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In this study, a detection method based on the Cascaded Hough Transform (or CHT for short) and an improved YOLOV5 model is proposed for the detection of vehicle pressing lines in straight lanes. This method combines traditional image processing with depth learning by integrating background modeling, cascade Hough transform principle and YOLOV5 depth learning framework. The specific process of this method is as follows. First, MOG2 background modeling is used to obtain the road background image, then the CHT principle is used to extract the lane lines in the road background image, and finally the improved YOLOV5 model is used to recognize the position of vehicles and judge whether the vehicles are pressing the line. The experimental results show that this method has good recognition speed and accuracy, can meet the basic requirements of road monitoring vehicle line detection, and also provides some reference experience for the research of vehicle line detection.

The upward progress in Data-Information-Knowledge-Wisdom (DIKW) has advanced manufacturing from digitalization to networking in Industry 3.0, and to intelligence and even wisdom in Industry 4.0, which reveals the inevitable trend of manufacturing evolution from “Computer Plus” to "Internet Plus" to "Knowledge Plus" and towards "Wisdom Plus". Wisdom manufacturing is the further development of intelligent/smart manufacturing in the form of social-cyber-physical system by integrating Internet of Things, Internet of Services, Internet by and for People, and Internet of Contents and Knowledge in manufacturing as a whole.

Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

Cloud service composition (CSC) is an effective way to carry out large-scale complicated applications by the ensemble of existing individual services. Each service typically involves several Quality of Service (QoS) criteria contracted for non-functional aspects like time or price, among others, which greatly influence the overall performance of the resulting applications. Service composition approaches have emerged as an important technique in leveraging the quality of composite service efficiently and have attracted significant attention. However, most existing proposals ignore the many-objective nature of CSC and consider up to three objectives, the optimization of diverse QoS aspects of CSC from a many-objective perspective (at least four) still lacks. On another aspect, due to the rapid growth of nondominated solutions in high-dimensional objective spaces, the traditional multi-objective optimization algorithms are usually not capable of handling problems possessing many objectives. To address the above issue, we develop an angle and adversarial direction based optimizer for many-objective CSC scenarios, which evolves a number of subpopulations with adversarial search directions in a parallel paradigm. Additionally, vector angle based selection criterion, which adaptively captures beacon individuals, is utilized to diversify the population. Extensive experiments are carried out on a series of CSC instances utilizing synthetic datasets and the results show that our proposition is competitive and has better versatility compared with the state-of-the-art.

The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

To further improve the search ability of the decomposition based many/multi-objective evolutionary algorithm (MOEA/D) in the tackling many-objective optimization problems (MaOPs) possessing complex characteristics (e.g., disconnected, degenerate, inverted, extremely convex or differently-scaled), we suggest an adaptive MOEA/D with better versatility, where the weight vector adaption and selection mechanism are improved. Firstly, a new niche-guided scheme by considering both the vector angle and Euclidean distance is proposed to leverage the search direction adaption upon different evolution phases, which is expected to be more robust for handling different types of irregular Pareto fronts (PFs). Secondly, in mating selection, a coordinated selection scheme aided by a multi-criterion decision procedure is utilized to enhance the effectiveness of recombination. Finally, in environmental selection, a steady state replacement strategy considering both the ensemble ranking of favorite subproblems with respect to solutions and improvement region restriction of subproblems is employed to alleviate misleading selection. Comparison experiments on benchmark MaOPs with diverse characteristics have been performed and the empirical results demonstrate the superiority of our proposal. The effects of direction vector adaption mechanism and other pertinent enhancements are also investigated.

Encouraged by the strong global search ability of the artificial bee colony (ABC) optimizer in single-objective problems (SOPs), we extend ABC to solve many-objective optimization problems (MaOPs) by exploiting the potential of direction vectors . Specifically, direction vectors are used not only to transform the original MaOP into a set of SOPs, but also to divide the bee colony into multiple subpopulations, while ABC serves the purpose of SOPs optimization . However, search equation of ABC is not efficient in convergence speed due to the overrated exploration property , to alleviate this issue, we suggest a search path statistical learning mechanism to predict the potential solutions, which is utilized to strengthen the exploitive search and accelerate the convergence rate. In addition, a new adaptive scalarization approach by exploiting population entropy and knee point information is developed to determine the elite in each subpopulation. For onlooker bee stage, a new fitness assignment scheme is proposed to achieve the computational effort allocation among food sources. The proposed algorithm is compared with several popular evolutionary optimizers on a wide range of test problems covering varying complexity, and empirical results show that it is very competitive and promising.

