Dynamic Routing Algorithm Research Articles

Capsule networks for image classification: A review

Over the past few years, the computer vision domain has evolved and made a revolutionary transition from human-engineered features to automated features to address challenging tasks. Computer vision is an ever-evolving domain with its roots deeply correlated with neuroscience; any new findings that trigger a more intuitive understanding and working of the human visual system generally impact the design strategy of computer vision algorithms. The convolutional neural network is one such algorithm that is currently the de facto standard for most computer vision tasks such as image classification, object detection, image segmentation, etc. As convolutional neural networks are associated with inherent constraints such as the requirement for an immense amount of labeled data and an inefficient data routing policy, capsule networks could be a viable alternative. Upheld by the backpropagation and the dynamic routing algorithm, the capsule network has set the new paradigm for developing reliable computer vision algorithms. Despite the phenomenal theoretical backing from neuroscience and the groundbreaking performance on benchmark datasets, the lack of information concerning the conception and working of capsule networks becomes the major impediment to adopting them for computer vision algorithms. This paper presents a concise overview of capsule network-based classification architectures, routing algorithms, performance analysis, limitations, and future scope, helping the research community to adopt capsule networks at the forefront of modern computer vision research.

THE DEVELOPMENT OF SUSTAINABLE TRANSPORTATION FOR E-GROCERY

Growing demand for organic food products requires to rethink the current supply chain approaches for e-grocery. The shifting trend for e-commerce is requiring products to be delivered in small quantities, to multiple delivery points, which causes negative effect from sustainability perspective. The distribution of goods in urban areas, together with private traffics flow, are among the main sources of energy consumption, air pollution and noise. Thus, the goal of the research is to conduct a literature review on development of sustainable transportation for e-grocery. The literature analysis indicated that the majority of vehicle routing approaches are design for general transportation problems, not specifically related to e-grocery. Also, the changing economic trends related to growing urbanization levels and traffic congestions are not taken into consideration. Thus, the authors of the publication indicate the need to develop dynamic routing algorithms for autonomous vehicles to cope with the traffic congestions and maintain higher sustainability levels from social, economic, and environmental perspectives.

DTAR: A Dynamic Threshold Adaptive Ranking-Based Energy-Efficient Routing Algorithm for WSNs

Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance ([Formula: see text]) and Residual Energy ([Formula: see text]). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency.

Dynamic Routing Algorithm for Hazmat Transportation Problems

After a series of gas pipeline explosions in Kaohsiung City, Taiwan, in 2014, the Environmental Protection Administration (EPA) issued regulations in 2017 requiring all chemical tank trucks to be equipped with GPS for real-time monitoring and control. Hazmat transportation problems, along with solution algorithms and applications, have been studied extensively. However, most of the research has focused on the route planning level. Dynamic characteristics should be incorporated to reflect possible dynamic traffic situations with more advanced information and communication capabilities for chemical tank trucks. This research formulates a bi-objective dynamic model and proposes a solution algorithm based on a genetic algorithm (GA) for dynamic hazmat route optimization. A dynamic bi-objective model, including transportation cost and risk, is developed to reflect time-varying traffic conditions. Dynamic traffic characteristics are reflected through traffic volume and travel time, simulated from simulation models. Numerical experiments are conducted in the Kaohsiung City network. The results show that dynamic routes for hazmat transportation vary with respect to traffic flow conditions. The discussions of this research are expected to provide some insights into policy implications.

Fault Diagnosis Method of Smart Meters Based on DBN-CapsNet

Rapid and accurate fault diagnosis of smart meters can greatly improve the operational and maintenance ability of power systems. Focusing on the historical fault data information of smart meters, a fault diagnosis model of smart meters based on an improved capsule network (CapsNet) is proposed. First, we count the sample size of each fault type, and a mixed sampling method combining undersampling and oversampling is used to solve the problem of distribution imbalance of sample size. The one-hot encoding method is adopted to solve the problem of the fault samples containing more discrete and disordered data. Then, the strong adaptive feature extraction capability and nonlinear mapping capability of the deep belief network (DBN) are utilized to improve the single convolution layer feature extraction part of a traditional capsule network; DBN can also address the problem of high data dimensions and sparse data due to one-hot encoding. The important features and key information of the input sample are extracted and used as the input of the primary capsule layer, and the dynamic routing algorithm is used to construct the digital capsule. Finally, the results of experiments show that the improved capsule network model can effectively improve the accuracy of diagnosis and shorten the training time.

