MOTIVATION: Crohn's disease (CD) exhibits substantial variability in response to biological therapies such as ustekinumab (UST), a monoclonal antibody targeting interleukin-12/23. However, predicting individual treatment responses remains difficult due to the lack of reliable histopathological biomarkers and the morphological complexity of tissue. While recent deep learning methods have leveraged whole-slide images (WSIs), most lack effective mechanisms for selecting relevant regions and integrating patch-level evidence into robust patient-level predictions. Therefore, a framework that captures local histological cues and global tissue context is needed to improve prediction performance. RESULTS: We propose a novel clustering-enhanced weakly supervised learning framework to predict UST treatment response from pre-treatment WSIs of CD patients. First, patches from WSIs were encoded using a pre-trained vision foundation model, and k-means clustering was applied to identify representative morphological patterns. Discriminative patches associated with treatment outcomes were selected via a DenseNet-based classifier, with Grad-CAM used to enhance interpretability. To aggregate patch-level predictions, we adopted a multi-instance learning approach, from which whole-slide features were extracted using both patch likelihood histograms and bag-of-words representations. These features were subsequently used to train a classifier for final response prediction. Experimental results on an independent test set demonstrated that our WSI-level model achieved superior predictive performance with an AUC of 0.938 (95% CI: 0.879-0.996), sensitivity of 0.951, and specificity of 0.825, outperforming baseline patch-level models. These findings suggest that our method enables accurate, interpretable, and scalable prediction of biological therapy response in CD, potentially supporting personalized treatment strategies in clinical settings. AVAILABILITY AND IMPLEMENTATION: https://github.com/caicai2526/USTAIM.

Weather image translation technologies aim to convert sunny images into various weather scenes, addressing the challenge of the costly acquisition of highly-demanded diverse weather samples. However, existing weather translation methods based on generative adversarial networks (GANs) have limited generalization capability, resulting in translated images that lack authenticity and diversity. In contrast, the emerging image generation technologies based on diffusion models have greatly surpassed GAN-based ones in performance, thus becoming the dominant paradigm in various visual tasks. This work pioneers the application of diffusion models to weather translation and presents a novel Instruction-driven Multi-Weather Translation (InstructWT) method. InstructWT is built on the large image editing model, InstructPix2Pix, and leverages the latter’s zero-shot generalization capacities. We develop a user-friendly translation instruction set through prompt engineering and introduce a weather intensity factor for precise control of weather effects, thereby well enhancing the authenticity and diversity of weather images translated. A weather correlation-based blended editing technique is employed to maintain the layout and structure of the original image content. Additionally, a physical rendering approach of rain and snow is incorporated to further improve the translations’ realism. The results of comparative experiments on a public dataset, Cityscapes, demonstrate that InstructWT outperforms existing methods in terms of authenticity and fidelity. Specifically, InstructWT achieves Contrastive Language-Image Pre-Training (CLIP) image embedding cosine similarity and directional CLIP similarity scores of 0.8302 and 0.1598, respectively. Furthermore, several semantic segmentation algorithms fine-tuned using the multi-weather scene dataset augmented by InsturctWT show significant improvement in the segmentation effect on all complex weather scenarios.

Container detection and localization are crucial for the automated loading and unloading of containers. In automating rubber-tired gantry cranes (RTGs) for container handling, small-and medium-sized terminals are increasingly favoring for vision-based solutions over expensive LiDAR (Light Detection and Ranging) systems. However, conventional image processing-based methods fail to meet robustness and real-time requirements in practical settings. Meanwhile, deep learning-based object detection approaches offer greater adaptability to complex environments but are limited by their dependence on large-scale datasets and insufficient localization accuracy. To address these challenges, this paper presents a three-stage container detection and localization method for RTGs, exploiting a Segment Anything Model and image processing. First, an Auto-Prompting Segment Anything Model (AP-SAM) is proposed for the first time. It strengthens the similarity between reference and target features by training a feature adaptor module that uses only a single reference image with segmentation labels. Next, local peak points of the similarity map are selected as prompts, guiding the SAM to achieve initial container segmentation. Subsequently, we design an image processing method considering a container’s structural and geometric characteristics to detect its top surface, castings, and lock holes. Furthermore, we propose a novel estimation strategy to infer the positions of unknown lock holes on the detected ones, significantly improving the location accuracy. Extensive experiments show that our proposed method achieves a container detection precision of 93.3% and an average location error of just 5.55 pixels, surpassing existing methods.

