Refine
Year of publication
- 2016 (81) (remove)
Document Type
- Conference Proceeding (81) (remove)
Keywords
- Bernstein polynomial (1)
- Body sensor networks (1)
- Business life-cycle (1)
- Business plan (2)
- Chassis dynamometer (1)
- Cognitive radio (1)
- Computational linguistics (1)
- Condition monitoring (1)
- Content analysis (1)
- Content analysis (keywords) (1)
Institute
Realistic traffic modeling plays a key role in efficient Dynamic Spectrum Access (DSA) which is considered as enabler for the employment of wireless technologies in critical industrial automation applications (IAA). The majority of models of spectrum usage are not suitable for this specific use case as they are based on measurement campaigns conducted in urban or controlled laboratory environments. In this work we present a time-domain traffic model for industrial communication in the 2.4 GHz industrial, scientific, medical (ISM) band based on measurements in an industrial automotive production site. As DSA is usually implemented on Software Defined Radios (SDR), our measurement campaign is based on SDR platforms rather than sophisticated spectrum analyzers. We show through the estimation of the Hurst parameter that industrial wireless traffic possesses inherent self-similarity that could be exploited for efficient DSA. We also show that wireless traffic could be modeled as a semi-Markov model with channel on and off durations Log-normally and Pareto distributed, respectively. We finally estimate the parameters of the derived models using Maximum Likelihood estimation.
Cognitive radio (CR) is a key enabler of wireless in industrial applications especially for those with strict quality-of-service (QoS) requirements. The cornerstone of CR is spectrum occupancy prediction that enables agile and proactive spectrum access and efficient utilization of spectral resources. Hidden Markov Models (HMM) provide powerful and flexible tools for statistical spectrum prediction. In this paper we introduce a HMM-based spectrum prediction algorithm for industrial applications that accurately predicts multiple slots in the future. Traditional HMM prediction approaches use two hidden states enabling the prediction of only one step ahead in the future. This one step is most often not enough due to internal hardware delays that render it outdated. We show in this work that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification approach enables a prediction span of multiple slots in the future even with fine spectrum sensing resolution. We verify the suitability of our approach to industrial wireless through extensive simulations that utilize a realistic measurement-based traffic model specifically tailored for industrial automotive settings.
Increasing robustness of handwriting recognition using character N-Gram decoding on large lexica
(2016)
Offline handwriting recognition systems often include a decoding step, that is retrieving the most likely character sequence from the underlying machine learning algorithm. Decoding is sensitive to ranges of weakly predicted characters, caused e.g. by obstructions in the scanned document. We present a new algorithm for robust decoding of handwriting recognizer outputs using character n-grams. Multidimensional hierarchical subsampling artificial neural networks with Long-Short-Term-Memory cells have been successfully applied to offline handwriting recognition. Output activations from such networks, trained with Connectionist Temporal Classification, can be decoded with several different algorithms in order to retrieve the most likely literal string that it represents. We present a new algorithm for decoding the network output while restricting the possible strings to a large lexicon. The index used for this work is an n-gram index with tri-grams used for experimental comparisons. N-grams are extracted from the network output using a backtracking algorithm and each n-gram assigned a mean probability. The decoding result is obtained by intersecting the n-gram hit lists while calculating the total probability for each matched lexicon entry. We conclude with an experimental comparison of different decoding algorithms on a large lexicon.
Recent years have seen the proposal of several different gradient-based optimization methods for training artificial neural networks. Traditional methods include steepest descent with momentum, newer methods are based on per-parameter learning rates and some approximate Newton-step updates. This work contains the result of several experiments comparing different optimization methods. The experiments were targeted at offline handwriting recognition using hierarchical subsampling networks with recurrent LSTM layers. We present an overview of the used optimization methods, the results that were achieved and a discussion of why the methods lead to different results.
Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.
Stress is a recognized as a predominant disease with growing costs of treatment. The approach presented here is aimed to detect stress using a light weighted, mobile, cheap and easy to use system. The result shows that stress can be detected even in case a person’s natural bio vital data is out of the main range. The system enables storage of measured data, while maintaining communication channels of online and post-processing.
The magneto-mechanical behavior of magnetic shape memory (MSM) materials has been investigated by means of different simulation and modeling approaches by several research groups. The target of this paper is to simulate actuators driven by MSM alloys and to understand the MSM element behavior during actuation, which shall lead to an increased performance of the actuator. It is shown that internal and external stresses should be taken into consideration using numerical computation tools for magnetic fields in an efficient way.
Corrosion
(2016)
Ein neuzeitlicher Pfahlbau
(2016)
Bauhäusler am Bodensee
(2016)
Present demographic change and a growing population of elderly people leads to new medical needs. Meeting these with state of the art technology is as a consequence a rapidly growing market. So this work is aimed at taking modern concepts of mobile and sensor technology and putting them in a medical context. By measuring a user’s vital signs on sensors which are processed on a Android smartphone, the target system is able to determine the current health state of the user and to visualize gathered information. The system also includes a weather forecasting functionality, which alerts the user on possibly dangerous future meteorological events. All information are collected centrally and distributed to users based on their location. Further, the system can correlate the client-side measurement of vital signs with a server-side weather history. This enables personalized forecasting for each user individually. Finally, a portable and affordable application was developed that continuously monitors the health status by many vital sensors, all united on a common smartphone.
