International Journal of Simulation and Process Modelling (49 papers in press)
Developing an evolution software architecture framework based on six dimensions
by Noureddine Gasmallah, Abdelkrim Amirat, Mourad Oussalah, Siridi Hassina
Abstract: Because of the vital need for software systems to evolve and change over time in order to account for new requirements, software evolution at higher levels of modelling is considered as one of the main foundation within software engineering used to reduce complexity and ensure flexibility, evolvability and usability. With the growing number of evolution methods, the need to develop a framework based on well defined dimensions to analyse these approaches, is now a prerequisite for practitioners in order to analyse, compare and classify methods within the field of architectural evolution. In similar studies for migration techniques and software engineering, presenting a framework does not usually cover the specification of systems based on software architecture subsequently, it does not give the opportunity to shape the interior design of software in terms of specific metrics. In this paper, we propose an evolution software architecture based on six dimensions for analysing, comparing and classifying existing and future evolution methods. The proposed architectural evolution framework adopts Zackman analytic tool by providing answers to What, Why, Where, Who, When and How questions. The process to formalize a framework for evolution methods relies upon identifying dimensions on which researchers would take into account while developing a new approach. The set of the proposed dimensions whether combined or individually can serve as a basis to explore further classification paradigms. Six explicit dimensions are identified confirming the conceptual framework consistency in accordance with the architectural view-point of the software. The proposed framework is supported by an empirical study that involves surveying and analysing 119 research methods related to area of architectural evolution from the literature. Furthermore, these dimensions are quantified and then analysed within a case study. This framework provides a blueprint to guide practitioners to position architectural evolution approaches and maps them according to a selected set of dimensions.
Keywords: conceptual framework; software architecture; classification; taxonomy.
Simulation of a remote runway solution for a congested airport: Mexico City airport
by Miguel Mujica Mota, Paolo Scala
Abstract: The airport of Mexico City has been declared saturated for most of the day. For that reason, the Mexican government announced a couple of years ago the construction of a completely new one which is supposed to be operative in 2020 in its first phase. However, the technical issues and the economic downturn in the country have jeopardised the project; for that reason, it is important to have alternatives that allow investing in a progressive fashion so that the investments are not lost or end up in useless infrastructure like the ones that have taken place in other parts of the world. The current work presents a simulation-based study of the alternative of using one of the runways of the new airport in a remote fashion in case the original project is delayed or even cancelled. The results indicate that the proposed infrastructure alleviates the congestion problem in the current airport, and at the same time allows the traffic growth with performance indicators similar to airports that have remote runways as it is the case of Schiphol in The Netherlands.
Keywords: simulation; airport; congestion; taxi time; failure; downturn; over dimension; NAICM.
Numerical simulation of thermal changes in womens breast tissue during menstrual cycle in different stages of its development
by Akshara Makrariya, K.R. Pardasani
Abstract: Investigators in the past have studied thermoregulation in womens breast tissue under normal conditions without taking into the account the benign changes taking place in the breast due to various physical and physiological conditions. In this paper, a model is proposed to study thermal changes taking place in womens breasts in different stages of its development due to the menstrual cycle. The changes in the physical and physiological parameters, such as blood mass flow rate, metabolic heat generation and thermal conductivity due to benign changes in the breast, have been incorporated into the model. Appropriate boundary conditions have been framed based on the physical conditions of the problem. The finite element method has been employed to obtain the solution for a two-dimensional steady-state case. The temperature profiles have been computed and the thermal changes in the breast have been analysed during the follicular and luteal phases of the menstrual cycle in different stages of its development. The thermal information of such models can be useful for diagnosis and proper health care of womens breast disorders. The finite element method has proved to be quite flexible and powerful in the present study as it was possible to incorporate the minor details of the physiology of breast in the proposed model to generate the thermal information of breast due to benign changes occurring in the breast, such as the menstrual cycle.
Keywords: women’s breast tissue; menstrual cycle; stages of development; mathematical modelling.
Performance optimisation of a pharmaceutical production line by integrated simulation and data envelopment analysis
by Naser Habibifar, Mahdi Hamid, Mahdi Bastan, Ahmad Taher Azar
Abstract: Pharmaceutical factories have a crucial role in the healthcare system and their products must be produced under optimal conditions. It seems that the use of mathematical models is not suitable for pharmaceutical production line optimisation, and the use of simulation leads to better outputs and provides more flexibility than mathematical models. Therefore, in this study, a novel methodology based on the integration of simulation and data envelopment analysis (DEA) is developed for performance optimisation of a pharmaceutical production line. For this purpose, first, an actual pharmaceutical production line was simulated, verified and validated. Then, the bottlenecks were identified through sensitivity analysis. Several practical scenarios, which were corrective solutions for production line improvement, were designed by the assistance of production managers and planning experts. Afterwards, DEA was developed for the scenario analysis. Moreover, two Banker, Chames and Cooper (BCC) and Charnes, Cooper and Rhodes (CCR) DEA models were employed for the evaluation and optimisation purposes. Operator use, machine use, total completion time (Cmax), waiting times and cost were optimisation indicators, among which operator use and machine use were outputs and Cmax, waiting times, and cost were inputs of DEA models. The input-oriented BCC DEA is selected as the best model scenario analysis. Finally, the weight of each indicator is identified through paired t-test. Results suggested that all the indicators are significant in the production unit. Optimisation model suggested that certain inputs and outputs had to be improved for the performance optimisation of the pharmaceutical unit. This is the first study in which an integrated simulation DEA is used for the performance optimisation of a pharmaceutical unit. Second, it identified the weights of each factor through an integrated approach. Third, it introduced the optimisation process through the proposed integrated approach.
Keywords: pharmaceutical plants; performance optimisation; data envelopment analysis; statistical analysis; sensitivity analysis.
NB-DEVS : a hybrid approach for modelling and simulation of imperfect systems
by Kadda Mostefaoui, Youcef Dahmani
Abstract: This article presents the BN-DEVS formalism for the modelling and simulation of Discrete EVent Systems (DEVS). Our objectives are to propose a new formal approach for describing the complex systems involving imperfectly. In our research, we propose to integrate the na
Keywords: modelling; simulation; DEVS; Bayesian networks; uncertainty.
Finite element analysis for engine crankshaft torsional stiffness
by Rang-Lin Fan, Chu-Yuan Zhang, Fang Yin, Cheng-Cheng Feng, Zhen-Dong Ma, Hua-Bing Gong
Abstract: Accurate crankshaft torsional stiffness is important to build the lumped parameter model (LPM) for crankshaft torsional vibration analysis. For the crankshaft system of a four-cylinder in-line gasoline engine, a procedure to build the finite element model (FEM) is presented. The applicability of two ways to set the equivalent mass or equivalent moment of inertia for piston-rod in FEM is described. When building the LPM, the concept of rigid rotation of the crank pin central section around the central line of the crankshaft is proposed, and a method to obtain the torsional angle of the central section is described. What is more, three post-processing methods are presented to obtain the torsional stiffness coefficients for all crank throws based on the FEA results of the full model of the crankshaft system. According to three load cases of no load, half load and full load, the tests are carried out. The simulation results of the FEM, the simulation results of the LPM, and the test results are compared, and all have good agreement with each other.
