In this dissertation, the general problem of the dimensionality reduction of hyperspectral imagery is considered. Data dimension can be reduced through compression, in which an original image is encoded into bitstream of greatly reduced size; through application of a transformation, in which a high-dimensional space is mapped into a low-dimensional space; and through a simple process of subsampling, wherein the number of pixels is reduced spatially during image acquisition. All three techniques are investigated in the course of the dissertation. For data compression, an approach to calculate an operational bitrate for JPEG2000 in conjunction with principal component analysis is proposed. It is shown that an optimal bitrate for such a lossy compression method can be estimated while maintaining both class separability as well as anomalous pixels in the original data. On the other hand, the transformation paradigm is studied for spectral dimensionality reduction; specifically, dataindependent random spectral projections are considered, while the compressive projection principal component analysis algorithm is adopted for data reconstruction. It is shown that, by incorporating both spectral and spatial partitioning of the original data, reconstruction accuracy can be improved. Additionally, a new supervised spectral dimensionality reduction approach using a sparsity-preserving graph is developed. The resulting sparse graph-based discriminant analysis is seen to yield superior classification performance at low dimensionality. Finally, for spatial dimensionality reduction, a simple spatial subsampling scheme is considered for a multitemporal hyperspectral image sequence, such that the original image is reconstructed using a sparse dictionary learned from a prior image in the sequence.

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The digitalization of the energy industry has made tremendous energy data available. This data is utilized across the entire energy value chain to provide value for customers and energy providers. One area that has gained recent attention in the energy industry is the electricity load forecasting for better scheduling and bidding on the electricity market. However, the electricity data that is used for forecasting is prone to have anomalies, which can affect the accuracy of forecasts. In this thesis we propose two anomaly detection methods to tackle the issue of anomalies in electricity demand data. We propose Long short-term memory (LSTM) and Feed-forward neural network (FFNN) based methods, and compare their anomaly detection performance on two real-world electricity demand datasets. Our results indicate that the LSTM model tends to produce a more robust behavior than the FFNN model on the dataset with regular daily and weekly patterns. However, there was no significant difference between the performance of the two models when the data was noisy and showed no regular patterns. While our results suggest that the LSTM model is effective when a regular pattern in data is present, the results were not found to be statistically significant to claim superiority of LSTM over FFNN. / Digitaliseringen inom energibranschen har tillgängliggjort enorma mängder energidata. Dessa data används över hela värdekedjan för energisystem i syfte att skapa värde för kunder och energileverantörer. Ett område som nyligen uppmärksammats inom energibranschen är att skapa prognoser för elbelastning för bättre schemaläggning och budgivning på elmarknaden. Data som används för sådana prognoser är dock benägna att ha avvikelser, vilket kan påverka prognosernas noggrannhet. I det här examensarbetet föreslår vi två metoder för detektering av avvikelser för att ta itu med frågan om avvikelser i data för elektricitetsbehov. Vi föreslår metoder baserade på Long short-term memory (LSTM) och Feedforward neural network (FFNN) och jämför dess prestanda att upptäcka avvikelser på två verkliga databanker över elbehovsdata. Våra resultat indikerar att LSTM-modellen tenderar att producera ett mer robust beteende än FFNN-modellen på data med upprepande dagliga samt veckovisa mönster. Det fanns dock ingen signifikant skillnad mellan prestanda för de två modellerna när data inte uppvisade regelbunda mönster. Även om våra resultat antyder att LSTM-modellen är effektiv när ett regelbundet datamönster finns närvarande, var resultaten inte statistiskt signifikanta för att påstå överlägsenhet av LSTM jämfört med FFNN.

