Neue Potenziale durch die automatisierte Unternehmenskommunikation

An automated enterprise communication enables enterprise spanning integration of costumers, suppliers, logistics service provider and other enterprises by exchanging data and providing services. This communication is now, by the standardization of web services, economical and reachable. After an explanation of web services as the technological enabler two examples of use of enterprise communication are presented. The first is enterprise spanning product configuration. The second considers virtual enterprises. A critical view of questions that are still open as well as an outlook close this article.     

用于锁模光纤激光陀螺的相敏检测

通过采用锁相放大器,演示了一种性能明显提高的锁模光纤激光陀螺.结果表明锁相放大器的输出与转动速率之间具有理想的线性关系,并对两脉冲(在时间间隔测量过程中引起相位误差)之间的光强差不敏感.与以往报道的时间间隔测量相比,演示了长期稳定性提高了二个数量级.     

30 Jahre EXACT-Bauelementesystem

Ohne Zusammenfassung     

Bug Triaging Based on Tossing Sequence Modeling

Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging:1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose iTriage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches.     

Automatic Detection and Repair Recommendation for Missing Checks

Missing checks for untrusted inputs used in security-sensitive operations is one of the major causes of various vulnerabilities. Efficiently detecting and repairing missing checks are essential for prognosticating potential vulnerabilities and improving code reliability. We propose a systematic static analysis approach to detect missing checks for manipulable data used in security-sensitive operations of C/C++ programs and recommend repair references. First, customized securitysensitive operations are located by lightweight static analysis. Then, the assailability of sensitive data used in securitysensitive operations is determined via taint analysis. And, the existence and the risk degree of missing checks are assessed. Finally, the repair references for high-risk missing checks are recommended. We implemented the approach into an automated and cross-platform tool named Vanguard based on Clang/LLVM 3.6.0. Large-scale experimental evaluation on open-source projects has shown its effectiveness and efficiency. Furthermore, Vanguard has helped us uncover five known vulnerabilities and 12 new bugs.     

Critical review on improved electrochemical impedance spectroscopy-cuckoo search-Elman neural network modeling methods for whole-life-cycle health state estimation of lithium-ion battery energy storage systems

Efficient and accurate health state estimation is crucial for lithium-ion battery (LIB) performance monitoring and economic evaluation. Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems. With high adaptability and applicability advantages, battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world. Artificial neural network (ANN)-based methods are often used for state estimations of LIBs. As one of the ANN methods, the Elman neural network (ENN) model has been improved to estimate the battery state more efficiently and accurately. In this paper, an improved ENN estimation method based on electrochemical impedance spectroscopy (EIS) and cuckoo search (CS) is established as the EIS-CS-ENN model to estimate the health state of LIBs. Also, the paper conducts a critical review of various ANN models against the EIS-CS-ENN model. This demonstrates that the EIS-CS-ENN model outperforms other models. The review also proves that, under the same conditions, selecting appropriate health indicators (HIs) according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently. In the calculation process, several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods. Through the analysis of the evaluation results and the selection of HIs, conclusions and suggestions are put forward. Also, the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.     

Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-Head Self-Attention

The source and load uncertainties arising from increased applications of renewable energy sources such as wind and photovoltaic energy in the power system have had adverse effects on optimal planning and dispatching. Models for generating typical renewable energy and load scenarios are constructed to reduce such effects and improve the applicability of a planning and optimal dispatching model of power systems with a high proportion of renewable energy. The traditional clustering-based model for representing such scenarios cannot handle high-dimensional time-series data and consequently the feature-related information obtained cannot fully reflect the characteristics of the data. Thus, a deep convolutional embedded clustering model based on multi-head self-attention is proposed. First, a variational mode decomposition model is optimized to reduce the influence of noise-related signals on the feature extraction. The deep features are then extracted from the data using an improved convolutional autoencoder, and the appropriate number of clusters is determined using the elbow method. Following this, the network parameters are optimized based on the sum of losses during reconstruction and clustering. Subsequently, typical scenarios are then generated based on the optimized network model. Finally, the proposed method is evaluated based on data visualization and evaluation metrics. It is shown that the quality of features and the accuracy of clustering can be effectively improved by the proposed scenario generation method.