간행물

한국인터넷정보학회> KSII Transactions on Internet and Information Systems (TIIS)

KSII Transactions on Internet and Information Systems (TIIS) update

  • : 한국인터넷정보학회
  • : 공학분야  >  기타(공학)
  • : KCI등재
  • : SCI,SCOPUS
  • : 연속간행물
  • : 월간
  • : 1976-7277
  • :
  • :

수록정보
수록범위 : 1권1호(2007)~14권12호(2020) |수록논문 수 : 2,788
KSII Transactions on Internet and Information Systems (TIIS)
14권12호(2020년 12월) 수록논문
최근 권호 논문
| | | |

KCI등재 SCI SCOPUS

1Generative Adversarial Networks: A Literature Review

저자 : Jieren Cheng , Yue Yang , Xiangyan Tang , Naixue Xiong , Yuan Zhang , Feifei Lei

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4625-4647 (23 pages)

다운로드

(기관인증 필요)

초록보기

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of “generative” and “adversarial”, researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

KCI등재 SCI SCOPUS

2Energy Efficient and Low-Cost Server Architecture for Hadoop Storage Appliance

저자 : Do Young Choi , Jung Hwan Oh , Ji Kwang Kim , Seung Eun Lee

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4648-4663 (16 pages)

다운로드

(기관인증 필요)

초록보기

This paper proposes the Lempel-Ziv 4(LZ4) compression accelerator optimized for scale-out servers in data centers. In order to reduce CPU loads caused by compression, we propose an accelerator solution and implement the accelerator on an Field Programmable Gate Array(FPGA) as heterogeneous computing. The LZ4 compression hardware accelerator is a fully pipelined architecture and applies 16 dictionaries to enhance the parallelism for high throughput compressor. Our hardware accelerator is based on the 20-stage pipeline and dictionary architecture, highly customized to LZ4 compression algorithm and parallel hardware implementation. Proposing dictionary architecture allows achieving high throughput by comparing input sequences in multiple dictionaries simultaneously compared to a single dictionary. The experimental results provide the high throughput with intensively optimized in the FPGA. Additionally, we compare our implementation to CPU implementation results of LZ4 to provide insights on FPGA-based data centers. The proposed accelerator achieves the compression throughput of 639MB/s with fine parallelism to be deployed into scale-out servers. This approach enables the low power Intel Atom processor to realize the Hadoop storage along with the compression accelerator.

KCI등재 SCI SCOPUS

3Study on Fast-Changing Mixed-Modulation Recognition Based on Neural Network Algorithms

저자 : Qingfeng Jing , Huaxia Wang , Liming Yang

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4664-4681 (18 pages)

다운로드

(기관인증 필요)

초록보기

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields. While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results. In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal. Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time. The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data. With the improvement of the SNR, the recognition accuracy increases rapidly. When the length of the training dataset increases, the neural network recognition effect is better. The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable. Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%. As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.

KCI등재 SCI SCOPUS

4Smart Anti-jamming Mobile Communication for Cloud and Edge-Aided UAV Network

저자 : Zhiwei Li , Yu Lu , Zengguang Wang , Wenxin Qiao , Donghao Zhao

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4682-4705 (24 pages)

다운로드

(기관인증 필요)

초록보기

The Unmanned Aerial Vehicles (UAV) networks consisting of low-cost UAVs are very vulnerable to smart jammers that can choose their jamming policies based on the ongoing communication policies accordingly. In this article, we propose a novel cloud and edge-aided mobile communication scheme for low-cost UAV network against smart jamming. The challenge of this problem is to design a communication scheme that not only meets the requirements of defending against smart jamming attack, but also can be deployed on low-cost UAV platforms. In addition, related studies neglect the problem of decision-making algorithm failure caused by intermittent ground-to-air communication. In this scheme, we use the policy network deployed on the cloud and edge servers to generate an emergency policy tables, and regularly update the generated policy table to the UAVs to solve the decision-making problem when communications are interrupted. In the operation of this communication scheme, UAVs need to offload massive computing tasks to the cloud or the edge servers. In order to prevent these computing tasks from being offloaded to a single computing resource, we deployed a lightweight game algorithm to ensure that the three types of computing resources, namely local, edge and cloud, can maximize their effectiveness. The simulation results show that our communication scheme has only a small decrease in the SINR of UAVs network in the case of momentary communication interruption, and the SINR performance of our algorithm is higher than that of the original Q-learning algorithm.

