Complicated Natural Environment Research Articles

An Advanced Motion Prediction for Targeted Small Moving Objects

For autonomous robots, small target motion detectionin complicated natural environments is an extremely challenging task. The extremely efficient visual systems of insects detect mates and trackprey, though the target occupy minute degrees of its visual field. Small target motion relies on the excellent sensitivityof specialized neurons, called Small TargetsMotion Detector (STMD). The already existing models based on STMD, hugely depend on visual contrast butshows poor performance in complicated natural environments, whereas small targets commonly exhibit highly low contrast against its backgrounds nearby. Here, we frame an attentionpredictionguided visual system to break the limitation. It hasfive main subsystems, a) Acquisition Moduleb) Process Modulec) Attention Module d) Action Module e) Log Module. These Modules Capture the image when an abnormal motion occurs and then enhanced the image, stores. The image gets validated and sends to the relevant destination receiver.detailed experiments on various real-world datasets prove the efficiency and majority of this system in the detection ofminute, lowcontrast movements of targets against complicated natural environment. This method is more efficient andproductive because the recording and storage take place when the abrupt changes occur. It is more cost effective.

Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods

• A systematic review of orchard yield prediction and estimation studies. • Summary of input features and modeling algorithms for indirect yield prediction. • Summary of applied platforms and counting strategies for direct yield estimation. • Application of engineering technologies needs coordinating with agronomic. • Recommendations of promising technologies to be use for different planting areas. Orchard pre-harvest yield data is important for fruit growers, which can be used for economic benefit evaluation, management mode adjustment and so on. However, traditional manual operation by sampling estimation is quite an onerous and time-consuming task. The main approach of automatic yield monitoring is by establishing multi-information comprehensive prediction systems or using intelligent equipment. A review is performed to investigate and analyze the past 12 years (from 2010 to 2021) of research work regarding orchard yield prediction and estimation. According to our investigation, the most widely used input features in yield prediction systems are various vegetation indices information of plants, while machine learning is the most modeling method applied. In addition, machine vision systems based on image processing and deep learning have been developed rapidly in the field of agriculture and is also widely applied in orchard yield prediction. Finally, major challenges and countermeasures for orchard yield prediction and estimation are discussed. Complicated natural environments with inconsistent horticultural management practices are still critical challenges for large scale commercialization of researched yield prediction methods. Therefore, combination of engineering technology optimization and standardization of agronomic management practices are necessary to realize automatic operation in complex agricultural fields. The review is intended to summarize the development of yield prediction and estimation technologies and provided suggestions for orchard intelligent management.

Organics-Coated Nanoclays Further Promote Hydrate Formation Kinetics.

A deeper understanding of the kinetics of CO2 hydrate formation in the complicated natural environment is required for its enhanced sequestration. Here we found that the organics-coated nanoclays enriched in the natural sediments could contribute to a 92% decline of the induction time of hydrate formation. This can be ascribed to the negative charges carried by the organics and the resulting ordered arrangement of the surrounding water molecules. It was, for the first time, proposed that the abundant functional groups from the coating organics could function as a protecting crust enabling the system more resistant to the acidification potentially upon the CO2 sequestration; besides, the negative charges could help prevent the deposition of the nanoclays via interparticle repulsive forces. These would consequently secure their sustainable promoting effect on hydrate formation. The findings suggest the deposits of gas hydrate a kinetically promising geological setting for the CO2 sequestration via forming hydrates.

Smart Cellulose-Based Electronic Skin with Humidity-Driven Dynamic Performance

Dynamic soft materials are requisite components not only for the survival of organisms in complicated natural environments but also for the advancement and operation of smart devices. A recently published article ( Zhao et al. ) reports new tunable polymeric materials exhibiting excellent reversible properties when the network hydration state is varied.

Bioinspired Photodetachable Dry Self-Cleaning Surface.

Geckos have adapted to the complicated natural environment with its excellent climbing ability. Current artificial gecko-inspired synthetic adhesives (GSAs) mimic gecko's attach-detach mechanism by creating anisotropic and hierarchical structures. Easy detachment and high self-cleaning capability are still the unsolved problems in GSAs. This study presents an unprecedented photodetachable mechanism of making bioinspired smart surfaces utilizing carbon dot (CD)-doped polydimethylsiloxane (PDMS) composites. Under ultraviolet (UV) irradiation, it could be triggered up to 80.46% reduction of adhesion force between PDMS-CDs bioinspired surfaces and contaminating particles. A load-drag-pull (i.e., LDP) test mimicking gecko's locomotion was adopted to test the dry self-cleaning capabilities of these bioinspired surfaces, where the falling rate of the model contaminates (PS micropellets; average size in diameter ∼8 μm) can reach up to 54.83% after seven repeated steps under UV irradiation. The significantly improved dry self-cleaning capability is attributed to the photothermal effect of CDs inside the PDMS matrix. The mechanism proposed in this work will find its applications in the realms of climbing robots, space adhesive devices, and self-cleaning, advanced gripping technologies for pick and place or assembly.

