Search Results (24)

Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 10³ average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article

To develop a grain flow sensor for combine auger grain outlets, a combine auger elevator was evaluated as the research object. A multi-point distributed array-style differential grain flow sensor for rice and wheat has been developed and tested on three field crops, rice, wheat, and rapeseed. The open system flow test bench was designed to compare the effects of differential processing in the time and frequency domains, as well as different filtering methods on the pre-processing of the collected raw sensor signals. Moreover, a random forest algorithm-based flow regression model was constructed for rice, wheat, and rapeseed based on the comparison of the flow signals of different grains. A weighted multiple linear regression model was constructed as the control group, and both bench and field tests were conducted. The results show that the sensor designed in this study can meet the needs of on-line grain flow monitoring. Meanwhile, the field monitoring errors for rice, wheat, and rapeseed based on the random forest flow regression model were −6.42~8.23%, −7.21~5.71%, and −4.19~4.78%, respectively, significantly better than the control group. The universal array-style grain flow sensor developed in this study provides significant practical value for the promotion and development of precision agriculture. Full article

To implement fine-grained progression signal control on arterial, it is essential to have access to the time-varying distribution of the origin–destination (OD) flow of the arterial. However, due to the sparsity of automatic vehicle identification (AVI) devices and the low penetration of connected vehicles (CVs), it is difficult to directly obtain the distribution pattern of arterial OD flow (i.e., path flow). To solve this problem, this paper develops a semi-supervised arterial path flow estimation method considering the consistency of path flow distribution by combining the sparse AVI data and the low permeability CV data. Firstly, this paper proposes a semi-supervised arterial path flow estimation model based on multi-knowledge graphs. It utilizes graph neural networks to combine some arterial AVI OD flow observation information with CV trajectory information to infer the path flow of AVI unobserved OD pairs. Further, to ensure that the estimation results of the multi-knowledge graph path flow estimation model are consistent with the distribution of path flow in real situations, we introduce a generative adversarial network (GAN) architecture to correct the estimation results. The proposed model is extensively tested based on a real signalized arterial. The results show that the proposed model is still able to achieve reliable estimation results under low connected vehicle penetration and with less observed label data. Full article

Grey cast iron releases energy in the form of stress waves when damaged. To analyse the evolution of the physical and mechanical properties and acoustic emission characteristics of grey cast iron under uniaxial compression, acoustic emission signals were collected at different rates (0.5, 1, and 2 mm/s). Combined with load-time curves, damage modes were identified and classified using the parametric RA-AF correlation analysis method. The results indicate the loading rate effects on the strength, deformation, acoustic emission (AE), and energy evolution of grey cast iron specimens. The acoustic emission counts align with the engineering stress–strain response. To better illustrate the entire failure process of grey cast iron, from its internal microstructure to its macroscopic appearance, X-ray diffraction (XRD) and optical microscopy (OM) were employed for qualitative and quantitative analyses of the material’s internal microstructural characteristics. The equivalent crystal model of grey cast iron was constructed using a Particle Flow Software PFC2D 6.00.30 grain-based model (GBM) to simulate uniaxial compression acoustic emission tests. The calibration of fine parameters with indoor test results ensured good agreement with numerical simulation results. Acoustic emission dynamically monitors the compression process, while discrete element particle flow software further analyses the entire damage process from the inside to the outside. It provides a new research method and idea for the study of crack extension in some metal materials such as grey cast iron. Full article

The radial distribution of material feeding onto a screen surface is an important factor affecting vibration screening performance, and it is also the main basis for the optimization of the operating parameters of a vibration screening system. In this paper, based on near-infrared properties, a real-time measurement method for the mass flow rate of grain vibration feeding was proposed. A laser emitter and a silicon photocell were used as the measuring components, and the corresponding signal processing circuit mainly composed of a T-type I/V convertor, a voltage follower, a low-pass filter, and a setting circuit in series was designed. Calibration test results showed that the relationship between grain mass flow rate and output voltage could be described using the Gaussian regression model, and the coefficient of determination was greater than 0.98. According to the working principle of the grain cleaning system of combine harvesters, the dynamic characteristics of grain vibration feeding were analyzed using discrete element method (DEM) simulations, and the monitoring range of the sensor was determined. Finally, grain mass flow rate measurement tests were carried out on a vibration feeding test rig. The results indicated that the grain mass measurement error could be controlled within 5.0% with the average grain mass flow rate in the range of 3.0–5.0 g/mm·s. The proposed measurement method has potential application value in the uniform feeding control systems of vibration feeders. Full article

