Earticle

Recognizing the problems such as the current execute process makes the bankruptcy process difficult to implement the bankruptcy trial resulted in the weakening of the function of the bankruptcy system and the public´s perception of bankruptcy is backward problems that lead to the deficiency of the bankruptcy system In order to solve the problems ofdifficulty in bankruptcyanddifficulty in execution, China has established the system of execute case transfer to bankruptcy review. After the establishment of the system, the theoretical and practical circles have shown great enthusiasm for research, scholars from the theoretical field emphasize on the choice of the starting mode, the path optimization and the concept of due process. But the experts in the practical field focus on improving the operability of the system. This article focuses on optimizing the function of execute the case transfer to bankruptcy review system, on the basis of analyzing the abnormal situation of the system, it analyzes its special function orientation, transfer of an enterprise legal person who is unable to repay or comply with the bankruptcy conditions to bankruptcy proceedings to solve the problem of liquidation. And think that In order to achieve this function, when establish this system it should focuses on the convergence of executive procedures and bankruptcy procedures from all aspects in order to give full play to the bridge function of the system. However, at present, the system has entered a difficult position in judicial practice, and the expected function of the system has not been brought into full play. It is not difficult to find out the reasons for it, The system has been neglected because of the internal system construction, namely, the execution system, the bankruptcy trial system and the execute of the case transfer to bankruptcy review system it also includes the influence of external operation environment including supporting system and bankruptcy concept. Therefore, this article advocates perfecting the system from two aspects: internal system and external operation environment. On the perfection of the internal system, strictly prohibit the execution of the court use the way of end the execution of the program to business entity to solve the situation that the debtor can´t pay off. Set up independent bankruptcy court and professional bankruptcy team. Further refining the linkage mechanism between the executive system and the bankruptcy system. With the perfection of the internal system, provide a strong system guarantee for the execute the case transfer to bankruptcy review system. As far as the optimization of external operation environment is concerned, we must strengthen hard system supply and soft culture construction, create a good external environment for ensuring the operation of the system and giving full play to its functions.

오일 및 CO2 가격을 반영한 CO2 자극공법의 최적 설계안을 검토하기 위하여 본 수치 연구를 수행하였다. 주입설계 최적화를 위하여 순현가와 연계된 목적함수를 정의하고 Computer Modeling Group의 최적화 도구인 CMOST를 이용하였다. 복수의 CO2-오일 가격 시나리오를 설정하고 순현재가치를 기준으로 자극공법의 생산성에 영향을 주는 주입량과 soaking 시간 등의 설계 인자들을 최적화하였다. 본 연구 결과에 따르면 CO2 가격이 상승하면서 주입량이 감소하였으며 이는 두 번째 단계에서 더 크게 나타났다. CO2 가격이 높을수록 주입의 효율성을 고려하여 첫 번째 단계에서 가능한 많은 양을 생산하는 것이 적절하였다. 오일 가격이 증가하면서 첫 번째 단계의 주입설계는 거의 변하지 않는다. soaking 시간의 경우 경향성이 뚜렷하지 않았으며 생산성에 미치는 영향도 매우 작았다.

This numerical study was undertaken to investigate optimal design of CO2 stimulation considering oil and CO2 prices. For injection design optimization, objective function associated with net present value (NPV) was defined. CMOST, the optimization tool of Computer Modeling Group, is used to determine optimum cases. Design parameters such as injection volume and soaking time, which are known to affect the performance of CO2 stimulation, were optimized in terms of NPV through different scenarios of oil and CO2 prices. Results from the study indicated that optimum injection volume was declined at higher cost of CO2, especially in the second cycle. For higher CO2 price, it was better to maximize production of the first cycle for improving the efficiency of injection. As oil price increases, there were no changes of injection scheme in the first cycle. Soaking time had no clear tendency related with operating condition and affected production performance very slightly.

In a sheet metal forming process, fracture and wrinkle are the most difficult task in new parts launching. The variation in process condition generates the fracture and wrinkle fluctuation. The fracture and wrinkle are very sensitive to the process conditions, then the main effects of the design variables cannot be obtained from the standard mean analysis. Therefore, in order to minimize the fracture and wrinkle in parts of automobile, a special method to counterpart is required. In this study, a new design method to achieve the optimal in the sheet metal forming process is proposed. The effectiveness of the proposed methods is shown with an example of the parts of fracture and wrinkle.

This paper presents a method which optimizes the cost allocation in the design process of a metal curtain-wall unit using Quality Function Deployment (QFD) method. From the literature survey, it was found that previous researches have been focused on maximizing customer-satisfaction using QFD without considering the constrained budget. This paper describes how the limited budget condition is considered in the QFD process for a curtain wall design. A case study is presented to demonstrate the applicability of the method proposed by the authors. The method contributes to deliver the curtain-wall design in a way to satisfy the level of requirements of both the customer and the curtain-wall contractor by calculating a solution vector allocating limited budget to the Technical Attributes(TAs) of the curtain wall unit according to the importance weight of Customer Attributes(CAs).

