Practical advancements in perceiving driving obstacles in adverse weather conditions are crucial to guaranteeing safe autonomous driving.
A machine-learning-driven wrist-worn device's design, architecture, implementation, and thorough testing are elaborated in this work. The newly developed wearable device, designed for use in the emergency evacuation of large passenger ships, enables real-time monitoring of passengers' physiological state and facilitates the detection of stress. Through a suitably prepared PPG signal, the device yields critical biometric data, namely pulse rate and oxygen saturation, complemented by a streamlined single-input machine learning approach. Employing ultra-short-term pulse rate variability, the embedded device's microcontroller now hosts a stress detection machine learning pipeline, successfully implemented. Therefore, the smart wristband demonstrated has the aptitude for real-time stress identification. Leveraging the publicly accessible WESAD dataset, the stress detection system's training was executed, subsequently evaluated through a two-stage testing procedure. The lightweight machine learning pipeline, when tested on a yet-untested portion of the WESAD dataset, initially demonstrated an accuracy of 91%. MDC A subsequent validation exercise, carried out in a dedicated laboratory, involved 15 volunteers exposed to established cognitive stressors while wearing the smart wristband, resulting in a precision score of 76%.
While feature extraction is crucial for automatically recognizing synthetic aperture radar targets, the increasing complexity of recognition networks obscures the features within the network's parameters, hindering the attribution of performance. Employing a profound fusion of an autoencoder (AE) and a synergetic neural network, we introduce the modern synergetic neural network (MSNN), which restructures the feature extraction process into a prototype self-learning algorithm. Nonlinear autoencoders, particularly those structured as stacked or convolutional autoencoders, are shown to converge to the global minimum when utilizing ReLU activation functions, provided their weights can be partitioned into sets of M-P inverse tuples. Therefore, MSNN is capable of utilizing the AE training process as a novel and effective self-learning mechanism for identifying nonlinear prototypes. Subsequently, MSNN elevates learning efficiency and robustness by guiding codes to spontaneously converge on one-hot representations utilizing the principles of Synergetics, in place of loss function adjustments. On the MSTAR dataset, MSNN exhibits a recognition accuracy that sets a new standard in the field. The feature visualization showcases that MSNN's strong performance originates from its prototype learning strategy, which focuses on extracting features not represented within the dataset itself. MDC New samples are reliably recognized thanks to these illustrative prototypes.
A critical endeavor in boosting product design and reliability is the identification of failure modes, which also serves as a vital input for selecting sensors for predictive maintenance. Acquiring failure modes often depends on expert knowledge or simulations, both demanding substantial computing power. Due to the rapid advancements in Natural Language Processing (NLP), efforts have been made to mechanize this ongoing task. Nevertheless, the process of acquiring maintenance records detailing failure modes is not just time-consuming, but also remarkably challenging. By using unsupervised learning methodologies, including topic modeling, clustering, and community detection, the automatic processing of maintenance records can facilitate the identification of failure modes. In spite of the rudimentary nature of NLP tools, the imperfections and shortcomings of typical maintenance records create noteworthy technical challenges. This paper advocates for a framework employing online active learning to extract failure modes from maintenance records to mitigate the difficulties identified. Human involvement in the model training stage is facilitated by the semi-supervised machine learning technique of active learning. This paper's hypothesis focuses on the efficiency gains achievable when a subset of the data is annotated by humans, and the rest is then used to train a machine learning model, compared to the performance of unsupervised learning models. Analysis of the results reveals that the model was trained using annotations comprising less than ten percent of the entire dataset. With an F-1 score of 0.89, the framework identifies failure modes in test cases with 90% precision. The paper also highlights the performance of the proposed framework, evidenced through both qualitative and quantitative measurements.