Artificial bee colony (ABC) is a well-known swarm intelligence based algorithm that simulates the foraging behavior of honey bees for food sources. However, the basic ABC only evolves one colony and both the employed bee phase and onlooker phase utilize the same solution search equation, which performs well in exploration but poorly in exploitation. Inspired by the good working efficiency of labor division and the coordination of complementary elements, we propose a novel individual dependent multi-colony ABC algorithm, abbreviated as IDABC, in which the whole colony is divided into three sub-colonies, i.e., inferior sub-colony, mid sub-colony and superior sub-colony, based on the fitness function values of the individuals involved. Three evolution operators with different searching biases are introduced into the corresponding sub-colonies in order to play different roles. Furthermore, we improve the related evolution operators by incorporating the fitness and distance information of individuals and the control parameters involved in the food source perturbation that are dynamically adjusted according to the search experience. And an orthogonal learning mechanism is suggested for scout bee searching so as to generate potential solutions. The proposed IDABC is examined in a set of benchmark instances taken from the CEC2013 and CEC2014 competition, and comparative results demonstrate the competitive performance of IDABC.

With the increasing prosperity of additive manufacturing, the 3D-printing shop scheduling problem has presented growing importance. The scheduling of such a shop is imperative for saving time and c...

The material transportation problem in workshops under complex environment can be idealized as the Generalized Traveling Salesman Problem (GTSP). To solve such a problem, an improved firefly algorithm is proposed. Firstly, two-layer coding is utilized to define the firefly individual, the inter-individual distance formula and position updating formula in standard firefly algorithm are improved, and the repaired method for infeasible solutions is defined. Then, in order to improve the local optimization ability of the proposed algorithm and accelerate the convergence speed, an improved Iterative Local Search (ILS) strategy and Complete 2-opt (C2opt) optimized operator are introduced. Moreover, the greedy firefly mutation strategy is used. Finally, 20 test cases are simulated with the algorithm. Experimental results indicate that the proposed algorithm has good convergence speed and problem solving accuracy.

As a key element of ITS (intelligent traffic systems), traffic information collection facilities play a key role, with ITS being able to analyze the state of mixed traffic more appropriately and can provide effective technical support for the design, management, and the evaluation of constructions. Traffic Infrastructure. Focusing on image processing technology, this study takes pedestrians, electric motor, and vehicles in mixed traffic flow as the research object, and Gaussian mixed model, Kalman filtering, and Fisher linear discriminant are introduced in the recognition system. On this basis, the mixed motion flow data acquisition framework model is elaborated in detail, which includes attribute extraction, object recognition, and object tracking. Given the difficulty in capturing reliable images of objects in real traffic scenes, this study adopted a novel background and foreground classification method with region proposal network so as to decrease the number of regions proposal from 2000 to 300, which can detect objects fast and accurately. Experiments demonstrate that the designed programme can collect the flow data by detecting and tracking moving object in the surveillance video for mixed traffic. Further integration of various modules to achieve integrated collection is another important task for further research and development. In the future, research on dynamic calibration of monocular vision will be carried out for distance measurement and speed measurement of vehicles and pedestrians.

Coverage model is often applied in spatial networks to evaluate site planning and robustness of networks. The visualized coverage model is helpful for people to understand and analyze these spatial data, in support for better decision. In this paper, we propose a novel dynamic coverage model for carparks around the city. Every carpark is quantified dynamically by its service capability, which is calculated in real time by the algorithm ParkingRank proposed in our previous work. Then an improved weighted Voronoi diagram featuring boundary constraint is introduced to map the quantified service capability of each carpark to the geospatial space, forming a seamless and non-overlapping coverage over the city. The best advantage of this model is the coverage scope of each carpark will dynamically adjust with its service capability varying, while the seamless and non-overlapping feature of coverage would be kept throughout from the perspective of the whole city. It is the first time that the network of carparks is modeled in a visual way, from which the city-wide parking management will get powerful decision supports in the future.

This paper presents a method on workpiece detection based on image processing and convolutional neural network(CNN). Firstly, four extreme points and center point of the workpiece are detected by image processing technologies such as canny edge detection operator, morphological processing and denoising processing. And the predicted boxes fitting the shape of image is generated. Then, according to the coordinates of the extreme points, the image with a single workpiece is cut out, and a novel CNN named workpiece-net(wp-net) is created to classify the object. As a result, the accuracy of image cutting is 0.9986, the average Intersection over Union(IoU) is 0.9235; the parameter size of wp-net is 98.25K and the average precision of classification is 0.9883. In addition, the average recall of classification is 0.9877 and the speed of classification is 0.1243s/fps without Graphics Processing Unit(GPU) and multithreading. Compared with the pure deep learning method, this method can detect more accurate coordinates which are consisted of extreme points. At the same time, the number of wp-net parameters and the complexity of the model structure used for classification are so far less than the popular deep neural network for detection that it can be easily deployed in embedded devices with limited storage space and computing power.

Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.

