| 1. |
- Amirhossein, Berenji, et al.
(författare)
-
curr2vib : Modality Embedding Translation for Broken-Rotor Bar Detection
- 2023
-
Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer Nature. - 9783031236334 ; , s. 423-437
-
Konferensbidrag (refereegranskat)abstract
- Recently and due to the advances in sensor technology and Internet-of-Things, the operation of machinery can be monitored, using a higher number of sources and modalities. In this study, we demonstrate that Multi-Modal Translation is capable of transferring knowledge from a modality with higher level of applicability (more usefulness to solve an specific task) but lower level of accessibility (how easy and affordable it is to collect information from this modality) to another one with higher level of accessibility but lower level of applicability. Unlike the fusion of multiple modalities which requires all of the modalities to be available during the deployment stage, our proposed method depends only on the more accessible one; which results in the reduction of the costs regarding instrumentation equipment. The presented case study demonstrates that by the employment of the proposed method we are capable of replacing five acceleration sensors with three current sensors, while the classification accuracy is also increased by more than 1%.
|
|
| 2. |
- Berenji, Amirhossein, et al.
(författare)
-
An Analysis of Vibrations and Currents for Broken Rotor Bar Detection in Three-phase Induction Motors
- 2022
-
Ingår i: Proceedings of the European Conference of the Prognostics and Health Management Society 2022. - State College, PA : PHM Society. - 9781936263363 ; , s. 43-48
-
Konferensbidrag (refereegranskat)abstract
- Selecting the physical property capable of representing the health state of a machine is an important step in designing fault detection systems. In addition, variation of the loading condition is a challenge in deploying an industrial predictive maintenance solution. The robustness of the physical properties to variations in loading conditions is, therefore, an important consideration. In this paper, we focus specifically on squirrel cage induction motors and analyze the capabilities of three-phase current and five vibration signals acquired from different locations of the motor for the detection of Broken Rotor Bar generated in different loads. In particular, we examine the mentioned signals in relation to the performance of classifiers trained with them. Regarding the classifiers, we employ deep conventional classifiers and also propose a hybrid classifier that utilizes contrastive loss in order to mitigate the effect of different variations. The analysis shows that vibration signals are more robust under varying load conditions. Furthermore, the proposed hybrid classifier outperforms conventional classifiers and is able to achieve an accuracy of 90.96% when using current signals and 97.69% when using vibration signals.
|
|
| 3. |
- Berenji, Amirhossein, 1995-, et al.
(författare)
-
Data-Centric Perspective on Explainability Versus Performance Trade-Off
- 2023
-
Ingår i: Advances in Intelligent Data Analysis XXI. - Cham : Springer. - 9783031300462 - 9783031300479 ; , s. 42-54
-
Konferensbidrag (refereegranskat)abstract
- The performance versus interpretability trade-off has been well-established in the literature for many years in the context of machine learning models. This paper demonstrates its twin, namely the data-centric performance versus interpretability trade-off. In a case study of bearing fault diagnosis, we found that substituting the original acceleration signal with a demodulated version offers a higher level of interpretability, but it comes at the cost of significantly lower classification performance. We demonstrate these results on two different datasets and across four different machine learning algorithms. Our results suggest that “there is no free lunch,” i.e., the contradictory relationship between interpretability and performance should be considered earlier in the analysis process than it is typically done in the literature today; in other words, already in the preprocessing and feature extraction step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
|
|
| 4. |
- Berenji, Amirhossein, et al.
