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Test-driven verification/validation of model transformations

László LENGYEL,Hassan CHARAF

《信息与电子工程前沿（英文）》 2015年 第16卷 第2期   页码 85-97 doi: 10.1631/FITEE.1400111

摘要： Why is it important to verify/validate model transformations? The motivation is to improve the quality of the transformations, and therefore the quality of the generated software artifacts. Verified/validated model transformations make it possible to ensure certain properties of the generated software artifacts. In this way, verification/validation methods can guarantee different requirements stated by the actual domain against the generated/modified/optimized software products. For example, a verified/validated model transformation can ensure the preservation of certain properties during the model-to-model transformation. This paper emphasizes the necessity of methods that make model transformation verified/validated, discusses the different scenarios of model transformation verification and validation, and introduces the principles of a novel test-driven method for verifying/validating model transformations. We provide a solution that makes it possible to automatically generate test input models for model transformations. Furthermore, we collect and discuss the actual open issues in the field of verification/validation of model transformations.

关键词： Graph rewriting based model transformations     Verification/validation     Test-driven verification    

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Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

《医学前沿（英文）》 2020年 第14卷 第4期   页码 488-497 doi: 10.1007/s11684-020-0762-0

摘要： Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computer-aided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.

关键词： knowledge representation     uncertain     causality     graphical model     artificial intelligence     diagnosis     dyspnea    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

《机械工程前沿（英文）》 2023年 第18卷 第2期 doi: 10.1007/s11465-022-0736-9

摘要： Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

关键词： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Knowledge enhanced graph inference network based entity-relation extraction and knowledge graph construction

《工程管理前沿（英文）》 2024年 第11卷 第1期   页码 143-158 doi: 10.1007/s42524-023-0273-1

摘要： With the escalating complexity in production scenarios, vast amounts of production information are retained within enterprises in the industrial domain. Probing questions of how to meticulously excavate value from complex document information and establish coherent information links arise. In this work, we present a framework for knowledge graph construction in the industrial domain, predicated on knowledge-enhanced document-level entity and relation extraction. This approach alleviates the shortage of annotated data in the industrial domain and models the interplay of industrial documents. To augment the accuracy of named entity recognition, domain-specific knowledge is incorporated into the initialization of the word embedding matrix within the bidirectional long short-term memory conditional random field (BiLSTM-CRF) framework. For relation extraction, this paper introduces the knowledge-enhanced graph inference (KEGI) network, a pioneering method designed for long paragraphs in the industrial domain. This method discerns intricate interactions among entities by constructing a document graph and innovatively integrates knowledge representation into both node construction and path inference through TransR. On the application stratum, BiLSTM-CRF and KEGI are utilized to craft a knowledge graph from a knowledge representation model and Chinese fault reports for a steel production line, specifically SPOnto and SPFRDoc. The F1 value for entity and relation extraction has been enhanced by 2% to 6%. The quality of the extracted knowledge graph complies with the requirements of real-world production environment applications. The results demonstrate that KEGI can profoundly delve into production reports, extracting a wealth of knowledge and patterns, thereby providing a comprehensive solution for production management.

关键词： knowledge graph construction     industrial     BiLSTM-CRF     document-level relation extraction     graph inference    

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A bilateral heterogeneous graph model for interpretable job recommendation considering both reciprocity

《工程管理前沿（英文）》 2024年 第11卷 第1期   页码 128-142 doi: 10.1007/s42524-023-0280-2

摘要： Amidst the inefficiencies of traditional job-seeking approaches in the recruitment ecosystem, the importance of automated job recommendation systems has been magnified. However, existing models optimized to maximize user clicks for general product recommendations prove inept in addressing the unique challenges of job recommendation, namely reciprocity and competition. Moreover, sparse data on online recruitment platforms can further negatively impact the performance of existing job recommendation algorithms. To counteract these limitations, we propose a bilateral heterogeneous graph-based competition iteration model. This model comprises three integral components: 1) two bilateral heterogeneous graphs for capturing multi-source information from people and jobs and alleviating data sparsity, 2) fusion strategies for synthesizing attributes and preferences to produce mutually beneficial job matches, and 3) a competition-enhancing strategy for dispersing competition realized through a two-stage optimization algorithm. Augmented by granular attention mechanisms for enhanced interpretability, the model’s efficacy, competition dispersion, and interpretability are validated through rigorous empirical evaluations on a real-world recruitment platform.

