An integrated method for effective behaviour analysis of distributed systems

This study examines a set of oculomotor measurements, or "oculometric" biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical perimetry measures of visual function in our patient group using Bivariate Pearson Correlation and a Linear Mixed-Effects Model (LMM). Significant visual and visuomotor deficits were found in 12 asymptomatic SLE patients on long-term HCQ therapy compared to a cohort of 17 age-matched healthy controls. Notably, six oculometrics were significantly different. The median initial pursuit acceleration was 22%, steady-state pursuit gain 16%, proportion smooth 7%, and target speed responsiveness 31% lower, while catch-up saccade amplitude was 46% and fixation error 46% larger. Excluding the two patients with diagnosed mild toxicity, four oculometrics, all but fixation error and proportion smooth, remained significantly impaired compared to controls. Across our population of 12 patients (24 retinae), we found that pursuit latency, initial acceleration, steady-state gain, and fixation error were linearly related to retinal thickness even when age was accounted for, while standard measures of clinical function (Mean Deviation and Pattern Standard Deviation) were not. Our data show that specific oculometrics are sensitive early biomarkers of functional deficits in SLE patients on HCQ that could be harnessed to assist in the early detection of HCQ-induced retinal toxicity and other visual pathologies, potentially providing early diagnostic value beyond standard visual field and OCT evaluations.

Inherent safety index module (ISIM) to assess inherent safety level during preliminary design stage

Preliminary design on high-end workstation cooling system using loop heat pipes

Advanced persistent threat (APT) attack is a malicious attack type which has intentional and clear targets. This attack technique has become a challenge for information security systems of organizations, governments, and businesses. The approaches of using machine learning or deep learning algorithms to analyze signs and abnormal behaviors of network traffic for detecting and preventing APT attacks have become popular in recent years. However, the APT attack detection approach that uses behavior analysis and evaluation techniques is facing many difficulties due to the lack of typical data of attack campaigns. To handle this situation, recent studies have selected and extracted the APT attack behaviors which based on datasets are built from experimental tools. Consequently, these properties are few and difficult to obtain in practical monitoring systems. Therefore, although the experimental results show good detection, it does not bring high efficiency in practice. For above reasons, in this paper, a new method based on network traffic analysis using a combined deep learning model to detect APT attacks will be proposed. Specifically, individual deep learning networks such as multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) will also be sought, built and linked into combined deep learning networks to analyze and detect signs of APT attacks in network traffic. To detect APT attack signals, the combined deep learning models are performed in two main stages including (i) extracting IP features based on flow: In this phase, we will analyze network traffic into networking flows by IP address and then use the combined deep learning models to extract IP features by network flow; (ii) classifying APT attack IPs: Based on IP features extracted in a task (i), the APT attack IPs and normal IPs will be identified and classified. The proposal of a combined deep learning model to detect APT attacks based on network traffic is a new approach, and there is no research proposed and applied yet. In the experimental section, combined deep learning models proved their superior abilities to ensure accuracy on all measurements from 93 to 98%. This is a very good result for APT attack detection based on network traffic.

Qualitative Assessment for Inherently Safer Design (QAISD) at preliminary design stage

To reduce development cost and to avoid late design fixes in aircraft design, methods that are fast and economic in estimating the aerodynamic characteristics of complex flight vehicles at the preliminary design stage are desired. This work and thesis focus on the adaptive-fidelity CFD approach, with emphasis on the high end of the CFD tools available today. The core idea of the method is to use computationally cheap modeling in the part of the flight envelope where it is applicable. When the complexity in the flow field increases more details and realism is included in the mathematical model, at a computationally higher cost. A typical case where this would be required could be at the border of the flight envelope, where flow phenomena such as shocks, flow separation, and interacting vortex systems could occur. Since the number of cases needed to resolve the flight envelope could be in the order of ten thousands automation is required. The bottlenecks are the discretization of the fluid volume and evaluation of raw CFD data and post processing of the data. These issues are also discussed in this work. The method has been tested on two real flying aircraft, the X-31 delta-winged aircraft with vector thrust, and the Ranger 2000 Jet trainer, as well as on the SACCON preliminary wing-body UCAV design. The results provide improved understanding of the usefulness of this method as an analysis tool during the preliminary design phase all the way into the flight test diagnostic phase of a new aircraft.