A workpiece detection method based on fusion of deep learning and image processing is proposed. Firstly, the workpiece bounding boxes are located in the workpiece images by YOLOv3, whose parameters are compressed by an improved convolutional neural network residual structure pruning strategy. Then, the workpiece images are cropped based on the bounding boxes with cropping biases. Finally, the contours and suitable gripping points of the workpieces are obtained through image processing. The experimental results show that mean Average Precision (mAP) is 98.60% for YOLOv3, and 99.38% for that one by pruning 50.89% of its parameters, and the inference time is shortened by 31.13%. Image processing effectively corrects the bounding boxes obtained by deep learning, and obtains workpiece contour and gripping point information.

Bayesian optimization (BO) is a versatile and robust global optimization method under uncertainty. However, most of the BO algorithms were developed for problems with only continuous variables. For practical engineering optimization, discrete variables are also prevalent. BO methods based on Gaussian process (GP) surrogates also suffers from the curse-of-dimensionality problem. To address these challenges, in this paper, a Bayesian discrete optimization algorithm is introduced to solve permutation-based combinatorial problems. A new kernel function is developed based on position distances for permutation. To improve the efficiency and scalability of the algorithm, a sparse GP model based on inducing points is further developed, where the simulated annealing algorithm is applied to select inducing points. The new algorithm is demonstrated and tested with the production scheduling problem for additive manufacturing. Experimental results show that the proposed algorithm can find a better solution with limited evaluations than state-of-the-art algorithms.

Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS, IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms’ linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.

Global competition and the growing need for rapid responsiveness of customers’ orders necessitate the integration of planning, scheduling and control in manufacturing systems. This paper proposes a...

Process of manufacturing service composition.Display Omitted A novel hybrid differential artificial bee colony algorithm for service composition in cloud manufacturing is proposed.Multiple subpopulations with distinct hybrid evolutionary operators are adopted during the evolution process.The size of each subpopulation is adaptively adjusted based on the information derived from its search process.The control parameters of each evolution operator are adapted independently.The proposed algorithm outperforms the state-of-the-art approaches known in the literature. As a new service-oriented smart manufacturing paradigm, cloud manufacturing (CMfg) aims at fully sharing and circulation of manufacturing capabilities towards socialization, in which composite CMfg service optimal selection (CCSOS) involves selecting appropriate services to be combined as a composite complex service to fulfill a customer need or a business requirement. Such composition is one of the most difficult combination optimization problems with NP-hard complexity. For such an NP-hard CCSOS problem, this study proposes a new approach, called multi-population parallel self-adaptive differential artificial bee colony (MPsaDABC) algorithm. The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution (DE) to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently. Moreover, the size of each subpopulation is dynamically adjusted based on the information derived from the search process. Different scales of the CCSOS problems are conducted to validate the effectiveness of the proposed algorithm, and the experimental results show that the proposed algorithm has superior performance over other hybrid and single population algorithms, especially for complex CCSOS problems.

Service composition and optimal selection (SCOS) is a key problem in cloud manufacturing (CMfg). The present study proposed a multi-objective hybrid artificial bee colony (HABC) algorithm to address the SCOS problem in consideration of both quality of service (QoS) and energy consumption, to which an improved solution update equation with multiple dimensions of perturbation was adopted in the employed bee phase. Likewise, a cuckoo search-inspired Levy flight was employed in the onlooker bee phase to overcome basic artificial bee colony (ABC) drawbacks such as poor exploitation and slow convergence. Moreover, a parameter adaptive strategy was applied to adjust the perturbation rate and step size of the Levy flight to improve the performance of the algorithm. The proposed algorithm was first tested on 21 multi-objective benchmark problems and compared with four other state-of-the-art multi-objective evolutionary algorithms (MOEAs). The effect of the improvement strategies was then experimentally verified. Finally, the HABC was applied to solve multiscale SCOS problems using comparison experiments, which resulted in more competitive results and outperformed other MOEAs.

This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and paramet...

Cloud manufacturing (CMfg) provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing services on-demand. The service composition with optimal overall quality of service (QoS) is of great significance since it affects the efficiency of resource sharing and optimal allocation in CMfg systems. However, some limitations still exist in manufacturing service composition methods especially in the complicated cloud environment. This owes to two aspects: (1) current service composition approaches assume that the QoS of services is fixed and not influenced by other services, the existence of the service correlation context is not considered adequately, thus resulting in no accordance with practical applications; (2) with the growing number of candidate services in cloud resource pools, the traditional methods might be inefficient for addressing large scale service composition problems. To overcome such drawbacks, this study proposes a new hybrid teaching–learning-based optimization (HTLBO) algorithm for optimal service composition with the consideration of service correlations. Experiments are conducted to verify the effectiveness and feasibility of the proposed algorithm.