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한국정보처리학회> JIPS(Journal of Information Processing Systems)

JIPS(Journal of Information Processing Systems) update

  • : 한국정보처리학회
  • : 공학분야  >  전자공학
  • : KCI등재
  • : SCOPUS
  • : 연속간행물
  • : 격월
  • : 1976-913x
  • : 2092-805X
  • : International journal of information processing systems(~2007)→Journal of information processing system(2008~)

수록정보
수록범위 : 1권1호(2005)~16권6호(2020) |수록논문 수 : 881
JIPS(Journal of Information Processing Systems)
16권6호(2020년 12월) 수록논문
최근 권호 논문
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KCI등재 SCOPUS

1Algorithms, Processes, and Services for Future ICT

저자 : Young-sik Jeong , Jong Hyuk Park

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1231-1237 (7 pages)

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In recent years, future information and communication technology (ICT) has influenced and changed our lives. Without various ICT-based applications, we would have difficulty in securely storing, efficiently processing, and conveniently communicating information. In the future, ICT will play a very important role in the convergence of computing, communication, and all other computational sciences and application. ICT will also influence various fields including communication, science, engineering, industry, business, law, politics, culture, and medicine. In this paper, we investigate the latest algorithms, processes, and services in future fields.

KCI등재 SCOPUS

2Big IoT Healthcare Data Analytics Framework Based on Fog and Cloud Computing

저자 : Hamoud Alshammari , Sameh Abd El-ghany , Abdulaziz Shehab

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1238-1249 (12 pages)

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Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

KCI등재 SCOPUS

3Multimodal Context Embedding for Scene Graph Generation

저자 : Gayoung Jung , Incheol Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1250-1260 (11 pages)

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This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

KCI등재 SCOPUS

4Secure Performance Analysis Based on Maximum Capacity

저자 : Xiuping Zheng , Meiling Li , Xiaoxia Yang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1261-1270 (10 pages)

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The physical security layer of industrial wireless sensor networks in the event of an eavesdropping attack has been investigated in this paper. An optimal sensor selection scheme based on the maximum channel capacity is proposed for transmission environments that experience Nakagami fading. Comparing the intercept probabilities of the traditional round robin (TRR) and optimal sensor selection schemes, the system secure performance is analyzed. Simulation results show that the change in the number of sensors and the eavesdropping ratio affect the convergence rate of the intercept probability. Additionally, the proposed optimal selection scheme has a faster convergence rate compared to the TRR scheduling scheme for the same eavesdropping ratio and number of sensors. This observation is also valid when the Nakagami channel is simplified to a Rayleigh channel.

KCI등재 SCOPUS

5A Study on the Fault Process and Equipment Analysis of Plastic Ball Grid Array Manufacturing Using Data-Mining Techniques

저자 : Hyun Sik Sim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1271-1280 (10 pages)

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The yield and quality of a micromanufacturing process are important management factors. In real-world situations, it is difficult to achieve a high yield from a manufacturing process because the products are produced through multiple nanoscale manufacturing processes. Therefore, it is necessary to identify the processes and equipment that lead to low yields. This paper proposes an analytical method to identify the processes and equipment that cause a defect in the plastic ball grid array (PBGA) during the manufacturing process using logistic regression and stepwise variable selection. The proposed method was tested with the lot trace records of a real work site. The records included the sequence of equipment that the lot had passed through and the number of faults of each type in the lot. We demonstrated that the test results reflect the real situation in a PBGA manufacturing process, and the major equipment parameters were then controlled to confirm the improvement in yield; the yield improved by approximately 20%.

KCI등재 SCOPUS

6Service Oriented Cloud Computing Trusted Evaluation Model

저자 : Hongqiang Jiao , Xinxin Wang , Wanning Ding

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1281-1292 (12 pages)

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More and more cloud computing services are being applied in various fields; however, it is difficult for users and cloud computing service platforms to establish trust among each other. The trust value cannot be measured accurately or effectively. To solve this problem, we design a service-oriented cloud trust assessment model using a cloud model. We also design a subjective preference weight allocation (SPWA) algorithm. A flexible weight model is advanced by combining SPWA with the entropy method. Aiming at the fuzziness and subjectivity of trust, the cloud model is used to measure the trust value of various cloud computing services. The SPWA algorithm is used to integrate each evaluation result to obtain the trust evaluation value of the entire cloud service provider.

