• International Journal of Computer Science & Network Security
• /
• 제22권4호
• /
• pp.17-26
• /
• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of Information Technology Applications and Management
• /
• 제29권3호
• /
• pp.43-55
• /
• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Nuclear Engineering and Technology
• /
• 제55권5호
• /
• pp.1838-1854
• /
• 2023

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A^* (IACO-A^*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A^* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A^* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A^* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A^* IACO-A^* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.

• Smart Structures and Systems
• /
• 제33권1호
• /
• pp.65-91
• /
• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• 지능정보연구
• /
• 제19권4호
• /
• pp.55-79
• /
• 2013

본 연구에서는 집중형 센터를 가진 역물류네트워크(Reverse logistics network with centralized centers : RLNCC)를 효율적을 해결하기 위한 혼합형 유전알고리즘(Hybrid genetic algorithm : HGA) 접근법을 제안한다. 제안된 HGA에서는 유전알고리즘(Genetic algorithm : GA)이 주요한 알고리즘으로 사용되며, GA 실행을 위해 0 혹은 1의 값을 가질 수 있는 새로운 비트스트링 표현구조(Bit-string representation scheme), Gen and Chang(1997)이 제안한 확장샘플링공간에서의 우수해 선택전략(Elitist strategy in enlarged sampling space) 2점 교차변이 연산자(Two-point crossover operator), 랜덤 돌연변이 연산자(Random mutation operator)가 사용된다. 또한 HGA에서는 혼합형 개념 적용을 위해 Michalewicz(1994)가 제안한 반복적언덕오르기법(Iterative hill climbing method : IHCM)이 사용된다. IHCM은 지역적 탐색기법(Local search technique) 중의 하나로서 GA탐색과정에 의해 수렴된 탐색공간에 대해 정밀하게 탐색을 실시한다. RLNCC는 역물류 네트워크에서 수집센터(Collection center), 재제조센터(Remanufacturing center), 재분배센터(Redistribution center), 2차 시장(Secondary market)으로 구성되며, 이들 각 센터 및 2차 시장들 중에서 하나의 센터 및 2차 시장만 개설되는 형태를 가지고 있다. 이러한 형태의 RLNCC는 혼합정수계획법(Mixed integer programming : MIP)모델로 표현되며, MIP 모델은 수송비용, 고정비용, 제품처리비용의 총합을 최소화하는 목적함수를 가지고 있다. 수송비용은 각 센터와 2차 시장 간에 제품수송에서 발생하는 비용을 의미하며, 고정비용은 각 센터 및 2차 시장의 개설여부에 따라 결정된다. 예를 들어 만일 세 개의 수집센터(수집센터 1, 2, 3의 개설비용이 각각 10.5, 12.1, 8.9)가 고려되고, 이 중에서 수집센터 1이 개설되고, 나머지 수집센터 2, 3은 개설되지 않을 경우, 전체고정비용은 10.5가 된다. 제품처리비용은 고객으로부터 회수된 제품을 각 센터 및 2차 시장에서 처리할 경우에 발생되는 비용을 의미한다. 수치실험에서는 본 연구에서 제안된 HGA접근법과 Yun(2013)의 연구에서 제안한 GA접근법이 다양한 수행도 평가 척도에 의해 서로 비교, 분석된다. Yun(2013)이 제안한 GA는 HGA에서 사용되는 IHCM과 같은 지역적탐색기법을 가지지 않는 접근법이다. 이들 두 접근법에서 동일한 조건의 실험을 위해 총세대수 : 10,000, 집단의 크기 : 20, 교차변이 확률 : 0.5, 돌연변이 확률 : 0.1, IHCM을 위한 탐색범위 : 2.0이 사용되며, 탐색의 랜덤성을 제거하기 위해 총 20번의 반복실행이 이루어 졌다. 사례로 제시된 두 가지 형태의 RLNCC에 대해 GA와 HGA가 각각 실행되었으며, 그 실험결과는 본 연구에서 제안된 HGA가 기존의 접근법인 GA보다 더 우수하다는 것이 증명되었다. 다만 본 연구에서는 비교적 규모가 작은 RLNCC만을 고려하였기에 추후 연구에서는 보다 규모가 큰 RLNCC에 대해 비교분석이 이루어 져야 할 것이다.

