• 제목/요약/키워드: Hybrid Memory

검색결과 282건 처리시간 0.031초

Hybrid CNN-LSTM 알고리즘을 활용한 도시철도 내 피플 카운팅 연구 (A Study on People Counting in Public Metro Service using Hybrid CNN-LSTM Algorithm)

  • 최지혜;김민승;이찬호;최정환;이정희;성태응
    • 지능정보연구
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    • 제26권2호
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    • pp.131-145
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    • 2020
  • 산업혁신의 흐름에 발맞추어 다양한 분야에서 활용되고 있는 IoT 기술은 빅데이터의 접목을 통한 새로운 비즈니스 모델의 창출 및 사용자 친화적 서비스 제공의 핵심적인 요소로 부각되고 있다. 사물인터넷이 적용된 디바이스에서 누적된 데이터는 사용자 환경 및 패턴 분석을 통해 맞춤형 지능 시스템을 제공해줄 수 있어 편의 기반 스마트 시스템 구축에 다방면으로 활용되고 있다. 최근에는 이를 공공영역 혁신에 확대 적용하여 CCTV를 활용한 교통 범죄 문제 해결 등 스마트시티, 스마트 교통 등에 활용하고 있다. 그러나 이미지 데이터를 활용하는 기존 연구에서는 개인에 대한 사생활 침해 문제 및 비(非)일반적 상황에서 객체 감지 성능이 저하되는 한계가 있다. 본 연구에 활용된 IoT 디바이스 기반의 센서 데이터는 개인에 대한 식별이 불필요해 사생활 이슈로부터 자유로운 데이터로, 불특정 다수를 위한 지능형 공공서비스 구축에 효과적으로 활용될 수 있다. 대다수의 국민들이 일상적으로 활용하는 도시철도에서의 지능형 보행자 트래킹 시스템에 IoT 기반의 적외선 센서 디바이스를 활용하고자 하였으며 센서로부터 측정된 온도 데이터를 실시간 송출하고, CNN-LSTM(Convolutional Neural Network-Long Short Term Memory) 알고리즘을 활용하여 구간 내 보행 인원의 수를 예측하고자 하였다. 실험 결과 MLP(Multi-Layer Perceptron) 및 LSTM(Long Short-Term Memory), RNN-LSTM(Recurrent Neural Network-Long Short Term Memory)에 비해 제안한 CNN-LSTM 하이브리드 모형이 가장 우수한 예측성능을 보임을 확인하였다. 본 논문에서 제안한 디바이스 및 모델을 활용하여 그간 개인정보와 관련된 법적 문제로 인해 서비스 제공이 미흡했던 대중교통 내 실시간 모니터링 및 혼잡도 기반의 위기상황 대응 서비스 등 종합적 메트로 서비스를 제공할 수 있을 것으로 기대된다.

조직의 정보 니즈와 ERP 기능과의 불일치 및 그 대응책에 대한 이해: 조직 메모리 이론을 바탕으로 (Understanding the Mismatch between ERP and Organizational Information Needs and Its Responses: A Study based on Organizational Memory Theory)

