New approach for air traffic management based on control theory

Air traffic management aims to provide solutions to congestion problems in air traffic networks (ATNs) which in turn are mainly generated by the variation in the capacity of air sectors or airports due to adverse weather conditions. Most of the existing approaches to dealing with these problems are based on mathematical programming techniques and inherit its computational difficulty. In this paper, we introduce a control scientist point of view to this topic by proposing an approach to solve the ground-holding problem based on discrete event systems control theory. An ATN can effectively be considered as a timed discrete event system and can be efficiently modelled based on a Time Petri net tool. The main advantage is an explicit representation of the position of each aircraft in the ATN at each time instant. The state space is modelled by a Discrete Time Reachability Graph and the capacity constraints on the air sectors are modelled by time floating general mutual exclusion constraints. Feasible flight plans can be constructed based on control synthesis techniques, while an algorithm to compute the optimal flight plan is proposed assuming a realistic cost function.     

Optimization of hybrid laminated composites using the multi-objective gravitational search algorithm (MOGSA)

The multi-objective gravitational search algorithm (MOGSA) technique is applied to hybrid laminates to achieve minimum weight and cost. The investigated laminate is made of glass–epoxy and carbon–epoxy plies to combine the economical attributes of the first with the light weight and high-stiffness properties of the second in order to make the trade-off between the cost and weight as the objective functions. The first natural flexural frequency was considered as a constraint. The results obtained using the MOGSA, including the Pareto set, optimum stacking sequences and number of plies made of either glass or carbon fibres, were compared with those using the genetic algorithm (GA) and ant colony optimization (ACO) reported in the literature. The comparisons confirmed the advantages of hybridization and showed that the MOGSA outperformed the GA and ACO in terms of the functions’ value and constraint accuracy.     

一种新型的智能云台控制系统

本文针对目前监控系统的智能化程度低的缺陷，提出了一套智能云台控制的方案，实现了对目标的自动跟踪、自动聚焦和自动光圈调整，并对自动跟踪和自动聚焦算法作了一些改进，缩短了跟踪和聚焦的时间。     

An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem

Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line-balancing problem (DLBP) is studied frequently. Recent research on disassembly lines has focused on a complete disassembly for optimising the balancing ability of lines. However, a partial disassembly process is widely applied in the current industry practice, which aims at reusing valuable components and maximising the profit (or minimising the cost). In this paper, we consider a profit-oriented partial disassembly line-balancing problem (PPDLBP), and a mathematical model of this problem is established, which is to achieve the maximisation of profit for dismantling a product in DLBP. The PPDLBP is NP-complete since DLBP is proven to be a NP-complete problem, which is usually handled by a metaheuristics. Therefore, a novel efficient approach based on gravitational search algorithm (GSA) is proposed to solve the PPDLBP. GSA is an optimisation technique that is inspired by the Newtonian gravity and the laws of motion. Also, two different scale cases are used to test on the proposed algorithm, and some comparisons with the CPLEX method, particle swarm optimisation, differential evolution and artificial bee colony algorithms are presented to demonstrate the excellence of the proposed approach.     

基于引力搜索神经网络的风电机组传动链故障识别

针对风电机组传动链故障识别由风电场制定合理维修策略可减少停机时间、降低维修费用问题,将引力搜索算法用于BP神经网络初始权值及阈值优化,提出基于引力搜索神经网络的风电机组传动链故障识别方法。算例结果表明,所提方法精度较BP神经网络高,能准确识别齿轮磨损、齿轮断齿、轴承松动等风电机组传动链典型故障,验证该方法的有效性。     

The cooperative shift change: an illustration using air traffic control

We define the cooperative shift change and consider it from the perspective of four phases identified in Grusenmeyer (International Journal of Human Factors in Manufacturing, 5, 163–176, 1995): end-of-shift, arrival, meeting, and taking post. We describe the differences expected for the phases for both outgoing and incoming operators, discuss various domains that differ in their shift-change profile, and consider why those profile differences obtain. With this framework as a backdrop, we explore in detail the shift change, or position relief briefing (PRB), of air traffic control. This theoretical framework has the potential to aid in the classification and comparison of the transfer of responsibility in a variety of industrial tasks. This study could aid in the implementation of changes for air traffic control PRBs.     

