Earticle

이 논문에서는 서로 다른 종류의 아이템들을 생산하기 위해서 필요한 템플릿을 구성하는 것과 관련된 일종의 패킹문제를 다룬다. 고정된 개수의 아이템이 배치된 하나 혹은 그 이상의 템플릿을 반복 인쇄함으로써 요구된 수량의 아이템들을 생산한다. 문제의 목적은 초과 생산량이 최소가 되도록 아이템들을 템플릿에 배치하는 것이다. 이 문제는NP-hard임이 밝혀져 있으며, 탐욕적 기법과 분기한정법에 기반한 근사 알고리즘이 알려져 있다. 본 논문에서는 이문제를 해결하기 위하여 잘 알려진 최적-적합 전략을 이용한 또 다른 다항시간 근사 알고리즘을 제시한다. 다양한실험 데이터를 통해 제시한 알고리즘의 성능을 평가하였으며 선행 연구에서 제시된 알고리즘과 비교하였다.

This paper deals with a kind of packing problem of which the goal is to compose one or more templates which will be used to produce the items of different types. Each template consists of a fixed number of slots which are assigned to the item types and the production of the items is accomplished by printing the template repeatedly. The objective is to minimize the total number of produced items. This problem is known to be NP-hard. Approximation algorithms based on either greedy approach or branch-and-bound technique have been proposed. We present another polynomial time approximation algorithm based on the well-known best-fit strategy. We perform experiments to compare the performance of the proposed algorithm with the previous ones.

This paper suggests a variation of a well-known probabilistic matrix factorization algorithm which is commonly used in data analysis and scientific computing, and which has been considered recently to serve natural language processing. The proposed variation is meant to take benefit from the fact that matrices processed in natural language processing tasks are normally sparse rectangular matrices with one dimension much larger than the other, and this can be used to ensure adequate accuracy with acceptable computation time. Preliminary experiments on real-world textual corpora show that the proposed algorithm achieves relevant improvements compared to the original one.

라벨 패킹 문제는 고정된 개수의 라벨이 배치된 하나 혹은 그 이상의 템플릿을 반복 인쇄함으로써 라벨들을 생산할 때 초과 생산량이 최소가 되도록 템플릿을 구성하는 최적화 문제이다. 선행연구에서 이 문제가 NP-hard임이 밝혀 졌으며, 탐욕적 기법에 기반한 의사 다항시간의 근사알고리즘이 제안되었다[6]. 본 논문에서는 이 문제를 해결하기 위한 또 다른 근사 알고리즘을 제시한다. 먼저 3~4개 정도의 소수의 템플릿을 사용하는 경우에 대해서 최적 해를 찾는 분기한정법 탐색 알고리즘을 제시하고, 템플릿의 개수가 많을 경우 문제를 작은 규모의 부분 문제들로 분할하 여 해결하는 휴리스틱을 제시한다. 다양한 실험 데이터를 통해 제시한 알고리즘의 성능을 평가하였으며 선행 연구에 서 제시된 알고리즘과 비교하였다.

The label packing problem is to compose one or more templates each of which consists of a fixed number of labels of different types and print them multiple times to produce the required amount of labels. The goal is to minimize the over-produced quantities. In our earlier work [6], we proved that this problem is NP-hard and proposed an approximation algorithm based on greedy approach. In this paper, we present an improved approximation algorithm. We first give an optimal algorithm based on branch-and-bound search technique that is applicable when the number of template is a small constant. For the general cases where the number of templates is large, we partition the problem into small subproblems, solve each subproblem using the optimal algorithm, and then merge the results. We tested the algorithm for a variety of sample data and analyse its performance.

신발이나 의류 등에 부착되는 라벨(label)은 먼저 고정 개수의 라벨이 배치된 템플릿(template)을 제작한 후 각각의 템플릿을 필요한 수량만큼 인쇄하여 생산한다. 이때 템플릿 내의 레이블 조합 방법에 따라 불필요한 초과생산이 발생하게 된다. 본 논문에서는 이 과정을 하나의 최적화 문제로 정형화하고, NP-hard임을 증명한 후, 의사 다항시간(pseudo-polynomial time)의 근사(approximation) 알고리즘을 제시한다. 알고리즘은 기본적으로 탐욕적(greedy) 기법을 따르며 알고리즘의 각 단계에서 동적 계획법(dynamic programming)을 이용한다. 다양한 실험 데이터를 통해 제시한 알고리즘의 성능을 평가하였다.

