Information systems play an important role in realizing business processes. Process mining uses event logs produced by information systems to construct the models of business processes. Then, the business process model can be used to verify and improve business processes. To represent a changing business process, some structures of the process model need to be replaced with other structures. However, existing model repair methods do not consider replacing the structures of the process model. This work proposes a logical Petri net-based approach to repair models based on the order relations among activities. First, we construct two relation sets of an event log and a process model, respectively. Comparing the two relation sets, the differences between a process model and an event log are collected in a deviation set. According to the deviation set, the model is repaired by constructing concurrent structures. Finally, we perform some experiments to illustrate the effectiveness and correctness of the proposed approach. The results show that the proposed method produces a more precise and simpler repaired model compared to state-of-the-art methods.

The business processes in the information system are complex and diverse, and a single machine learning method often relies excessively on the noise or specific patterns in the training data. When dealing with large datasets, the calculation amount of the model is heavy, resulting in poor performance on new data, and it is difficult to achieve accurate monitoring and prediction of business processes. For this reason, a two-layer machine learning framework is presented using stacking technology-Serial Stacking Framework. Based on the event log, the method carries out random grouping sampling with placement, trains the multi-objective regression model, and applies multiple machine learning models to predict in series. Generally speaking, it is to use the prediction results of the previous model to generate training data and use it for the prediction of the latter model, in order to achieve the sequential accumulation of the prediction efficiency of multiple models. Random Forest and XGBoost are used as specific stack ensemble models for prediction, and the proposed method is evaluated against the existing advanced method through experiments. The results show that the average absolute error of the model built by the serial stacking framework with random group sampling and multi-objective regression is at least 2.14% lower than that of the single machine learning model, the conventional stacking frameworks and the latest methods.

Business processes realized by enterprise information systems are usually designed and verified in advance by process models. Event logs generated from such systems can be used to ensure the correctness of the business process. With the upgrade of systems or changing of customers' requests, it often appears that the actual behaviors observed in event logs are not consistent with those in process models. Process mining techniques are thus used to construct and repair process models by mining event logs. In this work, we propose a new model repair approach based on logic Petri nets (LPNs). It can repair a Petri-net-based process model containing a loop return structure. According to alignments, relations between the activities in event logs and the process models are analyzed such that the deviations can be found. Then, logic expressions of logic transitions are constructed and an LPN-based process model is thus constructed. The repaired model can accurately describe the same processes as the event logs. Finally, some cases related to a thoracic surgery process in a hospital are given. The correctness and effectiveness of the proposed approach are illustrated by experiments.

In order to reduce the harm of earthquakes to human society, all governments actively promote the construction and development of earthquake emergency rescue work. The earthquake emergency response involves many departments, multiple personnel and large rescue forces, which presents a great challenge to the ability to carry out cross-departmental rescue work in a collaborative and joint manner. A novel collaboration approach based on traditional and logical Petri nets is proposed to improve the cross-departmental collaboration in earthquake emergency response. The approach extends the synchronization of transitions in traditional Petri nets to that of traditional and logical transitions in traditional and logical Petri nets, and defines the intersection of related logical functions. The approach builds a model for the earthquake emergency response plans of various departments with the help of traditional and logical Petri net models, and then performs synchronous intersection operations on the two kinds of Petri nets and merges the two kinds of Petri nets into new logical Petri nets. Meanwhile, through realizing the collaboration ability of cross-departmental work, the approach improves the rescue efficiency and reduces the damage of the earthquake emergency.

过程挖掘可以根据企业信息系统生成的事件日志建立业务过程模型.当实际业务过程发生变化时,过程模型与事件日志之间会产生偏差,这时需要对过程模型进行修正.对于含有并行结构的过程模型修复,由于加入自环和不可见变迁等因素,有些现有的修正方法的精度会降低.因此提出一种基于逻辑Petri网和托肯重演的并行结构过程模型修复方法.首先根据子模型的输入输出库所与日志的关系,确定子模型的插入位置;然后通过托肯重演的方式确定偏差所在位置;最后根据基于逻辑Petri网提出的方法进行过程模型的修复.在ProM平台上进行了仿真实验,验证了该方法的正确性和有效性,并与Fahland等方法进行对比分析.结果表明,所提方法的精度达到85%左右,相比Fahland、Goldratt方法分别提高了17和11个百分点;在简洁度方面该算法没有增加自环和不可见变迁,而Fahland和Goldratt方法均增加了不可见变迁和自环;三种方法的拟合度均在0.9以上,而Goldratt方法略低一些.以上证明用所提方法修正后的模型具有更高的拟合度和精度.

