• Korean journal of communication and information
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• v.46
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• pp.9-36
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• 2009

In a competitive media environment, Internet Protocol television (IPTV) has gained appeal because of its essential characteristics, 'Internet and Interactivity'. Our study aims to explore predictors that affect IPTV-VOD adoption within the context of competing media (i.e., existing multi-channel video programming services, such as cable television and digital broadcasting satellite services). In addition, this study examines if IPTV-VOD functions as a substitute for existing multi-channel services, such as cable television and direct broadcasting satellite services. A self-report survey through face-to-face interviews was conducted in Seoul metropolitan area and other six big cities. Our findings showed that primary factors for adopting an IPTV-VOD are: age of user, subjective importance of terrestial broadcasting services, real-time terrestrial broadcasting services, and other interactive service characteristics, such as time-shifting. Also, IPTV-VOD services were found to be a substitute for existing multi-channel video programming services.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1151-1160
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• 2016

In recent years, the energy storage systems such as LiB, NaS, RFB(Redox-Flow Battery), Super- capacitor, pumped hydro storage, flywheel, CAES(Compressed Air Energy Storage) and so on have received great attention as practical solutions for the power supply problems. They can be used for various purpose of peak shaving, load leveling and frequency regulation, according to the characteristics of each ESS(energy storage system). This paper will focus at 1 MWh RFB system, which is being developed through the original technology project of energy material. The output of ESS is mainly characterized by C-rate, which means that the total rated capacity of battery will be delivered in 1 hour. And it is a very important factor in the ESS operation scheduling. There can be several options according to the operation intervals 15, 30 and 60minutes. The operation scheduling is based on the optimization to minimize the daily electricity cost. This paper analyzes the cost-saving effects by the each operating time-interval in case that the RFB ESS is optimally scheduled for peak shaving and load leveling.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5C
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• pp.479-486
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• 2010

In this paper, we propose a novel low-complexity Alamouti coded orthogonal frequency division multiplexing (OFDM) scheme for asynchronous cooperative communications. Exploiting the combination of OFDM symbols at the source node and simple operations including sign change and complex product at the relay node, the proposed scheme can achieve cooperative diversity gain without use of time-reversion and shifting operations that the conventional scheme proposed by Li and Xia needs. In addition, by using the cyclic prefix (CP) removal and insertion operations at the relay node, the proposed scheme does not suffer from a considerable degradation of bit-error-rate (BER) performance even though perfect timing synchronization is not achieved at the relay node. From the simulation results, it is demonstrated that the BER performance of the proposed scheme is much superior to that of the conventional scheme in the presence of timing synchronization error at the relay node. It is also shown that the proposed scheme obtains two times higher diversity gain compared with the conventional scheme at the cost of half reduction in transmission efficiency.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.1
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• pp.35-42
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• 1998