关键词： job recommendation     competition     reciprocity     interpretability    

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基于探针图的并行型图顶点着色DNA计算模型 Article

许进, 强小利, 张凯, 张成, 杨静

《工程（英文）》 2018年 第4卷 第1期   页码 61-77 doi: 10.1016/j.eng.2018.02.011

摘要：
目前DNA 计算机研究中遇到的最大瓶颈是解空间指数爆炸问题，即随着问题规模的增大，所需要作为信息处理“数据”的DNA分子呈指数级增大。本文提出了一种新颖的图顶点着色DNA计算模型，该模型正是围绕着如何克服解空间指数爆炸问题以及如何提高运行速度而设计的。其主要贡献有：①通过如下三种方法来克服解空间指数爆炸问题：顶点颜色集的确定方法；子图分解方法以及子图中的顶点优化排序方法；②设计了一种并行型聚合酶链反应（PCR）操作技术，应用这种技术一次可以对图中多条边来删除非解，使得生物操作次数大大减少，极大地提高了运行速度。本文以一个3-着色的61 个顶点的图为例，实验表明，99% 的非可行解在构建初始解空间时就被删除，并利用DNA 自组装和并行PCR 方法，通过识别、拼接以及组装等技术得到解。由于该模型对任意61 个顶点的图是同样的操作方法，这就意味着，该模型的搜索能力可以达到O(359)。

关键词： DNA计算     图顶点着色问题     聚合酶链反应（PCR）    

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Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, Renzhi Wang

《医学前沿（英文）》 2020年 第14卷 第4期   页码 498-505 doi: 10.1007/s11684-020-0791-8

摘要： Disorders of sex development (DSD) are a group of rare complex clinical syndromes with multiple etiologies. Distinguishing the various causes of DSD is quite difficult in clinical practice, even for senior general physicians because of the similar and atypical clinical manifestations of these conditions. In addition, DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD. Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses. On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence. “Chaining” inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information. Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis. The model had an accuracy of 94.1%, which was significantly higher than that of interns and third-year residents. In conclusion, the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSD-related diseases.

关键词： disorders of sex development (DSD)     intelligent diagnosis     dynamic uncertain causality graph    

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

《结构与土木工程前沿（英文）》 2022年 第16卷 第11期   页码 1378-1396 doi: 10.1007/s11709-022-0855-8

摘要： It is of great significance to quickly detect underwater cracks as they can seriously threaten the safety of underwater structures. Research to date has mainly focused on the detection of above-water-level cracks and hasn’t considered the large scale cracks. In this paper, a large-scale underwater crack examination method is proposed based on image stitching and segmentation. In addition, a purpose of this paper is to design a new convolution method to segment underwater images. An improved As-Projective-As-Possible (APAP) algorithm was designed to extract and stitch keyframes from videos. The graph convolutional neural network (GCN) was used to segment the stitched image. The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has great potential of application in image segmentation and underwater image processing. The result shows that the improved APAP algorithm and GCN can adapt to complex underwater environments and perform well in different study areas.

关键词： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutional neural network    

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Identification of sources, characteristics and photochemical transformations of dissolved organic matter

《环境科学与工程前沿（英文）》 2021年 第15卷 第5期 doi: 10.1007/s11783-020-1340-z

摘要：

• The source of DOM in surface water and sediment is inconsistent.

关键词： Dissolved organic matter     Parallel factor analysis     Excitation-emission matrices     Photodegradation    

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Erratum to: Efficient keyword search over graph-structured data based on minimal covered Erratum

Asieh Ghanbarpour, Abbas Niknafs, Hassan Naderi,naderi@iust.ac.ir

《信息与电子工程前沿（英文）》 2020年 第21卷 第6期   页码 809-962 doi: 10.1631/FITEE.18e0133

摘要： Unfortunately the second author’s name has been misspelt. It should be read: Abbas NIKNAFS.
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Classifying multiclass relationships between ASes using graph convolutional network

《工程管理前沿（英文）》 2022年 第9卷 第4期   页码 653-667 doi: 10.1007/s42524-022-0217-1

摘要： Precisely understanding the business relationships between autonomous systems (ASes) is essential for studying the Internet structure. To date, many inference algorithms, which mainly focus on peer-to-peer (P2P) and provider-to-customer (P2C) binary classification, have been proposed to classify the AS relationships and have achieved excellent results. However, business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent years. Existing algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C relationships. In this study, we focus on multiclassification of AS relationship for the first time. We first summarize the differences between AS relationships under the structural and attribute features, and the reasons why multiclass relationships are difficult to be inferred. We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to solve this multiclassification problem under complex scenes. The proposed framework considers the global network structure and local link features concurrently. Experiments on real Internet topological data validate the effectiveness of our method, that is, AS-GCN. The proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task, with an overall metrics above 95%.