Shadow verification–based waterline detection for unmanned surface vehicles deployed in complicated natural environment

Boundary separation of operational regions would be helpful for unmanned surface vehicles deployed in dynamic outdoor environments. However, the feasibility and accuracy of current obstacle avoidance methods based on conventional optical images are comparatively poor for unmanned surface vehicle applications, with complicated natural illumination as one of the main sources of error. In this article, a new optical waterline detection method is proposed by combining shadow verification and global optimization (energy minimization). The method is then validated using an actual unmanned surface vehicle operating in outdoor environments. First, the basic principles of intrinsic image are introduced and then employed to evaluate the threshold for background segmentation so that the influence of complicated intensity distribution on the original image is reduced. The properties of different types of shadows are compared, and the basic principles of shadow verification are used to classify the different object regions. Subsequently, the intensity contrast between the shadow and non-shadow regions is used to measure the waterline position based on the relationship between the illumination and the shadow formation. Furthermore, the waterline detection problem is transformed into a problem involving the optimization of energy (minimization) described using differential equations. Finally, experiments are conducted with a series of practical images captured by the unmanned surface vehicle. The experimental results demonstrate the feasibility and robustness of the proposed method.

Airport pavement responses obtained from wireless sensing network upon digital signal processing

The intelligent measurement-based technology of Fibre Bragg Grating (FBG) sensor was proposed for establishing a wireless sensing network of airport asphalt pavement for structural information monitoring at Chongqing International Airport. Given the complicated natural environment and aircraft loadings, obtaining key information (i.e. dynamic response amplitude, duration and distribution, aircraft driving offset, speed, etc.) from the monitoring database is a crucial prerequisite to analyse pavement performance. Moving average filter and Fast Fourier Transform (FFT) filter were utilised to process and acquire vital dynamic responses information. The interval of the wandering determination sensors within the aircraft loading determination matrix was discussed and designed based on signal sampling theorem and three-dimensional finite element analysis. The batch program for aircraft taxiing speed calculation was compiled upon signal correlation method which was compared and verified using time difference method. Results show that the allowed interval of the wandering determination sensors is related to the aircraft gear sizes and sensor sizes simultaneously. The computed taxiing speeds using signal correlation method, which reveals higher calculation efficiency, are in good agreement with the results that employ the time difference method. The duration of dynamic response of aircraft shows an inverse proportional relationship with speed, while the forward and backward propagation distances of stress and strain are irrelevant to speed. Once the monitoring system could integrate all critical features, it becomes more useful for pavement health monitoring.

Management requiring conditions when manually falling trees

Manual tree fallers have to recognize potential hazards in complicated natural environments in order to develop falling plans that mitigate risk. This study developed a list of conditions that a manual tree faller could see before cutting into a tree and that had to be managed in order to fall the tree safely; these conditions were termed management requiring conditions (MRC). Fallers reported the MRCs and covariates while working and GLMM models were used to analyse the resulting count data. Intra faller correlation was found to be significant, and tree diameter and the ratio of dead trees to live trees were significant in explaining MRC frequency. Only 1.8% of the MRCs reported in this study had higher severity levels of two or three; however, this resulted in a faller potentially experiencing a higher severity MRC on average once every two days. The lack of correlation between slope and MRC frequency indicates that faller certification systems could be simplified; however, further study is required.

Parameter estimation for propagation along random rough surface by using line of sight data

This paper is concerned with a numerical procedure to estimate two parameters, amplitude modification factor α and propagation order of distance β, for electromagnetic (EM) propagation in complicated natural environments such as random rough surface (RRS). These two parameters are key parameters when we simulate field distributions along various types of RRS based on 1-ray model. We assume that the former parameter α can be evaluated by the visual planar angles of illuminated lines in case of 1D RRS and by the visual solid angles of illuminated planes in case of 2D RRS. We also assume that the latter parameter β can be estimated not only by base station (BS) antenna height, similar to Okumura-Hata model simulating EM propagation in urban and suburban areas, but also by mobile station (MS) antenna height. In order to demonstrate validity of the proposed parameter estimation, we compare the numerical field distributions obtained by the 1-ray model using estimated two parameters with those computed by discrete ray tracing method (DRTM) which is an effective EM field solver. It is shown that both numerical results are in good agreement.