• Journal of Digital Convergence
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• v.7 no.3
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• pp.49-60
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• 2009

The aim of this study was to develop a model for teaching-teaming by applying Ubiquitous at a learning experience field, in which connect characteristics of both ubiquitous application learning and experience teaming, making use of them. A literature survey of concepts was conducted, with the main areas to find out relationships between ubiquitous application learning and experience learning. Experience learning by applying ubiquitous learning methods maximizes its efficiency of experience learning in considering ubiquitous learning methods's characteristics of dynamic, interaction, sharing. Also it makes communications through positive participation and active interaction, and leads to a process of internal examination. The research data suggests that critical factors of experiencing learning applying ubiquitous are acquiring information and memory, information integration and exquisiteness, emotional and social activity, producing activity, help activity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.2
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• pp.102-114
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• 1987

In this paper, in order to develop the transmission system between the remote subscriber and the central office, the hardware and software implementation of a SMUX(Statistical Multiplexer)which can interleace eleven 16Kbps D-channels over a 64Kbps B-channel is obtained. As a result of this study, the high speed data transmission by the use of a dynamic buffer memory management algofithm for statistical multiplexing is realized. Especially a software architectur for interruption is proposed in order to improve performance of the transmission system more efficiently.

• Journal of Navigation and Port Research
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• v.29 no.4
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• pp.349-355
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• 2005

The use of the internet to facilitate commerce among companies promises vast benefits. Lots of e-marketplaces are building for several industries such as chemistry, airplane, and automobile industries. This study provides the new B2B EC business model for the shipping industry which concerns relatively massive fixed assets to be fully utilized. To be successful the proposed model gives participants useful information. To do this the expert system is constructed with the hybrid prediction system of neural network (NN) and memory based reasoning (MBR) with self-organizing map (SOM) and knowledge augmentation technique using qualitative reasoning (QR). The expert system supports participants useful information coping with dynamic market environment. with this shipping companies are induced to participate in the proposed e-marketplace and helped for exchanges easily. Also participants would utilize their assets fully through B2B exchanges.

• Journal of the Korea Safety Management & Science
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• v.6 no.4
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• pp.155-170
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• 2004

Among the semiconductor If-chips, unlike memory chips, a majority of Application Specific IC(ASIC) products are produced by customer orders, and meeting the customer specified due date is a critical issue for the case. However, to the one who understands the nature of semiconductor manufacturing, it does not take much effort to realize the difficulty of meeting the given specific production due dates. Due to its multi-layered feature of products, to be completed, a semiconductor product(called device) enters into the fabrication manufacturing process(FAB) repeatedly as many times as the number of the product specified layers, and fabrication processes of individual layers are composed with similar but not identical unit processes. The unit process called photo-lithography is the only process where every layer must pass through. This re-entrant feature of FAB makes predicting and planning of due date of an ordered batch of devices difficult. Parallel machines problem in the photo process, which is bottleneck process, is solved with restricted roll out algorithm. Roll out algorithm is a method of solving the problem by embedding it within a dynamic programming framework. Restricted roll out algorithm Is roll out algorithm that restricted alternative states to decrease the solving time and improve the result. Results of simulation test in condition as same as real FAB facilities show the effectiveness of the developed algorithm.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.100-110
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• 2001

In this paper, we investigated several buffer management schemes for the design of shared-memory type ATM switches, which can enhance the utilization of switch resources and can support quality-of-service (QoS) functionalities. Our results show that dynamic threshold (DT) scheme demonstrate a moderate degree of robustness close to pushout(PO) scheme, which is known to be impractical in the perspective of hardware implementation, under various traffic conditions such as traffic loads, burstyness of incoming traffic, and load non-uniformity across output ports. Next, we considered buffer management strategies to support QoS functions, which utilize parameter values obtained via connection admission control (CAC) procedures to set tile threshold values. Through simulations, we showed that the buffer management schemes adopted behave well in the sense that they can protect regulated traffic from unregulated cell traffic in allocating buffer space. In particular, it was observed that dynamic partitioning is superior in terms of QoS support than virtual partitioning.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.367-376
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• 2007

In this paper, we introduce an application-specific and adaptive power management technique for portable systems that support dynamic voltage scaling (DVS). We exploit both the idle time of multitasking systems running soft real-time tasks as well as memory- or CPU-bound code regions. Detailed power and execution time profiles guide an adaptive power manager (APM) that is linked to the operating system. A post-pass optimizer marks candidate regions for DVS by inserting calls to the APM. At runtime, the APM monitors the CPU's performance counters to dynamically determine the affinity of the each marked region. for each region, the APM computes the optimal voltage and frequency setting in terms of energy consumption and switches the CPU to that setting during the execution of the region. Idle time is exploited by monitoring system idle time and switching to the energy-wise most economical setting without prolonging execution. We show that our method is most effective for periodic workloads such as video or audio decoding. We have implemented our method in a multitasking operating system (Microsoft Windows CE) running on an Intel XScale-processor. We achieved up to 9% of total system power savings over the standard power management policy that puts the CPU in a low Power mode during idle periods.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.63-72
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• 2011

Binary translation is a kind of the emulation method which converts a binary code compiled on the particular instruction set architecture to the new binary code that can be run on another one. It has been mostly used for migrating legacy systems to new architecture. In recent, binary translation is used for instrumenting programs without modifying source code, because it enables inserting additional codes dynamically, For general application, there already exists some instrumentation software using binary translation, such as dynamic binary analyzers and virtual machine monitors. On the other hand, in order to be benefited from binary translation in kernel-level, a few issues, which include system performance, memory management, privileged instructions, and synchronization, should be treated. These matters are derived from the structure of the kernel, and the difference between the kernel and user-level application. In this paper, we present a scheme to apply binary translation and dynamic instrumentation on kernel. We implement it on Linux kernel and demonstrate that kernel-level binary translation adds an insignificant overhead to performance of the system.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.119-128
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• 2010

Computing environment in these days aim for user-oriented development called RIA (Rich Internet Application). As a representative development method of RIA, Flex Framework overcomes the weaknesses of the Mainframe and C/S (Client/Server). However, the issues, such as, difficulties in memory management, complexity of the binding structure and large capacities of the compile outputs are left to be solved. The purpose of this paper is to implement the framework which enables the fast and accurate development of user-oriented web application on the Flex platform. DDX (Dynamic Data eXchange) framework proposes standardized and efficient development environment in a Flex platform. And by using scalability-prepared library that is applicable for various job areas, the framework enhances the performance, increase development productivity and help construct stable system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.