The quantum particle swarm optimization (QPSO) algorithm exists some defects, such as premature convergence, poor search ability and easy falling into local optimal solutions. The adaptive adjustment strategy of inertia weight, chaotic search method and neighborhood mutation strategy are introduced into the QPSO algorithm in order to propose an improved quantum particle swarm optimization (AMCQPSO) algorithm in this paper. In the AMCQPSO algorithm, the chaotic search method is employed to promote the quality of initial population. The adaptive adjustment strategy of inertia weight is used to adjust the global search ability and local search ability of particles in the running process of QPSO algorithm. The neighborhood mutation strategy is used to increase the diversity of population and avoid premature convergence. Finally, in order to evaluate the performance of the AMCQPSO algorithm, several well-known benchmark functions are selected in this paper. The experiment simulations show that the proposed AMCQPSO algorithm can effectively improve the quality of solutions, and takes on powerful optimizing ability and more quickly convergence speed.

Particle swarm optimization (PSO) is a population-based stochastic optimization that has been widely applied to a variety of problems. However, it is easily trapped into the local optima and appears premature convergence during the search process. To address these problems, we propose a new particle swarm optimization by introducing chaotic maps (tent map and logistic map) and Gaussian mutation into the PSO algorithm. On the one hand, the chaotic map is employed to initialize uniform distributed particles so as to improve the quality of the initial population, which is a simple yet very efficient method to improve the quality of initial population. On the other hand, the Gaussian mutation mechanism based on the maximal focus distance is adopted to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space until the global optimal or the closer-to-optimal solutions can be found. Experimental results on two benchmark functions demonstrate the effectiveness and efficiency of the PSO algorithm proposed in this paper.

Student to business(Abbr. S2B) e-commerce network platform is designed to display the college students’ creative works, and build joint channel for the creative talents of college and small and medium-sized enterprises requiring innovative resources. Firstly, this paper analyses the targets and features of S2B e-commerce platform. Secondly, the user requirements-oriented explicit functional demands are listed from two aspects of the individual users and enterprise users through investigation. In addition, implicit functional demand is obtained based on ontology. According to the method of ontology mapping, the function description by natural language is mapped to semantic elements and demand items, then the relationship of demand items coming into semantic structures. According to the method of ontology inference, the semantic structure is mapped to domain ontology of S2B e-commerce network platform, as well as the implicit functional requirements of platform are digging out. As a result, the whole functional structure tree of S2B e-commerce network platform is constructed. Finally, the detail functions of the platform are described by UML modeling tool. Through the platform the college students’ creative works are integrated and flow to meet the innovative demand of enterprises. S2B e-commerce model can make the creative resources be fully used and put forward the innovative and creative industry developing well.

In order to improve the optimum speed, crease the diversity of the population and overcome the premature convergence problem in differential evolution(DE) algorithm for solving the complex optimization problems, the chaotic optimization algorithm with powerful local searching capacity and multi- strategy are introduced into the DE algorithm in order to propose an improved adaptive differential evolution(COMSIADE) algorithm in solving function optimization problems. In the COMSIADE algorithm, the ergodicity, regularity and internal randomness of the chaotic sequence are used to overcome the shortcoming of premature local optimum to improve the global searching capacity of the DE algorithm. The multi-population with parallel evolution is used to preserve the diversity of the population at the initial generation. The self-adaptive crossover operator probability is used to improve the global convergence ability, the stability and robustness. Finally, in order to test and verify the effectiveness of the COMSIADE algorithm, several benchmark functions are selected in this paper. The experimental results indicate that the proposed COMSIADE algorithm can improve the global searching capacity and avoid falling into local optimum. And it takes on the higher searching precision and faster convergence speed in solving the complex optimization problem.

This paper proposes a method to realize sensor function allocation and effective data aggregation simultaneously in wireless sensor networks. This method realizes dynamic allocation of sensor functions so as to balance the distribution of each sensor function in a target monitoring area. In addition, effective data aggregation is performed by using a tree network topology and time division multiple access (TDMA), which is a collision-free communication scheme. By comparing the results from the proposed method with the results from non-optimized methods, it can be validated that the proposed method is 1.7 times more efficient than non-optimized methods in distributing sensor functions. With this method, the network lifetime is doubled, and the number of data packets received at a base station is considerably increased by avoiding packet collisions.

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.

