• the MEAT Journal
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• s.36 summer
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• pp.61-71
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• 2009

The aim of this work was to analyze the effects of salt and NaNO2 on weight loss, proximate compositions, chemical parameters and texture characteristics of dry-cured ham processed using Korean methods. Four different treatments were considered: The H8 group of 3 hams (11.30 kg) was salted with 9.2 g/kg salt (w/w) (high salt batch), the HS+NaNO2 group of 3 hams (10.65 kg) was salted same as HS group and added 100 ppm NaNO2. The LS group of 3 hams (11.42 kg) was salted with 6.2 g/kg salt (w/w) (Low salt batch), the LS+NaNO2 group of 3 hams (10.62 kg) was salted same as L8 group and added 100 ppm NaNO2. The highest weight losses took place at the drying stage (27.46, 28.25, 26.99, and 28.42%). However, there were no significant differences in the weight losses between treatments (p>0.05). The moisture content was significantly affected with addition of NaNO2 (p<0.05), the L8 hams had significantly higher moisture content than HS + NaNO2 and L8 + NaNO2 (p<0.05). The level of salt and NaNO2 did not affect the fat, protein and ash contents. The hardness and chewiness in biceps femoris muscle from L8 hams were significantly lower than in the muscles from HS + NaNO2 hams (p<0.05). The NaNO2 did not affect the texture characteristics of dry-cured hams. The processing conditions significantly affected the chemical parameters of biceps femoris muscle (p<0.05). The water activity in biceps femoris muscle from L8 hams was significantly higher than in muscles from HS and H8+NaNO2 hams (p<0.04). The salt content in biceps femoris muscles from LS + NaNO2 hams was significantly lower than in the muscles from HS and HS + NaNO2 hams (p<0.05). The NaNO2 treatment did not affect the NaNO2 content in biceps femoris muscles (p>0.05). The processing conditions did not significantly affect the lightness (L), redness (a), and $h^{\circ}$ of biceps femoris muscles (p>0.05). The yellowness (b) and chroma in biceps femoris muscle from HS + NaNO2 hams were significantly higher than in the muscles from HS and LS hams.

• Food Science of Animal Resources
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• v.28 no.4
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• pp.493-498
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• 2008

The aim of this work was to analyze the effects of salt and $NaNO_2$ on weight loss, proximate compositions. chemical parameters and texture characteristics of dry-cured ham processed using Korean methods. Four different treatments were considered: The HS group of 3 hams (11.30 kg) was salted with 9.2 g/kg salt (w/w) (high salt batch), the HS+$NaNO_2$ group of 3 hams (10.65 kg) was salted same as HS group and added 100 ppm $NaNO_2$. The LS group of 3 hams (11.42 kg) was salted with 6.2 g/kg salt (w/w) (Low salt batch), the LS+$NaNO_2$ group of 3 hams (10.62 kg) was salted same as LS group and added 100 ppm $NaNO_2$. The highest weight losses took place at the drying stage (27.46, 28.25, 26.99, and 28.42%). However, there were no significant differences in the weight losses between treatments (p>0.05). The moisture content was significantly affected with addition of $NaNO_2$ (p<0.05), the LS hams had significantly higher moisture content than HS+$NaNO_2$ and LS+$NaNO_2$ (p<0.05). The level of salt and $NaNO_2$ did not affect the fat, protein and ash contents. The hardness and chewiness in biceps femoris muscle from LS hams were significantly lower than in the muscles from HS+$NaNO_2$ hams (p<0.05). The $NaNO_2$ did not affect the texture characteristics of dry-cured hams. The processing conditions significantly affected the chemical parameters of biceps femoris muscle (p<0.05). The water activity in biceps femoris muscle from LS hams was significantly higher than in muscles from HS and HS+$NaNO_2$ hams (p<0.05). The salt content in biceps femoris muscles from LS+$NaNO_2$ hams was significantly lower than in the muscles from HS and HS+$NaNO_2$ hams (p<0.05). The $NaNO_2$ treatment did not affect the $NaNO_2$ content in biceps femoris muscles (p>0.05). The processing conditions did not significantly affect the lightness (L), redness (a), and $h^{\circ}$ of biceps femoris muscles (p>0.05). The yellowness (b) and chroma in biceps femoris muscle from HS+$NaNO_2$ hams were significantly higher than in the muscles from HS and LS hams.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.53-58
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• 2005

