|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Impact of building on thermal performance of office building in Kuwait显示文摘 | Adnan Al-Anzi Donghyun Seo Moncef Krarti | 2009 | Energy Conversion and Management2009，50，3： | 1 |
| 2 | 富勒烯增强铝基纳米复合材料的力学性能(英文)显示文摘研究了富勒烯在铝基复合材料中的增强作用。该复合材料采用二次球磨结合热轧制技术制备。最初,富勒烯是由富勒烯分子通过范德华力结合在一起形成大的颗粒(~200μm),首先,通过行星球磨将大的颗粒破碎成更小的颗粒(~1μm);然后,采用高能球磨将富勒烯粉末分散在铝粉中;最后,将富勒烯/铝复合粉体在480 oC热轧成板材。由于富勒烯的晶粒细化和弥散强化作用,2%(体积分数)的富勒烯/铝复合材料板材表现出高的维氏硬度(HV^222)和高的屈服强度(~740 MPa)。 | Kwangmin CHOI Jiyeon SEO Donghyun BAE Hyunjoo CHOI | 2014 | Transactions of Nonferrous Metals Society of China2014，24，S1： | 1 |
| 3 | An unsupervised anomaly detection framework for detecting anomalies in real time through network system’s log files analysis显示文摘Nowadays,in almost every computer system,log files are used to keep records of occurring events.Those log files are then used for analyzing and debugging system failures.Due to this important utility,researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records.Research in log-based anomaly detection can be divided into two main categories:batch log-based anomaly detection and streaming log-based anomaly detection.Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies.On the other hand,streaming anomaly detection allows for immediate alert.However,current streaming approaches are mainly supervised.In this work,we propose a fully unsupervised framework which can detect anomalies in real time.We test our framework on hdfs log files and successfully detect anomalies with an F-1 score of 83%. | Vannel Zeufack Donghyun Kim Daehee Seo Ahyoung Lee | 2021 | High-Confidence Computing2021，1，2： | 0 |
| 4 | Secure verifiable aggregation for blockchain-based federated averaging显示文摘IoT devices’storage and computation capacities are constantly increasing in recent years,which brings critical challenges in data privacy protection.Federated learning(FL)and blockchain technology are two popular tech-niques used in IoT data aggregation,where FL enables data training with privacy protection,and blockchain provides a decentralized architecture for data storage and mining.However,very few the state-of-the-art works consider the applicability of the combination of FL and blockchain.In this paper,we adopt the federated aver-aging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance.We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy.Finally,we propose and implement a non-interactive Public Verifiable Secret Sharing(PVSS)algorithm with Distributed Hash Table(DHT)that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users.At last,we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides. | Saide Zhu Ruinian Li Zhipeng Cai Donghyun Kim Daehee Seo Wei Li | 2022 | High-Confidence Computing2022，2，1： | 0 |
| 5 | A Novel Transparent and Auditable Fog-Assisted Cloud Storage with Compensation Mechanism显示文摘This paper introduces a new fog-assisted cloud storage which can achieve much higher throughput compared to the traditional cloud-only storage architecture by reducing the traffics toward the cloud storage. The fog-storage service providers are transparency to end-users and therefore, no modification on the end-user devices is necessary. This new system is featured with(1) a stronger audit scheme which is naturally coupled with the proposed architecture and does not suffer from the replay attack and(2) a transparent and efficient compensation mechanism for the fog-storage service providers. We provide rigorous theoretical analysis on the correctness and soundness of the proposed system. To the best of our knowledge, this is the first paper to discuss about a storage data audit scheme for fog-assisted cloud storage as well as the compensation mechanism for the service providers of the fog-storage service providers. | Donghyun Kim Junggab Son Daehee Seo Yeojin Kim Hyobin Kim Jung Taek Seo | 2020 | Tsinghua Science and Technology2020，25，1： | 0 |
| 6 | Design the IoT Botnet Defense Process for Cybersecurity in Smart City显示文摘The smart city comprises various infrastructures,including health-care,transportation,manufacturing,and energy.A smart city’s Internet of Things(IoT)environment constitutes a massive IoT environment encom-passing numerous devices.As many devices are installed,managing security for the entire IoT device ecosystem becomes challenging,and attack vectors accessible to attackers increase.However,these devices often have low power and specifications,lacking the same security features as general Information Technology(IT)systems,making them susceptible to cyberattacks.This vulnerability is particularly concerning in smart cities,where IoT devices are connected to essential support systems such as healthcare and transportation.Disruptions can lead to significant human and property damage.One rep-resentative attack that exploits IoT device vulnerabilities is the Distributed Denial of Service(DDoS)attack by forming an IoT botnet.In a smart city environment,the formation of IoT botnets can lead to extensive denial-of-service attacks,compromising the availability of services rendered by the city.Moreover,the same IoT devices are typically employed across various infrastructures within a smart city,making them potentially vulnerable to similar attacks.This paper addresses this problem by designing a defense process to effectively respond to IoT botnet attacks in smart city environ-ments.The proposed defense process leverages the defense techniques of the MITRE D3FEND framework to mitigate the propagation of IoT botnets and support rapid and integrated decision-making by security personnel,enabling an immediate response. | Donghyun Kim Seungho Jeon Jiho Shin Jung Taek Seo | 2023 | Intelligent Automation & Soft Computing2023，37，9： | 0 |
| 7 | Language Model Using Differentiable Neural Computer Based on Forget Gate-Based Memory Deallocation显示文摘A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems.In this study,we apply a DNC to a language model(LM)task.The LM task is one of the reasoning problems,because it can predict the next word using the previous word sequence.However,memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory,which degrades performance.Therefore,we propose a forget gatebased memory deallocation(FMD)method,which searches for the minimum value of elements in a forget gate-based retention vector.The forget gatebased retention vector indicates the retention degree of information stored in each external memory address.In experiments,we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example.For LM tasks,we evaluated DNC using the Penn Treebank and enwik8 LM tasks.Although it does not yield SOTA results in LM tasks,the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character.For the speech recognition rescoring tasks,FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate. | Donghyun Lee Hosung Park Soonshin Seo Changmin Kim Hyunsoo Son Gyujin Kim Ji-Hwan Kim | 2021 | Computers, Materials & Continua2021，，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员