系统科学与系统工程学科研究热点与发展趋势

借助CiteSpace软件,对近30年系统科学与系统工程学科相关文献进行发文作者(机构)合作、学科研究热点和研究趋势等可视化分析,研究发现:作者之间以及机构之间合作松散,未形成核心作者群和核心机构群;学科研究热点包括人才培养、方法论、中国实践、复杂性、安全科学、地球科学、学科交叉、涌现性等;关于学科发展沿革与趋势分析,近三十年学科发展可划分为学科体系完善阶段、融合发展阶段和深度研究与拓展研究阶段,学科发展由注重理论研究向注重理论与实践并重研究转变,由微观研究到宏观研究转变。论文最后从加强合作、建立中国特色的学科体系和服务于中国实践三个方面提出学科发展建议。

霍尔辛赫煤矿智慧矿山系统工程的实践应用研究

智慧矿山系统是基于数字矿山系统而发展的,是当前煤矿管理的发展方向。论文以霍尔辛赫煤矿智慧矿山系统的建设和实践应用为研究对象,来分析其具体的开发方向和应用效果。期待此应用方案可以给其他同类型煤矿的智慧矿山系统工程软件的开发提供相应的参考。

系统工程方法在舰艇作战系统设计中的应用

阐述了舰艇作战系统的组成以及采用系统工程方法进行设计时的一般方法,并对采用系统工程方法研制时所具有的优点作了分析.

“系统的系统”综述

近年来,在各种期刊文献经常出现“System of Systems”(系统的系统)这一专用词汇,到目前为止,“系统的系统”还没有公认的定义。“系统的系统”的分类体系,设计原理,建设原则,安全体系以及与一般复杂大系统之间的关系等方面,目前都亟待出现突破性研究工具和基础理论。“系统的系统”应用由军事领域逐渐向企业、政府应用领域扩展,为加快该领域的研究进展,对“系统的系统”定义、特征、分类体系、建设原则,以及研究和实践发展前景等进行归纳和整理。

复杂系统体系的技术成熟度评价模型研究

从概念和特点等方面对国防装备工程中系统与系统体系,以及系统工程与系统之系统工程(SoSE)进行对比分析,找出异同点,在深入剖析系统工程中的技术成熟度模型内涵的基础上,围绕评价模型框架、评价标准和评价流程,对系统之系统中的技术成熟度评价模型进行探讨,提出了系统之系统技术成熟度评价的一些建议。

势科学理论的普适性与科学意义——信息人社会与势科学理论研究之二

"势"概念的科学定义揭示了信息的本质属性,即信息量与信息势是等价的。从自然科学到人文社会科学各领域的活动,其本质都可以归结为生产更多的信息量、营造更大的信息势。势科学理论本质上是一个求导过程,其构建了一个系统的逻辑体系,使社会科学与自然科学融通起来,为人文社会科学研究提供了强有力的工具,尤其为信息科学的研究提供了真正的逻辑起点。

Open multilevel hierarchic intelligent control system for eco-industrial system

In allusion to the characteristics of the open complex giant system, an open multilevel hierarchic intelligent control system is established for the eco-industrial system. With the idea of the open engineering system, using the hall for workshop of metasynthetic engineering （HWME）, intelligent control techniques, the expert system and the design of experiments are integrated within the framework of the nonlinear multiobjective decision support system to develop a robust, top-level design specification so as to make the system have the quality of adaptive control, self-organizing, self-learning and robustness. Finally, an illustrative example is given to clarify the effectiveness of the method.

A Model of Application System for Man-Machine-Environment System Engineering in Vessels Based on IDEF0

Applying man-machine-environment system engineering(MMESE)in vessels is a method to improve the effectiveness of the interaction between equipment, environment, and humans for the purpose of advancing operating efficiency, performance, safety, and habitability of a vessel and its subsystems. In the following research, the life cycle of vessels was divided into 9 phases, and 15 research subjects were also identified from among these phases. The 15 subjects were systemized, and then the man-machine-environment engineering system application model for vessels was developed using the ICAM definition method 0 (IDEF0), which is a systematical modeling method. This system model bridges the gap between the data and information flow of every two associated subjects with the major basic research methods and approaches included, which brings the formerly relatively independent subjects together as a whole. The application of this systematic model should facilitate the application of man-machine-environment system engineering in vessels, especially at the conceptual and embodiment design phases. The managers and designers can deal with detailed tasks quickly and efficiently while reducing repetitive work.