Traffic Dataset for Dynamic Routing Algorithm in Traffic Simulation

The purpose of this research is to create a simulated environment for teaching algorithms, big data processing, and machine learning. The environment is similar to Google Maps, with the capacity of finding the fastest path between two points in dynamic traffic situations. However, the system is significantly simplified for educational purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environments used in the project are VIPLE (Visual IoT/Robotics Programming Language Environment) and a traffic simulator developed in the Unity game engine. This paper focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithms in VIPLE. The traffic data is generated from the recorded real traffic data published on the Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation with support for dynamic changing data.

Capsule Boundary Network With 3D Convolutional Dynamic Routing for Temporal Action Detection

Temporal action detection is a challenging task in video understanding, due to the complexity of the background and rich action content impacting high-quality temporal proposals generation in untrimmed videos. Capsule networks can avoid some limitations of the invariance caused by pooling and inability from convolutional neural networks, which can better understand the temporal relations for temporal action detection. However, because of the extremely computationally expensive procedure, capsule network is difficult to be applied to the task of temporal action detection. To address this issue, this paper proposes a novel U-shaped capsule network framework with a k-Nearest Neighbor (k-NN) mechanism of 3D convolutional dynamic routing, which we named U-BlockConvCaps. Furthermore, we build a Capsules Boundary Network (CapsBoundNet) based on U-BlockConvCaps for dense temporal action proposal generation. Specifically, the first module is one 1D convolutional layer for fusing the two-stream with RGB and optical flow video features. The sampling module further processes the fused features to generate the 2D start-end action proposal feature maps. Then, the multi-scale U-Block convolutional capsule module with 3D convolutional dynamic routing is used to process the proposal feature map. Finally, the feature maps generated from the CapsBoundNet are used to predict starting, ending, action classification, and action regression score maps, which help to capture the boundary and intersection over union features. Our work innovatively improves the dynamic routing algorithm of capsule networks and extends the use of capsule networks to the temporal action detection task for the first time in the literature. The experimental results on benchmarks THUMOS14 show that the performance of CapsBoundNet is obviously beyond the state-of-the-art methods, e.g., the mAP@tIoU = 0.3, 0.4, 0.5 on THUMOS14 are improved from 63.6% to 70.0%, 57.8% to 63.1%, 51.3% to 52.9%, respectively. We also got competitive results on the action detection dataset of ActivityNet1.3.

Temporal information extraction with the scalable cross-sentence context for electronic health records.

Temporal information is essential for accurate understanding of medical information hidden in electronic health record texts. In the absence of temporal information, it is even impossible to distinguish whether the mentioned symptom is a current condition or past medical history. Hence, identifying the relationship between medical events and document creation time (DCT) is a critical component for medical language comprehension, which can link the mentioned medical information to the time dimension by marking temporal tags. Existing natural language processing (NLP) systems are typically based on the sentence where the medical event is located to extract the DCT relationship. Inevitably, the limited textual context can be insufficient as it is difficult to contain adequate document information. Introducing the surrounding sentences into models is a fitting way to enrich the information. However, in addition to document information, the added context can also bring noise to confuse the models. For effective utilization of the context, we design the DCDR (Dynamic Context and Dynamic Representation) model. Our model consists of two modules, i.e. the dynamic context mechanism and dynamic representation mechanism. The dynamic context mechanism is employed to bring the related texts into our model via the sliding windows and a scoring calculation. For the dynamic representation mechanism, a modified dynamic routing algorithm is adopted to filter the noise and generate an integrated representation for the whole context. Besides, the mentioned medical information is led into the routing process to enhance the dynamic representation module. The experiments show that our proposed model achieves improvement over existing models and achieves an F-score of 85.7% on the commonly used THYME corpus.

3D multi‐resolution attention capsule network for diagnosing multi‐pathological types of pulmonary nodules

AbstractTo achieve effective diagnosis of multi‐pathological types on pulmonary nodules, a three‐dimensional (3D) multi‐resolution attention (MRA) capsule network (3D MRA‐CapsNet‐DL) was proposed by using a self‐constructed lung CT image dataset. In the construction of 3D MRA‐CapsNet‐DL, an improved dynamic routing algorithm (DRA) with limiting the update increment (called DL) was first designed to solve the inactivation problem of a large number of vector neurons. Then, MRA was used to build three kinds of multi‐path 3D MRA‐CapsNet‐DL for multi‐pathological classification. Besides, we also investigated whether the interpolation perturbation introduced by multi‐resolution methods will affect the classification performance. Experiments showed that the proposed 3D MRA‐CapsNet‐DL can achieve a better classification (79.27% ACC and 0.9056 AUC) effect than classical convolutional neural networks (CNNs), and the interpolation disturbance has favorable and unfavorable effects on the classification performance of multi‐path and single‐path models, respectively.

