Update Speed Research Articles

Continuous Network Update With Consistency Guaranteed in Software-Defined Networks

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance. The single update focuses on processing one update event at a time, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , updating a set of flows from their initial routes to target routes, but it fails to handle continuously arriving update events in time incurred by high-frequency network changes. On the contrary, the continuous update proposed in “Update Algebra” can handle multiple update events concurrently and respond to the network condition changes at all times. However, “Update Algebra” only guarantees the blackhole-free and loop-free update. The congestion-free property cannot be respected. In this paper, we propose Coeus to achieve the continuous update while maintaining consistency, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , ensuring the blackhole-free, loop-free, and congestion-free properties simultaneously. Firstly, we establish the continuous update model based on the update operations in update events. With the update model, we dynamically reconstruct the operation dependency graph (ODG) to capture the relationship between update operations and link utilization variations. Then, we develop a composition algorithm to eliminate redundant operations in update events. To further speed up the update procedure, we present a partition algorithm to split the operation nodes of the ODG into a series of suboperation nodes that can be executed independently. The partition algorithm is proven to be optimal. Finally, extensive evaluations show that Coeus can improve the update speed by at least 179% and reduce redundant operations by at least 52% compared with state-of-the-art approaches when the arrival rate of update events equals three times per second.

Energy-Efficient III-V Tunnel FET-Based Synaptic Device with Enhanced Charge Trapping Ability Utilizing Both Hot Hole and Hot Electron Injections for Analog Neuromorphic Computing.

A charge trap device based on field-effect transistors (FET) is a promising candidate for artificial synapses because of its high reliability and mature fabrication technology. However, conventional MOSFET-based charge trap synapses require a strong stimulus for synaptic update because of their inefficient hot-carrier injection into the charge trapping layer, consequently causing a slow speed operation and large power consumption. Here, we propose a highly efficient charge trap synapse using III-V materials-based tunnel field-effect transistor (TFET). Our synaptic TFETs present superior subthreshold swing and improved charge trapping ability utilizing both carriers as charge trapping sources: hot holes created by impact ionization in the narrow bandgap InGaAs after being provided from the p+-source, and band-to-band tunneling hot electrons (BBHEs) generated at the abrupt p+n junctions in the TFETs. Thanks to these advances, our devices achieved outstanding efficiency in synaptic characteristics with a 5750 times faster synaptic update speed and 51 times lower sub-fJ/um2 energy consumption per single synaptic update in comparison to the MOSFET-based synapse. An artificial neural network (ANN) simulation also confirmed a high recognition accuracy of handwritten digits up to ∼90% in a multilayer perceptron neural network based on our synaptic devices.

Hybrid differential evolution particle swarm optimization (DEA-DCWPSO) for resource-constrained multi-project scheduling problem

In this paper, we studied the resource constrained project scheduling problem, and the research object is extended to the multi-project environment. On the basis of multi-project priority evaluation, with the goal of minimizing the weighted duration of multi-project, a multi-project schedule planning model is constructed. Through reasonable scheduling of multiple parallel projects sharing resources under resource constraints, it provides a decision-making basis for project managers to allocate resources reasonably under resource constraints to meet the duration requirements of each project and to shorten the weighted total duration of multiple projects as much as possible. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist Retention (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of particle swarm optimization algorithm with dynamically changing inertia weight. Through the dynamic changing inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can be used to solve the RCMPSP model more effectively.

Efficient hierarchical hash tree for OpenFlow packet classification with fast updates on GPUs

Packet classification is an important functionality of modern routers/switches, needed in packet forwarding, Quality of Service (QoS), firewall etc. In order to better utilize routers on the Internet, Software Defined Network (SDN) decouples control plane from data plane to fulfill centralized management. Based on OpenFlow standards, packet classification in SDN is designed for multi-field rules which are more complex than traditional 5-tuple rules. In the paper, we propose a novel packet classification algorithm, called hierarchical hash tree (H-HashTree), based on the two IP address fields and the 7 exact-match fields to partition rules into groups. An extended Bloom filter is also proposed to accelerate search process by skipping groups in the hash tree. To further improve the performance, H-HashTree is implemented on GPU. We tested on 100K rules including synthesized rules containing characteristics of ACL, FW, and IPC with different wildcard ratios in exact-match fields, and real OpenFlow rules from Open vSwitch. Compared with the existing state-of-the-art algorithms, CutTSS and TabTree [19][18], H-HashTree achieves the best performance on both search and update speeds. H-HashTree achieves 1.17-13.9 and 2.48-12.7 times faster in search speed and 2.03-6.0 and 1.87-4.53 times faster in rule updates from synthesized rulesets than CutTSS and TabTree, respectively. On the GPU platform, H-HashTree can achieve up to 114 MPPS in search speed and less than 0.04 usec/rule in rule updates.