Given the intricate situation of modelling gigapixel images, the usage of multiple instance learning (MIL) framework has recently increased to support clinical practice, encompassing cancer diagnosis, subtyping, survival prediction and other tasks. In current practice, most state-of-the-art MIL proposals typically apply a frozen pre-trained CNN or a pathological foundation model for feature extraction. While this paradigm lacks the capability for sequence feature fine-tuning within the downstream-specific tasks, which hinders the continuous performance promotion in Whole Slide Images (WSIs) Analysis. To address this issue, we propose a Sequence Feature Reconstruction Transformer (SeqFRT) for optimizing feature extraction of the foundation model, which can capture more discriminative features within pathological instance sequences. The proposed model comprises three main modules: 1) an offline foundation model as the pathological feature extractor; 2) a sequence position optimization architecture which aims at refining the correlations between instances in both sequential ordering and transpositional ordering; 3) a sequence sparsity enhancement strategy is designed to reconstruct the sequence feature and extract the latent representations instead of redundant information. Extensive experiments on six benchmark datasets for three computational pathology tasks demonstrated our model’s superiority over the state-of-the-art MIL methods. The source code is available at https://github.com/caicai2526/SeqFRT-MIL.

Lane detection, one of the crucial foundations of the autonomous driving of Rubber-Tired Gantries (RTGs), plays a vital role in automating manual container terminals. Deep-learning-based lane detection methods have robust and generalized global feature extraction capabilities to deal with complex scenarios well. However, the high preparation cost of large-scale labeled data has limited their application in RTG lane detection. Therefore, this paper presents a cost-effective, scalable incremental learning-based detection method. Specifically, some lane images are collected online, with reliable segmentation labels generated by an image-processing-based lane detection method. Next, a semi-supervised clustering approach is employed to construct a dynamically expanding sample pool, ensuring that samples are representative and diverse. Finally, a lane detection network model is self-trained by using all labeled and unlabeled samples. Extensive experimental results show that our proposed method outperforms existing methods and can achieve a lane detection accuracy of 94.87% and a detection success rate of 99.06%, with the potential for further performance improvement as data size increases..

This paper presents an improved Delaunay-Triangulation-based Variable Neighborhood Search (DVNS) using a new solution data structure, Satellite List (SL), to solve large-scale colored traveling salesman problem instances. SL encoding can reduce the time complexity of three basic operators in DVNS, i.e., insertion, swap, and flip to O(1), dramatically promoting the iteration efficiency of DVNS. Extensive experiments are conducted to validate the improved DVNS by comparing the presented method with the state-of-the-art algorithm DVNS. The results show that the improved DVNS outperforms the original in algorithm convergence, iteration efficiency, and solution quality.

Lane detection and tracking technique is the autonomous driving basis for Rubber-Tired Gantries (RTGs), vital to the automation and intelligence updating of man-driven container terminals. However, the existing lane detection methods developed for common road scenarios cannot meet the high-precision and robust all-weather requirements of RTG autonomous driving. In this article, we propose an Adaptive Edge-based Lane Detection and Tracking method considering RTG lanes’ characteristics in this paper. First, the candidate edges of lane lines are detected and paired based on the enhanced gradient features. Next, inverse perspective mapping is employed to search the right edges, followed by an adaptive sliding-window method. Ultimately, we develop an adaptive Kalman filter to track lane lines robustly, detecting confidence weighting by relaxing the constraint of lane line width. The proposed method is tested in an actual container yard, the lane centerline’s average position error is 2.051 pixels, and the detection success rate is close to 100%.

