Vol.39, No.2, 2021-Table of Contents
  • Task Allocation Approach for Minimizing Make-Span in Wireless Sensor Actor Networks
  • Abstract Wireless Sensor Actor Networks (WSANs) have contributed to the development of pervasive computing wherein time consideration to perform the tasks of pervasive applications is necessary. Hence, time constraint is one of the major challenges of WSANs. In this paper, we propose an analytical approach based on queuing theory to minimize the total time taken for completion of tasks, i.e., make-span, in WSANs with hybrid architecture. The best allocation rates of tasks to actor nodes are figured out through solving inequities and qualities resulting from a steady state analysis of the proposed model. Applying the calculated tasks arrival rates at each… More
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  • Research on the Novel Honeycomb-Like Cabin Based on Computer Simulation
  • Abstract The antiknock capability and thermal protection performance of rescue capsules mainly depend on the structural design of the cabin. By designing a new type of cabin structure, it can resist the impact of explosion shock waves and thermal shocks. In this paper, a new honeycomb-like cabin is proposed; the model has a novel thermal insulation layer design. Then, the antiknock capabilities and thermal protection analysis are carried out by using computer software. The “Autodyn” analysis module in ANSYS Workbench 17.0 has been used to simulate the explosion of TNT with a certain quality in a single room. The pressure map… More
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  • Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM
  • Abstract Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power. Temporal-spatial wind speed features contain rich information; however, their use to predict wind speed remains one of the most challenging and less studied areas. This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attention-based long short-term memory (LSTM), termed 2Attn-LSTM, a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data. To eliminate the unevenness of the original wind speed,… More
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  • Brain Storm Optimization Based Clustering for Learning Behavior Analysis
  • Abstract Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students’ learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data. This study proposes a… More
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  • Crowdsourced Requirements Engineering Challenges and Solutions: A Software Industry Perspective
  • Abstract Software crowdsourcing (SW CS) is an evolving software development paradigm, in which crowds of people are asked to solve various problems through an open call (with the encouragement of prizes for the top solutions). Because of its dynamic nature, SW CS has been progressively accepted and adopted in the software industry. However, issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained. If the requirements are not clear to the development team, it has a significant effect on the quality of the software product. This study aims to identify… More
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  • A Markov Model for Subway Composite Energy Prediction
  • Abstract Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard train operating conditions for simulation,… More
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  • Community Detection in Aviation Network Based on K-means and Complex Network
  • Abstract With the increasing number of airports and the expansion of their scale, the aviation network has become complex and hierarchical. In order to investigate the complex network characteristics of aviation networks, this paper constructs a Chinese aviation network model and carries out related research based on complex network theory and K-means algorithm. Initially, the P-space model is employed to construct the Chinese aviation network model. Then, complex network indicators such as degree, clustering coefficient, average path length, betweenness and coreness are selected to investigate the complex characteristics and hierarchical features of aviation networks and explore their causes. Secondly, using K-means… More
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  • A Novel PoW Scheme Implemented by Probabilistic Signature for Blockchain
  • Abstract PoW (Proof of Work) plays a significant role in most blockchain systems to grant an accounting right over decentralized participants and ensure tamper resistance. Though hash functions are generally exploited for PoW due to their merits on summering, anti-collision, and irreversibility, they cannot certify that the bookkeeper is exactly the worker. Thereafter, such insistence may lead to abuse or even embezzlement of computing power for the benefit of malicious miners. To preserve the functionality of PoW but also bind the miners’ signing keys with their works, we build a post-quantum PoW scheme by changing the approximate closest vector norm for… More
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  • An Effective CU Depth Decision Method for HEVC Using Machine Learning
  • Abstract This paper presents an effective machine learning-based depth selection algorithm for CTU (Coding Tree Unit) in HEVC (High Efficiency Video Coding). Existing machine learning methods are limited in their ability in handling the initial depth decision of CU (Coding Unit) and selecting the proper set of input features for the depth selection model. In this paper, we first propose a new classification approach for the initial division depth prediction. In particular, we study the correlation of the texture complexity, QPs (quantization parameters) and the depth decision of the CUs to forecast the original partition depth of the current CUs. Secondly,… More
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  • Using Blockchain Technology in Mobile Network to create decentralized Home Location Registry (HLR)
  • Abstract Blockchain can mean many things to many people. It is a set of protocols and encryption technologies for securely storing data on a distributed network for the developers. It is a distributed ledger for business and finance and the technology underlying the explosion of new digital currencies. For technologists, it is the driving force behind the next generation of the internet. On the other hand, it is a transformational technology facilitating large-scale human progress in previously unimagined ways for the rest of the people, a tool for radically reshaping society and economy. Some view it as a disruptive technology that… More
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