ERP System Design for Hydrogen Equipment Manufacturing Industry Based on Low Code Technology

Research Problem. Aiming at the problems such as fast update and iteration speed, high development cost, and isolated enterprise data in hydrogen equipment manufacturing industry, a low code programming system based on enterprise data center and business center was proposed and designed. Research Method. This paper designs the data center service and business center architecture of hydrogen equipment manufacturing industry. The data center collects, mines, and integrates massive data into unified standard structured data and stores them. The business center provides architectural support for the establishment of enterprise resource management system. With the data center service as the system data interaction center and the business center as the model to build the system architecture, the hydrogen equipment manufacturing industry resource management system is constructed through the steps of data interaction, process modeling, and UI editing. Research Results. According to the specific requirements of hydrogen equipment manufacturing industry, the business process of hydrogen enterprise resource management system is constructed, and the construction and development of low code system are studied and discussed by integrating data center and business center. Conclusions. Low code is not just a tool to increase productivity, it has the potential to be a catalyst for technology and culture to promote enterprise innovation and business agility, and ERP systems for hydrogen equipment manufacturing based on low coding technology are needed for the industry of the future. This article contributes to the hydrogen equipment manufacturing industry of low code programming system, users can according to their own needs, through the combination of components, quickly generate a complete management system, update system iteration, greatly reduce the development cost, solve the problem of enterprise data isolation, reduce the development cost, and realize the seamless integration of modern software development practice, so as to promote enterprise innovation and business agility.

BatchUp: Achieve fast TCAM update with batch processing optimization in SDN

The parallel access mechanism supported by TCAM has made it popular with commercial switches. However, due to the ordered arrangement of rules, TCAM’s update speed cannot keep up with policy update requests, especially for software-defined networks, which expect to achieve fine-grained control over the network. Meanwhile, the legacy single-rule update solutions cause redundant rule moves due to weak global view. In this paper, we present a new two-stage batch processing solution BatchUp to alleviate the high time cost of the rule update process. In stage 1, we propose the costless rule insertion algorithm CRIA. CRIA models the rules to be inserted with the available locations in TCAM as a bipartite graph. By solving for maximum matching, CRIA obtains an upper bound on the number of rules that can be inserted without TCAM movement. However, the high utilization of TCAM makes it unrealizable to find available locations for all rules to be inserted directly. Therefore, in stage 2, we model the remaining rules insertion as an optimization problem for solving the minimum movement cost and prove that it has NP-hard complexity. A heuristic updating algorithm HRIA based on simulated annealing and greedy algorithm is proposed to search for approximate optimal solution. The evaluation results present that BatchUp has advantages over existing state-of-the-art solutions in terms of the number of TCAM moves and overall time cost, and has applicability and robustness in different scenarios.

Complex Sports Target Tracking with Machine Learning: Take Basketball as an Example

Object tracking is a hot issue in vision technology research; it is used in scenarios like intelligent monitoring, autonomous driving, and robot visual perception. With the rapid development of sports, tracking of targets in complex sports scenes represented by basketball and football has gradually attracted attention. The target tracking algorithm based on machine learning (ML) has been gradually proposed. With the powerful feature extraction for convolutional neural network (CNN), it greatly improves accuracy and has better robustness in the face of complex sports scenes. However, the tracking algorithm based on deep learning has many network layers and parameters, which makes training and update speed of model slower. In this regard, taking basketball as an example, this paper designs a low-parameter deep learning-based complex sports target tracking algorithm, which greatly reduces the size of the model while ensuring the tracking accuracy. Aiming at the problem of large number of parameters and large model of deep learning tracking algorithm, this work proposes a network structure model with asymmetric convolution module. The asymmetric convolution module includes two convolutional layers, the compression layer and the asymmetric layer. To improve accuracy, this work designs a new triplet loss. Compared with original logistic loss, triplet loss function can fully utilize the latent relationship between the inputs, so that the network model can obtain higher tracking accuracy. Finally, this paper proposes a low-parameter deep learning-based target tracking algorithm combining asymmetric convolution and triple loss function. Comprehensive and systematic experiments demonstrate the effectiveness of this work in tracking complex sports objects.

