Abstracts:Physical layer key sharing has become a popular topic in today's literature, as it could provide an alternative to computationally expensive key-sharing protocols. Its importance is emphasized by several resource-constrained, battery-powered autonomous robots, and personal devices that must communicate within modern-day industrial complexes. Physical layer key sharing has been explored using temporally and spatially variant characteristics of signals to produce the same secret keys at different devices. In this paper, we propose a novel method for ultra-wideband (UWB)-based physical layer key sharing, leveraging an off-the-shelf plug-and-play UWB module and utilizing the frequency domain of the Channel Impulse Response (CIR) magnitude. The frequency domain effectively aligns and denoises CIR samples, increasing the spatial and temporal uniqueness of channel characteristics. In turn, our approach offers the advantage of creating high-entropy keys with a 92% success rate. Our paper is the first to examine employing an UWB module for key sharing under different dynamic scenarios. Finally, our system maintains a higher level of security against several types of attackers compared to standard CIR methods.

Abstracts:Li-ion batteries play a crucial role in green energy goals, but estimating their parameters is challenging due to their nonlinear structure, aging effects, and varying chemistries. In this article, a distribution, scale and context sensitive, convolutional neural network-based state of charge estimation model is proposed. First, the proposed model improves generalization by addressing data distribution shifts in batteries across different temperatures through individual sample handling. Second, by stacking convolutional layers with varied receptive fields, the model captures both local and global dependencies, providing the model with multiscale features and hierarchical representation. Finally, we add a self-attention module to enhance learning of input sequences by focusing on relevant parts and understanding the global context of features. Experiments were performed on single-domain and cross-domain settings to prove the effectiveness of the model. The results obtained demonstrate that the proposed model significantly outperforms state-of-the-art approaches in terms of both accuracy and generalization capability.

Abstracts:This article presents a linear motor mover position detection method based on an image sample database to achieve high-precision measurement of the position of a long-stroke linear motor mover. First, the aperiodic fringe image is constructed with a chirp signal as the target shooting source, and an image sample database is established by capturing the target image with a certain acquisition frequency using a line-scan camera. Second, a normalized correlation coefficient (NCC) matching method is proposed to search for the position of the current image in the database. A coarse positioning method based on velocity estimation is also employed to improve the efficiency of position matching in the sample database. Third, to overcome the position measurement error caused by incomplete matching between the current image and the image in the database, a subpixel measurement algorithm based on local upsampling NCC is proposed to improve the measurement accuracy. Finally, the actual displacement of the actuator is obtained by combining the calibration coefficients. To verify the feasibility of the proposed method, an experimental platform is built using a line-scan camera, a linear motor, an adjustable light source, and a target source image. Comparison tests, speed adaptation experiments, and light adaptation experiments are set up. The experimental results show that the average time of the proposed method is approximately 1 ms, and the average absolute measurement error is approximately 0.005 mm under various working conditions, which shows that the proposed method has real-time performance and strong environmental adaptability.

Abstracts:Data has become an integral part of our digital lives. Specifically, there is an explosive growth in the application of video data. Video information is quite different from other data formats in the sense that it possesses unique characteristics such as high dimensions, complex content, and multiple forms of representation. All these properties make the protection of privacy in videos a complex and challenging task. Simple obscuration techniques cannot address the conclusions or deductions extrapolated from the background information of the entities in the video. In this work, we explore a video sanitization technique that generates synthetic videos following the perturbation of the objects of interest. In our model, we combine the naive detect and obscure technique with randomization in the presence of the objects of interest in each frame and their trajectories. Essentially, our holistic model fulfills the privacy notion of $\epsilon$-object indistinguishability. The generated videos achieve our aim of preserving privacy while being accurate enough for utility analysis. We tested our system on the MOT16 videos dataset and observed a reasonable count of 20% lost objects, mean square error ranging in $[0.2-0.3]$, and trajectories deviation between $[0.2-0.6]$.

Abstracts:Deep learning offers promising solutions for quality prediction in industrial processes, improving decision-making and performance monitoring. In this article, we propose a novel deep learning framework that incorporates variable time delay (VTD) estimation and uncertainty quantification into quality prediction. The framework employs a collaborative method that integrates deep Q-network with random forest to estimate VTD values. It then utilizes a hybrid BMCR model, consisting of parallel bidirectional minimal gated unit and 1-D convolutional layers, along with a residual connection, specifically designed to capture both long-term and short-term features in industrial data. The framework produces prediction intervals directly to quantify the uncertainty in the prediction results. This combined method offers high-precision point predictions alongside uncertainty quantification, providing valuable insights for industrial decision-making. The effectiveness of the proposed method is validated through two numerical examples, a benchmark, and a real-world industrial case from the alumina digestion process.

