• 제목/요약/키워드: Point machine

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나노허니컴 구조물의 인장 및 굽힘 물성 측정 (Measurement of Tensile and Bending Properties of Nanohoneycomb Structures)

  • 전지훈;최덕현;이평수;이건홍;박현철;황운봉
    • Composites Research
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    • 제19권6호
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    • pp.23-31
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    • 2006
  • 나노허니컴 구조물의 영률, 굽힘 탄성 계수. 공칭파괴강도를 구하였다. 양극산화 알루미늄은 잘 정렬된 나노허니컴 구조물의 일종으로서 공정이 간단하고, 높은 종횡비, 자가 정렬된 기공구조를 가지고 있고, 기공의 크기를 조절할 수 있다. 원자현미경으로 외팔보 굽힘 시험을 수행하였고 나노-UTM을 이용한 3점 굽힘 실험결과와 비교하였다. 또한 나노-UTM으로 인장시험을 수행하였다. 나노허니컴 구조물의 한쪽 면은 막혀 있어서, 일반적인 샌드위치 구조물의 면재에 비유될 수 있다. 하지만 이러한 막힌 면은 굽힘 강도 증가에 영향을 끼치지 못하고 균열선단으로 작용한다는 것을 알 수 있었다. 본 연구로 나노허니컴 구조물을 설계하는데 기초적인 물성을 제공하고자 한다.

양수발전 설비에 적용 가능한 새로운 고장 예측경보 알고리즘 개발 (Development of a New Prediction Alarm Algorithm Applicable to Pumped Storage Power Plant)

  • 이대연;박수용;이동형
    • 산업경영시스템학회지
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    • 제46권2호
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    • pp.133-142
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    • 2023
  • The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

POI 에서 딥러닝을 이용한 개인정보 보호 추천 시스템 (Personal Information Protection Recommendation System using Deep Learning in POI)

  • 펭소니;박두순;김대영;양예선;이혜정;싯소포호트
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2022년도 추계학술발표대회
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    • pp.377-379
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    • 2022
  • POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

A Moderating Role of Personal Need for Structure on the Effects of Process versus Outcome Simulations on the Evaluation of Really New Products

  • Kim, Jun San;Hahn, Minhi;Yoon, Yeosun
    • Asia Marketing Journal
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    • 제14권4호
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    • pp.77-94
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    • 2013
  • Really new products (RNPs) provide novel benefits yet many consumers are reluctant to accept these highly innovative new products. Previous literature has shown that mental simulation is an effective method for enhancing the evaluation of RNPs. However, Castano et al. (2008) and Zhao, Hoeffler, and Zauberman (2011) demonstrate conflicting results as to which type of mental simulation (i.e., process versus outcome) is more effective for the enhancement of RNP evaluation. The authors try to reconcile these results by incorporating a moderating variable which is personal need for structure (PNS). PNS is an individual difference variable that taps the differences in people's propensity to cognitively structure and simplify their environment (Neuberg and Newsom 1993). From the analysis of the previous two works, the authors point out that consumers' susceptibility to uncertainty may contribute to the different results, and suggest that this susceptibility is dependent on consumers' PNS. To test the hypotheses established, an experiment was conducted. Waterless washing machine was presented as a RNP and PNS was measured by using the 12-item PNS Scale (Thompson et al. 2001). The results of the study show that for high-PNS consumers, process simulation is more effective than outcome simulation for enhancing the evaluation of a RNP, whereas for low-PNS consumers, outcome simulation is more effective than process simulation. This research contributes to the mental simulation and new product literature by suggesting and verifying that PNS moderates the effects of process versus outcome simulations for enhancing the evaluation of RNPs. This research provides important managerial implications for marketing managers of RNPs, indicating that they should take account of the target consumers' PNS in planning marketing communications. Specifically, when targeting high-PNS consumers, marketing communications that encourage process simulation may be more effective than those that encourage outcome simulation. In contrast, when targeting low-PNS consumers, marketing communications that encourage outcome simulation may be more effective than those that encourage process simulation.