A colored traveling salesman problem (CTSP) as a generalization of the well-known multiple traveling salesman problem utilizes colors to distinguish the accessibility of individual cities to salesmen. This work formulates a precedence-constrained CTSP (PCTSP) over hypergraphs with asymmetric city distances. It is capable of modeling the problems with operations or activities constrained to precedence relationships in many applications. Two types of precedence constraints are taken into account, i.e., 1) among individual cities and 2) among city clusters. An augmented variable neighborhood search (VNS) called POPMUSIC-based VNS (PVNS) is proposed as a main framework for solving PCTSP. It harnesses a partial optimization metaheuristic under special intensification conditions to prepare candidate sets. Moreover, a topological sort-based greedy algorithm is developed to obtain a feasible solution at the initialization phase. Next, mutation and multi-insertion of constraint-preserving exchanges are combined to produce different neighborhoods of the current solution. Two kinds of constraint-preserving k -exchange are adopted to serve as a strong local search means. Extensive experiments are conducted on 34 cases. For the sake of comparison, Lin-Kernighan heuristic, two genetic algorithms and three VNS methods are adapted to PCTSP and fine-tuned by using an automatic algorithm configurator-irace package. The experimental results show that PVNS outperforms them in terms of both search ability and convergence rate. In addition, the study of four PVNS variants each lacking an important operator reveals that all operators play significant roles in PVNS.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. In it, each city has one to multiple colors and allows a salesman in the same color to visit exactly once. This work presents for the first time a CTSP whose edge weights among the cities change over time. It can be applied to dynamic routing problems arising in logistic distribution systems with various goods accessibilities to different types of vehicles. A non-linear integer mathematical program is constructed. A Variable Neighborhood Search (VNS) algorithm with a direct-route encoding and random initialization is then presented to solve it. To increase the convergence rate and population diversity of VNS, it is further combined with two-stage greedy initialization and an appropriate population immigrant scheme to perform the population search in a dynamic environment. Then, off-line performance evaluation and Wilcoxon test of the proposed algorithms are performed. The results show that their solution quality is improved by 30% ~ 60% over the basic VNS’s. They can track the environmental changes of CTSP-VEW more rapidly and effectively. In the end, a case study with a practical scenario is conducted.

As a generalization of the well-known multiple traveling salesman problem, a colored traveling salesman problem (CTSP) utilizes colors to describe the accessibility of individual cities to salesmen. To expand its application scope, this work presents a bi-objective CTSP (BCTSP) over hypergraphs, taking into account the balance of workload among salesmen. To solve it, a bi-objective variable neighborhood search (BVNS) is proposed as a solution framework. In BVNS, we exploit a two-stage initialization and a probability-based insertion to produce feasible solutions and generate their neighborhood. Next, population-based multi-insertion, color-preserving exchange and 2-opt constitute a powerful local search procedure, where color-preserving exchange improves the solutions by modifying the route intersections among distinct salesmen. Besides, Delaunay triangulation is utilized to prepare candidates for multi-insertion, thereby increasing the possibility to find an optimal route by shortening links among vertices. Extensive experiments are conducted on 20 cases. To make a comprehensive comparison, four existing methods, i.e., two genetic algorithms and two variable neighborhood search methods, are adapted by exploiting an elitist non-dominated sorting for solving BCTSP instances. The experimental results show that BVNS is superior to its peers in achieving Pareto optima in terms of three popular performance metrics, i.e., hypervolume, inverted generational distance, and C-metric. In addition, the study of four BVNS variants reveals that probability-based insertion, population-based multi-insertion, and color-preserving exchange play significant roles in BVNS’s high performance.

Anomaly detection is an active area of computer vision and widely applied in diverse fields. As known, it is a considerable challenge to collect abnormalities in practice. To tackle it, researchers propose many unsupervised or semi-supervised algorithms based on autoencoders or their variants. They focus on reconstruction loss between input and reconstructed samples but ignore the latent features extracted by an autoencoder. Namely, the existing algorithms tend to learn the local features of samples and have the approximate capabilities to reconstruct normal and abnormal samples. To capture the latent spatial features of anomaly detection, we present an unsupervised latent feature autoencoder via adversarial training. Particularly, we propose a weighted feature consistency loss to exploit the correlation between the corresponding layers of an encoder and decoder in the autoencoder. A feature discrimination loss is also designed to improve its ability to identify real and reconstructed samples by utilizing the latent spatial features of a discriminator. Next, we develop a discriminator that consists of two networks, i.e., a feature extraction network and a classification one. The pre-trained model can extract the input features accurately and stably, while the classification network can avoid excessive information losses and strengthen the ability to acquire deep semantics. Extensive experiments conducted on six public datasets show that the proposed method is competitive with the existing mainstream methods.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. It utilizes colors to differentiate the accessibility of its cities to its salesmen. In our prior work, CTSPs are formulated over graphs associated with a city-color matrix. This work redefines a general colored traveling salesman problem (GCTSP) in the framework of hypergraphs and reveals several important properties of GCTSP. In GCTSP, the setting of city colors is richer than that in CTSPs. As results, it can be used to model and address various complex scheduling problems. Then, a Delaunay-triangulation-based Variable Neighborhood Search (DVNS) algorithm is developed to solve large-scale GCTSPs. At the beginning stage of DVNS, a divide and conquer algorithm is exploited to prepare a Delaunay candidate set for lean insertion. Next, the incumbent solution is perturbed by utilizing greedy multi-insertion and exchange mutation to obtain a variety of neighborhoods. Subsequently, 2-opt and 3-opt are used for local search in turn. Extensive experiments are conducted for many large scale GCTSP cases among which two maximal ones are up to 33000+ cities for 4 salesmen and 240 salesmen given 11000+ cities, respectively. The results show that the proposed method outperforms the existing four genetic algorithms and two VNS methods in terms of search ability and convergence rate.

