Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

YOLO-Banana:An Effective Grading Method for Banana Appearance Quality

The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.

Network Aggregation Process in Multilayer Air Transportation Networks

The air transportation network, one of the common multilayer complex systems, is composed of a collection of individual airlines, and each airline corresponds to a different layer. An important question is then how many airlines are really necessary to represent the optimal structure of a multilayer air transportation system. Here we take the Chinese air transportation network （CATN） as an example to explore the nature of multiplex systems through the procedure of network aggregation. Specifically, we propose a series of structural measures to characterize the CATN from the multilayered to the aggregated network level. We show how these measures evolve during the network aggregation process in which layers are gradually merged together and find that there is an evident structural transition that happened in the aggregated network with nine randomly chosen airlines merged, where the network features and construction cost of this network are almost equivalent to those of the present CATN with twenty-two airlines under this condition. These findings could shed some light on network structure optimization and management of the Chinese air transportation system.

A Resource Scheduling Scheme for Spectrum Aggregation in Cognitive Radio Based Heterogeneous Networks

In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectrum aggregation are not optimal or suitable for CR based heterogeneous networks(Het Nets). Consequently, the authors propose a novel resource scheduling scheme for spectrum aggregation in CR based Het Nets, termed as cognitive radio based resource scheduling(CR-RS) scheme. CR-RS has a three-level structure. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users(PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-RS. The simulation results show that CR-RS has the better performance than the current resource scheduling schemes in the CR based Het Nets. Meanwhile, CR-RS is also effective in other spectrum aggregation systems which are not CR based HetNets.

Four Types of Percolation Transitions in the Cluster Aggregation Network Model

We study the percolation transition in a one-species cluster aggregation network model, in which the parameter α describes the suppression on the cluster sizes. It is found that the model can exhibit four types of percolation transitions, two continuous percolation transitions and two discontinuous ones. Continuous and discontinuous percolation transitions can be distinguished from each other by the largest single jump. Two types of continuous percolation transitions show different behaviors in the time gap. Two types of discontinuous percolation transitions are different in the time evolution of the cluster size distribution. Moreover, we also find that the time gap may also be a measure to distinguish different discontinuous percolations in this model.

A multi-layer modelling approach for mining versatile ports of a global maritime transportation network

International ports play critical roles in maintaining transportation flows and sustaining the effectiveness of global maritime logistics.Although the concept of versatile ports has been introduced to represent the specific patterns that these ports play at the regional and global levels,there is still a need for the design and development of computational approaches that support these notions.This paper introduces a flexible multi-layer network approach for an international maritime network whose interest is that it offers a flexible model that favours the identification of the different transportation flows and versatile ports.The model is complemented by a series of structural indices complemented by a new overlap measure that evaluates the specific role of a given port across various transportation trades.The whole approach is implemented and experimented with using massive AIS maritime data that supports the automatic generation of the multi-layer network,and derivation of the structural measures while maintaining a flexible view of the maritime network.The experiments applied to the global maritime transportation network identify key versatile ports and highlight significant differences at the regional and trade flow levels.

Mixed-Vehicular Aggregated Transportation Network Design Considering En-route Recharge Service Provision for Electric Vehicles

This paper addresses the transportation network design problem （NDP） wherein the dis- tance limit and en-route recharge of electric vehicles are taken into account. Specifically, in this work, the network design problem aims to select the optimal planning policy from a set of infrastructure design scenarios considering both road expansions and charging station allocations under a specified construction budget. The user-equilibrium mixed-vehicular traffic assignment problem with en-route recharge （MVTAP-ER） is formulated into a novel convex optimization model and extended to a newly developed bi-level program of the aggregated NDP integrating recharge facility allocation （NDP-RFA）. In the algorithmic framework, a convex optimization technique and a tailored CA are adopted for, respectively, solving the subproblem MVTAP-ER and the primal problem NDP-RFA. Systematic ex- periments are conducted to test the efficacy of the proposed approaches. The results highlight the impacts of distance limits and budget levels on the project selection and evaluation, and the benefits of considering both road improvement policy and recharge service provision as compared to accounting for the latter only. The results also report that the two design objectives, to respectively minimize the total system travel time and vehicle miles travelled, are conflicting for certain scenarios.