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In practice, classification problems have appeared in many scientific fields, including finance, medicine and industry. It is critically important to develop an effective and accurate classification model. Although numerous useful classifiers have been proposed, they are unstable, sensitive to noise and slow in computation. To overcome these drawbacks, the combination of feature selection techniques with traditional machine learning models is of great help. In this paper, a novel feature selection method called the opposition-based seagull optimization algorithm (OSOA) is proposed and studied. The OSOA is constructed based on an SOA whose population is determined by the opposition-based learning (OBL) algorithm. To evaluate its overall classification performance, some measures, including classification accuracy, number of selected features, receiver operating characteristic curve (ROC), and computation time, are adopted. The empirical results indicate that the suggested method exhibits higher or similar accuracy and computational efficiency in comparison with genetic algorithm (GA)-, simulated annealing (SA)-, and Fisher score (FS)-based classification models. The experimental results show that the OSOA is a computationally efficient feature selection technique that has the ability to select relevant variables. Furthermore, it performs well with high-dimensional data whose number of variables exceeds the number of samples. Thus, the OSOA is an effective approach for the enhancement of classification performance.

This paper concerns interference estimation problem for device-to-device (D2D) communication networks. In the considered system, D2D users share common spectrum resources, such that the D2D links have interference with each other. To achieve effective interference management, it is necessary to have an accurate understanding of the interference relationship of the D2D devices, which is difficult since the locations and the number of mobile devices can vary over time. In this paper, we formulate an interference estimation problem for the D2D communication network where the D2D users change over time. Our objective is to get accurate estimations for the interference suffered by a generic D2D link and for the interference a D2D link introduces on other users. We propose a graph convolutional neural network (GCN) based estimation model which can estimate the interference of a D2D link based on the location information of the corresponding D2D pairs. Simulation results show the performance of our method.

This paper concerns the cooperative spectrum sensing (CSS) for cognitive radio (CR) networks, where the secondary users (SUs) collaborate to detect the presence of the primary users (PUs). With CSS, the information from different SUs is first fused, then, the detection of the PU signal is implemented based on the fused information. Most of the previous works focus on the design of a mapping function that calculates the probability of the existence of the PU signal based on the fused information. In this paper, we study the fusion process which combines the information from all SUs based on the local property of each SU. A graph-based multi-view learning framework for CSS (GMCSS) is designed to better fuse the information from different SUs. In the proposed framework, the information from each SU is considered as a view of the state of the target wireless channel and is fused with the information from other SUs through a graph-based learning process. Simulation results demonstrate the effectiveness of our method.

With the development of economic globalization and the deepening of China’s reform and opening up, international trade is frequent and the volume of foreign trade is increasing rapidly. Port economy has become an important force to promote the high-quality development of China’s economy. However, with the rapid development of port trade and transportation business, higher quality requirements have been put forward for port machinery manufacturing enterprises, so as to escort the development of marine economy. Based on this, this paper selects M company as the research case, deeply analyzes the quality management innovation mechanism of the company, so as to provide reference for the development of similar enterprises and boost the sustainable and healthy development of economy.

The high-end equipment manufacturing industry is the high-end part of the equipment manufacturing industry, the symbol of country's strategic emerging industries and industrial rise, and core competitiveness of the manufacturing industry. How to promote the high-end development of equipment manufacturing industry in the industrial chain through technological innovation and break the situation that developed countries hold the advanced technology in equipment manufacturing industry which is of great strategic significance for China's manufacturing power. This paper focuses on China's equipment manufacturing industry, and takes rail transit equipment manufacturing enterprise Z Company as an example to conduct benchmarking analysis with world-class enterprises, so as to explore and clarify the path and direction of China's equipment manufacturing enterprises to become world-class enterprises.

At present, China's economy is changing from the stage of high-speed growth to the stage of high-quality development, and the promotion of high-quality development is inseparable from the innovation of quality management. This paper takes the typical representative port manufacturing enterprises as an example to deeply analyze the development and experience of enterprise quality management innovation, so as to provide reference for the improvement of the quality level of similar enterprises and lead China's economy towards high-quality development.