An agent-based algorithm for dynamic routing in service networks

Service networks consisting of components and links are regarded as significant infrastructures to provide all kinds of services for users. They are supposed to select the best route for service requests in case of time-varying demand and different service patterns. In addition, it is necessary to choose appropriate routing metrics so as to satisfy user requirements for a variety of services. Multiple quality of service (QoS) requirements must be considered in the dynamic environment as a result of high demand for service networks. To that end, we propose an agent-based algorithm to address the routing problem in service networks. We construct a multi-layer network model to figure out component behaviors and complicated relationship between components under uncertainty. In order to reflect various service requirements, QoS metrics are defined from the perspectives of component and link. We also put forward an improved deep Q-learning method to achieve global convergence and enhance the efficiency of the routing algorithm. The numerical results on a case study illustrate the proposed algorithm finds high-quality solutions at acceptable costs, which routes service requests properly in the dynamic network environment. The proposed algorithm achieves outstanding performance compared with state-of-the-art routing algorithms in terms of delay and service factor.

Innovative method for pavement multiple damages segmentation and measurement by the Road-Seg-CapsNet of feature fusion

Deep learning algorithms have achieved promising results in the field of pavement damage segmentation. Since the texture features of map crack damage are complex, existing segmentation methods may experience the problem of edge information loss for the damaged area. In this study, we therefore propose Road-Seg-CapsNet, a feature fusion model combined with StyleGAN, to segment multiple complex forms of damage to asphalt pavements. Firstly, a data enhancement algorithm based on StyleGAN is developed to amplify the dataset. Next, padding convolution is used in the convolution layer of Road-Seg-CapsNet to retain more image edge information, which effectively solves the problem of information loss at the edges of areas of damage with map cracks. The feature map in the upsampling layer is fused with the features of the convolutional layer to improve the segmentation accuracy of the model. An optimised dynamic routing algorithm is then applied, which reduces the model parameters and improves the efficiency of model training. Our experimental results show that the segmentation mAP of the proposed model can reach 0.942. To verify the superiority of the proposed method, a comparative experiment with FCN, Mask R-CNN and optimised Mask R-CNN is carried out. Based on the segmentation results, a quantification and measurement experiment is carried out, and the minimum accuracy for the damaged areas is found to be 0.903.

Design and Analysis of Improved Dynamic Source Routing Protocol in Vehicular Ad-Hoc Network

Background and Objective: The present issues faced by the transport systems include enormous traffic jams and congestion, unexpected road accidents, unwanted time delays, pedestrians' crossings on main roads, wastage of fuel, and many more. ITS and some other technologies work together to overcome such factors contributing to a much more desirable transport system. This paper aims to identify the security measures that could increase the security of the routing protocol and improve the performance of the DSR routing protocol. If the number of establishing links is frequently broken, then the performance of the DSR routing protocol is not very good. A new modified DSR algorithm has been designed that would help in reducing the network overhead and increase the network quality. A comparison between the performances of both algorithms has also been studied, and the results show that improved DSR routing protocol has better efficiency in parameters like end-to-end network load delays and packet delivery ratio. Methods & Materials: The paper mainly focuses on securing the Dynamic Source routing algorithm by designing its modified version and simulating it. We have Proposed Modified DSR and discussed so far that the basic functionality of the DSR algorithm, its advantages and disadvantages. The main drawbacks include the end-to-end Delay, network overhead and packet delivery ratio. Following is a modified version of the dynamic source routing algorithm, which tries to overcome all the limitations of the basic algorithm at a certain level. In the Dynamic Source Routing algorithm, the Route Maintenance phase is used for the successful propagation of the packet within the network. The nodes that are involved in the transmission acknowledge the receiving of the packet to the preceding node. Results and Discussion: The present study aims at modifying the existing algorithm and designing a new modified algorithm that has less routing overhead, less Delay in packet transmission and high network quality. Modified DSR routing algorithm is examined on the parameters like E-2-E delay, Normalize Routing Overhead, PDR. When we compare the performance of the proposed method with the Existing protocol AODV, and DSR at a number of nodes equal to 50 then we get the Proposed DSR, which has the least end-to-end Delay among all the three protocols. Conclusion: In this paper, acomparison of Exiting DSR and Modified DSR in terms of end-to-end Delay, routing load and packet delivery ratio is made. After studying the readings, the conclusion drawn was that there is less network overhead and a high delivery ratio in modified DSR as compared to the existing DSR algorithm. The corrupted and destroyed packets are detected by the receiver, and in DSR, the mobility of mobile nodes is moderate. Under certain conditions, bidirectional and unidirectional links can be accepted. DSR routing protocols are highly dependent on the parameters such as area size, packet size, packet type, and others.

Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020

Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R2) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m3, 8 ± 3%, and 1.40 ± 0.2 μg/m3, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R2 values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m3, ~5%, and 0.60 ± 0.2 μg/m3, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R2 values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants.

A capsule network with pixel-based attention and BGRU for sound event detection

Sound event detection has recently become a hot topic in the sound process while discontinuous and overlapping sound events still pose challenges for sound event detection. In this paper, we propose a capsule network with pixel-based attention and bidirectional gated recurrent unit (PBA-AttCapsNet-BGRU) model which contains the high-level feature extraction module, the attention capsule network (AttCapsNet) module, and the bidirectional gated recurrent unit (BGRU) module. Specifically, pixel-based attention (PBA) is employed in the convolutional neural network named PBACNN to extract features more relevant to sound events from binaural log-Mel spectrograms (bin-LMS) features in the high-level feature extraction module. The module can solve the problem of discontinuous sound events. Furthermore, to detect overlapping sound events effectively, we propose an AttCapsNet module that combines capsule network (CapsNet) and soft attention mechanism. Also, the attention dynamic routing algorithm is introduced to validly distinguish the existence of sound events and focus on the significant frames in this paper. In addition, BGRU module is composed of BGRU and time-distributed fully-connected layers. It can obtain the context information and overcome the overfitting problem to a certain extent. We conducted the experiments on Task 4 of the DCASE 2017 Challenge. Experimental results show that the proposed PBA-AttCapsNet-BGRU model can achieve 0.032 improvements in F1 and 0.07 improvement in ER with the state-of-the-art models in sound event detection.

An effective prediction method for network state information in SD-WAN

In a software-defined wide area network (SD-WAN), a logically centralized controller is responsible for computing and installing paths in order to transfer packets among geographically distributed locations and remote users. Accordingly, this would necessitate obtaining the global view and dynamic network state information (NSI) of the network. Therefore, the centralized controller periodically collects link-state information from each port of each switch at fixed time periods. While collecting NSI in short periods causes protocol overhead on the controller, collecting in longer periods leads to obtaining inaccurate NSI. In both cases, packet losses are inevitable, which is not preferred for quality of service (QoS). Packet loss needs to be reduced by minimizing the protocol overload on the controller and collecting accurate NSI to provide better QoS. This work proposes an effective prediction method for collecting NSI (PM-NSI) that significantly reduces packet loss and controller protocol load allowing the controller to collect accurate NSI in longer periods. The proposed method is compared against the existing NSI collection method, which collects NSI periodically, in use on the RYU controller and the Mininet emulator by using a dynamic routing algorithm. The test results indicated that PM-NSI reduces controller load around 1000% by collecting NSI in longer periods and so outperforms the existing periodic NSI collection method in terms of packet loss, jitter, controller load, and thus QoS.

Research on dynamic routing algorithm based on gaussian mixture model

In this paper, based on the method of environmental sound detection, a neural network model based on capsule network and Gaussian mixture model is proposed. The model proposed in this paper mainly aims at the disadvantages of dynamic routing algorithm in the capsule network, and proposes a dynamic routing algorithm based on Gaussian mixture model. The improved dynamic routing algorithm assumes that the characteristics of the data conform to the multi-dimensional Gaussian distribution, so the model can learn the distribution of data features by building distribution functions of different classes. The information entropy is used as the activation value of the salient degree of the feature. Through experiments, the accuracy of the proposed algorithm on Urbansound8K data set is more than 92%, which is 4.8% higher than the original algorithm.

