Deep Attention Unet: A Network Model with Global Feature Perception Ability

Remote sensing image segmentation is a specific task of remote sensing image interpretation. A good remote sensing image segmentation algorithm can provide guidance for environmental protection, agricultural production, and urban construction. This paper proposes a new type of UNet image segmentation algorithm based on channel self attention mechanism and residual connection called . In my experiment, the new network model improved mIOU by 2.48% compared to traditional UNet on the FoodNet dataset. The image segmentation algorithm proposed in this article enhances the internal connections between different items in the image, thus achieving better segmentation results for remote sensing images with occlusion.Comment: 6 pages,7 figure

Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting

Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled texts belong to novel classes, thereby limiting the practicality of these methods. We argue that the OpenRE setting should be more aligned with the characteristics of real-world data. Specifically, we propose two key improvements: (a) unlabeled data should encompass known and novel classes, including hard-negative instances; and (b) the set of novel classes should represent long-tail relation types. Furthermore, we observe that popular relations such as titles and locations can often be implicitly inferred through specific patterns, while long-tail relations tend to be explicitly expressed in sentences. Motivated by these insights, we present a novel method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data. Experimental evaluations on several Open-world RE benchmarks demonstrate that KNoRD consistently outperforms other existing methods, achieving significant performance gains.Comment: 10 pages, 6 figure

Advanced Inverter control for mixed source microgrids

This thesis focuses on investigating virtual oscillator control (VOC) and applying it to mixed source microgrids to address several stability issues. A detailed comparison between VOC and droop control in a three-phase system is presented in terms of transient responses of a single inverter under small load disturbances and the synchronization speed in multiple paralleled inverters under various inverter terminal voltage amplitude and frequency regulation settings. In the single-inverter microgrid, it is demonstrated in both simulation and experiment that the two control models produce similar transient responses in the output voltage and current amplitudes. However, VOC has a faster instantaneous frequency transient response whilst still maintaining the terminal voltage amplitude transient response of the droop controller. In microgrids with multiple inverters, the synchronization speed of the VOC is faster than that of the droop control when the terminal voltage’s frequency regulation range is allowed to be wide. The conclusion is verified with different types of loads. A virtual inertia design method for the VOC inverter with a mixed source microgrid is presented to improve the frequency stability issues of the system. The per unit inertia constant of a VOC inverter is derived when coupled with a synchronous generator in an islanded microgrid. The control parameters of the virtual inertia are designed via small-signal analysis. A dual second order generalized integrator - frequency locked loop (DSOGI-FLL) is adopted for digital implementation of proposed virtual inertia based VOC. With the use of virtual inertia block, the frequency nadir is improved by 22% and rate of change of frequency is improved by 29% compared with the unmodified VOC inverter during the transient period induced by load disturbances. Simulation and experimental results verify the enhanced transient response of system frequency. A voltage and current dual-loop control structure is added to the VOC inverter to solve the voltage drop issues at the inverter terminals caused by the inverter dead-time effects, non-ideal semiconductor and LCL filter. A complete small-signal model for a multiple-inverters microgrid with the proposed control structure is presented in order to assess system stability using eigenvalue and participation factor analysis. Analytical results show that the parameter related to the frequency regulation and the integral gain of the voltage controller affect the location of the system’s dominant modes significantly. The stability margin is determined by modifying these control parameters. Experimental results on a laboratory test microgrid verify the predication from the small-signal analysis and time-domain simulations. Finally, a method to limit current in the VOC inverter under large disturbances in a mixed source microgrid is proposed. During a large load change in the islanded microgrid, the inverter based sources may get temporarily overloaded until other generations with sufficient power margin take the remaining load burden. The original VOC inverter lacks the ability to constrain the current within limits during the transient period. The dual-loop structure proposed in this thesis can limit the transient current with the use of virtual impedance. Such virtual impedance is presented by the desired maximum current magnitude and virtual voltage drop. Compared with a recently proposed fault ride through VOC inverter, the proposed virtual impedance based current limitation method can effectively constrain the inverter current within the pre-set value under large disturbances, which augments the range of application of VOC and enhances its robustness

