• Journal of Gastric Cancer
• /
• 제19권2호
• /
• pp.165-172
• /
• 2019

Purpose: The robotic system for surgery was introduced to gastric cancer surgery in the early 2000s to overcome the shortcomings of laparoscopic surgery. The more recently introduced da Vinci $Xi^{(R)}$ system offers benefits allowing four-quadrant access, greater range of motion, and easier docking through an overhead boom rotation with laser targeting. We aimed to identify whether the $Xi^{(R)}$ system provides actual advantages over the $Si^{(R)}$ system in gastrectomy for gastric cancer by comparing the operative outcomes. Materials and Methods: We retrospectively reviewed all patients who underwent robotic gastrectomy as treatment for gastric cancer from March 2016 to March 2017. Patients' demographic data, perioperative information, and operative and pathological outcomes were collected and analyzed. Results: A total of 109 patients were included in the $Xi^{(R)}$ group and 179 in the $Si^{(R)}$ group. Demographic characteristics were similar in both groups. The mean operative time was 229.9 minutes in the $Xi^{(R)}$ group and 223.7 minutes in the $Si^{(R)}$ group. The mean estimated blood loss was 72.7 mL in the $Xi^{(R)}$ group and 62.1 mL in the $Si^{(R)}$ group. No patient in the $Xi^{(R)}$ group was converted to open or laparoscopy, while 3 patients in the $Si^{(R)}$ group were converted, 2 to open surgery and 1 to laparoscopy, this difference was not statistically significant. Bowel function was resumed 3 days after surgery, while soft diet was initiated 4 days after surgery. Conclusions: We found no difference in surgical outcomes after robotic gastrectomy for gastric cancer between the da Vinci $Xi^{(R)}$ and da Vinci $Si^{(R)}$ procedures.

• 생물환경조절학회지
• /
• 제22권3호
• /
• pp.284-290
• /
• 2013

본 연구는 시설재배에서 참외를 수확할 수 있는 로봇의 엔드이펙터를 개발하기 위한 전단계로서, 참외의 엔드이펙트 중에서 소프트 핸드링이 가능한 그립퍼와 참외줄기를 절단하는 커터를 설계하기 위해 참외의 기하학, 압축, 절단, 마찰 특성 등을 분석하였다. 그 결과 참외의 길이는 평균 108mm, 직경은 중간지점에서 평균 70mm, 중량은 평균 188g, 부피는 평균 333mL, 진원도는 평균 3.8mm로 나타났다. 참외의 중량(W)에 대하여 길이(L)와 직경(D2)을 변수로 하는 식 $W=L^a{\times}D_2^b$로부터 비선형 회귀분석을 실시한 결과 a는 2.0279, b는 -0.9998의 상수값을 가지는 상관관계가 있는 것으로 나타났다. 참외줄기의 지름은 평균 3.8mm이며, 참외 줄기는 중심으로부터 반경 5mm 범위 내에서 대부분 분포하였다. 참외의 항복치와 압축강도, 경도의 평균값은 각각 $36.5N/cm^2$, $185.7N/cm^2$, $636.7N/cm^2$이며, 참외 줄기의 절단력과 절단강도는 각각 $2.87{\times}10^{-2}N$와 $5.60N/cm^2$로 나타났다. 참외의 마찰계수는 고무가 0.609으로 가장 높게 나타났고, 그 다음으로 알루미늄이 0.393, 스테인레스강이 0.177, 테프론이 0.079로 나타났다. 분석된 자료를 토대로 엔드이펙터 설계시 동작에 따른 위치 오차와 안전율을 감안하여, 그립퍼의 및 커터의 크기, 선회반경, 설치위치, 구동모터의 동력, 재료 및 재질의 선정 등에 적용할 수 있을 것으로 판단되었다.

• 한국정밀공학회지
• /
• 제30권1호
• /
• pp.11-17
• /
• 2013

'Multi-scale mass-deployable cooperative robots' is a next generation robotics paradigm where a large number of robots that vary in size cooperate in a hierarchical fashion to collect information in various environments. While this paradigm can exhibit the effective solution for exploration of the wide area consisting of various types of terrain, its technical maturity is still in its infant state and many technical hurdles should be resolved to realize this paradigm. In this paper, we propose to develop new design and manufacturing methodologies for the multi-scale mass-deployable cooperative robots. In doing so, we present various fundamental technologies in four different research fields. (1) Adaptable design methods consist of compliant mechanisms and hierarchical structures which provide robots with a unified way to overcome various and irregular terrains. (2) Soft composite materials realize the compliancy in these structures. (3) Multi-scale integrative manufacturing techniques are convergence of traditional methods for producing various sized robots assembled by such materials. Finally, (4) the control and communication techniques for the massive swarm robot systems enable multiple functionally simple robots to accomplish the complex job by effective job distribution.