This paper focuses on improving accuracy in constrained computing settings by employing the ReLU (Rectified Linear Unit) activation function. The research conducted involves modifying parameters of the ReLU function and comparing performance in terms of accuracy and computational time. This paper specifically focuses on optimizing ReLU in the context of a Multilayer Perceptron (MLP) by determining the ideal values for features such as the dimensions of the linear layers and the learning rate (Ir). In order to optimize performance, the paper experiments with adjusting parameters like the size dimensions of linear layers and Ir values to induce the best performance outcomes. The experimental results show that using ReLU alone yielded the highest accuracy of 96.7% when the dimension sizes were 30 - 10 and the Ir value was 1. When combining ReLU with the Adam optimizer, the optimal model configuration had dimension sizes of 60 - 40 - 10, and an Ir value of 0.001, which resulted in the highest accuracy of 97.07%.

Image edge detection is an important part of image processing, and the effect of edge detection is also directly affected by image analysis, recognition and understanding. Canny operator is the most commonly used image edge detection operator. However, this operator has some limitations. The traditional Canny operator uses Gaussian filtering which may bring problems such as missing edge information and false edge. Besides, the selection of high and low thresholds of the traditional Canny operator are not accurate, and cannot be carried out by self-adaption. In order to solve these problems, this paper presents an optimized algorithm for Canny operator. In this paper, an improved anisotropic diffusion function is used to filter the image, and the improved filtering not only reduces the noise, but also maintains the edge information of the image. Additionally, this paper has improved the maximum between-class variance method (OTSU) to select the high and low thresholds of Canny operator by self-adaption. The improved algorithm is applied to edge detection of various images, and the results indicated that the improved Canny operator is effective in reducing noise and extracting edge.

Properly allocation of virtual machines is important for computing infrastructures scheduling. This paper presents systemic method on virtual machine array optimization control based on artificial intelligence and matrix control theory. According to request service data from users to provide proper VMs roughly via intelligent pattern recognition based on RBFNN, the data is sent to a multiple-targets optimization process to produce VMs allocation matrix precisely, thus enable to minimize the cast and enhance efficiency of the whole array to achieve low consumption optimization and ensure the stability of the system. Simulation experiments confirmed the effectiveness of this model and adaption ability in online dynamics.

Developing a system architecture that satisfies the functional requirements and keeping the quality attributes high is still an open challenge.The task of selecting the optimal combination and redundancy levels of components (which is known as RAP problem) that meet system-level constraints is hard since each one of these alternatives must be modeled in order to obtain the global optimal solution. This problem is classified as NP-complete and many researches have suggested use of metaheuristic to encode this challenge as an optimization problem for improving architectures. The search for optimal solution in metaheuristics techniques is guided by objective function/functions. Majority of studies in RAP area were focused on reliability and mostly simple cost functions were used. An adopted cost-with-discount function is proposed in this paper to increase the efficiency of the approach within especial conditions. Single Objective - Particle Swarm Optimization (SO-PSO) to automatically generate and to evaluate architecture is implemented in order to support design decision. The Anti-lock Breaking System (ABS) has been used as a case study to motivate, demonstrate the applicability, and to validate the study. And statistical t-test is performed to evaluate the approach. The evaluation of the approach has proven that; it is significantly impact on providing an efficient solution and it can be used to assist architectures to easily achieve their objectives since they can decide on the automatically provided design options according to technical and financial inclinations.

본 연구에서는 다중 사용자 환경의 볼륨 가시화(Volume Rendering)에서 변환 함수(Transfer Function)의 최적화 방안을 연구한다. 볼륨 데이터에 따라 필요한 변환 함수의 형태가 다르기 때문에 여러 번의 시행착오를 겪어야 필요한 변환 함수를 얻을 수 있는데, 이를 방지하기 위해 크라우드 소싱 기법을 이용하여 변환 함수의 파라미터를 최적화 하는 방안을 제안한다. 다중 사용자 환경에서 각 사용자마다 신뢰도에 따른 레벨을 지정하여 가중치로 사용한다. 이전 사용자가 사용했던 변환 함수 파라미터는 가중치만큼 다음 사용자에게 제공되기 때문에 다음 사용자는 변환 함수의 최적 파라미터를 찾기 위한 시도횟수를 줄일 수 있다.

times. To prevent this, we propose transfer function optimization plan using crowd sourcing. In multi user environment, we use weight value for reliability level for each user. Because transfer function parameter used previous users is provided next users, they can be used effectively optimized transfer function and can reduce attempts. This Study is Transfer function optimization plan of volume rendering of multi user environment. Each volume data, for appropriate transfer function, they should be adjusted parameter many times. To prevent this, we propose transfer function optimization plan using crowd sourcing. In multi user environment, we use weight value for reliability level for each user. Because transfer function parameter used previous users is provided next users, they can be used effectively optimized transfer function and can reduce attempts.