• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1335-1351
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• 2015

In order to effectively understand the perception of businesses and consumers, the Bank of Korea has released Economic Sentiment Index (ESI), a composite indicator of business survey index (BSI) and consumer survey index (CSI), since 2102. The usefulness of ESI has been widely recognized. However, there exists a margin for improvement in terms of its predictive power. In this study, we evaluated the usefulness of ESI and improved the ESI by complementing its defaults. Our results of empirical analysis proved that dynamic optimal weight navigation process using the sliding window method is very useful in determining the optimal weights of configurations item of ESI based on economic situation.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1C
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• pp.138-144
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• 2009

This paper proposes an efficient scheme to estimate the channel parameters such as channel gain and delay for the IEEE802.15.4a LR-UWB systems. Sliding window (SW) method is generally used for the channel estimation of LR-UWB systems, which extracts the channel parameters by performing the cross-correlation with the repeatedly transmitted signal. However, the SW method experiences the severe performance degradation because the cross-correlation is performed just once for the received signal. In this paper, we propose a novel channel estimation scheme, which can achieve a great performance gain by performing the cross-correlation repeatedly with the repeated receive signal. In order to verify the performance gain of the proposed scheme, we performed the intensive simulation with the Saleh-Valenzuela channel model. Simulation results show that the proposed scheme has a performance improvement of 4dB compared to the conventional SW channel estimation scheme.

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.358-367
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• 2002

The IETF Differentiated Services (Diffserv) WG focused on Providing service differentiation on the Internet. One problem of the Diffserv Assured Services (AS) architecture is that it cannot guarantee fairness and throughput assurance. In this paper, we propose two schemes for guaranteeing fairness among the various target rates in the AS architecture. One is a variant of RED with IN and OUT (RIO), called the improved RIO (IRIO). The other is a variant of Time Sliding Window (TSW), called the improved TSW (ITSW). To validate the Proposed schemes, their behaviors are then examined under various simulation environments. The simulation results showed that IRIO and ITSW improved fairness and the throughput assurance in the AS architecture.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.49-60
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• 2023

The amount of data generated by electronic systems through e-commerce, social networks, and data computation has risen. However, the security of data has always been a challenge. The problem is not with the quantity of data but how to secure the data by ensuring its confidentiality and privacy. Though there are several research on cloud data security, this study proposes a security scheme with the lowest execution time. The approach employs a non-linear time complexity to achieve data confidentiality and privacy. A symmetric algorithm dubbed the Non-Deterministic Cryptographic Scheme (NCS) is proposed to address the increased execution time of existing cryptographic schemes. NCS has linear time complexity with a low and unpredicted trend of execution times. It achieves confidentiality and privacy of data on the cloud by converting the plaintext into Ciphertext with a small number of iterations thereby decreasing the execution time but with high security. The algorithm is based on Good Prime Numbers, Linear Congruential Generator (LGC), Sliding Window Algorithm (SWA), and XOR gate. For the implementation in C, thirty different execution times were performed and their average was taken. A comparative analysis of the NCS was performed against AES, DES, and RSA algorithms based on key sizes of 128kb, 256kb, and 512kb using the dataset from Kaggle. The results showed the proposed NCS execution times were lower in comparison to AES, which had better execution time than DES with RSA having the longest. Contrary, to existing knowledge that execution time is relative to data size, the results obtained from the experiment indicated otherwise for the proposed NCS algorithm. With data sizes of 128kb, 256kb, and 512kb, the execution times in milliseconds were 38, 711, and 378 respectively. This validates the NCS as a Non-Deterministic Cryptographic Algorithm. The study findings hence are in support of the argument that data size does not determine the execution.

• Korean Institute of Interior Design Journal
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• v.18 no.5
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• pp.101-110
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• 2009

In case of general residential house, photovoltaic can be installed at roof, wall, and any other places. But, in case of apartment building, there has not enough roof space to install photovoltaic panels to supply enough electricity. Actually, apartment building roof and facade wall (exclude the balcony window space) is not enough space to produce and supply the electricity to residents by installing PV panel. Generally, the space of facade balcony with windows in facade wall at apartment building occupied about $70{\sim}80%$, in all facade space. So, if we could use the balcony and windows space in facade as PV to generating electricity, there could contribute the energy saying. But, PV cell is opacify. So if it installed at front window area in apartment building, residents may have displeasure for that opacity character. But the other hand, residents are not always in house especially in day time that is exactly good time for generating electricity by PV. If we can use PV at the facade balcony with window without collusion of resident's displeasure, there have good attraction to using sustainable energy. Hence, this study suggests the design of facade balcony window style PV by considering resident's living pattern in apartment building. The methods of this study are as follows. At first, this study surveyed to the residents about residential time in their home and asked user demand by Delphi survey. At second, this study designed balcony open style PV system which oriented to the user demand. At third, this study tests designed result performance by computer simulation that compared design result with old design. As a result, For the purpose of satisfying the resident demand, there designed sliding window style which slide the several door systems to the one side. That would be make balcony absolute open scenery to the residents. Hence, the designed system performance results were as follows. When we compare the small apartment and large apartment, smaller one has good performance than larger one. Because resident's residential time characteristic. And that has more good electronic performance than vertical style that is similar to roof style.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.167-179
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• 2001

This paper discusses an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, we suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multi-dimensional index using a feature vector as indexing attributes. For query precessing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verily the superiority of our method, we perform extensive experiments. The results reseal that our method achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1383-1391
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• 2018

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.832-844
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• 2020

The degree of dispersion of a random variable can be described by the variance, which reflects the distance of the random variable from its mean. However, the time complexity of the traditional variance calculation algorithm is O(n), which results from full calculation of all samples. When the number of samples increases or on the occasion of high speed signal processing, algorithms with O(n) time complexity will cost huge amount of time and that may results in performance degradation of the whole system. A novel multi-step recursive algorithm for variance calculation of the time-varying data series with O(1) time complexity (constant time) is proposed in this paper. Numerical simulation and experiments of the algorithm is presented and the results demonstrate that the proposed multi-step recursive algorithm can effectively decrease computing time and hence significantly improve the variance calculation efficiency for time-varying data, which demonstrates the potential value for time-consumption data analysis or high speed signal processing.