Optimum Selection of Mechanism Type for Heavy Manipulators Based on Particle Swarm Optimization Method

The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effective and efficient methods for the optimum selection among different types of mechanism candidates. This paper presents a new strategy for the purpose of optimum mechanism type selection based on the modified particle swarm optimization method. The concept of sub-swarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the sub-swarms to reassign their population size according to the relative performances of the mechanism candidates to implement the optimization. Combining with a modular modeling approach for fast calculation of the performance index of the potential candidates, the proposed method is applied to determine the optimum mechanism type among the potential candidates for the desired manipulator. The effectiveness and efficiency of the proposed method is demonstrated through a case study on the optimum selection of mechanism type of a heavy manipulator where six feasible candidates are considered with force capability as the specific performance index. The optimization result shows that the fitness of the optimum mechanism type for the considered heavy manipulator can be up to 0.578 5. This research provides the instruction in optimum selection of mechanism types for robotic manipulators.

Relationship Between Plant Type and Grain Quality of Japonica Hybrid Rice in Northern China

Plant type and grain quality are two major aspects in rice breeding. Using canonical correlation analysis and canonical redundancy analysis, the relationship between plant type traits and rice grain quality traits was studied with 100 crosses derived from 10 sterile lines × 10 restorer lines. There was a complex relationship between parts of the traits of the two aspects. The angle of the 2nd leaf from the top and single panicle weight played important roles in plant type system and amylose content and grain length in grain quality system. The angle of the 2nd leaf from the top, plant height and single panicle weight had a great effect on grain quality traits, and amylose content, brown rice rate and translucency were easily influenced by plant type traits. Selection index model indicated that japonica hybrid rice in Northern China with good quality was characterized by broad flag leaf and 2nd leaf from the top, narrow and short 3rd leaf from the top, low plant height, short culm, long and more panicles and low single panicle weight.

GATE TYPE SELECTION BASED ON FUZZY MAPPING

Gate type selection is very important for mould design. Improper gate typemay lead to poor product quality and low production efficiency. Although numerical simulationapproach could be used to optimize gate location, the determination of gate type is still up todesigners' experience. A novel method for selecting gate type based on fuzzy logic is proposed. Theproposed methodology follows three steps: Design requirements for gate is extracted and generalized;Possible gate types (design schemes) are presented; The fuzzy mapping relationship between gatedesign requirements and gate design scheme is established based on fuzzy composition and fuzzyrelation transition matrices that are assigned by domain experts.

Type selection and maintenance of the control system of large-scale air separation units

Based on the practice of Baosteel＇ s 60000 m3/h air separation unit （ASU） ,which is the first domestically- integrated unit of such a scale, this paper studies the principles of type selection of the distribution control system （DCS）. It discusses the design of the unit＇s control system,which involves a compressor system,a purification system （molecular sieving）, a turbo expansion system and an air separation system. The final part of the paper discusses the maintenance and future development of the ASU control system at Baosteel.

Selective type II TRK inhibitors overcome xDFG mutation mediated acquired resistance to the second-generation inhibitors selitrectinib and repotrectinib

Neurotrophic receptor kinase(NTRK) fusions are actionable oncogenic drivers of multiple pediatric and adult solid tumors,and tropomyosin receptor kinase(TRK) has been considered as an attractive therapeutic target for "pan-cancer" harboring these fusions.Currently,two generations TRK inhibitors have been developed.The representative second-generation inhibitors selitrectinib and repotrectinib were designed to overcome clinic acquired resistance of the first-generation inhibitors larotrectinib or entrectinib resulted from solvent-front and gatekeeper on-target mutations.However,xDFG(TRKAG667C/A/S,homologous TRKCG696C/A/S) and some double mutations still confer resistance to selitrectinib and repotrectinib,and overcoming these resistances represents a major unmet clinical need.In this review,we summarize the acquired resistance mechanism of the first-and second-generation TRK inhibitors,and firstly put forward the emerging selective type Ⅱ TRK inhibitors to overcome xDFG mutations mediated resistance.Additionally,we concluded our perspectives on new challenges and future directions in this field.

Comparison of capacities at road intersections

In order to research the capacity differences of road intersections,this paper analyzed the influence factors and calculation models of various types of capacity at intersections and then obtained simplified models through reasonable assumption. 2 /2,4 /2,4 /4 intersections at which saturation and forecasted traffic volume were given were taken as examples,and an appropriate type was selected through capacity comparison. This paper can give a predictive estimation of the intersection capacity and provides theoretical data for the type selection of intersections.

Evaluation Method for Tunneling Stability of TBM Cutterhead

In the process of tunneling of tunnel boring machine (TBM), different geological conditions often correspond to different working conditions, and the randomness of geological conditions also causes the order of occurrence of each working condition to be different. Under the conversion of different working conditions, this makes the vibration of different types of cutterheads different. How to choose the appropriate type of cutterhead according to different geological conditions is very important for saving engineering cost and increasing cutterhead life. In view of the above situation, this paper proposes a stability evaluation method during the TBM tunneling process to select the appropriate cutterhead type. Firstly, the corresponding relationship between geology and working conditions is established according to different geological conditions, and the input loads corresponding to geological conditions are obtained. Then, it is substituted into the dynamic model of the cutterhead system, the vibration response boundaries of each degree of freedom are obtained by solving. And the average value of the maximum boundary amplitude of each degree of freedom is taken to represent the extreme vibration of the cutterhead under the corresponding working conditions. Finally, by comparing the fluctuation of the ultimate vibration amplitude of each type of cutterhead in the process of working condition conversion, the results are as follows: when the transition between homogeneous strata and composite strata is normal and there is no large turning and deviation correction, the vibration response of the two-part cutterhead is the smallest, and the two-part cutterhead is the best choice. Otherwise, the five-part cutterhead is the best choice, while the stability of the integrated cutterhead is the worst.

Image restoration techniques based on fuzzy neural networks

By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter-FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.