At present, the entrepreneurial market of small and medium-sized enterprises in China is increasingly active. However, in order to achieve sustainable growth and development, these new ventures cannot obtain competitive advantage in the market competition only relying on their own strength and resources, and even affect the survival of enterprises. External relationship has become an important way for new ventures to obtain supplementary resources and promote the growth and development of enterprises. Based on this, this paper uses the typical case analysis method to understand how the supply chain relationship quality growth performance in the initial stage of new ventures. This also provides some experience and reference for the new venture how to build the appropriate supply chain relationship quality according to its growth stage, and adopt the matching relationship management mode.

This paper investigates the frequency regulation and active power sharing problem in AC microgrids with mobile power resource. Considering the impact of communication time-delays, a novel distributed finite-time secondary control method is proposed. The stability of the proposed control method is analyzed based on Lyapunov method. The upper bound of convergence time is derived and is only a function of the designed parameters, the topology of communication network, and communication time-delays. Numerical simulations are conducted to verify the performance of the proposed control method.

Software requirements are constantly changing. Consequently, the development process is frequently under time pressure, which results in technical debt. To illustrate the symptoms of technical debt, 22 code smells have been introduced to indicate the poor design in code fragment, among which refused bequest is one of the most harmful smells and with high diffuseness. However, refused bequest is rarely taken into account because there is a lack of dataset. Moreover, it is difficult to design the detection rules for refused bequest compared with other popular smells.In this paper, we propose a machine-learning-based refused bequest smell detection framework SEADART, which features the utilization of a set of synthetic smelly instances. Specifically, SEADART comprises three components: (1) a smell generation approach, and (2) a model training strategy, and (3) an AdaBoost-based detection model. We evaluate the performance of the proposed framework. The evaluation results suggest that the generated smelly instances are reliable, and the trained AdaBoost model significantly outperforms the state-of-the-art over a real-world dataset.

In this paper, we investigate the economic dispatch problem in islanded microgrids and we propose a distributed finite-time algorithm to solve the economic dispatch problem. Different from the existing distributed algorithms which solve the economic dispatch problem asymptotically, the proposed algorithm can get the optimal solution in finite time and the bound of the convergence time only depends on the designed parameters in the proposed algorithm. The rapid convergence rate of the proposed algorithm makes it suitable for application in microgrids with penetration of large scale distributed generation resources. Numerical simulations are conducted to evaluate the performance of the proposed algorithm.

The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720x720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.

In recent years, China has made great progress in improving its business environment, and the ranking is gradually improving every year. But compared with the top countries in business environment, there is still room for further optimization. This article mainly combs the problems existing in the continuous optimization process of China's business environment from three aspects: the consciousness of optimizing business environment, the construction of new Qin-Qing government business relationship, and the evaluation criteria of business environment, and puts forward corresponding policy suggestions.

In the era of “mass innovation, mass entrepreneurship” vigorously advocated by the CPC Central Committee, entrepreneurship is the key measure to solve the employment problem. However, due to the late start of entrepreneurship education in China, there is a widespread phenomenon that theory teaching and practice teaching are divorced, so that many problems in the university entrepreneurship teaching in China cannot be solved by traditional teaching methods, so it is of great significance to strengthen the research on the teaching mode of entrepreneurship education for university students. The theory of bisection classroom teaching provides new ideas for the research and practice of entrepreneurship education of college students in China. This paper uses the theory for reference, and combines it with the teaching objectives of entrepreneurship education to improve the level of entrepreneurship education of college students, so as to guarantee the teaching quality of entrepreneurship education of college students.

DMAIC process implementation of Six Sigma methodology in this research, combined with the value Engineering (VE), launched the project implementation. This study uses a variety of advanced quality management theory and methods, such as permutation graph, incidence graph, line graph, control chart, histogram and other QC tools, value function analysis of value engineering (VE) method, and the DMAIC, SINPOC six sigma tools. In the improvement phase, this study uses the DOE orthogonal experiment method to reduce the number of experiments, the cost of the experiment, and improve the company's quality management level and core competitiveness.

The paper studies the dimensions of supply chain dynamic capability, through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to verify, the results of data fitting show that the supply chain dynamic capability including three dimensions: supply chain learning capability, supply chain coordination capability, supply chain reconfiguration capability. At the same time, construct of supply chain dynamic capability obtained by grounded theory has good reliability and validity, which can be used as a measurement tool for the next step of the study, the conclusion of this paper enriches the research results of the dynamic capability of the enterprise.

