procedural algorithm, and applies geodesign to evaluate the performances of design proposals. ② A focus on design flow: It elaborates how to define problems, what information is selected, and what criteria are used in making design decisions.③Multi-objective optimization: The test case produces indicators from performance modeling and derives principles through a multi-objective computational experiment to inform how the design can be improved. This paper concludes with issues and next steps in modeling urban design and infrastructure systems based on MDO tools.
Urbanization is a potential factor in economic development, which is a main route to social development. As the scale of urbanization expands, the quality of the urban water environment may deteriorate, which can have a negative impact on sustainable urbanization. Therefore, a comprehensive understanding of the functions of the urban water environment is necessary, including its security, resources, ecology, landscape, culture, and economy. Furthermore, a deep analysis is required of the theoretical basis of the urban water environment, which is associated with geographical location, landscape ecology, and a low-carbon economy. In this paper, we expound the main principles for constructing a system for the urban water environment (including sustainable development, ecological priority, and regional differences), and suggest the content of an urban water environmental system. Such a system contains a natural water environment, an economic water environment, and a social water environment. The natural water environment is the base, an effective economic water environment is the focus, and a healthy social water environment is the essence of such a system. The construction of an urban water environment should rely on a comprehensive security system, complete scientific theory, and advanced technology.
Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work.
With the rapid growth of vehicle population and vehicle miles traveled, automobile emission has become a severe issue in the metropolitan cities of China. There are policies that concentrate on the management of emission sources. However, improving the operation of the transportation system through apps on mobile devices, especially navigation apps, may have a unique role in promoting urban air quality. Real-time traveler information can not only help travelers avoid traffic congestion, but also advise them to adjust their departure time, mode, or route, or even to cancel trips. Will such changes in personal travel patterns have a significant impact in decreasing emissions? If so, to what extent will they impact urban air quality? The aim of this study is to determine how urban traffic emission is affected by the use of navigation apps. With this work, we attempt to answer the question of whether the real-time traffic information provided by navigation apps can help to improve urban air quality. Some of these findings may provide references for the formulation of urban traffic and environmental policies.
Social influence analysis (SIA) is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and evaluation aspects related to SIA. We divide SIA models into two types: microscopic and macroscopic models. Microscopic models consider human interactions and the structure of the influence process, whereas macroscopic models consider the same transmission probability and identical influential power for all users. We analyze social influence methods including influence maximization, influence minimization, flow of influence, and individual influence. In social influence evaluation, influence evaluation metrics are introduced and social influence evaluation models are then analyzed. The objectives of this paper are to provide a comprehensive analysis, aid in understanding social behaviors, provide a theoretical basis for influencing public opinion, and unveil future research directions and potential applications.
The objective of a bridge design is to produce a safe bridge that is elegant and satisfies all functionality requirements, at a cost that is acceptable to the owner. A successful bridge design must be natural, simple, original, and harmonious with its surroundings. Aesthetics is not an additional consideration in the design of a bridge, but is rather an integral part of bridge design. Both the structural configuration and the aesthetics of a bridge must be considered together during the conceptual design stage. To achieve such a task, the bridge design engineer must have a good understanding of structural theory and bridge aesthetics.
Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.
Geotagging is the process of labeling data and information with geographical identification metadata, and text mining refers to the process of deriving information from text through data analytics. Geotagging and text mining are used to mine rich sources of social media data, such as video, website, text, and Quick Response (QR) code. They have been frequently used to model consumer behaviors and market trends. This study uses both techniques to understand the resilience of infrastructure in Chennai, India using data mined from the 2015 flood. This paper presents a conceptual study on the potential use of social media (Twitter in this case) to better understand infrastructure resiliency. Using featureextraction techniques, the research team extracted Twitter data from tweets generated by the Chennai population during the flood. First, this study shows that these techniques are useful in identifying locations, defects, and failure intensities of infrastructure using the location metadata from geotags, words containing the locations, and the frequencies of tweets from each location. However, more efforts are needed to better utilize the texts generated from the tweets, including a better understanding of the cultural contexts of the words used in the tweets, the contexts of the words used to describe the incidents, and the least frequently used words.
