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Öge3D face animation generation from audio using convolutional neural networks(Graduate School, 2022)Problem of generating facial animations is an important phase of creating an artificial character in video games, animated movies, or virtual reality applications. This is mostly done manually by 3D artists, matching face model movements for each speech of the character. Recent advancements in deep learning methods have made automated facial animation possible, and this research field has gained some attention. There are two main variants of the automated facial animation problem: generating animation in 2D or in 3D space. The systems that work on the former problem work on images, either generating them from scratch or modifying the existing image to make it compatible with the given audio input. The second type of systems works on 3D face models. These 3D models can be directly represented by a set of points or parameterized versions of these points in the 3D space. In this study, 3D facial animation is targeted. One of the main goals of this study is to develop a method that can generate 3D facial animation from speech only, without requiring manual intervention from a 3D artist. In the developed method, a 3D face model is represented by Facial Action Coding System (FACS) parameters, called action units. Action units are movements of one or more muscles on the face. By using a single action unit or a combination of different action units, most of the facial expressions can be presented. For this study, a dataset of 37 minutes of recording is created. This dataset consists of speech recordings, and corresponding FACS parameters for each timestep. An artificial neural network (ANN) architecture is used to predict FACS parameters from the input speech signal. This architecture includes convolutional layers and transformer layers. The outputs of the proposed solution are evaluated on a user study by showing the results of different recordings. It has been seen that the system is able to generate animations that can be used in video games and virtual reality applications even for novel speakers it is not trained for. Furthermore, it is very easy to generate facial animations after the system is trained. But an important drawback of the system is that the generated facial animations may lack accuracy in the mouth/lip movement that is required for the input speech.
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ÖgeGesture recognition and customization on textile-based pressure sensor array(Graduate School, 2024-08-01)The tactile sensation plays an essential part in perceiving and interacting with our surroundings, making touch-based technologies increasingly significant in everyday life. The technologies cover a wide spectrum from cellphones and tablets to sensors made entirely of textiles. When creating tactile sensing systems, multiple parameters need to be taken into account. Although touchscreens are the ideal choice for systems that need visual feedback, wearable technology requires devices that are soft, flexible, adjustable to the human body's shapes, and free from safety concerns. Textile-based capacitive pressure sensor array is selected for pressure sensing in gesture recognition system because of its accurate pressure detection capability and lightweight design. A pressure sensor array consisting of 11x2 sensors has been manufactured that rely on the principle of determining the location of pressure applied through variations in capacitance. It generates 22-dimensional capacitance data vector. In order to detect regions with higher capacitance when pressure is applied with fingertips, a sequence of data processing procedures, such as calibration, scaling, and flattening are executed. The manipulated data reveal a series of consecutively pressed cells on the textile sensor. In order to analyze the patterns of pressure applied cells, a deep learning model called Long Short-Term Memory (LSTM) and a machine learning model Hidden Markov Model (HMM) are utilized. The results of the two models were compared, and based on the obtained results, a high level of accuracy was achieved. In considering the difficulties caused by memorizing gestures or the inability of users to execute pre-defined gestures, it was considered crucial to enable the creation of new gestures and customization of them. In order to solve this issue, a class-incremental approach was implemented. The proposed approach deals with two primary problems: missing previous data and the inability to identify a new class of data. Changes were implemented to the LSTM layer and output layer of the existent model. The amount of new data sample is a factor that affects the equilibrium between usability and accuracy. As the amount of data sample grows, the duration of training increases and the usability decreases. On the other hand, when there are fewer data samples, the accuracy of the model reduces. In order to tackle this issue, a compromise was made by gathering a small number of data samples from the user and then enlarging the dataset through the utilization of data augmentation techniques. In order to reduce the risk of forgetting previous classes, the model was enhanced by using past data as inputs. An experiment was carried out in two phases, involving a total of 20 people. During the first phase, the participants were instructed to execute four predetermined movements -up, down, pinch, and zoom—. During the second phase, participants were instructed to repeatedly execute a new gesture in order to collect data for the creation of the model of the new gesture. Afterwards, they were instructed to execute both predetermined and newly defined gestures in order to confirm the recognition of the new data and ensure that the previous data were not forgotten. This phase was repeated to demonstrate the capacity to establish several new gestures. The recognition rate of the predetermined movements in the first phase gave accuracy values of 95.3% for deep learning model, 91.5% for machine learning model. After including one new gesture, the overall system achieved an accuracy of 96.3% for deep learning model, 91.7% for machine learning model. Furthermore, after introducing a second new gesture, the accuracy dropped slightly to 94.7% for deep learning model, 89.4% for machine learning model. Further studies will investigate the utilization of this technology as a controller.
