• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.79-86
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• 2021

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.222-225
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• 2013

무선 네트워크 환경에서 스펙트럼 자원을 효율적으로 활용할 수 있는 무선 인지 (Cognitive Radio, CR) 중 기지국이나 접속점과 같은 중앙 제어장치가 없는 분산형 무선 인지 네트워크 (Distributed Cognitive Radio Network, DCRN)에서는 부 사용자 (Secondary User, SU)들의 하드웨어적 한계로 인해 주 사용자 (Primary User, PU)들에게 인가된 스펙트럼의 모든 정보를 알 수 없다. 이를 극복하기 위해 SU들은 협력적 스펙트럼 감지 (Cooperative Spectrum Sensing)를 통하여 지역적인 스펙트럼 정보를 서로 교환하여 전체 스펙트럼의 정보를 파악한다. 본 논문은 효율적인 협력적 스펙트럼 감지를 위하여 PU 채널 상태의 비율을 사용한 정책 기반 감지 채널 선택 방법인 비율 기반 감지 정책 (Proportion-based Sensing Policy, PSP)을 제안한다. 각 SU가 PU의 채널 사용을 감지하고 PU 채널들의 idle 상태의 비율을 계산하고, 이 결과를 바탕으로 감지하는 PU 채널 변경 시, 높은 idle 비율을 갖는 PU 채널을 우선적으로 선택하여 감지되는 PU 상태가 idle 일 확률을 높여 효율적인 채널 감지를 가능하게 한다. 시뮬레이션 결과를 통하여 기존에 제안된 채널 감지 정책보다 비율 기반의 채널 감지 정책이 효율적으로 PU 채널들을 감지하는 것을 알 수 있다.

• ETRI Journal
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• v.45 no.1
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• pp.28-44
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• 2023

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1479-1495
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• 2012

Spectrum scarcity seems to be the most challenging issue to be solved in new wireless telecommunication services. It is shown that spectrum unavailability is mainly due to spectrum inefficient utilization and inappropriate physical layer execution rather than spectrum shortage. Daily increasing demand for new wireless services with higher data rate and QoS level makes the upgrade of the physical layer modulation techniques inevitable. Orthogonal Frequency Division Multiple Access (OFDMA) which utilizes multicarrier modulation to provide higher data rates with the capability of flexible resource allocation, although has widely been used in current wireless systems and standards, seems not to be the best candidate for cognitive radio systems. Filter Bank based Multi-Carrier (FBMC) is an evolutionary scheme with some advantages over the widely-used OFDM multicarrier technique. In this paper, we focus on the total throughput improvement of a cognitive radio network using FBMC modulation. Along with this modulation scheme, we propose a novel uplink radio resource allocation algorithm in which fairness issue is also considered. Moreover, the average throughput of the proposed FBMC based cognitive radio is compared to a conventional OFDM system in order to illustrate the efficiency of using FBMC in future cognitive radio systems. Simulation results show that in comparison with the state of the art two algorithms (namely, Shaat and Wang) our proposed algorithm achieves higher throughputs and a better fairness for cognitive radio applications.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1295-1302
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• 2011

We present an analytic model of an unslotted opportunistic spectrum access (OSA) network and evaluate its performance such as interruption probability, service completion time, and throughput of secondary users. Numerical examples are given to show the performance of secondary users in cognitive networks. The developed modeling and analysis method can be used to evaluate the performance of various OSA networks.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.280-285
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• 2012

We propose self-encoded spread spectrum with two different iterative detection methods in multi-channel communication. The centralized iterative detection outperforms the iterative detection distributed over multiple channels. The results show that self-encoded spread spectrum with the centralized iterative detection is an excellent candidate for cognitive radio network.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.237-238
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• 2008

This paper presents a cognitive radio network where a base station exploits multi-antennas. For the system, a joint power control and user selection greedy algorithm which achieve a significant fraction of sum-capacity at lower complexity cost is proposed.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.73-76
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• 2003

신경생리학적으로 밝혀진 바에 의하면, 대뇌의 시상에 분포한 일차 감각영역에서 감각 정보를 수집한다. 수집된 감각 정보는 과거 기억과의 비교를 통해 인식되고 인식된 정보는 일차 운동영역으로 전달되어 행동으로 나타난다. 수집된 감각 정보를 판단하는 기관은 감각 연합 영역으로 알려져 있으며, 과거 정보를 통해 비교하여 판단하는 방식이다. 하지만, 과거 기억 정보로 존재하지 않는 새로운 감각 입력에 대해서는 대뇌피질 내의 파페츠 회로를 통해 새로이 기억하게 된다. 이 과정에는 변연계의 편도체(Amygdala)의 감정 반응을 이용하여 강한 감정 반응을 유도하는 감각 입력에 대해서는 강한 기억을 하게 되고, 반대의 경우에는 약한 기억을 하게 되는 특징이 고려된다. 본 논문에서는 기억되지 않은 새로운 감각 자극에 대해 감정 반응 정도에 따라 기억되는 정도의 변화를 관찰할 수 있는 모델을 제시하고자 한다. 이 모델은 대뇌피질의 정보 처리 및 감각 학습 과정을 인공적으로 구현하는 과정에 바탕이 될 것이다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.377-378
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• 2011

본 논문은 뇌기능 연구에 크게 기여하는 기능적 자기공명영상을 효과적으로 분석하기 위한 유효 연결성(Effective Connectivity, EC)을 이용한 대규모 네트워크(Large-Scale Network, LSN) 분석(LSN-EC)을 제안한다. 유효 연결성은 뇌영역간의 시공간적 인과관계를 표현한 연결성이며, 뇌의 기능적 연결성 및 구조탐색 사용된다. LSN-EC는 뇌영역간의 EC를 표현하고 그룹간의 차이분석을 통하여 뇌질환 분석 및 진단 연구로의 응용이 가능하다. 실험결과에서 알츠하이머병과 관련이 높다고 알려진 후대상피질(Posterior Cingulate Cortex)과 해마(Hippocampus)가 포함된 변연엽(Limbic Lobe), 기저핵 및 시상(Basal Ganglion and Thalamus) 주변 영역에서 감소된 EC를 확인하였다.