  • 정승렬;배억호
    • Asia pacific journal of information systems
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    • 제22권2호
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    • pp.21-38
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    • 2012
  • Until recently, successful implementation of ERP systems has been a popular topic among ERP researchers, who have attempted to identify its various contributing factors. None of these efforts, however, explicitly recognize the need to identify disparities that can exist between organizational information requirements and ERP systems. Since ERP systems are in fact "packages" -that is, software programs developed by independent software vendors for sale to organizations that use them-they are designed to meet the general needs of numerous organizations, rather than the unique needs of a particular organization, as is the case with custom-developed software. By adopting standard packages, organizations can substantially reduce many of the potential implementation risks commonly associated with custom-developed software. However, it is also true that the nature of the package itself could be a risk factor as the features and functions of the ERP systems may not completely comply with a particular organization's informational requirements. In this study, based on the organizational memory mismatch perspective that was derived from organizational memory theory and cognitive dissonance theory, we define the nature of disparities, which we call "mismatches," and propose that the mismatch between organizational information requirements and ERP systems is one of the primary determinants in the successful implementation of ERP systems. Furthermore, we suggest that customization efforts as a coping strategy for mismatches can play a significant role in increasing the possibilities of success. In order to examine the contention we propose in this study, we employed a survey-based field study of ERP project team members, resulting in a total of 77 responses. The results of this study show that, as anticipated from the organizational memory mismatch perspective, the mismatch between organizational information requirements and ERP systems makes a significantly negative impact on the implementation success of ERP systems. This finding confirms our hypothesis that the more mismatch there is, the more difficult successful ERP implementation is, and thus requires more attention to be drawn to mismatch as a major failure source in ERP implementation. This study also found that as a coping strategy on mismatch, the effects of customization are significant. In other words, utilizing the appropriate customization method could lead to the implementation success of ERP systems. This is somewhat interesting because it runs counter to the argument of some literature and ERP vendors that minimized customization (or even the lack thereof) is required for successful ERP implementation. In many ERP projects, there is a tendency among ERP developers to adopt default ERP functions without any customization, adhering to the slogan of "the introduction of best practices." However, this study asserts that we cannot expect successful implementation if we don't attempt to customize ERP systems when mismatches exist. For a more detailed analysis, we identified three types of mismatches-Non-ERP, Non-Procedure, and Hybrid. Among these, only Non-ERP mismatches (a situation in which ERP systems cannot support the existing information needs that are currently fulfilled) were found to have a direct influence on the implementation of ERP systems. Neither Non-Procedure nor Hybrid mismatches were found to have significant impact in the ERP context. These findings provide meaningful insights since they could serve as the basis for discussing how the ERP implementation process should be defined and what activities should be included in the implementation process. They show that ERP developers may not want to include organizational (or business processes) changes in the implementation process, suggesting that doing so could lead to failed implementation. And in fact, this suggestion eventually turned out to be true when we found that the application of process customization led to higher possibilities of failure. From these discussions, we are convinced that Non-ERP is the only type of mismatch we need to focus on during the implementation process, implying that organizational changes must be made before, rather than during, the implementation process. Finally, this study found that among the various customization approaches, bolt-on development methods in particular seemed to have significantly positive effects. Interestingly again, this finding is not in the same line of thought as that of the vendors in the ERP industry. The vendors' recommendations are to apply as many best practices as possible, thereby resulting in the minimization of customization and utilization of bolt-on development methods. They particularly advise against changing the source code and rather recommend employing, when necessary, the method of programming additional software code using the computer language of the vendor. As previously stated, however, our study found active customization, especially bolt-on development methods, to have positive effects on ERP, and found source code changes in particular to have the most significant effects. Moreover, our study found programming additional software to be ineffective, suggesting there is much difference between ERP developers and vendors in viewpoints and strategies toward ERP customization. In summary, mismatches are inherent in the ERP implementation context and play an important role in determining its success. Considering the significance of mismatches, this study proposes a new model for successful ERP implementation, developed from the organizational memory mismatch perspective, and provides many insights by empirically confirming the model's usefulness.

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소프트웨어 분산공유 메모리를 위한 향상된 하이브리드 프로토콜 (An Improved Hybrid Protocol for Software Distributed Shared Memory)

  • 이성우;김현철;유기영;하금숙
    • 한국정보과학회논문지:시스템및이론
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    • 제27권9호
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    • pp.777-784
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    • 2000
  • 최근 물리적으로 분산 메모리 하드웨어 상에서 공유메모리 프로그래밍 모델을 제공하는 3소프트웨어 분산 공유 메모리(Distributed Shared Memory, DSM) 시스템을 위해 여러 프로토콜이 등장하고 있다. 본 논문에서는 기존의 동적 복원 프로토콜인 하이브리드 프로토콜[11]의 성능향상을 제안하는 두 가지 문제를 밝혀내고 이를 개선하기 위한 향상된 하이브리드 프로토콜을 제안한다. 이 프로토콜은 동기화 시점에서 기존 프로토콜과 같이 과거에 어떤 페이지를 이미 접근한 프로세스에 대해서 복원 프로토콜을 적용할 뿐만 아니라. 그 페이지에 접근한 프로세스의 수가 선택된 파라미터 값 이상이면 모든 프로세스에 대해 복원 프로토콜을 적용한다. 제안한 프로토콜을 DSM 시스템인 CVM에 구현하고 100Mbps인 Ethernet으로 연결된 8대의 Sun ultral상에서 6개의 응용 프로그램에 대해 성능평가를 수행하였다. 그결과 원격 프로세스에 대한 수정정보 요구 메시지의 수를 평균 16% 감소시켰고, 4개의 응용프로그램에서 2-5%의 성능향상을 얻었다.

