The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Current PDA approaches primarily rely on adversarial learning for domain adaptation and use reweighting strategies to exclude source samples deemed outliers. However, the transferability of features diminishes from general feature extraction layers to higher task-specific layers in adversarial learning-based adaptation modules, leading to significant negative transfer in PDA settings. We term this issue the adaptation-discrimination paradox (ADP). Furthermore, reweighting strategies often suffer from unreliable pseudo-labels, compromising their effectiveness. In this work, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to provide a new perspective for the PDA challenge. Each domain-wise model is equipped with a residual domain adaptation (RDA) block to preserve the discriminative structure of each domain and mitigate the ADP. Additionally, we introduce a confident information flow via an interactive learning strategy, training the modules of IRDAN sequentially to avoid cross-interference. We also establish a reliable stopping criterion for selecting the best-performing model, ensuring practical usability in real-world applications. Experiments have demonstrated the superior performance of the proposed IRDAN.

Data-driven soft sensors have been widely applied to estimating important yet difficult-to-measure quality-relevant variables in industrial processes. The complex industrial data exhibits nonlinearity and dynamics due to changes in operating conditions. Soft sensors developed based on the assumption of identical and independent distributions often struggle to adapt to target domain data with significant distribution discrepancy, which poses a great challenge to traditional soft sensor approaches. Additionally, the dynamic features embedded in the practical industrial processes are of great importance for an accurate soft sensor. Existing soft sensor approaches pay little attention to the combined challenge of distribution discrepancy and dynamic feature transfer, referred to as the dynamic domain adaptation challenge. In this work, we propose a Self-modified Dynamic Domain Adaptation (SDDA) soft sensor approach to solve this problem. We develop a novel sequential optimization framework for dynamic domain adaptation, where the target samples are progressively incorporated and pseudo-labels are iteratively refined to preserve the underlying temporal dependency and enable efficient dynamic feature transfer. Also, we propose a feature alignment with the transfer component analysis (TCA) to avoid potential significant distribution discrepancy and guarantee a stable prediction. We demonstrate the superiority of the proposed method via two real-world industrial cases.

The widespread use of electronic devices has contributed to an increase in poor posture, particularly when it comes to the cervical spine, leading to various cervical vertebral pain disorders. In this article, we focus on accurately monitoring the motion status of the cervical spine using the accelerometers and gyroscope sensors embedded in earphones. Our aim is to gain a better understanding of cervical spine health. To address the individual differences among subjects, we introduce fuzzy rules to the Re-ISDA method, proposing a novel approach known as FuzRe-ISDA. Unlike traditional domain adaptation methods, the FuzRe-ISDA method offers flexibility in adjusting the contribution from different source domains. It takes into account the collective impact of multiple models on predicting new user behavior. Moreover, this method can quickly adapt to new users without requiring extensive datasets. Experimental results demonstrate that our FuzRe-ISDA approach outperforms popular domain adaptation methods in terms of accuracy when predicting cervical motion. This highlights the effectiveness of our approach in addressing individual differences and improving the reliability of cervical spine motion prediction.

Reconstructing keyboard input through side-channel attacks has posed significant threats to user security. While conventional keystroke eavesdropping attacks have demonstrated effectiveness using side channels such as acoustic signals, they are usually shorter in range and can be significantly affected by environmental noises. In this paper, we proposeLaserKey, a novel keystroke eavesdropping technique that leverages the long-range and noise-resistant nature of lasers to achieve a more stealthy side-channel attack. We utilize laser sensors to accurately capture the subtle vibrations induced on laptop screens by keystrokes, and innovatively design a laser-driven deep learning-based keystroke recognition model with the inputs being the Mel-frequency Cepstral Coefficien (MFCC), Time Difference of Arrival (TDoA), and amplitude features extracted from such vibration signals. Through systematic experiments, we demonstrate thatLaserKeyachieves a 92.2% single-key recognition accuracy. By combining multiple single-key recognition capabilities based on this, we then realize the end-to-end word-level recognition. Moreover, to mitigate the recognition errors caused by the changes in keystroke positions, we introduce a meta-learning based domain generalization approach for achieving robust laser position calibration. Results show thatLaserKeyachieves as low as 3% character error rate (CER) for word-level recognition, proving its effectiveness for long-range and high-accuracy keystroke eavesdropping, and highlighting the necessity for countermeasures in the future.

Material sensing holds significant potential in areas such as environmental awareness and security monitoring. While technologies like RFID, WIFI, and UWB offer potential solutions for portable, non-contact material identification, the need to place targets in fixed positions for identification has limited the flexibility of material sensing. In this article, we first innovatively apply the Range-Angle heatmap (RAheatmap) to effectively represent the distance, placement angle, and inherent material attributes to pave the way for precise material identification. Then propose an innovative system called Material-ID to utilize Commercial-Off-The-Shelf (COTS) millimeter wave (mmWave) radar for material sensing. Additionally, we endow the system with cross-domain adaptability to make it tailored to identify material reflection attributes and minimize the effects of variables such as distance and placement angle. The experiments prove the effectiveness of the proposed system.

The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA). This work is motivated by a cervical spine motion prediction problem, with the goal of predicting the cervical spine motion of different subjects using the measurements of “exterior features”. The joint distribution of the exterior features for each subject may vary with one’s distinct BMI, sex and other characteristics; the sample size of this problem is limited due to the high experimental cost. The proposed method is well suited for transfer learning problems with limited training samples. The learning problem is formulated as a dynamic programming model and the latter is then solved by an efficient greedy algorithm. Numerical studies show that the proposed method outperforms prevailing transfer learning methods. The proposed method also achieves high prediction accuracy for the cervical spine motion problem.Note to Practitioners—This paper is motivated by the problem of using human body exterior features (e.g., contours, surface markers) acquired by computer vision or other measurement means to determine the underlying physics or mechanics. The goal is to build a subject-specific model to predict cervical spine motion based on surface-based measurements. Conventional supervised learning and transfer learning methods fail to give an accurate prediction due to the subject-to-subject variation and/or the small sample size of the training data. This paper proposes a novel transfer learning method called renewing iterative self-labeling domain adaptation to modify the possible error of the pseudo labels and thus make the training process more stable. The proposed method improves the accuracy by over$35\%$on the traditional transfer learning methods. This method is applicable to limited-sample problems with different training and testing input distributions, and is particularly suitable for time-series data.