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JIPS(Journal of Information Processing Systems) update

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

수록정보
수록범위 : 1권1호(2005)~17권1호(2021) |수록논문 수 : 897
JIPS(Journal of Information Processing Systems)
17권1호(2021년 02월) 수록논문
최근 권호 논문
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KCI등재 SCOPUS

1SkelGAN: A Font Image Skeletonization Method

저자 : Debbie Honghee Ko , Ammar Ul Hassan , Saima Majeed , Jaeyoung Choi

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 1-13 (13 pages)

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In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.

KCI등재 SCOPUS

2Development of a Targeted Recommendation Model for Earthquake Risk Prevention in the Whole Disaster Chain

저자 : Xiaohui Su , Keyu Ming , Xiaodong Zhang , Junming Liu , Da Lei

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 14-27 (14 pages)

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Strong earthquakes have caused substantial losses in recent years, and earthquake risk prevention has aroused a significant amount of attention. Earthquake risk prevention products can help improve the self and mutualrescue abilities of people, and can create convenient conditions for earthquake relief and reconstruction work. At present, it is difficult for earthquake risk prevention information systems to meet the information requirements of multiple scenarios, as they are highly specialized. Aiming at mitigating this shortcoming, this study investigates and analyzes four user roles (government users, public users, social force users, insurance market users), and summarizes their requirements for earthquake risk prevention products in the whole disaster chain, which comprises three scenarios (pre-quake preparedness, in-quake warning, and post-quake relief). A targeted recommendation rule base is then constructed based on the case analysis method. Considering the user's location, the earthquake magnitude, and the time that has passed since the earthquake occurred, a targeted recommendation model is built. Finally, an Android APP is implemented to realize the developed model. The APP can recommend multi-form earthquake risk prevention products to users according to their requirements under the three scenarios. Taking the 2019 Lushan earthquake as an example, the APP exhibits that the model can transfer real-time information to everyone to reduce the damage caused by an earthquake.

KCI등재 SCOPUS

3Simulation of a Mobile IoT System Using the DEVS Formalism

저자 : Jung Hyun Im , Ha-ryoung Oh , Yeong Rak Seong

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 28-36 (9 pages)

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This paper proposes two novel methods to model and simulate a mobile Internet of Things (IoT) system using the discrete event system specification (DEVS) formalism. In traditional simulation methods, it is advantageous to partition the simulation area hierarchically to reduce simulation time; however, in this case, the structure of the model may change as the IoT nodes to be modeled move. The proposed methods reduce the simulation time while maintaining the model structure, even when the IoT nodes move. To evaluate the performance of the proposed methods, a prototype mobile IoT system was modeled and simulated. The simulation results show that the proposed methods achieve good performance, even if the number of IoT nodes or the movement of IoT nodes increases.

KCI등재 SCOPUS

4Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory

저자 : Li Wang , Guodong Wang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 37-50 (14 pages)

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Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.

KCI등재 SCOPUS

5New Two-Level L1 Data Cache Bypassing Technique for High Performance GPUs

저자 : Gwang Bok Kim , Cheol Hong Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 51-62 (12 pages)

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On-chip caches of graphics processing units (GPUs) have contributed to improved GPU performance by reducing long memory access latency. However, cache efficiency remains low despite the facts that recent GPUs have considerably mitigated the bottleneck problem of L1 data cache. Although the cache miss rate is a reasonable metric for cache efficiency, it is not necessarily proportional to GPU performance. In this study, we introduce a second key determinant to overcome the problem of predicting the performance gains from L1 data cache based on the assumption that miss rate only is not accurate. The proposed technique estimates the benefits of the cache by measuring the balance between cache efficiency and throughput. The throughput of the cache is predicted based on the warp occupancy information in the warp pool. Then, the warp occupancy is used for a second bypass phase when workloads show an ambiguous miss rate. In our proposed architecture, the L1 data cache is turned off for a long period when the warp occupancy is not high. Our two-level bypassing technique can be applied to recent GPU models and improves the performance by 6% on average compared to the architecture without bypassing. Moreover, it outperforms the conventional bottleneck-based bypassing techniques.

KCI등재 SCOPUS

6An Energy Efficient Multi-hop Cluster-Head Election Strategy for Wireless Sensor Networks

저자 : Liquan Zhao , Shuaichao Guo

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 63-74 (12 pages)

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According to the double-phase cluster-head election method (DCE), the final cluster heads (CHs) sometimes are located at the edge of cluster. They have a long distance from the base station (BS). Sensor data is directly transmitted to BS by CHs. This makes some nodes consume much energy for transmitting data and die earlier. To address this problem, energy efficient multi-hop cluster-head election strategy (EEMCE) is proposed in this paper. To avoid taking these nodes far from BS as CH, this strategy first introduces the distance from the sensor nodes to the BS into the tentative CH election. Subsequently, in the same cluster, the energy of tentative CH is compared with those of other nodes, and then the node that has more energy than the tentative CH and being nearest the tentative CH are taken as the final CH. Lastly, if the CH is located at the periphery of the network, the multi-hop method will be employed to reduce the energy that is consumed by CHs. The simulation results suggest that the proposed method exhibits higher energy efficiency, longer stability period and better scalability than other protocols.

KCI등재 SCOPUS

7Default Prediction of Automobile Credit Based on Support Vector Machine

저자 : Ying Chen , Ruirui Zhang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 75-88 (14 pages)

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Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

KCI등재 SCOPUS

8Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening

저자 : Hongbo Shi , Xin Chen , Min Guo

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 89-106 (18 pages)

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Different samples can have different effects on learning support vector machine (SVM) classifiers. To rebalance an imbalanced dataset, it is reasonable to reduce non-informative samples and add informative samples for learning classifiers. Safe sample screening can identify a part of non-informative samples and retain informative samples. This study developed a resampling algorithm for Rebalancing imbalanced data using Safe Sample Screening (Re-SSS), which is composed of selecting Informative Samples (Re-SSS-IS) and rebalancing via a Weighted SMOTE (Re-SSS-WSMOTE). The Re-SSS-IS selects informative samples from the majority class, and determines a suitable regularization parameter for SVM, while the Re-SSS-WSMOTE generates informative minority samples. Both Re-SSS-IS and Re-SSS-WSMOTE are based on safe sampling screening. The experimental results show that Re-SSS can effectively improve the classification performance of imbalanced classification problems.

KCI등재 SCOPUS

9Effective and Efficient Similarity Measures for Purchase Histories Considering Product Taxonomy

저자 : Yu-jeong Yang , Ki Yong Lee

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 107-123 (17 pages)

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In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

KCI등재 SCOPUS

10Multiple Properties-Based Moving Object Detection Algorithm

저자 : Changjian Zhou , Jinge Xing , Haibo Liu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 1호 발행 연도 : 2021 페이지 : pp. 124-135 (12 pages)

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Object detection is a fundamental yet challenging task in computer vision that plays an important role in object recognition, tracking, scene analysis and understanding. This paper aims to propose a multiproperty fusion algorithm for moving object detection. First, we build a scale-invariant feature transform (SIFT) vector field and analyze vectors in the SIFT vector field to divide vectors in the SIFT vector field into different classes. Second, the distance of each class is calculated by dispersion analysis. Next, the target and contour can be extracted, and then we segment the different images, reversal process and carry on morphological processing, the moving objects can be detected. The experimental results have good stability, accuracy and efficiency.

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