Abstract With the aim of identifying possible mechanical faults in unmanned aerial vehicle (UAV) rotors during operation, this paper proposes a method based on interval sampling reconstruction vibration signals and one-dimensional convolutional neural network (1D-CNN) deep learning. Firstly, experiments were designed to collect acceleration UAV working at high speed under three states (normal, rotor damage by varying degrees, crack different degrees). Then, considering powerful feature...

The increase in the number of cars on road has led to frequent traffic accidents, some which may be due human factors such as poor driving habits. In order reduce there is a need accurately analyze driver's behavior and identify actual state motion car. A method based one-dimensional convolutional neural network determine condition vehicle proposed. Vibration acceleration indications for four different fuel-powered traveling states (constant speed, acceleration, deceleration, deceleration)...

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by pattern recognition method, which makes it difficult solve end-to-end problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience technological change, and its application automotive field also many applications, such as image recognition, language processing, assisted driving. this paper, a one-dimensional...

Abstract The application of artificial intelligence methods in fault diagnosis is becoming more and extensive, exploring researching intelligent for automobile engines also a hot spot the field automotive engineering. Different machine learning have their own advantages disadvantages. By extracting different characteristic parameters optimizing combination multiple algorithms, faster stable results can be achieved, so that faults discovered repaired time. Aiming at potential fluctuation...

Seeking a straightforward and efficient method to predict expressway traffic noise, this study selected three segments in Guangdong Province, China conducted noise monitoring at ten different sites along these expressways. Data analysis revealed that the mean sound levels standard deviations were significantly positively negatively correlated with volume, respectively, frequency distribution of closely resembled normal distribution. A probability prediction model for based on distribution,...

This paper focuses on the distribution of passenger flow in Huoying Station, Line 13 Beijing subway system. The transformation measures taken by since operation are firstly summarized. Then authors elaborate facilities and equipment this station, especially node layout field. An optimization scheme is proposed to rapidly distribute Station adjusting time escalator direction Xizhimen. adopt Queuing theory Anylogic simulation software simulate original optimized schemes flow. results indicate...

In recent years, artificial intelligence has developed rapidly, and equipment greatly facilitated people's lives. During the operation of intelligent devices, accurate environmental perception is great importance. To provide more electric balance vehicle instructions, we collect vibration signal data in different driving environments, machine learning (ML) methods are adopted to extract analyse characteristics signal. Then, classification road type realized. study, a Bluetooth wireless...

Engine vibration signals are easy to be interfered by other noise, causing feature that represent its operating status get submerged and further leading difficulty in engine fault diagnosis. In addition, most of the utilized verify extraction method derived from numerical simulation, which far away real signals. To address these problems, this paper combines priority signal sparse decomposition finite element model research a novel for misfire Firstly, order highlight resonance regions...

By virtue of their convenience, reasonable cost and high efficiency, Unmanned Aerial Vehicles (UAVs) have been widely applied in every aspect life. However, complicated operating conditions are prone to causing mechanical failure UAVs, especially the rotor fault. Therefore, a novel dual attention convolutional neural network based on multisensory frequency features is proposed for UAV fault diagnosis this study. Firstly, according collected acceleration vibration signals rotors, time...

Abstract With the widespread application of UAV (UAV) in various fields, more and attention has been paid to operation status monitoring fault diagnosis UAV. During use UAV, motors, blades, connectors other components may inevitably experience wear, fatigue, breakage, which are difficult directly monitor through sensors. Therefore, a identification method based on one-dimensional convolutional neural network (1D-CNN) is proposed provide ideas for research mechanical A Bluetooth wireless...

The vibration modulation of the localized faults sun gears is complicated because structure and motion features planetary gearboxes. It challenging to completely determine mechanism. To address this issue, influences fluctuations in speed on factors that affect like transfer path function, time-varying projection meshing force were studied. Improved amplitude frequency models then established by combining affecting considering caused gears. Regarding gears, we concluded both fault feature...

The paper focuses on two kinds of rotating machinery, miniature table drilling machine and automobile engine, as the research object. Traditional learning has need for manual feature extraction, is very dependent expert diagnostic experience expertise, but also disadvantages low accuracy, timeliness, efficiency, etc. For traditional machinery fault diagnosis method more based model, this puts forward a one-dimensional convolutional neural network-based identification method. According to...

Abstract. In recent years, the quick upgrading and improvement of SAR sensors provide beneficial complements for traditional optical remote sensing in aspects theory, technology data. this paper, Sentinel-1A data GF-1 were selected image fusion, more emphases put on dryland crop classification under a complex planting structure, regarding corn cotton as research objects. Considering differences among various fusion methods, principal component analysis (PCA), Gram-Schmidt (GS), Brovey...

Support vector machine(SVM) got a good classification ability, but the recognition accuracy was easily affected by value of kernel parameters. Aiming at this problem, sparse autoencoder(SAE) has its unique advantages in dealing with complex structured data, so combination autoencoder and support machine(SAE+SVM) proposed on fault identification vehical automatic transmission. Firstly, eight indicators such as engine speed, throttle opening, water temperature are collected from acquisition...

Redundancy is commonly designed as a critical unit in complex engineering systems to improve their reliability. Among the redundant structures, k-out-of-n structure most useful. In this paper, reliability modeling and maintenance decisionmaking are investigated for k-out-of-n: F subsystem. Firstly, Weibull distribution used describe component degradation process, influence of external damage shock on process considered. Secondly, component-level model system-level system established. The...

Living off the Land (LotL) attacks have gained attention in recent years because they are sneaky. These exploit legitimate tools, scripts, and system permissions, making them hard to detect track. As a result, defense costs increase. However, most research focuses on detecting classifying malware rather than LotL attacks. This study aims explore novel approach that combines machine learning, deep natural language processing methods for We propose learning detection framework called LOLWTC....