遺伝的アルゴリズムの改良に基づくマルチターゲットの運輸問題に関する研究

With the rapid development of economic globalization and information technology, rapid changes have taken place in all fields of society. The status of modern logistics industry in the process of the flow of social means of production and commodities has become increasingly prominent, accompanied by profound changes in production and manufacturing, material circulation, commodity transactions and management methods. Logistics cost accounts for a large share of national GDP, which can reflect the quality and scale of a country\u27s national economy, reduce the logistics cost of enterprises, and greatly improve the profit space. Especially under the background of economic globalization, the competition among enterprises is increasingly fierce, and the impact of logistics on the competitiveness of enterprises is increasingly obvious. In the modern e-commerce environment, with the rapid development of science and technology, the space for enterprises to obtain profits from the products themselves has been greatly reduced. In order to reduce costs and improve profits as much as possible, enterprises focus on logistics. In the whole logistics system, transportation is a very important link. Therefore, efforts to reduce the cost of logistics and transportation can greatly reduce the cost of the entire logistics system. This paper starts from the main factors involved in the transportation logistics, optimizes the main factors affecting the logistics, reduces costs and improves profits.Firstly, this paper discusses and studies the distribution personnel, mainly including the logistics distribution under the limitation of personnel fatigue and the delivery distribution mode under the new mode of personnel allocation - "crowdsourcing logistics". Aiming at the research on the limitation of fatigue, aiming at the maximization of customer satisfaction and the minimization of total cost, this paper constructs a model of path optimization for driver\u27s fatigue driving, and designs a single Partheno-genetic algorithm for the model, which is verified by the distribution case of Japan\u27s otaku. On the research of crowdsourcing delivery, taking the delivery network as the research object, this paper analyzes the distribution process, mode and existing problems of crowdsourcing delivery mode. Based on the purpose of optimizing the distribution network, taking the shortest distribution path and the least time delay as the objective function, the basic optimization model and dynamic optimization model of crowdsourcing distribution path with time window are established, and the rationality of the model is evaluated.Secondly, from the perspective of vehicle research and analysis, mainly study the two-tier node logistics distribution mode based on heterogeneous vehicles. This paper analyzes the common transportation vehicle selection problem in the existing transportation. Based on the genetic algorithm, taking the transportation cost of the double-layer logistics node of a city\u27s seafood products as the optimization goal, and comprehensively considering the problem of taking delivery vehicle route and vehicle configuration strategy of different routes at the same time, the mathematical model of vehicle scheduling and transportation route problem in the double-layer node transportation route is established. In this paper, MATLAB software is used to solve the model based on traditional genetic algorithm and Partheno-genetic algorithm, and the correctness and effectiveness of the model and Partheno-genetic algorithm are verified.Then, from the perspective of transportation path mode, the research mainly involves the current hot "multimodal transport" problem. In this paper, the coal transportation in a country is taken as the research object. Under the mode of "iron water combined transportation", how to reasonably distribute the transportation capacity and correctly select the transportation mode can realize the enterprise to control the logistics cost and ensure the maximum profit. At the same time, based on the traditional genetic algorithm mechanism, aiming at the premature and local search ability of the traditional genetic algorithm in solving the logistics transportation path optimization problem are analyzed Due to the shortage of power, a hybrid genetic algorithm is proposed to solve the model.Finally, the optimization algorithm of logistics distribution is discussed. This paper presents a hybrid genetic algorithm based on information entropy and game theory. First, the initial population is generated by calculating population diversity with information entropy. Combined with parallel genetic algorithm, standard genetic algorithm (SGA), Partheno-genetic algorithm (PGA) and hybrid genetic algorithm (sga-pga) which integrates standard genetic algorithm and Partheno-genetic algorithm (sga-pga) are used to perform evolutionary operations. At the parallel node, information entropy and fitness value of each sub population are used Finally, three programs checking functions Rosenbrock function, Rastrigin function and Schaffer function are introduced to analyze the performance superiority of the algorithm.博士(工学)法政大学 (Hosei University