Whole faba beans (WFB) were dry roasted at different temperatures (110, 130, $150^{\circ}C$) for 15, 30, 45 minutes to determine the optimal heating conditions of time and temperature to increase nutritional value. Ruminant degradation characteristics of crude protein (CP) of WFB were determined by the nylon bag incubation technique in dairy cows fed 60% hay and 40% concentrate. Measured characteristics of crude protein (CP) were soluble (washable) fraction (S), undegradable fraction (U), lag time (T0), potentially degradable fraction (D) and the rate of degradation (Kd) of insoluble but degradable fraction. Based on measured characteristics, percentage bypass crude protein (%BCP) and bypass crude protein (BCP in g/kg) were calculated. Degradability of CP was reduced by dry roasting (p < 0.01). S was reduced rapidly with increasing time and temperature, from 49.0% in the raw WFB (RWFB) to 26.3% in $150^{\circ}C/45$ min. D varied from 50.7% in RWFB to 73.7% in $150^{\circ}C/45^{\prime}$. U varied from 0% in $130^{\circ}C/45^{\prime}$, $150^{\circ}/30^{\prime}$ and $150^{\circ}/45^{\prime}$ to 0.66% in $110^{\circ}/45^{\prime}$ (0.24% for the RWFB). Lag time (T0) varied from 1.58 h in $130^{\circ}C/30^{\prime}$ to 2.40 h in $150^{\circ}C/45^{\prime}$ (1.87 h for RWFB). Kd varied from 24.2% in the $110^{\circ}C/30^{\prime}$ to 4.3% in $150^{\circ}C/45^{\prime}$ (21.4% for the RWFB). Kd was significantly reduced with time and temperature. All these effects resulted in increasing % BCP from 8.9% in the $110^{\circ}C/45^{\prime}$, 11.3% in the RWFB to 43.1% in the $150^{\circ}C/45$. Therefore BCP increased from 31.3 and 39.9 to 148.4 g/kg respectively. Both %BCP and BCP at $150^{\circ}C/45$ increased nearly 4 times over the raw faba beans. The effects of dry roasting temperature and time on %BCP and BCP seemed to be linear up to the highest values tested. Therefore no optimal dry roasting conditions of time and temperature could be determined at this stage. It was concluded that dry roasting was effective in shifting crude protein degradation from rumen to intestine to reduce unnecessary nitrogen (N) loss in the rumen. To determine the optimal treatment, the digestibility of each treatment should be measured in the next trial using mobile bags technique.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.8
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• pp.1242-1251
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• 2020

Objective: The 3×3 factorial arrangement was used to investigate if either high water-soluble carbohydrates (WSC) cultivars or suitable time of day that the grass cut could improve nutrient values and in vitro fermentation characteristics. Methods: The 3 cultivars were mowed at 3 diurnal time points and included a benchmark WSC ryegrass cultivar 'Premium', and 2 high WSC cultivars AberAvon and AberMagic, which contained, on average, 157, 173, and 193 g/kg dry matter (DM) of WSC, and 36.0, 36.5, and 34.1 g/kg DM of N during 7th regrowth stage, respectively. The fermentation jars were run at 39℃ with gas production recorded and sampled at 2, 5, 8, 11, 14, 17, 22, 28, 36, and 48 h. The rumen liquid was collected from 3 rumen fistulated cows grazing on ryegrass pasture. Results: High WSC cultivars had significantly greater WSC content, in vitro DM digestibility (IVDMD) and total gas production (TGP), and lower lag time than Premium cultivar. Methane production for AberMagic cultivar containing lower N concentration was marginally lower than that for AberAvon and Premium cultivars. Grass cut at Noon or PM contained greater WSC concentration, IVDMD and TGP, and lower N and neutral detergent fiber (NDF) contents, but CH₄ production was also increased, compared to grass cut in AM. Meanwhile, the effects of diurnal cutting time were influenced by cultivars, such as in vitro CH₄ production for AberMagic was not affected by cutting time. The IVDMD and gas production per unit of DM incubated were positively related to WSC concentration, WSC/N and WSC/NDF, respectively, and negatively related to N and NDF concentrations. Conclusion: These results imply either grass cut in Noon or PM or high WSC cultivars could improve nutrient values, IVDMD and in vitro TGP, and that AberMagic cultivar has a slightly lower CH₄ production compared to AberAvon and Premium. Further study is necessary to determine whether the increase of CH₄ production response incurred by shifting from AM cutting to Noon and/or PM cutting could be compensated for by high daily gain from increased WSC concentration and DM digestibility.

