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作者：金志勋（jihoon kim）哈尔滨工业大学

学位级别：硕士

由于最近COVID-19的传播,在分销和购物领域,非面对面在线购买发展风速,现场商务服务正在从青少年到40多岁和50多岁的人群涌现。随着可随时随地访问的直播技术的发展,Naver、Coupang和11th Street等在线零售商也在通过新的在线分销渠道... 详细信息

由于最近COVID-19的传播,在分销和购物领域,非面对面在线购买发展风速,现场商务服务正在从青少年到40多岁和50多岁的人群涌现。随着可随时随地访问的直播技术的发展,Naver、Coupang和11th Street等在线零售商也在通过新的在线分销渠道加强营销能力。直播电商发展迅速,主要集中在中国的网红。因此,在韩国,还缺乏服务起步阶段等激活服务的相关研究。因此,本研究追求的研究目标如下。首先,我想调查一下网红(信息提供者)的特征对购买意愿的影响,它激活了中国的直播电商。其次,因为韩国直播电商营销处于起步阶段,所以要通过掌握韩国直播电商平台的功能局限,以及客户想要的需求,来明确发展方向.为此,首先,遵循以下研究方法和程序。通过前人的研究,将影响直播电商购买意愿的自变量分为直播电商服务的特征和信息提供者的特征。数据研究方面,从有经验的电子商务用户那里收集了200份有效问卷,并使用SPSS 21.0和AMOS 20.0进行了实证分析。作为本研究中实证分析的结果,15个假设中有6个被接受,9个被拒绝。在直播商务的特征中,无论是与顾客的互动还是与信息提供者的互动,都对沉浸感没有任何影响。在信息提供者的特征中,专业性和吸引力并没有影响沉浸感,而只是影响可靠性。另一方面,发现直播商务特征中与顾客的互动影响购买意愿,信息提供者的专业性和吸引力影响购买意愿。本研究揭示了影响韩国直播电商购买意愿的因素和因果关系,为直播电商奠定了理论基础。并且通过消费者行为分析揭示消费者对网络购物如何发展具有重要意义。预计本论文将为开始选择韩国直播电商的大型分销商提供一个发展方向,帮助他们不仅在韩国,而且作为全球平台的后来者,确保在市场上的竞争力。

关键词：现场商务服务王红需求投入现场商务的特点信息提供者的特点

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裂缝型致密气藏和页岩气藏的流一固耦合机制分析

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海洋地质 2013年第1期29卷 31-32页

作者： George J. MORIDIS jihoon kim Craig M. FREEMAN

本文通过数值模拟分析了页岩和致密砂岩这两种类型的裂缝型致密气藏的流一固耦合机制和过程。数学模型包括基于达西定律的多相流方程和有关储层流体热物理性质的准确描述。

关键词：致密气藏耦合机制裂缝型页岩数值模拟分析热物理性质致密砂岩储层流体

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Experimental Investigation and Numerical Analysis to Develop Low-Energy Large-Midwater Trawls

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China Ocean Engineering 2017年第6期31卷 700-708页

作者： jihoon LEE Taeho kim Chun-Woo LEE Subong PARK Division of Marine Technology Chonnam National University Yeosu 550-749 Korea Division of Marine Production System Management Pukyong National University Busan 608-737 Korea Department of Fisheries Physics Pukyong National University Busan 608-737 Korea

Fuel consumption in fisheries is a primary concern because of its effects on the environment and the costs incurred by fishermen. Many studies have been conducted to reduce the fuel consumption in fishing operations. Fuel consumption due to fishing gear during a fishing operation is generally related to the hydrodynamic resistance on the gear. This means that fuel consumption is proportional to the drag created by the towing speed. Based on numerical methods, this study suggests a new approach to reduce fuel consumption in fisheries. The results of the simulation are in good agreement with those of model experiments. The total as well as partial resistance forces on the gear are calculated by simulation. The simulation results suggest improved materials and gear structure for reducing the hydrodynamic forces on the gear while maintaining gear performance. The method for assessing the gear performance involves measuring the height and width of the net mouth. Furthermore, this study investigates the efficiency of a low-energy trawl from an economic point of view. The findings of this study will be useful in reducing greenhouse gas （GHG） emissions in fishing operations, and thereby contribute toward lowering fishing costs by saving fuel.