Keywords: engine; crankshaft; torsional vibration; torsional stiffness; rigid-rotation; finite element analysis; finite element model; lumped parameter model.
Two-sided M-Bayesian limits of credibility of reliability parameters in the case of zero-failure data and a case study
by Wanyi Dai, Siqi Li, Mei Zhang, Yueming Hu, Dongfang Mei
Abstract: In this paper, a novel method of two-sided M-Bayesian credible limit is proposed to deal with the interval estimation problem of reliability parameters with exponential distribution in the case of zero-failure data. The properties of two-sided M-Bayesian limits of credibility are discussed and some new theorems are proven, including the impact of the upper bound c of hyper parameters and the influence of different prior distributions of hyper parameters on two-sided M-Bayesian limits of credibility when the reliability of estimation is determined by the exponential distribution. The paper extends the conclusions drawn in two previous studies regarding the relationships among the many kinds of two-sided M-Bayesian limits of credibility and two-sided classical confidence. Finally, a real data set about engine was discussed with different model parameters. By means of an example, the presented method of this paper is compared with the classical confidence limits. The results verified the properties of two-sided M-Bayesian limits of credibility and indicated that the method is efficient and easy to perform.
Keywords: reliability; estimation; two-sided M-Bayesian limits of credibility; zero-failure data.
A novel method of reactive voltage optimisation for a photovoltaic system
by Weimin Zhang, Yanxia Zhang
Abstract: Based on improved dynamic teaching and learning reactive power optimisation technology based on moth drive algorithm, a new method for real-time adaptive reactive power optimisation with photovoltaic system is proposed. By constructing the mathematical model of reactive power voltage control of photovoltaic power station, learning is optimised continuously in the process of teaching and learning. According to real-time data, the optimal cooperation strategy between RPVC/AVC control system and intelligent power grid command is formed, and real-time, adaptive and dynamic control of the system is realised. The simulation results show that the method is reasonable and effective for testing the 220 kV substation and its feeder system.
Keywords: moth drive light algorithm; dynamic teaching and learning reactive power optimisation; photovoltaic; reactive power and voltage coordinated; automatic voltage control.
Special Issue on: I3M 2017 Concepts and Methodologies for the Next Generation of Modelling and Simulation Techniques in Industry and Logistics
Individualised modelling of affective data for intelligent tutoring systems: lessons learned
by Keith Brawner
Abstract: One on one tutoring from human expert tutors to human students is the most effective form of instruction found to date. There are many actions that human tutors perform that make them remarkably effective, including the attention that they pay to the cognitive and affective states of the human students that they tutor; and the use of this knowledge to modify the way that they instruct the material. According to theoretical models, learner state data is used to inform instructional data and decisions, which then influences the learning of the student. Naturally, the data about student state must be available in order to be used to adjust the instruction. Success amongst operational systems, however, has been observed with generalised modelling techniques. Individualised and adaptive modelling techniques from other domains in the literature present an alternative to the approach which isnt observing significant operational success. This paper investigates individualised adaptive models, validates the approach, and shows that it can produce models of acceptable quality, but that doing so does not obviate the experimenter from creating quality generalised models prior to individualising.
Keywords: adaptive and predictive computer-based training; intelligent tutoring systems; architectural components; emerging standards.
Identifying and modelling correlation between airport weather conditions and additional time in airport Arrival Sequencing and Metering Area
by Margarita Bagamanova, Juan Jose Ramos Gonzalez, Miquel Angel Piera Eroles, Jose Manuel Cordero Garcia, Alvaro Rodriguez-Sanz
Abstract: Different uncertainties during operational activities of modern airports can significantly delay some processes and cause chain-effect performance drop on the overall Air Traffic Management (ATM) system. The decision-making process to mitigate the propagation of perturbations through the different airport processes can be improved with the support of a causal model, build with a use of data mining and machine learning techniques. This paper introduces a new approach for modelling causal relationships between various ATM performance indicators, which can be used to predict by the means of simulation techniques the evolution of airport operations scenarios. The analysis of reachable airport states is a relevant approach to design mitigation mechanisms on those perturbations which drive the system to poor KPIs.
Keywords: ASMA time; holding; inbound traffic; weather impact; Bayesian networks; Coloured Petri net; airport; decision support tool.
Surrogate-assisted microscopic traffic simulation-based optimisation of routing parameters
by Bernhard Werth, Erik Pitzer, Christian Backfrieder, Gerald Ostermayer, Michael Affenzeller
Abstract: Reactive and predictive routing algorithms have to work fast and reliably for a large number of traffic participants. Therefore, simple rules and thresholds guide routing decisions rather than extensive data collection and machine learning. We optimize some of the thresholds governing the behavior of the PCMA* routing algorithm, by use of the microscopic traffic simulator TraffSim. Microscopic traffic simulation is more exact than its macroscopic counterpart and very well suited to test the efficiency of a reactive/predictive routing algorithm. Sadly, it is also tremendously more computationally expensive, impairing the applicability of 'conventional' heuristic optimization techniques like genetic algorithms or evolutionary strategies. Extensive use of surrogate models in an optimisation procedure is a promising alternative. Several variations of the efficient global optimisation algorithm (EGO) are tested and compared. Furthermore, a new type of surrogate model geared towards overcoming some characteristics of the parameter optimisation is presented.
Keywords: surrogate assisted optimisation; microscopic traffic simulation; TraffSim; HeuristicLab; efficient global optimisation; noisy optimisation; routing algorithms.
Sustainability in logistic hubs: a decision support system for investigating green practices at container terminals
by Francesco Longo
Abstract: Logistic hubs, and especially container terminals, are today responsible for a major percentage of the environmental pollution. Practices such as the Onshore Power Supply (OPS) or cold ironing may have relevant benefits, but it has been argued that container terminal operators perceive a paucity of evidence that the benefits derived from the implementation of green practices exceed the investment costs. In this perspective, there is an urgent need for guidance to sustainability-related decision-making in container terminals. To this end, an advanced decision support system, which leverages on modelling and simulation, is proposed in this article to represent a reliable test environment to investigate the impacts of sustainability practices on container terminals. Results show that the evaluation of the impacts of an OPS system must consider the source of the electric power needed by ships at berth, i.e. coal, biofuels or renewable energies. The study shows that, although a coal-powered OPS system is the most affordable one, pollutant emissions are reduced in the port area but it is not a zero-emission solution. Electric energy generated by biofuels or renewable energies is zero-emissions but results in higher costs. The unit costs of 1 m3 of saved CO2 and NOX (two of the most dangerous pollutants at port areas) by implementing an OPS system are calculated.
