Search Results (229)

A large number of sensors are required to collect information during the operation of nuclear power plants to ensure their absolutely safe operation. However, because of the unique nature of nuclear reactions, the physical environment of nuclear power production is prone to changes, leading to concept drift in the data collected by the sensors. Concept drift describes the phenomenon of sample distribution changing over time, which typically negatively impacts the model’s training and inference processes. We found that nongradual distribution changes could be guided by generating transitional intermediary distributions within the distribution, thereby achieving a gradual change process. Based on this, we designed a bridging distribution adaptive network (BDAN), which consisted of identical-depth TDoA (time difference of arrival) homomorphic backbone neural networks on both sides with a latent adaptive bridging module in the middle. By calculating the distribution differences over multiple timesteps, a series of bridge distributions were generated to guide the gradients in the latent space, updating the parameters of the latent adaptive guiding module in a directional manner and enabling the model to perceive nongradual distribution changes in the time domain. Experimental results showed that the BDAN outperformed the previous state-of-the-art benchmark methods by 5.6% in terms of mean squared error in the nuclear power data prediction task under concept drift, achieving the best fault prediction performance. Full article

In a real-world implementation, machine learning models frequently experience concept drift when forecasting the electricity load. This is due to seasonal changes influencing the scale, mean, and median values found in the input data, changing their distribution. Several methods have been proposed to solve this, such as implementing automated model retraining, feature engineering, and ensemble learning. The biggest drawback, however, is that they are too complex for simple implementation in existing projects. Since the drifted data follow the same pattern as the training dataset in terms of having different scale, mean, and median values, radian scaling was proposed as a new way to scale without relying on these values. It works by converting the difference between the two sequential values into a radian for the model to compute, removing the bounding, and allowing the model to forecast beyond the training dataset scale. In the experiment, not only does the constrained gated recurrent unit model with radian scaling have shorter average training epochs, but it also lowers the average root mean square error from 158.63 to 43.375, outperforming the best existing normalization method by 72.657%. Full article

This paper presents an advanced Controller Area Network (CAN) bus transceiver designed to enhance security using frame-level authentication with the concept of a nonphysical virtual auxiliary data channel. We describe the newly conceived transceiver security features and provide results concerning the design, implementation, fabrication and test of the transceiver to validate its functionality and robust operation in the presence of systemic error sources including Process, Voltage, and Temperature (PVT) variations. The virtual auxiliary channel integrates CAN frame authentication signatures into the primary data payload via phase modulation while also providing compatibility with existing CAN protocols, interoperability with non-enhanced systems and requiring no network or software modifications. Enhanced rail converters are designed to facilitate single-rail to dual-rail data conversion and vice versa, preserving phase information and minimizing phase errors across various nonideal effects such as frequency drift, Process, Voltage, and Temperature (PVT) variations, and cable phase mismatch. This ensures reliable data transmission and robust authentication in the presence of adversarial cyberattacks such as packet injection. The receiver recovers both the CAN frame data and the security signature, comparing the latter with an authorized signature to provide a real-time “GO/NO_GO” signal for verifying packet authenticity and without exceeding the CAN clock jitter specifications. Full article

Protocols to evaluate turfgrass quality rely on visual ratings that, depending on the rater’s expertise, can be subjective and susceptible to positive and negative drifts. We developed seasonal (spring, summer and fall) as well as inter-seasonal machine learning predictive models of turfgrass quality using multispectral and thermal imagery collected using unmanned aerial vehicles for two years as a proof-of-concept. We chose ordinal regression to develop the models instead of conventional classification to account for the ranked nature of the turfgrass quality assessments. We implemented a fuzzy correction of the resulting confusion matrices to ameliorate the probable drift of the field-based visual ratings. The best seasonal predictions were rendered by the fall (multi-class AUC: 0.774, original kappa 0.139, corrected kappa: 0.707) model. However, the best overall predictions were obtained when observation across seasons and years were used for model fitting (multi-class AUC: 0.872, original kappa 0.365, corrected kappa: 0.872), clearly highlighting the need to integrate inter-seasonal variability to enhance models’ accuracies. Vegetation indices such as the NDVI, GNDVI, RVI, CGI and the thermal band can render as much information as a full array of predictors. Our protocol for modeling turfgrass quality can be followed to develop a library of predictive models that can be used in different settings where turfgrass quality ratings are needed. Full article

