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ABSTRACT In order to realise automatic row alignment for sugar beet harvesting, and to improve the efficiency of sugar beet combine harvesters, it is essential to be able to detect the positions of sugar beet rows. In this paper, we consider beet harvesting as a scenario, and an extraction algorithm for beet crop rows is proposed based on videos of beets collected with a monocular camera. We focus on the characteristics of the root and stem features, which are relatively difficult to distinguish from the surrounding environment after cutting the tops and beating the leaves of sugar beet, and a crop row extraction algorithm is designed in which threshold segmentation is carried out based on histogram equalisation and MM-Filter (median filtering and minimum area filtering) is applied to remove noise. The proposed algorithm retains more complete feature information and has better denoising effect than the traditional algorithm, and the processing time is only 11.751 ms, a value 6.9% lower than for the alternative approach. Our algorithm is feasible in terms of recognition and extraction, and can provide a theoretical basis for the construction of an automatic row-to-row control system for a sugar beet combine harvester. It can also provide technical support for the design of an automatic sugar beet combine harvester.

ABSTRACT In complex environments, beet harvesters vibrate strongly under the influence of multiple sources of excitation. The modal constraints of the harvester's frame were obtained using modal simulation, and the accuracy of the finite element model was verified through SIMO modal testing. Additionally, field experiments were conducted to collect the vibration signals of the harvester under various conditions. Time-domain analysis revealed that the RMS value of the frame's Z-axis acceleration was highest in sugar beet fields and lowest on unpaved roads. There is a correlation between the operation of working components and changes in amplitude. Frequency domain analysis determined that the main vibration frequency of the frame was in the range of 0–75 Hz, and the operating frequency of the engine (35 Hz) and the power input shaft (12.7 Hz) excites the constrained modal of the frame, which may lead to resonance. Integrating the results of the modal response and vibration testing provides a more comprehensive approach to studying the vibration characteristics of agricultural machinery.

ABSTRACT As weeds cannot be eradicated in corn fields, a crab claw toe was used as the research object and combined with the agronomic requirements of corn fields to design a bionic weed claw suitable for corn fields. The contour curve of the crab claw toe was extracted and fitted through the Matlab image processing technology and fitting tool to establish a bionic claw finger 3D model using Solid works. According to the mechanical soil dynamics theory of dynamics analysis, the root–soil aggregates are easily broken, loosened, and drained under the action of the bionic claw fingers. Based on single image inverse reconstruction technology, the weed root system–soil aggregates discrete meta-model was established. Recur Dyn was used in the bionic weed claw kinematics simulation to verify the reliability of the movement of the mechanism and to realise the expected action of the weeding operation. We analysed the movement speed, displacement, and trajectory of soil particles in different depth layers through the joint simulation and comparison test. Using the mechanism of weeding and resistance reduction of the bionic weeding claw, under the same working conditions, the average resistance reduction rate of the bionic claw finger compared with the flat claw finger in the grasping process was 16.4%. The resistance of grass roots was reduced 23.3%, making it easier to pull out the more intact grass roots.

For C-V2X systems, the selection of the best beam in a real-time mode becomes an increasingly critical and yet open topic. Most of the existing approaches adopt either conventional ARIMA or ANN. Recently, there has been research on adopting sequence-to-sequence (Seq2Seq) predictors with attentions to extract time series features and emphasis on critical information to achieve data prediction. In this paper, a Seq2Seq predictor integrating with a Transitional Matrix based Hard attention is presented and validated through an artificial test dataset with predefined transitional states. At first, the transition probability matrix is generated from previous time series data and fed into the “hard” attention module of Seq2Seq predictor to determine the weights during the training phase. Secondly, the presented Seq2Seq predictor was implemented and adopted to predict the best beams of a C-V2X beamforming selector built up by the authors. Experiments were conducted and captured data were used to validate the performance of the predictor. When compared with baseline models, the presented predictor can achieve an enhanced prediction accuracy in a gain of 10-12%.