Keywords: green port; container terminal; logistics; sustainability; onshore power supply.
Analysing uncertainties and their impacts on deliveries of a logging company: a simulation model to foster supply chain resilience
by Peter Mensah
Abstract: The supply chain in todays competitive world faces uncertainties that might disrupt any part between the upper and lower levels affecting the flow of raw materials and products, as well as information and money. The lower level, consisting of the delivery of goods in the form of raw materials and/or finished products to customers on time, is vital as it enhances customers loyalty. This could boost competitive advantages yielding to higher profitability of organisations over their rivals. However, to achieve and sustain these advantages, organisations need to identify and analyse the risks and their impacts on deliveries especially during strategic and operational decision making processes. Hence, modelling and simulation may be used as an effective tool to support decision making when planning delivery patterns. Consequently, a research is conducted in a logging company, Company L, to comprehend the way uncertainties affect deliveries. This article therefore uses a simulation model as a tool to boost managerial decision making, with reference to Company L, by identifying and analysing uncertainties and portraying their impacts on deliveries. This will enable the organisation to be agile and flexible enough to combat uncertainties.
Keywords: supply chain; deliveries; uncertainties; risk impact; resilience; simulation model.
Microscopic modelling of international (re-)hospitalisation effects in the CEPHOS-LINK setting
by Guenther Zauner, Christoph Urach, Martin Bicher, Niki Popper, Florian Endel
Abstract: Avoiding hospitalisation has become mainstream in healthcare planning and policy, and the urge to identify so-called ambulatory care-sensitive conditions is increasing. In this and other contexts, re-hospitalisation rates have become popular as a quality indicator among healthcare researchers, planners and politicians, both for cost containment and quality improvement. Within the European Commission funded FP7 project CEPHOS-LINK, a dynamic agent-based model is integrated for Austria, Slovenia and the Veneto region of Italy for answering questions regarding long term and planning effects for psychiatric hospitalisation and re-hospitalisation under special constraints, using national routine databases combined by data pooling processes. The agent-based simulation framework is based on a modular concept with the core module GEPOC, a generic population concept developed within the DEXHELPP consortia project in Austria, and parametrised and calibrated using EUROSTAT and national statistics databases for regional forecast. Demographic changes and their simulated effects on (re)-hospitalsation are calculated as well as changes in distance to service and the effect of changes in the diabetes mellitus prevalence as forecast for Austria, Slovenia and the Veneto region. The distance information is calculated for weighted NUTS3 driving time to psychiatric hospitals. All results are gathered on a personal level and depicted and described on age and gender groups. Furthermore the (re)-hospitalisations in each country are mapped to costs, which are calculated with average inflation, based on purchasing power parity to get comparable results.
Keywords: healthcare simulation; agent-based model; spatial dynamics; international claims data; model parameterisation; pathway model; parameter calibration.
Management for safety and efficacy in industrial plan disaster: the medical competence in a simulation project for healthcare emergency.
by Marco Frascio, Katia Cortese, Kirill Sinelshchikov, Francesco Longo
Abstract: Industrial mass casualty incidents are an unfortunate reality in the 21st century, but there are few situational training exercises to prepare and to cope with emergencies management. Each phase of an emergency plan has particular needs, requires distinct tools, strategies, and resources and faces different challenges. This paper describes a project to carry out development of the activities devoted to face the complexities arising from emergencies in industrial plants. The DIEM-SSP is a simulation project working on two interoperable simulators, based on the IEEE 1516 High Level Architecture (HLA), used as a test-bed on specific case studies. The project is aimed to study innovative emergency procedures and proper routing of critical patients with severe traumas toward the most suitable first aid facilities. The project takes into account the emergency procedures considering the human factors and the possibility of mistakes. It is aimed to test and validate these methodologies through a test-bed based on distributed and interoperable simulation. The paper reports the medical contribution in this project.
Keywords: medical simulation; medical emergency management; triage; industrial plan disaster; healthcare emergency.
Simulation and performance improvement of a reverse logistics system for waste electrical and electronic equipment: a case study in Italy
by Eleonora Bottani, Marta Rinaldi, Roberto Montanari
Abstract: This paper presents a simulation study to evaluate the performance of a real waste electrical and electronic equipment (WEEE) management system of the north of Italy and to investigate alternative scenarios to enhance its effectiveness. A detailed simulation model was developed in Microsoft ExcelTM to reproduce the behaviour of the different players of the system. To this end, two real players, i.e. a WEEE collection company and a treatment plant, both located in the north of Italy, were initially observed. Data about the incoming flow of WEEE and the production of recycled goods was taken from direct observations. Specific key performance indicators were defined and used to evaluate the performance of the existing network and of some alternative configurations. As it is grounded on a real WEEE reverse logistics system, this study provides interesting proposals for future actions on WEEE management.
Keywords: waste electrical and electronic equipment; reverse logistics; simulation model; Italy; optimisation.
Innovative social networks modelling for population simulation
by Marina Massei, Riccardo Di Matteo
Abstract: Today's social networks are an important element of society that requires to develop new models as well as guidelines to create interoperable simulators covering these aspects. These new simulators are expected to enable research and development focused on people's reactions to simulated events social networks. This research area seems promising, however it is necessary to address several challenging issues including proper development of new conceptual models as well as validation and verification processes and definition of human behaviour modifiers. This paper presents a new methodological framework for modelling individuals and populations by means of web social networks, in order to reproduce their dynamics. The article proposes to create interoperable models of social networks along with a real case study related to training and decision making processes for authorities. The paper includes experimentations over a complex scenario where human factors and social networks play a crucial role.
Keywords: interoperable simulation; human behaviour modelling; hybrid warfare; CAX; serious games.
Special Issue on: ISSPM 2018 Integrating Nodes and Optimisation across Social and Scientific Areas in Sustainability Loops
Error of single-phase proton exchange membrane fuel cell model based on Brinkman-Darcys law in different flow fields
by Shizhong Chen, Zhongxian Xia, Xuyang Zhang, Yuhou Wu
Abstract: The proton exchange membrane (PEM) fuel cell is an auspicious energy device for the future with high energy efficiency and zero emissions. PEM fuel cell performance can be improved by optimising the flow field using numerical models based on Brinkman-Darcys law. However, errors made by applying Brinkman-Darcys law cannot be avoided; errors should be carefully investigated for different flow fields. In this paper, a single-phase PEM fuel cell model based on Brinkman-Darcys law is developed, considering the effects of flow field on both local electrochemical active area and effective permeability. The results showed that the model predicts well the performance of the flow field with a high resolution land width, such as 1 mm, but it over-estimated the performance under the low voltage region when the land width was 2 mm or larger, since the high mass transfer loss was under-estimated by the model.