Compound Fault Diagnosis Method of Modular Multilevel Converter Based on Improved Capsule Network

When submodule (SM) compound fault occurs in the Modular multilevel converter (MMC), the time-domain waveform characteristics of output current and internal circuiting current of MMC are not obvious, especially on the condition of high-level MMC. To address this issue, A compound fault diagnosis method based on an improved capsule network (CapsNet) is proposed in this paper. Firstly, The front end of the network adopts the feature extraction structure of One-dimensional Convolutional Neural Network (1DCNN) combined with Long and short-term memory network (LSTM). Then, the original data of MMC three-phase output currents and three-phase circulating currents are used as fault detection signal. The back-end employs a main capsule layer and a digital capsule layer structure, and realizes the transfer of feature vectors through a dynamic routing algorithm. This feature extraction structure combines the light weight of 1DCNN and the sequential sensitivity of LSTM. While ensuring that the information is fully extracted, the computational cost of the model is greatly reduced. Furthermore, the overlapping sampling method is used to construct and expand the sample sets. Compared with the other three common deep learning methods, their fault diagnosis performance under the condition of different levels and levels changing are analyzed respectively. The experimental results demonstrate that the proposed method has excellent cross-domain learning ability and high fault recognition accuracy.

Distributed Multi Criteria Routed for MPLS-TE Based on Machine Learning: Concept and Applications

MPLS-TE Networks are complex interacting systems, providers integrate absolutely the forwarding equivalent class involving in the dynamic routing algorithm "Dynamic Multi Criteria Routing", especially with network services such as an IPtv, a VPNs or a VoIP with the several types of video streaming and similar user applications, making its flagship technologies of tomorrow. Machine learning is very compatible for this kind of networks. In this paper, we first review how the main machine learning concepts can be integrated in communication networks and discuss concepts, supervised and unsupervised model suitable to the network, data and management strategies, and creating a new architecture of network controls and management tools. We then describe case studies networking in detail, anticipate anomalies at multiple network layers, covering predictive maintenance, descriptive network topology management, capacity optimization. Finally, we prove the importance of this work, and guess an overview of intelligent dynamic networks.

OTP-Based Software-Defined Cloud Architecture for Secure Dynamic Routing

In the current era, anyone can freely access the Internet thanks to the development of information and communication technology. The cloud is attracting attention due to its ability to meet continuous user demands for resources. Additionally, Cloud is effective for systems with large data flow such as the Internet of Things (IoT) systems and Smart Cities. Nonetheless, the use of traditional networking technology in the cloud causes network traffic overload and network security problems. Therefore, the cloud requires efficient networking technology to solve the existing challenges. In this paper, we propose one-time password-based software-defined cloud architecture for secure dynamic routing to mitigating the above-mention issues. The proposed cloud architecture provides a secure data path through dynamic routing using One-Time Internet Protocol (OTIP) algorithm between each layer. On the network side, we use software-defined technology to provide efficient network management and data security. We introduce a software-defined cloud architecture that applies OTIP algorithms for secure dynamic routing. We conduct a comparative analysis between general IP communication and proposed OTIP communication architecture. It evaluates the performance of OTIP algorithms. Finally, we examine the proposed software-defined cloud architecture, including how to apply OTIP in secure dynamic routing according to the results of the comparative analysis.

Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection

The development of social media enables fake news to be expressed in a multi-modal form, which is disseminated on various social platforms and brings harmful social impacts. To handle this challenge, the fake news detection task was proposed to examine whether false information is contained in multi-modal news. Existing methods exploit various approaches with cross-modal interaction and fusion, which have proven to be effective in detecting common fake news. However, although the description of multi-modal news is narrated around entities, the previously developed methods pay less attention to this characteristic. They do not explore its benefits to the detection task and underperform with respect to the detection of fake news that requires entity-centric comparisons. To make up for this omission, we explore a novel paradigm to detect fake news by aligning and fusing multi-modal entities and propose the Entity-oriented Multi-modal Alignment and Fusion network (EMAF). Our work adopts entity-centric cross-modal interaction, which can reserve semantic integrity and capture the details of multi-modal entities. Specifically, we design an Alignment module with the improved dynamic routing algorithm and introduce a Fusion module based on the comparison, the former aligns and captures the important entities and the latter compares and aggregates entity-centric features. Comparative experiments conducted on multiple public datasets, including Weibo, Twitter, and Reddit, reveal the superiority of the proposed EMAF method, and extensive analytical experiments demonstrate the effectiveness of our proposed modules.