• Earthquakes and Structures
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• v.22 no.5
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.2
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• pp.351-358
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• 2018

Reinforcement learning is a machine learning method which aims to determine a policy to get optimal actions in dynamic and stochastic environments. But reinforcement learning has high computational complexity and needs a lot of time to get solution, so it is not easily applicable to uncertain and continuous environments. To tackle the complexity problem, AC (actor-critic) method is used and it separates an action-value function into a value function and an action decision policy. Also, in transfer learning method, the knowledge constructed in one environment is adapted to another environment, so it reduces the time to learn in a reinforcement learning method. In this paper, we present AC method and transfer learning method to solve the problem of a reinforcement learning method. Finally, we analyze the case study which a transfer learning method is used to solve BS(base station) switching problem in wireless access networks.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.729-740
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• 2022

Netflix, Amazon Prime, and YouTube are the most popular and fastest-growing streaming services globally. It is a matter of great interest for the streaming service providers to preview their service infrastructure and streaming strategy in order to provide new streaming services. Hence, the first part of the paper presents a detailed survey of the Content Distribution Network (CDN) and cloud infrastructure of these service providers. To understand the streaming strategy of these service providers, the second part of the paper deduces a common quality-of-service (QoS) model based on rebuffering time, bitrate, progressive download ratio, and standard deviation of the On-Off cycle. This model is then used to analyze and compare the streaming behaviors of these services. This study concluded that the streaming behaviors of all these services are similar as they all use Dynamic Adaptive Streaming over HTTP (DASH) on top of TCP. However, the amount of data that they download in the buffering state and steady-state vary, resulting in different progressive download ratios, rebuffering levels, and bitrates. The characteristics of their On-Off cycle are also different resulting in different QoS. Hence a thorough adaptive bit rate (ABR) analysis is presented in this paper. The streaming behaviors of these services are tested on different access network bandwidths, ranging from 75 kbps to 30 Mbps. The survey results indicate that Netflix QoS and streaming behavior are significantly consistent followed by Amazon Prime and YouTube. Our approach can be used to compare and contrast the streaming services' strategies and finetune their ABR and flow control mechanisms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.61-67
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• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.

• Journal of The Korean Association For Science Education
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• v.29 no.6
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• pp.680-692
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• 2009

In this study, the facilities, educational program, and exhibition for students with disabilities in science museums were investigated. To do this, 19 science museums in Korea were surveyed by reviewing information and visiting investigation including interview. Facilities for access in consideration of the disabled were measured and found to averaged at 84.3 points well within the legal standard of 100 points. There were 11 moving science classes in which students with difficulties in accessing the museum can participate, but educational programs for the disabled were executed in 2 science museums. Exhibition in most science museum focused on basic science and dynamic operation, however there were little consideration for the disabled except for guides in braille and voice. In general, the facilities and educational program in science museum for students with disabilities were insufficient, but there are some possibilities to supplement by modifying the guide for the disabled or enlarging the educational program to include the disabled.

• Korean Journal of Materials Research
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• v.34 no.6
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• pp.283-290
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• 2024

The aggressive scaling of dynamic random-access memory capacitors has increased the need to maintain high capacitance despite the limited physical thickness of electrodes and dielectrics. This makes it essential to use high-k dielectric materials. TiO₂ has a large dielectric constant, ranging from 30~75 in the anatase phase to 90~170 in rutile phase. However, it has significant leakage current due to low energy barriers for electron conduction, which is a critical drawback. Suppressing the leakage current while scaling to achieve an equivalent oxide thickness (EOT) below 0.5 nm is necessary to control the influence of interlayers on capacitor performance. For this, Pt and Ru, with their high work function, can be used instead of a conventional TiN substrate to increase the Schottky barrier height. Additionally, forming rutile-TiO₂ on RuO₂ with excellent lattice compatibility by epitaxial growth can minimize leakage current. Furthermore, plasma-enhanced atomic layer deposition (PEALD) can be used to deposit a uniform thin film with high density and low defects at low temperatures, to reduce the impact of interfacial reactions on electrical properties at high temperatures. In this study, TiO₂ was deposited using PEALD, using substrates of Pt and Ru treated with rapid thermal annealing at 500 and 600 ℃, to compare structural, chemical, and electrical characteristics with reference to a TiN substrate. As a result, leakage current was suppressed to around 10^-6 A/cm² at 1 V, and an EOT at the 0.5 nm level was achieved.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• Applied Microscopy
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• v.51
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• pp.18.1-18.12
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• 2021

Hair, having distinct stages of growth, is a dynamic component of the integumentary system. Nonetheless, derangement in its structure and growth pattern often provides vital clues for the diagnosis of systemic diseases. Assessment of the hair structure by various microscopy techniques is, hence, a valuable tool for the diagnosis of several systemic and cutaneous disorders. Systemic illnesses like Comel-Netherton syndrome, Griscelli syndrome, Chediak Higashi syndrome, and Menkes disease display pathognomonic findings on hair microscopy which, consequently, provide crucial evidence for disease diagnosis. With minimal training, light microscopy of the hair can easily be performed even by clinicians and other health care providers which can, thus, serve as a useful tool for disease diagnosis at the patient's bedside. This is especially true for resource-constrained settings where access and availability of advanced investigations (like molecular diagnostics) is a major constraint. Despite its immense clinical utility and non-invasive nature, hair microscopy seems to be an underutilized diagnostic modality. Lack of awareness regarding the important findings on hair microscopy may be one of the crucial reasons for its underutilization. Herein, we, therefore, present a comprehensive overview of the available methods for hair microscopy and the pertinent findings that can be observed in various diseases.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of KIISE:Information Networking
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• v.35 no.5
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• pp.409-414
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• 2008

In mobile ad hoc networks, most of on demand routing protocols such as DSR and AODV do not deal with traffic load during the route discovery procedure. To solve the congestion and achieve load balancing, many protocols have been proposed. However, the existing load balancing schemes has only considered avoiding the congested route in the route discovery procedure or finding an alternative route path during a communication session. To mitigate this problem, we have proposed a new scheme which considers the packet bursting mechanism in congested nodes. The proposed packet bursting scheme, which is originally introduced in IEEE 802.11e QoS specification, is to transmit multiple packets right after channel acquisition. Thus, congested nodes can forward buffered packets promptly and minimize bottleneck situation. Each node begins to transmit packets in normal mode whenever its congested status is dissolved. We also propose two threshold values to define exact overloaded status adaptively; one is interface queue length and the other is buffer occupancy time. Through an experimental simulation study, we have compared and contrasted our protocol with normal on demand routing protocols and showed that the proposed scheme is more efficient and effective especially when network traffic is heavily loaded.