Keywords: Brinkman-Darcy’s law; flow fields; proton exchange membrane fuel cell; under-land cross-flow.
Dynamic performance of high supporting formwork under horizontal impact load
by Zhengran Lu, Chao Guo, Maosheng Zhang
Abstract: Dynamic performance of a high full-scale supporting formwork (HSF) subjected to horizontal impact loading was examined. It could be concluded that transverse X-bracings in the horizontal impact load direction have a more significant effect on the bearing performance and dynamic characteristics of an HSF than longitudinal X-bracings. An increase in the amount of poured concrete and the failure of the X-bracings reduces the natural frequency of the HSF. With the gradual increase in the amount of poured concrete, the maximum dynamic axial force of posts may increase from 4.33 kN to 12.77 kN under different X-bracing conditions during concrete placement. With failure of some main nodes, any impact load significantly increases the axial force acting on the posts. Dynamic axial force acting on some of the posts can increase by as much as 120% if 5% of fasteners fail during concrete pouring compared with that when all fasteners and X-bracings are intact.
Keywords: high supporting formwork; horizontal impact load; dynamic performance; experimental research.
Cloud resource orchestration optimisation Based on ARIMA
by Hua Qin, Min Yu, YingXu Lai, Liu Zenghui, Liu Jing
Abstract: This paper examines how to solve the problem of resources management in the cloud environment, especially on the PaaS, satisfy the demands of users and relieve the load on the server in high concurrency. After analysing the resource orchestration technology on PaaS, this paper presents a dynamic orchestration optimisation framework based on the ARIMA model. The architecture is based on OpenStack infrastructure as a service, and combines with Cloudify, a resource orchestration software on the PaaS by making adjustments in advance by predicting the resource consumption in the next time period. Experiments showed that this architecture can effectively shorten the concurrent response time and improve the use of memory.
Keywords: Cloudify; OpenStack; cloud computing; orchestration.
Morphological component analysis based on mixed dictionary for signal denoising of ground-penetrating radar
by Jianhua Zhang, Haohao Zhang, Yang Li, Xueli Wu
Abstract: Forward modelling is applied to simulate the ground-penetrating radar (GPR) detection environment, and a modified Morphological Component Analysis (MCA) algorithm is applied to GPR signal denoising. Finite-difference time-domain method (FDTD) is used to perform finite difference approximation to the space and time derivatives of Maxwell's equations. Under the forward simulation framework, The MCA algorithm applies a sparse dictionary to decompose the GPR signal. However, clutter is not represented as there is no corresponding sparse dictionary, the clutter is removed when the signal is reconstructed. The core of the MCA is to select a suitable dictionary. The combination of undecimated discrete wavelet transform dictionary (UDWT) and curvelet transform dictionary (CURVELET) is selected. The improved MCA algorithm is compared with Singular Value Analysis (SVD) and Principal Component Analysis (PCA), to confirm the high performance of the proposed algorithm.
Keywords: FDTD;signal processing; MCA; UDWT; CURVELET; ground-penetrating radar.
A data cleaning method for water quality based on improved hierarchical clustering algorithm
by Qingxuan Meng, Jianzhuo Yan
Abstract: Identifying and rectifying incomplete water quality data is of vital importance. A data cleaning method based on improved balanced iterative reducing and clustering using hierarchies (BIRCH) clustering algorithm is proposed. The clustering feature tree of water quality data is constructed and the cluster vector of the clustering feature tree is obtained by the agglomerative method. The optimal cluster number is determined according to the Bayesian information criterion and the nearest clustering ratio. The Pauta criterion is used to detect the global outlier and an Artificial Neural Network (ANN) is used to fill in outliers and missing values. Finally, the improved data cleaning method is applied to water quality monitoring data of Beijing Wastewater Treatment Plant. The experimental results show that the data cleaning method can not only detect abnormal values and missing values accurately, but also normalise and complete missing data.
Keywords: outliers; water quality monitoring; multivariate data; clustering; ANN.
Photovoltaic maximum power point tracking based on IWD-SVM
by Wenqing Zhao, Yayun Meng
Abstract: Photovoltaic system maximum power point tracking (MPPT) has great potential for improvement of power generation. To optimise MPPT, this paper presents a prediction model based on an Intelligent Water Drops optimisation Support Vector Machine (IWD-SVM) for maximum power point working voltage. IWD is used to optimise the penalty factor and kernel function parameters of the SVM, thus improving the training efficiency of the learning machine. Based on the optimisation algorithm, the SVM is used to model the PV array, the prediction results are compared to verify the accuracy and effectiveness of the IWD-SVM model. In addition, the IWD-SVM model is compared with the traditional neural network prediction results, which further verifies the validity of the proposed IWD-SVM model.
Keywords: IWD model; photovoltaic array; support vector machine; maximum power point tracking; neural network.
Simulation of multilateration system based on Chan algorithm and conjugate gradient optimisation algorithm
by Jianhua Zhang, Feng Gao, Yang Li, Xueli Wu
Abstract: For a multilateration (MLAT) system, the traditional Chan algorithm applies the theory of time-difference-of-arrival (TDOA) to solve the target position of the mathematical model. By introducing intermediate variables, the algorithm adopts a two-step weighted least-squares to locate, which has low computational complexity and high positioning accuracy. The introduction of intermediate variable will make the target position equation produce fuzzy solution, and then lead to the decline of positioning accuracy. Therefore, the location accuracy is directly related to the location algorithm. Conjugate gradient algorithm (CGA) is not only one of the most useful methods for solving large linear equations, but also one of the most effective algorithms in solving large-scale nonlinear optimisation. On the one hand, it avoids solving the inverse of the matrix, on the other hand it can also speed up the solution of the target position and improve the positioning accuracy. The TDOA principle is applied in this article, a four stations multipoint positioning system mathematical model is set up, and then a new fusion algorithm, Chan-CGA, is applied to the MLAT system. CGA is used to optimise the intermediate variable Chan algorithm solving process. Finally, the fusion algorithm is evaluated by simulation and comparison with the Chan-Taylor algorithm.
Keywords: multilateration; time-difference-of-arrival; Chan algorithm; conjugate gradient optimisation algorithm.