At present, no efficient method is proposed for the liveness analysis of general unbounded Petri nets (UPNs) except some of their subclasses. Our previous work presents a non-Karp-Miller finite reachability tree, i.e., lean reachability tree (LRT) to represent their markings. It faithfully expresses and folds the reachability set of an unbounded net. It can totally avoid the efforts made by the existing modified Karp-Miller trees on the expression of potentially unbounded nodes and elimination of all fake markings. By exploiting it, this paper presents a method for comprehensively analyzing the properties of general UPNs. Particularly, we reveal the repeatability of deadlock with the unfolding of some unbounded leaves in LRT and present a sufficient and necessary condition of deadlock existence. Then, LRT and some partial trees generated from it, instead of entire reachability graphs, are utilized to analyze the liveness and reversibility of general UPNs rather than some special ones. The related theoretical results are proven. A unified algorithm based on LRT for analysis of boundedness, liveness, deadlock, and reversibility of general UPNs is developed for the first time. The results of a case study show that the presented method is effective for general UPNs.

Cluster analysis is a data mining technique that has been widely used to exploit useful information in a great amount of data. Because of their evaluation mechanism based on an intracluster distance (ICD) function, traditional single-objective clustering algorithms are not appropriate for not-well-separated data. Specifically, they may easily result in the drop of the optimal solution accuracy on their late stages of search when dealing with the latter. To overcome the problem, in this paper a novel index reflecting the similarity of data within a cluster is presented and called intracluster cohesion (ICC). However, if a multiobjective method is used to cluster with ICD and ICC as the specified objectives, its clustering accuracy may depend on one's experience. Motivated by these, we propose an accelerated two-stage particle swarm optimization (ATPSO) in which K-means is utilized to accelerate particles' convergence during the population initialization. Its clustering process consists of two stages. First, the main objective of minimizing ICD is to execute preliminary clustering; second, ICC is optimized to promote the clustering accuracy. Extensive experiments with the help of 17 open-source clustering sets in various geometric distributions are conducted. The results show that ATPSO outperforms PSO, K -means PSO (KPSO), chaotic PSO (CPSO), and accelerated CPSO in terms of accuracy, and its efficiency is approximate to that of KPSO. Its convergence trend indicates that the adoption of the proposed ICC contributes to the clustering accuracy. Remarkably, compared with the Pareto-based multiobjective PSO, ATPSO can detect clusters more accurately and quickly through the proposed two-stage search.