To give play to the modeling advantages of the logical Petri net for batch processing and uncertainty of value transfer, this paper integralings the relevant game elements of multi-agent game process, excutes models for the multi-agent decision problem, solves the problem of multi-agent dynamic game decision optimization and puts forward logic game decision Petri net.Above all, this paper defines the properties of each token as rational persons and its utility function values, and provide the definition of utility functions and state probability transfer function.Next, this paper introduces decision transition, determines the optimal decision transition as per the comparison of token utility function value as well as provides related algorithm.Finally, the modeling and analysis of the dynamic game decision process of emergency are carried out based on logical game decision Petri net, and the dynamic game process is analyzed based on the reachability graph constructed by reachability identification.The algorithm is described for the generation of reachability graph, and how to solve the dynamic game decision problem is discussed, thus the optimal emergency pre-arranged plan is generated and the resource conflict in the process of emergency is analyzed by the logic game decision model of emergency.On this basis, this paper verifies the effectiveness and superiority of the model in the analysis of the emergency decision process.

Due to limitations of computer resources, when utilizing a neural network to process an image with a high resolution, the typical processing approach is to slice the original image. However, because of the influence of zero-padding in the edge component during the convolution process, the central part of the patch often has more accurate feature information than the edge part, resulting in image blocking artifacts after patch stitching. We studied this problem in this paper and proposed a fusion method that assigns a weight to each pixel in a patch using a truncated Gaussian function as the weighting function. In this method, we used the weighting function to transform the Euclidean-distance between a point in the overlapping part and the central point of the patch where the point was located into a weight coefficient. With increasing distance, the value of the weight coefficient decreased. Finally, the reconstructed image was obtained by weighting. We employed the bias correction model to evaluate our method on the simulated database BrainWeb and the real dataset HCP (Human Connectome Project). The results show that the proposed method is capable of effectively removing blocking artifacts and obtaining a smoother bias field. To verify the effectiveness of our algorithm, we employed a denoising model to test it on the IXI-Guys human dataset. Qualitative and quantitative evaluations of both models show that the fusion method proposed in this paper can effectively remove blocking artifacts and demonstrates superior performance compared to five commonly available and state-of-the-art fusion methods.

Several unexpected behaviors may occur during actual treatment of clinical pathways, which will have negative impact on the implementation and the future work. To increase the performance of current deviation detection algorithms, a method is presented according to business alignment, which can effectively detect the anomaly in the implementation of the clinical pathways, provide judgment basis for the intervention in the process of the clinical pathway implementation, and play a crucial role in improving the clinical pathways. Firstly, the noise in diagnosis and treatment logs of clinical pathways will be removed. Then, the synchronous composition model is constructed to embody the deviations between the actual process and the theoretical model. Finally, A ∗ algorithm is selected to search for optimal alignment. A clinical pathway for ST-Elevation Myocardial Infarction (STEMI) under COVID-19 is used as a case study, and the superiority and effectiveness of this method in deviation detection are illustrated in the result of experiments.

A method of repairing process models with non-free-choice constructs is proposed based on logical Petri nets, aiming at the problem of low precision in the existing repair methods. An extended successor matrix of transitions is determined according to the distance between any two transitions. There are two types of choice-construct transitions. One is a non-free-choice construct transition, and the other is a general choice construct transition. The type of choice-construct transitions can be determined based on the extended successor matrix and the relationship between the front and back sets of transitions. The location of the deviations is calculated by an improved replaying method. Finally, a model can be repaired according to remaining-token places and missing-token places. Based on the experiments on real event logs, the method proposed in this paper has a better performance in fitness, precision, and simplicity compared with its peers.

A method to enhance Web service clustering is proposed in this paper. Since current service clustering methods usually face low quality of service representation vectors and lack consideration of service collaboration, we try to provide an improved topic model to generate high-quality service representation vectors and design a service clustering method to integrate function similarity and collaboration similarity. First, by introducing feature word extraction and probability distribution correction into GSDMM, we present a model called TE-GSDMM (topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model). Then, a service collaboration graph is put forward to model cooperation relationships and generate service collaboration vectors. Collaboration similarity is assessed by the similarity of service collaboration vectors. Finally, the K-means++ algorithm is employed to cluster Web services by evaluating service function similarity and collaboration similarity. Experiments show that TE-GSDMM outperforms other topic models in generating high-quality service representation vectors for service clustering. Moreover, service clustering performance is further improved by integrating collaboration similarity. Thus, the proposed method effectively enhances Web service clustering by improving the quality of service representation vectors and integrating service collaboration similarity.

