Search Results (8)

Aiming to address the problems of nonlinearity, a large time delay, poor adjustment ability, and a difficult parameter setting process of the tension control system of belt conveyor tensioning devices, an adaptive Proportional-Integral-Derivative (PID) parameter self-tuning algorithm based on an improved seeker optimization algorithm (ISOA) is proposed in this paper. The algorithm uses inertia weight random mutation to determine step size. An improved boundary reflection strategy avoids the defect of a large number of out-of-bound individuals gathering on the boundary in a traditional algorithm, and projects the individual reflection beyond the boundary into the boundary, which increases the diversity of the population and improves the convergence accuracy of the algorithm. To improve the system response speed and suppress the overshoot problem of the control target, coefficients related to the proportional term are introduced into the fitness function to accelerate the convergence of the algorithm. The improved algorithm is tested on three test functions such as Sphere and compared with other classical algorithms, which verify that the proposed algorithm is better in accuracy and stability. Finally, the interference and tracking performance of the ISOA-PID controller are verified in industrial experiments, which show that the PID controller optimized using the ISOA has good control quality and robustness. Full article

For the purpose of detecting waterborne bacteria, a high-phase-sensitivity SPR sensor with an Ag–TiO₂–Franckeite–WS₂ hybrid structure is designed using an improved seeker optimization algorithm (ISOA). By optimizing each layer of sensor construction simultaneously, the ISOA guarantees a minimum reflectance of less than 0.01 by Ag (20.36 nm)–TiO₂ (6.08 nm)–Franckeite (monolayer)–WS₂ (bilayer) after 30 iterations for E. coli. And the optimal phase sensitivity is 2.378 × 10⁶ deg/RIU. Sensor performance and computing efficiency have been greatly enhanced using the ISOA in comparison to the traditional layer-by-layer technique and the SOA method. This will enable sensors to detect a wider range of bacteria with more efficacy. As a result, the ISOA-based design idea could provide SPR biosensors with new applications in environmental monitoring. Full article

In the human-in-loop (HIL) guidance mode, a pilot quickly identifies and flexibly locks on to a target through a real-time image signal transmitted by the aircraft. Then, the line-of-sight (LOS) angle error in the viewing field is tracked and compensated for in order to improve the guidance and control performance of the image-guided aircraft. Based on the physical structure and device parameters of the image seeker, an appropriate correction network is designed to improve the performance of the seeker stability loop. Aiming at a precise-extended crossover (PEC) pilot model, the structure of the dynamic model is optimized, and the maximum likelihood estimation (MLE) method of the output error structure is used to identify the dynamic parameters. This makes up for the deficiency of the existing modeling. In order to solve the nonlinear optimization problems encountered in the identification process, a hybrid strategy of a genetic algorithm (GA) and Gauss–Newton optimization algorithm is used to improve the probability of finding the global optimal solution. The simplex method is also used to improve the robustness of the algorithm. In addition, a hardware-in-the-loop simulation is designed and multi-round HIL experiment flow is performed. Moreover, based on the adaptability of the pilot to different image signal delays, the effects of different image signal delays on the stability and disturbance rejection rate (DRR) of the seeker control system are studied. The results demonstrate that the hybrid gradient optimization algorithm (HGOA) can find the global optimal value, and the identification model can accurately reflect the dynamic characteristics of the pilot. In the HIL guidance mode, the tracking compensation behavior of the pilot can reduce the influence of image signal delay on the disturbance of the aircraft body isolated by the seeker. The optimized PEC model and the identified dynamic parameters improve the efficiency of pilot training and screening. Full article

A chaotic adaptive seeker optimization algorithm (CASOA) is proposed in this study to improve the coupling efficiency and accuracy of a butterfly optical communication laser. It primarily relies on chaotic disturbance to improve seeker search performance. The chaotic disturbance enables the algorithm to jump out from local extremes. Furthermore, chaos is associated with a novel strategy for optimizing search paths with a small population. A simulation and experiment are conducted to demonstrate that the CASOA with a few seekers has an excellent search success rate with few iterations in the coupling alignment. These results indicate that the proposed CASOA can reliably improve the coupling accuracy and efficiency of laser diodes and single-mode fibers. Full article

