• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.6
• /
• pp.75-82
• /
• 2021

Low-temperature combustion (LTC) strategies, such as HCCI (Homogeneous Charge Compression Ignition), PCCI (Premixed Charge Compression Ignition), and RCCI (Reactivity Controlled Compression Ignition), have been developed to effectively reduce NOx and PM while increasing the thermal efficiency of diesel engines. Through numerical analysis, this study examined the effects of the injection timing and two-stage injection ratio of diesel fuel, a highly reactive fuel, on the performance and exhaust gas of RCCI engines using gasoline as the low reactive fuel and diesel as the highly reactive fuel. In the case of two-stage injection, combustion slows down if the first injection timing is too advanced. The combustion temperature decreases, resulting in lower combustion performance and an increase in HC and CO. The injection timing of approximately -60°ATDC is considered the optimal injection timing considering the combustion performance, exhaust gas, and maximum pressure rise rate. When the second injection timing was changed during the two-stage injection, considering the combustion performance, exhaust gas, and the maximum pressure increase rate, it was judged to be optimal around -30°ATDC. In the case of two-stage injection, the optimal result was obtained when the first injection amount was set to approximately 60%. Finally, a two-stage injection rather than a single injection was considered more effective on the combustion performance and exhaust gas.

• Journal of agriculture & life science
• /
• v.51 no.1
• /
• pp.187-192
• /
• 2017

In various crops, seedlings are preferred to seeds for faster and more effective growth, so transplanters are used for them. This 2-row transplanter was developed to promote the mechanization of vegetable transplanting work which depends on human power. and it can automatically supply seedling tray and transport picked up seedling to the planting hopper. Also, we judged performance of transplanter with comparing seedling missing plant ration according to two types of pick-up method. Result of experiment, in finger-type picking up of 265 seedlings, missing plant ratio was 13.7% with 17 failures of pick-up and 15 collapse of bed soil. and In fork-type picking up of 200 seedlings, failure of pick-up was not appeared and missing plant ratio was low as 4% with 6 dropped during transfer. Therefore for 2-row automatic transplanter, fork-type pick-up device was found to be compatible.

• Food Engineering Progress
• /
• v.22 no.4
• /
• pp.381-385
• /
• 2018

Various rheological methods to measure the hardness of instant cooked rice by a texture analyzer were investigated and compared. Specifically, instant white rice samples with a wide range of hardness were subjected to four different rheological tests with disk, cylinder, rod, and cone probe whose results were inter-correlated. All the measurements demonstrated that the hardness of instant rice was reduced with increasing moisture content and showed negatively linear relationships. Out of the four tests applied in this study, the highest coefficient of correlation ($R^2=0.9268$) was observed distinctly in the cone probe test, where both compressive and shear forces can be applied to deform individual rice grains. However, the cylinder probe test had the lowest coefficient of correlation ($R^2=0.7247$) because it may be ineffective in causing direct deformation of individual rice grains. Furthermore, when the hardness values (N) were converted to stress (Pa), highly linear correlations ($R^2{\approx}0.99$) were observed between the tests with similar probe geometry and force application.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.14 no.5
• /
• pp.19-30
• /
• 2018

Visual information understanding is known as one of the most difficult and challenging problems in the realization of machine intelligence. This paper proposes deriving visual verb and construction of ActionNet database as a video database for video semantic understanding. Even though development AI (artificial intelligence) algorithms have contributed to the large part of modern advances in AI technologies, huge amount of database for algorithm development and test plays a great role as well. As the performance of object recognition algorithms in still images are surpassing human's ability, research interests shifting to semantic understanding of video contents. This paper proposes candidates of visual verb requiring in the construction of ActionNet as a learning and test database for video understanding. In order to this, we first investigate verb taxonomy in linguistics, and then propose candidates of visual verb from video description database and frequency of verbs. Based on the derived visual verb candidates, we have defined and constructed ActionNet schema and database. According to expanding usability of ActionNet database on open environment, we expect to contribute in the development of video understanding technologies.

• The Journal of the Korea Contents Association
• /
• v.21 no.6
• /
• pp.35-43
• /
• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Journal of Internet Computing and Services
• /
• v.22 no.3
• /
• pp.27-35
• /
• 2021

With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.446-452
• /
• 2021

As a higher standard for food consumption is required, the consumption of chicken meat that can satisfy the subdivided food preferences is increasing. In March 2003, the quality criteria for chicken carcasses notified by the Livestock Quality Assessment Service suggested quality grades according to fecal contamination and the size and weight of blood and bruises. On the other hand, it is too difficult for human inspection to qualify mass products, which is key to maintaining consistency for grading thousands of chicken carcasses. This paper proposed the computer vision algorithm as a non-destructive inspection, which can identify chicken carcass parts according to the detailed standards. To inspect the chicken carcasses conveyed at high speed, the image calibration was involved in providing robustness to the side effect of external lighting interference. The separation between chicken and background was achieved by a series of image processing, such as binarization based on Expectation Maximization, Erosion, and Labeling. In terms of shape analysis of chicken carcasses, the features are presented to reveal geometric information. After applying the algorithm to 78 chicken carcass samples, the algorithm was effective in segmenting chicken carcass against a background and analyzing its geometric features.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.6
• /
• pp.36-42
• /
• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• Composites Research
• /
• v.35 no.2
• /
• pp.93-97
• /
• 2022

The application of infrared heating in the hot press forming of the thermoplastic composites is conducive to productivity with high-speed heating. However, high energy, high forming temperature, and high-speed heating derived from infrared heating can cause material degradation and deteriorate properties such as re-melting performance. Therefore, this study was conducted to optimize the process conditions of the hot press forming suitable for carbon fiber reinforced polyetherketoneketone(CF/PEKK) composites that are actively researched and developed as high-performance aviation materials. Specifically, the degradation mechanisms and properties that may occur in infrared high-speed heating were evaluated through morphological and thermal characteristics analysis and mechanical performance tests. The degradation mechanism was analyzed through morphological investigation of the crystal structure of PEKK. As a result, the size of the spherulite decreased as the degradation progressed, and finally, the spherulite disappeared. In thermal characteristics, the melting temperature, crystallization temperature and heat of crystallization tend to decrease as degradation progresses, and the crystal structure disappeared under long-term exposure at 460℃. In addition, the low bonding strength was observed on the degraded surface, and the bonding surfaces of PEKK did not melt intermittently. In conclusion, it was confirmed that the CF/PEKK composite material degraded at 420℃ in the infrared high-speed heating. Furthermore, the spherulite experienced morphological changes and the re-melting properties of thermoplastic materials were degraded.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.4
• /
• pp.349-357
• /
• 2021

Recently, drones are rapidly becoming common and expanding. There is a great need for diversity in whether drone flight data can be used as entertainment technology analysis data. In particular, it is necessary to check whether it is possible to analyze and utilize the flight and operation process of entertainment drones, which are developing through autonomous and intelligent methods, through data analysis and machine learning. In this paper, it was confirmed whether it can be used as a machine learning technology by using FC data in the evaluation of drones for entertainment. As a result, FC data from DJI and Parrot such as Mavic2 and Anafi were unable to analyze machine learning for entertainment. It is because data is collected at intervals of 0.1 second or more, so that it is impossible to find correlation with other data with GCS. On the other hand, it was found that machine learning technologies can be applied in the case of Fixhawk, which used an ARM processor and operates with the Nuttx OS. In the future, it is necessary to develop technologies capable of analyzing the characteristics of entertainment by dividing fixed-wing and rotary-wing flight information. For this, a model shoud be developed, and systematic big data collection and research should be conducted.