• 한국정밀공학회지
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• 제30권9호
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• pp.893-899
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• 2013

Recently, the requirement of automation in the cell production system is increasing due to a decrease of skilled workers who are the key point of a cell production system. This paper proposes a dual-arm robot designed and implemented with consideration of being applied to a cell production line of cellular phone. A specification was derived from the analysis of production process and the consideration of configuration for human-robot cooperation. Design and implementation results of the proposed dual-arm robot were suggested and the feasibility was verified through the demonstration of the proposed robot in some of packaging job of cellular phone.

• 한국산학기술학회논문지
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• 제18권10호
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• pp.756-760
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• 2017

자동차 부품 업체에서는 납기 준수, 생산 원가 절감, 품질 관리 향상 등의 고객의 필수적인 요구 사항를 만족하기 위하여 자동화를 추진하고 있다. 현재의 수작업을 통한 육안 검사 공정에서는 이러한 필수 요구 사항을 만족하기에는 불가능하다. 따라서 본 연구에서는 품질 관리 개선을 위하여 도어 힌지 브라켓 부품에 대한 현재의 수작업 육안 검사 공정을 대체할 수 있는 자동 선별 시스템을 제안하고자 한다. 본 제안은 도어 힌지 브라켓 부품의 용접너트 누락 불량 발생을 방지하여 고객사의 검사 요구사항 등을 만족할 수 있도록 설계하였다. 검사 공정 알고리즘 및 유사 척도 매칭 알고리즘 프로그램을 자동 선별 시스템에 적용하여 정상 제품과 불량 제품을 구별할 수 있도록 하였다. 검사 공정 알고리즘 및 유사 척도 매칭 알고리즘의 검증 시험을 통하여 검출정확도 98%의 성공적인 검사 결과를 나타내었고 이를 생산 현장에 적용하여 불량 제품감소에 따른 생산성 향상에 기여하였다.

• 대한안전경영과학회지
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• 제14권3호
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• pp.101-109
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• 2012

It is commonly recognized that railway is one of the representative transportation and it offers public service based on strategies for being rapid, automation, safety. Since the opening of high speed railway, 3-hundred-million people have used it and acknowledged its efficiency. However, derailed accident at Kwangmyeong station in February, 2011, frequent malfunction of KTX-Sancheon, and accidents by engineer's careless fault damaged on credibility of safety, Especially, spreaded accidents through social networking service by cell phones amplified anxiety of public, being criticized by the press. This study analyzed statistics of past accident and cases of passing-by accident, and surveyed 152 KTX captain engineers about their recognition of the accident by careless fault and experiences of possibility of occurrence for preventing engineer's careless fault and restoring trust According to the analysis, engineers worry about responsibility and disadvantages related to the accidents for the most, and they are nervous about malfunction for the second most. This study presents prevention methods regarding the result. First, it is required to improve mental stability and concentration on their work, secondly, advanced ability to cope with malfunction or error through repetitive education and training are required to increase confidence, and for the last, improvement of operational supporting system such as ATP, GPS to prevent errors by human factors. Improvement of the system is expected to lead engineers to prevent careless fault and regain the reputation of railway.

• 융합신호처리학회논문지
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• 제23권3호
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• pp.129-136
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• 2022

스마트 축사 시스템에서 가장 중요한 부분은 내·외부 대기환경 변화에도 가축이 고품질로 성장할 수 있도록 사육환경을 유지하는 것이다. 특히 여름과 겨울에는 여러 질병이 발생하기 때문에 축사환경에서 온·습도 유지가 매우 중요하다. 이러한 환경을 관리하기 위해 축사용 스마트 시스템이 도입되고 있으나 매우 고가이다. 본 연구에서는 퍼지제어와 HMI를 기반한 저가의 시스템 설계와 제어 방법을 제안한다. 제안된 시스템의 성능을 평가하기 위해 여름철과 겨울철의 대기환경 조건을 주어 시뮬레이션 실험하였으며, 그 결과 가축이 받은 온습도 스트레스를 최소화하는 성능을 보였다. 그리고 실제의 축사에 적용했을 때도 제안된 시스템은 외부대기 환경변화에도 안정적인 제어성능을 보였다. 본 연구에서 제안한 기법이 스마트팜 제어기로 적용된다면, 축사 환경관리에 있어 효과적일 것이다.

