Earticle

지식재산권은 현재 기업 발전의 핵심 경쟁력으로 막대한 경제적 가치를 지니고 있다. 그러나 지식재산권의 특성, 지식재산권법과 파산법의 차이, 일부 중국 기업들은 지식재산권에 충분한 주의를 기울이지 않고 있는 등으로 인해 중국 기업의 파산절차에서 지식재산권 범위의 불명확 한 정의, 지식재산권 계약의 부적절한 처리, 지식재산권 가치의 불명확 한 평가, 지식재산권 가치 실현의 미흡 등의 문제가 발생하고 있다. 따 라서 미국, 네덜란드, 일본의 경험을 참고해 중국 파산절차에서 지식재 산권 처리는 파산제도 개선, 기업의 지식재산권 보호 인식 제고, 자산 접수, 자산 관리 및 자산 평가, 지식재산권 실현 측면에서 개선되어야 한다.

Intellectual property, as the core competitiveness of current enterprise development, have enormous economic value. However, due to the characteristics of intellectual property, the differences between intellectual property law and bankruptcy law, and the lack of attention paid to intellectual property by some Chinese enterprises, there are problems with the treatment of intellectual property in bankruptcy proceedings of Chinese enterprises, such as unclear definition of the scope of intellectual property, improper handling of intellectual property contracts, unclear evaluation of the value of intellectual property, and insufficient realization of the value of intellectual property. Therefore, after drawing on the experience of countries such as the United States, the Netherlands and Japan, the treatment of intellectual property in bankruptcy proceeding in China should be improved, in terms of improving the bankruptcy system, enhancing the awareness of enterprises on intellectual property protection, and the asset acceptance, asset management and asset valuation transformation of intellectual property.

知识产权作为当前企业发展的核心竞争力，其有着巨大的经济价值。 但由于知识产权的特点，知识产权法与破产法的差异，以及中国部分企 业对知识产权的重视不够，导致了中国企业在破产程序中，对知识产权 的处理出现了知识产权范围界定不明、知识产权合同处理不当、知识产 权价值评估不清、知识产权价值变现不充分等问题。因此，在借鉴了美 国、荷兰和日本等国家的经验后，应当从破产制度的完善、企业知识产 权保护意识的增强，以及在知识产权的资产接收、资产管理和资产评估 变现等方面，完善中国破产程序中对知识产权的处置。

전통적인 ‘집행의 파산절차 전환’ 메커니즘은 그 고유한 결함으로 인해 끊임없이 발생하는 실행 문제에 대응할 수 없다. 집행과 파산절차 융 합은 집행절차와 파산절차의 이원적 분리 구조를 돌파하여, 이념, 업무, 자원의 심층적인 통합을 통해 ‘효율로 공정을 촉진하고 공정으로 효율성 을 보완하는’ 협력 메커니즘을 형성했다. 현재 집행과 파산절차 융합은 시작 동력의 부족, 절차 연결의 장벽, 사건 유형화 구분 부족, 지원 보 장 메커니즘 부재 등의 문제에 직면해 있다. 채권자와 채무자의 절차 선 택 선호, 집행 및 파산 부서의 기능 분할, 정보 공유 부족 등은 제도의 효율성을 충분히 발휘하는 데 제약을 주고 있다. 이에 대해 ‘집행 불가’ 의 실질심사 기준을 명확히 하고, 법원의 직권 발동 메커니즘을 강화하 며, 통합정보 공유 플랫폼을 구축하고, 집행 조치와 파산절차의 효과적 연계를 완비하며, 정부와 법원의 연계 및 전문화 건설을 심화하는 등의 방안을 통해 제도적 장애를 체계적으로 해결해야 한다.

