The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the application of QR Code position recognition during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the application of QR Code recognition system during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the speculative navigation using auxiliary encoder during the development of QR Code-aware indoor mobility robots.

본 연구의 목적은 겨울철 약광기 보광 시 줄기 유인 수가 온실 파프리카의 생육, 과실의 품질 및 생산량에 미치는 영향을 구명하는 것이다. 파프리카는 2020년 10월 26일에 m 2당 3.2 주를 정식하였고, 2020년 12월 1일부터 고압나트륨등을 작기 종료일인 2021년 5월 25일까지 하루 16시간의 광주기로 조사 하였다. 줄기 유인 처리는 분지 이후의 생장점이 각각 2, 3개로 유지되도록 유인해 주었다. 초장은 2줄기 유인 처리구에 비해 3줄기 유인 처리구에서 유의적으로 짧았고 마디수 및 엽수는 2줄기 유인 처리구에 비해 3줄기 유인 처리구에서 유의적으 로 증가했다. 엽면적 및 기관별 건물중은 통계적으로 유의한 차이를 보이지 않았다. 과실 생체중 및 건물중은 모든 처리구에서 생육단계에 따라 감소하는 경향을 보였으며, 3줄기 유인 처리구가 2줄기 유인 처리구에 비해 낮은 수치를 보였다. 3줄 기 유인 처리구의 상품과율은 95.4%로 2줄기 유인 처리구의 93.6%에 비해 높았다. 총 수확량은 3줄기 유인 처리구에서 30.2% 높게 나타났다. 결론적으로 온실 파프리카의 3줄기 유인 재배는 적정 수준의 영양생장을 유지함으로써 과실 생산량에 긍정적인 영향을 미쳤다고 할 수 있다. 본 연구 결과는 파프 리카의 생산량 증대를 위한 농가 의사결정에 활용될 수 있을 것으로 사료된다.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses on the QR Code recognition mobile robotics study during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a study on the position recognition control using QR Code during the development of QR Code-aware indoor mobility robots.

The role of QR Code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR Codes and the convenience of producing and attaching a lot of information within QR Codes have been raised, and many of these reasons have made QR Codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR Codes with the same horizontal and vertical sides, and the error is to create a QR Code robot with accuracy to reach within 3mm. This paper focuses a suggestion of control method in QR Code-aware indoor mobility robots.

본 연구는 광전환필름에 의해 변형된 태양광 하에서 채소류의 묘소질과 광합성 특성에 미치는 영향을 구명하기위해 수행 되었다. 상추와 배추, 오이 종자를 원예용 상토로 충진된 50구 플러그 트레이에 파종하고, 광전환 필름(SCF)과 상업용 폴리 에틸렌 필름(대조군) 하에서 재배하였다. 대조군 대비 SCF에 투과된 태양광 스펙트럼은 청-녹색광은 감소하고, 적색광은 증가했으며 R/FR 비와 R/B 비가 증가하여 묘의 광합성과 생육, 형태형성 측면에서 영향을 미치기에 충분한 수준이었다. 광합성속도는 SCF 하에서 자란 배추와 오이 묘에서 유의적으 로 증가했으며, 최대 광합성속도는 각각 23%, 19% 높게 나타 났다. 상추의 경우, SCF 처리구에서 생육이 유의적으로 감소 하였으며 도장하였다. SCF 필름 하에서 자란 배추의 경우, 형태적인 차이는 없었으나 잎의 건물중이 증가하여 묘의 충실도가 유의적으로 증가했고 왜화율은 10%로 나타났다. SCF 처리구의 오이 묘는 생육에 차이는 없었으나 엽병 및 초장이 유의적으로 감소하여 왜화율이 24%로 높게 나타났다. 결론적 으로 SCF에 의해 변형된 태양광 스펙트럼은 배추와 오이 묘의 광합성 효율을 증가시키고 묘소질을 향상시켰다고 판단되었다.

The role of QR code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR codes and the convenience of producing and attaching a lot of information within QR codes have been raised, and many of these reasons have made QR codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR codes with the same horizontal and vertical sides, and the error is to create a QR code robot with accuracy to reach within 3mm. This paper focuses on the driving operation techniques during the development of QR code-aware indoor mobility robots.