In the preliminary design phase of aircraft design, estimating the production cost accurately is a challenging task. At this stage, many design parameters that affect the overall cost are still undefined. This paper establishes cost-estimation models for civil, commercial aircraft using a parametric cost analysis (PCA) approach. Aircraft are characterised based on their size, ranging from a wide body to executive jets, into four categories. Key design parameters, such as maximum take-off weight, number of passengers, range, wing area, span, fuselage length, to name a few, are likely to be available in the preliminary design stage and significantly impact the aircraft design. These variables either directly or indirectly affect the overall production cost or performance. The PCA approach includes both correlation and multiple linear regression techniques. The empirical models thus developed were able to predict the aircraft cost with an error of less than ±4% for all aircraft categories considered. Two aircraft in each defined category were not part of the PCA models and were used to verify the models. The proposed models provide the ability to estimate the aircraft cost quickly in the early stages of the preliminary design phase and provide the possibility of performing parametric studies involving the key variables to determine the cost sensitivity to the main design parameters.

Web attack is a common attack technique that has been causing huge consequences for organizations and businesses. Therefore, the problem of early detecting and warning about web attack techniques is very necessary today. In this paper, we propose a method of detecting web attacks using machine learning techniques to analyze user behavior profiles. The user behavior profile represents the most basic characteristics of the access that the user is performing. The difference between our approach with other approaches is that instead of using techniques that seek and extract abnormal features and behaviors of access, we use the statistical method and then select and evaluate the values as the basis for the machine learning algorithm to classify normal access and abnormal access. The experimental results shown in section 4 of the paper have proved the optimal of our approach in the problem of abnormal access detection.

The design process of new air- and rotorcraft is commonly separated into three different consecutive phases. In the conceptual design phase, the viability of different designs is investigated with respect to customer requirements and/or the market situation. It usually ends with the identification of a basic aircraft lay-out. In the subsequent preliminary design stage the various disciplines are introduced, thus redefining the design process as a multidisciplinary optimization (MDO) task. The objective of this design stage is to enhance the initial aircraft configuration by establishing an advanced design comprising a loft provided with primary structure. This updated aircraft configuration represents a global optimum solution for the specified requirements which will then be optimized on a local level in the concluding detailed design phase with particular regard to manufacturing aspects. The investigations in the preliminary design phase comprise the generation of numerous similar but still different analytical and finite element (FE) models. Even though computational power is constantly increasing the model generation process is still a time-consuming task. Moreover, it is also a potential source of errors which — in the worst case — may lead to time- and cost-intensive redesign activities during the detailed design. As the preliminary design stage, therefore, is of particular importance during the overall design process the model generation process benefits from parametric models and automated process chains. The presented paper overviews the tools used for the automated generation of FE models developed and used at the Institute of Structures and Design (BT) of the German Aerospace Center (DLR) for the subsequent use in numerical simulations. Furthermore, basic requirements for the effective use of parametrization and automation like a common data format and infrastructure will be introduced. Exemplary models and applications will be presented to illustrate the positive impact on efficiency in aircraft design. Concluding, future development steps and possible applications will be discussed.

In this article, a topology optimization method is proposed to obtain a new preliminary shape design of the twin-web turbine disc considering the burst speed constraint. The burst speed constraint is converted to the constraints on the averaged hoop stress and the maximum averaged radial stress. The linear density filter and Heaviside projection are employed to prevent the mesh dependency and chequerboard pattern and ensure distinct solid/void interfaces in the optimization result. The geometric extraction is easily implemented using the least square fitting with B-spline method. Through illustrative examples, the importance of considering the burst speed constraint in the design of turbine disc is verified, which contributes towards obtaining a safer design at the preliminary design stage. The effectiveness of the proposed method in obtaining a lighter preliminary twin-web turbine disc design is also demonstrated compared with the parametric optimization method.

How can multi-robot self-organizing systems be designed so that they show the desired behaviors and are able to perform tasks specified by designers? Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of the system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct. The design framework consists of two stages — preliminary design and design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to enable designers to explore the design space and identify satisfactory solutions considering several performance indicators based on the trained system established in the previous stage. Between the two stages, surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in preliminary design. The surrogate models represent the goal functions in the cDSP. A multi-robot box-pushing problem is used as an example to test the efficacy of the proposed framework. The framework is general and can be extended to design other multi-robot self-organizing systems. Our focus in this paper is in describing the framework.

