基于协同多目标算法的多机器人路径规划

多机器人路径规划是群体机器人协同工作的前提,其特点是在防碰撞与避障的前提下追求多方面资源的最小消耗.针对这一特点,提出协同非支配排序遗传算法,解决具有多个优化目标的多机器人路径规划问题;运用改进的多目标优化算法,克服多目标优化取权值的不足,同时考虑机器人能源与时间两大资源,以多机器人的路径总长度、总平滑度、总耗时为规划目标.同时引入合作型协同算法框架,将难以求解的多变量问题分组求解.每个机器人的路径视为子种群,子种群通过带精英策略的非支配排序遗传算法,进化并筛选出子种群的部分进入协同进化,每次迭代更新外部的精英解集,最终生成一组非支配路径解.仿真结果表明,在栅格地图环境下,本文算法可有效实现多移动机器人的多优化目标路径规划.     

Multiemotional product color design using gray theory and nondominated sorting genetic algorithm-III

When users select products, they consider the emotional experience resulting from the color of the product. However, the emotional demands of users for product color are multidimensional and diverse. It is very important yet difficult to accurately grasp multiemotional image requirements and effectively convert them into design elements. Therefore, multiemotional product color design (MEPCD) has become a very important and challenging research topic. In this article, a novel MEPCD system using gray theory (GT) and nondominated sorting genetic algorithm-III (NSGA-III) is proposed to effectively solve the MEPCD problem. First, the image perception spaces of users, which exist in different emotional dimensions, were collected using factor analysis and the semantic differential technique. Then, GT was used to establish a multidimensional emotional product color image evaluation model. Finally, NSGA-III was used to optimize and design a multiemotional color scheme for a product. Furthermore, according to actual conditions, an MEPCD system was established based on the proposed method. The design case study shows that the method and design system proposed in this article have a certain range of applicability and can effectively improve the practicality of MEPCD.     

Using hybridized ANN-GA prediction method for DOE performed drying experiments

Abstract

Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.     

Improved multi-recessed 4H–SiC MESFETs with double-recessed p-buffer layer

An improved multi-recessed 4H–SiC metal semiconductor field effect transistor (MRD-MESFET) with double-recessed p-buffer layer (DRB-MESFET) is proposed in this paper. By introducing a double-recessed p-buffer layer, the gate depletion layer is further modulated, and higher drain saturation current and DC transconductance are obtained compared with the MRD-MESFET. The simulations show that the drain saturation current of the DRB-MESFET is about 42.4% larger than that of the MRD-MESFET. The DC transconductance of the DRB-MESFET is almost 15% higher than that of the MRD-MESFET and very close to that of double-recessed structure (DR-MESFET) at the bias conditions of V_gs=0 V and V_ds=40 V. The proposed structure has an improvement of 26.1% and 74.2% in the output maximum power density compared with that of the MRD-MESFET and DR-MESFET, respectively. In the meanwhile, the proposed structure possesses smaller gate-source capacitance, which results in better RF characteristics.     

基于自适应免疫遗传算法的IPPS问题研究

针对工艺规划与调度集成(Integration of Process Planning and Scheduling,IPPS)的多目标优化问题,将遗传算法基本理论与免疫应答机理相结合,并引入IPPS问题研究中,构造了融遗传算法和免疫机理为一体的自适应免疫遗传算法(Self-adaptive Immune Genetic Algorithm,SIGA)。该算法以遗传算法为主体并加入免疫算子,通过免疫系统的自适应、免疫识别、学习、记忆、激增和抑制等复杂的信息处理机理,有效地解决了规模大、约束多、目标函数非单一的IPPS问题。最后以实例进行仿真优化,验证了模型算法的正确性和有效性。     

Trans-Splicing Improvement by the Combined Application of Antisense Strategies

Spliceosome-mediated RNA trans-splicing has become an emergent tool for the repair of mutated pre-mRNAs in the treatment of genetic diseases. RNA trans-splicing molecules (RTMs) are designed to induce a specific trans-splicing reaction via a binding domain for a respective target pre-mRNA region. A previously established reporter-based screening system allows us to analyze the impact of various factors on the RTM trans-splicing efficiency in vitro. Using this system, we are further able to investigate the potential of antisense RNAs (AS RNAs), presuming to improve the trans-splicing efficiency of a selected RTM, specific for intron 102 of COL7A1. Mutations in the COL7A1 gene underlie the dystrophic subtype of the skin blistering disease epidermolysis bullosa (DEB). We have shown that co-transfections of the RTM and a selected AS RNA, interfering with competitive splicing elements on a COL7A1-minigene (COL7A1-MG), lead to a significant increase of the RNA trans-splicing efficiency. Thereby, accurate trans-splicing between the RTM and the COL7A1-MG is represented by the restoration of full-length green fluorescent protein GFP on mRNA and protein level. This mechanism can be crucial for the improvement of an RTM-mediated correction, especially in cases where a high trans-splicing efficiency is required.     

Multi-objective genetic algorithm for berth allocation problem considering daytime preference

Maximization of operational efficiency and minimization of cost are pursued by terminal operators, whereas daytime preference is increasingly emphasized by governments, terminal operators and workers. Daytime preference in berth allocation schedule refers to schedule the workloads in nights as fewer as possible, which improves working comfort, safety, and green and energy-savings degrees, but may decrease the throughput and total operational efficiency. By extending existing dynamic discrete berth allocation model, a bi-objective model considering daytime preference is established to minimize the delayed workloads and the workloads in nights. Based on the well known NSGA-II algorithm, a multi-objective genetic algorithm (moGA) is developed for solving the bi-objective model by using a two-part representation scheme. The sensitivities of the algorithmic parameters and tradeoffs between daytime preference and delayed workloads are analyzed by numerical experiments. The algorithmic aspects of the proposed approach and the effects of daytime preference on solutions are all examined. Finally, the managerial implications are discussed.     

Digital Microfluidics for Manipulation and Analysis of a Single Cell

The basic structural and functional unit of a living organism is a single cell. To understand the variability and to improve the biomedical requirement of a single cell, its analysis has become a key technique in biological and biomedical research. With a physical boundary of microchannels and microstructures, single cells are efficiently captured and analyzed, whereas electric forces sort and position single cells. Various microfluidic techniques have been exploited to manipulate single cells through hydrodynamic and electric forces. Digital microfluidics (DMF), the manipulation of individual droplets holding minute reagents and cells of interest by electric forces, has received more attention recently. Because of ease of fabrication, compactness and prospective automation, DMF has become a powerful approach for biological application. We review recent developments of various microfluidic chips for analysis of a single cell and for efficient genetic screening. In addition, perspectives to develop analysis of single cells based on DMF and emerging functionality with high throughput are discussed.     

Sensitivity Analysis on Ladder Network Equivalent Circuit Parameters of Power Transformer

In this article, novel methods and ideas are introduced, which altogether lead to an accurate model of power transformer windings. First, by the determination of non-dominant (hidden) resonances from frequency-response tests, a ladder model is proposed. Next, it is improved by assigning different values for similar elements of each section of the model. The parameters are obtained by minimizing the error function via a genetic algorithm. Sensitivity analysis is then applied to the obtained model to achieve further examinations and tests. Measurements have been driven from the windings of a 20/0.4-kV, 1600-kVA transformer. Modeling, methodologies, and sensitivity analysis in this article can be very useful for future research aiming to find internal faults of the transformer with the frequency response analysis.