The role of QR code robots in smart logistics is great. Cognitive robots, such as logistics robots, were mostly used to adjust routes and search for peripheral sensors, cameras, and recognition signs attached to walls. However, recently, the ease of making QR codes and the convenience of producing and attaching a lot of information within QR codes have been raised, and many of these reasons have made QR codes recognizable as visions and others. In addition, there have been cases in developed countries and Korea that control several of these robots at the same time and operate logistics factories smartly. This representative case is the KIVA robot in Amazon. KIVA robots are only operated inside Amazon, but information about them is not exposed to the outside world, so a variety of similar robots are developed and operated in several places around the world. They are applied in various fields such as education, medical, silver, military, parking, construction, marine, and agriculture, creating a variety of application robots. In this work, we are developing a robot that can recognize its current position, move and control in the directed direction through two-dimensional QR codes with the same horizontal and vertical sides, and the error is to create a QR code robot with accuracy to reach within 3mm. This paper focuses on the moving control model during the development of QR code-aware indoor mobility robots.

The molybdenum cup and molybdenum pin, which are the main materials of the molybdenum electrode used for the LCD BLU CCFL electrode, have not been developed in Japan and all of them are imported and used from Japan, is giving a competitive burden. In this research, to develop the manufacturing technology of molybdenum pin used for CCFL electrode of LCD BLU, development of linear processing technology, development of molybdenum wire surface treatment technology, development of wire cutting technology, production of molybdenum pin, design and fabrication of JIG and Fixture for inspection, molybdenum pin prototyping and analysis, and development of 100% molybdenum pin inspection technology. In this paper, especially, research on prototype manufacturing and CAE analysis is treated.

The molybdenum cup and molybdenum pin, which are the main materials of the molybdenum electrode used for the LCD BLU CCFL electrode, have not been developed in Japan and all of them are imported and used from Japan, is giving a competitive burden. In this research, the CCFL electrode of LCD BLU is used to develop the manufacturing technology of molybdenum pin. The development of linear processing technology is used to that. The development of molybdenum wire surface treatment technology is used to that. The development of wire cutting technology is ued to that. The design and fabrication of JIG and Fixture for inspection is used to that. The molybdenum pin prototyping and analysis is used to that and finally, the development of 100% molybdenum pin inspection technology is used to that. In this paper, especially, research on design technology of wire cutting is treated.

작물의 생체중을 추정하기 위해 다양한 연구가 시도되었지만, 이미지를 활용하여 생체중을 추정한 예는 없었다. 최근 합성곱 신경망을 사용한 이미지 처리 연구가 늘고 있으며, 합성곱 신경망은 미가공 데이터를 그대로 사용할 수 있다. 본 연구에서는 합성곱 신경망을 이용하여 미가공 데이터 상태인 특정 시점의 파프리카 이미지를 입력으로 작물의 생체중을 추정하도록 학습하였다. 실험은 파프리카(Capsicum annuum L.)를 재배하는 온실에서 수행하였다. 합성곱 신경망의 출력값인 생체중은 파괴조사를 통해 수집한 데이터를 기반으로 회귀 분석하였다. 학습된 합성곱 신경망의 결정 계수(R2)의 최고값은 0.95로 나타났다. 생체중 추정값은 실제 측정값과 매우 유사한 경향성을 보여주었다.

The molybdenum cup and molybdenum pin, which are the main materials of the molybdenum electrode used for the LCD BLU CCFL electrode, have not been developed in Japan and all of them are imported and used from Japan, is giving a competitive burden. In this research, to develop the manufacturing technology of molybdenum pin used for CCFL electrode of LCD BLU, development of linear processing technology, development of molybdenum wire surface treatment technology, development of wire cutting technology, production of molybdenum pin, design and fabrication of JIG and Fixture for inspection, molybdenum pin prototyping and analysis, and development of 100% molybdenum pin inspection technology. In this paper, especially, research on surface treatment technology of molybdenum wire is treated.