• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.15-24
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• 2024

One of the most significant issues in combinatorial optimization is the classical NP-complete conundrum known as the 0/1 Knapsack Problem. This study delves deeply into the investigation of practical solutions, emphasizing two classic algorithmic paradigms, brute force, and dynamic programming, along with the metaheuristic and nature-inspired family algorithm known as the Genetic Algorithm (GA). The research begins with a thorough analysis of the dynamic programming technique, utilizing its ability to handle overlapping subproblems and an ideal substructure. We evaluate the benefits of dynamic programming in the context of the 0/1 Knapsack Problem by carefully dissecting its nuances in contrast to GA. Simultaneously, the study examines the brute force algorithm, a simple yet comprehensive method compared to Branch & Bound. This strategy entails investigating every potential combination, offering a starting point for comparison with more advanced techniques. The paper explores the computational complexity of the brute force approach, highlighting its limitations and usefulness in resolving the 0/1 Knapsack Problem in contrast to the set above of algorithms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.205-206
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• 2000

In this paper, we consider variable sized bin packing problem, where the objective is not to minimize the total space used in the packing but to minimize the total cost of the packing when the cost of unit size of each bin does not increase as the bin size increases. A heuristic algorithm is described, and analyzed in two special cases: 1) b$\sub$m/｜…｜b$_1$and w$\sub$n/｜…｜w$_1$, and 2) b$\sub$m/｜…｜b$_1$, where b$\sub$i/ denotes the size of i-th type of bin and w$\sub$j/ denotes the size of j-th item. In the case 1), the algorithm guarantees optimality, and in the case 2), it guarantees asymptotic worst-case performance bounds of l1/9.

• The Journal of Advanced Prosthodontics
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• v.13 no.4
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• pp.226-236
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• 2021

PURPOSE. This study aimed to evaluate the effect of incorporating zirconium oxide nanoparticles (nano-ZrO₂) in polymethylmethacrylate (PMMA) denture base resin on flexural properties at different material thicknesses. MATERIALS AND METHODS. Heat polymerized acrylic resin specimens (N = 120) were fabricated and divided into 4 groups according to denture base thickness (2.5 mm, 2.0 mm, 1.5 mm, 1.0 mm). Each group was subdivided into 3 subgroups (n = 10) according to nano-ZrO₂ concentration (0%, 2.5%, and 5%). Flexural strength and elastic modulus were evaluated using a three-point bending test. One-way ANOVA, Tukey's post hoc, and two-way ANOVA were used for data analysis (α = .05). Scanning electron microscopy (SEM) was used for fracture surface analysis and nanoparticles distributions. RESULTS. Groups with 0% nano-ZrO₂ showed no significant difference in the flexural strength as thickness decreased (P = .153). The addition of nano-zirconia significantly increased the flexural strength (P < .001). The highest value was with 5% nano-ZrO₂ and 2 mm-thickness (125.4 ± 18.3 MPa), followed by 5% nano-ZrO₂ and 1.5 mm-thickness (110.3 ± 8.5 MPa). Moreover, the effect of various concentration levels on elastic modulus was statistically significant for 2 mm thickness (P = .001), but the combined effect of thickness and concentration on elastic modulus was insignificant (P = .10). CONCLUSION. Reinforcement of denture base material with nano-ZrO₂ significantly increased flexural strength and modulus of elasticity. Reducing material thickness did not decrease flexural strength when nano-ZrO₂ was incorporated. In clinical practice, when low thickness of denture base material is indicated, PMMA/nano-ZrO₂ could be used with minimum acceptable thickness of 1.5 mm.

• Carbon letters
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• v.16 no.4
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• pp.270-274
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• 2015

• Journal of Biosystems Engineering
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• v.5 no.2
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• pp.15-25
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• 1980