• 생물환경조절학회지
• /
• 제31권4호
• /
• pp.270-278
• /
• 2022

본 연구는 무대재배 복숭아 '미황'을 대상으로 성숙기간 중 RGB 영상을 취득한 후 다양한 품질 지표를 측정하고 이를 딥러닝 기술에 적용하여 복숭아 과실 숙도 분류의 가능성을 탐색하고자 실시하였다. 취득 영상 730개의 데이터를 training과 validation에 사용하였고, 170개는 최종테스트 이미지로 사용하였다. 본 연구에서는 딥러닝을 활용한 성숙도 자동 분류를 위하여 조사된 품질 지표 중 경도, Hue 값, a^*값을 최종 선발하여 이미지를 수동으로 미성숙(immature), 성숙(mature), 과숙(over mature)으로 분류하였다. 이미지 자동 분류는 CNN(Convolutional Neural Networks, 컨볼루션 신경망) 모델 중에서 이미지 분류 및 탐지에서 우수한 성능을 보이고 있는 VGG16, GoogLeNet의 InceptionV3 두종류의 모델을 사용하여 복숭아 품질 지표 값의 분류 이미지별 성능을 측정하였다. 딥러닝을 통한 성숙도 이미지 분석 결과, VGG16과 InceptionV3 모델에서 Hue_left 특성이 각각 87.1%, 83.6%의 성능(F1 기준)을 나타냈고, 그에 비해 Firmness 특성이 각각 72.2%, 76.9%를 나타냈고, Loss율이 각각 54.3%, 62.1%로 Firmness를 기준으로 한 성숙도 분류는 적용성이 낮음을 확인하였다. 추후에 더 많은 종류의 이미지와 다양한 품질 지표를 가지고 학습이 진행된다면 이전 연구보다 향상된 정확도와 세밀한 성숙도 판별이 가능할 것으로 판단되었다.

• 수완나부미
• /
• 제10권2호
• /
• pp.181-213
• /
• 2018

How to study Southeast Asia (SEA)? The need to explore and identify methodologies for studying SEA are inherent in its multifaceted subject matter. At a minimum, the region's rich cultural diversity inhibits both the articulation of decisive defining characteristics and the training of scholars who can write with confidence beyond their specialisms. Consequently, the challenges of understanding the region remain and a consensus regarding the most effective approaches to studying its history, identity and future seem quite unlikely. Furthermore, "Area Studies" more generally, has proved to be a less attractive frame of reference for burgeoning scholarly trends. This paper will propose a new tool to help address these challenges. Even though the science of artificial intelligence (AI) is in its infancy, it has already yielded new approaches to many commercial, scientific and humanistic questions. At this point, AI has been used to produce news, generate better smart phones, deliver more entertainment choices, analyze earthquakes and write fiction. The time has come to explore the possibility that AI can be put at the service of the study of SEA. The paper intends to lay out what would be required to develop SEABOT. This instrument might exist as a robot on the web which might be called upon to make the study of SEA both broader and more comprehensive. The discussion will explore the financial resources, ownership and timeline needed to make SEABOT go from an idea to a reality. SEABOT would draw upon artificial neural networks (ANNs) to mine the region's "Big Data", while synthesizing the information to form new and useful perspectives on SEA. Overcoming significant language issues, applying multidisciplinary methods and drawing upon new yields of information should produce new questions and ways to conceptualize SEA. SEABOT could lead to findings which might not otherwise be achieved. SEABOT's work might well produce outcomes which could open up solutions to immediate regional problems, provide ASEAN planners with new resources and make it possible to eventually define and capitalize on SEA's "soft power". That is, new findings should provide the basis for ASEAN diplomats and policy-makers to develop new modalities of cultural diplomacy and improved governance. Last, SEABOT might also open up avenues to tell the SEA story in new distinctive ways. SEABOT is seen as a heuristic device to explore the results which this instrument might yield. More important the discussion will also raise the possibility that an AI-driven perspective on SEA may prove to be even more problematic than it is beneficial.