关键词： low-energy fishing gear numerical methods greenhouse gasesemission hydrodynamic resistance fuelconsumption

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Context Awareness by Noise-Pattern Analysis of a Smart Factory

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Computers, Materials & Continua 2023年第8期76卷 1497-1514页

作者： So-Yeon Lee jihoon Park Dae-Young kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial *** of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training ***,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very ***,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw *** the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed *** efficient dataset was built formodel learning through a total of two steps of data *** addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.

关键词： Noise-pattern recognition context awareness deep learning fault detection smart factory

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Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series

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Computers, Materials & Continua 2024年第10期81卷 1275-1300页

作者： Byeongcheon Lee Sangmin kim Muazzam Maqsood jihoon Moon Seungmin Rho Department of Security Convergence Chung-Ang UniversitySeoul06974Republic of Korea Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of AI and Big Data Soonchunhyang UniversityAsan31538Republic of Korea Department of Industrial Security Chung-Ang UniversitySeoul06974Republic of Korea

In the context of rapid digitization in industrial environments,how effective are advanced unsupervised learning models,particularly hybrid autoencoder models,at detecting anomalies in industrial control system(ICS)datasets?This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things(IoT)devices,which can significantly improve the reliability and safety of these *** this paper,we propose a hybrid autoencoder model,called ConvBiLSTMAE,which combines convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)to more effectively train complex temporal data patterns in anomaly *** the hardware-in-the-loopbased extended industrial control system dataset,the ConvBiLSTM-AE model demonstrated remarkable anomaly detection performance,achieving F1 scores of 0.78 and 0.41 for the first and second datasets,*** results suggest that hybrid autoencoder models are not only viable,but potentially superior alternatives for unsupervised anomaly detection in complex industrial systems,offering a promising approach to improving their reliability and safety.

关键词： Advanced anomaly detection autoencoder innovations unsupervised learning industrial security multivariate time series analysis

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Energy Consumption and Greenhouse Gas Emission of Korean Offshore Fisheries

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Journal of Ocean University of China 2018年第3期17卷 675-682页

作者： LEE jihoon kim Taeho ELLINGSEN Harald HOGNES Erik Skontorp HWANG Bokyu Division of Marine Technology Chonnam National University 50 Daehak-ro Yeosu Jeollanam-Do 59626 Korea The University Center in Svalbard NO-9171 Longyearbyen Norway Asplan Viak AS Abels Gate 9 NO-7030 Trondheim Norway Department of Marine Science and Production Kunsan National University 558 Daehak-ro Kunsan 54150 Korea

This paper presents the energy and greenhouse gas(GHG) emission assessments of Korean offshore fisheries. The consumption of energy by fisheries is a significant concern because of its attendant environmental effect, as well as the cost of the fuel consumed in fishing industry. With the global attention of reducing GHG emission and increasing energy efficiency of fuel, the seafood industry needs to further understand its energy use and reduce its GHG emission. In the present study, the amount of energy consumed and the GHG emission of Korean offshore fisheries in a period from 2009 to 2013 were examined. Offshore fisheries accounted for 24% of Korean production in 2013 and 60% of fuel consumption related GHG emission. Whereas the total GHG emission intensity of this sector improved slightly between 2009 and 2012; as such emission decreased by approximately 1.9%, which increased again in 2013. The average amount of total GHG emission in this five years period was 1.78 × 10~6 tons of carbon dioxide equivalent/year(tCO_2eq. y^(-1)). Active fishing gear was found to consume 20% more fuel than passive gear. However, the production from passive gear was 28%, lower than 72% from active gear. The reason for this is that less abundant stationary resources are harvested using passive gear. Furthermore, the consumption of fuel was significantly influenced by the fishing method. Implementation and development of new fishing technologies and methods are important for improving energy efficiency and reducing the climate impact on fisheries. To realize these purposes, the fishery management system needs to be established by centralizing on energy efficiency and climate effect.

关键词： fuel consumption greenhouse gas emission Korean offshore fishery fuel use intensity

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