Labels of clothes or shoes are produced by making templates containing a fixed number of labels of different types and printing them multiple times. The composition of label types in templates determines the production loss which is defined to be the over-produced quantities. In this paper, we formulate this into an optimization problem and prove its NP-hardness. Then we present a pseudo-polynomial time approximation algorithm. Our algorithm takes a greedy approach and each step of it solves a sub-problem using dynamic programming technique. We tested the algorithm for a variety of sample data and analysed its performance.

This paper studies the problem of optimal multicast routing with multiple Quality-of- Service (QoS) constrains (OMRMC), which is regarded as a critical component in networks. However, it is not very efficient and effective for the existing algorithms. An undirected and connected graph G with m edges and n nodes is modeled to find a multicast routing tree from source to destination subset D in this paper, and a novel approximation algorithm called APP-OMRMC is proposed for OMRMC. The theoretical validations for the proposed method are presented to show its efficiency. After that, the different networks with different types of service are evaluated. Experimental results show that the proposed method APP-OMRMC is capable of finding the optimal (or minimum-cost) multicast routing tree with the approximation factor (1+ε) and time complexity of O(mτK-1).

In the control theory, a system has one or more input and output signals. With the development of control theory, a modern control system is carried out in the state space, and can process multi-input and multi-output (MIMO) systems. According to the complexity of a control system, a control system may have been described by a high order differential equation or transfer function, thus control system analysis and design becomes more difficult. Therefore a study to obtain a low order model with the dynamic characteristics similar to a high order system is very meaningful. In this paper, an approximation algorithm for multivariable system based on the generalized block pulse transform is presented. To solve the functional differential equation and transfer function, the generalized block pulse function is used. The algorithm adopted in this paper has shown that computational results are more accurate and convenient rather than conventional block pulse transform.

Based on the the characteristics of maximal function, the algorithm for solving Min- max problem was researched in this paper. A new differential approximation function for the nondifferentiability of the objection function was also constructed. At same time, the property of the new differential approximation function is discussed and the processes of proves show that using the new approximation function to solve the nonlinear unconstrained min-max problem is feasible and effective. The preliminary numerical example shows that the algorithm is effective, and that have a large of convergence characteristics.

Intelligent robot not only can realize reservation on conduct and actions, but also can be understand the characteristics of the unknown environment and adapt to changes in the environment through their own "sensory system", robots get outside information mainly by vision, this paper take the joint robot of binocular robot vision system as control plant, pointing on the problem of the uncertainties that existed in the dynamic model of binocular vision robot may cause instability. This paper has proposed a sliding model control scheme with RBF neural network adaptive control strategy based on sub-block approximation algorithm, in this control method, sliding model control was used to control trajectory of the joints of robot, and utilize the RBF neural network to approximate the each uncertain in the dynamic model of robot. The simulation results show that compared with the RBF neural network adaptive control strategy based on integral approximation for uncertainness, the proposed control method has features with good position tracking.

A human chromosome is a DNA molecule with approximately 108 base pairs. The techniques developed to date for sequencing are restricted to pieces of DNA with up to tens of thousands of base pairs. This means that when a piece is sequenced, only an extremely small part of a chromosome can be seen. Molecular biologists use special techniques to deal with DNA molecules comparable in size to a chromosome. These techniques enable them to create maps of an entire chromosome or of significant fractions of chromosomes. Computational techniques were studied that could potentially aid biologists in the map-generation process. An algorithm that solves the consecutive 1s problem was studied. Such a problem is a good model of hybridization mapping when there are no errors and when probes are unique. If errors are present, another approach is needed, and the approximation algorithm is a prospective problem solver for hybridization physical mapping of DNA with errors.

