440 research outputs found

    Japan’s Peacekeeping at a Crossroads

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    This open access book examines why Japan discontinued its quarter-century history of troop contribution to UN Peacekeeping Operations (1992–2017). Japan had deployed its troops as UN peacekeepers since 1992, albeit under a constitutional limit on weapons use. Japan’s peacekeepers began to focus on engineering work as its strength, while also trying to relax the constraints on weapons use, although to a minimal extent. In 2017, however, Japan suddenly withdrew its engineering corps from South Sudan, and has contributed no troops since then. Why? The book argues that Japan could not match the increasing “robustness” of recent peacekeeping operations and has begun to seek a new direction, such as capacity-building support

    Japan’s Peacekeeping at a Crossroads

    Get PDF
    This open access book examines why Japan discontinued its quarter-century history of troop contribution to UN Peacekeeping Operations (1992–2017). Japan had deployed its troops as UN peacekeepers since 1992, albeit under a constitutional limit on weapons use. Japan’s peacekeepers began to focus on engineering work as its strength, while also trying to relax the constraints on weapons use, although to a minimal extent. In 2017, however, Japan suddenly withdrew its engineering corps from South Sudan, and has contributed no troops since then. Why? The book argues that Japan could not match the increasing “robustness” of recent peacekeeping operations and has begun to seek a new direction, such as capacity-building support

    Effective data collection scheme for real-spatial group communication over hybrid infra-ad hoc wireless networks

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    This paper presents an effective data collection scheme to provide group communications among appropriate members selected by each user’s geographic situation and preference (real-spatial information). When each user directly notifies central servers of user’s information via wireless network infrastructure (Wi-infra), message delivery latency and losses drastically increase due to the network congestion. Therefore, we employ representative nodes (RNs) selected in a distributed manner. The RN first collects the real-spatial information from neighboring nodes via an ad hoc network and then notifies the server via Wi-infra. From simulation experiments, our scheme can drastically reduce both message delivery latency and losses

    Fast Data-driven Greedy Sensor Selection for Ridge Regression

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    We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator which is trained based on the data. The proposed algorithm greedily selects sensors for minimization of the cost function of the estimator. Sensor selection which prevents the overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. Furthermore, we show that sensor selection can be accelerated by dimensionality reduction of the target variables without large deterioration of the estimation performance. The effectiveness of the proposed algorithm is verified for two real-world datasets. The first dataset is a dataset of sea surface temperature for sensor selection for reconstructing large data, and the second is a dataset of surface pressure distribution and yaw angle of a ground vehicle for sensor selection for estimation. The experiments reveal that the proposed algorithm outperforms some data-drive selection algorithms including the orthogonal matching pursuit

    Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems

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    The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied straightforwardly to the group-greedy method, and a customized method is also considered. In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods. This strategy compensates for the deterioration of the solution due to compressed sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and numerical experiments are conducted using randomly generated sensor candidate matrices with potential sensor locations of 10,000--1,000,000. The proposed method can provide better optimization results than those obtained by the original group-greedy method when a similar computational cost is spent as for the original group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the compressed sensor candidates by the randomized algorithm. Similar results were also obtained in the real dataset. The proposed method is effective for the E-optimality criterion, in which the objective function that the optimization by the common greedy method is difficult due to the absence of submodularity of the objective function. The idea of the present method can improve the performance of all optimizations using a greedy algorithm

    Magnetic and metallographical studies of the Bocaiuva iron meteorite

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    The Bocaiuva iron meteorite (IAB) has been studied magnetically and metallographically in order to understand its stable natural remanent magnetization (NRM). This meteorite consists of a large amount of 6-7wt% Ni kamacite, associated with taenite, plessite, schreibersite and magnetite. Tetrataenite less than 0.2% in volume occurs along the high-Ni taenite lamellae and in the kamacite domain walls beside its lamellae. The NRM direction is almost parallel to a dominant plane of tetrataenite development. The Bocaiuva may have acquired the NRM in the slow cooling process under 300℃ of the meteorite\u27s parent body or after shock heating by collisions

    Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise

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    The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint. The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations. The problem can be efficiently solved by the alternating direction method of multipliers, and the computational complexity of the proposed method is proportional to the number of potential sensor locations when it is used in combination with a low-rank expression of the measurement noise model. The advantage of the proposed method over existing sensor selection methods is demonstrated through experiments using artificial and real datasets
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