Optimizing Base Placement of Surgical Robot: Kinematics Data-Driven Approach by Analyzing Working Pattern

In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Conventional base placement relies on the experience of trained medical staff. This study proposes a novel method for determining the optimal base pose based on the surgeon's working pattern. The proposed method analyzes recorded end-effector poses using a machine learning-based clustering technique to identify key positions and orientations preferred by the surgeon. We introduce two scoring metrics to address the joint limit and singularity issues: joint margin and manipulability scores. We then train a multi-layer perceptron regressor to predict the optimal base pose based on these scores. Evaluation in a simulated environment using the da Vinci Research Kit shows unique base pose score maps for four volunteers, highlighting the individuality of the working patterns. Results comparing with 20,000 randomly selected base poses suggest that the score obtained using the proposed method is 28.2% higher than that obtained by random base placement. These results emphasize the need for operator-specific optimization during base placement in RAMIS.Comment: 8 pages, 7 figures, 2 table

RIDESOURCING IN MANUFACTURING SITES: A FRAMEWORK AND CASE STUDY

With the recent innovations in transportation, ridesourcing services have been proliferating in many countries. There are increasing attempts to apply ridesourcing in the corporate context. Manufacturing companies now install the Industrial Internet of Things (IIOT) sensors to vehicles to obtain real-time data on the movement of goods and materials. Despite the massive amount of data accumulated, little attention has been paid to exploiting the data for vehicle fleet management (FM). This paper proposes an analytical framework to solve two FM problems: how to group organizational units for vehicle sharing and where to deploy the groups. The framework is then validated with a case study of a Korean shipbuilder. The results indicate that grouping departments with similar spatial patterns can reduce the current fleet

The PEX7-Mediated Peroxisomal Import System Is Required for Fungal Development and Pathogenicity in Magnaporthe oryzae

In eukaryotes, microbodies called peroxisomes play important roles in cellular activities during the life cycle. Previous studies indicate that peroxisomal functions are important for plant infection in many phytopathogenic fungi, but detailed relationships between fungal pathogenicity and peroxisomal function still remain unclear. Here we report the importance of peroxisomal protein import through PTS2 (Peroxisomal Targeting Signal 2) in fungal development and pathogenicity of Magnaporthe oryzae. Using an Agrobacterium tumefaciens-mediated transformation library, a pathogenicity-defective mutant was isolated from M. oryzae and identified as a T-DNA insert in the PTS2 receptor gene, MoPEX7. Gene disruption of MoPEX7 abolished peroxisomal localization of a thiolase (MoTHL1) containing PTS2, supporting its role in the peroxisomal protein import machinery. ΔMopex7 showed significantly reduced mycelial growth on media containing short-chain fatty acids as a sole carbon source. ΔMopex7 produced fewer conidiophores and conidia, but conidial germination was normal. Conidia of ΔMopex7 were able to develop appressoria, but failed to cause disease in plant cells, except after wound inoculation. Appressoria formed by ΔMopex7 showed a defect in turgor generation due to a delay in lipid degradation and increased cell wall porosity during maturation. Taken together, our results suggest that the MoPEX7-mediated peroxisomal matrix protein import system is required for fungal development and pathogenicity M. oryzae

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Empirical Study on Contextual Bandit Algorithms with Neural Processes

Graduate School of Artificial Intelligence ArtificiMulti-armed bandit is a well-formulated test bed for designing sequential decision-making algorithms that deal with the exploration-exploitation dilemma. Bandit algorithm balances exploration towards uncertain domain and exploitation of the observed history to accurately estimate the reward distribution of each arm. Contextual bandit incorporates a context that contains rich information about the structure of bandit environment and determines the reward function, making the algorithms devised in this setting be more applicable to real-world problems like news recommendation. Focus on the practicality of contextual bandit algorithm, we consider the diversity and non-stationarity of bandit environment. Also, we assume that there exists a number of accumulated dataset from previous evaluations. To this end, we propose offline training of a reward prediction model via meta-learning so that the model can adapt to the changing environment. We consider Neural Processes (NP), a probabilistic few-shot learner that can estimate the uncertainty with its prediction. Adopting the upper confidence bound (UCB) exploration strategy, we propose NP-UCB, the exploration strategy based on the uncertainty estimate of trained neural processes. We evaluate the proposed algorithm with various neural processes on wheel bandit and news recommendation system. The results show that our method works well with the latest neural process model called Neural Bootstrapping Attentive Neural Processes (NEUBANP), which can adapt to dynamically changing environments with the help of its reliable uncertainty estimates.ope

자원 효율적인 네트워크 가상화를 위한 스케쥴링 전략

OAIID:RECH_ACHV_DSTSH_NO:A201620374RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A001118CITE_RATE:DEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:CONFIRM:

분할 Bi-LSTM 모델을 활용한 패션 아웃핏 추천

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