KCI등재 SCOPUS

7Mid-level Feature Extraction Method Based Transfer Learning to Small-Scale Dataset of Medical Images with Visualizing Analysis

저자 : Dong-ho Lee , Yan Li , Byeong-seok Shin

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1293-1308 (16 pages)

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In fine-tuning-based transfer learning, the size of the dataset may affect learning accuracy. When a dataset scale is small, fine-tuning-based transfer-learning methods use high computing costs, similar to a large-scale dataset. We propose a mid-level feature extractor that retrains only the mid-level convolutional layers, resulting in increased efficiency and reduced computing costs. This mid-level feature extractor is likely to provide an effective alternative in training a small-scale medical image dataset. The performance of the mid-level feature extractor is compared with the performance of low- and high-level feature extractors, as well as the fine-tuning method. First, the mid-level feature extractor takes a shorter time to converge than other methods do. Second, it shows good accuracy in validation loss evaluation. Third, it obtains an area under the ROC curve (AUC) of 0.87 in an untrained test dataset that is very different from the training dataset. Fourth, it extracts more clear feature maps about shape and part of the chest in the X-ray than fine-tuning method.

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Crowd evacuation simulation is an important research issue for designing reasonable building layouts and planning more effective evacuation routes. The social force model (SFM) is an important pedestrian movement model, and is widely used in crowd evacuation simulations. The model can effectively simulate crowd evacuation behaviors in a simple scene, but for a multi-obstacle scene, the model could result in some undesirable problems, such as pedestrian evacuation trajectory oscillation, pedestrian stagnation and poor evacuation routing. This paper analyzes the causes of these problems and proposes an improved SFM for complex multi-obstacle scenes. The new model adds navigation points and walking shortest route principles to the SFM. Based on the proposed model, a crowd evacuation simulation system is developed, and the crowd evacuation simulation was carried out in various scenes, including some with simple obstacles, as well as those with multi-obstacles. Experiments show that the pedestrians in the proposed model can effectively bypass obstacles and plan reasonable evacuation routes.

KCI등재 SCOPUS

9Boundary-RRT* Algorithm for Drone Collision Avoidance and Interleaved Path Re-planning

저자 : Je-kwan Park , Tai-myoung Chung

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1324-1342 (19 pages)

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Various modified algorithms of rapidly-exploring random tree (RRT) have been previously proposed. However, compared to the RRT algorithm for collision avoidance with global and static obstacles, it is not easy to find a collision avoidance and local path re-planning algorithm for dynamic obstacles based on the RRT algorithm. In this study, we propose boundary-RRT*, a novel-algorithm that can be applied to aerial vehicles for collision avoidance and path re-planning in a three-dimensional environment. The algorithm not only bounds the configuration space, but it also includes an implicit bias for the bounded configuration space. Therefore, it can create a path with a natural curvature without defining a bias function. Furthermore, the exploring space is reduced to a half-torus by combining it with simple right-of-way rules. When defining the distance as a cost, the proposed algorithm through numerical analysis shows that the standard deviation (σ) approaches 0 as the number of samples per unit time increases and the length of epsilon ε (maximum length of an edge in the tree) decreases. This means that a stable waypoint list can be generated using the proposed algorithm. Therefore, by increasing real-time performance through simple calculation and the boundary of the configuration space, the algorithm proved to be suitable for collision avoidance of aerial vehicles and replanning of local paths.

KCI등재 SCOPUS

10Stagewise Weak Orthogonal Matching Pursuit Algorithm Based on Adaptive Weak Threshold and Arithmetic Mean

저자 : Liquan Zhao , Ke Ma

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 16권 6호 발행 연도 : 2020 페이지 : pp. 1343-1358 (16 pages)

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In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsity estimation is determined via maximum iterations. Different maximum iterations correspond to different thresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variable weak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the residual error value to control the weak threshold. When the residual value decreases, the threshold value continuously increases, so that the atoms contained in the atomic set are closer to the real sparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved the generalized Jaccard coefficient in order to replace the inner product method that is used in the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace the joint expectation for two variables based on the generalized Jaccard coefficient. The improved generalized Jaccard coefficient can be used to generate a more accurate calculation of the correlation between the measurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selecting the wrong atoms. We demonstrate using simulations that the proposed algorithm produces a better reconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.

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