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플랫폼 기반 의사결정 품질 요인의 영향력 연구 (Impact of Quality Factors on Platform-based Decisions)

  • 윤성복;송호준;신완선
    • 산업경영시스템학회지
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    • 제46권3호
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    • pp.109-122
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    • 2023
  • As platforms become primary decision making tools, platforms for decision have been introduced to improve quality of decision results. Because, decision platforms applied augmented decision-making process which uses experiences and feedback of users. This process creates a variety of alternatives tailored for users' abilities and characteristics. However, platform users choose alternatives before considering significant quality factors based on securing decision quality. In real world, platform managers use an algorithm that distorts appropriate alternatives for their commercial benefits. For improving quality of decision-making, preceding researches approach trying to increase rational decision -making ability based on experiences and feedback. In order to overcome bounded rationality, users interact with the machine to approach the optional situation. Differentiated from previous studies, our study focused more on characteristics of users while they use decision platforms. This study investigated the impact of quality factors on decision-making using platforms, the dimensions of systematic factors and user characteristics. Systematic factors such as platform reliability, data quality, and user characteristics such as user abilities and biases were selected, and measuring variables which trust, satisfaction, and loyalty of decision platforms were selected. Based on these quality factors, a structural equation research model was created. A survey was conducted with 391 participants using a 7-point Likert scale. The hypothesis that quality factors affect trust was proved to be valid through path analysis of the structural equation model. The key findings indicate that platform reliability, data quality, user abilities, and biases affect the trust, satisfaction and loyalty. Among the quality factors, group bias of users affects significantly trust of decision platforms. We suggest that quality factors of decision platform consist of experience-based and feedback-based decision-making with the platform's network effect. Through this study, the theories of decision-making are empirically tested and the academic scope of platform-based decision-making has been further developed.

Development of an Automatic PCR System Combined with Magnetic Bead-based Viral RNA Concentration and Extraction

  • MinJi Choi;Won Chang Cho;Seung Wook Chung;Daehong Kim;Il-Hoon Cho
    • 대한의생명과학회지
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    • 제29권4호
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    • pp.363-370
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    • 2023
  • Human respiratory viral infections such as COVID-19 are highly contagious, so continuous management of airborne viruses is essential. In particular, indoor air monitoring is necessary because the risk of infection increases in poorly ventilated indoors. However, the current method of detecting airborne viruses requires a lot of time from sample collection to confirmation of results. In this study, we proposed a system that can monitor airborne viruses in real time to solve the deficiency of the present method. Air samples were collected in liquid form through a bio sampler, in which case the virus is present in low concentrations. To detect viruses from low-concentration samples, viral RNA was concentrated and extracted using silica-magnetic beads. RNA binds to silica under certain conditions, and by repeating this binding reaction, bulk samples collected from the air can be concentrated. After concentration and extraction, viral RNA is specifically detected through real-time qPCR (quantitative polymerase chain reaction). In addition, based on liquid handling technology, we have developed an automatic machine that automatically performs the entire testing process and can be easily used even by non-experts. To evaluate the system, we performed air sample collection and automated testing using bacteriophage MS2 as a model virus. As a result, the air-collected samples concentrated by 45 times then initial volume, and the detection sensitivity of PCR also confirmed a corresponding improvement.

인공지능 의료윤리: 영상의학 영상데이터 활용 관점의 고찰 (Ethics for Artificial Intelligence: Focus on the Use of Radiology Images)

