Due to simplicity, rapidity and non-destructiveness, Raman spectroscopy is very suitable for mineral classification and identification. A Raman spectral model-fitting method does not need to build a reference spectral database and complex spectral matching, which is advantageous in mineral classification. However, there is a lack of comprehensive comparison of the existing model-fitting methods based on machine learning and deep learning since they use relatively single-learning models. To this end, this paper comprehensively evaluates the model-fitting classification methods of mineral Raman spectral using the RRUFF mineral Raman spectrum dataset. It compares the classification performance of four traditional machine learning methods of KNN, XGBoost, SVM, and RF, and three deep learning models of CNN, DNN, and RNN, as well as four data preprocessing methods and sample size on the classification effect. To improve the classification performance, we also propose a data preprocessing method of Raman spectral intensity curvature, which calculates the curvature of the baseline-corrected Raman spectral sequence intensity as a construction feature so that the model can extract the position of the spectra peaks more effectively. The experimental results showed that data preprocessing greatly improved the classification performance of machine learning models but had little effect on deep learning models. Additionally, the size of the sample is a key factor of the model performance. When the size is large, the deep learning models outperform the traditional machine learning models, whereas when the size is small, it is difficult for the deep learning models to exert their advantages, while the traditional machine learning models combined with data preprocessing work better.

Prediction of Chlorophyll-a (Chl-a) concentration is significant for marine ecology and environmental protection. This paper presents an integrated approach to forecast seasonal Chl-a concentration in coastal waters. Before modeling, feature construction procedures, such as simplification, combination, and normalization, are conducted to identify the potentially significant features. The feature extraction method based on Random Forest (RF) and eXtreme Gradient BOOSTing (XGBoost) is applied to select relevant variables. Then, we propose a Cluster-stacking-based approach which includes a station-oriented clustering model and a stacking-based regression model. The former model is used to divide the observation stations into several groups, thus partitions the study region into several sub-regions and the study dataset into several subsets according to the corresponding stations. In each subset, single regression models including K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Multi-Layer Perceptron regression (MLP) and XGBoost are established in level 0 space and integrated by RF in level 1 space via stacked generalization. We compare the performance of the Cluster-stacking model with that of Cluster-KNN, Cluster-SVR, Cluster-MLP, Cluster-XGBoost and the regression stacking model without cluster. The model evaluation shows that the Cluster-stacking-based approach outperforms others in forecasting Chl-a concentration with a coefficient of determination (R 2 ) of 0.848 and a mean absolute error (MAE) of 0.665 ug/l.

In order to run a dataflow with as low cost as possible, it is often faced with deciding which data-sets in a data-set sequence should be stored, with the rest regenerated. The Intermediate Data-set Storage problem arises from this situation. The current best algorithm for this problem takes O(n4) time. In this paper, we present two improved algorithms for this problem, the first of which can achieve a time complexity O(n2), the second of which O(rn), where n is the number of data-sets in a dataflow, r is a numerical number which indicates how large it is for the maximum storage cost to be divided by the minimum computation cost in the dataflow.

Running a dataflow in a cloud environment usually generates many useful intermediate datasets. A strategy for running a dataflow is to decide which datasets should be stored, while the rest of them are regenerated. The intermediate dataset storage (IDS) problem asks to find a strategy for running a dataflow, such that the total cost is minimized. The current best algorithm for linear-structure IDS takes O(n(4)) time, where "linear-structure" means that the structure of the datasets in the dataflow is a pipeline. In this paper, we present a new algorithm for this problem, and improve the time complexity to O(n(3)), where n is the number of datasets in the pipeline.

With the maturity of the industrial robotic technology, robotic cells are gradually regarded as a kind of stand equipment to replace human work in every walk of life. How to obtain the maximum or approximate maximum throughput in a robotic cell is always the highlighted goal, especially in the rapid growing 3C industry market. In this paper, the objective is to get a 1-unit cycle sequence of robot actions that approximately minimizes the cycle time to produce a part and maximizes the throughput by using a new hybrid algorithm in the robotic cell with a dual-gripper robot. In this algorithm, different constrains are considered during computing the cycle time, including free/non-free process, allowed time window. The resulting diagrams provide very intuitive insights into the accuracy of the hybrid algorithm compared with the exact algorithm. Additional 100 simulation results prove the effectiveness of the hybrid algorithm, with a solid performance to achieve the maximum productivity of robotic cell.

Execution performance is critical for large-scale and data-intensive workflows. This paper proposes DISWOP, a novel scheduling algorithm for data-intensive workflow optimizations; it consists of three main steps: workflow process generation, task & resource mapping, and task clustering. To evaluate the effectiveness and efficiency of DISWOP, a comparison evaluation of different workflows is conducted a prototype workflow platform. The results show that DISWOP can speed up execution performance by about 1.6-2.3 times depending on the task scale.

In a large-scale workflow, the workflow structure needs to be simplified before execution so as to improve the completion performance. This paper puts forward an approach to structure simplifying for structured workflows. First, we present a task planning method based on differential evolution algorithm to map the tasks into available resources; then, based on the mapping relationship, the workflow structure will be simplified by task clustering. To evaluate the performance of the proposed approach, the proposed algorithms are evaluated through a comparison study using simulated workflows executed on a prototype workflow platform. The simulation results prove the effectiveness of our approach. © 2012 Springer Science+Business Media B.V.

