• Journal of KIISE:Computing Practices and Letters
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• v.15 no.2
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• pp.89-97
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• 2009

Embedded systems widely used in ubiquitous environments usually employ an event-driven programming model instead of thread-based programming model in order to create a more robust system that uses less memory. However, as the software for embedded systems becomes more complex, it becomes hard to program as a single event handler using the event-driven programming model. This paper discusses the implementation of non-preemptive real-time scheduling theory for the design of embedded systems. To this end, we present an efficient schedulability test method for a given non-preemptive task set using a sufficient condition. This paper also shows that the notion of sub-tasks in embedded systems can overcome the problem of low utilization that is a main drawback of non-preemptive scheduling.

• Structural Engineering and Mechanics
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• v.35 no.4
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• pp.459-478
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• 2010

This paper contains detailed descriptions of a dynamic time-history modal analysis to calculate deflection, inter-storey drift and storey shear demand in single-storey and multi-storey buildings using an EXCEL spreadsheet. The developed spreadsheets can be used to obtain estimates of the dynamic response parameters with minimum input information, and is therefore ideal for supporting the conceptual design of tall building structures, or any other structures, in the early stages of the design process. No commercial packages, when customised, could compete with spreadsheets in terms of simplicity, portability, versatility and transparency. An innovative method for developing the stiffness matrix for the lateral load resistant elements in medium-rise and high-rise buildings is also introduced. The method involves minimal use of memory space and computational time, and yet allows for variations in the sectional properties of the lateral load resisting elements up the height of the building and the coupling of moment frames with structural walls by diaphragm action. Numerical examples are used throughout the paper to illustrate the development and use of the spreadsheet programs.

• Electronics and Telecommunications Trends
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• v.33 no.6
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• pp.129-138
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• 2018

As recent applications are requiring more CPUs for their performance, manycore systems have evolved. Since existing operating systems do not provide performance scalability in manycore systems, Azalea, a multi-kernel based system, has been developed for supporting performance scalability. Unikernel is a new operating system technology starting with the concept of a library OS. Applying unikernel to Azalea enables an improvement in performance. In this paper, we first analyze the current technology trends of unikernel, and then discuss the applications and effects of unikernel to Azalea. Azalea-unikernel was built in a single image consisting of libOS, runtime libraries, and an application, and executed with the desired number of cores and memory size in bare-metal. In particular, it supports source and binary compatibility such that existing linux binaries can be rebuilt and executed in Azalea-unikernel, and already built binaries can be run immediately without modification with a better performance. It not only achieves a performance enhancement, it is also a more secure OS for manycore systems.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.249-258
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• 2019

In the cyber-physical system, big data collected from numerous sensors and IoT devices is transferred to the Cloud for processing and analysis. When transferring data to the Cloud, merging data into one single file is more efficient than using the data in the form of split files. However, current merging and splitting operations are performed at the user-level and require many I / O requests to memory and storage devices, which is very inefficient and time-consuming. To solve this problem, this paper proposes kernel-level partitioning and combining operations. At the kernel level, splitting and merging files can be done with very little overhead by modifying the file system metadata. We have designed the proposed algorithm in detail and implemented it in the Linux Ext4 file system. In our experiments with the real Cloud storage system, our technique has achieved a transfer time of up to only 17% compared to the case of transferring split files. It also confirmed that the time required can be reduced by up to 0.5% compared to the existing user-level method.

• Smart Structures and Systems
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• v.24 no.2
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• pp.157-171
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• 2019

This study investigated the effects of the geometric parameters of superelastic shape memory alloy (SE SMA) fibers on the pullout displacement recovering and self-healing capacity of reinforced cementitious composites. Three diameters of 0.5, 0.7 and 1.0 mm and two different crimped lengths of 5.0 and 10.0 mm were considered. To provide best anchoring action and high bond between fiber and cement mortar, the fibers were crimped at the end to create spear-head shape. The single fiber cement-based specimens were manufactured with the cement mortar of a compressive strength of 84 MPa with the square shape at the top and a dog-bone shape at the bottom. The embedded length of each fiber was 15 mm. The pullout test was performed with displacement control to obtain monotonic or hysteretic behaviors. The results showed that pullout displacements were recovered after fibers slipped and stuck in the specimen. The specimens with fiber of larger diameter showed better displacement recovering capacity. The flag-shaped behavior was observed for all specimens, and those with fiber of 1.0 mm diameter showed the clearest one. It was observed that the length of fiber anchorage did not have a significant effect on the displacement recovery, pullout resistance and self-healing capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1385-1402
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• 2019

Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value. Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper. The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams. Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology. At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3390-3405
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• 2022

Long Short-Term Memory (LSTM) combined with attention mechanism is extensively used to generate semantic sentences of images in image captioning models. However, features of salient regions and spatial information are not utilized sufficiently in most related works. Meanwhile, the LSTM also suffers from the problem of underutilized information in a single time step. In the paper, two innovative approaches are proposed to solve these problems. First, the Synergy-Gated Attention (SGA) method is proposed, which can process the spatial features and the salient region features of given images simultaneously. SGA establishes a gated mechanism through the global features to guide the interaction of information between these two features. Then, the Recurrent Fusion LSTM (RF-LSTM) mechanism is proposed, which can predict the next hidden vectors in one time step and improve linguistic coherence by fusing future information. Experimental results on the benchmark dataset of MSCOCO show that compared with the state-of-the-art methods, the proposed method can improve the performance of image captioning model, and achieve competitive performance on multiple evaluation indicators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.126-132
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• 2023

Web technology is evolving with the passage of time, from a single node server to high availability and then in the form of Kubernetes. In recent years, the research community have been trying to provide high availability in the form of multi master cluster with a solid election algorithm. This is helpful in increasing the resources in the form of pods inside the worker node. There are new impact of known DDoS attack, which is utilizing the resources at its peak, known as Yoyo attack. It is kind of burst attack that can utilize CPU and memory to its limit and provide legit visitors with a bad experience. In this research, we tried to mitigate the Yoyo attack by introducing a firewall at load-balancer level to prevent the attack from going to the cluster network.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.