• Computers and Concrete
• /
• v.30 no.4
• /
• pp.277-287
• /
• 2022

Due to the scarcity of extortionate experimental data, fatigue failure of the reinforced concrete (RC) element might be achieved economically adopting nonlinear finite element (FE) analysis as an alternative approach. However, conventional implicit dynamic analysis is expensive, quasi-static method overlooks interaction effects and inertia, direct cyclic analysis computes stabilized responses. Apart from this, explicit dynamic analysis may provide a numerical operating system for factual long-term responses. The study explores the fatigue behavior based on a simplified explicit dynamic solution employing nonlinear time domain analysis. Among fourteen RC beams, one beam is selected to validate under static loading, one under fatigue with the experimental study and other twelve to check the detail fatigue behavior. The SWOT (Strength, Weakness, Opportunities, Threats) analysis has been carried out to pinpoint the detail scenario in the adoption of numerical approach as an alternative to the experimental study. Excellent agreement of FE and experimental results is seen. The 3D nonlinear RC beam model at service fatigue limits is truthful to be used as an expedient contrivance to envisage the precise fatigue behavior. The simplified analysis approach for RC beam under fatigue offers savings in computation to predict responses providing acceptable accuracy rather than the complicated laboratory investigation. At higher frequency, the flexural failure occurs a bit earlier gradually compared to the repeated loading case of lower frequency. The deflection increases by 6%-10% at the end of first cycle for beams with increasing frequency of cyclic loading. However, at the end of fatigue loading, greater deflection occur earlier for higher load range because of more rapid stiffness degradation. For higher frequency, a slight boost in concrete compressive strains at an initial stage of loading has been seen indicating somewhat stepper increment. Stiffness degradation in larger loading cycle at same duration escalates the upsurge of the rate of strain in case of higher frequency.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.27 no.1
• /
• pp.69-75
• /
• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.117-127
• /
• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Journal of Software Assessment and Valuation
• /
• v.16 no.2
• /
• pp.127-133
• /
• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4345-4363
• /
• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Journal of Positioning, Navigation, and Timing
• /
• v.11 no.3
• /
• pp.163-172
• /
• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• Journal of Korea Society of Industrial Information Systems
• /
• v.14 no.1
• /
• pp.43-55
• /
• 2009

We consider a Direct Input Output Manufacturing System(DIOMS) which has a number of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the SIR machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• KNOM Review
• /
• v.22 no.1
• /
• pp.60-67
• /
• 2019

Graphical Processing Units(GPUs) achieve high performance undertaking from relatively uniformed computation in parallel. The technology related to General Purpose GPU(GPGPU) has been enhanced, which provides concurrent kernel execution of multi and diverse applications at the same time, but it is still limited to support resource sharing or planning. NVIDIA recently introduces Multi-Process Service(MPS), which allows kernels from different applications can be execute concurrently. However, the strength of MPS comes along with the characteristics of applications and the order of their execution. This paper shows the performance analysis of diverse scientific applications in real world. Based on the analysis, we prove that it is important to the identify characteristics of co-run applications, and to schedule multiple applications via profiling to maximize MPS functionality.

• Journal of the Korea Institute of Building Construction
• /
• v.23 no.3
• /
• pp.315-326
• /
• 2023

The design principles and implementation of rebar lap splice in architectural structures are governed by building regulations. Nevertheless, the minimization of rebar-cutting waste (RCW) is often impeded by the mandatory requirements pertaining to the rebar lapping zone as prescribed in design codes. In real-world construction scenarios, compliance with these rules often falls short due to hurdles concerning productivity, quality, safety, time, and cost. This discrepancy between code stipulations and on-the-ground construction practices necessitates an academic exploration. The goal of this research was to delve into the effect of rebar lap splice placement on the robustness and constructability of building edifices. The study initially took on a review of the computation of rebar lapping length and the rules revolving around the lapping zone. Following this, a structural robustness and constructability examination was undertaken, focusing on adherence to the lap splice zone. The interpretations and deductions of the research led to the following insights: (1) the efficacy of rebar lap splice is not solely contingent on the moment, and (2) the implementation of rebar lap splice beyond the specified zone can match the structural integrity and robustness of those confined within the designated area. As a result, the constraints on the rebar lapping zone ought to be revisited and possibly relaxed. The conclusions drawn from this research are anticipated to reconcile the disconnect between building codes and practical construction conditions, furnishing invaluable academic substantiation to further the endeavor of achieving near-zero RCW.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.3
• /
• pp.133-138
• /
• 2009

Recently Lin et al. proposed the remote user authentication scheme using smart cards. But the proposed scheme has not been satisfied security requirements considering in the user authentication scheme using the password based smart card. In this paper, we showed that he can get the user's password using the off-line password guessing attack on the scheme when the adversary steals the user's smart card and extracts the information in the smart card. Also, we proposed the seven security requirements for evaluating remote user authentication schemes using smart card. As a result of analysis, in Lin et al's scheme we have found the deficiencies of security requirements. So we suggest the improved scheme, the mutual authentication scheme that does not store the user's password verifier in server and can authenticate each other at the same time between the user and server.