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멀티코어 환경에서 효율적인 트랜잭션 처리를 위한 메모리 관리 기반 하이브리드 트랜잭셔널 메모리 기법 (Memory Management based Hybrid Transactional Memory Scheme for Efficiently Processing Transactions in Multi-core Environment)

  • 장연우;강문환;장재우
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2017년도 춘계학술발표대회
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    • pp.795-798
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    • 2017
  • 최근 멀티코어 프로세서가 개발됨에 따라 병렬 프로그래밍은 멀티코어를 효과적으로 활용하기 위한 기법으로 그 중요성이 높아지고 있다. 트랜잭셔널 메모리는 처리 방식에 따라 HTM, STM, HyTM으로 구분되며, 최근 HTM 및 STM 결합한 HyTM 이 활발히 연구되고 있다. 그러나 기존의 HyTM 는 HTM과 STM의 동시성 제어를 위해 블룸필터를 사용하는 반면, 블룸필터의 자체적인 긍정 오류를 해결하지 못한다. 아울러, 트랜잭션 처리를 위한 메모리 할당/해제를 기존의 락 메커니즘을 사용하여 관리한다. 따라서 멀티코어 환경에서 스레드 수가 증가할수록 트랜잭션 처리 효율이 떨어진다. 본 논문에서는 멀티코어 환경에서 효율적인 트랜잭션 처리를 위한 메모리 관리 기반 하이브리드 트랜잭셔널 메모리 기법을 제안한다. 제안하는 기법은 트랜잭션 처리에 최적화된 블룸필터를 제공함으로써, 병렬적으로 동시에 수행되는 서로 다른 환경의 트랜잭션에 대해 일관성 있는 처리를 지원한다. 아울러, CPU 캐시라인에 최적화된 메모리 기법을 통해, 메모리 할당량이 적은 트랜잭션은 로컬 캐시에 할당함으로써 트랜잭션의 빠른 처리를 지원한다.

Active shape change of an SMA hybrid composite plate

  • Daghia, Federica;Inman, Daniel J.;Ubertini, Francesco;Viola, Erasmo
    • Smart Structures and Systems
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    • 제6권2호
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    • pp.91-100
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    • 2010
  • An experimental study was carried out to investigate the shape control of plates via embedded shape memory alloy (SMA) wires. An extensive body of literature proposes the use of SMA wires to actively modify the shape or stiffness of a structure; in most cases, however, the study focuses on modeling and little experimental data is available. In this work, a simple proof of concept specimen was built by attaching four prestrained SMA wires to one side of a carbon fiber laminate plate strip. The specimen was clamped at one end and tested in an environmental chamber, measuring the tip displacement and the SMA temperature. At heating, actuation of the SMA wires bends the plate; at cooling deformation is partially recovered. The specimen was actuated a few times between two fixed temperatures $T_c$ and $T_h$, whereas in the last actuation a temperature $T_f$ > $T_h$ was reached. Contrary to most model predictions, in the first actuation the transformation temperatures are significantly higher than in the following cycles, which are stable. Moreover, if the temperature $T_h$ is exceeded, two separate actuations occur during heating: the first follows the path of the stable cycles; the second, starting at $T_h$, is similar to the first cycle. An interpretation of the phenomenon is given using some differential scanning calorimeter (DSC) measurements. The observed behavior emphasizes the need to build a more comprehensive constitutive model able to include these effects.

경도인지장애 노인의 인지능력 향상을 위한 로봇 콘텐츠 개발 (Development of Robot Contents to Enhance Cognitive Ability for the Elderly with Mild Cognitive Impairment)

  • 이연화;김갑묵;트란 트렁 틴;김종욱
    • 로봇학회논문지
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    • 제11권2호
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    • pp.41-50
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    • 2016
  • This paper describes the effect of a robot cognitive rehabilitation program on cognitive functions for the elderly with mild cognitive impairment, and compares it with traditional cognitive therapy programs. Three experiment groups including cognition therapy group, robot cognitive rehabilitation group, and hybrid group have been sampled and one comparative group has been organized for this research. 32 old people whose ages are between 61 and 88 with mild cognitive impairment participated in the programs with an admission of W care hospital. According to the program results, the cognitive therapy program alone had shown a positive effect on the attention function, and the robot cognitive rehabilitation program alone had a positive effect on the total intelligence and memory function. However, a simultaneous operation with both programs had shown a positive effect on the three intelligence areas such as total, basic, and management quotients as well as attention and memory functions as subsidiary factors. This paper has verified that the proposed robot cognitive rehabilitation program makes a positive effect on a cognitive function and plays a complementary role with traditional cognitive therapy programs.