• 한국토양비료학회지
• /
• 제43권4호
• /
• pp.458-465
• /
• 2010

가축분 퇴비 시용 수준에 따른 수수${\times}$수단그라스 ($S{\times}S$ hybrid)의 수량 및 양분 (N, P)흡수 변이를 조사하기 위해 전남대학교 부속 농장 초지에서 3반복 난괴법으로 실험을 실시하였다. 6개 처리 (무비구, 화학비료관행구, 퇴비 1, 2, 4, 6 수준)를 두었는데, 화학비료관행구의 비료 처리량은 질소 20 g N $m^{-2}$과 인산 20 g $P_2O_5\;m^{-2}$이고, 가축분 퇴비는 6 수준을 기준시비량 (20.2 g N $m^{-2}$과 21.6 g $P_2O_5\;m^{-2}$)으로 두고 퇴비 1, 2, 4 수준은 그 비율대로 감비하였다. 처리 90일 후 최종 지상부 건물중과 양분 (N, P) 흡수량을 조사하였다. 화학비료 처리구의 건물중 (2.4 kg $m^{-2}$)과 질소 (38.3 g N $m^{-2}$) 및 인산 (15.3 g $P_2O^5\;m^{-2}$) 흡수량이 가장 높았으며, 퇴비 시용량이 증가함에 따라 건물중과 양분 흡수량이 증가하는 경향을 보였다 (P<0.01). 하지만, 퇴비 4와 6 수준의 건물중은 각각 1.9 kg $m^{-2}$과 1.8 kg $m^{-2}$으로 차이가 없었다. 따라서, 가축분 퇴비 단독 시비로는 화학비료와 대등한 건물 생산이 어려울 것으로 판단되었다. 양분흡수효율 분석 결과에 의하면 퇴비의 인산흡수 효율이 화학비료보다 높았기 때문에, 퇴비를 인산 급원으로 시용하고 부족한 질소는 농가의 비료자원 수급 가능성과 목표 수량을 고려하여 액비, 화학비료, 녹비 등으로 공급하는 것이 적절한 시비 전략으로 판단된다.

• 생명과학회지
• /
• 제25권8호
• /
• pp.925-931
• /
• 2015

미립 품질 향상을 위하여 미립 형태를 결정하는 특성을 위한 분자육종기술을 확립하기 위하여 미립과 관련된 양적형질 유전자좌를 탐색하고, 이들 환경요인과 상호작용 효과를 분석한 결과는 다음과 같다. 인디카 품종인 ‘청청’과 자포니카형인 ‘낙동’이 교배된 조합 F₁의 약배양에 의해 양성된 120 계통(DH 집단)과 217개의 DNA 마커를 이용하여 전체 길이가 2,067cM이고, 마커간 평균거리가 9.5cM인 유전자 지도를 작성하였다. 미립형태 관련 유전자좌 분석에서 미립의 외형인 길이, 폭, 두께, 장폭비, 천립중과 관련하여 14개의 QTL이 탐색되었다. 현미의 미립길이 관련 3개의 QTL (qGL2, qGL5, qGL7), 미립 폭 관련 3개의 QTL (qGW2-1, qGW2-2, qGW2-3), 미립 두께 관련 1개의 QTL (qGT2), 장폭비 관련 6개의 QTL (qLWR2-1, qLWR2-2, qLWR2-3, qLWR2-4, qLWR7, qLWR12) 및 천립중 관련 1개의 QTL (qTGW8)이 선발되었다. 미립 장폭비 관련 4개의 QTL은 미립길이와 미립두께에서 동일한 염색체 상에서 확인되었다. 본 연구에서 구명된 QTL 마커들은 쌀 품종개량을 위하여 이용될 수 있을 것이라 판단된다.

• 대한간호학회지
• /
• 제30권5호
• /
• pp.1279-1291
• /
• 2000

The main objectives of this study were to analyze the concept of hope, so to provide basic data to develop a valid instrument to measure hope, and to develop hope enhancing nursing intervention a program for cancer patients. The hybrid model approach was applied in three phases, the theoretical phase, the empirical phase, and the analytic phase. The study was developed on universal attributes explaining generalized hope and specific hope, which were revealed in a comprehensive review of the literature. In the empirical phase, eight cancer patients undergoing chemotherapy were interviewed to reveal causes, motivation, and their resource of hope according to The Hope Assessment Guide (Farren, Herth, & Popovich, 1995). In the analytical phase, the results of the two previous stages of the study were compared. The results were as follows : In the theoretical phase, six dimensions of hope emerged; affective, cognitive, behavioral, affiliative, temporal and contextual dimension. The antecedent of hope was loss, crisis, uncertainity, and stress. The consequences were renewal, development of new methods, safety, peace and transcendental competence. In the empirical phase, these six dimensions emerged as theoretical phases were verified and specified as these descriptive terms: feeling, intention, expectation, activity, relation, future- orientation, reality and goal-setting. The antecedent factor of hope was occurrence or recurrence of cancer. The consequence of hope was ability to cope with real condition, feeling of safety and comfort, peace, development of new strategy and recovery of disease. The major content of hope in this phase was related to specific hope, but it was also influenced on by general hope. In the analytic phase, general and specific hope was renamed as trait and state hope. All attributes emerged at the empirical phases, and also emerged at the theoretical phase. However, cognitive and contextual dimensions were revised and specified. In conclusion, the concept of hope is divided into trait hope and state hope, and state hope is an anticipatory expectation that occurs at the time of a stressful stimulus, such as being diagnosed with cancer. Hope is a multidimensional dynamic energized mental state which has the dimensions of affective, cognitive, behavioral, affiliative, temporal and contextual. There should be further studies to develope the state and trait hope scale according to definition and attributes of hope investigated in this study. In addition, considering results of the empirical phase, the family is very a important factor as a resource of hope, so it is necessary to consider family in implementing a nursing intervention program to enhance hope.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
• /
• 제10권1호
• /
• pp.99-113
• /
• 2004