关键词： autonomous system     multiclass relationship     graph convolutional network     classification algorithm     Internet topology    

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基于工业互联网的产业链数字孪生系统框架及信息模型 Research Article

王文宣1,刘永钦1,2,柴旭东3,张霖1

《信息与电子工程前沿（英文）》 2024年 第25卷 第7期   页码 951-967 doi: 10.1631/FITEE.2300123

摘要： 工业互联网、云计算、大数据技术的融合正在改变产业链的经营和管理模式。然而，产业链涉及领域广泛、发展环境复杂、影响因素众多，给工业大数据的高效整合与利用带来挑战。针对当前产业链物理空间与虚拟空间的融合，本文建立基于工业互联网的产业链数字孪生系统框架。进一步，本文提出一种基于知识图谱的产业链信息模型，以整合复杂异构的产业链数据并抽取产业知识。首先，建立产业链本体，提出基于科技成果的实体对齐方法。第二，提出基于Transformer的双向编码器表示（BERT）与多头选择模型的产业链信息实体关系联合抽取方法。第三，提出基于关系图卷积网络与图采样聚合网络的关系补全模型，该模型同时考虑了知识图谱的语义信息和图结构信息。实验结果表明，本文所提出的实体关系联合抽取模型和关系补全模型的性能明显优于其他基线模型。最后，本文基于基础机械领域的18条产业链数据建立了产业链信息模型，证明了该方法的可行性。

关键词： 产业链；数字孪生；工业互联网；知识图谱；图神经网络    

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Research on polyhydroxyalkanoates and glycogen transformations: Key aspects to biologic nitrogen and

Hongjing LI, Yinguang CHEN

《环境科学与工程前沿（英文）》 2011年 第5卷 第2期   页码 283-290 doi: 10.1007/s11783-010-0243-9

摘要： In this paper, a study was conducted on the effect of polyhydroxyalkanoates (PHA) and glycogen transformations on biologic nitrogen and phosphorus removal in low dissolved oxygen (DO) systems. Two laboratory-scale sequencing batch reactors (SBR1 and SBR2) were operating with anaerobic/aerobic (low DO, 0.15–0.45 mg·L ) configurations, which cultured a propionic to acetic acid ratio (molar carbon ratio) of 1.0 and 2.0, respectively. Fewer poly-3-hydroxybutyrate (PHB), total PHA, and glycogen transformations were observed with the increase of propionic/acetic acid, along with more poly-3-hydroxyvalerate (PHV) and poly-3-hydroxy-2-methyvalerate (PH2MV) shifts. The total nitrogen (TN) removal efficiency was 68% and 82% in SBR1 and SBR2, respectively. In the two SBRs, the soluble ortho-phosphate (SOP) removal efficiency was 94% and 99%, and the average sludge polyphosphate (poly-P) content (g·g-MLVSS ) was 8.3% and 10.2%, respectively. Thus, the propionic to acetic acid ratio of the influent greatly influenced the PHA form and quantity, glycogen transformation, and poly-P contained in activated sludge and further determined TN and SOP removal efficiency. Moreover, significant correlations between the SOP removal rate and the (PHV+ PH2MV)/PHA ratio were observed ( >0.99). Accordingly, PHA and glycogen transformations should be taken into account as key components for optimizing anaerobic/aerobic (low DO) biologic nitrogen and phosphorus removal systems.

