• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.205-214
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• 2019

Android uses app cache files to improve app execution performance. However, this optimization technique may raise security issues that need to be examined. In this paper, we present a practical design of "Android app cache manipulation attack" to intentionally modify the cache files of a target app, which can be misused for stealing personal information and performing malicious activities on target apps. Even though the Android framework uses a checksum-based integrity check to protect app cache files, we found that attackers can effectively bypass such checks via the modification of checksum of the target cache files. To demonstrate the feasibility of our attack design, we implemented an attack tool, and performed experiments with real-world Android apps. The experiment results show that 25 apps (86.2%) out of 29 are vulnerable to our attacks. To mitigate app cache manipulation attacks, we suggest two possible defense mechanisms: (1) checking the integrity of app cache files; and (2) applying anti-decompilation techniques.

• Journal of Powder Materials
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• v.25 no.6
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• pp.475-481
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• 2018

Selective laser melting (SLM), a type of additive manufacturing (AM) technology, leads a global manufacturing trend by enabling the design of geometrically complex products with topology optimization for optimized performance. Using this method, three-dimensional (3D) computer-aided design (CAD) data components can be built up directly in a layer-by-layer fashion using a high-energy laser beam for the selective melting and rapid solidification of thin layers of metallic powders. Although there are considerable expectations that this novel process will overcome many traditional manufacturing process limits, some issues still exist in applying the SLM process to diverse metallic materials, particularly regarding the formation of porosity. This is a major processing-induced phenomenon, and frequently observed in almost all SLM-processed metallic components. In this study, we investigate the mechanical anisotropy of SLM-produced 316L stainless steel based on microstructural factors and highly-oriented porosity. Tensile tests are performed to investigate the microstructure and porosity effects on mechanical anisotropy in terms of both strength and ductility.

• Journal of Drive and Control
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• v.18 no.1
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• pp.1-8
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• 2021

This paper presents research on methods for the reduction of forklifts' noise level for the increased comfort and safety of its operator. A cooling fan with a high air volume flow rate installed in the forklift acts as an important design parameter which efficiently cools the heat exchanger system, helping to transfer internal heat from the engine room to the outdoors with both transmitted and diffracted opening noises. The cooling fan contributes significantly to both the forklift's emitted sound power and the operator room's noise level, thereby necessitating research on the forklift's reduction of acoustic power level and transmission. A noise analysis for various fan models with a biomimetic design based on eagle-wing geometry was conducted. In addition to the acoustic power generation, the aerodynamic performance of the cooling blade is also strongly influenced by the design of airfoil distribution, thereby requiring optimization. The cooling fans were fabricated and installed in the forklift in order to check the efficacy of the forklift engine's cooling, and the final version of the fan was measured for its ability to lower acoustic power level and cool the engine room. This study explains the aerodynamic and acoustic features of the designed fans with the use of BEM analysis and forklift test results.

• The Journal of Korean Institute of Information Technology
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• v.17 no.10
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• pp.49-55
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• 2019

In this paper, we propose a simple precoding vector design scheme for multi-user multiple-input single-output (MISO) interference channel when there are multiple eavesdroppers. We aim to obtain a mathematical closed-form solution of the secrecy rate optimization problem. For this goal, we design the precoding vector based on the signal-to-leakage plus noise ratio (SLNR). More specifically, the proposed precoding vector is designed to completely eliminate a wiretap channel capacity for refraining the eavesdroppers from detecting the transmitted information, and to maximize the transmitter-receiver link achievable rate. We performed simulation for the performance investigation. Simulation results show that the proposed scheme has better secrecy rate than the conventional scheme over all signal-to-noise ratio (SNR) range even though the special condition among the numbers of transmit antennas, transmitter-receiver links, and eavesdroppers is not satisfied.

• Advances in nano research
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• v.13 no.2
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• pp.199-206
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• 2022

We present the high-brightness large-area 10.1" in-cell polarizer display panel integrated with a wire grid polarizer (WGP) and metal reflector, from the initial design to final system development in a commercially feasible level. We have modeled and developed the WGP architecture integrated with the metal reflector in a single in-cell layer, to achieve excellent polarization efficiency as well as brightness enhancement through the light recycling effect. After the optimization of key experimental parameters via Taguchi method, the roll nanoimprint lithography employing a flexible large-area tiled mold has been utilized to create the 90 nm-pitch polymer resist pattern with the 54.1 nm linewidth and 5.1 nm residual layer thickness. The 90 nm-pitch Al gratings with the 51.4 nm linewidth and 2150 Å height have been successfully fabricated after subsequent etch process, providing the in-cell WGPs with high optical performance in the entire visible light regime. Finally we have integrated the WGP in a commercial 10.1" display device and demonstrated its actual operation, exhibiting 1.24 times enhancement of brightness compared to a conventional film polarizer-based one, with the contrast ratio of 1,004:1. Polarization efficiency and transmittance of the developed WGPs in an in-cell polarizer panel achieve 99.995 % and 42.3 %, respectively.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.293-303
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• 2022

As infrastructure ages and traffic load increases, serious public concerns have arisen for the well-being of bridges. The current health monitoring practice focuses on large-scale bridges rather than short span bridges. However, it is critical that more attention should be given to these behind-the-scene bridges. The relevant information about the construction methods and as-built properties are most likely missing. Additionally, since the condition of a bridge has unavoidably changed during service, due to weathering and deterioration, the material properties and boundary conditions would also have changed since its construction. Therefore, it is not appropriate to continue using the design values of the bridge parameters when undertaking any analysis to evaluate bridge performance. It is imperative to update the model, using finite element (FE) analysis to reflect the current structural condition. In this study, a FE model is established to simulate a concrete culvert bridge in New South Wales, Australia. That model, however, contains a number of parameter uncertainties that would compromise the accuracy of analytical results. The model is therefore updated with a neural network (NN) optimisation algorithm incorporating Monte Carlo (MC) simulation to minimise the uncertainties in parameters. The modal frequency and strain responses produced by the updated FE model are compared with the frequency and strain values on-site measured by sensors. The outcome indicates that the NN model updating incorporating MC simulation is a feasible and robust optimisation method for updating numerical models so as to minimise the difference between numerical models and their real-world counterparts.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.37-44
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• 2022

Plug-in Hybrid electric vehicles (PHEV) show great potential to reduce gas emission, improve fuel efficiency and offer more driving range flexibility. Moreover, PHEV help to preserve the eco-system, climate changes and reduce the high demand for fossil fuels. To address this; some basic components and energy resources have been used, such as batteries and proton exchange membrane (PEM) fuel cells (FCs). However, the FC remains unsatisfactory in terms of power density and response. In light of the above, an electric storage system (ESS) seems to be a promising solution to resolve this issue, especially when it comes to the transient phase. In addition to the FC, a storage system made-up of an ultra-battery UB is proposed within this paper. The association of the FC and the UB lead to the so-called Fuel Cell Battery Electric Vehicle (FCBEV). The energy consumption model of a FCBEV has been built considering the power losses of the fuel cell, electric motor, the state of charge (SOC) of the battery, and brakes. To do so, the implementing a reinforcement-learning energy management strategy (EMS) has been carried out and the fuel cell efficiency has been optimized while minimizing the hydrogen fuel consummation per 100km. Within this paper the adopted approach over numerous driving cycles of the FCBEV has shown promising results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.365-380
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• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.