• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1333-1343
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• 2016

Spectrum sharing in centrally controlled cognitive radio (CR) networks has been widely studied, however, research on channel access for distributively controlled individual cognitive users has not been fully characterized. This paper conducts an analysis of random channel access of cognitive users controlled in a distributed manner in a CR network. Based on the proposed estimation method, each cognitive user can estimate the current channel condition by using its own Markov-chain model and can compute its own blocking probability, collision probability, and forced termination probability. Using the proposed scheme, CR with distributed control (CR-DC), CR devices can make self-controlled decisions based on the status estimations to adaptively control its system parameters to communicate better.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2964-2983
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• 2015

As the Android operating system has become a key target for malware authors, Android protection has become a thriving research area. Beside the proved importance of system permissions for malware analysis, there is a lot of overlapping in permissions between malware apps and goodware apps. The exploitation of them effectively in malware detection is still an open issue. In this paper, to investigate the feasibility of neuro-fuzzy techniques to Android protection based on system permissions, we introduce a self-adaptive neuro-fuzzy inference system to classify the Android apps into malware and goodware. According to the framework introduced, the most significant permissions that characterize optimally malware apps are identified using Information Gain Ratio method and encapsulated into patterns of features. The patterns of features data is used to train and test the system using stratified cross-validation methodologies. The experiments conducted conclude that the proposed classifier can be effective in Android protection. The results also underline that the neuro-fuzzy techniques are feasible to employ in the field.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.4
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• pp.33-39
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• 2014

In this paper, a new on-chip aging sensing circuit based on flip-flops is proposed to detect a circuit failure of MOSFET digital circuits casued by aging phenomenon such as HCI and BTI. The proposed circuit uses timing windows to warn against a guardband violation of sequential circuits, and generates three warning bits right before circuit failures occur. The generated bits can apply to an adaptive self-tuning method for reliable system design as control signals. The aging sensor circuit has been implemented using 0.11um CMOS technology and evaluated by $4{\times}4$ multiplier with power gating structure.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.12-20
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• 1998

As an approach to develope a control system with high robustness in changing control environment conditions, this paper will propose a robust control system, using multilayer neural network and biological immune system. The proposed control system adjusts weights of the multilayer neural network(MNN) with the immune algorithm. This algorithm is made up of two major divisions, the innate immune algorithm as a first line of defence and the adaptive immune algorithm as a barrier of self-adjustment. Using the proposed control system based on immune algorithm, we will work out a design for the controller of a robot manipulator. And we will demonstrate the effectiveness of the control system of robot manipulator with computer simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.91-99
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• 2011

Several repeater systems are used to enhance the cell coverage to location such as shadow and rural areas in mobile systems. But the general RF repeater solutions are not suitable for high power outdoor environment because it has the weakness such as self oscillation problem With adoption of a adaptive digital filter technology, feedback interference cancellation repeater prevents oscillation by detecting and canceling the unwanted feedback signal between transmission and receiver antenna. In this paper, dual NLMS based interference cancellation method is proposed and the step size adaptation can be implemented by the estimation of the feedback channel Doppler frequency characteristics. The performance of the proposed algorithm is quantified via analysis and simulation for the static and multipath fading feedback channels.

• BMB Reports
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• v.57 no.1
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• pp.40-49
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• 2024

Prokaryotes encode clustered regularly interspaced short palindromic repeat (CRISPR) arrays and CRISPR-associated (Cas) genes as an adaptive immune machinery. CRISPR-Cas systems effectively protect hosts from the invasion of foreign enemies, such as bacteriophages and plasmids. During a process called 'adaptation', non-self-nucleic acid fragments are acquired as spacers between repeats in the host CRISPR array, to establish immunological memory. The highly conserved Cas1-Cas2 complexes function as molecular recorders to integrate spacers in a time course manner, which can subsequently be expressed as crRNAs complexed with Cas effector proteins for the RNA-guided interference pathways. In some of the RNA-targeting type III systems, Cas1 proteins are fused with reverse transcriptase (RT), indicating that RT-Cas1-Cas2 complexes can acquire RNA transcripts for spacer acquisition. In this review, we summarize current studies that focus on the molecular structure and function of the RT-fused Cas1-Cas2 integrase, and its potential applications as a directional RNA-recording tool in cells. Furthermore, we highlight outstanding questions for RT-Cas1-Cas2 studies and future directions for RNA-recording CRISPR technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2389-2413
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• 2015

High data rates in long-term evolution (LTE) networks can affect the mobility of networks and their performance. The speed and motion of user equipment (UE) can compromise seamless connectivity. However, a proper handover (HO) decision can maintain quality of service (QoS) and increase system throughput. While this may lead to an increase in complexity and operational costs, self-optimization can enhance network performance by improving resource utilization and user experience and by reducing operational and capital expenditure. In this study, we propose the self-optimization of HO parameters based on fuzzy logic control (FLC) and multiple preparation (MP), which we name FuzAMP. Fuzzy logic control can be used to control self-optimized HO parameters, such as the HO margin and time-to-trigger (TTT) based on multiple criteria, viz HO ping pong (HOPP), HO failure (HOF) and UE speeds. A MP approach is adopted to overcome the hard HO (HHO) drawbacks, such as the large delay and unreliable procedures caused by the break-before-make process. The results of this study show that the proposed method significantly reduces HOF, HOPP, and packet loss ratio (PLR) at various UE speeds compared to the HHO and the enhanced weighted performance HO parameter optimization (EWPHPO) algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.169-174
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• 2003

The trial and success of malicious cyber attacks has been increased rapidly with spreading of Internet and the activation of a internet shopping mall and the supply of an online, or an offline internet, so it is expected to make a problem more and more. The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators in real time. In fact, the general security system based on Internet couldn't cope with the attack properly, if ever. other regular systems have depended on common vaccine softwares to cope with the attack. But in this paper, we will use the positive selection and negative selection mechanism of T-cell, which is the biologically distributed autonomous system, to develop the self/nonself recognition algorithm and AIS (Artificial Immune System) that is easy to be concrete on the artificial system. For making it come true, we will apply AIS to the network environment, which is a computer security system.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.5-24
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• 2010

Data fusion is an attractive technology because it allows various trade-offs related to performance metrics, e.g., energy, latency, accuracy, fault-tolerance and security in wireless sensor networks (WSNs). Under a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets, so that the complexity for the fusion process is increased due to the existence of various physical attributes. In this paper, we first investigate the process and performance of multi-attribute fusion in data gathering of WSNs, and then propose a self-adaptive threshold method to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Based on our proposed methods, we design a novel energy equilibrium routing method for WSNs, viz., multi-attribute fusion tree (MAFT). Simulation results demonstrate that MAFT achieves very good performance in terms of the network lifetime.