关键词： low dissolved oxygen (DO)     biological nitrogen and phosphorus removal     polyhydroxyalkanoates (PHA)     glycogen    

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A 7-year follow-up study of the features and transformations of elderly male patients with OGTT-1h hyperglycemia

TIAN Hui, LI Chunlin, ZHONG Wenwen, PAN Changyu, LU Juming, CAO Xiutang

《医学前沿（英文）》 2008年 第2卷 第4期   页码 396-399 doi: 10.1007/s11684-008-0076-0

摘要： The aim of this paper is to investigate the clinical features and transformation of elderly male patients with normal blood glucose levels at fasting and 2 hours after glucose intake but with hyperglycemia (≥ 11.1 mmol/L) 1 hour after oral glucose tolerance test (OGTT-1h HG). Seven years of follow-up visits were performed on 189 elderly male outpatients with OGTT-1h HG and data was recorded on their body mass index (BMI), blood pressure, serum cholesterol and triglyceride test results and on their glucose tolerance changes every 1–2 years after taking OGTT; their possible causes were analysed. Follow-up visits revealed that 19 patients with OGTT-1h HG were diagnosed with diabetes (10.1%), 78 patients with impaired glucose tolerance (IGT, 41.3%), 2 patients transformed to normal glucose tolerance (NGT, 1.1%) and the remaining 90 patients (47.6%) remained unchanged. Synchronized comparison with IGT patients showed that the ratio of OGTT-1h HG patients turning to diabetes was lower than that of IGT patients (21.1%, = 13.05, < 0.01), and the ratio of OGTT-1h HG patients transforming to NGT was slightly higher (0.4%, = 2.46, > 0.05). The prevalence of complications of hypertension, coronary heart disease, cerebral vascular diseases and dyslipidemia in patients with OGTT-1h HG were higher than those with NGT ( < 0.05) and were similar to that of IGT patients. As a special phenotype of OGTT and as part of an abnormal glucose tolerance conformation, patients with OGTT-1h HG warrant special attention, since about half of them were found to have developed diabetes or IGT, and their risk of suffering from vascular diseases were also increased.

关键词： special attention     prevalence     unchanged     dyslipidemia     elderly    

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A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

《化学科学与工程前沿（英文）》 2024年 第18卷 第4期 doi: 10.1007/s11705-024-2403-7

摘要： Methanol-to-olefins, as a promising non-oil pathway for the synthesis of light olefins, has been successfully industrialized. The accurate prediction of process variables can yield significant benefits for advanced process control and optimization. The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes, such as high nonlinearities, dynamics, and data distribution shift caused by diverse operating conditions. In this paper, we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues. Firstly, a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions. Subsequently, convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns. Meanwhile, a multi-graph convolutional network is leveraged to model the spatial interactions. Afterward, the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction. Finally, the outputs are denormalized to obtain the ultimate results. The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices, making the model more interpretable. Lastly, this model is deployed onto an end-to-end Industrial Internet Platform, which achieves effective practical results.

关键词： methanol-to-olefins     process variables prediction     spatial-temporal     self-attention mechanism     graph convolutional network    

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标题 作者 时间 类型 操作

Test-driven verification/validation of model transformations

László LENGYEL,Hassan CHARAF

期刊论文

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

期刊论文

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

期刊论文

Knowledge enhanced graph inference network based entity-relation extraction and knowledge graph construction

期刊论文

A bilateral heterogeneous graph model for interpretable job recommendation considering both reciprocity

期刊论文

基于探针图的并行型图顶点着色DNA计算模型

许进, 强小利, 张凯, 张成, 杨静

期刊论文

Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, Renzhi Wang

期刊论文

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

期刊论文

Identification of sources, characteristics and photochemical transformations of dissolved organic matter

期刊论文

Erratum to: Efficient keyword search over graph-structured data based on minimal covered

Asieh Ghanbarpour, Abbas Niknafs, Hassan Naderi,naderi@iust.ac.ir

期刊论文

Classifying multiclass relationships between ASes using graph convolutional network

期刊论文

基于工业互联网的产业链数字孪生系统框架及信息模型

王文宣1,刘永钦1,2,柴旭东3,张霖1

期刊论文

Research on polyhydroxyalkanoates and glycogen transformations: Key aspects to biologic nitrogen and

Hongjing LI, Yinguang CHEN

期刊论文

A 7-year follow-up study of the features and transformations of elderly male patients with OGTT-1h hyperglycemia

TIAN Hui, LI Chunlin, ZHONG Wenwen, PAN Changyu, LU Juming, CAO Xiutang

期刊论文

A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

期刊论文