(författare)
-
Fault identification with limited labeled data
- 2024
-
Ingår i: Journal of Vibration and Control. - London : Sage Publications. - 1077-5463 .- 1741-2986. ; 30:7-8, s. 1502-1510
-
Tidskriftsartikel (refereegranskat)abstract
- Intelligent fault diagnosis (IFD) based on deep learning methods has shown excellent performance, however, the fact that their implementation requires massive amount of data and lack of sufficient labeled data, limits their real-world application. In this paper, we propose a two-step technique to extract fault discriminative features using unlabeled and a limited number of labeled samples for classification. To this end, we first train an Autoencoder (AE) using unlabeled samples to extract a set of potentially useful features for classification purpose and consecutively, a Contrastive Learning-based post-training is applied to make use of limited available labeled samples to improve the feature set discriminability. Our Experiments—on SEU bearing dataset—show that unsupervised feature learning using AEs improves classification performance. In addition, we demonstrate the effectiveness of the employment of contrastive learning to perform the post-training process; this strategy outperforms Cross-Entropy based post-training in limited labeled information cases. © The Author(s) 2023.
|
|
| 5. |
- Bobek, Szymon, et al.
(författare)
-
Towards Explainable Deep Domain Adaptation
- 2024
-
Ingår i: Artificial Intelligence. ECAI 2023 International Workshops. - Cham : Springer. - 9783031503955 - 9783031503962 ; , s. 101-113
-
Konferensbidrag (refereegranskat)abstract
- In many practical applications data used for training a machine learning model and the deployment data does not always preserve the same distribution. Transfer learning and, in particular, domain adaptation allows to overcome this issue, by adapting the source model to a new target data distribution and therefore generalizing the knowledge from source to target domain. In this work, we present a method that makes the adaptation process more transparent by providing two complementary explanation mechanisms. The first mechanism explains how the source and target distributions are aligned in the latent space of the domain adaptation model. The second mechanism provides descriptive explanations on how the decision boundary changes in the adapted model with respect to the source model. Along with a description of a method, we also provide initial results obtained on publicly available, real-life dataset. © The Author(s) 2024.
|
|
| 6. |
- Esmaeilian, Maryam, et al.
(författare)
-
Experimental Evaluation of Delayed-Based Detectors Against Power-off Attack
- 2023
-
Ingår i: 2023 IEEE 29th International Symposium on On-Line Testing and Robust System Design (IOLTS). - : IEEE. - 9798350341355 - 9798350341362
-
Konferensbidrag (refereegranskat)abstract
- Embedded systems are vulnerable to significant security threats from Fault Injection Attacks (FIAs), which allow attackers to gain access to confidential information. While various attack detectors have been proposed in the literature to detect different types of FIAs, these detectors themselves are susceptible to such attacks and can be compromised. Hence, the robustness of these detectors is critical in maintaining the security of embedded systems. The focus of this study is to evaluate the robustness of digital circuits and delay-based digital detectors against a new type of FIA called Power-Off Attack (POA). POA occurs when the power to the chip is turned off, and the detectors are not active. Following a POA attack, the circuit or its detectors may not function properly when the power is turned back on, which can allow other attacks to be applied without being detected if the detectors are less sensitive. This study implements two detectors on Xilinx Artix-7 FPGAs and examines the impact of heating cycles on detector characteristics when the FPGA is in various states, including power-off, power-on, and inactive states (such as clock-freezing mode). Our experiments reveal that heating cycles in power-off mode can alter the FPGA component delays and the accuracy of its detectors, which highlights the vulnerability of these systems to POA and potential issues for embedded system security. © 2023 IEEE.
|
|
| 7. |
- Kazemi, Zahra, et al.
(författare)
-
An Offline Hardware Security Assessment Approach using Symbol Assertion and Code Shredding
- 2022
-
Ingår i: Proceedings of the Twenty Third International Symposium on Quality Electronic Design. - : IEEE. - 9781665494663 - 9781665494656 - 9781665494670
-
Konferensbidrag (refereegranskat)abstract
- This paper presents an evaluation approach to analyze and prioritize the embedded software vulnerabilities against FIAs by using symbolic execution. The proposed approach is based on the code review analysis and highlights the potential software weakness points. It uses LLVM and its add-on named KLEE tool, which applies the symbolic assertion into the code under review. These tools are employed to obtain a vulnerability factor that is used to spot the corner cases in the execution paths of the code blocks. A case study has shown the effectiveness of the generated assertions in pinpointing the actual vulnerabilities. © 2022 IEEE.
|
|
| 8. |
- Kharazian, Zahra, et al.
