A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

Research computer numerical simulation on heat dissipation model of submarine data centre based on TOPSIS model

Due to the huge energy consumption of land-based data centers, it is necessary to establish undersea data centers as soon as possible in order to alleviate the problem of resource tension. In this paper, the uniformity of containers is assumed. Through force analysis, it is found that the stress of containers is uniformly distributed, so only the external stress should be considered. Hypothesis submarine data center in the 50 m deep seawater, calculate the container need to withstand stress is 76.417403 Mpa, common material in engineering field, then physical parameters of material to deal with the dimensional and normalization, and establishes a comprehensive evaluation model material, the Topsis method is adopted to solve, to solve the optimal evaluation of the results can be divided into: 7-4 PH Stainless Stee 0.7450 points, so choose it as IU server container material.

A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue

The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to different tasks with global traversal over a finite set, then an optimization result can be converged to the global optimal value avoiding the problem that individual optimization easy to fall into local optimal results. In the numerical simulation, for fixed set of multi-object and multi-task, we used different numbers of robots allocation operation. We then compared with other methods: Hungarian, the auction method, and the method based on game theory. The results showed that Global Optimal Evaluation of Revenue reduced the number of robots used by at least 17%, and the delay time could be reduced by at least 16.23%.

Idiopathic Interstitial Pneumonias and COVID-19 Pneumonia: Review of the Main Radiological Features and Differential Diagnosis

COVID-19 pneumonia represents a challenging health emergency, due to the disproportion between the high transmissibility, morbidity, and mortality of the virus and healthcare systems possibilities. Literature has mainly focused on COVID-19 pneumonia clinical-radiological diagnosis and therapy, and on the most common differential diagnoses, while few papers investigated rare COVID-19 pneumonia differential diagnoses or the overlapping of COVID-19 pneumonia on pre-existing lung pathologies. This article presents the main radiological characteristics of COVID-19 pneumonia and Idiopathic Interstitial Pneumonias (IIPs) to identify key radiological features for a differential diagnosis among IIPs, and between IIPs and COVID-19 pneumonia. COVID-19 pneumonia differential diagnosis with IIPs is challenging, since these entities may share common radiological findings as ground glass opacities, crazy paving patterns, and consolidations. Multidisciplinary discussion is crucial to reach a final and correct diagnosis. Radiologists have a pivotal role in identifying COVID-19 pneumonia patterns, reporting possible overlapping with long-lasting lung diseases, and suggesting potential differential diagnoses. An optimal evaluation of HRTC may help in containing the disease, in promoting better treatment for patients, and in providing an efficient allocation of human and economic resources.

Comparing a Single Clinician Versus a Multidisciplinary Consensus Conference Approach for Dementia Diagnostics

Background: Evidence-based recommendations on the optimal evaluation approach for dementia diagnostics are limited. This impedes a harmonized workup across clinics and nations. Objective: To evaluate the diagnostic performance of a multidisciplinary consensus conference compared to a single clinician approach. Methods: In this prospective study, we enrolled 457 patients with suspected cognitive decline, from two European memory clinics. A diagnostic evaluation was performed at baseline independently in two ways: 1) by a single clinician and 2) at a multidisciplinary consensus conference. A syndrome diagnosis and an etiological diagnosis was made. The confidence in the diagnosis was recorded using a visual analogue scale. An expert panel re-evaluation diagnosis served as reference for the baseline syndrome diagnosis and a 12-24-month follow-up diagnosis for the etiological diagnosis. Results: 439 patients completed the study. We observed 12.5%discrepancy (k = 0.81) comparing the baseline syndrome diagnoses of the single clinician to the consensus conference, and 22.3%discrepancy (k = 0.68) for the baseline etiological diagnosis. The accuracy of the baseline etiological diagnosis was significantly higher at the consensus conference and was driven mainly by increased accuracy in the MCI group. Confidence in the etiological diagnosis at baseline was significantly higher at the consensus conference (p < 0.005), especially for the frontotemporal dementia diagnosis. Conclusion: The multidisciplinary consensus conference performed better on diagnostic accuracy of disease etiology and increased clinicians’ confidence. This highlights the importance of a multidisciplinary diagnostic evaluation approach for dementia diagnostics, especially when evaluating patients in the MCI stage.