Special Issue on: ISSPM 2018 Internet of Things and Smart City Technologies
A DT-CWT-based infrared-visible image fusion method for smart city
by Guanqiu Qi, Mingyao Zheng, Zhiqin Zhu
Abstract: Following the development of smart city, informative images play a more and more important role in recognition, detection, and perception. As an efficient way, image fusion technique integrates information from multiple images. Multi-scale transform (MST) and sparse representation (SR) are widely used in infrared-visible image fusion. Traditional MST-based fusion methods are difficult to represent all features of source images. At the same time, traditional SR-based fusion methods do not consider morphological information of image features in dictionary learning processes. To overcome the defects of both MST- and SR-based fusion methods, this paper presents a infrared-visible image fusion framework by combining double-tree complex wavelet transform (DTCWT) and SR. The source images are decomposed and clustered into high- and low-pass bands by DTCWT. The high-pass bands are fused by the Sum Modified Laplacian (SML). The low-pass bands are fused by the SR-based approach. The fused high- and low-pass bands are integrated and reconstructed by DTCWT to form the final fused image. Comparing with five mainstream image fusion solutions, the proposed fusion framework can achieve state-of the-art performance in infrared-visible fusion images.
Keywords: DT-CWT; sparse representation; SML; infrared-visible; image fusion.
A two-level identification model for selecting the coordination strategy for the urban arterial road based on fuzzy logic
by Haochen Sun, Feng Qiao, Lingzhong Guo, Zhaoyan Wang
Abstract: A novel model for identifying the traffic condition of urban arterial roadways is proposed in this paper to improve the operational efficiency and safety of the urban traffic arterial road system. During the identification process, fuzzy analytic hierarchy process and fuzzy integrated evaluation are employed to identify the traffic condition on the arterial road; according to the fuzzy logic scheme, a proper coordination strategy is then generated based on the resulting identification of each way of the artery. To verify the effectiveness of the proposed method, a numerical experiment is carried out by using the microscopic traffic simulation software VISSIM, where a traffic flow simulation system is generated according to the real-time traffic data. The comparison results show that the proposed model works well to fit with the actual operating condition of the arterial traffic and the proposed coordination strategy can provide a better performance for the traffic management.
Keywords: traffic condition identification; coordination strategy; urban arterial road; fuzzy logic.
Research on optimal collaborative method for microgrid environmental and economic dispatch in grid-connected mode
by Juan Chen, Bin Lu, Lingling Hao
Abstract: In recent years, real-time optimisation of microgrid dispatch with minimal total costs of energy generation and emission has been an urgent problem. In this paper, a collaborative method is considered for microgrid connected mode. At first, we construct the microgrid schematic diagram of energy flow. Then, a mathematical model is presented for dispatch optimisation environmentally and economically. After we define the objective function according to the energy consumption, maintenance and systemic carbon emissions, two types of constraint are given, including the single input-output balance and the overall energy flow balance. Thirdly, we analyse the cooperative co-evolutionary genetic algorithm and apply it to a large number of cases. The better optimisation performance and convergence rate are obtained by comparing the method with the traditional genetic algorithms. The experimental results show it may play a significant role to the optimal scheduling of distributed energy generations in the grid-connected mode.
Keywords: optimal method; collaborative method; microgrid; environmental dispatch; economic dispatch; connected mode; optimisation; genetic algorithm; energy generation; emission; distributed energy.
An improved artificial fishes swarm algorithm for traffic signal control
by Yang Wang, Qiang Wang, Bin Lu
Abstract: The excessive growth of car ownership has caused great pressure on urban traffic. The traffic congestion is the most acute problem. One of the main causes of traffic congestion is the unreasonable scheme of traffic signal timing at road intersections. In view of the limitation of the Webster algorithm, we combine the artificial fishes swarm algorithm, chaos search and feed back strategy based on the optimisation theory of the signal timing to solve this problem. Furthermore, we apply the algorithm to the field of the traffic signal control. We set the average of vehicle delays and parking numbers as the target and improve the target road intersection timing scheme by using the optimisation algorithm. This method enhances the capacity of the target intersection effectively. Taking the condition of the target road intersection and the basic data into consideration, we construct the simulation model of the road intersection through the VISSIM simulation modelling tool. Then we import the relevant data and obtain a new timing plan, which sets a new cycle and the green light duration of each phase. Compared with the original method, the algorithm based on the artificial fish-swarm is feasible and effective.
Keywords: artificial fish swarm algorithm; road intersection; traffic light control; signal timing; VISSIM simulation.
Simulation and analysis of user-side transaction technology for energy blockchain considering multi-chain structure
by Guping Zheng, Jingya Hu, Gang Li
Abstract: Blockchain advantages, such as decentralised tamper-proof and smart contracts, are naturally suitable for the trading needs of the Energy Internet, dealing with the problem of low efficiency, waste of resources, privacy leakage, etc. In this paper, an energy trading method is proposed based on blockchain expansion technology. The lightning network is used to expand the blockchain trading process, and divides the transaction into on-chain trading and off-chain trading; and the chain structure of the energy blockchain is expanded using a multi-chain architecture, and the blockchain is divided into an account blockchain and a transaction blockchain. A case study is carried out, in this research work, in combination with multiple energy trading scenarios in the energy local area network. The results show that the proposed method possesses a significant performance of improving the efficiency of energy trading and reducing the complexity of trading information.
Keywords: blockchain; Energy Internet; energy trading; energy blockchain; lightning network; multi-chains; off-chain trading.
WLAN indoor positioning method based on gradient boosting and particle filtering
by Libin Hu, Zhongtao Li
Abstract: Indoor positioning technology has shown its great application prospects in smart cities. The main purpose of this paper is to study a low-cost, low-error indoor positioning method that can get a accurate indoor position when communicating with Wireless Local Area Networks (WLAN). The paper optimises the traditional WLAN indoor positioning method based on location fingerprint database, and algorithms about indoor signal simulation, similarity matching of vector and continuous positioning are tested. The WLAN indoor positioning method based on gradient boosting and particle filtering is proposed. The paper finds the indoor positioning result with an average error of 1.7 metres, which verifies the feasibility of WLAN indoor positioning and shows that the positioning accuracy will be improved with the further optimisation of the positioning method. The potential application values of WLAN technology make it more convenient for Internet of Things technology.
Keywords: IOT-SCT; gradient boosting; particle filter; WLAN indoor positioning.
iCampusGuide: a multi-purpose guide system in the intelligent campus
by Chunyan Yu, Hui Qi, Haibao Chen, Shenghui Zhao
Abstract: As an important application of intelligent campus, Campus Guide aims to help visitors get to their destination. In this paper, a flexible and low-cost campus guide model named iCampusGuide is proposed, which generalises the problem of university campus guide including objective, constraints, system and algorithms. The proposed iCampusGuide provides visiting reservation, route navigation, parking guideline and broadcasting introduction of buildings during the navigation. Technically, iCampusGuide adopts ibeacons for accurate location and navigation, and provides the parking suggestion by sending real-time snapshots from the parking lot nearest to the destination. Particularly, iCampusGuide provides business mode, tour mode, driving mode, and walking mode for different purposes. Each mode uses different route navigation algorithms. To verify the model, a client-server software is developed and implemented in a real university campus. Experiments show that the model is effective.