Leakage incidents on LNG bunker vessels will result in a serious degree of hazard. This paper investigates typical high-risk scenarios such as hose ruptures and valve joint leakages. The consequences of an LNG leakage accident during the simultaneous operation of a bunker vessel and a container carrier are simulated using the FLACS software, and the dispersion range of the combustible vapor cloud is quantitatively analyzed under both ballast and laden conditions. Under the ballast condition, the diffusion range of the combustible vapor cloud on the side of the bunker vessel is 58 × 15.5 m from the front wall of the cargo equipment room to the bow of the vessel, and 35 × 9.5 m between the cargo equipment room and the transom of the vessel. Under the laden condition, the diffusion range on the side of the bunker vessel is 58 × 15.5 m from the front wall of the cargo equipment room to the bow of the vessel, and 15 × 4 m between the rear end wall of the cargo equipment room and the front wall of the stern of the vessel. These results provide important technical guidance and reference values for the safe operation of LNG bunker vessels.

We develop a novel three-dimensional (3D) cluster-based channel model for vehicle-to-vehicle (V2V) communications under the scenarios of urban street scattering environments. The proposed model combines the flexibility of geometrical channel models with the existing state-of-the-art 3D V2V models. To provide an accurate representation of specific locations and realistic V2V fading environments in a computationally manageable fashion, all clusters are divided into three groups of use cases including “ahead,” “between,” and “behind” clusters according to the relative locations of clusters. Using the proposed V2V model, we first derive the closed-form expressions of the channel impulse response (CIR), including the line-of-sight (LoS) components and cluster components. Subsequently, for three categories of clusters, the corresponding statistical properties of the reference model are studied. We additionally derive the expressions of the 3D space-time correlation function (STCF), the autocorrelation function (ACF), and 2D STCF. Finally, comparisons with on-road measurement data and numerical experiments demonstrate the validity and effectiveness of the proposed 3D cluster-based V2V model.

Cognitive radio networks (CRNs) are susceptible to primary user emulation (PUE) attacks. Conventional PUE attack detection approaches consider television broadcasting as the primary user. In this work, however, we study a special kind of PUE attack named wireless microphone user emulation (WMUE) attack. Existing work on WMUE attack detection deals with single channel senario. Although multi-channelWM(MCWM) systems are common, detecting WMUE attacks under a multi-channel setting in noisy environments has not been well studied. In this work, we propose a novelmulti-channelWMUEattack detection scheme which operates in low signal-to-noise ratio (SNR) environments with low computational complexity, thanks to the first 1.5-bit FM demodulator whose outputs are represented by only 0, 1 and -1. Experimental results show that, the proposed scheme can effectively detect multi-channel WMUE attacks within 0.25 second when SNR is lower than 6 dB.

Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others.

In this paper, an extension of spatial channel model (SCM) for vehicle-to-vehicle (V2V) communication channel in roadside scattering environment is investigated for the first time theoretically and by simulations. Subsequently, to efficiently describe the roadside scattering environment and reflect the nonstationary properties of V2V channels, the proposed SCM V2V model divides the scattering objects into three categories of clusters according to the location of effective scatterers by introducing critical distance. We derive general expressions for the most important statistical properties of V2V channels, such as channel impulse response, power spectral density, angular power density, autocorrelation function, and Doppler spread of the proposed model. The impact of vehicle speed, traffic density, and angle of departure, angle of arrival, and other statistical performances on the V2V channel model is thoroughly discussed. Numerical simulation results are presented to validate the accuracy and effectiveness of the proposed model.

We demonstrated a synthetic process on the glass doped with AgInS2 nanocrystals through sol-gel method under a controlled atmosphere. X-ray powder diffraction and X-ray photoelectron spectra revealed that the AgInS2 crystalline phase had formed in the glass matrix. Transmittance electron microscopy showed that these AgInS2 crystals had spherical shape and good dispersed form in the glass matrix, and their diameter distribution was mainly focused on three size regions. Furthermore, the glass doped with AgInS2 nanocrystals exhibited three photoluminescence peaks located at 1.83 eV, 2.02 eV, and 2.21 eV, which were ascribed to the introduction of AgInS2 nanocrystals in the glass.