As a low-frequency and smooth signal, the bias field has a certain destructive effect on magnetic resonance (MR) images and is the main obstacle for doctors' diagnosis and image processing (such as segmentation, texture analysis, and registration). Before analyzing a damaged MR image, a preprocessing step is required to correct the bias field in the image. Unlike traditional bias field removal algorithms based on signal models and a priori assumptions, deep learning methods do not require precisely modeling signals and bias fields and do not need to adjust parameters. An MR image with the bias field is input and the corrected MR image is output after the deep neural network being trained on a large training set. In this paper, we propose taking the original image and the local feature images of the bias field in multiple frequency bands obtained by a Log-Gabor filter bank as input, correcting the bias field of a brain MR image through a deep separable convolutional neural network. Meanwhile, to speed up the training process and improve bias correction performance, we apply residual learning and batch normalization. We conducted the same test on BrainWeb simulation database and Human Connectome Project real data set, the consistency of qualitative and quantitative evaluation shows that our training model demonstrates better performance than the traditional state-of-the-art N4 and non-iterative multi-scale (NIMS) methods. Especially for the images with high-intensity non-uniformity level, the bias field has been well corrected.

Process mining is an emerging technology used to extract, detect, and improve actual processes by extracting knowledge from event logs generated from information systems. In the production process, we can obtain the optimal process based on practical experience. Indirect dependencies may exist among different structures in the optimal process model discovered from the event log of the executions that perform better. However, the existing process mining algorithms cannot effectively mine the indirect dependencies among different structures. To compensate for this deficiency, an algorithm named AlphaID is proposed in this article, and it can mine the indirect dependencies in a loop-choice-driven loop structure. First, two algorithms are proposed to efficiently identify loop sequences and choice sequences from event logs. Then, the concept of association rules is proposed to describe indirect dependencies among different structures. Next, we expand the ordinary Petri net and redefine the new transition firing rules to represent the process model obtained by AlphaID . Finally, the correctness and effectiveness of the algorithm are verified by an artificial case and a real case. AlphaID is integrated into the ProM which is an open-source process mining tool platform as a plug-in.

Process mining is a technique that can discover and enhance business processes by extracting knowledge from the event logs generated in information systems. A mixed multiple-concurrency short-loop structure is a frequently appearing structure in business processes. It cannot be mined accurately (or well) from incomplete logs by existing methods to the best of our knowledge. In this paper, an AlphaMining algorithm is proposed to discover mixed multiple-concurrency short-loop structures via Petri nets. First, the activities are matched with a triangular two-degree loop or a quadrilateral two-degree loop. Then, two kinds of short-loop structures are identified from the incomplete logs. Algorithms are proposed to correctly construct models with multiple-concurrency short-loop structures. Finally, the proposed method is integrated as a plug-in into an open-source process mining tool named ProM. The correctness and effectiveness of the proposed method are verified by experiments.

为解决逻辑Petri网不能详尽地描述模型在规定时间点变迁引发和引发完成的时间问题,提出逻辑时延Petri网.首先在普通变迁的基础上引入变迁的引发时间和变迁完成时间形成决策变迁,为每个token定义到达时间和自身时间属性等;其次重新定义引发规则和可达图算法,并针对决策变迁和可达图生成进行算法描述;最后使用逻辑时延Petri网对停车预订系统进行建模,构建可达图分析系统中重分配问题以及车位的利用率等问题.在此基础上设计实验验证了逻辑时延Petri网的可行性和智能停车预订系统的优势.

Enterprise information systems (EIS) play an important role in busi-ness process management. Process mining techniques that can mine a large num-ber of event logs generated in EIS become a very hot topic. There always exist some deviations between a process model of EIS and event logs. Therefore, a pro-cess model needs to be repaired. For the process model with selection structures, the mining accuracy of the existing methods is reduced because of the additional self-loops and invisible transitions. In this paper, a method for repairing Logical-Petri-nets-based process models with selection structures is proposed. According to the relationship between the input and output places of a sub-model, the de- viation position is determined by a token replay method. Then, some algorithms are designed to repair the process models based on logical Petri nets. Finally, the effectiveness of the proposed method is illustrated by some experiments, and the proposed method has relatively high fitness and precision compared with its peers.

针对目前传统的Snake模型图像分割算法的力场捕捉范围小、对初始轮廓的选取敏感以及对轮廓曲线难以收敛到细小深凹边界的缺陷,提出一种基于Snake模型的脑部CT图像分割新算法.算法首先运用Canny边缘算子对图像进行边缘检测,将边缘检测图像叠加到原始图像上,然后再运用Snake模型和梯度向量流(GVF)Snake模型分别对叠加图像进行分割.实验结果表明,该算法克服了传统Snake模型和GVF Snake模型因边缘轮廓不清晰造成的漏分割情况,防止了GVF Snake模型由于GVF力场的相互作用所造成的过分割现象,同时,还能促使轮廓线收敛到细小深凹边界,提高定位精度,具有更好的分割效果.