In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected. Full article

Non-invasive crop analysis through image-based methods holds great promise for applications in plant research, yet accurate and robust trait inference from images remains a critical challenge. Our study investigates the potential of AI model ensembling and hybridization approaches to infer sorghum crop traits from RGB images generated via unmanned aerial vehicle (UAV). In our study, we cultivated 21 sorghum cultivars in two independent seasons (2021 and 2022) with a gradient of fertilizer and water inputs. We collected 470 ground-truth N measurements and captured corresponding RGB images with a drone-mounted camera. We computed five RGB vegetation indices, employed several ML models such as MLR, MLP, and various CNN architectures (season 2021), and compared their prediction accuracy for N-inference on the independent test set (season 2022). We assessed strategies that leveraged both deep and handcrafted features, namely hybridized and ensembled AI architectures. Our approach considered two different datasets collected during the two seasons (2021 and 2022), with the training set from the first season only. This allowed for testing of the models’ robustness, particularly their sensitivity to concept drifts, in the independent season (2022), which is fundamental for practical agriculture applications. Our findings underscore the superiority of hybrid and ensembled AI algorithms in these experiments. The MLP + CNN-VGG16 combination achieved the best accuracy (R² = 0.733, MAE = 0.264 N% on an independent dataset). This study emphasized that carefully crafted AI-based models applied to RGB images can achieve robust trait prediction with accuracies comparable to the similar phenotyping tasks using more complex (multi- and hyper-spectral) sensors presented in the current literature. Full article

This paper discusses the iteration of a seismic retrofit solution for shear-deficient end regions of 19 reinforced concrete (RC) moment-resisting frame (MRF) T-beams located in a 12-story RC MRF building in downtown Los Angeles, California. Local strengthening with externally bonded (EB) fiber-reinforced polymer (FRP) fabric was chosen as the preferred retrofit strategy due to its cost-effectiveness and proven performance. The FRP-shear-strengthening scheme for the deficient end-hinging regions of the MRF beams was designed and evaluated through large-scale cyclic testing of three replica specimens. The specimens were constructed at 4/5 scale and cantilever T-beam configurations with lengths of 3.40 m or 3.17 m. The cross-sectional geometry was 0.98 × 0.61 m with a top slab of 1.59 m in width and 0.12 m in thickness. Applied to these specimens were three different retrofit configurations, tested sequentially, namely: (a) unanchored continuous U-wrap; (b) anchored continuous U-wrap with conventional FRP-embedded anchors at the ends; and (c) fully closed external FRP hoops made of discrete FRP U-wrap strips and FRP through-anchors that penetrate the top slab and connect both ends of the FRP strips, combined with intermediate crack-control joints. The strengthening concept with FRP hoops precluded the premature debonding and anchor pullout issues of the two more conventional retrofit solutions and, despite a more challenging and labor-intensive installation, was selected for the in-situ implementation. The proposed hooplike EB-FRP shear-strengthening scheme enabled the deficient MRF beams to overcome a 30% shear overstress at the end-yielding region and to develop high-end rotations (e.g., 0.034 rad [3.4% drift] at peak and 0.038 rad [3.8% drift]) at strength loss for a beam that, otherwise, would have prematurely failed in shear. These values are about 30% larger than the ASCE 41 prescriptive value for the Life Safety (LS) performance objective. Energy dissipation achieved with the fully closed scheme was 108% higher than that of the unanchored FRP U-wrap and 45% higher than that of the FRP U-wrap with traditional embedded anchors. The intermediate saw-cut grooves successfully attracted crack formation between the strips and away from the FRP reinforcement, which contributed to not having any discernable debonding of the strips up to 3% drift. This paper presents the experimental evaluation of the three large-scale laboratory specimens that were used as the design basis for the final retrofit solution. Full article

Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings. Full article

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research. Full article

In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual–inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of mobile robots in indoor environments, we propose a visual SLAM perception method that integrates wheel odometry information. First, the robot’s body pose is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jacobian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relative pose residuals and their Jacobians for loop closure constraints. This approach solves the nonlinear optimization problem to obtain the optimal pose and landmark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorithm demonstrates significantly higher perception accuracy, with root mean square error (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE). The overall trajectory localization accuracy ranges between 5 and 17 cm, validating the effectiveness of the proposed algorithm. These findings can be applied to preliminary mapping for the autonomous navigation of indoor mobile robots and serve as a basis for path planning based on the mapping results. Full article

