• Title/Summary/Keyword: Machine Learning

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Development of Journal Recommendation Method Considering Importance of Decision Factors Based on Researchers' Paper Publication History (연구자의 논문 게재 이력을 고려한 저널 결정 요인별 중요도 학습 기반의 저널 추천 방법론)

  • Son, Yeonbin;Chang, Tai-Woo;Choi, Yerim
    • Journal of Internet Computing and Services
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    • v.20 no.4
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    • pp.73-79
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    • 2019
  • Selecting a proper journal to submit a research paper is a difficult task for researchers since there are many journals and various decision factors to consider during the decision process. For this reason, journal recommendation services are exist as a kind of intelligent research assistant which recommend potential journals. The existing services are executing a recommendation based on topic similarity and numerical filtering. However, it is impossible to calculate topic similarity when a researcher does not input paper data, and difficult to input clear numerical values for researchers. Therefore, the journal recommendation method which consider the importance of decision factors is proposed by constructing the preference matrix based on the paper publication history of a researcher. The proposed method was evaluated by using the actual publication history of researchers. The experiment results showed that the proposed method outperformed the compared methods.

A Study on PCS for ML-Based Electrical Propulsion System (ML 기반의 전기추진시스템을 위한 PCS에 관한 연구)

  • Lee, Jong-Hak;Lee, Hun-Seok;Oh, Jin-Seok
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.9
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    • pp.1025-1031
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    • 2019
  • This study proposes a PCS that enables efficient operation of seawater pumps for ships by implementing ML-based algorithms. Seawater temperature, RPM and power consumption data are acquired from two ships with PCS, analyzed with regression analysis method, and new algorithms are presented. Using the algorithms presented, Ship A saved about 36% compared to the PCS application, and ML-based algorithms in certain sea temperatures of 19 to 27 degrees Celsius and above 32 degrees Celsius were about 1% lower than Ship A's PCS. Ship B saved about 50% compared to PCS not applied, and about 2% more than Ship B's PCS in waters above $19^{\circ}C$, a specified sea temperature. The derived data can be used to suggest the optimum pump speed and sea route. In addition, the trend of acquired data can be used to infer the performance of the pump or the timing of elimination of the MGPS when efficiency becomes poor.

Real-Time Detection on FLUSH+RELOAD Attack Using Performance Counter Monitor (Performance Counter Monitor를 이용한 FLUSH+RELOAD 공격 실시간 탐지 기법)

  • Cho, Jonghyeon;Kim, Taehyun;Shin, Youngjoo
    • KIPS Transactions on Computer and Communication Systems
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    • v.8 no.6
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    • pp.151-158
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    • 2019
  • FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.

Analysis of Tensor Processing Unit and Simulation Using Python (텐서 처리부의 분석 및 파이썬을 이용한 모의실행)

  • Lee, Jongbok
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.3
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    • pp.165-171
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    • 2019
  • The study of the computer architecture has shown that major improvements in price-to-energy performance stems from domain-specific hardware development. This paper analyzes the tensor processing unit (TPU) ASIC which can accelerate the reasoning of the artificial neural network (NN). The core device of the TPU is a MAC matrix multiplier capable of high-speed operation and software-managed on-chip memory. The execution model of the TPU can meet the reaction time requirements of the artificial neural network better than the existing CPU and the GPU execution models, with the small area and the low power consumption even though it has many MAC and large memory. Utilizing the TPU for the tensor flow benchmark framework, it can achieve higher performance and better power efficiency than the CPU or CPU. In this paper, we analyze TPU, simulate the Python modeled OpenTPU, and synthesize the matrix multiplication unit, which is the key hardware.

Improved Environment Recognition Algorithms for Autonomous Vehicle Control (자율주행 제어를 위한 향상된 주변환경 인식 알고리즘)

  • Bae, Inhwan;Kim, Yeounghoo;Kim, Taekyung;Oh, Minho;Ju, Hyunsu;Kim, Seulki;Shin, Gwanjun;Yoon, Sunjae;Lee, Chaejin;Lim, Yongseob;Choi, Gyeungho
    • Journal of Auto-vehicle Safety Association
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    • v.11 no.2
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    • pp.35-43
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    • 2019
  • This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

A Study on the Collaboration between Government Departments in the Fourth Industrial Revolution Era (4차산업혁명시대의 정부부처 간 협력에 관한 연구)

  • Lee, Sun Young;Cho, Kyung Ho;Park, Kwang Kook
    • Journal of Digital Convergence
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    • v.17 no.6
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    • pp.35-42
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    • 2019
  • This study was conducted to identify the determing factors of the success and constraints based on the perception of public officials preparing for the Fourth Industrial Revolution(4IR) and the collaboration among ministries. The analytic method performed an average value analysis based on the survey of public officials' awareness, and the results of the study are as follows. First, officials from nine ministries who are responsible for the 4IR recognized that they were regarded that the 4IR as a new opportunity, but if it failed to respond properly, there might be a crisis. Second, it recognizes the era of 4IR as the number one priority in big data, second in artificial intelligence and machine learning, and third in cloud computing technology. Third, they recognized that 'flexibility of the institutions' and 'recruitment of experts' were needed to prepare for the 4IR effectively.

A Study on Fingerprint-Based Coil Alignment Improvement Technique for Magnetic Resonant Wireless Power Transfer System (핑거프린트 방식의 자기 공진형 무선전력전송 코일 정렬 상태 개선 기법 연구)

  • Kim, Sungjae;Lee, Euibum;Ku, Hyunchul
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.30 no.1
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    • pp.38-44
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    • 2019
  • This paper proposes fingerprint-based positioning methods which can be used in a magnetic resonant wireless power transfer(WPT) system and verifies their performance. A new receiver coil with small orthogonal auxiliary coils is proposed to measure magnetic field signals in three axial directions. The magnitude and phase characteristics of the three-axis electromotive force can be obtained by using the proposed coil. To predict a position with the measured values, we propose a lookup table-based method and linear discriminant analysis-based method. For verification, the proposed methods are applied to predict 75 positions of the 6.78 MHz WPT system, and the performances such as accuracy and computation time are compared.

Detection of Cropland in Reservoir Area by Using Supervised Classification of UAV Imagery Based on GLCM (GLCM 기반 UAV 영상의 감독분류를 이용한 저수구역 내 농경지 탐지)

  • Kim, Gyu Mun;Choi, Jae Wan
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.6
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    • pp.433-442
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    • 2018
  • The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks

  • Park, Sung Bae;Chung, Chun Kee;Gonzalez, Efrain;Yoo, Changwon
    • Journal of Bone Metabolism
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    • v.25 no.4
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    • pp.251-266
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    • 2018
  • Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies

  • Park, Hyeoun-Ae;Jung, Hyesil;On, Jeongah;Park, Seul Ki;Kang, Hannah
    • Healthcare Informatics Research
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    • v.24 no.4
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    • pp.253-262
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    • 2018
  • Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.