Hybrid Differential Evolution Particle Swarm Optimization Algorithm for Solving Resource Leveling Problem of Multi-project with Fixed Duration

This paper attempts to substitute Resource Leveling Problem (RLP) into multi-project environment and construct Resource Leveling Problem of Multi-project (RLPMP) model with the goal of minimizing the sum of weighted mean square deviations of multi-resource requirements. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist reservation (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of Particle Swarm Optimization (PSO) algorithm with dynamic inertia weight. Through the dynamic change of inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can effectively solve the RLPMP model, and then effectively improve the balance of multi-project resources.

UAV dynamic obstacle avoidance based on improved reciprocal velocity obstacle

This paper aims to solve the problem that when the UAV flies from the starting point to the target point in the velocity space, the obstacle avoidance speed provided by the mutual velocity obstacle method will appear too large turning angle in order to avoid dynamic obstacles. In order to better meet the flight performance of UAV, an improved mutual velocity obstacle method is proposed in this paper. This method can not only solve the problem of path jitter caused by the speed obstacle method, but also solve the problem of excessive turning angle when the velocity direction selected by the mutual velocity obstacle method is flying towards the target point. In addition, the UAV will not return to the original path after the obstacle avoidance, but fly along the target point according to the updated best speed direction, so as to reduce the number of turns. The simulation results show that the improved mutual velocity obstacle method can effectively reduce the turning angle of UAV in dynamic environment and meet the flight performance of UAV.

HBase-based spatial-temporal index model for trajectory data

The development of global positioning technology and the popularization of smart mobile terminals has led to a trend of rapid growth in the data volume and coverage of trajectory data. This type of data has the characteristics of fast update speed, high dimensional characteristics, and a large amount of information that can be mined. Many technology companies will use trajectory data to provide location-based services, such as vehicle scheduling and road condition estimation. However, the storage and query efficiency of massive trajectory data have increasingly become bottlenecks in these applications, especially for large-scale spatiotemporal query scenarios. This paper solves this problem by designing a trajectory data index model based on GeoSOT-ST spatiotemporal coding. Based on this model, the HBase-based trajectory data storage scheme and spatiotemporal range query technology are studied, and MapReduce is used as the calculation engine to complete the query attribute condition filtering in parallel. Comparative experiments prove that the index model proposed in this paper can achieve efficient trajectory data query management.

Research on Assembly Scheduling of Complex Electromechanical Products Based on Improved Particle Swarm Optimization

The assembly shop production scheduling of complex electromechanical products is a typical discrete variable NP-hard problem. In order to solve the assembly job scheduling problem of a large transformer assembly shop, an improved particle swarm algorithm is used to solve the optimal assembly task scheduling result. In order to minimize the maximum completion time, the traditional particle swarm optimization (PSO) algorithm was improved according to the characteristics of large transformer assembly process. First, the two-stage coding method of process and assembly team is adopted. Then, in order to improve the quality of the initial solution, the initialization method that minimizes the completion time is selected, and the adaptive inertia weight optimization speed update formula is adopted. Finally, the simulation study is carried out with relevant example data. Compared with the traditional PSO, the maximum completion time and convergence speed are significantly improved, which verifies the effectiveness of the improved PSO, and obtains the production scheduling Gantt chart of the workshop.