The use of computers for control and monitoring of industrial process has expanded greatly in recent years. The computer used in such applications is shared between a certain number of time-critical control and monitor function and non time-critical batch processing job stream. Embedded systems encompass a variety of hardware and software components which perform specific function in host computer. Many embedded system must respond to external events under certain timing constraints. Failure to respond to certain events on time may either seriously degrade system performance or even result in a catastrophe. In the design of real-time embedded system, decisions made at the architectural design phase greatly affect the final implementation and performance of the system. Flexibility indicates how well a particular system architecture can tolerate with respect to satisfying real-time requirements. The degree of flexibility of real-time system architecture indicates the capability of the system to tolerate perturbations in timing related specifications. Given degree of flexibility, one may compare and rank different implementations. A system with a higher degree of flexibility is more desirable. Flexibility is also an important factor in the trade-off studies between cost and performance. In this paper, it is identified the need for flexibility function and shows that the existing real-time analysis result can be effective. This paper motivated the need for a flexibility for the efficient analysis of potential design candidates in the architectural design exploration or real time embedded system.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.487-498
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• 2005

Recently, It has focused on decision tree algorithm that can handle large dataset. However, because most of these algorithms for large datasets process data in a batch mode, if new data is added, they have to rebuild the tree from scratch. h more efficient approach to reducing the cost problem of rebuilding is an approach that builds a tree incrementally. Representative algorithms for incremental tree construction methods are BOAT and ITI and most of these algorithms use a local discretization method to handle the numeric data type. However, because a discretization requires sorted numeric data in situation of processing large data sets, a global discretization method that sorts all data only once is more suitable than a local discretization method that sorts in every node. This paper proposes an incremental tree construction method that efficiently rebuilds a tree using a global discretization method to handle the numeric data type. When new data is added, new categories influenced by the data should be recreated, and then the tree structure should be changed in accordance with category changes. This paper proposes a method that extracts sample points and performs discretiration from these sample points to recreate categories efficiently and uses confidence intervals and a tree restructuring method to adjust tree structure to category changes. In this study, an experiment using people database was made to compare the proposed method with the existing one that uses a local discretization.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.12
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• pp.2197-2204
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• 2000

Lignin is a major component of wastewater generated in the chemical processing of wood. Because it is recalcitrant, it inhibits biological treatment of wastewater of pulp manufacturing, especially high concentration of lignin may inhibit the anaerobic digestion. The objective of this study was to evaluate the toxicity of high molecular hardwood lignin (lignosulfonate, MW $\geq$ 20,000) on aceticlastic methanogens in the batch reactors at different temperatures with different sludge concentrations, using anaerobic serum bottles. The hardwood lignin was found to inhibit anaerobic conversion of acetate to methane and carbon dioxide, shown with a long lag-phase before methanogenesis started. The methanogens assumed not to be able to acclimate to the lignin were found to be acclimated slowly in the batch experiments, finally reaching non-toxic levels in which methane production could start. The hardwood lignin was found not to be bacteriocidal but bacteriostatic to aceticlastic methanogens. Hardwood lignin(lignosulfonate) at 1.3, 2.6, and 3.9%(w/w) inhibited the acetateutilizing methanogens of anaerobic digester sludge by 14.5, 17.8, 21.1 days(in noninhibitory condition it took 10 days) to produce the same amount of methane. The inhibitory effect of lignin was examined at temperature ranges of $30^{\circ}C$ to $50^{\circ}C$. When 2.6% of lignin was contained in wastewater, methane production was highest at $30^{\circ}C$ during initial 8 days. At $4^{\circ}C$, methane production rapidly increased after 12 days of digestion, the value became higher than that at $30^{\circ}C$ after 14 days. However, the methane production was completely inhibited during whole digestion period at $50^{\circ}C$. High ratio of lignin concentration to initial anaerobic sludge concentration gave tolerance to the inhibition. In this experiment, high molecular hardwood lignin was not degraded and decolorized.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.159-159
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• 2017

