• Industrial Engineering and Management Systems
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• 제15권1호
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• pp.1-10
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• 2016

This paper presents a fast sequencing algorithm for a mixed model assembly line with multiple workstations which minimize the total utility work and idle time. We compare the proposed algorithms with another heuristic, the Tsai-based heuristic, for a sequencing problem that minimizes the total utility works. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. The Tsai-based heuristic performs best in terms of utility work, but the fast sequencing algorithm performs well for both utility work and idle time. However, the computational complexity of the fast sequencing algorithm is O (KN) while the Tsai-based algorithm is O (KNlogN). Actual computational time of the fast sequencing heuristic is 2-6 times faster than that of the Tsai-based heuristic.

• 대한산업공학회지
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• 제24권3호
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• pp.417-429
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• 1998

This paper considers two-sided (left and right side) assembly lines which are often used, especially in assembling large-sized products such as trucks and buses. A large number of exact algorithms and heuristics have been proposed to balance one-sided lines. However, little attention has been paid to balancing two-sided assembly lines. We present an efficient algorithm based on a branch and bound for balancing two-sided assembly lines. The algorithm involves a procedure for generating an enumeration tree. To efficiently search for the near optimal solutions to the problem, assignment rules are used in the method. New and existing bound strategies and dominance rules are else employed. The proposed algorithm can find a near optimal solution by enumerating feasible solutions partially. Extensive computational experiments are carried out to make the performance comparison between the proposed algorithm and existing ones. The computational results show that our algorithm is promising and robust in solution quality.

• Journal of information and communication convergence engineering
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• 제11권2호
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• pp.132-138
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• 2013

In this paper, an accelerated generation algorithm to effectively generate an elemental image array in computational integral imaging system is proposed. In the proposed method, the depth information of 3D object is extracted from the images picked up by a stereo camera or depth camera. Then, the elemental image array can be generated by using the proposed accelerated generation algorithm with the depth information of 3D object. The resultant 3D image generated by the proposed accelerated generation algorithm was compared with that the conventional direct algorithm for verifying the efficiency of the proposed method. From the experimental results, the accuracy of elemental image generated by the proposed method could be confirmed.

• 대한음성학회지:말소리
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• 제60호
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• pp.109-123
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• 2006

Since computing the log-likelihood of Gaussian mixture density is a major computational burden for the speech recognizer based on the continuous HMM, several techniques have been proposed to reduce the number of mixtures to be used for recognition. In this paper, we propose a modified Bucket Box Intersection (BBI) algorithm, in which two relative thresholds are employed: one is the relative threshold in the conventional BBI algorithm and the other is used to reduce the number of the Gaussian boxes which are intersected by the hyperplanes at the boxes' edges. The experimental results show that the proposed algorithm reduces the number of Gaussian mixtures by 12.92% during the recognition phase with negligible performance degradation compared to the conventional BBI algorithm.

• 인터넷정보학회논문지
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• 제15권3호
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• pp.71-77
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• 2014

상호정보 포텐셜과 델타 함수열 (cross-information potential and Dirac-delta functions, CIPD) 을 이용한 Equalizer 알고리듬이 충격성 잡음 하에서도 채널의 ISI 제거 성능이 우수한 반면, 블록 처리 방식으로 가중치 갱신을 행하고 있어서 계산량이 많다는 단점을 갖고 있다. 이 논문에서는 CIPD 알고리듬의 계산량을 크게 줄일 수 있는 방법으로서 매 샘플 시간마다 수행하는 CIPD 알고리듬의 이중 합산을 단일 합산으로 바꿀 수 있는 방법을 제시하였다. 실험 결과에서 제안된 방식은 기존 CIPD 알고리듬과 동일한 기울기 학습 곡선을 나타냈다. 또한 충격성 잡음 상황에서도 기존 방식이 블록처리 데이터 수에 비례하는 계산량을 나타낸 반면 제안된 방식은 이와 관계없이 더 작은 계산량을 유지하면서 CIPD 알고리듬과 동일한 기울기 값을 산출해낸다.

• 융합신호처리학회논문지
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• 제19권2호
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• pp.42-46
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• 2018

다양한 신호처리 및 통신환경에서 적응신호처리는 매우 중요하다. 적응신호처리 방식 중에서 least mean square(LMS) 알고리즘은 단순하면서도 강인하기 때문에 널리 사용되고 있다. 가변스텝 LMS 알고리즘은 스텝을 가변하므로 빠른 수렴속도와 작은 초과자승오차를 얻을 수 있는 방식이다. 성능향상을 위하여 다양한 가변스텝 LMS 알고리즘이 연구되어 왔다. 하지만 성능향상을 위하여 가변스텝 LMS 알고리즘의 계산 복잡도는 일부 방식에서는 크게 높아지게 되었다. 계산 복잡도가 낮은 고정스텝 LMS 알고리즘과 빠른 수렴속도의 가변스텝 LMS 알고리즘의 장점을 같이 가질 수 있는 간헐적 스텝 갱신 알고리즘을 제안한다. 간헐적으로 스텝 갱신을 할 때 피보나치 수열을 사용하여 스텝 갱신 횟수를 상당히 낮추면서도 가변스텝 LMS 알고리즘의 성능을 유지할 수 있었다. 적응 등화기에 제안한 가변스텝 LMS 알고리즘을 적용하여 그 성능을 확인하였다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2005년도 추계학술대회 논문집
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• pp.250-255
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• 2005

본 논문에서는 적응 신호처리의 수렴속도를 향상 시키고 복잡한 계산량을 줄이는 새로운 필터 구조를 제안한다. 그리고 제안한 알고리즘을 웨이블렛 기반 적응 알고리즘에 적용한다. 실제로 합성 음성을 사용하여 적응 잡음 제거기에 적용하여 컴퓨터 시뮬레이션을 통해 제안한 알고리즘과 기존 알고리즘과의 성능을 비교한다. 그 결과 변환 영역 알고리즘은 기존의 시간영역의 알고리즘보다 수렴속도의 향상을 보였고, 웨이블렛 알고리즘, short-length fast running FIR 알고리즘, fast-short-length fast FIR 알고리즘 그리고 제안한 알고리즘에 대한 비교 연구를 수행하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1437-1459
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• 2020

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1262-1270
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• 2017

Among various decoding algorithms of low-density parity-check (LDPC) codes, the min-sum (MS) algorithm and its modified algorithms are widely adopted because of their computational simplicity compared to the sum-product (SP) algorithm with slight loss of decoding performance. In the MS algorithm, the magnitude of the output message from a check node (CN) processing unit is decided by either the smallest or the next smallest input message which are denoted as min1 and min2, respectively. It has been shown that multiplying a scaling factor to the output of CN message will improve the decoding performance. Further, Zhong et al. have shown that multiplying different scaling factors (called a 2-dimensional scaling) to min1 and min2 much increases the performance of the LDPC decoder. In this paper, the simplified 2-dimensional scaled (S2DS) MS algorithm is proposed. In the proposed algorithm, we figure out a pair of the most efficient scaling factors which multiplications can be replaced with combinations of addition and shift operations. Furthermore, one scaling operation is approximated by the difference between min1 and min2. The simulation results show that S2DS achieves the error correcting performance which is close to or outperforms the SP algorithm regardless of coding rates, and its computational complexity is the lowest comparing to modified versions of MS algorithms.

• 한국멀티미디어학회논문지
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• 제23권1호
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.