Approximation methods are best way to deal NP optimization problems and MVC is one of these. In this research paper we have presented a new extra fast approximation algorithm for solving MVC generally in all graphs. The proposed algorithm is named degree contribution algorithm (DCA), a new data structure proposed and employed in this algorithm, name degree contribution. It is first time in literature that such a sophisticated data structure for graphs is proposed which take account of whole graph for each node contribution value. All decisions regarding vertices are made on the basis of the proposed data structure. Effectiveness of DCA is shown by applying it to best available benchmarks and after large number of experiments worst approximation ratio recorded was 1.041 and an average approximation ratio was 1.005. These results show that algorithm can perform well in solving graphs faster as compared to other algorithms present.

Minimum vertex cover is very important among the NP-optimization problems and got the attention of the researchers in the past decade. Approximation techniques are used to solve the NP problems to get either optimal or near optimal solutions in polynomial time. In this paper, a modified vertex support algorithm is proposed that make use of same data structure as that of VSA but with different vertex selection criteria. Proposed algorithm is called advance vertex support algorithm (AVSA), it takes into account all the neighbors of a vertex twice while selection as surrounding vertices are very important for the decision. AVSA is analyzed experimentally against all benchmarks and compared with some better algorithms already present. The experimental results show that the worst approximation ratio was 1.032 with a ratio of average approximation 1.006. A comparative analysis with other algorithms indicates that AVSA can provide the best solution among them the minimum time requirements. Simplicity in design can give advantages to implement in various fields of application with minimum overhead.

A novel neural network algorithm optimized by particle swarm optimization (PSO) for function approximation is proposed in this paper. The prior information extracted from the upper and lower bound of the approximated function is coupled into PSO. Since the prior information narrows the search space and guides the movement direction of the particles, the convergence rate and the approximation accuracy are improved. Experimental results demonstrate that the new algorithm is more effective than traditional methods.

Given the moments of a symmetric random variable, its density and distribution functions can be accurately approximated by making use of the algorithm proposed in this paper. This algorithm is specially designed for approximating symmetric distributions and comprises of four phases. This approach is essentially based on the transformation of variable technique and moment-based density approximants expressed in terms of the product of an appropriate initial approximant and a polynomial adjustment. Probabilistic quantities such as percentage points and percentiles can also be accurately determined from approximation of the corresponding distribution functions. This algorithm is not only conceptually simple but also easy to implement. As illustrated by the first two numerical examples, the density functions so obtained are in good agreement with the exact values. Moreover, the proposed approximation algorithm can provide the more accurate quantities than direct approximation as shown in the last example.

This paper presents a power-efficient hardware realization for a motion estimation technique that is based on the full-search block matching algorithm (FSBMA). The considered input is the quarter common intermediate format of digital video. The mean of absolute difference (MAD) is the distortion criteria employed for the block matching process. The conventional architecture considered for the hardware realization of FSBMA is that of the shift register-based 2-D systolic array. For this architecture, a conservative approximation technique is adapted to eliminate unnecessary MAD computations involved in the block matching process. Upon introducing the technique to the conventional architecture, the power and complexity of its implantation is reduced, while the accuracy of the motion vector extracted from the block matching process is preserved. The proposed architecture is verified for its functional specifications. A performance evaluation of the proposed architecture is carried out using parameters such as power, area, operating frequency, and efficiency.

The projection approximation subspace tracking (PAST) is one of the attractive subspace tracking algorithms, because it estimatesthe signal subspace adaptively and continuously. Furthermore, the computational complexity is relatively low. However, the algorithm still has room for improvement in the subspace estimation accuracy. In this paper, we propose a new algorithm to improve the subspace estimation accuracy using a normally ordered input vector and a reversely ordered input vector simultaneously.

This paper is proposed a approximation control method(ACM) for the maximum power of a photovoltaic system. It is designed for power systems application and utilities. The proposed Maximum Power Point Tracking(MPPT) control has the advantage to provide a new simple way to approximate the optimal or rated voltage, the optimal or rated current and maximum power rating produced by a solar panel and the photovoltaic inverter. And this straightforward method has the advantage that Pmax and $V_{op}$ can be approximated using the same variable as the dynamic model without using complicate approximations or Taylor series. This paper is proposed MPPT using AMC using weather condition of domestic moderate program technique. This paper is proposed the experimental results to verify the effectiveness of the new methods.