Chinese government strongly advocates mass entrepreneurship and innovation, which provides a good business environment for start-ups. Many start-ups adapt to the change of environment, improve their own ability and obtain rapid development by establishing and expanding external supply chain relationship. Through the theoretical analysis and model construction of this paper, the internal mechanism of supply chain relationship quality affecting the growth performance of new enterprises is further clarified, which provides theoretical support for further empirical research.

the market competition has broken through the boundary of enterprises and developed into the competition among the supply chains of enterprises. The cooperation between supply chains has become a key factor for the sustainable growth of start-ups. Through cross case study, this paper establishes an IPO Model of supply chain relationship quality influencing the growth performance of new start-ups, analyzes the internal intermediary mechanism between them, and enriches the theory of supply chain relationship quality.

At present, with the growing influence of the world bank's business environment report, business environment construction has become an important strategic measure to promote economic development in various countries. In the process of economic system reform in China, the government should actively build a new business environment to promote the healthy and sustainable development of enterprises and other market entities. In recent years, China's state-owned business environment construction has achieved positive results. However, there are many problems. Based on the practical background, this paper aims to promote the high-quality development of China's economy, analyze the problems existing in the construction of the state-owned business environment, sort out the principles of optimization, and put forward the corresponding strategies.

This paper concerns the dynamic spectrum access problem for femtocell networks, where traffic loads are different among cells. We model the interference relationship of the femtocell networks with conflict graphs, and a graphical game is employed as the channel access coordination mechanism. A graph neural network based architecture is proposed, which directly maps traffic loads to the channel access scheme for each femtocell. With our method, each femtocell first estimates the qualities of all available channels based on the information from its neighbors, and then the channels of the highest quality are accessed. A multiagent reinforcement learning framework is designed to train the proposed architecture to make accurate estimations of channel quality.

This paper concerns the energy efficiencyoptimization for distributed cooperative spectrum sensing. In the considered distributed spectrum sensing system, each sensor measures the local test statistic for the target spectrum bands and these measurements will be combined together through a weighted consensus protocol. In this way, all the sensors are able to make contributions to the improvement of the spectrum sensing performance. However, the significance of a sensor’s contribution depends on its local signal to noise ratio. From energy efficiency perspective, it is not reasonable to invest energy on the sensors that bring little benefits. In this paper, we formulate an energy efficiency optimization framework for distributed spectrum sensing. Our objective is to achieve the target spectrum sensing performance with as few sensors as possible. Accordingly, a genetic algorithm based approach and a particle swarm optimization based approach are proposed for this problem.

The cyber-physical security of smart grid is of great importance since it directly concerns the normal operating of a system. Recently, researchers found that organized sequential attacks can incur large-scale cascading failure to the smart grid. In this paper, we focus on the line-switching sequential attack, where the attacker aims to trip transmission lines in a designed order to cause significant system failures. Our objective is to identify the critical line-switching attack sequence, which can be instructional for the protection of smart grid. For this purpose, we develop an evolutionary computation based vulnerability analysis framework, which employs particle swarm optimization to search the critical attack sequence. Simulation studies on two benchmark systems, i.e., IEEE 24 bus reliability test system and Washington 30 bus dynamic test system, are implemented to evaluate the performance of our proposed method. Simulation results show that our method can yield a better performance comparing with the reinforcement learning based approach proposed in other prior work.

We propose a novel cooperative spectrum sensing (CSS) framework for cognitive radio networks based on imbalanced learning techniques, which aims to resolve the skewed category distribution problems of signal data. For a radio channel shared by primary users (PUs) and secondary users (SUs), the signal data composed of energy vectors, in which each energy level is estimated by SU, can be used to detect the channel availability via a classifier. However, due to the nature of this application, the existing category-imbalance problem hinders the detection performance since the trained classifier has a better effect on the dominated category. To enhance the performance, sampling (e.g., oversampling, under-sampling, and combination) algorithms are employed to balance the training data set based on the imbalance degree metric of imbalance-ratio. The balanced training set then can be used to train classifiers with initial parameters, and the validation set can be utilized to tune as well as evaluate the classifiers. In the testing phase, the actual desired performance on unseen signal data can be determined based on the testing set, i.e., whether the channel is available or not. The performance of each sampling algorithm is measured in terms of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). The simulation results demonstrate the effectiveness of our proposed framework compared to traditional CSS methods.