• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.431-434
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• 2016

Currently, the radial basis function network (RBFN) and various other neural networks are employed to classify gases using chemical sensors arrays, and their performance is steadily improving. In particular, the identification performance of the RBFN algorithm is being improved by optimizing parameters such as the center, width, and weight, and improved algorithms such as the radial basis function network-stochastic gradient (RBFN-SG) and radial basis function network-normalized stochastic gradient (RBFN-NSG) have been announced. In this study, we optimized the number of centers, which is one of the parameters of the RBFN-NSG algorithm, and observed the change in the identification performance. For the experiment, repeated measurement data of 8 samples were used, and the elbow method was applied to determine the optimal number of centers for each sample of input data. The experiment was carried out in two cases(the only one center per sample and the optimal number of centers obtained by elbow method), and the experimental results were compared using the mean square error (MSE). From the results of the experiments, we observed that the case having an optimal number of centers, obtained using the elbow method, showed a better identification performance than that without any optimization.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.418-424
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• 2023

Natural killer (NK) cells play a crucial role in combating infections and tumors. However, their therapeutic application in solid tumors is hindered by challenges, such as limited lifespan, tumor penetration, and delivery precision. Our research introduces a novel ultrasonic actuation technique to navigate NK cells more effectively in the vascular system, particularly at vessel bifurcations where targeted delivery is most problematic. We use a hemispherical ultrasonic transducer array that generates phase-modulated traveling waves, focusing on an ultrasound beam to steer NK cells using blood-flow dynamics and a focused acoustic field. This method enables the precise obstruction of non-target vessels and efficiently directs NK cells toward the tumor site. The simulation results offer insights into the behavior of NK cells under various conditions of cell size, radiation pressure, and fluid velocity, which inform the optimization of their trajectories and increase targeting efficiency. The experimental results demonstrate the feasibility of this ultrasonic approach for enhancing NK cell targeting, suggesting a potential leap forward in solid tumor immunotherapy. This study represents a significant step in NK cell therapeutic strategies, offering a viable solution to the existing limitations and promising enhancement of the efficacy of cancer treatments.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.23-29
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• 2024

In this scholarly investigation, the focus is placed on the transformative potential of edge computing in enhancing Intelligent Transportation Systems (ITS) for the facilitation of autonomous driving. The intrinsic capability of edge computing to process voluminous datasets locally and in a real-time manner is identified as paramount in meeting the exigent requirements of autonomous vehicles, encompassing expedited decision-making processes and the bolstering of safety protocols. This inquiry delves into the synergy between edge computing and extant ITS infrastructures, elucidating the manner in which localized data processing can substantially diminish latency, thereby augmenting the responsiveness of autonomous vehicles. Further, the study scrutinizes the deployment of edge servers, an array of sensors, and Vehicle-to-Everything (V2X) communication technologies, positing these elements as constituents of a robust framework designed to support instantaneous traffic management, collision avoidance mechanisms, and the dynamic optimization of vehicular routes. Moreover, this research addresses the principal challenges encountered in the incorporation of edge computing within ITS, including issues related to security, the integration of data, and the scalability of systems. It proffers insights into viable solutions and delineates directions for future scholarly inquiry.