An Artificial Neural Network Model to Predict Efficiency and Emissions of a Gasoline Engine

With global warming, and internal combustion engine emissions as the main global non-industrial emissions, how to further optimize the power performance and emissions of internal combustion engines (ICEs) has become a top priority. Since the internal combustion engine is a complex nonlinear system, it is often difficult to optimize engine performance from a certain factor of the internal combustion engine, and the various parameters of the internal combustion engine are coupled with each other and affect each other. Moreover, traditional experimental methods including 3D simulation or bench testing are very time consuming or expensive, which largely affects the development of engines and the speed of product updates. Machine learning algorithms are currently receiving a lot of attention in various fields, including the internal combustion engine field. In this study, an artificial neural network (ANN) model was built to predict three types of indicators (power, emissions, and combustion phasing) together, including 50% combustion crank angle (CA50), carbon monoxide (CO), unburned hydrocarbons (UHC), nitrogen oxides (NOx), indicated mean effective pressure (IMEP), and indicated thermal efficiency (ITE). The goal of this work was to verify that only one machine learning model can combine power, emissions, and phase metrics together for prediction. The predicted results showed that all coefficients of determination (R2) were larger than 0.97 with a relatively small RMSE, indicating that it is possible to build a predictive model with three types of parameters (power, emissions, phase) as outputs based on only one ANN model. Most importantly, when optimizing the powertrain control strategy of a hybrid vehicle, only a surrogate model can help establish the relationship between the input and output parameters of the whole engine, which is the need of the future research. Overall, this study demonstrated that it is feasible to integrate three types of combustion-related parameters in a single machine learning model.

SplitTrie: A Fast Update Packet Classification Algorithm with Trie Splitting

Software Defined Network (SDN) currently is widely used in the implementation of new network technologies owing to its distinctive advantages. In changeable SDN environments, the update performance of SDN switches has significant importance for the overall network performance because packet processing could be interrupted by ruleset updating in SDN switches. In order to guarantee high update performance, we propose a new classification algorithm, SplitTrie, based on trie structures and trie splitting. SplitTrie splits rulesets according to the field type vectors of rules. The splitting can improve the update performance because it reduces the trie structure sizes. Experimental results demonstrated that SplitTrie could achieve 20 times of update speed in the complex rulesets comparing the method without trie splitting.

Radiation avoiding algorithm for nuclear robot path optimization

Improved elephant herding-ant colony hybrid algorithm (IEHO-ACO) is proposed for the nuclear robot path planning problem under the dual constraints of distance and radiation in the radiation environment. In this algorithm, the particle swarm algorithm speed update method and “hybrid operation” are introduced to increase the ability of the elephant herding optimization to jump out of the local optimum. The shortest path obtained by the improved elephant herding optimization is converted into the increment of the initial pheromone distribution of the ant colony optimization to increase the convergence speed of the algorithm and reduce the “blindness” of the ants. At the same time, the “Z” transformation strategy is introduced to dynamically adjust the combination of pheromone heuristic factor and expectation heuristic factor, so as to avoid the occurrence of premature stagnation and falling into local optimum in the search process. Taking full account of the particularity of the radiation environment, the way heuristic factors and pheromone updates in the ant colony algorithm are adapted. Also, the attraction of the target point to the ants is increased when the fallback strategy solves the deadlock problem. The simulation results show that the paths obtained by IEHO-ACO are more reliable and converge faster, and it can be applied to complex environments, especially in environments containing concave obstacles, IEHO-ACO shows good search performance.

An improved atomic search algorithm for optimization and application in ML DOA estimation of vector hydrophone array

&lt;abstract&gt;&lt;p&gt;The atom search optimization (ASO) algorithm has the characteristics of fewer parameters and better performance than the traditional intelligent optimization algorithms, but it is found that ASO may easily fall into local optimum and its accuracy is not higher. Therefore, based on the idea of speed update in particle swarm optimization (PSO), an improved atomic search optimization (IASO) algorithm is proposed in this paper. Compared with traditional ASO, IASO has a faster convergence speed and higher precision for 23 benchmark functions. IASO algorithm has been successfully applied to maximum likelihood (ML) estimator for the direction of arrival (DOA), under the conditions of the different number of signal sources, different signal-to-noise ratio (SNR) and different population size, the simulation results show that ML estimator with IASO algorithum has faster convergence speed, fewer iterations and lower root mean square error (RMSE) than ML estimator with ASO, sine cosine algorithm (SCA), genetic algorithm (GA) and particle swarm optimization (PSO). Therefore, the proposed algorithm holds great potential for not only guaranteeing the estimation accuracy but also greatly reducing the computational complexity of multidimensional nonlinear optimization of ML estimator.&lt;/p&gt;&lt;/abstract&gt;

Information Disruption in Online Journalism in the Era of the Industrial Revolution 5.0