Precision agriculture relies heavily on measuring grain production per unit plot, and a grain flow monitoring system performs this using a combine harvester. In response to the high cost, complex structure, and low stability of the yield monitoring system for grain combine harvesters, the objective of this research was to design a convex curved grain mass flow sensor to improve the accuracy and practicality of grain yield monitoring. In addition, it involves the development of a grain yield monitoring system based on a cut-and-flow combine harvester prototype. This research examined the real output signal of the convex curved grain mass flow sensor. Errors caused by variations in terrain were reduced by establishing the zero point of the sensor’s output. Measurement errors under different material characteristics, flow rates, and grain types were compared in indoor experiments, and the results were subsequently confirmed through field experiments. The results showed that a sensor with a cantilever beam-type elastic element and a well-constructed carrier plate may achieve a measurement error of less than 5%. After calibrating the sensor’s zero and factors, it demonstrated a measurement error of less than 5% during the operation of the combine harvester. These experimental results align with the expected results and can provide valuable technical support for the widespread adoption of impulse grain flow detection technology. In future work, the impact of factors such as vehicle vibration will be addressed, and system accuracy will be improved through structural design or adaptive filtering processing to promote the commercialization of the system. Full article

The automotive industry is experiencing a transformation with the rapid integration of software-based systems inside vehicles, which are complex systems with multiple sensors. The use of vehicle sensor data has enabled vehicles to communicate with other entities in the connected vehicle ecosystem, such as the cloud, road infrastructure, other vehicles, pedestrians, and smart grids, using either cellular or wireless networks. This vehicle data are distributed, private, and vulnerable, which can compromise the safety and security of vehicles and their passengers. It is therefore necessary to design an access control mechanism around the vehicle data’s unique attributes and distributed nature. Since connected vehicles operate in a highly dynamic environment, it is important to consider context information such as location, time, and frequency when designing a fine-grained access control mechanism. This leads to our research question: How can Attribute-Based Access Control (ABAC) fulfill connected vehicle requirements of Signal Access Control (SAC), Time-Based Access Control (TBAC), Location-Based Access Control (LBAC), and Frequency-Based Access Control (FBAC)? To address the issue, we propose a data flow model based on Attribute-Based Access Control (ABAC) called eXtensible Access Control Markup Language for Mobility (XACML4M). XACML4M adds additional components to the standard eXtensible Access Control Markup Language (XACML) to satisfy the identified requirements of SAC, TBAC, LBAC, and FBAC in connected vehicles. Specifically, these are: Vehicle Data Environment (VDE) integrated with Policy Enforcement Point (PEP), Time Extensions, GeoLocation Provider, Polling Frequency Provider, and Access Log Service. We implement a prototype based on these four requirements on a Raspberry Pi 4 and present a proof-of-concept for a real-world use case. We then perform a functional evaluation based on the authorization policies to validate the XACML4M data flow model. Finally, we conclude that our proposed XACML4M data flow model can fulfill all four of our identified requirements for connected vehicles. Full article