(författare)
-
AID4HAI : Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning
- 2023
-
Ingår i: Advances in Intelligent Data Analysis XXI. - Cham : Springer. - 9783031300462 - 9783031300479 ; , s. 195-207
-
Konferensbidrag (refereegranskat)abstract
- This research is an interdisciplinary work between data scientists, innovation management researchers, and experts from a Swedish hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection to control and prevent healthcare-associated infections (HAI). The principal idea of this study is to use machine learning methods to extract informative ideas from social media to assist healthcare professionals in reducing the rate of HAI. Therefore, the proposed package offers a corpus of data collected from Twitter, associated expert-created labels, and software implementation of an annotation framework based on the Active Learning paradigm. We employed Transfer Learning and built a two-step deep neural network model that incrementally extracts the semantic representation of the collected text data using the BERTweet language model in the first step and classifies these representations as informative or non-informative using a multi-layer perception (MLP) in the second step. The package is AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is made fully available (software code and the collected data) through a public GitHub repository (https://github.com/XaraKar/AID4HAI). We believe that sharing our ideas and releasing these ready-to-use tools contributes to the development of the field and inspires future research.
|
|
| 9. |
- Kharazian, Zahra, et al.
(författare)
-
Increasing safety at smart elderly homes by Human fall detection from video using transfer Learning approaches
- 2020
-
Ingår i: e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15). - 9789811485930
-
Konferensbidrag (refereegranskat)abstract
- In this study, we investigate the problem of detecting humans fall from video images. Many of the existing methods try to solve the problem by manually defining a set of hand-crafted features for detecting fall, which is not only a suboptimal approach but also cumbersome. On the contrary, the proposed method puts the burden of feature extraction on a pre-trained deep neural network. In this way, we can extract a comprehensive set of conceptual features automatically and efficiently. An important challenge of employing deep neural networks is the need for a large collection of training data. While the available labeled data for human fall detection is very limited, we propose three approaches based on transfer learning, and we trained them on two standard RGB and depth datasets for fall detection. The pre-trained models explored in this study are VGG16, Inception V3, and ResNet50. Support vector machine and logistic regression are used to classify the extracted features from videos into two classes of fall and normal daily activities. The experimental results obtained from the proposed approach suggest that the transfer learning tactic is able to compensate for the low training data issue. It is also shown that the proposed approach can efficiently extract important features from the sequences of video and boost the accuracy of the system on the task of human fall detection.
|
|
| 10. |
- Mahdavi, Ehsan, et al.
(författare)
-
ITL-IDS : Incremental Transfer Learning for Intrusion Detection Systems
- 2022
-
Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 253
-
Tidskriftsartikel (refereegranskat)abstract
- Utilizing machine learning methods to detect intrusion into computer networks is a trending topic in information security research. The limitation of labeled samples is one of the challenges in this area. This challenge makes it difficult to build accurate learning models for intrusion detection. Transfer learning is one of the methods to counter such a challenge in machine learning topics. On the other hand, the emergence of new technologies and applications might bring new vulnerabilities to computer networks. Therefore, the learning process cannot occur all at once. Incremental learning is a practical standpoint to confront this challenge. This research presents a new framework for intrusion detection systems called ITL-IDS that can potentially start learning in a network without prior knowledge. It begins with an incremental clustering algorithm to detect clusters’ numbers and shape without prior assumptions about the attacks. The outcomes are candidates to transfer knowledge between other instances of ITL-IDS. In each iteration, transfer learning provides target environments with incremental knowledge. Our evaluation shows that this method can combine incremental and transfer learning to identify new attacks. © 2022 Published by Elsevier B.V.
|
|
