A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

Traffic condition estimation with pre-selection space time model

A pre-selection space time model was proposed to estimate the traffic condition at poor-data-detector,especially non-detector locations.The space time model is better to integrate the spatial and temporal information comprehensibly.Firstly,the influencing factors of the "cause nodes" were studied,and then the pre-selection "cause nodes" procedure which utilizes the Pearson correlation coefficient to evaluate the relevancy of the traffic data was introduced.Finally,only the most relevant data were collected to compose the space time model.The experimental results with the actual data demonstrate that the model performs better than other three models.

A Spectrum Prediction-Based Frequency Band Pre-Selection over Deteriorating HF Electromagnetic Environment

As the earliest invented and utilized communication approach, shortwave, known as high frequency(HF) communication now experience the deterioration of HF electromagnetic environment. Finding quality frequency in efficient manner becomes one of the key challenges in HF communication. Spectrum prediction infers the future spectrum status from history spectrum data by exploring the inherent correlations and regularities. The investigation of HF electromagnetic environment data reveals the correlations and predictability of HF frequency band in both time and frequency domain. To solve this problem, we develop a Spectrum Prediction-based Frequency Band Pre-selection(SP-FBP) for HF communications. The pre-selection of HF frequency band mainly incorporated in prediction of HF spectrum occupancy and prediction of HF usable frequency, which provide the frequency band ranking of spectrum occupancy and alternative frequency for spectrum sensing, respectively. Performance evaluation via real-world HF spectrum data shows that SP-FBP significantly improves the efficiency of finding quality frequency in HF communications.

A Layered Energy-Efficient Multi-Node Scheduling Mechanism for Large-Scale WSN

In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.

Comparative Study on the Dwarfing Pre-selected Indices of Pear Germplasms with Different Growth Potentials

[ Objective] The aim of this study was to provide a theoretical basis for breeding selection, matching parents and the identification of traits during early period. [ Method ] With Shanli （ Pyrus ussuriensis Maxim） , S2 × Shanli （vigorous）, S2 x ShanU （dwarfing）, S2, super-dwarfing germplasm as the matedais, the dwarfing traits of each germplasm were identified by indices including leaf stomata density, branch-cortex ratio, leaf thickness, palisade tissue thickness, paisade-spongy ratio and vessel density. [Result] Among five kinds of pear germplasms, Shanli with strong growth potential had the smallest branch-cortex ratio, leaf thickness, palisade tissue thickness and palisade-spengy ratio, but the largest stomata density and vessel density. On the contrary, super-dwarfing germplasm with weak growth potential had the largest branch-cortex ratio, leaf thickness, palisade tissue thickness and palisade-spongy ratio, but the smallest stomata density and vessel density. There was a difference in stomata density, branch-cortex ratio, leaf thickness, palisade tissue thickness, palisade-spongy ratio and vessel density for every germplasm. [ Conclusion] Stomata density, branch-cortex ratio, leaf thickness, palisade tissue thickness, palisade-spongy ratio and vessel density can be used as indices of identification for pear growth potential in early period.

A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven fault state prediction and quantitative degreemeasurement,a fast small-sample supersphere one-class SVMmodelingmethod using support vectors pre-selection is systematically studied in this paper.By theorem-proving the irrelevance between themodel’s learning result and the non-support vectors(NSVs),the distribution characters of the support vectors are analyzed.On this basis,a modeling method with selected samples having specific geometry character fromthe training sets is also proposed.The method can remarkably eliminate theNSVs and improve the algorithm’s efficiency.The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method.The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.

A hierarchical spatiotemporal adaptive fusion model using one image pair

Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community.Over the past decade,although many advances have been made in the spatiotemporal fusion models,there still remain several shortcomings in existing methods.In this article,a hierarchical spatiotemporal adaptive fusion model(HSTAFM)is proposed for producing daily synthetic fine-resolution fusions.The suggested model uses only one prior or posterior image pair,especially with the aim being to predict arbitrary temporal changes.The proposed model is implemented in two stages.First,the coarse-resolution image is enhanced through super-resolution based on sparse representation;second,a pre-selection of temporal change is performed.It then adopts a two-level strategy to select similar pixels,and blends multi-sensor features adaptively to generate the final synthetic data.The results of tests using both simulated and actual observed data show that the model can accurately capture both seasonal phenology change and land-cover-type change.Comparisons between HSTAFM and other developed models also demonstrate our proposed model produces consistently lower biases.