플래시 메모리의 데이터 신뢰성 향상 및 수명 연장을 위한 하이브리드 메모리기반의 FTL알고리즘 제안 (A proposal of hybrid memory based FTL algorithm for improving data reliability and lifetime of flash memory)

  • 이하림;권세진;김성수;정태선
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2014년도 추계학술발표대회
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    • pp.30-32
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    • 2014
  • 최근 낸드 플래시 메모리는 임베디드 저장 장치로 많이 사용되고 있다. 비휘발성인 플래시 메모리는 기존의 하드디스크와 달리 저 전력, 좋은 내충격성 및 집적도 등 많은 장점이 있지만 데이터 업데이트 시 덮어쓰기가 안 되어 쓰기 연산 전 해당 블록을 지우는 작업이 선 진행되어야 하며 이로 인해 부분 페이지 업데이트가 자주 일어난다. 이런 플래시메모리와 더불어 최근 차세대 메모리연구가 많이 진행 중인데, 이 중에서 PCM 이라는 메모리는 비휘발성으로 정전 시 데이터가 날라 가버리는 DRAM에 반해 전원이 공급 안 되더라도 데이터가 보존되는 특성이 있다. 하지만 PCM 역시 플래시 메모리와 마찬가지로 블록 당 쓰기연산 작업이 제한되어 있어서 근래에 DRAM과 같이 사용하는 하이브리드 구조를 채택하여 많은 연구가 진행되고 있다. 따라서 본 논문에서는 플래시 메모리의 문제점을 해결함으로서 수명을 연장시키고 정 전시 데이터가 보존되지 않는 DRAM의 단점을 하이브리드 메모리를 기반으로하여 데이터의 신뢰성을 높이는 FTL알고리즘을 제안한다.

A Novel Whale Optimized TGV-FCMS Segmentation with Modified LSTM Classification for Endometrium Cancer Prediction

  • T. Satya Kiranmai;P.V.Lakshmi
    • International Journal of Computer Science & Network Security
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    • 제23권5호
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    • pp.53-64
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    • 2023
  • Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

Practical methods for GPU-based whole-core Monte Carlo depletion calculation

  • Kyung Min Kim;Namjae Choi;Han Gyu Lee;Han Gyu Joo
    • Nuclear Engineering and Technology
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    • 제55권7호
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    • pp.2516-2533
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    • 2023
  • Several practical methods for accelerating the depletion calculation in a GPU-based Monte Carlo (MC) code PRAGMA are presented including the multilevel spectral collapse method and the vectorized Chebyshev rational approximation method (CRAM). Since the generation of microscopic reaction rates for each nuclide needed for the construction of the depletion matrix of the Bateman equation requires either enormous memory access or tremendous physical memory, both of which are quite burdensome on GPUs, a new method called multilevel spectral collapse is proposed which combines two types of spectra to generate microscopic reaction rates: an ultrafine spectrum for an entire fuel pin and coarser spectra for each depletion region. Errors in reaction rates introduced by this method are mitigated by a hybrid usage of direct online reaction rate tallies for several important fissile nuclides. The linear system to appear in the solution process adopting the CRAM is solved by the Gauss-Seidel method which can be easily vectorized on GPUs. With the accelerated depletion methods, only about 10% of MC calculation time is consumed for depletion, so an accurate full core cycle depletion calculation for a commercial power reactor (BEAVRS) can be done in 16 h with 24 consumer-grade GPUs.

A hybrid deep neural network compression approach enabling edge intelligence for data anomaly detection in smart structural health monitoring systems

  • Tarutal Ghosh Mondal;Jau-Yu Chou;Yuguang Fu;Jianxiao Mao
    • Smart Structures and Systems
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    • 제32권3호
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    • pp.179-193
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    • 2023
  • This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.