Fairness and efficiency are two key requirements that have to be guaranteed in channel resource allocation in cooperative vehicle safety systems. Existing channel access control strategies, however, rely on each individual node to adjust networking parameters independently according to its locally measured state information, thus leading to unfairness. Although some coordinated strategies have been proposed to resolve this issue, they pay little attention to the efficiency. In order to achieve the tradeoff between fairness and efficiency, in this paper, we propose a utility function in terms of inter-packet reception time required to capture the performance of consecutive successful packets’ reception under various vehicle densities. A channel access control problem among vehicles is then formulated as a non-cooperation game model. This model utilizes a punishment function to penalize a node that monopolizes the channel resources and hence can enable nodes to coordinate with each other to achieve desired fairness and efficiency. Next, a distributed decision-making scheme for channel access control is designed. It adjusts transmission rate in a coordinated manner and can guide each node to reach a Pareto-optimal Nash equilibrium point. The experimental results validate that the proposed strategy can result in fair channel resource allocation and ensure high-tracking accuracy for each vehicle under dynamic traffic conditions.

The trust-based routing mechanisms are proposed to enhance the security of the mobile ad hoc network (MANET), which use the performance metrics of a node to evaluate the trust value of the node. However, some performance metrics are fuzzy, which are easier to be described qualitatively than to be expressed quantitatively. Therefore, the inability to quantitatively express these performance metrics leads to the inaccuracy in the calculation of the trust values of nodes. Meanwhile, some routing mechanisms add the path with the highest credibility to routing table without considering the hop counts of the route in route selection, which reduces quality of service (QoS) of the routing. Aiming at the above problems, firstly, we use cloud model to deal with the fuzziness of performance metrics. Specifically, a trust reasoning model based on cloud model and fuzzy Petri net (FPN) is presented to evaluate the credibility of nodes. Then we propose a routing algorithm based on trust entropy. Routes with the minimum trust entropy are selected to add to routing table. This routing algorithm can reflect the comprehensive effect of route hops and the trust values of nodes on routing selection, thus improving QoS in MANET. Finally, the TUE-OLSR protocol is established based on the trust entropy routing algorithm and the optimized link state routing (OLSR) protocol. What's more, the effectiveness of TUE-OLSR protocol is verified by simulation experiments, which illustrate that TUE-OLSR protocol performs better than existing trust-based OLSR protocols in terms of packet delivery ratio and average latency.

Extended colored logic Petri nets (ECLPNs) are extensions of logic Petri nets (LPNs) and colored logic Petri nets (CLPNs). They are equivalent to LPNs and CLPNs, which can describe the batch processing and indeterminacy functions of resources in cooperative systems. The advantage of ECLPNs is that their net structures are much simpler than their equivalent CLPNs, and therefore, ECLPNs can be easily used to model and analyze cooperative systems. For systems containing several subsystems with the same function and structure, we can describe them by a single ECLPN. Then we propose a composition method of ECLPNs. We define the robustness of a system based on ECLPN which reflects the validity of the collaboration of subsystems. We define a strict conservativeness that guarantees data security. The robustness and strict conservativeness of composed ECLPNs are analyzed. An E-commerce example is presented to illustrate the modeling capacity and the advantage of ECLPNs.

逻辑Petri网是一种增广Petri网模型,具有与图灵机等价的建模能力.颜色逻辑Petri网解决了逻辑Petri网中输出的不确定性表达问题.然而颜色逻辑Petri网描述不同子系统的并发过程,需要对每一个子系统建立一个子网模型.如果所有子网模型的结构相同,则可以引入多个有色托肯,从而用一个子网模型表示多个子系统的并发过程.因此,提出了扩展颜色逻辑Petri网模型及其可达性分析方法.首先,为了方便可达标识的表示和计算,引入多重集的素数表示法,用素数幂的乘积来表示一个多重集,并给出了判断变迁使能的方法.其次,通过定义颜色逻辑关联矩阵,给出了一步可达标识的计算公式以及可达树构造方法.最后,针对一个电子商务实例,分别用颜色逻辑Petri网和所提出的扩展颜色逻辑Petri网建立模型并进行比较分析,从而证明了提出的模型具有更简单的网结构,丰富了逻辑Petri网理论.