Urban eco-environmental degradation is becoming inevitable due to the extensive urbanization, population growth, and socioeconomic development in China. One of the traffic arteries in Shenzhen is an urban expressway that is under construction and that runs across environmentally sensitive areas (ESAs). The environmental pollution from urban expressways is critical, due to the characteristics of expressways such as high runoff coefficients, considerable contaminant accumulation, and complex pollutant ingredients. ESAs are vulnerable to anthropogenic disturbances and hence should be given special attention. In order to evaluate the environmental sensitivity along this urban expressway and minimize the influences of the ongoing road construction and future operation on the surrounding ecosystem, the environmental sensitivity of the relevant area was evaluated based on the application of a geographic information system (GIS). A final ESA map was classified into four environmental sensitivity levels; this classification indicates that a large proportion of the expressway passes through areas of high sensitivity, representing 11.93 km or 52.3% of the total expressway, and more than 90% of the total expressway passes through ESAs. This study provides beneficial information for optimal layout schemes of initial rainfall runoff treatment facilities developed from low-impact development (LID) techniques in order to minimize the impact of polluted road runoff on the surrounding ecological environment.
Low-impact development (LID) technologies have a great potential to reduce water usage and stormwater runoff and are therefore seen as sustainable improvements that can be made to traditional water infrastructure. These technologies include bioretention areas, rainwater capturing, and xeriscaping, all of which can be used in residential zones. Within the City of Atlanta, residential water usage accounts for 53% of the total water consumption; therefore, residential zones offer significant impact potential for the implementation of LID. This study analyzes the use of LID strategies within the different residential zones of the City of Atlanta from an ecological perspective by drawing analogies to natural ecosystems. The analysis shows that these technologies, especially with the addition of a graywater system, work to improve the conventional residential water network based upon these ecological metrics. The higher metric values suggest greater parity with healthy, natural ecosystems.
Green process engineering, which is based on the principles of the process intensi?cation strategy, can provide an important contribution toward achieving industrial sustainable development. Green process engineering refers to innovative equipment and process methods that are expected to bring about substantial improvements in chemical and any other manufacturing and processing aspects. It includes decreasing production costs, equipment size, energy consumption, and waste generation, and improving remote control, information ?uxes, and process ?exibility. Membrane-based technology assists in the pursuit of these principles, and the potential of membrane operations has been widely recognized in the last few years. This work starts by presenting an overview of the membrane operations that are utilized in water treatment and in the production of energy and raw materials. Next, it describes the potential advantages of innovative membrane-based integrated systems. A case study on an integrated membrane system (IMS) for seawater desalination coupled with raw materials production is presented. The aim of this work is to show how membrane systems can contribute to the realization of the goals of zero liquid discharge (ZLD), total raw materials utilization, and low energy consumption.
To understand the resource features and geology in the deep Jinchuan nickel deposit, difficult geological conditions were systematically analyzed, including high stress, fragmentized ore rock, prevalent deformation, difficult tunnel support, complicated rock mechanics, and low mining recovery. An integrated technology package was built for safe, efficient, and continuous mining in a deep, massive, and complex nickel and cobalt mine. This was done by the invention of a large-area continuous mining method with honeycomb drives; the establishment of ground control theory and a technology package for high-stress and fragmented ore rock; and the development of a new type of backfilling cement material, along with a deep backfilling technology that comprises the pipeline transport of high-density slurry with coarse aggregates. In this way, good solutions to existing problems were found to permit the efficient exploitation and comprehensive utilization of the resources in the deep Jinchuan nickel mine. In addition, a technological demonstration in an underground mine was performed using the cemented undercut-and-fill mining method for stressful, fragmented, and rheological rock.
The rapid growth of lithium ion batteries (LIBs) for portable electronic devices and electric vehicles has resulted in an increased number of spent LIBs. Spent LIBs contain not only dangerous heavy metals but also toxic chemicals that pose a serious threat to ecosystems and human health. Therefore, a great deal of attention has been paid to the development of an efficient process to recycle spent LIBs for both economic aspects and environmental protection. In this paper, we review the state-of-the-art processes for metal recycling from spent LIBs, introduce the structure of a LIB, and summarize all available technologies that are used in different recovery processes. It is notable that metal extraction and pretreatment play important roles in the whole recovery process, based on one or more of the principles of pyrometallurgy, hydrometallurgy, biometallurgy, and so forth. By further comparing different recycling methods, existing challenges are identified and suggestions for improving the recycling effectiveness can be proposed.
