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Offline Deduplication for Solid State Disk Using a Lightweight Hash Algorithm

  • Park, Eunsoo;Shin, Dongkun
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.15 no.5
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    • pp.539-545
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    • 2015
  • Deduplication technique can expand the lifespan and capacity of flash memory-based storage devices by eliminating duplicated write operations. The deduplication techniques can be classified into two approaches, i.e., online and offline approaches. We propose an offline deduplication technique that uses a lightweight hash algorithm, whereas the previous offline technique uses a high-cost hash algorithm. Therefore, the memory space for caching hash values can be reduced, and more pages can be examined for deduplication during short idle intervals. As a result, it can provide shorter write latencies compared to the online approach, and can show low garbage collection costs compared to the previous offline deduplication technique.

Performance Analysis of MUSIC-Based Jammer DOA Estimation Technique for a Misaligned Antenna Array

  • Park, Kwansik;Seo, Jiwon
    • Journal of Positioning, Navigation, and Timing
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    • v.9 no.1
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    • pp.7-13
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    • 2020
  • As a countermeasure against the threat of jamming which can disrupt operation of the Global Positioning System (GPS) receivers, various kinds of technique to estimate the Direction-Of-Arrivals (DOAs) of incoming jamming signals have been widely studied, and among them, the MUltiple SIgnal Classification (MUSIC) algorithm is known to provide very high resolution. However, since the previous studies regarding the MUSIC algorithm does not consider the orientation of each antenna element of antenna arrays, there is a possibility that DOA estimation performance degrades in the case of a misaligned antenna array whose antenna elements are not oriented along the same direction. As an effort to solve this problem, there exists a previous work which presents an MUSIC-based method for DOA estimation. However, the error between the real and measured values of each antenna orientation is not taken into consideration. Therefore, in this paper, the effect of the aforementioned error on the DOA estimation performance in the case of a misaligned antenna array is analyzed by simulations.

PCB 생산라인에서의 호이스트 스케쥴링을 위한 유전자알고리즘의 응용

  • 임준묵
    • Journal of Korea Society of Industrial Information Systems
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    • v.1 no.1
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    • pp.29-62
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    • 1996
  • In this paper, the problem of determining cyclic schedules for a material handling hoist in the printed-circuit-board(PCB) electroplating line is considered. The objective of this research is to determine an optimal simple-cycle schedule of the hoist which in turn maximizes the line throughput rate. Previous approaches to the cyclic hoist scheduling problem are all mathematical programming-based approaches to develop cyclic schedules(Mixed Integer Programming, Linear Programming based Branch and Bound, Branch and Bound Search Method and so on). In this paper, a genetic algorithm-based approach for a single hoist scheduling in the PCB electroplating line is described. Through some experiments for the well known example data and randomly generated data, the proposed algorithm is shown to be more efficient than the previous mathematical programming-based algorithm.

Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season (신경회로망과 하절기 온도 민감도를 이용한 단기 전력 수요 예측)

  • Ha Seong-Kwan;Kim Hongrae;Song Kyung-Bin
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.54 no.6
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    • pp.259-266
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    • 2005
  • Short-term load forecasting algorithm using neural networks and the sensitivity of temperatures in the summer season is proposed. In recent 10 years, many researchers have focused on artificial neural network approach for the load forecasting. In order to improve the accuracy of the load forecasting, input parameters of neural networks are investigated for three training cases of previous 7-days, 14-days, and 30-days. As the result of the investigation, the training case of previous 7-days is selected in the proposed algorithm. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

Scence Change Adaptive Bit Rate Control Using Local Variance (국부 분산을 이용한 장면 전환 적응 비트율 제어)

  • 이호영;김기석;박영식;송근원;남재열;하영호
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.22 no.4
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    • pp.675-684
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    • 1997
  • The bit rate control algorithm which is capable of handing scene change is proposed. In MPEG-2 TM5, block variance is used to measure block activity. But block variance is not consistent with human visual system and does not differenciate the distribution of pixel values within the block. In target bit allocation process of TM5, global complexity, obtained by results of previous coded pictures, is used. Since I pictures are spaced relatively far apart, their complexity estimate is not very accurate. In the proposed algorithm local variance is used to measure block activity and detect scene change. Local variance, using deviation from the mean of neighboring pixels, well represents the distribution of pixel values within the block. If scene change is detected, the local variance information is used for target bit allocation process. Allocating target bits for I picture, the average local variance difference between previous and current I picture is considered. The experimental results show that the proposed algorithm can detect scene change very precisely and gives better picture quality and higher PSNR values than MPEG-2 TM5.

