This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed incorporates sensor fusion by taking advantage the CNN structure to achieve higher and more robust accuracy. Both temporal spatial information raw data from multiple sensors is considered during training process CNN. Representative features can be extracted automatically signals. It avoids manual feature extraction or selection, which relies heavily on prior...

Recent works on domain adaptation reveal the effectiveness of adversarial learning filling discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from domains alone are not sufficient to ensure domain-invariance at most part latent space. Second, discriminator involved these methods can only judge real or fake with guidance hard label, while it is more reasonable use soft scores evaluate generated...

The degradation of bearings plays a key role in the failures industrial machinery. Prognosis is critical adopting an optimal maintenance strategy to reduce overall cost and avoid unwanted downtime or even casualties by estimating remaining useful life (RUL) bearings. Traditional data-driven approaches RUL prediction rely heavily on manual feature extraction selection using human expertise. This paper presents innovative two-stage automated approach estimate deep neural networks (DNNs). A...

Condition monitoring and fault diagnosis are important for maintaining the system performance guaranteeing operational safety. The traditional data-driven approaches mostly incorporate well-defined features methodologies such as supervised artificial intelligence algorithms. Prior knowledge of possible a large quantity labelled condition data needed. Besides, many require rebuilding or retraining original model to new conditions. present study proposes an intelligent approach that uses deep...

This paper presents the design, implementation, and evaluation of PyTorch distributed data parallel module. is a widely-adopted scientific computing package used in deep learning research applications. Recent advances argue for value large datasets models, which necessitates ability to scale out model training more computational resources. Data parallelism has emerged as popular solution thanks its straightforward principle broad applicability. In general, technique replicates on every...

Robots are increasingly operating in indoor environments designed for and shared with people. However, robots working safely autonomously uneven unstructured still face great challenges. Many modern wheelchair accessibility mind. This presents an opportunity wheeled to navigate through sloped areas while avoiding staircases. In this paper, we present integrated software hardware system autonomous mobile robot navigation environments. modular reusable framework incorporates capabilities of...

Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and LCC. Convolutional neural networks (CNNs) improve performance hyperspectral image with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they ineffective in solving spatial relationships. To address issue insufficient...

In a distributed stream data processing system, an application is usually modeled using directed graph, in which each vertex corresponds to source or unit, and edges indicate flow. this paper, we propose novel predictive scheduling framework enable fast processing, features topology-aware modeling for performance prediction scheduling. For prediction, present method accurately predict the average tuple time of given solution, according topology graph runtime statistics. scheduling, effective...

This letter presents a novel and integrated framework for Next-Best-View (NBV) selection toward autonomous robotic exploration in indoor environments. A topological map, named semantic road map (SRM), is proposed to represent the explored environment during exploration. The basic concept of SRM construct graph with nodes containing states edges satisfying collision-free constraints. Especially, integrates both structure information environment, which possesses beneficial properties using It...

Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance. However, the availability data is limited due significant human resources and time costs labeling data. Unsupervised classification thus received increasing attention. In this paper, we propose novel unsupervised framework based on contrastive method transformer model The...

In structural health monitoring (SHM), measuring and evaluating dynamic responses are critical for safety management of civil infrastructures. Particularly, online forecasting the under extreme external loading conditions (e.g., earthquakes) takes a significant role in SHM to provide early warning ensure safe operation. practice, complex causality intrinsic interactions between seismic excitation response make it challenging establish reliable predictive scheme. The present paper proposes...

Bitcoin (BTC)-the first cryptocurrency-is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due arbitrary nature, thus limiting use skepticism among businesses and households. However, is vast scope of machine learning approaches predict future prices precisely. One the major problems with previous research on BTC price predictions that they primarily empirical lacking sufficient analytical support back...

An Internet of Things (IoT) platform with capabilities sensing, data processing, and wireless communication has been deployed to support remote aquatic environmental monitoring. In this paper, the design development an IoT multiple Mobile Sensor Nodes (MSN) for spatiotemporal quality evaluation surface water is presented. A survey planner proposed distribute Sampling Locations Interest (SLoIs) over study area generate paths MSNs visit SLoIs, given limited energy time budgets. The SLoIs are...

Drug–drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such critical threat, many studies conducted clarify mechanism underlying each DDI, based on which alternative therapeutic strategies successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, highly dependent reliable DDI data set with clear mechanistic information....

Software framework serves as a skeleton for the offline data processing software many high energy physics (HEP) experiments. The event management, including model (EDM), transient store and input/output, implements core functionalities of framework, has great impact on performance entire software. Future HEP experiments are generating increasingly large amounts data, bringing challenges to processing. To address this issue, common management system that supports efficient parallelized...

Abstract Bridge structural health monitoring (SHM) measures the real-time responses of bridges via instrumented sensors. This paper focuses on bridge displacement reconstruction at unmeasured locations interest (LoIs) using measurements from Increasing number sensors can capture more information. However, considering complex structure and scale bridges, minimizing selecting critical sensing (CSL) to place for are significant in SHM. To achieve efficient SHM under limited sensors, this...

This paper presents a new technique for communication over channels with memory where the channel state is unknown at transmitter and receiver. A deep interleaver combined successive decoding decomposes into an array of parallel memoryless on which conventional coding system can operate individually. The problems joint estimation thus are separated without loss capacity. achieves capacity so may be used to evaluate capacities different channels. general information-theoretic framework...