Monitoring Newly Adopted Technologies Using Keyword Based Analysis of Cited Patents

This paper proposes a method that can reliably monitor the adoption of existing technology by term frequency-inverse document frequency (11-IDF) and K-means clustering using cited patents. 11-IDF and K-means clustering can extract patent information when the number of patents is sufficiently large. When the number of patents is too small for 11-IDF and K-means clustering to be reliable, the method considers patents that were cited by the originally set of patents. The mixed set of citing patents and cited patents is the new subject of analysis. As a case study, we have focused in agricultural tractor in which new technologies were adopted to achieve automated driving. TF-IDF and K-means clustering alone failed to monitor the adoption of new technology but the proposed method successfully monitored it. We anticipate that our method can ensure the reliability of patent monitoring even when the number of patents is small.11Ysciescopu

Generating Information Relation Matrix Using Semantic Patent Mining for Technology Planning: A Case of Nano-Sensor

For the purposes of technology planning and research and development strategy development, we present a semi-automated method that extracts text information from patent data, uses natural language processing to extract the key technical information of the patent, and then visualizes this information in a matrix form. We tried to support qualitative analysis of patent contents by extracting functions, components, and contexts, which are the most important information about inventions. We validated the method by applying it to patent data related to nanosensors. The matrix can emphasize technical information that have not been exploited in patents, and thereby identify development opportunities.111Ysciescopu

In/Out Status Monitoring in Mobile Asset Tracking with Wireless Sensor Networks

A mobile asset with a sensor node in a mobile asset tracking system moves around a monitoring area, leaves it, and then returns to the region repeatedly. The system monitors the in/out status of the mobile asset. Due to the continuous movement of the mobile asset, the system may generate an error for the in/out status of the mobile asset. When the mobile asset is inside the region, the system might determine that it is outside, or vice versa. In this paper, we propose a method to detect and correct the incorrect in/out status of the mobile asset. To solve this problem, our approach uses data about the connection state transition and the battery lifetime of the mobile node attached to the mobile asset. The connection state transition is used to classify the mobile node as normal or abnormal. The battery lifetime is used to predict a valid working period for the mobile node. We evaluate our method using real data generated by a medical asset tracking system. The experimental results show that our method, by using the estimated battery life time or by using the invalid connection state, can detect and correct most cases of incorrect in/out statuses generated by the conventional approach

Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms

This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.Comment: 2019 IEEE/IFIP International Conference on Dependable Systems and Networks Supplementa

Robust algorithm for arrhythmia classification in ECG using extreme learning machine

<p>Abstract</p> <p>Background</p> <p>Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.</p> <p>Methods</p> <p>In this paper we propose a novel arrhythmia classification algorithm which has a fast learning speed and high accuracy, and uses Morphology Filtering, Principal Component Analysis and Extreme Learning Machine (ELM). The proposed algorithm can classify six beat types: normal beat, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature beat, and paced beat.</p> <p>Results</p> <p>The experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average specificity, and 98.72% in terms of average accuracy. These accuracy levels are higher than or comparable with those of existing methods. We make a comparative study of algorithm using an ELM, back propagation neural network (BPNN), radial basis function network (RBFN), or support vector machine (SVM). Concerning the aspect of learning time, the proposed algorithm using ELM is about 290, 70, and 3 times faster than an algorithm using a BPNN, RBFN, and SVM, respectively.</p> <p>Conclusion</p> <p>The proposed algorithm shows effective accuracy performance with a short learning time. In addition we ascertained the robustness of the proposed algorithm by evaluating the entire MIT-BIH arrhythmia database.</p

Dynamic Vehicular Route Guidance Using Traffic Prediction Information

We propose a dynamic vehicular routing algorithm with traffic prediction for improved routing performance. The primary idea of our algorithm is to use real-time as well as predictive traffic information provided by a central routing controller. In order to evaluate the performance, we develop a microtraffic simulator that provides road networks created from real maps, routing algorithms, and vehicles that travel from origins to destinations depending on traffic conditions. The performance is evaluated by newly defined metric that reveals travel time distributions more accurately than a commonly used metric of mean travel time. Our simulation results show that our dynamic routing algorithm with prediction outperforms both Static and Dynamic without prediction routing algorithms under various traffic conditions and road configurations. We also include traffic scenarios where not all vehicles comply with our dynamic routing with prediction strategy, and the results suggest that more than half the benefit of the new routing algorithm is realized even when only 30% of the vehicles comply

Microvasculature remodeling in the mouse lower gut during inflammaging

Inflammaging is defined as low-grade, chronic, systemic inflammation in aging, in the absence of overt infection. Age-associated deterioration of gastrointestinal function could be ascribed to the inflammaging, although evidence is yet to emerge. Here we show that microvessels in aging mouse intestine were progressively deprived of supportive structures, microvessel-associated pericytes and adherens junction protein vascular endothelial (VE)-cadherin, and became leaky. This alteration was ascribed to up-regulation of angiopoetin-2 in microvascular endothelial cells. Up-regulation of the angiopoietin-2 was by TNF-α, originated from M2-like residential CD206 + macrophages, proportion of which increases as animal ages. It was concluded that antigenic burdens encountered in intestine throughout life create the condition of chronic stage of inflammation, which accumulates M2-like macrophages expressing TNF-α. The TNF-α induces vascular leakage to facilitate recruitment of immune cells into intestine under the chronic inflammatory setting. © Author(s) 2017.1