높은 가용성, 확장성, 시스템 성능의 요구를 만족시키기 위해 SAN(Storage Area Network)이 등장했다. 대부분의 SAM 운영 S/W들은 SAN을 보다 효과적으로 활용하기 위해서 SAN에 부착된 물리적 저장장치들을 가상적으로 하나의 커다란 볼륨으로 보이게 하는 저장장치 가상화 개념을 지원한다. 저장장치 가상화의 핵심적인 역할을 하는 것이 바로 논리볼륨 관리자이다. 논리볼륨 관리자는 논리주소를 물리 주소로 매핑 시킴으로서 저장장치 가상화를 실현한다. 더불어 논리볼륨 관리자는 특정 시점의 볼륨이미지를 유지할 수 있는 스냅샷과 시스템을 정지시키지 않고 SAN에 저장장치를 추가 또는 삭제할 수 있는 온라인 재구성 기능을 지원한다. 이러한 기능을 지원하기 위해 수식 기반의 매핑 방법보다 테이블 기반의 매핑 방법이 제안되고 있다. 그러나 이 방법은 관리해야 할 데이타 양이 저장장치 용량에 비례하여 증가하고 메인 메모리에서 모두 관리할 수 없어 성능 저하의 요인이 되었다. 이 논문에서는 기존의 수식 기반의 매핑 방법을 이용하면서 스냅샷과 온라인 재구성 기능과 같은 동적인 환경을 효과적으로 지원할 수 있는 혼합 매핑 방법을 설계하고 구현한다. 제안하는 방법의 스냅샷과 재구성은 되도록이면 정상 입출력 연산에 영향을 주지 않기 위해서 별도의 예약된 공간에서 수행된다. 마지막으로, 이 논문에서 제안한 기법에 대한 성능 평가를 수행하여 제안하는 기법이 우수함을 보인다.

• 한국재료학회:학술대회논문집
• /
• 한국재료학회 2012년도 춘계학술발표대회
• /
• pp.72-72
• /
• 2012

Lithium rechargeable batteries have been widely used as key power sources for portable devices for the last couple of decades. Their high energy density and power have allowed the proliferation of ever more complex portable devices such as cellular phones, laptops and PDA's. For larger scale applications, such as batteries in plug-in hybrid electric vehicles (PHEV) or power tools, higher standards of the battery, especially in term of the rate (power) capability and energy density, are required. In PHEV, the materials in the rechargeable battery must be able to charge and discharge (power capability) with sufficient speed to take advantage of regenerative braking and give the desirable power to accelerate the car. The driving mileage of the electric car is simply a function of the energy density of the batteries. Since the successful launch of recent Ni-MH (Nickel Metal Hydride)-based HEVs (Hybrid Electric Vehicles) in the market, there has been intense demand for the high power-capable Li battery with higher energy density and reduced cost to make HEV vehicles more efficient and reduce emissions. However, current Li rechargeable battery technology has to improve significantly to meet the requirements for HEV applications not to mention PHEV. In an effort to design and develop an advanced electrode material with high power and energy for Li rechargeable batteries, we approached to this in two different length scales - Atomic and Nano engineering of materials. In the atomic design of electrode materials, we have combined theoretical investigation using ab initio calculations with experimental realization. Based on fundamental understanding on Li diffusion, polaronic conduction, operating potential, electronic structure and atomic bonding nature of electrode materials by theoretical calculations, we could identify and define the problems of existing electrode materials, suggest possible strategy and experimentally improve the electrochemical property. This approach often leads to a design of completely new compounds with new crystal structures. In this seminar, I will talk about two examples of electrode material study under this approach; $LiNi_{0.5}Mn_{0.5}O_2$ based layered materials and olivine based multi-component systems. In the other scale of approach; nano engineering; the morphology of electrode materials are controlled in nano scales to explore new electrochemical properties arising from the limited length scales and nano scale electrode architecture. Power, energy and cycle stability are demonstrated to be sensitively affected by electrode architecture in nano scales. This part of story will be only given summarized in the talk.