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ÖgeAerial link orchestration(Graduate School, 2024-08-23)Unmanned Aerial Vehicles (UAVs) have become indispensable tools due to their superior maneuverability and flexibility in a variety of activities such as mapping, infrastructure monitoring, and object tracking. Their applications are many, ranging from industrial and military surveillance to commercial delivery and other operations. Because of their hardware architectures, atmospheric factors such as wind and turbulence restrict the movement of UAVs, particularly drones. These conditions not only interfere with their responsiveness but also limit the operation of integrated systems and communication between the drone and the ground control station (GCS). It is critical in drone operations to maintain communication systems with the GCS and ensure the correct functioning of integrated systems, including managing the drone's movement parameters. These different uses, as well as the associated environmental circumstances, highlight the crucial requirement for UAVs to function dependably, as well as the importance of suitable regulations and adaptations. Drones and UAVs utilize a variety of communication methods in order to create a data link between the vehicle and GCS and sometimes between multiple aircraft (swarm technology). UAV communication systems can be utilized for data and image transmission from sensors and payloads to the control station, broadcasting telemetry systems, and command and control. Additionally, they provide bidirectional communication from air to ground and ground to air by allowing data and commands to be received at the ground station. The most common ways of drone communication employ radio-frequency (RF) signals in bands such as HF (high frequency) , satellites, cellulars, and other wireless infrastructures. However, radio technologies are the most widely used. RF datalinks can be analog or digital and have a longer range than Wi-Fi, although they are still limited to line-of-sight (LOS). The range of the UAV communications system is determined by the direction and size of the antenna, the strength of the transmitter, and the frequency, with lower frequencies allowing longer ranges but lower data rates. By addressing these technical difficulties, we develop new techniques to improve UAV communication quality and identify drone flight parameters that influence communication quality. Our goal is to create communication systems that are less impacted by these elements. Our research aims to overcome constraints in high-frequency transmission imposed by drone instability and antenna limitations. Our primary goal is to provide safe, continuous communication while greatly increasing the packet delivery ratio (PDR). We create resilient and adaptive UAV systems that can function well in a variety of dynamic operational scenarios by taking advantage of the inherent flexibility of Software Defined Radio (SDR) technology. This holistic approach encompasses proactive measures against signal interference, noise mitigation, and the management of flight-induced vibrations, harnessing SDR's configurability to meet the evolving demands of modern UAV operations effectively. Our approach involves: * Addressing Drone Flight Patterns and Aerial Conditions: We classify different aerial conditions affecting UAVs. *Enhancing the Modulation and Coding Scheme (MCS): We improve the MCS table to be aware of aerial and flight conditions. *Exhaustive Real-World Experimentation: Utilizing a "train on day, test on the next day" methodology on a real test bed. To increase drone PDR, we use Digital Twin architecture to detect influential parameters. Using the "train one day, test another day" method, we include real-world test flight log data from drones and SDR communication attributes into our digital twin model. This allows us to discover the best parameter values for getting a high PDR, which we then feed back into our system. Based on these results, we update the existing static MCS table to reflect the effect of the identified drone flying factors on communication performance. Our results validated methodologies have demonstrated significant improvements in PDR, achieving an average increase of 27\% across multiple drone platforms and environmental scenarios. These findings underscore the effectiveness of our approach in optimizing communication performance under real-world conditions. Furthermore, our research provides valuable insights into the intricate interactions between UAV flight dynamics and communication efficacy, guiding future advancements in UAV technology. In summary, our research underscores the critical importance of maintaining robust communication networks in dynamic UAV environments. By proposing and validating innovative methodologies, we lay the groundwork for enhanced UAV communication resilience and efficiency. Future endeavors will build upon these foundations, expanding system capabilities across broader operational scenarios and pushing the boundaries of UAV communication technology to new heights.