With the advance on wireless energy transfer, it is reliable and favorable to power a directional sensor network (DSN) by wireless charging. This paper investigates how to deploy a rechargeable DSN using a mobile charger (MC) with the least number of nodes for perpetual target coverage subject to the limited sensing angles of directional sensors and limited energy capacity of the MC. We prove that the proposed problem is NP-hard. Next, we formulate it as a mixed integer nonlinear program to determine the smallest subset of sites to place sensors and the working directions of sensing nodes. Then, we propose two algorithms, i.e., an energy-bounded minimum-cost deployment and a relaxed-linear-program and repairing-based deployment. The simulation results demonstrate that the latter has higher success rate and solution quality than the former at the expense of more computational time.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. In our prior CTSP, each salesman is allocated a particular color and each city, carrying 1, 2, or all salesmen's colors depending on the problem types, allows any salesmen with the same color to visit exactly once. This paper presents a more common CTSP, in which city colors are diverse, i.e., each city has one to all salesmen's colors while other elements of the problem keeps unchanged. It is a generalization of the existing CTSPs, i.e., the radial and serial ones, and can be used to model the scheduling problems with different accessibility of jobs toward executors. A city color matrix is introduced to describe the accessibility difference of cities to all salesmen. Since CTSP is NP-hard, this paper presents a variable neighborhood search (VNS) approach, instead of computationally intractable exact solutions. First, the repetitive solution space due to the dual-chromosome encoding for the prior genetic algorithms can be entirely avoided by using direct-route encoding. Then, a two-stage greedy initialization algorithm is utilized by VNS to generate the initial solution. A city removal mechanism and a reinsertion operation are introduced to change the neighborhood space of the current solution and 2-opt method is adopted for the local search. Extensive simulation is conducted and the results show that the proposed VNS is an efficient heuristics to solve CTSP.

Elaborate efforts have been made to eliminate fake markings and refine ω-markings in the existing modified or improved Karp-Miller trees for various classes of unbounded Petri nets since the late 1980s. The main issues fundamentally are incurred due to the generation manners of the trees that prematurely introduce some potentially unbounded markings with ω symbols and keep their growth into new ones. Aiming at addressing them, this work presents a non-Karp-Miller tree called a lean reachability tree (LRT). First, a sufficient and necessary condition of the unbounded places and some reachability properties are established to reveal the features of unbounded nets. Then, we present an LRT generation algorithm with a sufficiently enabling condition (SEC). When generating a tree, SEC requires that the components of a covering node are not replaced by ω symbols, but continue to grow until any transition on an output path of an unbounded place has been branch-enabled at least once. In return, no fake marking is produced and no legal marking is lost during the tree generation. We prove that LRT can faithfully express by folding, instead of equivalently representing, the reachability set of an unbounded net. Also, some properties of LRT are examined and a sufficient condition of deadlock existence based on it is given. The case studies show that LRT outperforms the latest modified Karp-Miller trees in terms of size, expressiveness, and applicability. It can be applied to the analysis of the emerging discrete event systems with infinite states.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. This paper investigate a class of CTSP, called serial CTSP (S-CTSP). Each of its salesmen has his exclusive cities and shares some cities with its neighbor(s) in a serial manner. It can be used to model the scheduling problem of multimachine engineering systems with linearly arranged machines. S-CTSP is NP-hard. Developing effective and efficient approaches to S-CTSP is important to enable its industrial applications. This paper presents a population-based incremental learning (PBIL) approach to it. After analyzing its solution space, we set up some probability matrix models to guide the individual search of the algorithm. Then, a distance penalty is introduced into the state transfer function that can select the cities with small penalty values by the Roulette method to form a good route. By adding a powerful local search operation, 2-opt, to the algorithm, we can further enhance its search ability. Extensive simulation is conducted and its results show that the augmented PBIL is effective and well outperforms the genetic algorithms and CPLEX.

Owing to large production capacity and high efficiency, multibridge machining systems (MBMSs) have gained increasing attention in industry. Their multiple bridge machines work concurrently in their serially arranged and partially overlapping workspaces. To solve totally the job scheduling and collision resolution problems of MBMS, our prior work proposes a serial-colored traveling salesman problem (S-CTSP)-based method. Each salesman in S-CTSP visits his exclusive cities and some cities shared with his neighbor(s). To endow MBMS with fault-tolerance ability, this paper presents a two-stage method for both static path planning and dynamic collision avoidance of multiple machines. At the first stage, the path planning problem abstracted as an S-CTSP is solved by a population-based incremental learning (PBIL) algorithm. The PBIL introduces a local search operation, two types of possibility vectors, and a selection strategy of exclusive and shared cities. The second stage uses a Petri net supervisor to dynamically avoid any emerging collision when performing the scheduled work. This work also provides a novel priority net structure to prevent mutual waiting of two neighboring machines ready to enter their overlapping workspace. Then, we apply the presented method to a large tribridge waterjet cutting case to show its performance.

