Multimodal object detection has attracted great attention in recent years since the information specific to different modalities can complement each other and effectively improve accuracy stability of model. However, compared processing inputs from a single modality, fusing multiple significantly increase computational complexity model, thus impairing its efficiency. Therefore multi-modal fusion module needs be carefully designed enhance performance model while keeping consumption low. In...

The integrity of geomagnetic data is a critical factor in understanding the evolutionary process Earth's magnetic field, as it provides useful information for near-surface exploration, unexploded explosive ordnance detection, and so on. Aimed to reconstruct undersampled data, this paper presents reconstruction approach based on machine learning techniques. traditional linear interpolation approaches are prone time inefficiency high labor cost, while proposed has significant improvement. In...

Surface defects directly affect the mechanical properties of industrial strip steel products. To evaluate integrity surface, a channel-wise global Transformer-based dual-branch network (CGTD-Net) for surface defect detection, dubbed CGTD-Net, is proposed in this study. First, images are preprocessed using saturation adjustment and random flipping strategies to remove unnecessary background information improve generalization. Second, Swin Transformer employed at end backbone negative impacts...

The intelligent fault diagnosis powered deep learning (DL) is widely applied in various practical industries, but the conventional methods cannot fully juggle manifold structure information with multiple-order similarity from massive unlabeled industrial data. Thus, a new Multiple-Order Graphical Deep Extreme Learning Machine (MGDELM) algorithm for unsupervised (UFD) of rolling bearing proposed this study. Specifically, developed MGDELM mainly contains two parts: 1) one feature extraction,...

Detection of the liquefied natural gas (LNG) leakage attracts increasing attention for preventing environments and governments from severe pollution economic loss. Existing frameworks take advantage stationary surveillance thermal cameras to detect LNG leakage, which comprises background subtraction classification. However, these methods are limited in rural areas due lack sensitivity accuracy. In this article, a generalized framework, i.e., tensor-based detection (TBLD), is proposed area...

Automated valuation model (AVM) is a mathematical program to estimate the market value of real estates based on analysis locations, neighborhood characteristics, and relevant property characteristics. The most common AVMs employed by appraisal industry are multiple regression analysis. Other analytic tools such as statistical learning fuzzy algorithms have become more popular because increasing capability collecting high volume data advancement machine learning. new thus becomes possible...

A miniature magnetic gradient tensor system may be used in small spacecraft for mineral prospecting and detection of unexploded explosive ordnance. The consists a magneto-inductive (MI) sensors array, signal extraction circuit, microcontroller. full-tensor configuration utilizes four MI arranged planar cross structure is employed to minimize orientation orthogonality errors. system's performance was checked by data validation anomaly detection. Numerous experimental results demonstrate that...

In‐service bridge structural performance analysis and prediction are usually complicated challenging because of many unknown uncertain factors. Contrary to the traditional appearance inspections load tests, health monitoring (SHM) can provide a perspective for online analysis, prediction, early warning. So far, SHM has been widely used in structures, lot data have also collected. However, existing studies focus on some independent unsystematic methods, which hard use engineering applications...

Wind power is one of the majority sustainable sources in world. However, wind turbine facing highly possible blade frozen event on blade. While numerous ice detection methods have been reported, with data acquired by SCADA (supervisory control and acquisition) system has not investigated yet. Thus, this study we propose a assessment framework using an ITL (inductive transfer learning) approach. We applied different machine learning to problem, which fully-connected neural networks (FNN)...

The proton precession magnetometer (PPM) is a commonly used device to measure the varying magnetic field. Since frequency of PPM sensing free induction decay (FID) signal proportional field, signal-to-noise ratio (SNR) always critical issue that influences measurement accuracy severely due external interferences such as harmonic noise and random noise. In this study, boost SNR FID signal, an effective filtering algorithm based on time-frequency peak (TFPF) analyzed with pseudo-Wigner-Ville...

A leak detection and repair survey (LDAR) is essential to ensure a reliable safe liquefied natural gas (LNG) supply. Modern LDAR systems deploy numerous fixed thermal imaging devices automatically monitor the risk of potential leaks empowered by computational intelligence frameworks. Existing frameworks employ either background subtraction-based (BGS-based) or deep neural network-based (DNN-based) for LNG from images. However, BGS-based feature high sensitivity perceive emissions with low...

The free induction decay (FID) transversal signal is always employed by a proton precession magnetometer (PPM) to evaluate the time-domain geomagnetic field. Nevertheless, signal-to-noise ratio (SNR) an important factor that severely affects detection accuracy of magnetic field due uncontrollable interference sources, including random noise and power frequency noise. In this study, aiming boost SNR FID signal, novel filtering algorithm based on prewhiten (PW) strategy proposed PW was...

Concealed metallic object detection is one of the critical tasks for any security system. It has been proved that different objects have their own magnetic fingerprints, which are a series anomalies determined by shape, size, physical composition, etc. This study addresses design low-cost power system according to response field. The consists three anisotropic magnetoresistance (AMR) sensor arrays, circuits, and microcontroller. A gradient full-tensor configuration, utilizing four AMR...

Automatic Identification System (AIS) was initially developed for tracking ships and avoiding collisions. As numerous small satellites were launched in recent years, millions of AIS messages are captured by these satellite providers every day. The massive amount data allow shipping firms port operators to better predict vessels' movement. In this study, a computational framework is future trajectories destinations vessels applying deep learning the containing self-reporting positioning data....