A transfer learning framework for traffic video using neuro-fuzzy approach

One of the main challenges in the Traffic Anomaly Detection (TAD) system is the ability to deal with unknown target scenes. As a result, the TAD system performs less in detecting anomalies. This paper introduces a novelty in the form of Adaptive Neuro-Fuzzy Inference System-Lossy-Count-based Topic Extraction (ANFIS-LCTE) for classification of anomalies in source and target traffic scenes. The process of transforming the input variables, learning the semantic rules in source scene and transferring the model to target scene achieves the transfer learning property. The proposed ANFIS-LCTE transfer learning model consists of four steps. (1) Low level visual items are extracted only for motion regions using optical flow technique. (2) Temporal transactions are created using aggregation of visual items for each set of frames. (3) An LCTE is applied for each set of temporal transaction to extract latent sequential topics. (4) ANFIS training is done with the back-propagation gradient descent method. The proposed ANFIS model framework is tested on standard dataset and performance is evaluated in terms of training performance and classification accuracies. Experimental results confirm that the proposed ANFIS-LCTE approach performs well in both source and target datasets.     

Concurrent optimization of assembly tolerances for quality with position control using scatter search approach

Tolerance allocation to individual parts in any assembly should be a vital design function with which both the design and manufacturing engineers are concerned. Generally design engineers prefer to have tighter tolerances to ensure the quality of their design, whereas manufacturing engineers prefer loose tolerances for ease of production and the need to be economical. This paper introduces a concurrent tolerance approach, which determines optimal product tolerances and minimizes combined manufacturing and quality related costs in the early stages of design. A non-linear multivariable optimization model is formulated here for assembly. A combinatorial optimization problem by treating cost minimization as the objective function and stack-up conditions as the constraints are solved using scatter search algorithm. In order to further explore the influence of geometric tolerances in quality as well as in the manufacturing cost, position control is included in the model. The results show how position control enhances quality and reduces cost.     

Optimal age-based inspection scheme for condition-based maintenance using A* search algorithm

In condition-based maintenance (CBM) with periodic inspection, the system is preventively replaced if failure risk, which is calculated based on the information obtained from inspection, exceeds a pre-determined threshold. The determination of optimal replacement threshold is often based on the minimisation of average maintenance costs per unit time due to preventive and failure replacements over a long time horizon. It is often assumed that inspections are performed at equal time intervals with no cost. However, in practice, inspections require labour, specific test devices, and sometimes suspension of operations and, thus, it is reasonable to inspect less frequently during the time the system is in its early age and/or in a healthier state and to perform inspections more frequently as time passes and/or as the system degrades. In other words, an age-based inspection scheme.

This paper proposes a novel two-phase approach for the determination of an optimal replacement threshold and an optimal age-based inspection scheme for CBM such that the total long-run average costs of replacements and inspections are minimised. First, it takes into account failure and preventive replacement costs to determine the optimal replacement threshold assuming that inspections are performed at equal time intervals with no cost. This assumption is, subsequently, relaxed and its consequences on total average cost are evaluated using a proposed iterative procedure based on A* search algorithm to obtain the optimal age-based inspection scheme. The proposed approach is illustrated through a numerical example.     

空气处理机组风阀控制算法分析

针对空气处理机组风阀常规PID控制算法中出现的2个风阀调节量不均衡、开机稳定慢的现象,提出一种基于串级PID控制的改进算法。首先根据阿里巴巴张北云计算数据中心二期工程中空气处理机组的实际运行情况搭建数学模型,然后利用该模型分别对常规PID和改进后的串级PID算法进行模拟运行与对比分析。结果表明,改进的串级PID算法使得2个风阀调节量均衡,并有效缩短系统进入稳定运行阶段的时间。     

A new approach for rebalancing of U-lines with stochastic task times using ant colony optimisation algorithm

