SLAM of Mobile Robot for Wireless Communication Based on Improved Particle Filter

The mobile robot is moved by receiving instructions through wireless communication, and the particle filter is used to simultaneous localization and mapping. Aiming at the problem of the degradation of particle filter weights and loss of particle diversity, which leads to the decrease of filter accuracy, this paper uses the plant cell swarm algorithm to optimize the particle filter. First of all, combining the characteristics of plant cells that affect the growth rate of cells when the auxin content changes due to light stimulation realizes the optimization of the particles after importance sampling, so that they are concentrated in the high-likelihood area, and the problem of particle weight degradation is solved. Secondly, in the process of optimizing particle distribution, the auxin content of each particle is different, which makes the optimization effect on each particle different, so it effectively solves the problem of particle diversity loss. Finally, a simulation experiment is carried out. During the experiment, the robot moves by receiving control commands through wireless communication. The experimental results show that the algorithm effectively solves the problem of particle weight degradation and particle diversity loss and improves the filtering accuracy. The improved algorithm is verified in the simultaneous localization and mapping of the robot, which effectively improves the robot’s performance at the same time positioning accuracy. Compared with the classic algorithm, the robot positioning accuracy is increased by 49.2%. Moreover, the operational stability of the algorithm has also been improved after the improvement.

Mechanical Modification of Resin Capsules by Adding Steel Particles

Five kinds of steel particles with sizes ranging from 0.6 mm to 2.2 mm with increments of 0.4 mm were mixed with mining resin materials, and the mixing ratio of the particles was also varied. By using this approach, the film gloving problem of coal mine bolting should be effectively solved due to the shredding effects of the particles during bolt rotation. The premise is that the mechanical behavior should not be weakened under such conditions. A total of 47 standard cylindrical specimens were manually prepared, which included pure resin specimens and specimens containing particles with different sizes and weights. First, the homogeneity of a prepared standard specimen was verified by computed tomography (CT) scanning technology. Second, the mechanical improvements provided by each type of particle were evaluated. Thirdly, the effectiveness of both the particle weight and particle size was comprehensively discussed, and the eventual recommendation was to set for the particle size and weight as 1.4 mm and 40 g, respectively, and the particles weight percentage was 7.27%. Finally, the failure patterns for all specimens were collected and comprehensively compared. Additionally, pullout tests were carried out to vindicate the recommended particle size and weight.

Efficient Prediction of Microplastic Counts from Mass Measurements

Microplastics must be characterized and quantified to assess their impact. Current quantification procedures are time-consuming and rely on expensive equipment. This study evaluates the use of machine learning to estimate the number of microplastic particles based on aggregate particle weight measurements. Synthetic datasets are used to test the performance of linear regression, kernel ridge regression and decision trees. Kernel ridge regression achieves the strongest performance, and it is also tested with experimental datasets. The numerical results show that the algorithm is better at predicting the counts of larger and more homogenous samples, and that contamination by organics does not significantly increase error. In mixed samples, prediction error is lower for heavier particles, with an error rate comparable to or better than that of manual counting. Overall, the proposed method is faster, cheaper and easier than current approaches.SynopsisUsing generated and real datasets, this study demonstrates that Kernel Ridge Regression can estimate microplastic counts from weight measurements as accurately as traditional visual sorting techniques.

Formation of stable aggregates by fluid-assembled solid bridges

<p>Earh's surface is covered with soil; particulate mixtures subject to cycles of wetting and drying. The role of this transient hydrodynamic forcing in creating and destroying aggregates is virtually unexplored. We examine this process at the grain scale. When a colloidal suspension is dried, capillary pressure may overwhelm repulsive electrostatic forces, assembling aggregates that are out of thermal equilibrium. This poorly understood process confers cohesive strength to many geological and industrial materials. Here we observe evaporation-driven aggregation of natural and synthesized particulates, and then probe their stability under rewetting using a microfluidics channel as a flume to determine the entrainment threshold. We also directly measure bonding strength of aggregates using an atomic force microscope. Cohesion arises at a common length scale (~5 microns), where interparticle attractive forces exceed particle weight. In polydisperse mixtures, smaller particles condense within shrinking capillary bridges to build stabilizing “solid bridges” among larger grains. This dynamic repeats across scales forming remarkably strong, hierarchical clusters, whose cohesion derives from grain size rather than mineralogy. Transient capillary pressures are even sufficiently large to sinter the smallest particles together. These results may help to understand the strength and erodibility of natural soils, and other polydisperse particulates that experience transient hydrodynamic forces.</p>