To support the application of ocean surface radiance data from the ultraviolet imager (UVI) payload of the HY-1C oceanographic satellite and to improve the quantification level of ocean observation technology, the authenticity check study of ocean surface radiance data from the UVI payload was conducted to provide a basis for the quantification application of data products. The UVI load makes up for the lack of detection capabilities of modern ocean remote sensing satellites in the ultraviolet band. The UVDRAMS (Ultra-Violet Dual-band RadiAnce Measurement System) was used to verify the surface radiance data collected at 16 stations in the study area and the pupil radiance data collected by the UVI payload to establish an effective radiative transfer model and to identify the model parameters using the seeker optimization algorithm (SOA). The study of the UVDRAMS measurement system based on the SOA algorithm and the validation of the sea surface radiance of the UVI payload of the HY-1C satellite shows that 97.2% of the incident pupil radiance of the UVI payload is contributed by the atmospheric reflected radiance, and only 2.8% is from the real radiation of the water surface, while the high signal-to-noise ratio of the UVI payload of the HY-1C ocean satellite can effectively distinguish the reflectance of the water body. The high signal-to-noise ratio of the UVI payload of the HY-1C ocean satellite can effectively distinguish the amount of standard deviation in the on-satellite radiation variation, which meets the observation requirements and provides a new way of thinking and technology for further quantitative research in the future. Full article

As a new paradigm, fog computing (FC) has several characteristics that set it apart from the cloud computing (CC) environment. Fog nodes and edge computing (EC) hosts have limited resources, exposing them to cyberattacks while processing large streams and sending them directly to the cloud. Intrusion detection systems (IDS) can be used to protect against cyberattacks in FC and EC environments, while the large-dimensional features in networking data make processing the massive amount of data difficult, causing lower intrusion detection efficiency. Feature selection is typically used to alleviate the curse of dimensionality and has no discernible effect on classification outcomes. This is the first study to present an Effective Seeker Optimization model in conjunction with a Machine Learning-Enabled Intrusion Detection System (ESOML-IDS) model for the FC and EC environments. The ESOML-IDS model primarily designs a new ESO-based feature selection (FS) approach to choose an optimal subset of features to identify the occurrence of intrusions in the FC and EC environment. We also applied a comprehensive learning particle swarm optimization (CLPSO) with Denoising Autoencoder (DAE) for the detection of intrusions. The development of the ESO algorithm for feature subset selection and the DAE algorithm for parameter optimization results in improved detection efficiency and effectiveness. The experimental results demonstrated the improved outcomes of the ESOML-IDS model over recent approaches. Full article

For now, the open humidification method is applied in the tobacco bulk curing barn, which has some disadvantages, such as the loss of the oil content and aroma components of the tobacco leaves and the waste heat loss of the exhaust air flow. In this context, a tobacco bulk curing barn with totally closed hot air circulation is designed to perfect the curing quality of tobacco and avoid the loss of residual heat in the bulk curing barn. Meanwhile, due to the balance and symmetry of input and output of the curing barn temperature, according to the law of conservation of energy, a mathematical model of the temperature control system of the closed hot air circulation tobacco bulk curing barn is established, and the temperature transfer function of the system is obtained. On this basis, 10 algorithms are used to optimize the full closed hot air circulation tobacco bulk curing barn temperature control system PID parameters. The result of the sobol sequence seeker optimization algorithm (SSOA) is better than the other algorithms. So, the PID control strategy based on the SSOA is used to simulate and experiment the temperature control system of tobacco bulk curing barn. The simulation and experimental results show that for the tobacco bulk curing barn temperature control system, the sobol sequence seeker optimization algorithm PID control has better dynamic characteristics compared with fuzzy PID control, and the temperature control system of tobacco bulk curing barn has fast adjustment and small overshoot. Therefore, the new baking barn with proper PID parameters can improve the tobacco’s curing quality and save energy by reducing the residual heat. Full article

Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP₀), response index to side-dress N based on harvested yield (RI_Harvest), and side-dress N agronomic efficiency (AE_NS). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015–2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP₀ (R² = 0.44–0.78) than only using NDVI or RVI (R² = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AE_NS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AE_NS. The INFOA-based PNM strategies could increase marginal returns by 212 $ ha⁻¹ and 70 $ ha⁻¹, reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%–40% and 11%–65% compared with farmer’s typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information. Full article