In real world, line balancing involves existing lines in existing factories and the line typically needs to be rebalanced rather than balanced. Rebalancing of a U-line can be defined as a changeover process from its initial configuration to a new configuration for a while due to the reasons such as demand variations, changes in product design and changes in task times, etc. This study defines U-line rebalancing problem with stochastic task times and proposes a solution procedure based on ant colony optimisation. The objective of the proposed algorithm is to minimise total cost of rebalancing which is the sum of task transposition costs, workstation opening/closing costs and operating costs of workstations for a particular planning horizon. A comprehensive experiment is conducted to generate problem instances and to compare rebalancing costs of U-lines by means of several factors. A total of 6600 rebalancing solutions are obtained and several comparisons are performed.     

A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm

In this paper, a bioinspired path planning approach for mobile robots is proposed. The approach is based on the sparrow search algorithm, which is an intelligent optimization algorithm inspired by the group wisdom, foraging, and anti-predation behaviors of sparrows. To obtain high-quality paths and fast convergence, an improved sparrow search algorithm is proposed with three new strategies. First, a linear path strategy is proposed, which can transform the polyline in the corner of the path into a smooth line, to enable the robot to reach the goal faster. Then, a new neighborhood search strategy is used to improve the fitness value of the global optimal individual, and a new position update function is used to speed up the convergence. Finally, a new multi-index comprehensive evaluation method is designed to evaluate these algorithms. Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-of-the-art studies.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00366-x     

An algorithm for spectral factorization using random search techniques

An approximation method for the energy spectrum of a stationary stochastic dynamical system is presented, which allows approximate functional rational factorization.This paper is in three parts. The first deals with a theoretical problem of approximation in Hardy Spaces, whose main result is the following:Let S⁽ⁱⁿ⁾, S be positive functions belonging to L¹(R_gw), such that log S⁽ⁿ⁾ and log S belong to L¹(R_ω, dω/1 + ω²).Let h⁽ⁿ⁾, h be the outer functions of the Hardy Space H²(Π₊) such that S^(g) = |h⁽ⁿ⁾|² and S = |h|² on iR.If S⁽ⁿ⁾ n^→∝ S in L¹(R_ω), and log S⁽ⁿ⁾ n^→∝ log S in L¹(R_ω, dω/1 + ω²), then: h⁽ⁿ⁾ n^→∝ h in H²(Π₊).The second part describes an effective algorithm, using random search methods, and gives an almost sure convergence result for it.The third part treats numerically two examples, permitting comparison of this algorithm with others (whenever there are…): the first example is a problem of approximation for a nonrational process (turbulence) that was considered in Ref. 22: the second example is a problem of model reduction (automatic) considered in Ref. 4.     

A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.     

Towards computer vision-based approach for an adaptive traffic control system

In this paper, an adaptive traffic control system (ATCS) is proposed using the state of the art of video processing techniques. We illustrate how the system controls standard four-way intersections using three parameters; namely, average vehicles flow speed, level of crowdedness of vehicles, and a critical state timer. These parameters are detected from traffic videos using our computer vision algorithm. The ATCS decision-making process has been designed to adapt to predefined priorities over the traffic parameters. The validation of the proposed ATCS has been tested using four synchronized test videos in order to feed the proposed ATCS with different traffic information. Experimental results show a complete adaptation for the traffic flow.     

A generic approach for examining the effectiveness of traffic control devices in school zones

The effectiveness and performance of traffic control devices in school zones have been impacted significantly by many factors, such as driver behavioral attributes, roadway geometric features, environmental characteristics, weather and visibility conditions, region-wide traffic regulations and policies, control modes, etc. When deploying traffic control devices in school zones, efforts are needed to clarify: (1) whether traffic control device installation is warranted; and (2) whether other device effectively complements this traffic control device and strengthens its effectiveness. In this study, a generic approach is developed to examine and evaluate the effectiveness of various traffic control devices deployed in school zones through driving simulator-based experiments. A Traffic Control Device Selection Model (TCDSM) is developed and two representative school zones are selected as the testbed in Beijing for driving simulation implementation to enhance its applicability. Statistical analyses are conducted to extract the knowledge from test data recorded by a driving simulator. Multiple measures of effectiveness (MOEs) are developed and adopted including average speed, relative speed difference, and standard deviation of acceleration for traffic control device performance quantification. The experimental tests and analysis results reveal that the appropriateness of the installation of certain traffic control devices can be statistically verified by TCDSM. The proposed approach provides a generic framework to assess traffic control device performance in school zones including experiment design, statistical formulation, data analysis, simulation model implementation, data interpretation, and recommendation development.     

