No abstract

No abstract

No abstract

We are developing a method to acquire position information of a cow outdoors using Received Signal Strength Indicator (RSSI) of Bluetooth Low Energy (BLE). As existing research, there is a localization method using fingerprint database as learning data in deep learning. However, that method has the problem that it costs to create a database by measurement in a vast outdoor environment. Therefore, we considered to build a part of the fingerprint database using virtual space modeling received power measurement environment in a pasture. Experimental results showed that an average distance error to GPS data is about 6 m by training DNN using the database and additionally training DNN using actual GPS data.

No abstract

Researches on computing an optimal route according to user's preference or spatial contexts have been gaining focus recently, as car navigation and pedestrian navigation systems are getting accustomed to be used in daily lives. In this paper, the authors propose a method to compute a path to pass through as many context areas as possible along its route, according to the user's needs. Specifically, the authors apply the concept to construct a Delaunay Diagram from neighbor nodes in Voronoi Diagram, and use Network Voronoi Diagram to construct a subgraph based on contexts. For route search, the authors use A* shortest path algorithm over the subgraph. To verify the efficiency of the proposed method with subgraphs, the authors perform route search over subgraphs and base graph, which was generated from the road network of the real space, and compare the results. The results show that the route search by the proposed method consist of more paths with context areas than those performed over the base graph. Moreover, the proposed method reduces the number of steps for path search.

Influence maximization (IM) has been widely studied in recent years. Given fixed number of seed users and certain diffusion models, the IM problem aims to select proper seed users in a social networks such that they can achieve the maximal spread of influence. Most previous work assumes that there are only positive relationships between users, and thus users spread influence positively. However, negative relationships also universally exist in various social networks and are complementary to positive relationships in information diffusion. In this paper, the influence maximization problem is addressed in signed social networks that contain both positive and negative relationships. We propose a novel diffusion model called LT-S and two influence spread functions. The proposed LT-S model extends the classical linear threshold model with opinion formation that incorporates both positive and negative opinions and simulates information diffusion in real-world social networks. The influence spread functions under the LT-S model are neither monotone nor submodular which bring challenges to maximization. The RLP algorithm is proposed to tackle the issue, which is improved from R-Greedy algorithm by incorporating two proposed accelerating techniques, the live-edge based and propagation-path based techniques. The results of the extensive experiments on public real signed social network datasets demonstrate that our algorithm outperforms the baseline algorithms in terms of both efficiency and effectiveness.

No abstract