The multiple traveling salesman problem (MTSP) is an important combinatorial optimization problem. It has been widely and successfully applied to the practical cases in which multiple traveling individuals (salesmen) share the common workspace (city set). However, it cannot represent some application problems where multiple traveling individuals not only have their own exclusive tasks but also share a group of tasks with each other. This work proposes a new MTSP called colored traveling salesman problem (CTSP) for handling such cases. Two types of city groups are defined, i.e., each group of exclusive cities of a single color for a salesman to visit and a group of shared cities of multiple colors allowing all salesmen to visit. Evidences show that CTSP is NP-hard and a multidepot MTSP and multiple single traveling salesman problems are its special cases. We present a genetic algorithm (GA) with dual-chromosome coding for CTSP and analyze the corresponding solution space. Then, GA is improved by incorporating greedy, hill-climbing (HC), and simulated annealing (SA) operations to achieve better performance. By experiments, the limitation of the exact solution method is revealed and the performance of the presented GAs is compared. The results suggest that SAGA can achieve the best quality of solutions and HCGA should be the choice making good tradeoff between the solution quality and computing time.

This work formulates for the first time a multiple traveling salesman problem (MTSP) with ordinary and exclusive cities, denoted by MTSP for short. In the original MTSP, a city can be visited by any traveling salesman and is thus renamed as an ordinary one in MTSP. A new class of cities is introduced in MTSP, called exclusive ones. They are divided into groups, each of which can be exclusively visited by a specified or predetermined salesman. To solve MTSP, a genetic algorithm is presented. It encodes cities and salesman into two single chromosomes. Accordingly, three modes of crossover and mutation operators are designed, i.e., simple city crossover and mutation (CCM), simple salesman crossover and mutation, and mixed city-salesman crossover and mutation. All the operations of crossover and mutation follow the proper relationship between cities and salesman. With the help of an MTSP example, the performance of the proposed algorithm with three modes of crossover and mutation operators is compared and analyzed. The simulation results show that the algorithm can solve MTSP with rapid convergence with CCM being the best mode of the operators.

Petri net (PN) transformation is a method for converting a net from one structure to another. The existing approaches are net content dependent, i.e., all elements of the right- and left-hand-side nets or the operand nets participate in matching and related operations during transformation. They incur high computational complexity and difficulty to predict the transformation results. Reduction and refinement as two kinds of elementary net transformation have not been brought into a unified framework of net transformation approaches. In addition, the criteria for steering net transformation have not been separated from the transformation manipulations in the existing reduction and refinement methods. To solve these problems, this work proposes a net algebraic system for the general transformation of nets. It possesses node operation, block operation, and basic place- and transition-interfaced net operation algebras. Net-content-independent transformation, in which only the location and operation of the operand nets' interfaces are involved, can be achieved in a net algebraic way. Furthermore, by composing several operations, new operations for reduction and refinement are defined within a unified net transformation framework in which they are independent of the specific transformation criteria. Subsequently, the equivalence between the original and transformed nets with respect to PN properties as the criterion is investigated and proved when several newly proposed subnet structures are used as operands. Finally, the algebraic reduction operation is applied to analyze a complicated PN model of a mail sorting system.

The advent of reconfigurable manufacturing systems (RMSs) has given rise to a challenging problem, i.e., how to reconfigure rapidly and validly a RMS supervisory controller in response to frequent changes in the manufacturing system configuration driven by fluctuating market. This paper presents an improved net rewriting system (INRS)-based method for automatic reconfiguration of Petri net (PN) supervisory controllers for RMS. We begin with presenting the INRS which overcomes the limitations of the net rewriting system and can dynamically change the structure of a PN without damaging its important behavioral properties. Based on INRS, a method for design reconfigurable PN controllers of RMS is introduced. Subsequently, we presented an INRS-based method for rapidly automatic reconfiguration of this class of PN controllers. In the reconfiguration method, changes in a RMS configuration can be formalized and act on an existing controller to make it reconfigure rapidly into a new one. Noticeably, no matter the design or reconfiguration, the expected behavioral properties of the resultant PN controllers are guaranteed. Thus, efforts for verification of the results can be avoided naturally. We also illustrate the reconfiguration of a PN controller for a reconfigurable manufacturing cell.