Publikationen

Capturing the current statistical features of a process and its dynamic evolution is important for controlling and monitoring its overall operational status. In terms of capturing the process dynamics, existing probabilistic latent-variable methods mostly consider autoregressive relationships, and thus, the causality from the control inputs to the pattern, or key hidden variable, remains unmodeled or implicit. To bridge this gap, a model structured by a newly designed dynamic-controlled Bayesian network (DCBN) is proposed in this paper for pattern modeling, especially pattern control and optimization. Significantly, the innovation and advantage of the DCBN lie in explicitly quantifying the impulse response of the pattern under control inputs. As well, the expectation-maximization algorithm is specially designed for learning the DCBN model. Finally, a new framework for pattern-based process control and optimization is presented in which online pattern filtering and control can be implemented. A case study on the combustion process from an industrial boiler illustrates the advantages of the proposed method in that it can capture the controlled dynamics of the process and achieve optimization by tracking the pattern set point or trajectory.

A process controlled using model predictive control is required to be re-identified when significant plant-model mismatch (PMM) occurs. During data acquisition for re-identification, the process is excited to enable accurate re-identification. However, the process excitation worsens the control performance. To prevent this problem, a new model-update framework that consists of targeted excitation (TE) and targeted re-identification (TR) is proposed. In TE, only the manipulated variables corresponding to problematic transfer functions that have significant PMM are excited during data acquisition. On the other hand, the other manipulated variables are optimized to suppress the variations of the controlled variables. After data is acquired using TE, the TR method re-identifies only the problematic transfer functions by using the other transfer-function models without large PMM. The validity of the proposed framework is examined by theoretical analysis and numerical case studies. In the theoretical analysis, the stability during data acquisition using TE and the asymptotic bias of the parameters re-identified using TR were considered. In the numerical case studies, the applicability of the proposed framework to several processes including a fluid catalytic cracking (FCC) process was examined. As a result, it was shown that, for all the processes, the proposed framework can improve both the control performance during data acquisition and the model accuracy after re-identification, compared to an existing method that excites all the inputs during data acquisition.

Intracranial aneurysms (IAs) located in the anterior and posterior circulations of the Circle of Willis present differential rupture risks. This study aimed to compare the rupture risk and clinical outcomes of anterior communicating artery aneurysms (AcomA) and basilar tip aneurysms (BAs); two IA types located along the midline within the Circle of Willis. We retrospectively collected data from 1026 patients presenting with saccular IAs. Only AcomA and BAs with a 3D angiography were included. Out of 186 included IAs, a cohort of 32 BAs was matched with AcomA based on the patients’ pre-existing conditions and morphological parameters of IAs. Clinical outcomes, including rupture risk, hydrocephalus development, vasospasm incidence, and patients’ outcome, were compared. The analysis revealed no significant difference in rupture risk, development of hydrocephalus, need for ventricular drainage, or vasospasm incidence between the matched AcomA and BA cohorts. Furthermore, the clinical outcomes post-rupture did not significantly differ between the two groups, except for a higher Fisher Grade associated with BAs. Once accounting for morphological and patient factors, the rupture risk between AcomA and BAs is comparable. These findings underscore the importance of tailored management strategies for specific IA types and suggest that further investigations should focus on the role of individual patient and aneurysm characteristics in IA rupture risk and clinical outcomes.

Evolving software is a highly complex and creative problem in which a number of different strategies are used to solve the tasks at hand. These strategies and reoccurring coding patterns can offer insights into the process. However, they can be highly project or even task-specific. We aim to identify code change patterns in order to draw conclusions about the software development process. For this, we propose a novel way to calculate high-level file overarching diffs, and a novel way to parallelize pattern mining. In a study of 1000 Java projects, we mined and analyzed a total of 45,000 patterns. We present 13 patterns, showing extreme points of the 7 pattern categories we identified. We found that a large number of high-level change patterns exist and occur frequently. The majority of mined patterns were associated with a specific project and contributor, where and by whom it was more likely to be used. While a large number of different code change patterns are used, only a few, mostly unsurprising ones, are common under all circumstances. The majority of code change patterns are highly specific to different context factors that we further explore.

The Internet makes use of high performance network switches in order to route network traffic from end users to servers. Despite line-rate performance, the current switches consume huge energy and cannot support more expressive learning models, like cognitive functions using neuromorphic computations. The major reason is the use of transistors in the underlying Ternary Content-Addressable Memory (TCAM) which is volatile and supports digital computations only. These shortcomings can be bypassed by developing network memories building on novel components, like Memristors, due to their nonvolatile, nanoscale and analog storage/processing characteristics. In this paper, we propose the use of a novel memristor-based Probabilistic Associative Memory, PAmM, which provides both digital (deterministic) and analog (probabilistic) outputs for supporting cognitive computational models in network switches. The traditional digital operations can be supported by a memristor-based energy efficient TCAM, called TCAmMCogniGron. Building on PAmM and TCAmMCogniGron, we propose a novel network switching architecture and analyze its energy efficiency over the experimental dataset of a Nb-doped SrTiO3 memristive device. The results show that the proposed network switching architecture consumes only 0.01 fJ/bit/cell energy for analog compute operations which is at least 50 times less than the digital operations.