Influence of dynamic stimulation,visual perception and individual susceptibility to car sickness during controlled stop-and-go driving

Forschung im Ingenieurwesen - The enjoyment and usage of highly automated vehicles might be reduced by motion sickness. This study addresses the influencing factors of dynamic stimulation, visual...     

Detection of cross-channel anomalies

The data deluge has created a great challenge for data mining applications wherein the rare topics of interest are often buried in the flood of major headlines. We identify and formulate a novel problem: cross-channel anomaly detection from multiple data channels. Cross-channel anomalies are common among the individual channel anomalies and are often portent of significant events. Central to this new problem is a development of theoretical foundation and methodology. Using the spectral approach, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single-channel anomalies. We also derive the extension of the proposed detection method to an online settings, which automatically adapts to changes in the data over time at low computational complexity using incremental algorithms. Our mathematical analysis shows that our method is likely to reduce the false alarm rate by establishing theoretical results on the reduction of an impurity index. We demonstrate our method in two applications: document understanding with multiple text corpora and detection of repeated anomalies in large-scale video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large-scale data stream analysis.     

Mapping of coastal-water turbidity using LANDSAT imagery

Secchi disk depth was recorded in the field all along the Swedish coastline and compared with LANDSAT data. Chromaticity analysis was applied in the evaluation to allow for Sun angle and atmospheric corrections. The data were used to study the relative nutrient and solids loading situations around the Swedish coast and as a basis for the applicability of laser bathymetry for water depth soundings     

Age replacement policy in a random environment using systemability

Preventive maintenance is a group of maintenance policies based on preventive actions in order to predate the failure of a component or a system. Usually, these policies are designed using a series of data related to the studied units. All policies do not consider the effect of the environment where the components or systems operate. In this article, one of the most used policies, the age replacement policy, is also discussed taking into consideration the environmental effects using an innovative concept, introduced by Pham, called systemability. Several numerical examples are carried out in order to illustrate the aim of this work. The importance of environmental factors is also demonstrated thanks to the application to a real case.     

Accurate junction detection and characterization in line-drawing images

In this paper, we present a new approach for junction detection and characterization in line-drawing images. We formulate this problem as searching for optimal meeting points of median lines. In this context, the main contribution of the proposed approach is three-fold. First, a new algorithm for the determination of the support region is presented using the linear least squares technique, making it robust to digitization effects. Second, an efficient algorithm is proposed to detect and conceptually remove all distorted zones, retaining reliable line segments only. These line segments are then locally characterized to form a local structure representation of each crossing zone. Finally, a novel optimization algorithm is presented to reconstruct the junctions. Junction characterization is then simply derived. The proposed approach is very highly robust to common geometry transformations and can resist a satisfactory level of noise/degradation. Furthermore, it works very efficiently in terms of time complexity and requires no prior knowledge of the document content. Extensive evaluations have been performed to validate the proposed approach using other baseline methods. An application of symbol spotting is also provided, demonstrating quite good results.     

Process synthesis and optimization of biorefinery configurations

Recently, there has been a growing interest in the development of cost‐effective technologies for the production of biofuels. A common approach to biofuel research is to invent or improve a biochemical or thermochemical conversion step. Subsequently, other conversion and separation steps are added to form a complete biorefinery flowsheet. Because this approach is structured around a specific conversion step, it may limit the possibilities of configuring optimal and innovative biorefineries. This article proposes a novel and systematic two‐stage approach to the synthesis and optimization of biorefinery configurations, given available feedstocks and desired products. In the synthesis stage, a systems‐based approach is developed to create a methodical way for synthesizing integrated biorefineries. This method is referred to as “forward‐backward” approach. It involves forward synthesis of biomass to possible intermediates and reverse synthesis starting with the desired products and identifying necessary species and pathways leading to them. In the optimization stage, Bellman's principle of optimality is applied to decompose the optimization problem into subproblems in which an optimal policy of available technologies is determined for every conversion step. An optimization formulation is utilized to determine the optimal configuration based on screening and connecting the optimal policies and generating the biorefinery flowsheet. A case study of alcohol‐producing pathways from lignocellulosic biomass is solved to demonstrate the merits of the proposed approach. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self-powered sensors, machine learning algorithms, and speaker recognition.