Anwendung von maschinellen Lernverfahren (ML) in der Produktionstechnik, in Zeiten der Industrie 4.0, stark angestiegen. Insbesondere die Datenverfügbarkeit ist an dieser Stelle elementar und für die erfolgreiche Umsetzung einer ML-Applikation Voraussetzung. Falls für eine gegebene Problemstellung die Datenmenge oder -qualität nicht ausreichend ist, können Techniken, wie die Datenaugmentierung, der Einsatz von synthetischen Daten sowie das Transferlernen von ähnlichen Datensätzen Abhilfe schaffen. Innerhalb dieser Ausarbeitung wird das Konzept des Transferlernens im Bereich das Radial-Axial Ringwalzens (RAW) angewendet und am Beispiel der Zeitreihenprognose des Außendurchmessers über die Prozesszeit durchgeführt. Das Radial-Axial Ringwalzen ist ein warmumformendes Verfahren und dient der nahtlosen Ringherstellung.

Due to the increasing computing power and corresponding algorithms, the use of machine learning (ML) in production technology has risen sharply in the age of Industry 4.0. Data availability in particular is fundamental at this point and a prerequisite for the successful implementation of a ML application. If the quantity or quality of data is insufficient for a given problem, techniques such as data augmentation, the use of synthetic data and transfer learning of similar data sets can provide a remedy. In this paper, the concept of transfer learning is applied in the field of radial-axial ring rolling (rarr) and implemented using the example of time series prediction of the outer diameter over the process time. Radial-axial ring rolling is a hot forming process and is used for seamless ring production.

Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate information for emergency response and field operation. While the focus of most previously proposed techniques is predicting the number of accidents in a certain region, the problem of predicting the accident occurrence or fast detection of the accident has been little studied. To address this gap, we propose a deep learning approach and build a deep neural network model based on long short term memory (LSTM). We apply it to forecast the expected speed values on freeways’ links and identify the anomalies as potential accident occurrences. Several detailed features such as weather, traffic speed, and traffic flow of upstream and downstream points are extracted from big datasets. We assess the proposed approach on a traffic dataset from Sacramento, California. The experimental results demonstrate the potential of the proposed approach in identifying the anomalies in speed value and matching them with accidents in the same area. We show that this approach can handle a high rate of rapid accident detection and be implemented in real-time travelers’ information or emergency management systems. / M.S. / Rapid traffic accident detection/prediction is essential for scaling down non-recurrent conges- tion caused by traffic accidents, avoiding secondary accidents, and accelerating emergency system responses. In this study, we propose a framework that uses large-scale historical traffic speed and traffic flow data along with the relevant weather information to obtain robust traffic patterns. The predicted traffic patterns can be coupled with the real traffic data to detect anomalous behavior that often results in traffic incidents in the roadways. Our framework consists of two major steps. First, we estimate the speed values of traffic at each point based on the historical speed and flow values of locations before and after each point on the roadway. Second, we compare the estimated values with the actual ones and introduce the ones that are significantly different as an anomaly. The anomaly points are the potential points and times that an accident occurs and causes a change in the normal behavior of the roadways. Our study shows the potential of the approach in detecting the accidents while exhibiting promising performance in detecting the accident occurrence at a time close to the actual time of occurrence.

During several decades the stock market has been an area of interest forresearchers due to its complexity, noise, uncertainty and nonlinearity of thedata. Most of the studies regarding this area use a classical stochastics method,an example of this is ARIMA which is a standard approach for time seriesprediction. There is however another method for prediction of the stock marketthat is gaining traction in the recent years; Artificial Neural Network (ANN).This method has mostly been used in research on the American and Asian stockmarkets so far. Therefore, the purpose of this essay was to explore if ArtificialNeural Network could be used instead of ARIMA to predict the Swedish stockmarket (OMXS30). The study used data from the Swedish Stock Marketbetween 1991-07-09 to 2018-12-28 for the training of the ARIMA model anda forecast data that ranged between 2019-01-02 to 2019-04-26. The forecastdata of the ANN was composed of 80% of the data between 1991-07-09 to2019-04-26 and the evaluation data was composed of the remaining 20%. TheANN architecture had one input layer with chunks of 20 consecutive days asinput, followed by three Long Short-Term Memory (LSTM) hidden layers with128 neurons in each layer, followed by another hidden layer with RectifiedLinear Unit (ReLU) containing 32 neurons, followed by the output layercontaining 2 neurons with softmax activation. The results showed that theANN, with an accuracy of 0,9892, could be a successful method to forecast theSwedish stock market instead of ARIMA.