스마트 시설환경은 대표적으로 원예, 축산 분야 등 여러 형태의 농업현장에 정보 통신 및 데이터 분석 기술을 도입하고 있는 시설화된 생산 환경이라 할 수 있다. 근래에 하드웨어적으로 급증한 스마트 시설환경에서 생산되는 방대한 생육/환경 데이터를 올바르고 적합하게 사용하기 위해서는 일반 산업 현장과는 차별화 된 분석기법이 요구된다고 할 수 있다. 소프트웨어 공학 분야에서 연구된 빅데이터 처리 기술을 기계적으로 농업 분야의 빅데이터에 적용하기에는 한계가 있을 수 있다. 시설환경 내/외부의 다양한 환경 변수는 시계열 데이터의 난해성, 비가역성, 불특정성, 비정형 패턴 등에 기인하여 예측 모델 연구가 매우 난해한 대상이기 때문이라 할 수 있다. 본 연구에서는 근래에 관심이 급증하고 있는 인공신경망 연구 소프트웨어인 Tensorflow (www.tensorflow.org)와 대표적인 Open source인 OpenNN (www.openn.net)을 스마트 시설환경 환경변수 상호간 상관성 분석에 응용하였다. 해당 소프트웨어 라이브러리의 운영환경을 살펴보면 Tensorflow 는 Linux(Ubuntu 16.04.4), Max OS X(EL capitan 10.11), Windows (x86 compatible)에서 활용가능하고, OpenNN은 별도의 운영환경에 대한 바이너리를 제공하지 않고 소스코드 전체를 제공하므로, 해당 운영환경에서 바이너리 컴파일 후 활용이 가능하다. 소프트웨어 개발 언어의 경우 Tensorflow는 python이 기본 언어이며 python(v2.7 or v3.N) 가상 환경 내에서 개발이 수행이 된다. 주의 깊게 살펴볼 부분은 이러한 개발 환경의 제약으로 인하여 Tensorflow의 주요한 장점 중에 하나인 고속 연산 기능 수행이 일부 운영 환경에 국한이 되어 제공이 된다는 점이다. GPU(Graphics Processing Unit)의 제공하는 하드웨어 가속기능은 Linux 운영체제에서 활용이 가능하다. 가상 개발 환경에 운영되는 한계로 인하여 실시간 정보 처리에는 한계가 따르므로 이에 대한 고려가 필요하다. 한편 근래(2017.03)에 공개된 Tensorflow API r1.0의 경우 python, C++, Java언어와 함께 Go라는 언어를 새로 지원하여 개발자의 활용 범위를 매우 높였다. OpenNN의 경우 C++ 언어를 기본으로 제공하며 C++ 컴파일러를 지원하는 임의의 개발 환경에서 모두 활용이 가능하다. 특징은 클러스터링 플랫폼과 연동을 통해 하드웨어 가속 기능의 부재를 일부 극복했다는 점이다. 상기 두 가지 패키지를 이용하여 2016년 2월부터 5월 까지 충북 음성군 소재 딸기 온실 내부에서 취득한 온도, 습도, 조도, CO2에 대하여 Large-scale linear model을 실험적(시간단위, 일단위, 주단위 분할)으로 적용하고, 인접한 세그먼트의 환경변수 예측 모델링을 수행하였다. 동일한 조건의 학습을 수행함에 있어, Tensorflow가 개발 소요 시간과 학습 실행 속도 측면에서 매우 우세하였다. OpenNN을 이용하여 대등한 성능을 보이기 위해선 병렬 클러스터링 기술을 활용해야 할 것이다. 오프라인 일괄(Offline batch)처리 방식의 한계가 있는 인공신경망 모델링 기법과 현장 보급이 불가능한 고성능 하드웨어 연산 장치에 대한 대안 마련을 위한 연구가 필요하다.

• Applied Chemistry for Engineering
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• v.26 no.2
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• pp.234-238
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• 2015

The separation of nitrogen heterocyclic compound (NHC) contained in a model coal tar fraction was compared by the methanol and formamide extraction. The model coal tar fraction comprising four kinds of NHC (NHCs : quinoline, iso-quinoline, indole, quinaldine) and three kinds of bicyclic aromatic compound (BACs : 1-methylnaphthalene, 2-methylnaphthalene, dimethylnaphthalene), biphenyl and phenyl ether was used as a raw material. The aqueous solution of methanol and formamide were used as solvents. A batch-stirred tank was used as the raw material - a solvent contact unit of this work. Independent of the solvent used, the distribution coefficient of NHCs sharply increased by decreasing the initial volume ratio of water to the solvent and increasing the equilibrium operation temperature, whereas, the selectivity of NHCs in reference to BACs decreased. Decreasing the initial volume ratio of solvent to feed resulted in deteriorating distribution coefficients, but the selectivity of NHCs in reference to BAC was almost the constant. The distribution coefficient of NHCs by the methanol extraction was 3~5 times higher than that of NHCs by the formamide extraction, inversely, the selectivity of NHCs based on BACs by the formamide extraction was 3~7 times higher than that of NHCs by the methanol extraction. Furthermore, two different solvent extraction methods by adding the extraction processing speed to the balance between solvency and selectivity of NHCs were compared.