Properties of green particleboard manufactured from coconut fiber using a potato starch based adhesive

Particleboards were manufactured using coconut fibers (Cocos nucifera). The panels were made using different green adhesives, i.e., native potato starch, citric acid, and glutardialdehyde modified potato starch, that were applied at 10%, 12%, and 15% based on oven-dry particle weight for each green adhesive type. The properties of the panels were determined according to the Japanese industrial standard. The results showed that the panels that were bonded with the 15% citric acid-modified starch green adhesive yielded the best mechanical properties (the modulus of elasticity, modulus of rupture, and internal bonding strength). The modified potato starch had potential as a green adhesive used for the production of particleboards from coconut fibers.

Formation of stable aggregates by fluid-assembled solid bridges

When a colloidal suspension is dried, capillary pressure may overwhelm repulsive electrostatic forces, assembling aggregates that are out of thermal equilibrium. This poorly understood process confers cohesive strength to many geological and industrial materials. Here we observe evaporation-driven aggregation of natural and synthesized particulates, probe their stability under rewetting, and measure bonding strength using an atomic force microscope. Cohesion arises at a common length scale (∼5 μm), where interparticle attractive forces exceed particle weight. In polydisperse mixtures, smaller particles condense within shrinking capillary bridges to build stabilizing “solid bridges” among larger grains. This dynamic repeats across scales, forming remarkably strong, hierarchical clusters, whose cohesion derives from grain size rather than mineralogy. These results may help toward understanding the strength and erodibility of natural soils, and other polydisperse particulates that experience transient hydrodynamic forces.

Thawing process calculation of egg ingredients particles in minced meat

Sausages from poultry meat are present on the domestic market. In this regard, the problem of improving the competitiveness of these products is urgent. One way to solve this problem is to replace part of the raw meat in the minced meat for sausages with chicken egg products (pasteurized liquid egg, egg white, yolk). Using egg products as ingredients of minced meat will help stabilize minced meat for sausages before and after heat treatment, increase the nutritional value of the finished product, and reduce its cost. However, currently, the mass fraction of egg ingredients in meat and egg products formulations does not exceed 5–10 %. This is due to the appearance of a specific taste, if it is exceeded, and because it is impossible to obtain a pattern of egg fragments in the section of the finished products, if necessary. This fact restricts the use of egg products as ingredients of meat and egg products. A technological method to eliminate these problems is freezing egg ingredients before adding to minced meat. In order to control the application of this method, the mechanism of changes in frozen egg ingredients during the preparation and heat treatment of minced meat for sausages is revealed. It was found that at the stage of minced meat mixing, the liquid part of the egg ingredients resulting from the thawing of frozen particles surface mixes with meat ingredients. Moreover, when unmoved relative to the surrounding minced meat, the frozen particles of egg ingredients are caught by the minced meat, and then locally coagulate in the process of meat and egg product heat treatment. The weight of the liquid phase resulting from the thawing of frozen egg ingredient particle and the weight of its remaining local part depend on the duration of the minced meat mixing process, its temperature and particle weight. Based on the knowledge about this mechanism, analytical equations are obtained using the energy balance method. They describe the duration of egg ingredients thawing in meat and egg products depending on the particle weight and the temperature of minced meat. The experimental data of the authors are used as a basis for calculating the process of egg ingredients thawing. The proposed calculation method will allow purposeful controlling the process of change in frozen egg ingredients aggregative state in minced meat for sausages, under production conditions.

Improved Particle Filter Resampling Architectures

The most challenging aspect of particle filtering hardware implementation is the resampling step. This is because of high latency as it can be only partially executed in parallel with the other steps of particle filtering and has no inherent parallelism inside it. To reduce the latency, an improved resampling architecture is proposed which involves pre-fetching from the weight memory in parallel to the fetching of a value from a random function generator along with architectures for realizing the pre-fetch technique. This enables a particle filter using M particles with otherwise streaming operation to get new inputs more often than 2M cycles as the previously best approach gives. Results show that a pre-fetch buffer of five values achieves the best area-latency reduction trade-off while on average achieving an 85% reduction in latency for the resampling step leading to a sample time reduction of more than 40%. We also propose a generic division-free architecture for the resampling steps. It also removes the need of explicitly ordering the random values for efficient multinomial resampling implementation. In addition, on-the-fly computation of the cumulative sum of weights is proposed which helps reduce the word length of the particle weight memory. FPGA implementation results show that the memory size is reduced by up to 50%.