Geo-engineered buffer capacity of two-layered absorbing system under the impact of rock avalanches based on Discrete Element Method

Many rock avalanches were triggered by the Wenchuan earthquake on May 12, 2008 in southwest China. Protection galleries covered with a single soil layer are usually used to protect against rockfall. Since one-layer protection galleries do not have sufficient buffer capacity, a two-layered absorbing system has been designed. This study aims to find whether an expanded poly-styrol （EPS） cushion, which is used in the soil-covered protection galleries for shock absorption, could be positioned under dynamic loadings. The dynamic impacts of the two-layered absorbing system under the conditions of rock avalanches are numerically simulated through a 2D discrete dement method. By selecting reasonable parameters, a series of numerical experiments were conducted to find the best combination for the two- layered absorbing system. The values of the EPS layer area as a percentage of the total area were set as 0% （Sl）, 22~ （S2）, and 70% （$3）. 22~ of the area of the EPS layer was found to be a reasonable value, and experiments were conducted to find the best position of the EPS layer in the two-layered absorbing system. The numerical results yield useful conclusions regarding the interaction between the impacting avalanches and the two-layered absorbing system. The soil layer can absorb the shock energy effectively and S2 （0.4-m thick EPS cushion covered with soil layer） is the most efficient combination, which can reduce the impact force, compared with the other combinations.

Systems Neuroengineering: Understanding and Interacting with the Brain

In this paper, we review the current state- of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering： neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity （via a neuroimaging modality） through a neural interface （invasive or noninvasive） and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering--to develop neurotechniques for enhancing the understanding of whole- brain function and dysfunction, and the management of neurological and mental disorders.

Mathematical models and expert system for grate-kiln process of iron ore oxide pellet production(Part Ⅰ):Mathematical models of grate process

Grate process is an important step in grate-kiln pellet production.However,as a relatively closed system,the process on grate is inaccessible to direct detection,therefore,it is hard to control.As a result,mathematical models of temperature distribution,moisture distribution and oxidation degree distribution in pellet bed,with good universality,computation speed and calculation accuracy,are presented based on analysis of heat transfer and physical-chemical reactions during grate process.And real-time visualization of temperature,moisture and oxidation degree distribution in pellet bed during grate process is realized.Model validation is displayed,and the similarity of 91% is proved.The results can reveal real time status on grate,and provide a solid foundation for the subsequent study of artificial intelligence control system of pellet production.

Application of Neural Networks for Predictive Variables in Engineering

This paper proposes a method in order to detect the importance of the input variables in multivariate analysis problems. When there is correlation among predictor variables, the importance of each input variable, when adding variables in the model, can be detected from the knowledge stored in Artificial Neural Network （NN） and it must be taken into account. Neural networks models have been used with the analysis of sensibility, these models predict more accurately the relationship between variables, and it is the way to find a set of forecasting variables in order to be included in the new prediction model. The obtained results have been applied in a system to forecast the volume of wood for a tree, and to detect relationships between input and output variables.

A system dynamics approach for studying the resilience of irrigation systems

One of the most interesting outcomes from the recent collaboration between natural and social scientists is the concept of resilience, which imported from engineering to ecology. The problem with that concept is that it is hard if not impossible to get simple measures for resilience as far as social-ecological systems are complex ones. Using a system dynamics model, the author shows that, for assessing resilience of systems like irrigation systems, it probably helps to see the process of resilience loss as a systemic one, in which dynamics is given by positive self-reinforcing loops, like the one we have labeled in this paper--the death spiral. The author also presents a list of symptoms of collapse in irrigation systems, in order to assess the resilience of those systems, and suggest some future avenues of research on the subject.

Optimal Filtering Algorithm for Stochastic 2-D FMM Ⅱ with Multiplicative Noise

A stochastic two dimensional Fornasini Marchesini’s Model Ⅱ (2 D FMM Ⅱ) with multiplicative noise is given, and a filtering algorithm for this model, which is optimal in the sense of linear minimum variance, is developed. The stochastic 2 D FMM Ⅱ with multiplicative noise can be reduced to a 1 D model, and the proposed optimal filtering algorithm for the stochastic 2 D FMM Ⅱ with multiplicative noise is obtained by using the state estimation theory of 1 D systems. An example is given to illustrate the validity of this algorithm.

SOA Information Engineering System Based on Knowledge Grid Applications in Smart Grids

Smart grids have the characteristics of being observable,controllable,adaptive,self-healing,embedded independent processing,and real-time analysis.With the development of smart grids,constructing a grid to cover global,unified information systems,which should be adapted to fulf ill the requirements of the characteristics,is essential.This paper presents an service-oriented architecture(SOA)for smart grid information-engineering systems based on knowledge grid,which could form as a service-oriented architecture through business,technology and management;it would extract potentially valuable information from the massive amount of information on the generation side,the grid side,and the electricity side,then share the useful information to improve availability,security and stability.