Interest in blockchain technology has extended to a diverse array of industries, spanning healthcare, supply chains, and the realm of cryptocurrencies. Although blockchain possesses potential, it struggles with a limited capacity for scaling, causing low throughput and high latency. Several options have been explored to mitigate this. Blockchain's scalability predicament has been significantly advanced by the implementation of sharding, which has proven to be one of the most promising solutions. Two primary categories of sharding encompass (1) sharding-integrated Proof-of-Work (PoW) blockchain systems, and (2) sharding-integrated Proof-of-Stake (PoS) blockchain systems. Excellent throughput and reasonable latency are observed in both categories, yet security concerns persist. The focus of this article is upon the second category and its various aspects. Within this paper, we first present the key components which structure sharding-based proof-of-stake blockchain protocols. To begin, we will provide a concise introduction to two consensus mechanisms, Proof-of-Stake (PoS) and Practical Byzantine Fault Tolerance (pBFT), and evaluate their uses and limitations within the broader context of sharding-based blockchain protocols. Subsequently, a probabilistic model is presented for assessing the security of these protocols. More explicitly, we compute the probability of a faulty block being created and evaluate security by calculating the expected time to failure in years. A network of 4000 nodes, partitioned into 10 shards with a 33% resiliency level, exhibits a failure period estimated at approximately 4000 years.
The geometric configuration employed in this study is defined by the state-space interface between the railway track (track) geometry system and the electrified traction system (ETS). Primarily, achieving a comfortable drive, smooth operation, and full compliance with the Environmental Testing Specifications (ETS) are vital objectives. Direct methods of measurement were employed during interactions with the system, specifically concerning the fixed-point, visual, and expert-based evaluations. In particular, the utilization of track-recording trolleys was prevalent. The integration of certain techniques, such as brainstorming, mind mapping, the systems approach, heuristics, failure mode and effects analysis, and system failure mode effects analysis, was also a part of the subjects belonging to the insulated instruments. The three principal subjects of this case study are represented in these findings: electrified railway lines, direct current (DC) systems, and five specific scientific research objects. MDC This scientific research work on railway track geometric state configurations is driven by the need to increase their interoperability, contributing to the ETS's sustainable development. This research's conclusions unequivocally demonstrated the validity of their assertions. In order to first estimate the D6 parameter of railway track condition, the six-parameter defectiveness measure D6 was meticulously defined and implemented. This approach not only improves preventative maintenance and decreases corrective maintenance but also innovatively complements the existing direct measurement method for railway track geometric conditions, further enhancing sustainability in the ETS through its interaction with indirect measurement techniques.
Currently, 3D convolutional neural networks (3DCNNs) are a frequently adopted method in the domain of human activity recognition. Despite the differing methods for recognizing human activity, we introduce a new deep learning model in this work. The core mission of our work is to augment the standard 3DCNN, and we propose a novel model which seamlessly blends 3DCNN with Convolutional Long Short-Term Memory (ConvLSTM) units. Our findings, derived from trials conducted on the LoDVP Abnormal Activities, UCF50, and MOD20 datasets, unequivocally showcase the 3DCNN + ConvLSTM method's superior performance in human activity recognition. Our model, tailored for real-time human activity recognition, is well-positioned for enhancement through the inclusion of supplementary sensor data. In order to provide a complete evaluation of our 3DCNN + ConvLSTM approach, we scrutinized our experimental results on these datasets. Employing the LoDVP Abnormal Activities dataset, we attained a precision rate of 8912%. Using the modified UCF50 dataset (UCF50mini), the precision obtained was 8389%. Meanwhile, the precision for the MOD20 dataset was 8776%. Our findings, resulting from the synergistic use of 3DCNN and ConvLSTM layers, establish an improvement in human activity recognition accuracy, implying promising real-time performance of the proposed model.
Despite their reliability and accuracy, public air quality monitoring stations, which are costly to maintain, are unsuitable for constructing a high-spatial-resolution measurement grid. Air quality monitoring, employing low-cost sensors, is now facilitated by recent technological advancements. Portable, affordable, and wirelessly communicating devices stand as a highly promising solution within hybrid sensor networks. These networks integrate public monitoring stations alongside numerous inexpensive devices for supplementary measurements. However, low-cost sensors are impacted by both weather and the degradation of their performance. Because a densely deployed network necessitates numerous units, robust, logistical calibration solutions become paramount for accurate readings.