• 현장농수산연구지
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• 제23권2호
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• pp.99-111
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• 2021

농경지, 농기계, 농작업자 간 IoT 등의 통신 기술을 이용한 유기적인 정보교환을 통해 생산성, 효율성, 수익성을 높이는 지능형 데이터 농업 형태인 커넥티드 팜이 상용화 단계에 있다. 본 연구는 지능형 농업기계의 교육과정과 농업기계 안전교육 성과지표를 개발하고자 ICT, 로봇, 인공지능 등 첨단 기술을 적용한 농업생산의 무인화 및 고효율화 변화에 따른 농업기계의 특허 동향을 조사 분석하였다. 노지용 자동화 기술과 관련해서 미국, 일본, 유럽, 한국의 특허 건수는 각각 541건, 326건, 128건, 85건으로 미국에서의 특허 활동이 가장 활발한 것으로 나타났고, 일본, 유럽, 한국의 순으로 조사되어 한국에서의 농업 자동화 기술이 선진국에 비해 뒤쳐져있는 것으로 조사되었다. 노지 자동화 기술의 세분기술 분야로 보면, 경로 생성 및 추종 기술, 환경 인식을 통한 작업기 제어 기술, 로봇 농작업 시스템 설계 기술, 작물 및 환경 센싱 기술, 수확량 및 품질 모니터링 기술 분야 순으로 출원 점유율이 높은 것으로 나타났다.

• 센서학회지
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• 제19권3호
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• pp.169-175
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• 2010

Urinalysis is an important clinical test to diagnose urinary diseases, and dipstick method with visual inspection is widely applied in practice. Automated optical devices recently developed have disadvantages of long measurement time, big size and heavy weight, accuracy degradation with time, etc. The present study proposed a new computer scanning technique, in which the test strip and the standard chart were simultaneously scanned to remove any environmental artifacts, followed by automated differentiation with the minimum distance algorithm, leading to significant enhancement of accuracy. Experiments demonstrated an accuracy of 100 % in that all test results were identical with the human visual inspection. The present technique only uses a personal computer with scanner and shortens the test time to a great degree. The results are also stored and accumulated for later use which can be transmitted to remote locations through a network, thus could be easily integrated to any ubiquitous health care systems.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• 복식
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• 제51권4호
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• pp.71-80
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• 2001

Since the beginning of the 90's, Fashion shows appear to be a type of performance form of art combining with other areas to visually entertain the viewers. This can be explained by the modern tendency to escape from society which requires formality and complicated lifestyles. Fashion shows take place in a scene Influenced by the idea of post-modernism which redefines the definition of space. A church, old factory, unoccupied ground, subway stations, or even place like a waste disposal are used as a setting. The stage set is no longer the T shape run way and the procinium arch has disappeared. The gap between audience and stage has diminished and theatrical element is added to the fashion performance by using viewers living and working environments as setting of the show. The human relation with machine based on the cutting edge technology such as the stage automation, robots and mist making sprinkler system introduces new elements with spontaneity and detailed planning in the stage performance. Music also plays an Important role in attracting viewers. The sound track covers house music to techno music. Instead of music being abstract, folks orchestral music, choirs, piano. even live concert performed by pop artist provide the liveliness of the fashion show. And the catwalk itself is a performance. Model needs to be well trained as the capable talent who can handle sensitive gestures, facial expressions, dancing and choreography. The improvisatorial interaction between audience and model lead to audience participation. Models now range from pop star, ordinary people, handicapped people, to elderly and so on. John Galliano introduced the theatrical factors for the fashion show and Alexander Mcqueen approached the fashion show as the visual art of communication. Hussein Chalayan utilized high technology skewing futurism as if in a magic show. Today the Fashion show tends to be a total performance which includes audience participation, impromptu, and that break the limitation that fashion shows previously had. This will lead the fashion industry in opening new horizon of its own.

• 전기전자학회논문지
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• 제15권3호
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• pp.227-234
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• 2011

The Interest in robotics has been steadily increasing in recent times both in Korea as well as abroad. Research on robots for new and diverse fields is ongoing. This study discusses the current research and development on robot actuator, which are used to control the joints of robots, and focuses on developing more efficient technology for joint control, as compared with the current technologies. It also aims to find means to apply the abovementioned technology to diverse industrial fields. We found that easy and effective control of actuators could be achieved by using ZigBee sensor networks, which were widely being used on wireless communications. Throughout the experiments it is proved that the developed wireless actuator could be used for easy control of various robot joints. This technology can be effectively applied to develop two-legged robots that will be able to walk like human, or even quadruped and hexapod robots. It can also be applied to motors used in industry. In this study, we develop an extremely minimized ZigBee sensor network module that can be used to control various servo motors with low power consumption even if it is long distances. We realized effective wireless control by optimizing the ZigBee antenna, and were able to quickly check the status of relevant Tree node through mutual communication between the servo motors composing the ZigBee sensor network and the main server control modules. The developed Servo Motor with ZigBee sensor network modules can be applied in both robotics as well as for home or factory automation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.