Abstracts:Detecting leaks in natural gas pipelines using acoustic signals typically requires extensive prior knowledge and complex parameter designs, making it challenging to handle background noise and data distribution disparities simultaneously. This article proposes a dual-feature drift framework utilizing a nonparametric design approach for acoustic signal-based leak detection. This framework consists of two core technologies: first, feature backward normalization. Low-dimensional drift factors are designed based on transformed acoustic signals to exponentially normalize the time-periodic features of the signal feature matrix, thereby eliminating strong background noise. Second, constructing the feature drift layer within a one-dimensional convolutional neural network. Weighted parameters constrain a high-dimensional feature matrix, developing drift factors that perform exponential drift on each feature, thus enhancing gradient constraints and eliminating data distribution differences during model training. This framework achieves a fault identification accuracy of 95.46% for natural gas pipeline leaks, outperforming competing methods and representing a novel approach to intelligent pipeline leak detection.

Abstracts:Although distributed renewable energy sources (DRESs) provide a sustainable solution to future microgrids (MGs), their fluctuant power outputs can incur frequency instability. The work studies the load frequency control (LFC) for MGs with the integration of wind energy under a hierarchical architecture. At the DRES level, a model predictive control method is employed together with an intensified event-triggered scheme considering multiple historic released signals to improve the computation efficiency. At the MG level, robustness specification is addressed in mean-square asymptotic stability to relieve the fluctuations caused by wind power penetration. Furthermore, the phasor measurement unit (PMU) failure and intermittent transmissions are considered in the control design, leading to the resilient control policy. Besides, this article extends the applicability of conventional small-signal LFC model by adding an uncertain matrix to tolerant the parameter variation due to the shift of the steady-state operating point caused by wind energy integration. The closed-loop performance based on the deployed resilient LFC strategy is verified through hardware-in-the-loop experiments, by which the frequency regulations against PMU failures and intermittent communication at different levels are effectively exhibited.

Abstracts:Stainless steel/carbon steel double-layer structures are commonly used in industries, but they are prone to generate internal defects (such as delamination and corrosions) at the bonding interface of the carbon steel layer. Eddy current pulsed thermography (ECPT) has shown promise for subsurface defect detection due to its high excitation and concentrated heating area. However, the complex heat transfer in multilayer structures lead to poor signal-to-noise ratios and poses challenges for defect identification. Moreover, the data-driven algorithms like PCA and ICA, though widely used in postprocessing, are faced with unstable performance and poor interpretability due to the lack of attention to the specific physical mechanism. To address these issues, this article proposes a thermal parameter reconstruction (TPR) imaging method to better detect the defect regions. Specifically, TPR regards the test sample as a 3-D thermal impedance space and projects it onto a parameterized grid plane. According to the thermal diffusion mechanism in ECPT, TPR builds a physical model to calculate the spatial thermal parameters of the projected surface. Through the reconstructed visual thermal parameters grid, the shape information of internal defects can be more clearly detected. An experiment on double-layer stainless steel/carbon steel structures is conducted, which validates the enhancement effectiveness of TPR on both round and irregularly shaped interlayer defects.

Abstracts:The brake pads are a critical component for the friction braking system of high-speed trains, which are prone to damage during long-term service. Friction-induced vibration signals include rich friction interface features, and reasonable analysis of vibration signals can predict brake damage in advance. However, the characteristics of braking interface signal are different under variable speeds, making it difficult to precisely recognize the health state of brake pads at unknown speeds. This article proposes an invariant feature exploration generalization network (IFEGN). The proposed method focuses on the feature alignment among domains and the differences within the domains. Thus, the invariant features of brake pads under variable rotational speeds are fully explored. This enables the model to generalize well to unseen data, even if there are huge differences among the source domains. The network utilizes knowledge distillation to learn the Fourier phase information to obtain internally-invariant features. It employs the local maximum mean discrepancy to align sub-domains to obtain mutually-invariant features. Various experiments show that the IFEGN model has better generalization ability than other models.

Abstracts:This article deals with an islanded three-phase four-wire battery-supported system with integration of solar and wind. Voltage and frequency of point of common coupling (PCC) are regulated by a battery-supported converter. On other side, voltage across stator of wind driven doubly fed induction generator (DFIG) is developed by stage-I rotor side converter control. After matching with grid code, the stator connects with PCC. Stage-II control is based on field-oriented control and it is used to activate power loop of controller. Moreover, this control offers required reactive power by DFIG and it also follows reference rotor speed. An improved three phase comb sliding mode control (CSM) regulates dc-link voltage of grid side converter and mitigates power quality issues. Dynamic performances during load variation and change in wind speed are handled by this advanced CSM controller. On other hand, double-stage photovoltaic (PV) power is fed to PCC via a voltage source converter. This VSC is controlled by dq-control, which regulates dc-link voltage of corresponding converter and also improves performance during solar dynamics. These results are displayed through system and controller behavior during wind variation, different insolation, load variation, and stator synchronization. Moreover, developed system is maintaining grid current total harmonic distortion (THD) below 2%.