Real-time crop harvest data acquisition from harvesters during harvesting operations is an important way to understand the distribution of crop harvest in the field. Most real-time monitoring systems for grain yield using sensors are vulnerable to factors such as low accuracy and low real-time performance. To address this phenomenon, a real-time grain yield monitoring system was designed in this study. The real-time monitoring of yield was accomplished by adding three pairs of photoelectric sensors to the elevator of the corn kernel harvester. The system mainly consists of a signal acquisition and processing module, a positioning module and a visualization terminal; the signal acquisition frequency was set to 1 kHz and the response time was 2 ms. When the system operated, the signal acquisition and processing module detected the sensor signal duration of grain blocking the scrapers of the grain elevator in real-time and used the low-potential signal-based corn grain yield calculation model constructed in this study to complete the real-time yield measurement. The results of the bench tests, conducted under several different operating conditions with the simulated elevator test bench built, showed that the error of the system measurement was less than 5%. Field tests were conducted on a Zoomlion 4YZL-5BZH combined corn kernel harvester and the results showed that the average error of measured yield was 3.72%. Compared to the yield measurement method using the weighing method, the average error of the bench test yield measurement was 7.6% and the average error of yield measurement in field trials with a mass flow sensor yield measurement system was 16.38%. It was verified that the system designed in this study has high yield measurement accuracy and real-time yield measurement, and can provide reference for precision agriculture and high yield management. Full article

Stock price prediction is crucial but also challenging in any trading system in stock markets. Currently, family of recurrent neural networks (RNNs) have been widely used for stock prediction with many successes. However, difficulties still remain to make RNNs more successful in a cluttered stock market. Specifically, RNNs lack power to retrieve discerning features from a clutter of signals in stock information flow. Making it worse, by RNN a single long time cell from the market is often fused into a single feature, losing all the information about time which is essential for temporal stock prediction. To tackle these two issues, we develop in this paper a novel hybrid neural network for price prediction, which is named frequency decomposition induced gate recurrent unit (GRU) transformer, abbreviated to FDGRU-transformer or FDG-trans). Inspired by the success of frequency decomposition, in FDG-transformer we apply empirical model decomposition to decompose the complete ensemble of cluttered data into a trend component plus several informative and independent mode components. Equipped with the decomposition, FDG-transformer has the capacity to extract the discriminative insights from the cluttered signals. To retain the temporal information in the observed cluttered data, FDG-transformer utilizes hybrid neural network of GRU, long short term memory (LSTM) and multi-head attention (MHA) transformers. The integrated transformer network is capable of encoding the impact of different weights from each past time step to the current one, resulting in the establishment of a time series model from a deeper fine-grained level. We appy the developed FDG-transformer model to analyze Limit Order Book data and compare the results with that obtained from other state-of-the-art methods. The comparison shows that our model delivers effective price forecasting. Moreover, an ablation study is conducted to validate the importance and necessity of each component in the proposed model. Full article

For various sorts of hard coal, enrichment by flotation is used for feed consisting of grains smaller than 0.5 mm. Regarding process automation, coal flotation is a multidimensional, dynamic nonlinear object of control, for which the main control signal is the flow rate of the flotation agent. Typically, in Polish coal-processing facilities the monitoring and control systems of the flotation process can only measure the parameter of the waste quality (content of ash in flotation tailings). This naturally becomes an output signal, enabling an indirect assessment of the ongoing process. Therefore, knowledge of the dynamic properties of the flotation process, analysed as an object with one control input (the flow rate of the flotation agent) and with one output for measuring (content of ash in flotation tailings) may be material in designing automatic control systems for this operation. It is important to use an appropriate identification method when developing a model of the dynamics of the flotation process, especially if the model parameters are to be determined on an ongoing basis. This article discusses the research method and presents the results of applying the method of identifying the dynamic properties of the coal flotation process with the use of the Kalman filter. We carried out a comparative analysis of the results obtained by this method based on the Kalman algorithm and the method of least squares, taken as the reference method. The presented parameters of the dynamic models were calculated based on actual data obtained from industrial tests conducted at the coal-processing plant at one of the Polish mines. It was demonstrated that, for control purposes, the Kalman algorithm can be successfully applied in identification of the coal flotation process. This is due to the fact that it gives satisfactory results in relation to the adopted reference method despite the fact that it is a recursive algorithm. Full article

