• Resources Recycling
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• v.29 no.6
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• pp.104-113
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• 2020

Bubble size in froth flotation has long been recognized as a key factor which affects the bubble residence time, the bubble surface area flux (Sb) and the carrying rate (Cr). This paper presents method of bubble size measurement, relationship between operating variables and gas dispersion properties in flotation column. Using high speed camera and image analysis system, bubble size has been directly measured as a function of operating parameters (e.g., superficial gas rate (Jg), superficial wash water rate (Jw), frother concentration) in flotation column. Relationship compared to measured and estimated bubble size was obtained within error ranges of ±15~20% and mean bubble size was 0.718mm. From this system the empirical relationship to control the bubble size and distribution has been developed under operating conditions such as Jg of 0.65~1.3cm/s, Jw of 0.13~0.52cm/s and frother concentration of 60~200ppm. Surface tension and bubble size decreased as frother concentration increased. It seemed that critical coalescence concentration (CCC) of bubbles was 200ppm so that surface tension was the lowest (49.24mN/m) at frother concentration of 200ppm. Bubble size tend to increase when superficial gas rate (Jg) decreases and superficial wash water rate Jw and frother concentration increase. Gas holdup is proportional to superficial gas rate as well as frother concentration and superficial wash water rate (at the fixed superficial gas rate).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.268-277
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• 2020

This study emphasizes that the survival strategy of universities should be a co-evolution strategy based on ecological thinking. Therefore, the purpose of the research is to present a theoretical framework for dividing the university innovation ecosystem into four stages and building a co-evolution strategy for each step, as universities play a prominent role in regional innovation ecosystems. Thus, our research method focused on literature research, and the theoretical framework for the university innovation ecosystem used Moore's Enterprise Ecosystem Model (1996). The university's ecological innovation strategy is divided into four stages of development, and a step-by-step co-evolution strategy is presented. Findings are summarized as follows. The pioneering stage involves the creation of values of the university-led innovation ecosystem. The expansion stage focuses on the establishment of critical mass. The authority stage covers maintaining authority and bargaining power. The renewal stage features continuous performance improvement. In particular, this theoretical model of the university-regional innovation ecosystem is meaningful in that it provides a theoretical basis for enhancing the effectiveness of government financial support projects, and for individual universities, it provides a framework for strategies suitable for their ecosystem building process.

• Journal of Marine Life Science
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• v.7 no.1
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• pp.45-51
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• 2022

The profer salinity range for aquaculture of hybrid grouper (Epinephleus bruneus♀ × Epinephleus lanceolatus♂) were evaluated in terms of their survival, growth, feed intake and physiological responses after 30 days of exposure to different salinities (0 psu, 3 psu, 5 psu, 10 psu, 15 psu, 20 psu, 25 psu, 30 psu). Hybrid grouper all died at 0 psu salinity on the 4th day of exposure but all hybrid survived at a salinity of 5 psu or higher during the exposure period. Body length and weight increased above 5 psu salinity and the growth rate was the highest at 30 psu. Hybrid exposed to 3 psu did not eat the feed pellet. Feed intake did not show a significant difference between salinity 20 psu, 25 psu and 30 psu. Plasma osmolality of the juvenile hybrids exposed to different salinities was 341~368 mg Osmol/kg above 5 psu. The oxygen consumption rate was the highest at 30 psu with 163.6±22.3 mg O₂/kg fish/h. SOD, CAT and GSH-PX were significantly higher at 15 psu. Therefore, the critical salinity for survive of the juvenile hybrid was 5 psu, and the proper salinity for optimal culture was analyzed with 20~30 psu.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.297-297
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• 2023

최근 친수성, 경관, 생태계 보전 등을 위해 다양한 호안블록의 시공이 이루어지고 있어 호안블록의 수리적 안정성에 관한 관심이 증가하고 있다. 이러한 배경 하에 한국건설기술연구원 안동하천실험센터(이하 센터)에서는 2019년부터 실규모 수로를 이용하여 여러 건의 호안블록 실규모 수리검토 실험을 실시한 바 있다. 본 연구는 그간의 실험 결과를 종합적으로 고찰하고 수리 검토 실험의 현황과 한계점, 그리고 개선 방향을 제언하는데 그 목적이 있다. A1 수로(급경사수로, 하상경사 1/70)에서는 7건(21회), B1 수로(고유속수로, 하상경사 1/7)에서는 2건(6회)의 실험이 수행되었다. A1 수로 실험의 유량-소류력 관계는 1.0 m³/s에서 약 20 N/m²이며, 1.0 m³/s 증가당 약 11 N/m²이 증가하는 관계를 나타낸다. 7건의 실험 결과 30분 이상 지속된 최대 실험 유량은 6~7 m³/s 정도이며, 이는 A1 최대 공급 유량의 75 % 정도로서 안정적인 수준이라고 판단된다. 이 때의 최대 소류력은 75 N/m² 정도로 나타났다. B1 수로는 5 m/s 이상의 고유속 흐름을 발생시킬 수있으며, 2건의 실험 결과 0.5 m³/s에서 약 100 N/m², 최대 4.5 m³/s에서 330 N/m²까지 소류력을 제공하여 실험을 수행한 바 있다. 따라서 A1, B1 수로를 통해 제공할 수 있는 소류력 범위는 10~330 N/m²이지만, 75~100 N/m²는 실험에서 제공된 바 없었다. 한편, 토양유실의 경우 수준측량에 의해 측정되는데, 대부분의 실험에서 Clopper의 토양손실 지수(1.27 cm) 미만의 결과가 발생하였다. 이는 시험체에 따라 여건이 다르기는 하지만, 수리 검토 실험시 3회 실험을 기본으로 하고 있고 호안재료의 침식이 기준 이하로 유지되면서 최대한의 성능을 발휘할 수 있는 소류력 조건을 얻으려는 실험 목적에 부합하도록 조절된 것으로 볼 수 있다. 이러한 실험 결과를 토대로 고려해볼 수 있는 개선 방향은 다음과 같다. 강성 재료가 아닌 연성 또는 친환경적 호안재료의 허용 소류력 범위를 보다 넓게 평가하기 위해 A1 수로가 제공하는 최대 소류력을 높일 필요가 있다. 이를 위해 기본 3회의 실험 외에 추가로 호안블록이 파괴되거나 토양유실 임계치를 초과할 수 있는 실험을 수행함으로써 각 제품의 한계 성능을 평가하는 것이 필요할 것으로 보인다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.485-492
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• 2008