Keywords: intelligent campus guide; beacon; intelligent campus; intelligent parking.
Special Issue on: ISSPM 2018 Theory, Methodology and Application of Modelling and Simulation
Modelling and application of laparoscopic simulation system for panhysterectomy
by Xue Wang, Lili Xuan, Ying Pan, Haoying Wang, Xiaochen Huang, Ming Liu
Abstract: The laparoscopic surgery simulation training system uses human anatomy visual reproduction and force-feedback technology, and uses a variety of medical image data on the computer to establish a virtual environment with vision, hearing, speaking, dynamic, smell, feel, and touch. Surgeons conduct surgical training in the virtual environment, which includes suture, knotting, hand-eye coordination, organisational separation, and directional adaptation. In this paper, the issue of simulation-based training with the laparoscopic simulation system is dealt with for training of internship surgeons in panhysterectomy. The model is built and the practical training results are analysed and compared for the performance of two groups of internship surgeons under model-based training and conventional training, respectively. The results show that there was no significant difference in total abdominal hysterectomy (TAH) and crossover test for the two groups, whereas practice test scores increased significantly (p < 0.05), with the group using the model of laparoscopic simulation system scoring higher than the other group. It proves that the laparoscopic simulation system application in gynaecological surgery will help to improve the clinical skills of internship surgeons. The advanced simulation system makes a great contribution to reducing medical accidents, adding to the progress of surgery and its training.
Keywords: laparoscopic simulation system; panhysterectomy; laparoscopic surgery; medical modelling; clinical skill.
Seamless development in Java of distributed real-time systems using actors
by Franco Cicirelli, Libero Nigro, Paolo F. Sciammarella
Abstract: The work described in this paper is concerned with a model-driven development of distributed real-time systems, such as cyber-physical systems. One challenge in such systems development consists of ensuring that a final implementation is compliant with its model used for functional and temporal property checking. In particular, a modern Java framework called Theatre is described, which enables the modelling, analysis and synthesis of distributed real-time systems in a way that preserves such compliance. Theatre is based on the paradigm of actors, that is, highly modular and encapsulated software entities that communicate each other by asynchronous message-passing. Theatre rests on lightweight actors regulated by a customisable reflective control layer. Key features of Theatre are its support to seamless application development and timing predictability. The same model can be developed, without distortions, from early analysis through design and to a series of implementations. The paper describes Theatre and demonstrates its application to the development of a distributed dependable real-time system. Modelling, property checking by simulation and real-time prototyping are all illustrated. Theatres current level of maturity and practical implementation in Java are detailed.
Keywords: distributed timed actors; Java; modelling and simulation; timing constraints; seamless development.
Simulation modelling and analysis of balance mechanisms of innovation search in innovation networks
by Linling Xie, T.I.E. Wei
Abstract: Based on the scale-free weighted dynamic network, this paper proposes an agent-based model to investigate the dynamics characterising the interaction between balance mechanisms of innovation search and the innovation network. Specifically, it explores how two balance mechanisms of innovation search, punctuated equilibrium and ambidexterity, influence the evolution of the growth of network knowledge and performance through the simulation of the three-stage innovation search processes in the innovation network. In addition, it considers and assesses the impact of the rate of knowledge diffusion. The results show that the balance mechanisms of innovation search and the innovation network are co-evolutionary. The balance mechanisms of innovation search have an impact on the growth of network knowledge and performance, and the rate of knowledge diffusion has a contingent effect. This study provides a valid theoretical analysis framework and approach for future research in balance mechanisms of search.
Keywords: balance mechanisms of innovation search; innovation network; agent-based simulation; network evolution.
Research on NOx emission of coal-fired unit based on multi-model clustering ensemble
by Chenggang Zhen, Huaiyuan Liu, Hanyong Hao
Abstract: The predictive control of NOx emission generated by coal-fired units has an important impact on the economic benefits of a power station and control of environmental pollution. In order to enhance the accuracy of the prediction model, a modelling method of boiler NOx emission based on Voting Multi-model Soft Clustering (VMSC) ensemble is proposed. The data space is divided into three subspaces according to the level of NOx emission, and the variables that participate in clustering are determined by using variable weight based on relevant analysis and hierarchical clustering using information entropy. The proposed algorithm VMSC is used to obtain a new membership degree matrix of each subspace. The multiple Least Squares Support Vector Machine (LSSVM) models of each subspace are compromised by the least-squares method fused membership degree. The simulation results show that the VMSC algorithm, which merges Soft Fuzzy C-Means clustering (SFCM) and Genetic Algorithm-Soft Fuzzy C-Means clustering (GA-SFCM), improves the accuracy of clustering, and the simulation performance is better than other selected models. The integrated model VMSC-LSSVM can achieve accurate prediction for NOx emission of the utility boiler and effectively solve the problem that the model uses a single method to model is a weak generalisation.
Keywords: prediction of NOx emission; soft clustering; cluster ensemble; SFCM; GA-SFCM; multi-LSSVM; ensemble model;.
Decision support for ship collision avoidance in the narrow channel
by Yuanqiang Zhang, Guoyou Shi, Hu Liu, Weifeng Li
Abstract: To solve the problem of ship collision avoidance in restricted waters, a method is proposed, in this paper, for obtaining the Time to the Closest Point of Approach (TCPA), the Distance to the Closest Point of Approach (DCPA) and the meeting position considering multileg route, proposed method considers the turning position and turning time of the ship. For ships with a collision risk, a safe speed can be obtained by setting a new meeting position. The safe speed considers the time elapsed for altering speed. At the end, the proposed method is used to get the collision risk and avoidance measures for three different encounter situations. A navigation simulator is used to verify the avoidance measures. The experimental results show that the proposed algorithm can obtain more accurate results than the existing algorithms.
Keywords: restricted waters; waters intersection; TCPA; DCPA; ship collision avoidance.
Modelling and simulation of intelligent collision avoidance based on ship domain
by Weifeng Li, Jiaxuan Yang, Xiaori Gao, Jiagen Yu
Abstract: Intelligent collision avoidance systems can increase the safety of ship navigation and reduce the influence of human error. Thus, related technologies are critical to the development of the world's shipping industry. Risk of collision is a central concept in the Convention on International Regulations for Preventing Collision at Sea, 1972 (COLREGs). Collision risk is also a key factor in determining whether a collision avoidance action is required, specifically with regard to intelligent collision avoidance decisions. On the basis of the ship domain proposed by the Fujii, at first, this paper defined the risk of collision index λ, which can be used to measure the risks between owe ship ant the target ships, then, according to the COLREGs, categorised ship encounter types as head-on situation, crossing situation, and overtaking situation, and established the action flow charts of each of these situations, at last, With the permission of Dalian Maritime University, the training ships Yu Peng and Yu Kun were selected as simulation objects, the paper presented simulations of head-on, crossing and overtaking situations to verify the usability of the proposed models.