  • 박성호
    • 대한영상의학회지
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    • 제83권4호
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    • pp.759-770
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    • 2022
  • 인공지능의 연구 개발 및 활용에서 윤리의 중요성이 의료분야뿐 아니라 전 사회적으로 점차 널리 인식되고 있다. 이 종설은 영상의학 영상데이터를 인공지능 연구에 활용할 때 개인정보의 보호 및 데이터에 대한 권리 측면에서 윤리적으로 고려할 사항들에 대해서 국내 독자들에게 실용적인 정보를 제공하고자 한다. 따라서 이 글에 담긴 내용은 많은 부분이 관련된 국내 법과 정부 제도에 바탕을 두고 있다. 인공지능의 연구 개발 및 활용에서 개인정보 보호는 매우 중요한 윤리적 원칙이며 연구 데이터의 적절한 가명처리는 개인정보 보호를 위한 핵심 방법이다. 아울러 인공지능 연구 개발에 의료 데이터를 상업적 이해관계를 최소화하며 윤리적으로 공유할 필요성도 부각되고 있다. 연구 데이터 공유는 개인정보 유출의 위험을 증가시키므로 개인정보 보호에 더욱 주의가 필요하다.

A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism

  • Xiangyu Ma;Yuntao Zhao;Yongxin Feng;Yutao Hu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권2호
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    • pp.456-477
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    • 2024
  • With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

The gene expression programming method for estimating compressive strength of rocks

  • Ibrahim Albaijan;Daria K. Voronkova;Laith R. Flaih;Meshel Q. Alkahtani;Arsalan Mahmoodzadeh;Hawkar Hashim Ibrahim;Adil Hussein Mohammed
    • Geomechanics and Engineering
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    • 제36권5호
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    • pp.465-474
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    • 2024
  • Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

차세대 웨어러블 디바이스를 위한 높은 기계적/전기적 특성을 갖는 CNT-Ni-Fabric 유연기판 (CNT-Ni-Fabric Flexible Substrate with High Mechanical and Electrical Properties for Next-generation Wearable Devices)

  • 김형구;노호균;차안나;이민정;하준석
    • 마이크로전자및패키징학회지
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    • 제27권2호
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    • pp.39-44
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    • 2020
  • 최근 웨어러블 장치에 적용하기 위한 유연성 기판에 대한 연구가 활발히 진행되고 있다. 특히, 유연성 기판 중 의복에 웨어러블 장치를 적용하기 위한 전도성 섬유기판에 대한 연구가 진행되고 있다. 본 연구에서는, 면섬유 기판 표면에 CNT와 Pd복합 용액을 스프레이 법을 이용하여 형성하였고, 무전해 도금법을 이용하여 금속층을 도금하였다. 도금된 섬유기판의 형상을 분석하기 위하여 SEM 장비를 이용하였고, CNT를 증착한 섬유기판의 표면에 Ni 레이어가 형성된 것을 확인하였다. EDS 분석을 통하여 섬유기판의 표면에 형성된 물질이 Ni임을 알 수 있었다. 전기적 특성을 확인하기 위하여 4-point probe로 무전해 도금을 진행한 섬유기판의 표면저항 및 저항 분포를 확인하기 위한 맵핑을 진행하였다. 무전해 도금의 진행 시간이 길어질수록 전도성이 향상되었음을 확인할 수 있었고, 표면 위치 별 저항의 분포가 균일함을 알 수 있었다. 인장력, 굽힘, 뒤틀림 시험을 통하여 기계적 스트레스로 인한 저항변화를 측정하였다. 그 결과 도금 시간이 길어질수록 유연성 기판의 저항변화가 점점 사라지는 것을 확인하였다. UTM(Universal testing machine)을 이용하여 도금시간 변화에 대한 무전해 도금 기판의 기계적 특성 향상 여부에 대하여 분석하였다. 인장강도는 무전해 도금을 2 시간 동안 진행한 전도성 섬유기판의 경우, 면섬유 기판보다 약 16 MPa 증가하였다. 이러한 결과들을 토대로 Ni-CNT-Fabric 유연기판은 의류 일체형 전도성 기판으로 이용되기에 충분함을 확인하였고, 이러한 연구 결과는 유연기판, 웨어러블 디바이스뿐만 아니라 유연성이 필요한 배터리, 촉매, 태양전지 등에 적용되어 발전에 기여할 수 있을 것으로 기대한다.