A procedure for evaluation of multiple attributes in the preliminary design stage is necessary to the development of a more formal theory and methodology of design. In this paper we prescribe, demonstrate, and compare two such techniques: fuzzy set analysis and multiattribute utility analysis. We analyze the problem of preliminary material selection for an automotive bumper beam to illustrate the application of both analytical procedures. Preliminary assessment of incommensurate performance measures of weight, cost, shape restriction, stiffness, and corrosion are made for steel and polymer composite alternatives. Fuzzy analysis readily accommodates design evaluation based on semantic assessment of relative attribute levels, although it can also employ information which is entirely numerical. In contrast, utility analysis requires considerable numerical quantification. For the example studied, the ordinal ranking of alternatives was the same for both methods. Recommendations include using fuzzy analysis in the earliest stages of preliminary design evaluation or in situations limited to semantic input from design decision makers. Utility analysis may be used in later stages of preliminary design, where numerical quantification of attribute levels is possible and when decision makers are able to assess the individual attribute utility functions in the standard way. The results of utility analysis can be used to quantify acceptable design tradeoffs between attribute levels and can be used in later stages of the iterative design process.

Release planning is a cornerstone problem in incremental software development. It deals with the assignment of requirements to sequence of releases in order to maximize profit, minimize the delay of feedback and return of investment in such a way that dependency and resource constraints are met. Release planning decisions are required at an early stage in the development cycle, when uncertainty is unavoidable in the project estimates. Recently, there are some works concerning the use of fuzzy theory to address issues concerning the uncertainty in the release planning problem: fuzzy effort constraints and fuzzy dependency constraints. In this paper, we study the application of fuzzy theory to handle the uncertainty concerning dependency constraints from a holistic perspective, i.e. the whole set of fuzzy dependency constraints is considered as a fuzzy graph. The satisfaction of dependency constraints in a solution plan is measured by the distance between this plan and an ideal plan (in terms of the dependency constraints). The distance is materialized as the distance between two fuzzy graphs. This is considered to be an essential support for the actual decision-making. All the concepts and the complete approach are illustrated by a case study example.

Correct evaluation of rudder performance is a key issue in assessing ship maneuverability. This paper presents a simplified approach based on a viscous flow solver to address propeller and rudder interactions. Viscous flow solvers have been applied to this type of problems, but the large computational requests limit (or even prevent) their application at a preliminary ship design stage. Based on this idea, a simplified approach to include the propeller effect in front of the rudder is considered to speed up the solution. Based on the concept of body forces, this approach enables sufficiently fast computation for a preliminary ship design stage, thereby maintaining its reliability. To define the limitations of the proposed procedure, an extensive analysis of the simplified method is performed and the results are compared with experimental data presented in the literature. Initially, the reported results show the capability of the body-force approach to represent the inflow field to the rudder without the full description of the propeller, also with regard to the complex bollard pull condition. Consequently, the rudder forces are satisfactorily predicted at least with regard to the lift force. However, the drag force evaluation is more problematic and causes higher discrepancies. Nevertheless, these discrepancies may be accepted due to their lower influence on the overall ship maneuverability performance.

This paper proposes an integrated design and optimization approach for radial inflow turbines consisting of an automated preliminary design module and a flexible three-dimensional multidisciplinary optimization module. The latter was constructed by an evolution algorithm, a genetic algorithm-assisted self-learning artificial neural network and a dynamic sampling database. The 3-D multidisciplinary optimization approach was validated by the original T-100 turbine and the T-100re turbine obtained from the automated preliminary design approach, for maximizing the total-to-static efficiency and minimizing the rotor weight while keeping the mass flow rate constant and stress limitation satisfied. The validation results indicate that the total-to-static efficiency is 89.6%, increased by 1.3%, and the rotor weight is reduced by 0.14 kg (14.6%) based on the T-100re turbine, while the efficiency is 88.2%, increased by 2.2% and the weight is reduced by 0.49 kg (37.4%) based on the original T-100 turbine. Moreover, the T-100re turbine shows better performance at the preliminary design stage and conserves this advantage to the end, though both the aerodynamic performance of the T-100 and the T-100re turbine are improved after 3-D optimization. At the same time, it is implied that the preliminary design plays an essential role in the radial inflow turbine design process, and it is hard for only 3-D optimization to get a further performance improvement.