• Journal of the Korean Wood Science and Technology
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• v.14 no.1
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• pp.3-44
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• 1986

In order to obtain the basic physical and mechanical properties of steel wire reinforced particleboard, particleboards were formed with large particles through 2.11 mm (12 meshes) and retained on 1.27mm (20 meshes) sieves and small particles through 1.27mm (20 meshes) and retained on 0.42mm (60 meshes) sieves from the plywood mill wastes of meranti (Shorea spp.) in the form of pallmanchips, applying urea-formaldehyde resin as an adhesive on the particle surface in 10 percent on the oven dried weight of particles, and arranging steel wires of 1mm in diameter 5,10,15,20, and 25mm in longitudinal and transverse direction with crossing in the mid of the board depth in single layer boards, 10mm in longitudinal or transverse direction without crossing in two layers and 10mm in longitudinal and transverse directions with and without crossing in three steel wire layers boards. The stepwise 9-minutes-multi-pressing schedule in 5 minutes at 35 kgf/$cm^2$, 2.5 minutes at 25 kgf/$cm^2$. and 1.5 minutes at 15 kgf/$cm^2$ was applied for $300{\times}200{\times}13$mm board at the temperature of 160$^{\circ}C$ in a hot press. Specific gravity, thickness swelling, bending properties of modulus of rupture (MOR), modulus of elasticity(MOE), work to proportional limit, and work to ultimate load, internal bond (IB), and screw holding power(SHP) of the reinforced boards were analyzed on the wire openings and wire layers. The results obtained are summarized as follows; 1) In specific gravity, particleboards with large particles and small particles had higher value with more steel wire placements and more steel layers composition, 2) Particleboards with large particles in accordance with more steel wire liners composition gave very poor thickness swelling. 3) The mechanical properties of particleboards formed with large or small particles were reinforced with more steel wire layers. Therefore, bending strength was improved in modulus of rupture, modulus of elasticity, and work to ultimate load. Especiallv, particleboards with two or three steel wire layers showed the tension lamination effect when the steels in lower steel wire layer were oriented parallel to the board length. 4) The modulus of rupture, modulus of elasticity, and work to ultimate load in bending varied with opening area, distance of lengthwise wires multipled by distance of transverse wires. Particleboards formed with large particles resulted in higher value in modulus of rupture with 1.5-3 $cm^2$ opening area, 1-2cm distance between transverse wires, and 1.5-2.5cm distance between lengthwise wires. Particle boards formed with small particles showed higher value with 0.5-1.5$cm^2$ or 3.75-6.25 $cm^2$ opening area, 0.5 or 2.5cm distance between transverse wires. 5) In modulus of elasticity, particleboards formed with large particles with one steel wire layer suggested higher value with 5-3$cm^2$ opening area, 1-2.5cm distance between transverse wires and also 1-2.5 cm distance between lengthwise wires. Particleboards formed with small particles showed higher value with 0.75-1.25$cm^2$ or 3-6.25$cm^2$ opening area and 0.5 or 2.5cm distance between transverse wires. 6) Particleboards formed with large particles gaved higher value in work to ultimate load with 1-3$cm^2$ opening area. Particleboards formed with small particles showed increasing tendancy with decreasing opening area. 7) In internal bond and screw holding power, particleboards formed with large particles had increasing value in two and three steel wire layers compositions, but particleboards formed with small particles showed no difference. Particleboards formed with large particles containing one steel wire layer showed no difference in internal bond and screw holding power, and particleboards formed with small panicles containing one steel wire layer resulted in increasing value in internal bond and decreasing value in screw holding power in accordance with increase in opening area.