The corporate entrepreneur
(2016)
Corporate entrepreneurship is one tool for established companies to strengthen their capabilities for strategic renewal and innovativeness. The question, however, which factors are influencing the success of a corporate entrepreneurship initiative requires further attention. The corporate entrepreneur who is acting as both the leader of embedded entrepreneurial teams and linking pin to the corporate, is providing one possible perspective. Based on 6 interviews conducted in 6 German organizations this study contributes to the understanding of the role of the corporate entrepreneur and how this role can be distinguished from other roles in the context of innovation.
A lot of procedures for estimating the spool position in linear electromagnetic actuators using voltage and current measurements only, can be found in the literature. Subject to the accuracy of the estimated spool position some achieve better, some worse results. However, in almost every approach hysteresis has a huge impact on the estimation accuracy that can be achieved. Regardless whether these effects are caused by magnetic or mechanical hysteresis, they will limit the accuracy of the position estimate, if not taken into account. In this paper, a model is introduced which covers the hysteresis effects as well as other nonlinear ities occurring in estimated position-dependent parameters. A classical Preisach model is deployed first, which is then adjusted by using novel elementary preceding Relay-Operators. The resulting model for the estimated position-dependent parameters including the adjusted Preisach model can be easily applied to position estimation tasks. It is shown that the considered model distinctly improves the accuracy for the spool position estimate, while it is kept as simple as possible for real-time implementation reasons.
Loch im Putz = alles neu?
(2016)
This paper broadens the resource-based approach to explaining survival of new technology-based firms (NTBFs) by focusing on the entrepreneur's ability to transform resources in response to triggers resulting from market interactions. Network theory is used to define a construct that allows determining the status of venture emergence (VE).The operationalization of the VE construct is built on the firm's value network maturity in the four market dimensions customer, investor, partner, and human resource. Business plans of NTBFs represent the artifact that contains this data in the form of transaction relation descriptions. Using content analysis, a multi-step combined human and computer coding process has been developed to empirically determine NTBFs' status of VE.Results of the business plan analysis suggests that the level of transaction relations allows to draw conclusions on the status of VE. Moreover, applying the developed process, a business plan coding test shows that the transaction relation based VE status significantly relates to NTBFs' survival capabilities.
Nowadays there is a rich diversity of sleep monitoring systems available on the market. They promise to offer information about sleep quality of the user by recording a limited number of vital signals, mainly heart rate and body movement. Typically, fitness trackers, smart watches, smart shirts, smartphone applications or patches do not provide access to the raw sensor data. Moreover, the sleep classification algorithm and the agreement ratio with the gold standard, polysomnography (PSG) are not disclosed. Some commercial systems record and store the data on the wearable device, but the user needs to transfer and import it into specialised software applications or return it to the doctor, for clinical evaluation of the data set. Thus an immediate feedback mechanism or the possibility of remote control and supervision are lacking. Furthermore, many such systems only distinguish between sleep and wake states, or between wake, light sleep and deep sleep. It is not always clear how these stages are mapped to the four known sleep stages: REM, NREM1, NREM2, NREM3-4. [1] The goal of this research is to find a reduced complexity method to process a minimum number of bio vital signals, while providing accurate sleep classification results. The model we propose offers remote control and real time supervision capabilities, by using Internet of Things (IoT) technology. This paper focuses on the data processing method and the sleep classification logic. The body sensor network representing our data acquisition system will be described in a separate publication. Our solution showed promising results and a good potential to overcome the limitations of existing products. Further improvements will be made and subjects with different age and health conditions will be tested.
An approach for an adaptive position-dependent friction estimation for linear electromagnetic actuators with altered characteristics is proposed in this paper. The objective is to obtain a friction model that can be used to describe different stages of aging of magnetic actuators. It is compared to a classical Stribeck friction model by means of model fit, sensitivity, and parameter correlation. The identifiability of the parameters in the friction model is of special interest since the model is supposed to be used for diagnostic and prognostic purposes. A method based on the Fisher information matrix is employed to analyze the quality of the model structure and the parameter estimates.
In this paper, utilisation of an Unscented Kalman Filter for concurrently performing disturbance estimation and wave filtering is investigated. Experimental results are provided that demonstrate very good performance subject to both tasks. For the filter, a dynamic model has been used which was optimised via correlation analysis in order to obtain a minimum set of relevant parameters. This model has also been validated by experiments deploying a small vessel. A simulation study is presented to evaluate the performance using known quantities. Experimental trials have been performed on the Rhine river. The results show that for instance flow direction and varying current velocities can continuously be estimated with decent precision, even while the boat is performing turning manoeuvres. Moreover, the filtering properties are very satisfactory. This makes the filter suitable for being used, for instance, in autonomous vessel applications or assistance systems.
The IT unit is not the only provider of information technology (IT) used in business processes. Aiming for increased performance, many business workgroups autonomously implement IT resources not covered by their organizational IT service management. This is called shadow IT. Risks and inefficiencies associated with this phenomenon challenge organizations. Organizations need to decide how to deal with identified shadow IT and if the business or the IT unit should be responsible for corresponding tasks and components. This study proposes design principles for a method to control identified shadow IT following action design research in four organizational settings. The procedure results in an allocation of IT task responsibilities between the business workgroups and the IT unit following risk considerations and transaction cost economics, leading to an IT service governance. This contributes to governance research regarding adaptive and efficient arrangements with reduced risks for business-located IT activities.