&lt;p&gt;&lt;em&gt;&lt;span&gt;This study examines the barriers to online journalism in the 5.0 industry era in facing market competition. This research was conducted on suaramerdeka.com as a large newspaper company in Central Java that can still survive in the midst of global competition in the mass media competition. Critical perspective with Fairclough's Critical Discourse Analysis design used in this study. The novelty of this research is specifically to answer the challenges of online journalism in the era of the industrial revolution 5.0 in terms of human resources. The findings of this study are related to the routine of news production in the suaramerdeka.com media, the speed of news updates, the accuracy and balance of the news. The human resources of news seekers and editing teams are not yet fully prepared to face the challenges of the new era. The results of this study shows that 1) the news production dependent on HR factor, company factor and managing editor; 2) the news delivery planning dependent on the market need, the news content of the printed Suara Merdeka aims at fulfilling the needs of the people of Central Java, suaramerdeka.com seeks to meet the needs of the people of Central Java around the world.&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;

Fast Online Packet Classification With Convolutional Neural Network

Packet classification is a critical component in network appliances. Software Defined Networking and cloud computing update the rulesets frequently for flexible policy configuration. Tuple Space Search (TSS), implemented in Open vSwitch (OVS), achieves fast rule updating at the sacrifice of the classification rate. In TSS, each tuple is managed by a hash table and classifying a packet needs to go through all hash tables. Merging tuples can reduce the number of hash tables, but inevitably increases the hash conflicts that may even worsen the classification performance in some cases. No existing algorithm meets the need of both fast packet classification and online rule updating. In this paper, we propose Convolutional Neural Network (CNN)-based Range Partition (CRP) to achieve fast packet classification and online update simultaneously. CRP exploits CNN-based image recognition to quickly partition tuples into range spaces upon the change of ruleset distribution, which reduces hash operations while avoiding rule overlapping caused by hashing many rules to the same location of the hash table. Experimental results demonstrate that CRP achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.2\times $ </tex-math></inline-formula> classification speed and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.2\times $ </tex-math></inline-formula> update speed on average compared with state-of-the-art algorithms. We also implement CRP in OVS. The throughput of CRP-OVS is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> that of native OVS.

An Intelligent Transient Stability Assessment Framework With Continual Learning Ability

Data driven method based on big data and deep model is an effective tool to solve the problem of online power system transient stability assessment. Nevertheless, when the system suffers from some large disturbances such as the changes of topology, there is no proper way for a model to supplement the original knowledge base but to retrain it from scratch. Meanwhile, with the continuous emergence of new scenarios, data management has become a new dilemma reflected in the increase of data accumulation and retraining time. To address these issues, a novel TSA framework with continual learning ability is proposed. First, the improved convolutional neural network-based orthogonal weight modification algorithm is selected as a transient stability predictor. Then, large disturbance is regarded as a new recognition task which can be continuously learned by the predictor. Through the proposed updating process, the original knowledge base can be updated and supplemented only by the data corresponding to new scenario. The test results on two power systems show that the proposed scheme can realize the tradeoff between the integrity of knowledge base and the update speed without losing too much accuracy in a limited capacity.

Retraction Note: GIS-based marine atmospheric environment and fishery company governance structure

During the water cycle, the energy and water vapor between the sea and the air are converted. Changes in water evaporation and water vapor encountering cold precipitation will have an important impact on the water flow in the ocean. There is a non-adiabatic heating rate and moisture in the air. The flow of fresh water from the sea plays an extremely important role in the circulation of the sea, especially in the circulation of salt-containing seawater. Water is presented in different forms. There are two functions of heating and cooling in the air temperature circulation system. With the rapid development of remote sensing information and geographic information technology, the use of GIS technology proves that the system has a very large data storage capacity. The coverage is relatively wide, and the information update speed is fast. It can create quick and accurate theoretical knowledge support for the public and promotes the scientific development of eco-environmental resources and the rapid and healthy development of social economy in the surrounding area of ​​the study area. This article discusses the impact of changes in the flow of fresh water in the world’s oceans on global temperature warming. Correlative experimental researches on the water cycle and water vapor content on different time scales are carried out. Through research, it is found that there are certain problems in the internal management structure of fishery enterprises. Based on the theory of internal standards, suggestions and measures for improvement of related issues can be put forward, which can provide reference and reference for the healthy development of fishery companies.

Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging.

Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.