KCI등재 SCI SCOPUS

5The Impact of Transforming Unstructured Data into Structured Data on a Churn Prediction Model for Loan Customers

저자 : Hoon Jung , Bong Gyou Lee

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4706-4724 (19 pages)

다운로드

(기관인증 필요)

초록보기

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

KCI등재 SCI SCOPUS

6Controller Backup and Replication for Reliable Multi-domain SDN

저자 : Junli Mao , Lishui Chen , Jiacong Li , Yi Ge

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4725-4747 (23 pages)

다운로드

(기관인증 필요)

초록보기

Software defined networking (SDN) is considered to be one of the most promising paradigms in the future. To solve the scalability and performance problem that a single and centralized controller suffers from, the distributed multi-controller architecture is adopted, thus forms multi-domain SDN. In a multi-domain SDN network, it is of great importance to ensure a reliable control plane. In this paper, we focus on the reliability problem of multi-domain SDN against controller failure from perspectives of backup controller deployment and controller replication. We firstly propose a placement algorithm for backup controllers, which considers both the reliability and the cost factors. Then a controller replication mechanism based on shared data storage is proposed to solve the inconsistency between the active and standby controllers. We also propose a shared data storage layout method that considers both reliability and performance. Besides, a fault recovery and repair process is designed based on the controller backup and shared data storage mechanism. Simulations show that our approach can recover and repair controller failure. Evaluation results also show that the proposed backup controller placement approach is more effective than other methods.

KCI등재 SCI SCOPUS

7Exploring Working Group's Psychological Subjectivity on Public Smart Work Services in a Cloud-based Social Networking

저자 : Ki Youn Kim , In Kuk Song

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4748-4762 (15 pages)

다운로드

(기관인증 필요)

초록보기

Recently, the COVID 19 pandemic has affected on our daily lives and society in many ways. Specifically, it has brought rapid changes in the working environment from office working to smart telecommuting. In addition, cloud computing technology and services not only provided ubiquitous access, but also led to a sharing of information, internal-external communication channels, telework, and innovative smart work for the business process. As a result, smart work services based on social cloud networking have spread to the public sector. However, existing academic research examining smart work merely remains to focus on the theoretical conceptualization or to deal with merely several examples of private views. Best practices of smart work services based on cloud computing technology in the public field rarely exists. Moreover, many studies have been differently measured the values of smart work for private and public sectors depending on organizational singularities. Therefore, the study aims to define new theoretical implications and to explore future business strategies and policy directions based on a technical working group's personal psychological subjectivity. The research applied Q methodology, and selected five public organizations in Korea, that they have adopted or currently plan to adopt some part of smart work services.

KCI등재 SCI SCOPUS

8Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network

저자 : Hyeonho Kim , Suchul Lee , Seokmin Han

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4763-4775 (13 pages)

다운로드

(기관인증 필요)

초록보기

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

KCI등재 SCI SCOPUS

9EER-ASSL: Combining Rollback Learning and Deep Learning for Rapid Adaptive Object Detection

저자 : Minhaz Uddin Ahmed , Yeong Hyeon Kim , Phill Kyu Rhee

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4776-4794 (19 pages)

다운로드

(기관인증 필요)

초록보기

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

KCI등재 SCI SCOPUS

10Deeper SSD: Simultaneous Up-sampling and Down-sampling for Drone Detection

저자 : Han Sun , Wen Geng , Jiaquan Shen , Ningzhong Liu , Dong Liang , Huiyu Zhou

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 12호 발행 연도 : 2020 페이지 : pp. 4795-4815 (21 pages)

다운로드

(기관인증 필요)

초록보기

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.

12
권호별 보기
가장 많이 인용된 논문

(자료제공: 네이버학술정보)

가장 많이 인용된 논문
| | | |
1연안해역에서 석유오염물질의 세균학적 분해에 관한 연구

(2006)홍길동 외 1명심리학41회 피인용

다운로드

2미국의 비트코인 규제

(2006)홍길동심리학41회 피인용

다운로드

가장 많이 참고한 논문

(자료제공: 네이버학술정보)

가장 많이 참고한 논문

다운로드

2미국의 비트코인 규제

(2006)홍길동41회 피인용

다운로드

해당 간행물 관심 구독기관

동국대학교 영남이공대학교 한양대학교 아주대학교 건양대학교
 12
 11
 9
 8
 7
  • 1 동국대학교 (12건)
  • 2 영남이공대학교 (11건)
  • 3 한양대학교 (9건)
  • 4 아주대학교 (8건)
  • 5 건양대학교 (7건)
  • 6 서울과학기술대학교 (6건)
  • 7 이화여자대학교 (6건)
  • 8 장로회신학대학교 (4건)
  • 9 University of Toronto (3건)
  • 10 연세대학교 (3건)

내가 찾은 최근 검색어

최근 열람 자료

맞춤 논문

보관함

내 보관함
공유한 보관함

1:1문의

닫기