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ÖgeSemi-supervised learning strategy for improved flash point prediction(Graduate School, 2024-08-20)This thesis explores the application of semi-supervised learning techniques to enhance the prediction of flash points in the oil industry, which are critical for ensuring the safety of transporting and storing petroleum products. Flash points denote the lowest temperature at which a substance's vapors ignite in air, a crucial parameter that traditional methods ascertain through costly and time-consuming laboratory tests. This study proposes a data-driven approach to optimize these processes more efficiently and effectively. Semi-supervised learning, which leverages both labeled and unlabeled data, provides a robust framework especially valuable in scenarios where data labeling is prohibitively expensive or logistically challenging. This research integrates sensor data such as pressure, temperature, and flow rates with sparse flash point measurements to develop a predictive model. The aim is to reduce dependency on extensive laboratory testing while enhancing operational efficiency and safety protocols. The central research questions addressed are: How can flash points be accurately predicted in the oil industry when only a limited number of labeled data points are available? Given these constraint, could semi-supervised learning method be an effective solution? What are the specific advantages and limitations of these technique within the oil industry context? The study validates the effectiveness of semi-supervised learning method and develops a model that improves upon traditional approaches. To address the research questions, particularly in the context of improving flash point predictions with limited labeled data, the study employs data preprocessing techniques and modeling processes that are essential for optimizing model performance. The methodology employs two principal data preprocessing techniques: Winsorization and Min-Max Scaling. Winsorization mitigates the effects of outliers by limiting extreme data points within a designated percentile range, ensuring the model is not skewed by anomalies. Min-Max Scaling normalizes the data, allowing for equitable evaluation of all features and preventing any single feature from dominating the model's output. The modeling process involves the Gaussian Process Regressor and the Random Forest model. The Gaussian Process Regressor, suitable for continuous data, provides uncertainty estimates to gauge the reliability of predictions. The Random Forest model enhances stability and accuracy by aggregating predictions from multiple decision trees. Initially trained on labeled data, the Gaussian Process Regressor subsequently predicts labels for unlabeled data, incorporating those predictions within a specified confidence interval into the training set. This expanding dataset further trains the Random Forest model, applying an expanding window approach to incrementally improve prediction capabilities. Performance metrics such as Mean Absolute Error and Root Mean Squared Error assess model efficacy. The baseline model initially yielded an mean absolute error of 1.1 degrees in flash point predictions. With the application of the semi-supervised learning model, Mean Absolute Error improved to 1.01 and Root Mean Squared Error decreased to 1.63, demonstrating significant enhancements in accuracy through the inclusion of unlabeled data. In conclusion, this thesis illustrates the potential of semi-supervised learning to bridge the gap caused by a scarcity of labeled data, particularly in critical industrial applications like oil processing. The findings suggest that semi-supervised learning not only reduces the financial and temporal expenditures associated with traditional testing methods but also offers a scalable, efficient alternative poised to transform industry practices. The methodologies developed here have broader implications, suggesting that semi-supervised learning could be similarly beneficial in other sectors where data labeling is a significant constraint and even small performance improvements are critical due to the importance of the parameters being predicted.