Researchers have developed many visual saliency models in order to advance the technology in computer vision. Neural networks, Convolution Neural Networks (CNNs) in particular, have successfully differentiate objects in images through feature extraction. Meanwhile, Cummings et al. has proposed a proto-object image saliency (POIS) model that shows perceptual objects or shapes can be modelled through the bottom-up saliency algorithm. Inspired from their work, this research is aimed to explore the imbedding features in the proto-object representations and utilizing artificial neural networks (ANN) to capture and predict the saliency output of POIS. A combination of CNN and a bi-directional long short-term memory (BLSTM) neural network is proposed for this saliency model as a machine learning alternative to the border ownership and grouping mechanism in POIS. As ANNs become more efficient in performing visual saliency tasks, the result of this work would extend their application in computer vision through successful implementation for proto-object based saliency.

In this paper we explore various approaches to using deep neural networks to per- form cryptanalysis, with the ultimate goal of having a deep neural network deci- pher encrypted data. We use long short-term memory networks to try to decipher encrypted text and we use a convolutional neural network to perform classification tasks on encrypted MNIST images. We find that although the network is unable to decipher encrypted data, it is able to perform classification on encrypted data. We also find that the networks performance is depending on what key were used to en- crypt the data. These findings could be valuable for further research into the topic of cryptanalysis using deep neural networks.

The use of Long Short Term Memory (LSTM) networks continues to yield better results in natural language processing tasks. One area which recently has seen significant improvements is semantic dependency parsing, where the current state-of-the-art model uses a multilayer LSTM combined with an attention-based scoring function to predict the dependencies. In this thesis the state of the art model is first replicated and then extended to include features based on syntactical trees, which was found to be useful in a similar model. In addition, the effect of part-of-speech tags is studied. The replicated model achieves a labeled F1 score of 93.6 on the in-domain data and 89.2 on the out-of-domain data on the DM dataset, which shows that the model is indeed replicable. Using multiple features extracted from syntactic gold standard trees of the DELPH-IN Derivation Tree (DT) type increased the labeled scores to 97.1 and 94.1 respectively, while the use of predicted trees of the Stanford Basic (SB) type did not improve the results at all. The usefulness of part-of-speech tags was found to be diminished in the presence of other features.

A “biomedical event” is a broad term used to describe the roles and interactions between entities (such as proteins, genes and cells) in a biological system. The task of biomedical event extraction aims at identifying and extracting these events from unstructured texts. An important component in the early stage of the task is biomedical trigger classification which involves identifying and classifying words/phrases that indicate an event. In this thesis, we present our work on biomedical trigger classification developed using the multi-level event extraction dataset. We restrict the scope of our classification to 19 biomedical event types grouped under four broad categories - Anatomical, Molecular, General and Planned. While most of the existing approaches are based on traditional machine learning algorithms which require extensive feature engineering, our model relies on neural networks to implicitly learn important features directly from the text. We use natural language processing techniques to transform the text into vectorized inputs that can be used in a neural network architecture. As per our knowledge, this is the first time neural attention strategies are being explored in the area of biomedical trigger classification. Our best results were obtained from an ensemble of 50 models which produced a micro F-score of 79.82%, an improvement of 1.3% over the previous best score.

This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentation, we decided to explore an object detection task using a particular model architecture which maintains a sweet spot between computational cost and accuracy. The model architecture which we had used is LSTM-Decoder. The model had been experimented with different CNN feature extractor and found their pros and cons in variant scenarios. The results which we had obtained on different datasets elucidates that CNN plays a major role in obtaining higher accuracy and we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection Dataset. In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as follows: using the detected annotation from object detection, a small cropped image is generated which would be feed into a small cascade network which was trained for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently, we had generated our results for shoulder keypoint detection. However, this idea could be extended to full-body pose Estimation by modifying the cascaded network for pose estimation purpose and this had become an important topic of discussion for the future work of this thesis.

Long short-term memory (LSTM) neural networks have been proven to be effective for time series prediction, even in some instances where the data is non-stationary. This lead us to examine their predictive ability of stock market returns, as the development of stock prices and returns tend to be a non-stationary time series. We used daily stock trading data to let an LSTM train models at predicting daily returns for 60 stocks from the OMX30 and Nasdaq-100 indices. Subsequently, we measured their accuracy, precision, and recall. The mean accuracy was 49.75 percent, meaning that the observed accuracy was close to the accuracy one would observe by randomly selecting a prediction for each day and lower than the accuracy achieved by blindly predicting all days to be positive. Finally, we concluded that further improvements need to be made for models trained by LSTMs to have any notable predictive ability in the area of stock returns.