The key to endpoint control in basic oxygen furnace (BOF) steelmaking lies in accurately predicting the endpoint carbon content and temperature. However, BOF steelmaking data are complex and change distribution due to variations in raw material batches, process adjustments, and equipment conditions, leading to concept drift and affecting model performance. In order to resolve these problems, this paper proposes a dynamic soft sensor model based on an adaptive feature matching variational autoencoder (VAE-AFM). Firstly, this paper innovatively proposes an adaptive feature matching (AFM) method. This method utilizes the maximum mean discrepancy to calculate the values of the marginal and conditional distributions. Based on the discrepancy between these two values, a dynamic adjustment algorithm is designed to adaptively assign different weights to the two distributions. This approach dynamically and quantitatively evaluates and adjusts the relative importance of different distributions in the domain adaptation process, thereby enhancing the effectiveness of cross-domain data alignment. Secondly, a variational autoencoder (VAE) is employed to process the data, as the VAE model can capture the complex data structures and latent features in the steelmaking process. Finally, the features extracted by the VAE are processed with the adaptive feature matching method, thereby constructing the VAE-AFM dynamic soft sensor model. Experimental studies on actual BOF steelmaking data validate the efficacy of the offered approach, offering a reliable solution to the challenges of high complexity and concept drift in BOF steelmaking data. Full article

“Inoperative Education as Drift Between Eastern and Western Philosophies” expands upon recent notions of “inoperativity” in educational philosophy in the West through an encounter with the Taoist philosophy of Zhuangzi. Thus far, the concept of inoperativity has largely been inspired by Giorgio Agamben, the contemporary Italian critical theorist. Educational theory has taken up inoperativity in order to rethink the school as a space of free time, the student as a studier, and the gymnastic body, to name only a few. Through a comparative, philosophical analysis, inoperativity is rethought in a decisively Taoist register in order to generate three movements of inoperativity: drift as use, drift as use of uselessness, and drift as deactivation of learning (un-learning). Full article

Learning from multiclass imbalanced data streams with concept drift and variable class imbalance ratios under a limited label budget presents new challenges in the field of data mining. To address these challenges, this paper proposes an adaptive active learning method for multiclass imbalanced data streams with concept drift (AdaAL-MID). Firstly, a dynamic label budget strategy under concept drift scenarios is introduced, which allocates label budgets reasonably at different stages of the data stream to effectively handle concept drift. Secondly, an uncertainty-based label request strategy using a dual-margin dynamic threshold matrix is designed to enhance learning opportunities for minority class instances and those that are challenging to classify, and combined with a random strategy, it can estimate the current class imbalance distribution by accessing only a limited number of instance labels. Finally, an instance-adaptive sampling strategy is proposed, which comprehensively considers the imbalance ratio and classification difficulty of instances, and combined with a weighted ensemble strategy, improves the classification performance of the ensemble classifier in imbalanced data streams. Extensive experiments and analyses demonstrate that AdaAL-MID can handle various complex concept drifts and adapt to changes in class imbalance ratios, and it outperforms several state-of-the-art active learning algorithms. Full article

Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept drift will greatly affect the classification efficiency of the original classifier, that is, the old decision-making model is not suitable for the new data environment. Therefore, dealing with concept drift from changing data streams is crucial to guarantee classifier performance. Currently, most concept drift detection methods apply the same detection strategy to different data streams, with little attention to the uniqueness of each data stream. This limits the adaptability of drift detectors to different environments. In our research, we designed a unique solution to address this issue. First, we proposed a variance estimation strategy and a variance feedback strategy to characterize the data stream’s characteristics through variance. Based on this variance, we developed personalized drift detection schemes for different data streams, thereby enhancing the adaptability of drift detection in various environments. We conducted experiments on data streams with various types of drifts. The experimental results show that our algorithm achieves the best average ranking for accuracy on the synthetic dataset, with an overall ranking 1.12 to 1.5 higher than the next-best algorithm. In comparison with algorithms using the same tests, our method improves the ranking by 3 to 3.5 for the Hoeffding test and by 1.12 to 2.25 for the McDiarmid test. In addition, they achieve a good balance between detection delay and false positive rates. Finally, our algorithm ranks higher than existing drift detection methods across the four key metrics of accuracy, CPU time, false positives, and detection delay, meeting our expectations. Full article

An innovative solution for aluminium-glass partition walls that can withstand seismic actions without damage is presented. The key feature characterising the proposed innovation is a dissipative coupling between the components of the partition wall, i.e., the glass plates and the surrounding aluminium frame, accomplished through the interposition of high-damping rubber pads (HDRPs). Sliding mechanisms between glass panels and the aluminium frame are permitted through specific detailing solutions, which allow the partition wall to be unsensitive to the inter-storey drift imposed by the hosting structure. A detailed discussion of the system conception is illustrated, showing the main intermediate steps that led to the final solution. The implementation of a refined numerical model is illustrated, and its characteristic parameters are calibrated according to a set of experimental tests previously performed on materials and subcomponents. A numerical application to a case study consisting of a partition wall system installed within a three-storey building is provided to assess the performance of the proposed innovative solution under severe earthquakes. Full article