Multi-agent Systems and Cloud Computing for Controlling and Managing Chemical and Food Processes

Cloud computing can offer a very powerful, reliable, predictable and scalable computing infrastructure for the execution of MAS （multi-agent systems） implementing complex agent-based applications such when modelling, simulation and real-time running of complex systems must be provided. Multi-agent systems appears as an adequate approach to current challenges in many areas. Between important qualities of MAS also belongs to, that they are open, interoperable, and heterogenous systems. The agent is active, a program entity, has its own ideas how to perform the tasks of the own agenda. Agents： perceive, behave ＂reasonably＂, act in the environment, communicate with other agents. Cloud infrastructures can offer an ideal platform where run MAS systems simulations, applications and real-time running because of its large amount of processing and memory resources that can be dynamically configured for executing large agent-based software at unprecedented scale. Cloud computing can help chemical and food companies drive operational excellence; meet growing and changing customer demands; accelerate new product innovation and ramp-to-volume manufacturing in key markets; reduce IT spending; manage and mitigate supply chain risks; and enable faster and more flexible delivery of new IT system. Production type of SOC （service-oriented computing） can be inspired by a ＂Cloud＂, for the production of ＂Cloud＂ offers an attractive and natural solutions in several computing trends such as delivery system over the Internet, use of utilities, flexibility, virtualization, a ＂grid＂ distributed computing, outsourcing, Web 2.0, etc.. Production of the ＂Cloud＂ is also considered as a new multidisciplinary field that includes ＂network＂ production, virtual manufacturing, agile manufacturing, and of course cloud computing. Examples of cloud computing and MAS applications in food and chemistry development and industry, proposition of using multi-agent systems in the control of batch processes, modified ACO （ant colony optimization） approach for the diversified service allocation and scheduling mechanism in cloud paradigma, examples of applications in a business area were studied in the paper.

Dynamic Deformation Monitoring for Long-Span Bridges under Construction and EMD De-noising Method

Construction progress of long-span bridge is complicated and the quality control is strict. Any disadvantage during construction may potentially affect the internal forces and deck alignments after it is open to traffic. To exactly evaluate the periodic alignments, internal forces and safety, geometrical and physical monitoring are needed during construction. This study aims at the requirement of dynamic geometric monitoring during Sutong Bridge construction, and introduces the realization and observing schemes of the self-developed GPS real-time dynamic geometrical deformation monitoring system. Affected by wind load and construction circumstance, GPS （global positioning system） monitoring signal contains a variety of noise. And the useful signal can be extracted from the signal after de-noising the noises. A de-noising method based on EMD （empirical mode decomposition） model is introduced here to process the bridge dynamic monitoring data, and with the wavelet threshold de-noising method are compared. The result shows that the EMD method has good adaptability, is free from the choice of wavelet bases and the number of decomposition layer. The method is an effective de-noising method for dynamic deformation monitoring to large-span bridges.

Interaction Analysis and Decomposition Principle for Control Structure Design of Large-scale Systems

Industrial processes are mostly large-scale systems with high order.They use fully centralized control strategy,the parameters of which are difficult to tune.In the design of large-scale systems,the decomposition according to the interaction between input and output variables is the first step and the basis for the selection of control structure.In this paper,the decomposition principle of processes in large-scale systems is proposed for the design of control structure.A new variable pairing method is presented,considering the steady-state information and dynamic response of large-scale system.By selecting threshold values,the related matrix can be transformed into the adjoining matrixes,which directly measure the couple among different loops.The optimal number of controllers can be obtained after decomposing the large-scale system.A practical example is used to demonstrate the validity and feasibility of the proposed interaction decomposition principle in process large-scale systems.

Proposal for a Project of Integration of IT Tools With Focus on Planning and Control of Operations on the Shop Floor

Manufacturing system, with high level of complexity and with a mix of semi-repetitive and repetitive products, to become productive, should seek the standardization of products and processes to obtain the optimization of use of production resources. However, it is necessary to measure the productivity, so that the system of measurement and control of manufacturing processes are an element critical as to ensure greater visibility of the flow＇s restrictions, minimized when detected properly. In this case, the automation of factory＇s measurement process can effectively contribute to ensuring the effectiveness of the function control of a manufacturing system. It is important to consider that the automation of the system of measurement and control of manufacturing processes, of complex environment, is heavily dependent of IT tools applied directly in the interface computational between the operation systems and the corporate systems. This heavy reliance, if exploited technically properly, allows that automation of the system of measurement and control of production makes the access to time real of availability of manufacturing process＇s data, such as processing time and setup time that it can export to a specialist software in programming production, for example, feasible. In this paper, the automation of the system of measurement and control of production is approached, in order to identify the main possibilities of the design of an information system capable to integrate the flow of information in an environment internal on manufacturing organizations, with emphasis in the digital manufacturing paradigm.