Embedded platforms are often used as a sensor data processing node to collect data and transmit the data to the remote server. Due to the poor performance and power limitation, data processing was often left to the remote server. With the improvement of the computation ability, it is becoming possible to do some partial data processing on the embedded platforms, which would reduce the power and time consumption on the data transmission. Moreover, processing the data locally on the embedded platforms could reduce the dependence on the network. The platform could even do some tasks offline. This project aims to explore effective data analysis methods, especially for anomaly detection, which could be implemented on the embedded platform to be analyzed and detected locally. In this project, we select four methods: Seasonal and Trend Decomposition Using Loess (STL), Autoregressive Integrated Moving Average Model (ARIMA), Vector Autoregression (VAR), Long ShortTerm Memory (LSTM), to implement on the embedded platform ESP32. To test which methods could better fit the platform, we evaluate and compare the result from two aspects: the time overhead and the accuracy. The results show that the STL has the highest detection accuracy, but its time overhead is significantly higher than all other methods. ARIMA has the smallest time overhead and higher accuracy than LSTM and VAR. For LSTM, the method performs better with univariable input than multivariable input. Finally, we discuss the factors that may influence the result and future works. / Inbäddade plattformar används ofta som en sensor databehandlingsnod för att samla in och sedan överföra data till fjärrservern. Databehandling lämnades ofta till fjärrservern på grund av den dåliga prestandan och effektbegränsningen. Med förbättrad beräkningsförmåga blir det framkomligt att göra en del databehandling på de inbäddade plattformarna, vilket skulle minska ström och tidsförbrukningen för dataöverföringen. För övrigt kan lokal behandling av data på de inbäddade plattformarna minska beroendet av nätverket. Plattformen kan till och med utföra vissa uppgifter I nedkopplat läge. Detta projekt avser att utforska effektiva dataanalysmetoder särskilt för avvikelsedetektering, som kan verkställas på den inbäddade plattformen för att analyseras och upptäckas lokalt. I det här projektet väljer vi fyra metoder för att införa på den inbäddade plattformen ESP32: Seasonal and Trend Decomposition Using Loess (STL), Autoregressive Integrated Moving Average Model (ARIMA), Vector Autoregression (VAR), Long Short-Term Memory (LSTM). För att testa vilka metoder som bättre passar plattformen utvärderar och jämför vi resultatet med hänsyn till två aspekter: tidsomkostnaderna och noggrannheten. Resultaten visar att STL har den högsta detektionsnoggrannheten, men dess tidsomkostning är betydligt högre än alla andra metoder. ARIMA har den minsta tidsomkostningen och högre noggrannhet än LSTM och VAR. För LSTM fungerar metoden bättre med univariable input än multivariable input. Slutligen diskuterar vi faktorerna som möjligtvis påverkar resultatet och framtida arbeten.

Ore is transported using belt conveyor systems. The transported ore has various anomalous objects that must be removed to prevent damage to the system. Currently anomalies are detected manually using humans. This leads to increased costs of wages and damage to the system overmissed anomalies. The thesis aims to solve this problem via the use of trained neural networks which can run on relatively cheap systems with a greater accuracy than humans. A set of neural networks were trained on both the BCS dataset consisting of data collected from the belt conveyor system and on the MVTec dataset. The latter dataset was used as a way of checking the correctness of the implementation of the models. As training neural networks usually requires large datasets, this thesis also focuses on the effect of the portion of labelled versus unlabelled data on the models. Labelling data can be time consuming and expensive so investigating if or how much data can be unlabelled without any or minimal loss to accuracy could lead to further cost reductions. The convolutional autoencoder (CAE) performed best on the classification based task on the BCS dataset where it managed to classify most of the dataset correctly, with an F1-score of 0.94 on data without anomalies and an F1-score of 0.86 on data with anomalies, as long as suitable thresholds were set. ResNet performed somewhat well with a 0.91 F1-score in detecting anomaly free data and a 0.50 F1-score in detecting anomaly containing data. The SimCLR and SimCLRv2 models were unable to learn from the data and defaulted to always assuming the data contained anomalies. The CAE model trained using the L1 loss function performed best with an IoU of 0.272 and performed worst with the SSIM based loss function with an IoU of 0.160. The effect of labelled versus unlabelled data using the MVTec dataset was tested using the SimCLR and SimCLRv2 models and the models performed best with the fully labelled dataset which was expected. The SimCLR model was able to identify all categories with an F1-score greater than 0.67 whereas the other splits performed worse overall with two or more categories completely misclassified. The SimCLRv2 was able to classify six categories with an F1-score greater than 0.0 which was significantly better than all other labelled and unlabelled splits.