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ÖgeA condition coverage-based black hole inspired meta-heuristic for test data generation(Graduate School, 2022)As software becomes more complex, the importance of software testing increases by the day. It is very important to get as close to bug-free software as possible, especially for safety-critical systems. Some standards, such as DO-178, have been established to ensure that the safety requirements of safety-critical software are met. In these standards, the code coverage ratio is one of the parameters to measure test quality. To increase the coverage rate, test data must be generated in a systematic manner. Combinatorial Testing is one of the most commonly used methods to deal with this problem. However, for software that takes a large number of input parameters, CT causes test case explosion problems and it is not possible to test all produced test cases. To reduce the number of test cases, T-way testing is a technique for selecting a subset of huge numbers of test cases. However, because this technique does not choose test cases based on code coverage, the software is not tested with coverage values at the required height. The motivation of this study is to produce test cases by considering the condition coverage value. Thus, the condition coverage value and test quality will be increased without the need to test too many test cases of all possible combinations. This thesis focuses on the research question (RQ): How can we conduct a meta-heuristic method in Search-Based Combinatorial Testing (SBCT) that generates test data to achieve high coverage rates while avoiding local minima? To find answers to our research question, we reviewed the literature and discovered that Search-based Software Testing and Search (SBST) and Search-based Combinatorial Testing (SBCT) techniques are used to generate optimum test data using meta-heuristic approaches. Among the studies in the literature, we chose studies that work on problems similar to ours and studies that differ in terms of fitness function and data set, so we aimed to examine studies with various features as much as possible. As the results of our literature review, we discovered that meta-heuristics in Search-Based Combinatorial Testing (SBCT) could be used to generate test data for enhanced condition coverage in software testing. During our literature review, we realized that the Black Hole Algorithm (BHA), which is also a meta-heuristic approach, can be also used for our problem. Hence, after analysing alternative solutions, we decided to work on BHA for the following three main characteristics of our inspired study: (1) That study focused on a problem that is very similar to our problem, (2) Although BHA is a data clustering method, it is a novel method used in test data generation, (3) BHA was reported to be more efficient than another optimization algorithm (i.e., Particle Swarm Optimization (PSO)); and this finding inspired us to propose a stronger method. To achieve our goal, we present a novel approach based on a binary variation of the Black Hole Algorithm (BBH) in SBCT and adapt it to the CT difficulties. We reused some of the techniques in BBH, modified some of them, and introduced new methods as well. The proposed BBH version, BH-AllStar, aims the following: (1) obtaining higher condition coverage, (2) avoiding local minima, and (3) handling discrete input values. The main two significant differences between BH-AllStar and BBH are the new elimination mechanism and avoiding local minima by reassessing earlier removed stars and selecting the useful ones to add to the final population. Our contributions are as follows: (1) we perform more detailed tests by using detailed condition coverage rate criteria per each condition branch while generating test cases, (2) we develop a condition coverage-based test cases selecting mechanism and reduce the risk of eliminating the beneficial test cases wrongly, (3) we avoid local minima by providing variety achieved by re-evaluating the test cases which are destroyed before and giving chance them to be added to the final test case pool (4) we give higher priority to coverage rate than test case number unlike existing studies in the literature and thus, we provide more efficient tests with higher condition coverage rates and (5) we provide the validity of our BH-AllStar method by applying it to three different Software Under Test (SUT): one of them is a real-life safety-critical software and two of them are toy examples. Our new metaheuristic method can be applied on different software settings and all types of SUTs can be used on the experiment setup. We analyzed our approach in terms of condition coverage, number of test cases, and execution time. As a result, we observed that our BH-AllStar method provided up to 43% more coverage than BBH. Although BH-AllStar produced more test cases than BBH, this level of increase was acceptable to achieve higher coverage. Finally, we answered RQ by determining that the Black Hole phenomenon, which is provided as a meta-heuristic in SBCT, is a suitable strategy for producing test data in order to reach larger condition ratios while avoiding local minima by modifying and proposing novel features to it. As future work, BH-AllStar can be tested on different SUT, the randomization operation in initialization processes can be optimized and MC/DC tests can be studied. BH-AllStar.