Keywords: simulation; modelling; ship; intelligent collision avoidance; risk of collision; ship domain.
Validating trustworthy service composition through VIPLE and pi-calculus
by Shenghui Zhao, Yuemin Li, Yang Wang, Yinong Chen
Abstract: The current formal verification practice focuses on functionality and does not consider the non-functional attributes verification. In this study, we propose a method in which non-functional attributes are incorporated into the logic rules of inference in terms of composition of the linear logic and pi-calculus. Giving credibility to non-functional attributes is important, especially in the cloud computing platform and IoT environments, where trust and security are ultra-important. Such studies have not been paid much attention by researchers and practitioners. In our approach, the evolvement of the non-functional attributes are included in the process of formal verification of the service composition scheme. In addition to theoretical analysis, we applied a tool named VIPLE (Visual IoT/Robotics Programming Language Environment) to execute and verify the validity of the service composition model's function. We translate the proving process of the linear logic into the corresponding pi-calculus expressions. VIPLE can translate visual work flow into pi-calculus and can verify the correctness of pi-calculus expressions.
Keywords: formal verification; pi-calculus; service composition; visual programming; VIPLE.
Modelling of ship collision avoidance behaviours based on AIS data
by Miao Gao, Guoyou Shi
Abstract: The original automatic identification system (AIS) data are so large that they cannot be directly applied to learning and training, so the collision avoidance data must be filtered, identified, and extracted. AIS data from the Laotieshan channel are used as raw data to identify successful cases of collision avoidance. Ship navigation statuses are screened according to AIS message codes. The improved density-based spatial clustering of applications with noise algorithm (DBSCAN) is used to cluster the four types of habitual route of ship trajectory, with the rest of the data as candidate data for ship matching. Ship encounter situations are planned for 13 categories considering the ship light arc range and the requirements of the International Regulations for Preventing Collisions at Sea (COLREGs). The matched data use a sliding window algorithm for extracting ship navigation behaviour, which is then stored in the form of segmented ship trajectory unit sequences. This study suggests a new knowledge base of intelligent ship collision avoidance data, providing a novel method and theoretical guidance for future developments in ship collision avoidance methods.
Keywords: USV; ship collision avoidance; modelling; AIS; big data; extraction of behaviour feature; ship trajectory unit.
Real-time health status evaluation for electric power equipment based on cloud model
by Wenqing Zhao, Min Cui
Abstract: The health status evaluation of electric power equipment is an important issue with extensive concerns in power system communities around the globe. In consideration of the uncertain characteristics of the monitoring data of wind turbines, a real-time health status evaluation method for wind turbines is presented employing the advantages of the cloud model in dealing with uncertain information. In the presented method, real-time data are analysed based on the well-established unsupervised clustering to partition the operational space. The health evaluation model is then trained based on the cloud model and cloud transformation, combining with SCADA historical state data and fully considering the uncertain information of wind turbines. The proposed model is applied to evaluate the health conditions of a 1.5 MW wind turbine located in northern China, and it is demonstrated that this model can detect the changing trend, and hence to promote reliability of wind turbines, and reduce maintenance costs.
Keywords: electric power equipment; cloud model and cloud transformation; health evaluation; unsupervised clustering.
Fast fractal image retrieval algorithm based on HV partition
by Hejin Yuan, Mingjie Li, Weihua Niu, Linna Zhang, Kebin Cui
Abstract: Existing quadtree-based fractal algorithms and fractal algorithms based on Horizontal Vertical (HV) have the problems of long encoding time and low accuracy in the task of image retrieval. In this paper, an improved fast fractal image retrieval algorithm based on HV segmentation is proposed, which speeds up the coding time and improves the accuracy for real-time searching. In order to improve the coding efficiency, the proposed algorithm restricts R block segmentation to certain direction and location in the coding phase and uses the local codebook to find the optimal matching of the partitioned blocks. We also introduce a weighting equation calculating method of area intersection to the image matching. New weighting parameters with respect to the sizes of partitioning blocks are proposed to improve the accuracy of image retrieval. The constraint-based HV segmentation algorithm and the local codebook matching strategy are tested on the texture and Olivetti Research Laboratory (ORL) face datasets. The experimental results show that the proposed algorithm accelerates the speed of image encoding. When the recall ratio is 100%, the precision of our algorithm has improved significantly. The proposed algorithm based on HV segmentation outperforms traditional fractal search algorithms in terms of adaption adaptivity.
Keywords: HV segmentation; fractal coding; precision; image retrieval.
Large-scale text classification with deeper and wider convolution neural network
by Min Huang, Wei Huang
Abstract: The dominant approaches for most Natural Language Processing (NLP) tasks such as text classification are recurrent neural networks (RNNs) and convolutional neural networks (CNNs). These architectures are usually shallow and only have one or two layers, which cannot easily extract inner patterns in natural language. Different from the original feature of image pixels with regularity, words and phrases are highly abstracted from human knowledge without direct correlation. Shallow models only capture the surface relation between them while deep models cannot directly apply to them. Therefore, a Shuffle Convolution Neural Network (SCNN) is proposed to address the shallow learning problem by introducing wider inception cell and deeper residual connection. In the paper, the difficulty of applying deep models to NLP problems is overcome by tricks of shuffling channel input and reshaping the output dimension in first layer. The results of the experiments carried out in this research work demonstrate that the proposed SCNN makes a great improvement in accuracy and efficiency compared to shallow models.
Keywords: text classification; shuffle channel; inception cell; residual connection.
Parameter co-evolution mechanism of particle swarm optimisation algorithm
by Ming Zhao, Xiaoyu Song, Yichen Gao
Abstract: PSO (Particle Swarm Optimisation) algorithm is a kind of evolution optimisation algorithm, which simulates the intelligent behaviour of birds foraging. The running parameters are the important factors that influence the performance of PSO, and the optimisation of the fixed setting and the adjustment mechanism on them is one of the hot research directions for the improvement of PSO. Based on the related research, this paper designs the co-evolution mechanism for the parameters, including both inertia weight and accelerating factors, which defines the stochastic evolution speed to reflect the current state of population evolution during the iterative process, and uses it as feedback to set the inertia weight and the two accelerating factors. PSO with the parameter co-evolution mechanism can realise cooperative evolution of the running parameters with the population by dynamically adjusting parameter values according to the population evolution state. Finally, compared with five widely recognised parameter fixed settings or adjustment mechanisms, on 20 optimisation benchmark functions of different categories, the effectiveness and the efficiency of the proposed mechanism are verified based on indicators including success rate, solution quality, stability and convergence speed.