Optimizing traffic signal timing can effectively alleviate urban traffic congestion. However, most of the existing signal timing methods struggle to deal with conflicting traffic flows in heavy traffic; therefore, more effective methods are urgently required. In this paper, we propose a flexible signal timing method that combines all-red control and adaptive timing (FMAA) to deal with conflicting traffic flows at an isolated intersection. First, we consider a Vehicle-to-Infrastructure (V2I) communication-based vehicular network environment, in which fine-grained traffic information can be collected by Road Side Units (RSUs) and uploaded to a cloud server for designing signal timing methods. Second, the congestion degree of Conflict Area (CA) is defined and utilized to trigger all-red control in congested cases. Third, the tolerance degree of the Waiting Area (WA) is defined and utilized to perform adaptive timing in other cases. Finally, simulations were conducted using SUMO, and the proposed FMAA method performed better than the comparative methods in terms of average speed, waiting time, and congested vehicles, thus improving traffic efficiency at an isolated intersection with conflicting traffic flows. Full article

Ground rubber from automobile tires is very difficult to recycle due to the cross-linking of the macromolecules and thus the lack of thermoplastic properties. The research consisted of assessing the processing possibility via the injection of highly filled PP/GTR compounds modified with 1.5 wt.% 2.5-dimethyl-2.5-di-(tert-butylperoxy)-hexane. GTR dosing ranged from 30 wt.% up to 90 wt.%. The evaluation of the processing properties of the obtained materials was carried out on the basis of the melt flow index test results and the signals recorded during processing by the injection molding by temperature and pressure sensors placed in the mold. The influence of the applied modifier on the changes in the mechanical properties of PP/GTR was determined with hardness, impact and static tensile tests. Moreover, thermal properties were obtained by the differential scanning calorimetry method. It has been found that it is possible to efficiently process compounds with high GTR content using injection molding. The presence of the filler allows to significantly reduce the cooling time in the injection mold and thus the time of the production cycle. It has been confirmed that 2.5-dimethyl-2.5-di-(tert-butylperoxy)-hexane modifies the rheological properties of PP and thus the PP/GTR composition. The lower viscosity of the matrix results in a more accurate bonding with the developed surface of the GTR grains, which results in better mechanical properties of the rubber-filled polypropylene. Full article

Computational models of the basal ganglia (BG) provide a mechanistic account of different phenomena observed during reinforcement learning tasks performed by healthy individuals, as well as by patients with various nervous or mental disorders. The aim of the present work was to develop a BG model that could represent a good compromise between simplicity and completeness. Based on more complex (fine-grained neural network, FGNN) models, we developed a new (coarse-grained neural network, CGNN) model by replacing layers of neurons with single nodes that represent the collective behavior of a given layer while preserving the fundamental anatomical structures of BG. We then compared the functionality of both the FGNN and CGNN models with respect to several reinforcement learning tasks that are based on BG circuitry, such as the Probabilistic Selection Task, Probabilistic Reversal Learning Task and Instructed Probabilistic Selection Task. We showed that CGNN still has a functionality that mirrors the behavior of the most often used reinforcement learning tasks in human studies. The simplification of the CGNN model reduces its flexibility but improves the readability of the signal flow in comparison to more detailed FGNN models and, thus, can help to a greater extent in the translation between clinical neuroscience and computational modeling. Full article

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method. Full article

Grain moisture is one of the key indexes of grain quality, and acquiring an accurate moisture value is critical for grain storage security. However, the sensors used in the traditional methods for testing grain moisture are based on capacitance, microwave, or radio-frequency methods and still exhibit low accuracy and instability because they are susceptible to the temperature, moisture, and micro gas flow of the air in the granary. In this study, we employed a new design for a radio-frequency moisture sensor for grain. The structure of the sensor is based on the difference method and consists of two parallel probe units. These units are at different distances to the tested grain, resulting in different sensitivities in the moisture measurements. Through a phase difference operation on the test signals, the disturbance variable was reduced. The specific size of the two parallel probes was confirmed by calculation and simulation using High Frequency Structure Simulator (HFSS) software. The simulated and measured parameters of a prototype sensor agreed well. The linear relationship yielded a correlation coefficient of 0.9904, and the average error of the moisture testing was within ±0.3% under the conditions where the VSWR (voltage standing wave ratio) value and return losses were 1.5896 and −20 dB, respectively, at a measured central frequency of 100 MHz. The results indicate that the performance of the sensor was excellent. Full article