간행물

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)~17권6호(2021) |수록논문 수 : 966
JIPS(Journal of Information Processing Systems)
17권6호(2021년 12월) 수록논문
최근 권호 논문
| | | |

KCI등재 SCOPUS

1Study on Data Processing of the IOT Sensor Network Based on a Hadoop Cloud Platform and a TWLGA Scheduling Algorithm

저자 : Guoyu Li , Kang Yang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1035-1043 (9 pages)

다운로드

(기관인증 필요)

초록보기

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

KCI등재 SCOPUS

2The Nexus among Globalization, ICT and Economic Growth: An Empirical Analysis

저자 : Ximei Liu , Zahid Latif , Daoqi Xiong , Mengke Yang , Shahid Latif , Kaif Ul Wara

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1044-1056 (13 pages)

다운로드

(기관인증 필요)

초록보기

Globalization has integrated the world through interaction among countries and people with the help of information and telecommunication technology (ICT). The rapid mode of globalization has put a new life in ICT and economic sector. The key focus of this study is to examine the nexus among the globalization, ICT and economic growth. This study uses autoregressive distributed lag model (ARDL), vector error correction model (VECM) and econometric method spanning from 1990 to 2015. The empirical result highlights that the globalization stimulates economic growth of a country. In addition, both the internet penetration and the mobile phone usage contribute to the economic growth. Lastly, this article contributes important policy lessons on strengthening the economy by utilizing ICT with the rapid globalization.

KCI등재 SCOPUS

3Case-Related News Filtering via Topic-Enhanced Positive-Unlabeled Learning

저자 : Guanwen Wang , Zhengtao Yu , Yantuan Xian , Yu Zhang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1057-1070 (14 pages)

다운로드

(기관인증 필요)

초록보기

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

KCI등재 SCOPUS

4Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes

저자 : Guik Jung , Hyunsoo Ha , Sangjun Lee

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1071-1082 (12 pages)

다운로드

(기관인증 필요)

초록보기

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

KCI등재 SCOPUS

5Massive MIMO Channel Estimation Algorithm Based on Weighted Compressed Sensing

저자 : Zhiguo Lv , Weijing Wang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1083-1096 (14 pages)

다운로드

(기관인증 필요)

초록보기

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value uop by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and uop. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

KCI등재 SCOPUS

6Wireless Mobile Sensor Networks with Cognitive Radio Based FPGA for Disaster Management

저자 : G. A. Preethi Ananthachari

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1097-1114 (18 pages)

다운로드

(기관인증 필요)

초록보기

The primary objective of this work was to discover a solution for the survival of people in an emergency flood. The geographical information was obtained from remote sensing techniques. Through helpline numbers, people who are in need request support. Although, it cannot be ensured that all the people will acquire the facility. A proper link is required to communicate with people who are at risk in affected areas. Mobile sensor networks with field-programmable gate array (FPGA) self-configurable radios were deployed in damaged areas for communication. Ad-hoc networks do not have a centralized structure. All the mobile nodes deploy a temporary structure and they act as a base station. The mobile nodes are involved in searching the spectrum for channel utilization for better communication. FPGA-based techniques ensure seamless communication for the survivors. Timely help will increase the survival rate. The received signal strength is a vital factor for communication. Cognitive radio ensures channel utilization in an effective manner which results in better signal strength reception. Frequency band selection was carried out with the help of the GRA-MADM method. In this study, an analysis of signal strength for different mobile sensor nodes was performed. FPGA-based implementation showed enhanced outcomes compared to software-based algorithms.

KCI등재 SCOPUS

7Improved Deep Residual Network for Apple Leaf Disease Identification

저자 : Changjian Zhou , Jinge Xing

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1115-1126 (12 pages)

다운로드

(기관인증 필요)

초록보기

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

KCI등재 SCOPUS

8Research on the Impact of Agricultural Mechanization Service on Wheat Planting Cost: A Case Study of Henan Province

저자 : Zhang Cheng

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1127-1137 (11 pages)

다운로드

(기관인증 필요)

초록보기

Given the different effects of agricultural mechanization on various stages of wheat planting in Henan, this article selects 78 observation samples from Henan, a major wheat-growing province. It uses different research methods (multiple linear regression, social network analysis model, multi-layer sensory nerves network) to conduct a comparative study, and the calculation results of the model show that the experimental results have a strong convergence and consistency. Agricultural mechanization services have significant effects on the three stages of wheat planting: harvesting, plowing and sowing. A higher degree of mechanized service in several stages can reduce the cost of growing wheat on family farms.

KCI등재 SCOPUS

9A Survey on Image Emotion Recognition

저자 : Guangzhe Zhao , Hanting Yang , Bing Tu , Lei Zhang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1138-1156 (19 pages)

다운로드

(기관인증 필요)

초록보기

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

KCI등재 SCOPUS

10KAWS: Coordinate Kernel-Aware Warp Scheduling and Warp Sharing Mechanism for Advanced GPUs

저자 : Viet Tan Vo , Cheol Hong Kim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 6호 발행 연도 : 2021 페이지 : pp. 1157-1169 (13 pages)

다운로드

(기관인증 필요)

초록보기

Modern graphics processor unit (GPU) architectures offer significant hardware resource enhancements for parallel computing. However, without software optimization, GPUs continuously exhibit hardware resource underutilization. In this paper, we indicate the need to alter different warp scheduler schemes during different kernel execution periods to improve resource utilization. Existing warp schedulers cannot be aware of the kernel progress to provide an effective scheduling policy. In addition, we identified the potential for improving resource utilization for multiple-warp-scheduler GPUs by sharing stalling warps with selected warp schedulers. To address the efficiency issue of the present GPU, we coordinated the kernel-aware warp scheduler and warp sharing mechanism (KAWS). The proposed warp scheduler acknowledges the execution progress of the running kernel to adapt to a more effective scheduling policy when the kernel progress attains a point of resource underutilization. Meanwhile, the warp-sharing mechanism distributes stalling warps to different warp schedulers wherein the execution pipeline unit is ready. Our design achieves performance that is on an average higher than that of the traditional warp scheduler by 7.97% and employs marginal additional hardware overhead.

12
권호별 보기

내가 찾은 최근 검색어

최근 열람 자료

맞춤 논문

보관함

내 보관함
공유한 보관함

1:1문의

닫기