• Title/Summary/Keyword: machine learning

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Research Trend on Internet of Things and Smart City Using Keyword Fequency and Centrality Analysis : Focusing on United States, Japan, South Korea (키워드 빈도와 중심성 분석을 이용한 사물인터넷 및 스마트 시티 연구 동향: 미국·일본·한국을 중심으로)

  • Lee, Taekkyeun
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.18 no.3
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    • pp.9-23
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    • 2022
  • This study aims to examine research trends on the Internet of Things and smart city based on papers from the United States, Japan, and Korea. We collected 7113 papers related to the Internet of Things and smart city published from 2016 to 2021 in Elsevier's Scopus. Keyword frequency and centrality analysis were performed based on the abstracts of the collected papers. We found keywords with high frequency of appearance by calculating keyword frequency and identified central research keywords through the centrality analysis by country. As a result of the analysis, research on security, machine learning, and edge computing related to the Internet of Things and smart city were the most central and highly mediating research conducted in each country. As an implication, studies related to deep learning, cybersecurity, and edge computing in Korea have lower degree centrality and betweenness centrality compared to the United States and Japan. To solve the problem it is necessary to combine these studies with various fields. The future research direction is to analyze research trends on the Internet of Things and smart city in various regions such as Europe and China.

Cost-optimal Preventive Maintenance based on Remaining Useful Life Prediction and Minimum-repair Block Replacement Models (잔여 유효 수명 예측 모형과 최소 수리 블록 교체 모형에 기반한 비용 최적 예방 정비 방법)

  • Choo, Young-Suk;Shin, Seung-Jun
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.45 no.3
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    • pp.18-30
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    • 2022
  • Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

AI를 이용한 차량용 침입 탐지 시스템에 대한 평가 프레임워크

  • Kim, Hyunghoon;Jeong, Yeonseon;Choi, Wonsuk;jo, Hyo Jin
    • Review of KIISC
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    • v.32 no.4
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    • pp.7-17
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    • 2022
  • 운전자 보조 시스템을 통한 차량의 전자적인 제어를 위하여, 최근 차량에 탑재된 전자 제어 장치 (ECU; Electronic Control Unit)의 개수가 급증하고 있다. ECU는 효율적인 통신을 위해서 차량용 내부 네트워크인 CAN(Controller Area Network)을 이용한다. 하지만 CAN은 기밀성, 무결성, 접근 제어, 인증과 같은 보안 메커니즘이 고려되지 않은 상태로 설계되었기 때문에, 공격자가 네트워크에 쉽게 접근하여 메시지를 도청하거나 주입할 수 있다. 악의적인 메시지 주입은 차량 운전자 및 동승자의 안전에 심각한 피해를 안길 수 있기에, 최근에는 주입된 메시지를 식별하기 위한 침입 탐지 시스템(IDS; Intrusion Detection System)에 대한 연구가 발전해왔다. 특히 최근에는 AI(Artificial Intelligence) 기술을 이용한 IDS가 다수 제안되었다. 그러나 제안되는 기법들은 특정 공격 데이터셋에 한하여 평가되며, 각 기법에 대한 탐지 성능이 공정하게 평가되었는지를 확인하기 위한 평가 프레임워크가 부족한 상황이다. 따라서 본 논문에서는 machine learning/deep learning에 기반하여 제안된 차랑용 IDS 5가지를 선정하고, 기존에 공개된 데이터셋을 이용하여 제안된 기법들에 대한 비교 및 평가를 진행한다. 공격 데이터셋에는 CAN의 대표적인 4가지 공격 유형이 포함되어 있으며, 추가적으로 본 논문에서는 메시지 주기 유형을 활용한 공격 유형을 제안하고 해당 공격에 대한 탐지 성능을 평가한다.

Research Trends in Quantum Error Decoders for Fault-Tolerant Quantum Computing (결함허용 양자 컴퓨팅을 위한 양자 오류 복호기 연구 동향)

  • E.Y. Cho;J.H. On;C.Y. Kim;G. Cha
    • Electronics and Telecommunications Trends
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    • v.38 no.5
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    • pp.34-50
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    • 2023
  • Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.

Phrase-Chunk Level Hierarchical Attention Networks for Arabic Sentiment Analysis

  • Abdelmawgoud M. Meabed;Sherif Mahdy Abdou;Mervat Hassan Gheith
    • International Journal of Computer Science & Network Security
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    • v.23 no.9
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    • pp.120-128
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    • 2023
  • In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.

A Stay Detection Algorithm Using GPS Trajectory and Points of Interest Data

  • Eunchong Koh;Changhoon Lyu;Goya Choi;Kye-Dong Jung;Soonchul Kwon;Chigon Hwang
    • International Journal of Internet, Broadcasting and Communication
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    • v.15 no.3
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    • pp.176-184
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    • 2023
  • Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

Human Normalization Approach based on Disease Comparative Prediction Model between Covid-19 and Influenza

  • Janghwan Kim;Min-Yong Jung;Da-Yun Lee;Na-Hyeon Cho;Jo-A Jin;R. Young-Chul Kim
    • International Journal of Internet, Broadcasting and Communication
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    • v.15 no.3
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    • pp.32-42
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    • 2023
  • There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

Assessing Convolutional Neural Network based Malicious Network Traffic Detection Methods (컨볼루션 신경망 기반 유해 네트워크 트래픽 탐지 기법 평가)

  • Yeom, Sungwoong;Nguyen, Van-Quyet;Kim, Kyungbaek
    • KNOM Review
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    • v.22 no.1
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    • pp.20-29
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    • 2019
  • Recently, various machine learning based traffic classification methods are focused on detecting malicious network traffic. In this paper, convolutional neural network based malicious network traffic classification method is introduced and its performance is evaluated. In order to utilize the convolutional neural network which is excellent in analyzing images, a image transform method from important information of network traffic to a standardized image is proposed, and the transformed images are used as learning input of a CNN network traffic classifier. By using the real network traffic dataset, the proposed image transform method and CNN based network traffic classification method are evaluated. Especially, under various configurations of CNN, the performance of the proposed method is evaluated.

Machine Learning-based model for predicting changes in user evaluation reflecting the period of the product (제품 사용 기간을 반영한 기계학습 기반 사용자 평가 변화 예측 모델)

  • Boo Hyunkyung;Kim Namgyu
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.1
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    • pp.91-107
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    • 2023
  • With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

Camouflaged Adversarial Patch Attack on Object Detector (객체탐지 모델에 대한 위장형 적대적 패치 공격)

  • Jeonghun Kim;Hunmin Yang;Se-Yoon Oh
    • Journal of the Korea Institute of Military Science and Technology
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    • v.26 no.1
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    • pp.44-53
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    • 2023
  • Adversarial attacks have received great attentions for their capacity to distract state-of-the-art neural networks by modifying objects in physical domain. Patch-based attack especially have got much attention for its optimization effectiveness and feasible adaptation to any objects to attack neural network-based object detectors. However, despite their strong attack performance, generated patches are strongly perceptible for humans, violating the fundamental assumption of adversarial examples. In this paper, we propose a camouflaged adversarial patch optimization method using military camouflage assessment metrics for naturalistic patch attacks. We also investigate camouflaged attack loss functions, applications of various camouflaged patches on army tank images, and validate the proposed approach with extensive experiments attacking Yolov5 detection model. Our methods produce more natural and realistic looking camouflaged patches while achieving competitive performance.