• Fashion & Textile Research Journal
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• v.24 no.6
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• pp.667-685
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• 2022

This paper aims to investigate 4D printing materials for soft robots. 4D printing is a targeted evolution of the 3D printed structure in shape, property, and functionality. It is capable of self-assembly, multi-functionality, and self-repair. In addition, it is time-dependent, printer-independent, and predictable. The shape-shifting behaviors considered in 4D printing include folding, bending, twisting, linear or nonlinear expansion/contraction, surface curling, and generating surface topographical features. The shapes can shift from 1D to 1D, 1D to 2D, 2D to 2D, 1D to 3D, 2D to 3D, and 3D to 3D. In the 4D printing auxetic structure, the kinetiX is a cellular-based material design composed of rigid plates and elastic hinges. In pneumatic auxetics based on the kirigami structure, an inverse optimization method for designing and fabricating morphs three-dimensional shapes out of patterns laid out flat. When 4D printing material is molded into a deformable 3D structure, it can be applied to the exoskeleton material of soft robots such as upper and lower limbs, fingers, hands, toes, and feet. Research on 4D printing materials for soft robots is essential in developing smart clothing for healthcare in the textile and fashion industry.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.3
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• pp.358-365
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• 1999

The effects of dry roasting whole lupin seeds (lupinus albus, WLS) at 110, 130 or $150{^{\circ}C}$ for 15, 30 or 45 minutes on the in sacco rumen degradation characteristics, optimal heating conditions of time and temperature and in vitro enzyme digestibility were determined. Ruminant degradation characteristics (RDC) of crude protein (CP) of WLS were determined by in sacco technique in dairy cows. Measure ROC were soluble (S), undegradable (U), potentially degradable (D) fractions, lag time (TO) and rate of degradation (Kd) of insoluble but degradable fraction. Based on measured ROC, percentage bypass CP (%BCP) and bypass CP (BCP in g/kg, DM) were calculated. Degradability of CP was significantly reduced by dry roasting (p<0.001). The interaction of dry roasting temperature and time had significant effects on D (p<0.05), Kd (p<0.01), U (p<0.01), %BCP (p<0.001) and BCP (p<0.001) but not on S (p=0.923>0.05). With increasing time and temperature, S, D, Kd and U varied from 31.8%, 67.4%, 10.3%/h and 0.8% in the raw WLS (RWLS) to 27.1 %, 35.8%, 3.6%/h, 38.4% in $150{^{\circ}C}/45\;min$, respectively. All these effects resulted in increasing %BCP from 25.9 in RWLS to 61.0% in the $150{^{\circ}C}/45\;min$. Therefore BCP increased form 111.2 to 261.2 g/kg DM, respectively. Both %BCP and BCP at $150{^{\circ}C}/45\;min$ increased nearly 2.5 times over the RWLS. The effects of dry roasting on %BCP and BCP seemed to be linear up to the highest value tested. Although ROC had been altered by dry roasting, the In vitro perpsin-cellulase digestibility was generally unchanged. It was concluded that dry roasting was effective in shifting CP degradation from rumen to the lower gastrointestinal tract to potential reduce unnecessary N loss in the rumen. It might be of great value in successfully synchronizing the rhythms of release of nitrogen and energy in the rumen, thus achieving a more efficient fermentation of diets with high proportions of lignocellulosic resources. To determine the optimal dry roasting conditions, the digestibility of each treatment in the cows will be measured in the next trial using mobile bags technique.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.102-111
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• 2023

Automatic Music Transcription (AMT) is a task that detects and recognizes musical note events from a given audio recording. In this paper, we focus on reducing the latency of real-time AMT systems on piano music. Although neural AMT models have been adapted for real-time piano transcription, they suffer from high latency, which hinders their usefulness in interactive scenarios. To tackle this issue, we explore several techniques for reducing the intrinsic latency of a neural network for piano transcription, including reducing window and hop sizes of Fast Fourier Transformation (FFT), modifying convolutional layer's kernel size, and shifting the label in the time-axis to train the model to predict onset earlier. Our experiments demonstrate that combining these approaches can lower latency while maintaining high transcription accuracy. Specifically, our modified model achieved note F1 scores of 92.67 % and 90.51 % with latencies of 96 ms and 64 ms, respectively, compared to the baseline model's note F1 score of 93.43 % with a latency of 160 ms. This methodology has potential for training AMT models for various interactive scenarios, including providing real-time feedback for piano education.