Designing manufacturing cells: A staged approach and a tabu search algorithm

This paper proposes a three-stage approach for the manufacturing cells design problem and a tabu search scheme for its solution. The proposed approach is based on the observation that almost all computationally complex aspects can be incorporated in a middle stage optimization model. A preliminary parts grouping stage precedes and a stage eliminating remaining intercellular traffic follows. The tabu search scheme that solves the middle stage problem integrates proper short- and long-term memory structures and an overall search strategy for their use. At the code-development phase, special care has been taken to enhance the explorative capability of the algorithm by correlating search statistics with the values of the search parameters. The resulting algorithm is robust and produces more than promising results with negligible computational effort. In the absence of known optimal solutions for larger dimensions (more than 30 machines), datasets from mathematically similar areas were used. Tests with problems of size up to 50 also give encouraging results.     

A safety assessment methodology applied to CNS/ATM-based air traffic control system

In the last decades, the air traffic system has been changing to adapt itself to new social demands, mainly the safe growth of worldwide traffic capacity. Those changes are ruled by the Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM) paradigm , based on digital communication technologies (mainly satellites) as a way of improving communication, surveillance, navigation and air traffic management services. However, CNS/ATM poses new challenges and needs, mainly related to the safety assessment process. In face of these new challenges, and considering the main characteristics of the CNS/ATM, a methodology is proposed at this work by combining “absolute” and “relative” safety assessment methods adopted by the International Civil Aviation Organization (ICAO) in ICAO Doc.9689 [14], using Fluid Stochastic Petri Nets (FSPN) as the modeling formalism, and compares the safety metrics estimated from the simulation of both the proposed (in analysis) and the legacy system models. To demonstrate its usefulness, the proposed methodology was applied to the “Automatic Dependent Surveillance-Broadcasting” (ADS-B) based air traffic control system. As conclusions, the proposed methodology assured to assess CNS/ATM system safety properties, in which FSPN formalism provides important modeling capabilities, and discrete event simulation allowing the estimation of the desired safety metric.     

A multi-level heuristic search algorithm for production scheduling

This paper introduces a multi-level heuristic search algorithm for identifying the optimal production schedule considering different levels of manufacturing requirements and constraints. The multi-level heuristic search algorithm generates search nodes at different levels. An upper level search node is composed of lower level search nodes, and evaluated based upon the evaluation of these lower level search nodes using a heuristic function. A production scheduling system was developed based upon the multi-level heuristic search algorithm. In this scheduling system, production requirements and constraints are represented at three different levels: task level, process level, and resource level. A task describes a manufacturing requirement. A process defines a method to achieve the goal of a task. A resource, such as a machine or a person, is a facility for accomplishing a required process. The multi-level heuristic search-based scheduling system was implemented using Smalltalk, an object-oriented programming language. Discussions on scheduling quality and efficiency are addressed at the end of this paper.     

Magnetic charged system search: a new meta-heuristic algorithm for optimization

An improved version of the charged system search (CSS) algorithm is introduced which is called magnetic charged system search (MCSS). In the new algorithm, magnetic forces are considered in addition to electrical forces, using the Biot–Savart law. Each charged particle (CP), as a search agent, exerts magnetic forces on other CPs based on the variation of its objective function value during its last movement and its distance between other CPs. This additional force provides useful information for the optimization process and enhances the performance of the CSS algorithm. The efficiency of the MCSS is examined by application of this algorithm to well-known mathematical benchmarks and three well-studied engineering design problems. The results are compared to those of the CSS, and the improvements are highlighted.