This study presents a viscoelastic characterization of flexible pavement subjected to moving loads. A series of field tests have been conducted on three pavement sections (A2, A5, and A8) at the Korea Expressway Corporation (KEC) test road. The effect of vehicle speed on the responses of each test section was investigated at three speeds: 25 km/hr, 50 km/hr, and 80 km/hr. During the test, both longitudinal and lateral strains were measured at the bottom of asphalt layers and in-situ measurements were compared with the results of finite element (FE) analyses. A commercial FE package, ABAQUS was used to model each test section and a step loading approximation has been adopted to simulate the effect a moving vehicle. For viscoelastic analysis, relaxation moduli of asphalt mixtures were obtained from laboratory test. Field responses reveals the strain anisotropy (i.e., discrepancy between longitudinal and lateral strains) and the amplitude of strain normally decreases as the vehicle speed increases. In most cases, lateral strain was smaller than longitudinal strain, and strain reduction was more significant in lateral direction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6C
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• pp.251-258
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• 2009

In this study, the optimum design conditions for embankment construction on soft clay layer improved by soil compaction pile (SCP) are discussed by comparing the practical design method to the reliability design which is based on the loss function and advanced first order second moment (AFOSM) method. The results are summarized as follows; 1) the relationship between safety factor and failure probability becomes heavy exponentially, failure probability decreases rapidly till 1% approximately until safety factor is smaller than 1.2 and after then, failure probability decrease gradually along the increase of the safety factor. The design safety factor of 1.2 may be the critical value that has been established on considering both relationships appropriately, 2) the safety factor of 1.15 at the minimum expected total cost is a little smaller than the design safety factor of 1.2 and the failure probability is about 1%, 3) the sensitivities of the ratio of stress share and the internal friction angle of sand is larger than the variables related the undrained shear strength of soft layer. This result means that the distribution characteristic of n and ${\phi}$ influences on the stability analysis considerably and they should be considered necessarily on stability analysis of embankment on soft layer improved by SCP, 4) new failure points of the input variables at the design safety factor of 1.2(below failure probability of 0.1~0.3%) is far 1~2 times of standard deviation from the initial design values of themselves.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.69-78
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• 2024

Recently, the use of CCTV to prevent crowd accidents has been promoted, but research is needed to compensate for the spatial limitations of CCTV. In this study, pedestrian density was measured using drone footage, and based on a review of existing literature, a threshold of 6.7 people/m² was selected as the cutoff risk level for crowd accidents. In addition, we conducted a preliminary study to determine drone parameters and found that the pedestrian recognition rate was high at a drone altitude of 20 meters and an angle of 60°. Based on a previous study, we selected a target area with a high concentration of pedestrians and measured pedestrian density, which was found to be 0.27~0.30 per m². The study shows it is possible to measure risk levels by determining pedestrian densities in target areas using drone images. We believe drone surveillance will be utilized for crowd safety management in the near future.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.77-83
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• 2009

This paper proposes a new noise-biased compensation of minimum statistics(MS) method using a nonlinear function and a priori speech absence probability(SAP) for speech enhancement in non-stationary noisy environments. The minimum statistics(MS) method is well known technique for noise power estimation in non-stationary noisy environments. It tends to bias the noise estimate below that of true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function and a priori speech absence probability (SAP) for biased compensation. Specifically. we apply the adaptive parameter according to the a posteriori SNR. In addition, when the a priori SAP equals unity, the adaptive biased compensation factor separately increases ${\delta}_{max}$ each frequency bin, and vice versa. We evaluate the estimation of noise power capability in highly non-stationary and various noise environments, the improvement in the segmental signal-to-noise ratio (SNR), and the Itakura-Saito Distortion Measure (ISDM) integrated into a spectral subtraction (SS). The results shows that our proposed method is superior to the conventional MS approach.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.355-373
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• 2024

The Generalized Hoek-Brown (GHB) criterion is a nonlinear failure criterion specialized for rock engineering applications and has recently seen increased usage. However, the GHB criterion expresses the relationship between minimum and maximum principal stresses at failure, and when GSI≠100, it has disadvantage of being difficult to express as an explicit relationship between the normal and shear stresses acting on the failure plane, i.e., as a Mohr failure envelope. This disadvantage makes it challenging to apply the GHB criterion in numerical analysis techniques such as limit equilibrium analysis, upper-bound limit analysis, and the critical plane approach. Consequently, recent studies have attempted to express the GHB Mohr failure envelope as an approximate analytical formula, and there is still a need for continued interest in related research. This study presents improved formulations for the approximate GHB Mohr failure envelope, offering higher accuracy in predicting shear strength compared to existing formulas. The improved formulation process employs a method to enhance the approximation accuracy of the tangential friction angle and utilizes the tangent line equation of the nonlinear GHB failure envelope to improve the accuracy of shear strength approximation. In the latter part of this paper, the advantages and limitations of the proposed approximate GHB failure envelopes in terms of shear strength prediction accuracy and calculation time are discussed.

• 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.