Keywords: particle swarm optimisation; parameter co-evolution; inertia weight; accelerating factor; adjustment mechanism; stochastic evolution speed.
Special Issue on: ISSPM 2018 Modelling, Simulation and Optimisation of Manufacturing and Production Processes
Modelling and implementation of an intelligent stowage simulator for container ships
by Qingwu Wang, Jian Zhao, Lin Ma
Abstract: To solve the problems that conventional loading master for container ships merely provides the stability, strength, floating condition, etc., a comprehensive intelligent simulator was worked out, which can also obtain the amount of restowage containers, verify segregation scheme for dangerous containers, etc. The BLOCK algorithm was proposed for the first time for restowage problems with respect to the containers on deck jam that in hold and all in hold or on deck. In the case of all in hold or on deck, independent stowage and mixed stowage were studied in some detail. A fast and effective algorithm was also put forward to perform the segregation verifying. The proposed algorithms were verified by the loading condition from the loading manual.
Keywords: container ship; intelligent stowage simulator; restowage; container segregation.
An optimised steelmaking-continuous casting scheduling simulation system with Unity 3D
by Liangliang Sun, Yaqian Yu, Li Zhang, Tingting An
Abstract: This work considers the transportation time involved in ladle allocation during the steelmaking-continuous casting production. The optimisation problem of equipment allocation is presented, the optimal production sequence is determined, and a schedule for charges is determined. In order to ensure that the operation time and the energy consumption are minimised, a solution methodology combining a heuristic algorithm and a conflict elimination algorithm is developed. A two-stage heuristic algorithm is used to solve the problems of steel leakage continuous casting, equipment idling, and equipment overload, which improves the logistics of clogging and shortens the waiting time of charges. Then a conflict elimination algorithm is presented to solve the conflicts that may exist among charges and realise the optimisation of a rough scheduling plan. The simulation experiment based on actual production data shows that this methodology can effectively solve the steelmaking and continuous casting scheduling problem. To validate the scheduling plan more intuitively and modify it further, we present an optimisation system based on Unity 3D simulation platform. The 3D model of ladle, converter, refining position and caster made by 3ds Max is imported into Unity 3D engine and renders a reasonable distribution. The system can effectively evaluate the optimisation of the solution and adjust the related data to modify the scheduling plan and ultimately get a reasonable rescheduling plan.
Keywords: steelmaking-continuous casting; production scheduling plan; conflict elimination algorithm; heuristic algorithm; Unity 3D.
Test and effect analysis of hydraulic automatic pressure regulating water injection device in Shengli Oilfield
by Yuhai Cui, Jiehua Feng, Dongya Zhao
Abstract: In the later stage of oilfield exploitation, owing to the decrease of fossil oil, the oil recovery of the oilfield will be reduced, and the production may even be stopped. In order to stabilise the production of the oilfield, it is an effective measure to develop the oilfield by water injection. Therefore, it is very important to study the pressure-regulating water injection technology, and the hydraulic automatic regulating device is the key. In this paper, a set of hydraulic automatic pressure-regulating water injection devices has been developed, which mainly includes three parts: screw motor module, screw pump module, and transmission device between screw motor output shaft and screw pump input shaft. The shunt injection of each layer can be realised without deployment by the automatic pressure-regulating device. The change of total water injection volume can automatically adapt to the reservoirs according to the flow distribution ratio. At the same time, the test and field experiments in Shengli Oilfield, Dongying, China, show that the developed device has good energy-saving effects.
Keywords: Shengli Oilfield; regulating device; energy use; application effect.
A study of flexible flow shop scheduling problem with variable processing times Based on improved bat algorithm
by Jianyong Bian, Liying Yang
Abstract: In order to solve the problem of flexible flow shop scheduling with variable processing time (FFSP-VPT), a mathematical model is established, in this paper, based on analysing the processing time selection, defect detection, and rework of jobs with variable processing time. According to the characteristics of FFSP-VPT, a two-stage coding method is designed using ranked over value (ROV) coding. Based on the Bat Algorithm (BA), a self-adaptive elite bat algorithm (SEBA) is developed as the global optimisation method by using Hamming distance-based elite individual set and adaptive position update. Crossover tests are designed to determine the optimal values of critical parameters in SEBA. Validity of SEBA on solving FFSP-VPT in actual production is verified by comparing the simulation test results of SEBA with those of other algorithms.
Keywords: flexible flow shop; bat algorithm; variable processing time; Hamming distance; adaptive position update.
Bus manufacturing workshop scheduling method with routing buffer
by Zhonghua Han, Jingyuan Zhang, Shiyao Wang, Yuanwei Qi
Abstract: Aiming at solving the problem that the moving route is complicated and the scheduling is difficult in the routing buffer of the bus in the manufacturing workshop, a routing buffer mathematical programming model for bus manufacturing workshop is proposed. We design a moving approach for minimising the total setup cost for moving in routing buffer. The framework and the solution of the optimisation problem of such a bus manufacturing workshop scheduling with routing buffer are presented. The evaluation results show that, compared with the irregularly guided moving method, the proposed method can provide a better guideline for the bus movement in the routing buffer by reducing the total setup time of all buses processed at the next stage, and a better scheduling optimisation solution that minimises the maximum total completion time.
Keywords: bus manufacturing workshop; flexible flow shop scheduling; routing buffer; moving method.
Special Issue on: ICSCIB 2018 Green Progression in Smart Cities and Intelligent Buildings
Deep activity recognition in smart buildings with commercial Wi-Fi devices
by Qizhen Zhou, Jianchun Xing, Yuhan Zhang, Qiliang Yang
Abstract: Activity recognition acts as a key enabler of smart building applications, such as behaviour analysis, health diagnosis and user authentication. However, existing methods require either burdensome equipment, or light and Line-Of-Sight (LOS) working conditions. To address this challenge, we propose DeepAR, a device-free human activity recognition system with prevailing Wi-Fi signals, which circumvents the use of dedicated devices. DeepAR mainly exploits two key techniques to recognise human daily activities. Firstly, a novel principle component extraction method is presented to capture the motion-induced distortions and discard the irrelevant interferences. Secondly, deep feature maps are constructed with time and frequency domain characteristics, and a deep Convolutional Neural Network (CNN) model is further applied to classify the activity labels. DeepAR is implemented with commercial Wi-Fi devices, and the performance is evaluated through extensive experiments. Experiment results show that DeepAR can achieve an average accuracy of 98.6% in a meeting room and 96.4% in a student office.
Keywords: channel state information; wireless sensing; deep learning; principle component analysis; smart building.