Mobile agents act as the task executors in migrating workflow system. As the size of workflow is increased by including many tasks and branches, multiple mobile agents should be used where each agent is responsible for a branch of the workflow process and fulfills the workflow goal by cooperating with its partners. In this case, the workflow process needs to be partitioned into a set of sub-processes before execution so that each sub-process can be assigned to one mobile agent. This paper proposes a structured process partition approach that includes process structure partition and Quality of Service(QoS) objective partition. The former partitions a structured process into a set of sub-processes with dominant relations and each sub-process consists of a sequence of tasks. The latter distributes QoS objectives, such as the expected budget or deadline of the whole workflow, over all of the sub-processes. In addition, a sub-process execution planning algorithm with QoS objective based on Markov Decision Process (MDP) is also put forward in this paper. The experiment results show that the effects of workflow partition method and MDP based process planning method are sound.

In a migrating workflow, the concrete process is fine-grained defined for the flexibility and scalability in structure, yet the large number of fine-grained tasks have a great effect on the execution efficiency. To this end, the workflow process need be partitioned into a set of sub-processes for execution optimization. This paper proposes a two-phase approach to workflow process partitioning which comprises task allocation and task clustering. First, we map the tasks into available service sites based on Particle Swarm Optimization, aimed at minimizing execution time and migrating time. Then, we propose a level-adjacent task clustering algorithm for workflow partitioning with the objective of reducing the queue waiting time during execution. The proof of the convexity of the task clustering is also elaborated. Finally, the proposed algorithms are evaluated through a comparison study using simulated workflow cases executed on a migrating workflow platform. The results obtained show the efficiency of the algorithms as well as the advantages of structure optimization with workflow partitioning. © 2011 Binary Information Press.

In migrating workflow system, process fragmentation is to partition a process into a set of sub-processes which can be allocated to the migrating instances for the process execution. Due to the distributed and constrained workplace resource, process fragmentation needs take the execution environment into consideration. In addition, the balanced granularity of the fragmentation also need to be taken into account so as to cater for the applications in which each migrating instance is generated with a balanced executing ability. This paper proposes a two-phase approach to the process partitioning. First, we abstract the process resource allocation as a multi-objective combinatorial optimization problem and present a particle swarm optimization algorithm for the solution of the problem. And then, based on the mapping relationship between the tasks and the available resources, we propose a task clustering algorithm to generate the set of sub-processes with balanced granularity. Simulation experiment results show the effectiveness and feasibility of the fragmentation approach.

针对计算机专业本科生"计算机网络"课程教学过程中普遍存在的问题,基于作者的教学经验和对计算机网络技术的理解.从课堂教学、实验教学和考核环节展开讨论,提出了若干探讨性的改革建议.

如何培养高校本科生的科研能力一直受到教师和教育学者的广泛关注,本文基于作者在计算机专业课教学过程中的实际经验和对这一问题的思考,从理论教学和实践教学两方面展开分析,提出了若干具体的探讨性的建议.

In the resource-constrained workflow, a task is usually decomposed into a set of dependent subtasks. Since resources are limited, the task decomposition and planning should be optimized according to the specific executing environment. Based on the software component concepts, in this paper, a task is defined as a set of dependent task units, and the subtasks are clips of a task, in which all of the task units can be executed in a single limited resource. This paper presents a task decomposition and planning model for the resources-constrained workflow environment, by which the task decomposition and planning can be abstracted to a combinatorial optimization problem whose objective is to minimize executing and communicating overheads. In order to solve this problem, a discrete particle swarm optimization algorithm is proposed and the simulation experiments prove the effectiveness of the algorithm.

Workplace planning is a key problem in migrating workflow system. Different from general resource allocation or task schedule problems, workplace planning is a multi-constrained combinatorial optimization problem, the main objective of which is to minimize the migrating path length. This paper firstly proposes the model of workplace planning problem, and then provides a greedy discrete particle swarm optimization (GDPSO) algorithm for the problem solving. The simulation experimental results demonstrate the effectiveness of the algorithm.

In order to support process construction in wireless migrating workflow system, this paper presents an approach based on task components for resource-limited executing environment. First, we propose a business process construction model based on the idea that the process logic is separated from the process instances, and then abstract the process generation as a resource-constrained combinatorial optimization problem whose objective is to maximize the QoS of the whole process. For the problem solving, a novel discrete differential evolution (DDE) algorithm is detailed and the simulation experimental results demonstrate the effectiveness of the algorithm.

A migrating instance path planning model based on navigating tree is proposed. The routing process of migrating instances and the self-maintaining mechanism of navigating tree are described in detail. By means of this model, the navigating tree can update and maintain navigation knowledge dynamically according to the updating of anchorages. Since the updating information is transferred only within a local migrating domain,the rate of information flow in migrating workflow networks can be reduced greatly,thus the model fits very well into the large inter -organization workflow services in which the anchorages may update frequently. The order form example shows that migrating instance can acquire an effective migrating path and avoid vain migration by means of the migrating instance path planning mechanism.

Introduces the development of the applied graphics software based on VC++. The software is developed according to the basic principle of computer graphics and the MFC. It provides some basic function as drawing,editing, modifying, and so on. It uses the OOP technology, and is easily expanded and maintained. The design and development of the software provide a directly and friendly integrated circumstance for the research of graphics system. Furthermore, it also provides a good platform for large-scale development of the graphics system.

许多田径项目的世界纪录之所以不断被刷新,专项技术的不断改进和完善是重要因素之一.文章进而从理论和实践的双重角度,对蹲踞式起跑的预备姿势、铅球投掷出手角度和跳远的助跑距离及落地方式等技术环节的改进进行了探讨.