• Journal of Korean Society of Forest Science
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• v.59 no.1
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• pp.67-91
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• 1983

In order to examine the alcohol production from softwoods (Pinus densiflora Sieb. et Zucc., Pinus rigida Miller, Larix leptolepis Gordon) and hardwoods (Alnus japonica Steud., Castanea crenata Sieb. et Zucc. Populus euramericana CV 214), chemical compositions were analyzed and conditions of acid hydrolysis with wood meals were established. Also strains which could remarkably decompose the cellulose were identified, and conditions of cellulase production of strains, characteristics of cellulase, and alcohol fermentation were examined. The results were summarized as follows. 1) In acid hydrolysis of wood, the high yield of reducing sugars was shown from 1.0% to 2.0% of hydrochloric acid and 2.0% of sulfuric acid. The highest yield was produced 23.4% at wood meals of Alnus japonica treated with 1.0% of hydrochloric acid. 2) The effect of raising the hydrolysis was good at $1.5kg/cm^2$, 30 times (acid/wood meal), and 45 min in treating hydrochloric acid and 30 min in treating sulfuric acid. 3) The pretreatments with concentrated sulfuric acid were more effective concentration ranged from 50% to 60% than that with hydrochloric acid and its concentration ranged from 50% to 60%. 4) The quantative analysis of sugar composition of acid hydrolysates revealed that glucose and arabinose were assayed 137.78mg and 68.24mg with Pinus densiflora, and 102.22mg and 65.89mg with Alnus janonica, respectively. Also xylose and galactose were derived. 5) The two strains of yeast which showed remarkably high alcohol productivity were Saccharomyces cerevisiae JAFM 101 and Sacch. cerevisiae var. ellipsoldeus JAFM 125. 6) The production of alcohol and the growth of yeasts were effective with the neutralization of acid hydrolysates by $CaCO_3$ and NaOH. Production of alcohol was excellent in being fermented between pH 4.5-5.5 at $30^{\circ}C$ and growth of yeasts between pH 5.0-6.0 at $24^{\circ}C$. 7) The production of alcohol was effective with the addition of 0.02% $(NH_2)_2CO$ and $(NH_4)_2SO_4$, 0.1% $KH_2PO_4$, 0.05% $MgSO_4$, 0.025% $CaCl_2$, 0.02% $MnCl_2$. Growth of yeasts was effective with 0.04-0.06% $(NH_2)_2CO$ and $(NH_4)_2SO_4$, 0.2% $K_2HPO_4$ and $K_3PO_4$, 0.05% $MgSO_4$, 0.025% $CaCl_2$, and 0.002% NaCl. 8) Among various vitamins, the production of alcohol was effective with the addition to pyridoxine and riboflavin, and the growth of yeasts with the addition to thiamin, Ca-pantothenate, and biotin. The production of aocohol was increased in 0.1% concentration of tannin and furfural, but mas decreased in above concentration. 9) In 100ml of fermented solution, alcohol and yeast were produced 2.201-2.275ml and 84-114mg for wood meals of Pinus densiflora, and 2.075-2.125ml and 104-128mg for that of Alnus japonica. Residual sugars were 0.55-0.60g and 0.60-0.65g for wood meals of Pinus densiflora and Alnus japonica, respectively, and pH varied from 3.3 to 3.6. 10) A strain of Trichoderma viride JJK. 107 was selected and identified as its having the highest activity of decomposing cellulose. 11) The highest cellulase production was good when CMCase incubated for 5 days at pH 6.0, $30^{\circ}C$ and xylanase at pH 5.0, $35^{\circ}C$. The optimum conditions of cellulase activity were proper in case of CMCase at pH 4.5, $50^{\circ}C$ and xylanase at pH 4.5, $40^{\circ}C$. 12) In fermentation with enzymatic hydrolysates, the peracetic acid treatment for delignification showed the best yields of alcohol and its ratio was effective with the addition of about 10 times. 13) The production of alcohol was excellent when wood meals and Koji of wheat bran was mixed with 10 to 8 and the 10g of wood meals of Pinus densiflora produced 2.01-2.14ml of alcohol and Alnus japonica 2.11-2.20ml.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Korean Society of Forest Science
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• v.42 no.1
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• pp.83-100
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• 1979

