• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.4
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• pp.7-18
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• 2023

The continuous increase in air traffic emphasizes the importance of capacity calculation. Research on the calculation method of Terminal Control Area (TMA) capacity has been treated as a partial aspect of the airspace sector capacity or has been limitedly studied. This study aims to propose a mathematical model for calculating TMA capacity, taking into account the Standard Terminal Arrival Route (STAR), separation standards, TMA entry speed, and runway threshold passing speed. The proposed model has the advantage of being able to calculate the instantaneous arrival capacity, which has not been noted in previous studies, along with the throughput. Additionally, it is meaningful as the model can easily calculate the arrival capacity of the TMA considering airport construction, runway expansion, or new procedures.

• Annual Conference of KIPS
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• 2008.05a
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• pp.92-94
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• 2008

In this paper we describe an approach for template based detection and tracking of objects by chamfer matching in real time video. Detecting and tracking of any objects is the key problem in computer vision. In our case we try for hand and head of human for detection and tracking by chamfer matching technique. Matching involves correlating the templates with the distance transformed scene and determining the locations where the mismatch is below a certain user defined threshold.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.106-111
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• 2023

The importance of load-to-use latency has been highlighted as state-of-the-art computing cores adopt deep pipelines and high clock frequencies. The cascaded cache was recently proposed to reduce the access cycle of the L1 cache by utilizing differences in latencies among banks of the cache structure. However, this study assumes the cache is comprised of SRAM, making it unsuitable for direct application to non-volatile memory-based systems. This paper proposes a novel mechanism and structure for lowering dynamic energy consumption. It inserts monitoring logic to keep track of swap operations and write counts. If the ratio of swap operations to total write counts surpasses a set threshold, the cache controller skips the swap of cache blocks, which leads to reducing write operations. To validate this approach, experiments are conducted on the non-volatile memory-based cascaded cache. The results show a reduction in write operations by an average of 16.7% with a negligible increase in latencies.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.248-254
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• 2024

Artificial intelligence techniques have improved fire-detection methods; however, false alarms still occur. Conventional methods detect fires using current sensors, which can lead to detection errors due to temporary environmental changes or noise. Thus, fire-detection methods must include a trend analysis of past information. We propose a deep-learning-based fire detection method using multi-sensor data and Kendall's tau. The proposed system used a BiLSTM model to predict fires using pre-processed multi-sensor data and extracted trend information. Kendall's tau indicates the trend of a time-series data as a score; therefore, it is easy to obtain a target pattern. The experimental results showed that the proposed system with trend values recorded an accuracy of 99.93% for BiLSTM and GRU models in a 20-tap moving average filter and 40% fire threshold. Thus, the proposed trend approach is more accurate than that of conventional approaches.

• Advances in environmental research
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• v.4 no.3
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• pp.143-153
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• 2015

The ventilation system is a key device to ensure both healthful indoor air quality (IAQ) and thermal comfort in buildings. The ventilation system should make the IAQ meet the standards such as ASHRAE 62. This study deals with a new approach to modeling the ventilation and IAQ requirement in residential buildings. In that approach, Elite software is used to calculate the air supply volume, and CONTAM model as a multi-zone and contaminant dispersal model is employed to estimate the contaminants' concentrations. Amongst various contaminants existing in the residential buildings, two main contaminates of carbon dioxide ($CO_2$) and carbon monoxide (CO) were considered. CO and $CO_2$ are generated mainly from combustion sources such as gas cooking and heating oven. In addition to the mentioned sources, $CO_2$ is generated from occupants' respirations. To show how that approach works, a sample house with the area of $80m^2$ located in Tehran was considered as an illustrative case study. The results showed that $CO_2$ concentration in the winter was higher than the acceptable level. Therefore, the air change rate (ACH) of 4.2 was required to lower the $CO_2$ concentration below the air quality threshold in the living room, and in the bedrooms, the rate of ventilation volume should be 11.2 ACH.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4B
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• pp.270-277
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• 2006

In the wireless adaptive modulation and coding(AMC) systems, the modulation type of user's connection can be changed dynamically. and the ongoing connection might fail due to the change of modulation. In this paper, we approach the AMC-induced CAC problem by focusing on the guaranteed connection. Three kinds of calls, new, handoff, and modulation-changed calls, are considered. We propose a modified guard channel CAC scheme that allows the modulation-changed and handoff calls to use the guard channel. Then we analyze a Markov model for the CAC scheme with long-term AMC in mind. According to the simulation results, the proposed approach reduces the call dropping probability for modulation-changed calls, which suggests the threshold of guard channels can be determined based on the proposed approach.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.309-319
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• 2016

For binary classification models, we consider a risk score that is a function of linear scores and estimate the coefficients of the linear scores. There are two estimation methods: one is to obtain MLEs using logistic models and the other is to estimate by maximizing AUC. AUC approach estimates are better than MLEs when using logistic models under a general situation which does not support logistic assumptions. This paper considers imbalanced data that contains a smaller number of observations in the default class than those in the non-default for credit assessment models; consequently, the AUC approach is applied to imbalanced data. Various logit link functions are used as a link function to generate imbalanced data. It is found that predicted coefficients obtained by the AUC approach are equivalent to (or better) than those from logistic models for low default probability - imbalanced data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.199-209
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• 2008

This paper is described the concepts and limitations for the traditional directional design hour volume estimation. The main objective of this paper is to establish an estimation method of probabilistic directional design hour volume in order to improve the limitation for the traditional approach method. To express the traffic congestion of specific road segment, this paper proposed the link travel time as the probability that the road capacity can accommodate a certain traffic demand at desired service level. Also, the link travel time threshold was derived from chance-constrained stochastic model. Such successive probabilistic process could determine optimal ranked design hour volume and directional design hour volume. Therefore, the probabilistic directional design hour volume can consider the traffic congestion and economic aspect in road planning and design stage. It is hoped that this study will provide a better understanding of various issues involved in the short term prediction of directional design hourly volume on different types of roads.