Due to its inherent defects, the traditional “enforcement to bankruptcy” mechanism is unable to cope with the ever-growing enforcement problems. The integration of enforcement and bankruptcy breaks through the dichotomy of enforcement and bankruptcy procedures, and forms a synergistic mechanism of “promoting fairness with efficiency and supplementing efficiency with fairness” through in-depth integration of concepts, operations and resources. Currently, “enforcement and bankruptcy integration” faces problems such as insufficient start-up motivation, procedural articulation barriers, insufficient differentiation of case types, and the absence of supporting safeguard mechanisms. The procedural choice preferences of creditors and debtors, the functional division between enforcement and bankruptcy departments, and the lack of information sharing constrain the system's effectiveness from being brought into full play. In this regard, systematic efforts should be made to break down the institutional barriers by clarifying the substantive review criteria for “inability to execute”, strengthening the mechanism for activation of the court's powers and authority, constructing an integrated information-sharing platform, perfecting the connection between the effectiveness of execution measures and bankruptcy proceedings, and deepening the linkage between the government and the courts and the construction of specialization.

传统的“执转破”机制因其固有缺陷，无法应对层出不穷的执行难题。 “执破融合”突破了执行程序与破产程序的二元分立格局，通过理念、业 务、资源的深度整合，形成“以效率促公平、以公平补效率” 的协同机 制。当前“执破融合” 面临启动动力不足、程序衔接壁垒、案件类型化区 分不足及配套保障机制缺位等问题。债权人与债务人的程序选择偏好、 执行与破产部门的职能分割、信息共享缺失等，制约了制度效能的充分 发挥。对此，应当通过明确“执行不能” 的实质审查标准、强化法院职权 启动机制、构建一体化信息共享平台、完善执行措施与破产程序的效力 衔接、深化府院联动及专业化建设等路径，系统性地破解制度障碍。

Test functions play an important role in validating and comparing the performance of optimization algorithms. The test functions should have some diverse properties, which can be useful in testing of any new algorithm. The efficiency, reliability and validation of optimization algorithms can be done by using a set of standard benchmarks or test functions. For any new optimization, it is necessary to validate its performance and compare it with other existing algorithms using a good set of test functions. Optimization problems are widely used in various fields of science and technology. Sometimes such problems can be very complex. Particle Swarm Optimization is a stochastic algorithm used for solving such optimization problems. This paper transplants some of the test functions which can be used to test the performance of Particle Swarm Optimization (PSO) algorithm, in order to improve its performance and have better results. Different test functions can be used for different types of problems. These test functions have a specific range and values, which can be applied in different situations. These functions, when applied to the PSO algorithm, can give the better comparison of results. The test functions that have been the most commonly adopted to assess performance of PSO-based algorithms and details of each of them are provided, such as the search range, the position of their known optima, and other relevant properties.

For solving traveling salesman problem (TSP), the ant colony optimization (ACO) algorithm and simulated annealing (SA) algorithm are used to propose a two-phase hybrid optimization (TPASHO) algorithm in this paper. In proposed TPASHO algorithm, the advantages of parallel, collaborative and positive feedback of the ACO algorithm are used to implement the global search in the current temperature. And adaptive adjustment threshold strategy is used to improve the space exploration and balance the local exploitation. When the calculation process of the ACO algorithm falls into the stagnation, the SA algorithm is used to get a local optimal solution. And the obtained best solution of the ACO algorithm is regarded as the initial solution of the SA algorithm, and then a fine search is realized in the neighborhood. Finally, the probabilistic jumping property of the SA algorithm is used to effectively avoid falling into local optimal solution. In order to verify the effectiveness and efficiency of the proposed TPASHO algorithm, some typical TSP is selected to test. The simulation results show that the proposed TPASHO algorithm can effectively obtain the global optimal solution and avoid the stagnation phenomena. And it has the better search precision and the faster convergence speed.

This paper aims to present a self-adaptive global particle swarm optimization (SGPSO) algorithm for solving unconstrained optimization problems. In the new algorithm, the inertia weights are generated based on Gaussian distribution, which is helpful to improve the diversity of the population. In addition, the worst particle is updated by averaging the other particles, which is beneficial to improving the quality of the population. Finally, a global disturbance is adopted to increase the convergence rate of SGPSO. In the disturbance process, a disturbance factor is utilized to control the searching ranges of the population, which can effectively keep a balance between the global exploration and local exploitation. Twenty well-known benchmark functions are considered to evaluate the performance of SGPSO, and 50 runs are implemented in each case. Numerical experiments and comparisons demonstrate that SGPSO is superior to the other three algorithms according to means, standard deviations and convergence rate.