본 연구는 현재 국내 농촌에서 문제시되고 있는 벼의 건조와 저장방법을 개선하기위한 한 가지 방법으로써 Grain bin의 이용에 따른 기술적인 적용가능성을 구명하는데 있었으며 건조열원으로서는 상온공기와 Solar collector 에 의한 보충가열공기를 사용하였다. 건조시험에서는 벼의 건조속도, 층별함수율의 변화, 동력소모량, 도청수율 등을 측정 비교하였으며 건조가 완료된 후에는 저장시험도 아울러 실시하였다. 본 시험을 통하여 얻은 결과는 다음과 같다. 1. 본시험에 사용된 Solar Collector 는 집열면적이 $27.7m^2M$의 Flat-plate 형식이며 내부에 태양열의 저장모체로서 약 $7m^2M$의 검은 돌을 사용하였다. Collector 의 효율은 35%이었으며 Collector를 통과하여 Bin으로 들어가는 공기의 온도는 외기온에 비하여 주간에는 약 $4^\circ C$, 야간에는 약 $8^\circ C$, 정도 상승된 것으로 나타났다. 2. 상온공기와 Collector를 이용한 건조험결과 안전저장함수율에 도달하는 데 약 7일과 약 5일이 소요되었다. 3. 태양열 건조는 상온통풍건조에 비하여 곡물층간의 함수율차이가 약간 크게 나타났으나 건조속도가 빠를뿐만 아니라 동력소모량도 적은 것으로 분석되었다. 4. 건조시험이 완료된 직후 이차에 걸쳐 Bin 내에서 저장시험을 실시한 결과 저장기간중 벼의 안전보전이 가능했으며 평균함수율이 12.0~14.5%범위에서 유지되었다.

• Information Display
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• v.19 no.2
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• pp.24-31
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• 2018

• Korean Management Science Review
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• v.22 no.1
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• pp.167-178
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• 2005

The 2DBPP(Two-Dimensional Bin Packing Problem) is a problem of packing each item into a bin so that no two items overlap and the number of required bins is minimized under the set of rectangular items which may not be rotated and an unlimited number of identical .rectangular bins. The 2DBPP is strongly NP-hard and finds many practical applications in industry. In this paper we discuss a tabu search approach which includes tabu list, intensifying and diversification Strategies. The HNFDH(Hybrid Next Fit Decreasing Height) algorithm is used as an internal algorithm. We find that use of the proper parameter and function such as maximum number of tabu list and space utilization function yields a good solution in a reduced time. We present a tabu search algorithm and its performance through extensive computational experiments.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_2
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• pp.249-254
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• 2023

The use of robots in automated factories requires accurate bin-picking to ensure that objects are correctly identified and selected. In the case of atypical objects with multiple reflections from their surfaces, this is a challenging task. In this paper, we developed a random 3D bin picking system by integrating the low-cost vision system with the robotics system. The vision system identifies the position and posture of candidate parts, then the robot system validates if one of the candidate parts is pickable; if a part is identified as pickable, then the robot will pick up this part and place it accurately in the right location.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.126-132
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• 2008

The bin packing problem (BPP) is an NP-Complete Problem. The problem can be described as there are $N=\{1,2,{\cdots},n\}$ which is a set of item indices and $L=\{s1,s2,{\cdots},sn\}$ be a set of item sizes sj, where $0<sj{\leq}1$, ${\forall}j{\in}N$. The objective is to minimize the number of bins used for packing items in N into a bin such that the total size of items in a bin does not exceed the bin capacity. Assume that the bins have capacity equal to one. In the past, many researchers put on effort to find the heuristic algorithms instead of solving the problem to optimality. Then, the quality of solution may be measured by the asymptotic worst-case ratio or the average-case ratio. The First Fit Decreasing (FFD) is one of the algorithms that its asymptotic worst-case ratio equals to 11/9. Many researchers prove the asymptotic worst-case ratio by using the weighting function and the proof is in a lengthy format. In this study, we found an easier way to prove that the asymptotic worst-case ratio of the First Fit Decreasing (FFD) is not more than 11/9. The proof comes from two ideas which are the occupied space in a bin is more than the size of the item and the occupied space in the optimal solution is less than occupied space in the FFD solution. The occupied space is later called the weighting function. The objective is to determine the maximum occupied space of the heuristics by using integer programming. The maximum value is the key to the asymptotic worst-case ratio.

• Communications of the Korean Mathematical Society
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• v.26 no.2
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• pp.163-168
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• 2011

In this paper we introduce the notion of the center ZBin(X) in the semigroup Bin(X) of all binary systems on a set X, and show that if (X,${\bullet}$) ${\in}$ ZBin(X), then x ${\neq}$ y implies {x,y}=${x{\bullet}y,y{\bullet}x}$. Moreover, we show that a groupoid (X,${\bullet}$) ${\in}$ ZBin(X) if and only if it is a locally-zero groupoid.