• Journal of Fashion Business
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• v.17 no.1
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• pp.81-97
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• 2013

The purpose of this study is to summarize systematically and understand the characteristics of the production process of whole garments in order to develop knitwear using a real whole garment machine and propose this as a development case for high value added knitwear design. Concerning research methods, the study looked at existing research into whole garment knitwear and relevant data, data on websites, and the whole garment knitting machine made by Shima Seiki, a Japanese company, which has been the most commonly used machine in Korea. Also the study collected program data concerning a knitting machine and knitting by participating in the production process of whole garment knitwear, and the production line was filmed directly. In addition, the study conducted research into the development of knitwear design using the SWG-X 12 gauge. The conclusions obtained from the production process of whole garments and product development include the following. First, whole garment knitwear is appropriate for expressing a sophisticated look that makes the body appear to be in one form through natural connection without any seam allowance. Second, it is very suitable for response production since it does not go through the pattern, cutting, and processing stages. Furthermore, because of the consistent management of the entire process by computer control, it may be the highest cutting-edge fashion area in which planning and proposal style industry may be realizable. Third, it is easy to approach design through a programming process, and it is possible to develop diverse patterns; thereby, it is appropriate for producing high value added knitwear products.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.239-244
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• 2022

Neural Machine Translation (NMT) is gradually breaking down the boundary between human and machine translation. We look at actual cases of human and machine translation and discuss why machine translation needs a human touch. In this paper, we raise three driving questions: Can humans be replaced by machines?; How human translators can remain successful in a NMT-driven world?; Is it possible to eliminate language barrier in the era of NMT and World Englishes? The answers to these questions are all negative. We suggest that machine translation is a useful tool with rapidity, accuracy, and low cost productivity. However, the machine translation is limited in the areas of culture, borrowing, ambiguity, new words and (national) dialects. The machines cannot imitate the emotional and intellectual abilities of human translators since machines are based on machine learning, while humans are on intuition. The machine translation will be a useful tool that does not cause moral problems when using methods such as back translation and human post-editing. To conclude, we propose the blended approach that machine translation cannot be completed without the touch of human translation.

• Journal of Fashion Business
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• v.3 no.4
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• pp.113-129
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• 1999

The purpose of this study is to discover the changes and characteristis of modern knitwears from the 1920's to the 1990's. The result of this study is as follows: The popular knitwears in the 1920's had a simple boxy-line and modern unisex image with a sporty look. The knitwears in the 1930's-1950's had seen more short, feminine detail and texture, fit silhouette with syntheyic fibers. In the 1960's, the knitwears of sporty look and unisex style was popularized by young peaples, and crochet with handkniting by the fiber artists was popularized. In the 1970's-80's, the layered looks and unisex styles of knitwears were popularized by influence of a ecology mood and a woman movemant. And various patterns was developed with the computer machine. The items of outer knitwear was increased by the use of fancy yarns and the knitwears as the total fashion was popularized. In the 1990s, the knitwears had a very forceful characteristics by the mixed style and the technique of design developed into the post-modernism. The key words of the character of the modern knitwears are funtional pursuit, unisex, high value, hightechnique, art. By the change of the life style and the develope of technical innovation, the knitwears will be the key item to leading the fashion in the 21th.

• Archives of Plastic Surgery
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• v.36 no.1
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• pp.46-50
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• 2009

Purpose: Finger injury by green onion cutting machine is one of the common hand injuries in the kitchen. It has a unique feature: there are multiple parellel laceration 3 - 5 mm wide. There are two directions of injuries(vertical, oblique). It may involve bone, tendon, nerve, and vessel injuries. We discuss its management and the long - term progress. Methods: We have treated six patients from 2003 to 2007. We carried out low tension approximation with thin suture materials to avoid ischemia and performed the additional operation as nail bed repair, tenorrhaphy, open reduction, vessel anastomosis, and composite graft. We reviewed the record of initial injury and collected the follow - up record. Results: They were all middle aged - women who had worked in the kitchen. Right hand was dominent over left hand. The ratio of the directions was 3 : 3 (vertical : oblique). They were all competely healed although there were three atrophy, four hyperesthesia, and one nail deformity. Conclusion: Finger injury by green onion cutting machine is a unique pattern of laceration with various accompanied injuries. It may look like a severe form of injury, but in most cases have relatively favorable progress. We have to perform careful examination of accompanied injuries and carry out the proper management. First and foremost, the user especially in the middle aged women should be warned to be careful in handling this risky machine.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• Smart Media Journal
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• v.4 no.2
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• pp.26-33
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• 2015

In this paper, we propose an efficient automatic pedestrian removal system from a frame in a video sequence. It firstly finds pedestrians from the frame using a Histogram of Oriented Gradient(HOG) / Linear-Support Vector Machine(L-SVM) classifier, searches for proper background patches, and then the patches are used to replace the deleted pedestrians. Background patches are retrieved from the reference video sequence and a modified feather blender algorithm is applied to make boundaries of replaced blocks look naturally. The proposed system, is designed to automatically detect object and generate natural-looking patches, while most existing systems provide search operation in manual. In the experiment, the average PSNR of the replaced blocks is 19.246

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.211-215
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• 2012

Surgery equipment in operating room is very important at treatment procedure of patients. In this circuit of large equipment, a sudden change of current make big problem such as short circuit. when the current is converted suddenly, the current converter becomes in saturate and it caused by the induction curve of the inductor. in this case, the rates of the primary and secondary winding are broken and it becomes a open circuit. in this paper, we will look around the current transform of the primary and secondary winding when current converter becomes in saturate.

• Journal of architectural history
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• v.17 no.5
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• pp.55-69
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• 2008

One of the most notable architectural aphorisms in modern period must be undeniably Louis Sullivan's, 'Form Follows Function.' The aphorism has been not only an important source of new aesthetic but also an formal principle of machine age. Other most famous source in order to justify modern aesthetic was the short essay by an cynical critic, Adolf Loos(1870-1933), 'Ornament and Crime' of 1908. Apart from what the essay asserted it is also famous for the influence of Sullivan's architectural notion during Loos' States staying. For Architectural historians of the early 20th century this connection is so useful to create a legacy of modern architecture. The historians seemed to believe that Loos understanded Sullivan's aphorism on which the historians wanted to focused. When we however look into two buildings designed and built in the period of publishing both aphorism and essay there must be a big fissure between the buildings and the historians interpretations. With this view point this study aims at showing the true meanings of Sullivan's aphorism and Loos' essay and also the big difference between the machine age's aesthetic and theirs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.254-255
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• 2016

The Human have sought convenience through advancing Science skill, The Generation that unman control all machine have came. the unman - vehicle have used and applied flight, ship, car, manufacturing all over the world. plus which, that is researching. but pros and cons of unman - vehicle is that unman control machine, It mean that unman - vehicle have high possibility which have collision with obstacle on driving. I will show you that this evading collision will be made from fuzzy control and video recognition and sensor recognition.I look for good effect for this system.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.