The objective of this experiment was to investigate the effect of drip irrigation volume on tomatoes (Solanum lycopersicum L.) grown in a greenhouse using perlite medium. Plants were treated by three different irrigation treatment I0, I25, and I50 (where irrigation volume of I25 and I50 was 25% and 50% higher than I0, having limited or no leaching). Growth characteristics of plants, yield and water use efficiency were measured. The result showed that plant height, leaf length and leaf width were lowest in the I0 treated plants. However, these parameters were not statistically significant differences between the plants that were grown in the I25 and I50 treatment. Soluble solids content, acidity and dry matter of 111th, 132nd, and 143rd days harvested tomato were higher in the plants irrigated with lowest volume (I0) than the higher volume (I25 or I50). In addition, water content was lower in the 111th and 132nd days of harvested tomatoes from the I0 treatment. The number of big-size tomatoes (>180 g) was significantly higher in the I25 irrigated plants. There was no significant difference in the total number of harvested fruits among the treatments. The average fruit weight and total yield of harvested tomatoes were lowest in the I0 treated plants. The water consumption of tomato was not significantly different amongst the treatments but water use efficiency was lowest in the I0 treatment. Principal component analysis revealed that total soluble solid and acidity of tomato showed a positive correlation between each other. These results suggest that I25 was the optimum irrigation treatment for tomato based on its measured growth characteristics, yield and water use efficiency.

The PoN (Proof of Nonce) distributed consensus algorithm basically uses a non-competitive consensus method that can guarantee an equal opportunity for all nodes to participate in the block generation process, and this method was expected to resolve the first trilemma of the blockchain, called the decentralization problem. However, the decentralization performance of the PoN distributed consensus algorithm can be greatly affected by the network transaction transmission delay characteristics of the nodes composing the block chain system. In particular, in the consensus process, differences in network node performance may significantly affect the composition of the congress and committee on a first-come, first-served basis. Therefore, in this paper, we presented a problem by analyzing the decentralization performance of the PoN distributed consensus algorithm, and suggested a fairness control algorithm using a learning-based probabilistic acceptance rule to improve it. In addition, we verified the superiority of the proposed algorithm by conducting a numerical experiment, while considering the block chain systems composed of various heterogeneous characteristic systems with different network transmission delay.

In recent years, importance of blockchain systems has been grown after success of bitcoin. Distributed consensus algorithm is used to achieve an agreement, which means the same information is recorded in all nodes participating in blockchain network. Various algorithms were suggested to resolve blockchain trilemma, which refers conflict between decentralization, scalability, security. An algorithm based on Byzantine Agreement among Decentralized Agents (BADA) were designed for the same manner, and it used limited committee that enables an efficient consensus among considerable number of nodes. In addition, election of committee based on Proof-of-Nonce guarantees decentralization and security. In spite of such prominence, application of BADA in actual blockchain system requires further researches about performance and essential features affecting on the performance. However, performance assessment committed in real systems takes a long time and costs a great deal of budget. Based on this motivation, we designed and implemented a simulator for measuring performance of BADA. Specifically, we defined a simulation framework including three components named simulator Command Line Interface, transaction generator, BADA nodes. Furthermore, we carried out response surface analysis for revealing latent relationship between performance measure and design parameters. By using obtained response surface models, we could find an optimal configuration of design parameters for achieving a given desirable performance level.

Recently, a lots of studies have been conducted to imitate real meat with the factors including texture, aroma, taste, and color of meat analogues. Since the color component of meat is diverse and complex, as well as large of degree of change according to cooking, the color imitating has been still challenging. In this study, we selected up a representative meat analogue (tteok-galbi), tracked the color change at three making points as steps of batter, steaming, and cooking. In order to realize the color at these points, three plant-based natural pigments including prickly pear cactus, sweet pumpkin, and mugwort were selected, and the color with optimized ratio was applied to the tteok-galbi. As a result, it was found that 1:3:1 in the batter, 2:1:2 in steaming, and 2:1:2 in cooking were optimal ratios. The results in this study were significant in that it could apply the colors to meat analogues according to the combination of natural pigments derived from plants.

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

Process mining is an analytical technique aimed at obtaining useful information about a process by extracting a process model from events log. However, most existing process models are deterministic because they do not include stochastic elements such as the occurrence probabilities or execution times of activities. Therefore, available information is limited, resulting in the limitations on analyzing and understanding the process. Furthermore, it is also important to develop an efficient methodology to discover the process model. Although genetic process mining algorithm is one of the methods that can handle data with noises, it has a limitation of large computation time when it is applied to data with large capacity. To resolve these issues, in this paper, we define a stochastic process tree and propose a tabu search-genetic process mining (TS-GPM) algorithm for a stochastic process tree. Specifically, we define a two-dimensional array as a chromosome to represent a stochastic process tree, fitness function, a procedure for generating stochastic process tree and a model trace as a string of activities generated from the process tree. Furthermore, by storing and comparing model traces with low fitness values in the tabu list, we can prevent duplicated searches for process trees with low fitness value being performed. In order to verify the performance of the proposed algorithm, we performed a numerical experiment by using two kinds of event log data used in the previous research. The results showed that the suggested TS-GPM algorithm outperformed the GPM algorithm in terms of fitness and computation time.