As an optimization technique, particle swarm optimization (PSO) has obtained much attention during the past decade. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to realize. To enhance the performance of PSO, an improved hybrid particle swarm optimization (IPSO) is proposed to solve complex optimization problems more efficiently, accurately and reliably. It provides a new way of producing new individuals through organically merges the harmony search (HS) method into particle swarm optimization (PSO). During the course of evolvement, harmony search is used to generate new solutions and this makes IPSO algorithm have more powerful exploitation capabilities. Simulation results and comparisons with the standard PSO based on several well-studied benchmarks demonstrate that the IPSO can effectively enhance the searching efficiency and greatly improve the search quality.

As an optimization technique, particle swarm optimization (PSO) has obtained much attention during the past decade. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to realize. To enhance the performance of PSO, an improved hybrid particle swarm optimization (IPSO) is proposed to solve complex optimization problems more efficiently, accurately and reliably. It provides a new way of producing new individuals through organically merges the harmony search (HS) method into particle swarm optimization (PSO). During the course of evolvement, harmony search is used to generate new solutions and this makes IPSO algorithm have more powerful exploitation capabilities. Simulation results and comparisons with the standard PSO based on several well-studied benchmarks demonstrate that the IPSO can effectively enhance the searching efficiency and greatly improve the search quality.

The paper provides an improved evolutionary strategy (ES) of genetic algorithm (GA) on the basis of the existing literature. The ES overcomes the shortage of traditional GA whose excellent child individuals obtained in the crossover process may not survive in the process of mutation. In addition, the crossover probability and mutation probability which is hard to determine in traditional GA is removed for this proposed strategy. At the same time, it increases the number of individuals produced in process of crossover. This may increase the possibility of producing excellent individuals, thus lead to better improvement of the traditional GA. The test result of finding the optimal values of four functions using transitional GA and the proposed GA is presented in this paper. The result shows that the improved ES presented in this paper has faster calculation speed and significantly smaller number of iterations than the traditional GA. Thus, the improvement of improved ES is powerfully illustrated. Based on articles in the existing research literature, the initial population generation methods were further explored when using the genetic algorithm(GA) for solving constrained optimization problem. Through the research we present a new method about initial interior point’s generation. Firstly, construct a constraint posed by the objective function, which is based on the characteristics of constrained optimization problems. Then translate the problem of evaluating the initial interior point into a problem of solving a series of unconstrained optimization. By solving the unconstrained optimization problem, we achieve the solution of the initial interior point. Based on this idea, the research has given a method on the generation of the rest initial population individuals. In addition, through the research we concluded that the key to generate the initial population is to obtain an initial point. The production of other individuals will take less time after the initial internal point is obtained. Finally, we verified by examples that the initial population generation method given by this paper is a fast and reliable method. Thus the shortage of the GA of which the initial population is difficult to be produced in some constrained optimization problem is overcome

Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi objective parameter optimization problems and often require an optimal solution for the success of a business organization by balancing demand and supply. This work reviews some of the fundamental theory of differential evolution, the methodology for master production scheduling calculation and most important results. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithm. The analysis reveals that the DE algorithm provides a better solution with reasonable computational time.

In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper. In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm. In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here. The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.

Research the Incident Vehicle Routing Problem with Soft Time-windows (IVRP-STW). Associated logistics scheduling problem with soft time windows using the logistic function optimization method to improve the chaotic particle algorithm, compared with the GA and the standard PSO algorithm. Simulation results show that such optimization method can effectively improve the global search of the particles in the particle swarm and the ability of local search is effective in resolving such problems.