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A Sequential Approximate Optimization Technique Using the Previous Response Values (응답량 재사용을 통한 순차 근사최적설계)

  • Hwang Tae-Kyung;Choi Eun-Ho;Lim O-Kaung
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.1 s.232
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    • pp.45-52
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    • 2005
  • A general approximate optimization technique by sequential design domain(SDD) did not save response values for getting an approximate function in each step. It has a disadvantage at aspect of an expense. In this paper, previous response values are recycled for constructing an approximate function. For this reason, approximation function is more accurate. Accordingly, even if we did not determine move limit, a system is converged to the optimal design. Size and shape optimization using approximate optimization technique is carried out with SDD. Algorithm executing Pro/Engineer and ANSYS are automatically adopted in the approximate optimization program by SDD. Convergence criterion is defined such that optimal point must be located within SDD during the three steps. The PLBA(Pshenichny-Lim-Belegundu-Arora) algorithm is used to solve approximate optimization problems. This algorithm uses the second-order information in the direction finding problem and uses the active set strategy.

Reversible Data Hiding Algorithm Based on Pixel Value Ordering and Edge Detection Mechanism

  • Nguyen, Thai-Son;Tram, Hoang-Nam;Vo, Phuoc-Hung
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.10
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    • pp.3406-3418
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    • 2022
  • Reversible data hiding is an algorithm that has ability to extract the secret data and to restore the marked image to its original version after data extracting. However, some previous schemes offered the low image quality of marked images. To solve this shortcoming, a new reversible data hiding scheme based on pixel value ordering and edge detection mechanism is proposed. In our proposed scheme, the edge image is constructed to divide all pixels into the smooth regions and rough regions. Then, the pixels in the smooth regions are separated into non overlapping blocks. Then, by taking advantages of the high correlation of current pixels and their adjacent pixels in the smooth regions, PVO algorithm is applied for embedding secret data to maintain the minimum distortion. The experimental results showed that our proposed scheme obtained the larger embedding capacity. Moreover, the greater image quality of marked images are achieved by the proposed scheme than that other previous schemes while the high EC is embedded.

A study of creative human judgment through the application of machine learning algorithms and feature selection algorithms

  • Kim, Yong Jun;Park, Jung Min
    • International journal of advanced smart convergence
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    • v.11 no.2
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    • pp.38-43
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    • 2022
  • In this study, there are many difficulties in defining and judging creative people because there is no systematic analysis method using accurate standards or numerical values. Analyze and judge whether In the previous study, A study on the application of rule success cases through machine learning algorithm extraction, a case study was conducted to help verify or confirm the psychological personality test and aptitude test. We proposed a solution to a research problem in psychology using machine learning algorithms, Data Mining's Cross Industry Standard Process for Data Mining, and CRISP-DM, which were used in previous studies. After that, this study proposes a solution that helps to judge creative people by applying the feature selection algorithm. In this study, the accuracy was found by using seven feature selection algorithms, and by selecting the feature group classified by the feature selection algorithms, and the result of deriving the classification result with the highest feature obtained through the support vector machine algorithm was obtained.

Knowledge Distillation for Unsupervised Depth Estimation (비지도학습 기반의 뎁스 추정을 위한 지식 증류 기법)

  • Song, Jimin;Lee, Sang Jun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.17 no.4
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    • pp.209-215
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    • 2022
  • This paper proposes a novel approach for training an unsupervised depth estimation algorithm. The objective of unsupervised depth estimation is to estimate pixel-wise distances from camera without external supervision. While most previous works focus on model architectures, loss functions, and masking methods for considering dynamic objects, this paper focuses on the training framework to effectively use depth cue. The main loss function of unsupervised depth estimation algorithms is known as the photometric error. In this paper, we claim that direct depth cue is more effective than the photometric error. To obtain the direct depth cue, we adopt the technique of knowledge distillation which is a teacher-student learning framework. We train a teacher network based on a previous unsupervised method, and its depth predictions are utilized as pseudo labels. The pseudo labels are employed to train a student network. In experiments, our proposed algorithm shows a comparable performance with the state-of-the-art algorithm, and we demonstrate that our teacher-student framework is effective in the problem of unsupervised depth estimation.

Improved Route Search Method Through the Operation Process of the Genetic Algorithm (유전 알고리즘의 연산처리를 통한 개선된 경로 탐색 기법)

  • Ji, Hong-il;Seo, Chang-jin
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.64 no.4
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    • pp.315-320
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    • 2015
  • Proposal algorithm in this paper introduced cells, units of router group, for distributed processing of previous genetic algorithm. This paper presented ways to reduce search delay time of overall network through cell-based genetic algorithm. As a result of performance analysis comparing with existing genetic algorithm through experiments, the proposal algorithm was verified superior in terms of costs and delay time. Furthermore, time for routing an alternative path was reduced in proposal algorithm, in case that a network was damaged in existing optimal path algorithm, Dijkstra algorithm, and the proposal algorithm was designed to route an alternative path faster than Dijkstra algorithm, as it has a 2nd shortest path in cells of the damaged network. The study showed that the proposal algorithm can support routing of alternative path, if Dijkstra algorithm is damaged in a network.