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

본 논문에서는 컴퓨터 네트워크의 설계에 유용한 등가 라우팅 알고리즘(Equal- cost Bifurcated Routing Algorithm)을 제안하였다. 이 제안한 알고리즘의 성능은 기존의 몬테카를로 시뮬레이션 및 비정상 큐잉 근사화(Transient queueing approximation)를 이용하여 비교되었으며 그 결과 큐잉 근사화는 몬테카를로 시뮬레이 션에 상당히 근접한 결과를 제공하였다. 또한, 큐잉 근사화는 몬테카를로 시뮬레이션 에 비하여 매우 적은 수행시간을 요구하므로 제안한 등가 라우팅 알고리즘은 대부분 의 경우에 우수한 결과를 제공하였다.

In this paper, we propose an equal-cost bifurcated routing algorithm which may be useful in practical computer network design problem. The performance of the routing algorithm is evaluated using the conventional Monte Carlo simulation and a transient queueing approximation. The relative errors between the closure approximation and the Monte Carlo simulation was fairly small. The closure approximation may be used to evaluate the performance of the load splitting algorithms, which results in considerable execution time reduction. The performance of the proposed algorithm is compared to that of the known algorithms based on average packet delay. For networks that have many non-disjoint equal-paths, the proposed algorithm performed better than other algorithms.

본 연구는 추가제약이 있는 최소 신장나무 문제(Constrained Minimum Spanning Tree : CMST문제)에 대한 유사다항시간 알고리듬 및 근사 해법 개발에 관한 것이다. CMST문제는 NP-hard문제임이 이미 증명되었으며, 이후 이 문제에 대해서는 근사해법 개발이 주된 관심이 되어왔다 [Ravi and Goemans 96]는 다항시간 근사 해법(PTAS)을 이미 개발하였고, [Marathe et at 98]은 가능해(feasible solution)는 아니지만, 앞으로 서술할 $(1+1/\varepsilon,\;+\epsilon)$사해를 구하는 완전다항시간 근사해법 (FPTAS)을 제시하였다. 이와는 달리 [Papa. and Yan, 00]는 파레토 근사 최적해를 구하는 FPTAS를 제시하였는데, 본 연구는 이들의 연구에서 주로 의존하고 있는 행렬-나무 정리(Tree-Matrix Theorem)를 보다 일반화하여, CMST문제에 대한 유사다항시간 알고리듬과 $(1+\varepsilon,\;1+\epsilon)$근사해를 구하는 FPTAS를 제시할 것이다.

다수 에이전트 시스템(Multi-agent system)은 에이전트 각자의 결정으로 최상의 조직화 된 결정을 달성하는 것을 목표로 하는 시스템으로 본 논문에서는 다수 에이전트-다수 작업의 할당 문제를 제시한다. 본 문제는 각 에이전트가 하나의 작업에 할당이 되어 수행하고, 작업 수행에 대한 작업 완료 확률(completion probability)이 있으며 모든 작업의 수행 확률을 최대화하는 할당을 결정한다. 비선형(non-linearity)의 목적함수와 조합 최적화(combinatorial optimization)로 표현되는 본 문제는 NP-hard로, 효과적이면서 효율적인 문제 해결 방법론 제시가 필요하다. 본 연구에서는 한계 이익(marginal gain)의 감소를 의미하는 하위모듈성(submodularity)을 활용한 근사 알고리즘(approximation algorithm)을 제안하고, 확장성(scalability)과 강건성(robustness) 측면에서 우수한 알고리즘임을 이론 및 실험적으로 제시한다.

A multi-agent system is a system that aims at achieving the best-coordinated decision based on each agent's local decision. In this paper, we consider a multi agent-multi task assignment problem. Each agent is assigned to only one task and there is a completion probability for performing. The objective is to determine an assignment that maximizes the sum of the completion probabilities for all tasks. The problem, expressed as a non-linear objective function and combinatorial optimization, is NP-hard. It is necessary to design an effective and efficient solution methodology. This paper presents an approximation algorithm using submodularity, which means a marginal gain diminishing, and demonstrates the scalability and robustness of the algorithm in theoretical and experimental ways.