The optimal generation dispatch problem with only one fuel option for each generating unit has been solved for many recent years. However, it is more realistic to represent the fuel cost function for each fossil fired plant as a segmented piece-wise quadratic functions. This is because of development of technology in thermal plants to reach maximum fuel save. Those units are faced with the difficulty of determining which the most economical fuel to burn is. This paper presents two effective methods for solving economic load dispatch problem with multiple fuel options. An advantage of the methods is to formulate Lagrange mathematical function easily based on the Lagrange multiplier theory. The proposed methods are tested on one test system consisting of ten generating units with various load demands and compared to other methods. The simulation results show that the methods are very efficient for the optimal generation dispatch problem with multiple fuel options

본 연구에서는 GeoGebra를 활용한 부등식 영역의 최대 최소 문제의 지도 과정에서 나타나는 학생들의 등고선 개념에 대한 이해 수준의 특징을 분석하였다. 또한 부등식 영역의 최대・최소 학습 지도에서 GeoGebra의 활용이 학생들의 수학적 사고에 미치는 영향과 함께 교실 수업에서 GeoGebra의 활용을 위한 시사점을 알아보았다. 연구의 결과로 첫째, 연구대상 학생들 중에서 주희는 등고선 개념에 대해서 과정 수준, 혜민이와 다영이는 행동 수준의 특성을 보였다. 둘째, 부등식 영역의 최대・최소 지도에서 GeoGebra의 활용은 학생들의 등고선 개념의 이해 수준을 향상시킬 수 있었다. 또한 GeoGebra의 활용을 통해 학생들은 목적함수의 의미를 파악할 수 있었으며, 목적함수식에서의 변수적 측면을 이해하고, 최댓값과 최솟값이 경계에서만 나오는 이유를 알 수 있었다.

This study was intended to suggest realistic knowledge for learning and teaching the optimization problems in regional inequalities using GeoGebra. For this purpose, learning material that is adequate to apply GeoGebra was developed and applied to experimental teaching. At first, through reviewing previous studies about learning of the optimization problems in regional inequalities, it is found that the concept of level curve is essential in studying the content. So analysis was conducted for the concept of level curve and development of the concept based on APOS theory that explains the development of understanding mathematical concept as the construction of mental structures: action, process, object. From the reviewing cases of utilizing GeoGebra for math class, availability of certain functions of GeoGebra like coordinate indicating function, drawing graph, dragging graph, and slider emerged as suitable teaching methods to understand the level curve. As a result, learning materials of activities with GeoGebra was designed so that student's understanding of the level curve can be improved. Secondly, the effects of using GeoGebra in teaching optimization problems in regional inequalities were investigated by analyzing the feature of learning・teaching through the developed learning materials. The analysis was conducted focusing on the change in level of student's understanding of the level curve. The followings are the results of this study. Through the concrete activities with GeoGebra, students' were able to understand the meaning of level curve, the variability of k in the expression f(x,y)=k, and relation between k and level curve f(x,y)=k. During the class with GeoGebra activities, students could correct their misconception and understand the reason why the maximum and minimum values are assumed only on the boundary of the area of solution of inequalities.

In this paper, using nonsmooth analysis, we established equivalence results between a locally Lipschitz vector optimization problem and its associated approximated problem under the proper efficiency.

In this paper, we establish the necessary and sufficient Karush-Kuhn-Tucker (KKT) conditions for an optimization problem with difference of set-valued maps under generalized cone convexity assumptions. We also study the duality results of Mond-Weir (MW D), Wolfe (W D) and mixed (Mix D) types for the weak solutions of the problem (P).

Zoning problems are the process of grouping several small geographic areas into a cluster. They include a variety of partitioning problems such as school district problems, sales territory designs, and political redistricting. A successful political redistricting plan must meet a number of neutral requirements such as population equality and contiguity as well as compactness. Generally, equal population and compactness are conflicting each other, which means the more equally populated districts, the less compact those. This paper aims to find various redistricting plans depending on the tradeoff plot between population equality and compactness. In this paper, a multiobjective optimization heuristic model called Pareto Simulated Annealing (PSA) will be used to develop redistricting problems. Computational results show that PSA to redistricting problems produces various redistricting plans so that decision makers can make a choice. These approaches can be nonpartisan alternatives to zoning problems.

We consider the linearized (approximated) problem for differentiable vector optimization problem, and then we establish equivalence results between a differentiable vector optimization problem and its associated linearized problem under the proper efficiency.

The article is devoted to mathematical modeling of prob-lems of stochastic optimization of the plastic metal working. Classifi-cation and mathematical statements of such problems are proposed. Several calculation techniques of the single goal function are pre-sented. The probability theory and the Fuzzy numbers were applied for solution of the problems of stochastic optimization.