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In pursuit of significant economy growth, most of the developed countries often ignore the importance of radioactive waste management, so the vast majority of nuclear power plants are still based on labor management, which is considered as inefficient way. The nuclear plant has limited operating life, which means it has to be dismantled after designated life limit, and the radioactive waste is also need to be disposed appropriately. In this research, an intelligent nuclear power plant decommissioning mechanism with cloud based was proposed, which include a comprehensive solution for dismantled parts transportation, radioactive waste management platform for radioactive waste storage facilities, and real-time nuclear waste drum status monitoring. Meanwhile, this mechanism also deploys software agents and cloud-based system, RFID as infrastructure to setup integrated information system. The result of this research could improve the current monitoring method for radioactive waste, from locating to storing, with the assistance of cloud-based information system. Furthermore, the solution for dismantled parts transportation may bring more safety for related workers. [ABSTRACT FROM AUTHOR]

Course scheduling is a combinatorial optimization problem with multiple constraints. To achieve the reasonable allocation of teaching resources, all courses should be arranged under a number of specified constraints. With the increased number of courses, the solution space increases exponentially. However, the traditional methods cannot quickly find the optimal solution. In order to overcome the disadvantages of low efficiency and the possibility of high conflict in traditional course scheduling, this research study proposes a course scheduling, method based on an improved binary cuckoo search algorithm (IBCS). First of all, a multi-objective and multi-constraint mathematical model of course scheduling is established. Course scheduling involves issues, such as allocating teachers, courses, classes, classrooms and time under specific constraints. The class element is defined to represent the class and the course that the teachers will teach. Therefore, the problem of course scheduling is transformed into the process of mapping from the class element to the classroom-time pair. Six hard constraints and three soft constraints are also defined accordingly. Then, the BCS algorithm is used to search the best course scheduling scheme in the binary-encoded solution space. In order to control the convergence rate effectively and avoid falling into the local optimum, a dynamically adjustable, equilibrium coefficient is introduced. Therefore, the algorithm retains the diversity of the solutions while converging. Finally, this research study constructs the data set and carries out the simulation experiment. It also compares the IBCS algorithm in this paper with the genetic algorithm and the standard binary cuckoo search algorithm (BCS). Experimental results show that the IBCS algorithm can converge in effective time. Meanwhile, it can achieve an improved global scheduling scheme with higher stability. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT), real-time decision making has gradually become one of the important characteristics in mobile edge computing (MEC) environment of IoT. Therefore, in the Integrated Circuit (IC) design of MEC environment for IoT, low delay is one of the important optimization objectives. In the many routing competitions, wirelength, overflow, and runtime are the main evaluation standards, so how to reduce wirelength, overflow, and runtime has become a major challenge. However, the existing work lacks the excellent optimization ability in wirelength, overflow, and runtime, or only considers some of the optimization objectives. Therefore, we consider the wirelength, overflow, and runtime as the optimization objectives and propose a high-quality global routing algorithm of IC design in MEC environment, including the following effective strategies: (1) A hybrid topology optimization strategy combining Prim algorithm and divide-and-conquer method, (2) a heuristic search algorithm considering the congestion and the wirelength of nets. Due to the use of the Fast Lookup Table (FLUTE) algorithm to construct the topology of each net, there are too many Steiner points. For this reason, we used Prim algorithm and divide-and-conquer method to construct the topology, and thus it can avoid the problem of redundant Steiner points. In addition, we propose a congestion area identification method based on interval division to determine the area and order of nets to rip-up and reroute (R&R). Furthermore, a heuristic search algorithm that considers both the congestion and the wirelength of nets is used to optimize the total wirelength in the R&R stage. In terms of the total overflow, the total wirelength and the runtime, the experimental results show that the proposed strategies have achieved effective optimization, which can better satisfy the demand of low delay of IC design for data processing in MEC environments of IoT. [ABSTRACT FROM AUTHOR]

Data stream mining is one of the hot topics in data mining. Most existing algorithms assume that data stream with concept drift is balanced. However, in real-world, the data streams are imbalanced with concept drift. The learning algorithm will be more complex for the imbalanced data stream with concept drift. In online learning algorithm, the oversampling method is used to select a small number of samples from the previous data block through a certain strategy and add them into the current data block to amplify the current minority class. However, in this method, the number of stored samples, the method of oversampling and the weight calculation of base-classifier all affect the classification performance of ensemble classifier. This paper proposes a dynamic weighted selective ensemble (DWSE) learning algorithm for imbalanced data stream with concept drift. On the one hand, through resampling the minority samples in previous data block, the minority samples of the current data block can be amplified, and the information in the previous data block can be absorbed into building a classifier to reduce the impact of concept drift. The calculation method of information content of every sample is defined, and the resampling method and updating method of the minority samples are given in this paper. On the other hand, because of concept drift, the performance of the base-classifier will be degraded, and the decay factor is usually used to describe the performance degradation of base-classifier. However, the static decay factor cannot accurately describe the performance degradation of the base-classifier with the concept drift. The calculation method of dynamic decay factor of the base-classifier is defined in DWSE algorithm to select sub-classifiers to eliminate according to the attenuation situation, which makes the algorithm better deal with concept drift. Compared with other algorithms, the results show that the DWSE algorithm has better classification performance for majority class samples and minority samples. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation has always been a popular issue and has attracted much attention. Traditional exhaustive search methods take considerable time to solve multilevel thresholding problems. However, heuristic search algorithms have potential advantages in terms of solving such multilevel thresholding problems. Based on this idea, in this paper, a novel adaptive gravitational search algorithm (AGSA) is proposed to solve the optimal multilevel image thresholding problem; this algorithm is more efficient than the traditional exhaustive search method for grayscale image segmentation. In the AGSA, an adaptive parameter optimization strategy is used to tune the gravitational constant and the inertia weight. To verify the performance of the proposed algorithm, a series of classic test images are used to perform several experiments. In addition, the standard GSA and some optimization algorithms are compared with the proposed algorithm. The experimental results show that the proposed algorithm is obviously better than the other six algorithms. These promising results suggest that the AGSA is more suitable than existing methods for multilevel image thresholding. [ABSTRACT FROM AUTHOR]

To solve the problems of poor real-time collision accuracy and low efficiency of modeling in the virtual reality environment, we propounded a depth image-based 3D modeling system and a hybrid intelligent collision detection algorithm. With the development of the 3D animation interactive system as an example, this paper uses 3D modeling technology based on depth images to build single role models, then reorganizes and merges the models to form 3D scenes. The hybrid intelligent collision detection algorithm, which combines the quantum behavior particle swarm optimization algorithm and the differential algorithm, improves the collision detection efficiency and accuracy and realizes behavior control of the characters in the interactive system. The experimental result shows that the 3D modeling technology based on depth images has greatly improved the accuracy and quantity of model texture and motion rate. By comparing the hybrid intelligent collision detection algorithm, the QPSO algorithm, and the FDH bounding box for collision detection, we conclude that the algorithm used in this paper has a shorter average collision time, more stable role behavior control, and better robustness. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems of complex image background and difficulties in the later image segmentation, an image segmentation algorithm based on the chaotic particle swarm algorithm and fuzzy clustering is proposed. First, the color space is converted from the RGB color space into the HIS color space. Then, a hybrid algorithm consisted of chaotic particle swarm optimization and fuzzy clustering is introduced. Each color component is processed by the algorithm, and the corresponding partition graph is obtained. Finally, the color space is converted into the RGB color space to achieve the segmentation effect. Experimental results show that the new algorithm has higher accuracy to segment the image and has good robustness to noises. [ABSTRACT FROM AUTHOR]