This paper reviews the recent achievements made by our team in the mitigation of rockburst risk. It includes the development of neural network modeling on rockburst risk assessment for deep gold mines in South Africa, an intelligent microseismicity monitoring system and sensors, an understanding of the rockburst evolution process using laboratory and in situ tests and monitoring, the establishment of a quantitative warning method for the location and intensities of different types of rockburst, and the development of measures for the dynamic control of rockburst. The mitigation of rockburst at the Hongtoushan copper mine is presented as an illustrative example.
With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership (AMP) in 2011 and the National Network for Manufacturing Innovation (NNMI) in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further “leverage existing resources… to nurture manufacturing innovation and accelerate commercialization” by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features: integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing (i2M) systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Internet of Things and Services (IoTS), cyber-physical systems (CPSs), and cloud computing are discussed. Challenges are addressed with applications that are based on commercially available platforms such as General Electric (GE)’s Predix and PTC’s ThingWorx.
Mineral consumption is increasing rapidly as more consumers enter the market for minerals and as the global standard of living increases. As a result, underground mining continues to progress to deeper levels in order to tackle the mineral supply crisis in the 21st century. However, deep mining occurs in a very technical and challenging environment, in which significant innovative solutions and best practice are required and additional safety standards must be implemented in order to overcome the challenges and reap huge economic gains. These challenges include the catastrophic events that are often met in deep mining engineering: rockbursts, gas outbursts, high in situ and redistributed stresses, large deformation, squeezing and creeping rocks, and high temperature. This review paper presents the current global status of deep mining and highlights some of the newest technological achievements and opportunities associated with rock mechanics and geotechnical engineering in deep mining. Of the various technical achievements, unmanned working-faces and unmanned mines based on fully automated mining and mineral extraction processes have become important fields in the 21st century.
An increased global supply of minerals is essential to meet the needs and expectations of a rapidly rising world population. This implies extraction from greater depths. Autonomous mining systems, developed through sustained R&D by equipment suppliers, reduce miner exposure to hostile work environments and increase safety. This places increased focus on “ground control” and on rock mechanics to define the depth to which minerals may be extracted economically. Although significant efforts have been made since the end of World War II to apply mechanics to mine design, there have been both technological and organizational obstacles. Rock in situ is a more complex engineering material than is typically encountered in most other engineering disciplines. Mining engineering has relied heavily on empirical procedures in design for thousands of years. These are no longer adequate to address the challenges of the 21st century, as mines venture to increasingly greater depths. The development of the synthetic rock mass (SRM) in 2008 provides researchers with the ability to analyze the deformational behavior of rock masses that are anisotropic and discontinuous—attributes that were described as the defining characteristics of in situ rock by Leopold Müller, the president and founder of the International Society for Rock Mechanics (ISRM), in 1966. Recent developments in the numerical modeling of large-scale mining operations (e.g., caving) using the SRM reveal unanticipated deformational behavior of the rock. The application of massive parallelization and cloud computational techniques offers major opportunities: for example, to assess uncertainties in numerical predictions; to establish the mechanics basis for the empirical rules now used in rock engineering and their validity for the prediction of rock mass behavior beyond current experience; and to use the discrete element method (DEM) in the optimization of deep mine design. For the first time, mining—and rock engineering—will have its own mechanics-based “laboratory.” This promises to be a major tool in future planning for effective mining at depth. The paper concludes with a discussion of an opportunity to demonstrate the application of DEM and SRM procedures as a laboratory, by back-analysis of mining methods used over the 80-year history of the Mount Lyell Copper Mine in Tasmania.
Topology optimization is a powerful design approach that is used to determine the optimal topology in order to obtain the desired functional performance. It has been widely used to improve structural performance in engineering fields such as in the aerospace and automobile industries. However, some gaps still exist between topology optimization and engineering application, which significantly hinder the application of topology optimization. One of these gaps is how to interpret topology results, especially those obtained using the density framework, into parametric computer-aided design (CAD) models that are ready for subsequent shape optimization and manufacturing. In this paper, a new method for interpreting topology optimization results into stereolithography (STL) models and parametric CAD models is proposed. First, we extract the skeleton of the topology optimization result in order to ensure shape preservation and use a filtering method to ensure characteristics preservation. After this process, the distribution of the nodes in the boundary of the topology optimization result is denser, which will benefit the subsequent curve fitting. Using the curvature and the derivative of curvature of the uniform B-spline curve, an adaptive B-spline fitting method is proposed in order to obtain a parametric CAD model with the fewest control points meeting the requirement of the fitting error. A case study is presented to provide a detailed description of the proposed method, and two more examples are shown to demonstrate the validity and versatility of the proposed method.