• Journal of Construction Engineering and Project Management
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• 제5권1호
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• 디지털융복합연구
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• 제10권9호
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• pp.375-381
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• 2012

이동통신망에서는 한정된 채널을 효과적으로 할당하기 위한 여러 연구들이 진행되고 있다. 이동국에서 호를 요청하면 교환국에서 각 기지국에 속한 이동국에 채널을 할당한다. 채널할당방식에는 크게 고정채널할당방식, 동적채널할당방식 그리고 이를 조합한 하이브리드방식이 있다. 본 논문에서는 채널을 할당 할 때 채널 간 간섭을 최소로 하고 채널을 할당하기까지의 시간과 횟수를 최소화하는 방안을 제안한다. 본 논문에서는 제안하고자 하는 알고리즘은 기지국, 제어국, 교환국 등 특정 장비당 채널수에 상관이 없이 범용으로 사용할 수 있는 시스템 모델을 기준으로 제안하였으며 기존의 통신사업자들이 통계를 근거로 채널을 할당하는 유사한 고정방식과 할당 시 기존의 방식과는 개선된 방식을 제시한다. 시뮬레이션을 통해 다른 방식과 비교 검토하여 제안 방식의 효율성을 검증한다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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• pp.382-388
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• 2007

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 개선된 퍼지 클러스터(Modified Fuzzy C-Means: MFCM) 알고리즘을 제안한다. 제안된 MFCM은 기존의 유클리디언 거리 값 대신 Bayesian Likelihood 목적함수(fitness function)를 이용하여 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태값(optimal channel output states)을 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적인 채널 상태(desired channel states) 벡터들을 구성하고 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우스 노이즈를 추가한 데이터를 사용하여 하이브리드 유전자 알고리즘 (GA merged with simulated annealing (SA): GASA)과 그 성능을 비교 하였으며, 제안된 MFCM을 이용한 등화기가 GASA를 활용한 것 보다 상대적으로 정확도와 속도 면에서 우수함을 보였다.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.55-68
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• 2023

In recent years, the Television White Space (TVWS) has attracted the interest of many researchers due to its propagation characteristics obtainable between 470MHz and 790MHz spectrum bands. The plenty of unused channels in the TV spectrum allows the secondary users (SUs) to use the channels for broadband services especially in rural areas. However, when the number of SUs increases in the TVWS wireless network the aggregate interference also increases. Aggregate interferences are the combined harmful interferences that can include both co-channel and adjacent interferences. The aggregate interference on the side of Primary Users (PUs) has been extensively scrutinized. Therefore, resource allocation (power and spectrum) is crucial when designing the TVWS network to avoid interferences from Secondary Users (SUs) to PUs and among SUs themselves. This paper proposes a model to improve the resource allocation for reducing the aggregate interface among SUs for broadband services in rural areas. The proposed model uses joint power and spectrum hybrid Firefly algorithm (FA), Genetic algorithm (GA), and Particle Swarm Optimization algorithm (PSO) which is considered the Co-channel interference (CCI) and Adjacent Channel Interference (ACI). The algorithm is integrated with the admission control algorithm so that; there is a possibility to remove some of the SUs in the TVWS network whenever the SINR threshold for SUs and PU are not met. We considered the infeasible system whereby all SUs and PU may not be supported simultaneously. Therefore, we proposed a joint spectrum and power allocation with an admission control algorithm whose better complexity and performance than the ones which have been proposed in the existing algorithms in the literature. The performance of the proposed algorithm is compared using the metrics such as sum throughput, PU SINR, algorithm running time and SU SINR less than threshold and the results show that the PSOFAGA with ELGR admission control algorithm has best performance compared to GA, PSO, FA, and FAGAPSO algorithms.

• 대한전자공학회논문지TC
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• 제44권3호
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• pp.26-32
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• 2007

개구면 결합 공진기 급전 마이크로스트립 패치 안테나의 단순하고도 정확한 등가 회로를 추출한다. 이 등가회로는 이상적인 트랜스포머, 어드미턴스 소자, 그리고 전송선으로 구성되고 각 소자 값들은 가역 정리와 스펙트럼 영역 이미턴스 방법에 기반한 복소 전력 개념으로부터 구할 수 있다. 기 게재된 논문의 연구 결과를 이용하여 제안한 등가회로의 타당성을 검증한 후 이중 대역 안테나를 유전 알고리즘과 Holder-Mead 방법을 통한 이종 진화적 최적화 방법으로 설계하였다. 설계 목표치에 적합한 결과를 도출하였고, 이 결과는 이종 진화적 최적화 방법이 설계에 매우 효율직임을 확인해 준다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.53-63
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• 2017

The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. Two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.

• Journal of information and communication convergence engineering
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• 제8권6호
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• pp.609-617
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• 2010

This paper is concerned with the use of a selforganizing map (SOM) to estimate the desired channel states of an unknown digital communication channel for blind equalization. The modification of SOM is accomplished by using the Bayesian likelihood fitness function and the relation between the desired channel states and channel output states. At the end of each clustering epoch, a set of estimated clusters for an unknown channel is chosen as a set of pre-defined desired channel states, and used to extract the channel output states. Next, all of the possible desired channel states are constructed by considering the combinations of extracted channel output states, and a set of the desired states characterized by the maximal value of the Bayesian fitness is subsequently selected for the next SOM clustering epoch. This modification of SOM makes it possible to search the optimal desired channel states of an unknown channel. In simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The performance of the proposed method is compared with those of the "conventional" SOM and an existing hybrid genetic algorithm. Relatively high accuracy and fast search speed have been achieved by using the proposed method.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• 융합정보논문지
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• 제11권8호
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• pp.207-211
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• 2021

이 논문은 단계 1에 기계 한 대, 단계 2에 2대의 병렬 기계가 있는 유연 흐름 생산 스케줄링 문제를 다룬다. 목적 함수는 평균 완료 시간을 최소화하는 것이다. 이 문제를 혼합 정수 2차 문제로 정식화하여 혼합 시뮬레이티드 어닐링을 이용하여 풀었다. 혼합 시뮬레이티드 어닐링은 유전자 알고리즘의 탐색 능력을 이용하고 시뮬레이티드 어닐링을 적용하여 너무 이른 수렴 현상을 줄이는 방법이다. 실험을 통하여 혼합 시뮬레이티드 어닐링의 성능을 평가하였다.

• Advances in Computational Design
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• 제7권2호
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• pp.153-171
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• 2022

The present study aimed to find a way to create forms that can simultaneously meet several architectural requirements by applying generative design methods specifically focused on cellular automata. In other words, it is tried to find various forms of architecture that all have common features. Because of the useful features of cellular automata, we decided to use it to generate various forms, but make a relation between the discrete nature of cellular automata and the continuous nature of architecture, was the major problem of our project. To achieve this goal, three consecutive stages were designed. In the first stage, independent variables including the location of the building, the height of the building, and the building area were considered as the inputs of the model. In the second stage, after locating the building, the building's main shell was designed as a hidden geometry for the cellular automata and then the cellular automata were determined based on this shell. The main result of this research is establishing a logical relationship between the discrete geometry of the cellular automata and the continuous search space such that it creates various optimized forms. Although we specify the site plan of this project at Iran-Tehran, this research can be generalized to various design sites as well as different projects, allowing the architectsto alter the cell dimensions, cell density, etc., based on their opinion and project needs.