Critical networks require defence in depth incorporating many different security technologies including intrusion detection. One important intrusion detection approach is called anomaly detection where normal (good) behaviour of users of the protected system is modelled, often using machine learning or data mining techniques. During detection new data is matched against the normality model, and deviations are marked as anomalies. Since no knowledge of attacks is needed to train the normality model, anomaly detection may detect previously unknown attacks. In this thesis we present ADWICE (Anomaly Detection With fast Incremental Clustering) and evaluate it in IP networks. ADWICE has the following properties: (i) Adaptation - Rather than making use of extensive periodic retraining sessions on stored off-line data to handle changes, ADWICE is fully incremental making very flexible on-line training of the model possible without destroying what is already learnt. When subsets of the model are not useful anymore, those clusters can be forgotten. (ii) Performance - ADWICE is linear in the number of input data thereby heavily reducing training time compared to alternative clustering algorithms. Training time as well as detection time is further reduced by the use of an integrated search-index. (iii) Scalability - Rather than keeping all data in memory, only compact cluster summaries are used. The linear time complexity also improves scalability of training. We have implemented ADWICE and integrated the algorithm in a software agent. The agent is a part of the Safeguard agent architecture, developed to perform network monitoring, intrusion detection and correlation as well as recovery. We have also applied ADWICE to publicly available network data to compare our approach to related works with similar approaches. The evaluation resulted in a high detection rate at reasonable false positives rate. / <p>Report code: LiU-Tek-Lic-2006:12.</p>

Anomaly detection is an important first step of quality control in manufacturing processes. In wooden planks, anomalies such as broken knots and resin pockets can lower the quality of the final product. With the help of machine vision, inspections can be made faster, at higher accuracy, and at a lower cost. Therefore, this Master's Thesis project aims to explore different machine vision-based machine learning methods for anomaly detection in images of wooden planks. Both unsupervised and supervised methods were used. The evaluated unsupervised methods were two variations of student-teacher frameworks, while the supervised methods were different semantic segmentation models. The evaluation results showed that the pre-trained DeepLabV3 semantic segmentation model performed the best, with a pixel-level IoU of 0.780, an object-level precision of 89.3% and object-level recall of 96.9%. Findings suggest that for this data set of images of wooden planks, the benefits of training on labeled data outweigh the time cost of annotation.

Anomaly detection plays a crucial role in various domains, such as transportation, cybersecurity, and industrial monitoring, where the timely identification of unusual patterns or outliers is of utmost importance. Traditional statistical techniques have limitations in handling complex and highdimensional data, which motivates the use of deep learning approaches. The project proposes designing and implementing deep neural networks, tailored explicitly for time series multivariate data from sensors incorporated in vehicles, to effectively capture intricate temporal dependencies and interactions among variables. As this project is conducted in collaboration with Scania, Sweden, the models are trained on datasets encompassing various vehicle sensor data. Different deep learning architectures, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), are explored and compared to identify the most suitable model for anomaly detection tasks for the specified time series data and CNN found to perform well for the data used in the study. Furthermore, interpretability techniques are incorporated into the developed models to enhance their transparency and provide insights into the reasons behind detected anomalies. Interpretability is crucial in real-world applications to facilitate trust, understanding, and decision-making. Both model-agnostic and model-specific interpretability methods were employed to highlight the relevant features and contribute to the interpretability of the anomaly detection models. The performance of the proposed models is evaluated using test datasets with anomaly data, and comparisons are made against existing anomaly detection methods to demonstrate their effectiveness. Evaluation metrics such as precision, recall, false positive rate, F1 score, and composite F1 score are employed to assess the anomaly detection models' detection accuracy and robustness. For evaluating the interpretability method, Kolmogorov-Smirnov Test is used on counterfactual examples. The outcomes of this research project will contribute to developing advanced anomaly detection techniques that can effectively analyse time series multivariate data collected from sensors incorporated in vehicles. Incorporating interpretability techniques will provide valuable insights into the detected anomalies, enabling better decision-making and improved trust in the deployed models. These advancements can potentially enhance anomaly detection systems across various domains, leading to more reliable and secure operations.

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Aircraft-mounted cameras have potential to greatly increase the effectiveness of wilderness search and rescue efforts by collecting photographs or video of the search area. The more data that is collected, the more difficult it becomes to process it by visual inspection alone. This work presents a method for automatically detecting unusual objects in aerial video to assist people in locating signs of missing persons in wilderness areas. The detector presented here makes use of anomaly detection methods originally designed for hyperspectral imagery. Multiple anomaly detection methods are considered, implemented, and evaluated. These anomalies are then aggregated into spatiotemporal objects by using the video's inherent spatial and temporal redundancy. The results are therefore summarized into a list of unusual objects to enhance the search technician's video review interface. In the user study reported here, unusual objects found by the detector were overlaid on the video during review. This increased participants' ability to find relevant objects in a simulated search without significantly affecting the rate of false detection. Other effects and possible ways to improve the user interface are also discussed.