It was planned and performed to study the possibility on the use of inexpensive and easily acquirable foliage powder, which processed by pulverizing after dried, instead of imported expensive wheat flour for the extending of plywood adhesives. Pine leaves of softwood trees, Poplar, Oak and Sycamore leaves of broad leaved species were selected and harvested to pulverize into the minute foliage powder. The harvested foliages from each selected species were pulverized into 40 mesh particles after dried at $100{\sim}105^{\circ}C$ condition during 24 hours in drying oven. To compare the extending effect of plywood adhesives with these foliage powders 100 mesh wheat flour using at current plywood industry was also prepared. Foliage powder and wheat flour were extended into 10, 20, 30, 50 and 100% to the urea and phenol formaldehyde resin. After plywoods were processed by the above extending method shear strength of extended plywoods were analyzed and discussed. The results obtained at this study are as follows: 1) Among 10% extensions of urea formaldehyde resin plywood, dry shear strength of plywood extended by wheat flours was the highest and that of non-extended plywood the next. Plywood extended with foliage powder showed the lowest dry shear strength. The order of dry shear strength of plywoods extended by foliage powder was that of Oak foliage powder extension, the best, that of Sycamore, that of Pine, and that of Poplar. 2) Among 20% extensions of urea formaldehyde resin plywood, plywood extended by wheat flour showed the highest dry shear strength, and the next was plywood by Poplar foliage powder. All these two showed higher dry shear strength than non-extension plywoods. Except Poplar, dry shear strength of foliage powder extension plywoods was bad, but the order of dry shear strength of plywoods extended by foliage powder was Pine, Poplar and Oak. 3) In the case of 30% extensions of urea formaldehyde resin plywood, dry shear strength of wheat flour extension was the highest and non-extension the next. Dry shear strength of foliage powder extension plywoods was poor with a rapid falling-off in strength. 4) Among 50% and 100% extensions of urea formaldehyde resin plywood, only wheat flour showed excellent dry shear strength. In the case of foliage powder extension, low dry shear strength showed at the 50% extension of Pine and Poplar, and plywoods of 50% extension of Oak foliage powder delaminated without measured strength. All plywoods of 100% foliage powder extension delaminated, and then shear strength were not measured. 5) Among wet shear strength of 10% extensions of urea formaldehyde resin plywood, wheat flour extension was the highest as in the case of dry shear strength, and non-extension plywood the next. Except Poplar foliage extension, all foliage powder extension plywoods showed low shear strength. 6) Wet shear strength of plywoods of 20% extension lowered in order of non-extension plywood, plywood of wheat flour extension and plywood of foliage powder extension, but other plywoods of foliage powder extension except plywoods of Poplar and Oak foliage powder extension delaminated. 7) Wet shear strength of 30% or more extension of urea formadehyde resin plywood were weakly measured only at 30% and 50% extension of wheat flour, and wet shear strength of plywoods extended by foliage powder were not measured because of delaminating. 8) Dry shear strength of phenol formaldehyde plywoods extended by 10% wheat flour was the best, and shear strength of plywoods extended by foliage powder were low, but the order was Oak, Poplar, and Pine. Plywood of Sycamore foliage powder extension delaminated. 9) In the case of 20% extensions of phenol formaldehyde resin, dry shear strength of plywood extended by wheat flour was the best, but plywood of Pine foliage powder extension the next, and the next order was Oak and Poplar foliage powder. Plywood of Sycamore foliage powder extension delaminated. 10) Among dry shear strength of 30% extensions of phenol formaldehyde plywood, that of Pine foliage powder extension was on the rise and more excellent than plywood of wheat flour extension, but Poplar and Oak showed the tendency of decreasing than the case of 20% extension. Plywood of Sycamore foliage powder extension delaminated. 11) While dry shear strength of 50% and 100% extension plywoods were excellent in the case of Pine foliage powder and wheat flour extension, that of hardwood such as Poplar, Oak, and Sycamore foliage powder extension were not measured because of delaminating. 12) As a filler the foliage powder extension of urea formaldehyde resin is possible up to 20% with Poplar foliage powder. And also as an extender for phenol formaldehyde resin, Pine foliage powder can be added up to the same amount as that in the case of wheat flour.