분재는 우리나라의 주요 수출 임산물이다. 외국으로의 분재수출 요구도는 증가하고 있지만 수출을 위해서는 검역적 위해 요소가 제거 되어 야 하는데 식물기생선충도 검역의 중요 대상이다. 우리나라 6개 분재원에서 활엽수 분재[단풍나무(Acer palmatum), 백일홍(Zinnia elegans), 쥐똥나무(Ligustrum obtusiflium), 소사나무(Carpinus turcaninowii), 애기사과(Malus sieboldii)]를 대상으로 발생하는 선충을 조사하였다. 세 종의 식물기생선충[미국검선충(Xiphinema americanum), 둥근꼬리붙이나선선충(Rotylenchus blothrotylus), 한국껍질선충(Hemicycliophora koreana)] 과 세 종의 비기생 선충 [등화육각창선충(Aporclaimellus donghwaens), 둥근꼬리붙이나선선충(Egtitus andhricus), 계룡중간창선충(Mesodorylaimus usitatus)]들이 분재로부터 분리되었다. 비기생선충인 계룡중간창선충은 모든 조사 수종에서 검출되었고, 미국과 EU연합 검역대상 선충으로는 한국껍질선충과 미국검선충의 두 종만 발견되었다. 등화육각창선충과 계룡중간창선충은 분재목 뿌리에서 분리되었고, 나머지 선충들은 토양에서 분리되었다.

Recently, blockchain technology has been recognized as one of the most important issues for the 4th Industrial Revolution which can be represented by Artificial Intelligence and Internet of Things. Cryptocurrency, named Bitcoin, was the first successful implementation of blockchain, and it triggered the emergence of various cryptocurrencies. In addition, blockchain technology has been applied to various applications such as finance, healthcare, manufacturing, logistics as well as public services. Distributed consensus algorithm is an essential component in blockchain, and it enables all nodes belonging to blockchain network to make an agreement, which means all nodes have the same information. For example, Bitcoin uses a consensus algorithm called Proof-of-Work (PoW) that gives possession of block generation based on the computational volume committed by nodes. However, energy consumption for block generation in PoW has drastically increased due to the growth of computational performance to prove the possession of block. Although many other distributed consensus algorithms including Proof-of-Stake are suggested, they have their own advantages and limitations, and new research works should be proposed to overcome these limitations. For doing this, above all things, we need to establish an evaluation method existing distributed consensus algorithms. Based on this motivation, in this work, we suggest and analyze assessment items by classifying them as efficiency and safety perspectives for investigating existing distributed consensus algorithms. Furthermore, we suggest new assessment criteria and their implementation methods, which can be used for a baseline for improving performance of existing distributed consensus algorithms and designing new consensus algorithm in future.

Polyphenol profiles, physicochemical properties, antioxidant activities, and inhibitory effect of adipocyte differentiation of Houttuynia cordata fermented with Lactobacillus brevis B84 were evaluated. Six polyphenols were characterized for this plant by using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), and the results were compared with total phenolic content by a spectrophotometric method. The total amount of the identified polyphenols was lower than that determined by the spectrophotometric method. However, the fermentation process influenced polyphenol composition such as content of vanillic acid and caffeic acid. The phytochemical profiles were evaluated by high-performance liquid chromatography with UV and electrospray ionization mass spectrometry detection (HPLC-DAD-ESI-MSn). Total sugar and reducing sugar contents decreased after fermentation. Antioxidant activities such as DPPH, ABTS, and superoxide anion radical scavenging and reducing power were evaluated to compare the beneficial effect after fermentation. Fermented H. cordata increased the lipolytic effect in 3T3-L1 adipocytes. Overall, the results indicate that the fermentation of H. cordata with L. brevis B84 produces changes of phenolic compounds, antioxidant activity, and lipolytic effect.

개미 사육 및 관찰을 위한 사용되는 투명 젤리형 개미사육장은 간편한 활용성으로 아동의 자연관찰 학습용 교구로 꾸준히 애용되고 있다. 하지만 투명 젤리는 다량의 수분 함유 문제뿐만 아니라 부패방지를 위해 단백질을 포함하고 있지 않기에 사육 시 단백질을 필요로 하는 산란용 여왕개미나 애벌레의 사육이 불가능하다는 단점이 있다. 이에 본 연구는 산란용 여왕개미와 애벌레의 사육이 가능한 단백질 함유 곤충 젤리를 만드는 방법을 제안하고자 한다. 단백질 공급원으로는 우유를, 부패 방지제로는 천연물 기반 키토산을 사용하였으며, 기능이 첨가된 사육용 젤리의 항균성은 곤충 대량폐사 질병인 물렁병의 원인균주(Serratia marcescens)로 테스트(in vitro)하였다. 실험군인 곤충사육용 젤리는 박토아가(1.5%,w/v)가 함유된 키토산수용액(1%,w/v)에 1:1 용량비율로 우유를 섞어 제조하며, 대조군은 키토산수용액을 제외하였다. 제조 배지는 Serratia marcescens를 도말 배양하여 효과성을 검증하였다. 그 결과 3배 희석한 균주(4.8×106CFU/ml)를 접종 후 2일간 배양한 그룹에서 대조군(키토산 없음)은 균이 많이(TNTC) 자랐으나, 실험군(키토산 있음)은 전혀 자라지 않았다. 따라서 위 실험은 키토산을 첨가하는 간단한 방법으로도 유해균의 생장 억제와 유충의 성장 촉진이 동시에 가능한 기능성 단백질 곤충젤리의 제조 가능성을 확인하였다.

OLED Display fabrication system is one of the most complicated discrete processing systems in the world. As the glass size grows from 550×650mm to 1,500×1,850mm in recent years, the efficiency of Automated Material Handling System (AMHS) has become very important and OLED glass manufacturers are trying to improve the overall efficiency of AMHS. Aiming to meet the demand for high efficiency of transportation, various kind of approaches have been applied for improving dispatching rules and facility layout, while simultaneously considering the system parameters such as glass cassettes due date, waiting time, and stocker buffer status. However, these works did not suggest the operational policy and conditions of distribution systems, especially for handling unnecessary material flows such as detour. Based on this motivation, in this paper, we proposed an efficient algorithm for improving detour transportation in OLED FAB. Specifically, we considered an OLED FAB simplifying OLED production environment in a Korean company, where four stockers are constructed for the delivery of Lot in a bay and linked to processing equipments. We developed a simulation model using Automod and performed a numerical experiment using real operational data to test the performance of three operation policies under considerations. We showed that a competitive policy for assigning alternative stocker in case of detour was superior to the current dedicated policy using a specified stocker and other considered policies.

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

The aim of the study was to investigate the effects of incentive spirometry and Ujjayi breathing technique on the pulmonary function of smokers. Subjects were individuals who had a smoking habit of at least a year. Subjects were randomly divided into 3 groups: the incentive spirometry group (n=8), Ujjayi breathing technique (n=9), and a group applying both incentive spirometry as well as Ujjayi breathing technique (n=8). Each intervention was performed twice a day, 5 times a week, for a total of 8 weeks whereupon the change in pulmonary function was evaluated. A spirometer was used to measure FVC, FEV1, and FEV1/FVC. The survey used for this study included the Fagerström Test of Nicotine Dependence (FTND) and the Shortness of Breath Questionnaire (SOBQ). Study results for the comparison within groups showed that in the group that performed both the incentive spirometry and Ujjayi breathing technique, FEV1 improved with statistical significance (p<.05). Furthermore, within this comparison the FEV1/FVC improved with statistical significance. Comparison amongst the groups showed no statistically significant differences in all areas. Following, to effectively increase pulmonary function in young adult smokers, both incentive spirometry and Ujjayi breathing technique should be employed together.

In the painting process of automotive factory, color changeover cost is incurred every time the color of vehicle is changed. To solve this problem, automotive company usually uses storage space such as Selectivity Banks(SB) or Car Rescheduling Storage and carries out sequence planning so that vehicles of the same color are consecutive, which is called Car Resequencing Problem (CRP). So far, research works for CRP has focused on algorithms finding optimal or approximated optimal solutions under the condition that the number of vehicles is fixed in SB. However, these results cannot be directly applied to the actual automotive paint shops since they have continuous flows of cars into SB to be handled in a day. Therefore, in this paper, we propose an efficient cyclic scheduling method that starts the painting process using the result of Accelerated Dynamic Programming (ADP) and then reapplies the ADP to the vehicles in SB for renewing the painting schedule whenever a certain number of vehicles is painted, represented as a threshold. To show the effectiveness of the proposed method, we performed a numerical experiment by designing system configurations, based onthe actual vehicle painting process, and proposed a good threshold that can reduce overall color changeover cost.

The global small and mid-sized display market is changing from thin film transistor-liquid crystal display to organic light emitting diode (OLED). Reflecting these market conditions, the domestic and overseas display panel industry is making great effort to innovate OLED technology and incease productivity. However, current OLED production technology has not been able to satisfy the quality requirement levels by customers, as the market demand for OLED is becoming more and more diversified. In addition, as OLED panel production technology levels to satisfy customers’ requirement become higher, product quality problems are persistently generated in OLED deposition process. These problems not only decrease the production yield but also cause a second problem of deteriorating productivity. Based on these observations, in this study, we suggest TRIZ-based improvement of defects caused by glass pixel position deformation, which is one of quality deterioration problems in small and medium OLED deposition process. Specifically, we derive various factors affecting the glass pixel position shift by using cause and effect diagram and identify radical reasons by using XY-matrix. As a result, it is confirmed that glass heat distortion due to the high temperature of the OLED deposition process is the most influential factor in the glass pixel position shift. In order to solve the identified factors, we analyzed the cause and mechanism of glass thermal deformation. We suggest an efficient method to minimize glass thermal deformation by applying the improvement plan of facilities using contradiction matrix in TRIZ. We show that the suggested method can decrease the glass temperature change by about 23% through an experiment.