Results 1 to 10 of about 5,097,290 (214)

KAGRA: 2.5 generation interferometric gravitational wave detector [PDF]

Nature Astronomy, 2019
6 pages, 3 figures (quality of some figures has been reduced)
Akutsu T., Ando M., Arai K., Arai Y., Araki S., Araya A., Aritomi N., Asada H., Aso Y., Atsuta S., Awai K., Bae S., Baiotti L., Barton M. A., Cannon K., Capocasa E., Chen C. -S., Chiu T. -W., Cho K., Chu Y. -K., Craig K., Creus W., Doi K., Eda K., Enomoto Y., Flaminio R., Fujii Y., Fujimoto M. -K., Fukunaga M., Fukushima M., Furuhata T., Haino S., Hasegawa K., Hashino K., Hayama K., Hirobayashi S., Hirose E., Hsieh B. H., Huang C. -Z., Ikenoue B., Inoue Y., Ioka K., Itoh Y., Izumi K., Kaji T., Kajita T., Kakizaki M., Kamiizumi M., Kanbara S., Kanda N., Kanemura S., Kaneyama M., Kang G., Kasuya J., Kataoka Y., Kawai N., Kawamura S., Kawasaki T., Kim C., Kim J., Kim J. C., Kim W. S., Kim Y. -M., Kimura N., Kinugawa T., Kirii S., Kitaoka Y., Kitazawa H., Kojima Y., Kokeyama K., Komori K., Kong A. K. H., Kotake K., Kozu R., Kumar R., Kuo H. -S., Kuroyanagi S., Lee H. K., Lee H. M., Lee H. W., Leonardi M., Lin C. -Y., Lin F. -L., Liu G. C., Liu Y., Majorana E., Mano S., Marchio M., Matsui T., Matsushima F., Michimura Y., Mio N., Miyakawa O., Miyamoto A., Miyamoto T., Miyo K., Miyoki S., Morii W., Morisaki S., Moriwaki Y., Morozumi T., Musha M., Nagano K., Nagano S., Nakamura K., Nakamura T., Nakano H., Nakano M., Nakao K., Narikawa T., Naticchioni L., Quynh L. N., Ni W. -T., Nishizawa A., Obuchi Y., Ochi T., Oh J. J., Oh S. H., Ohashi M., Ohishi N., Ohkawa M., Okutomi K., Ono K., Oohara K., Ooi C. P., Pan S. -S., Park J., Arellano F. E. P., Pinto I., Sago N., Saijo M., Saitou S., Saito Y., Sakai K., Sakai Y., Sakai Y., Sasai M., Sasaki M., Sasaki Y., Sato S., Sato N., Sato T., Sekiguchi Y., Seto N., Shibata M., Shimoda T., Shinkai H., Shishido T., Shoda A., Somiya K., Son E. J., Suemasa A., Suzuki T., Suzuki T., Tagoshi H., Tahara H., Takahashi H., Takahashi R., Takamori A., Takeda H., Tanaka H., Tanaka K., Tanaka T., Tanioka S., Martin E. N. T. S., Tatsumi D., Tomaru T., Tomura T., Travasso F., Tsubono K., Tsuchida S., Uchikata N., Uchiyama T., Uehara T., Ueki S., Ueno K., Uraguchi F., Ushiba T., van Putten M. H. P. M., Vocca H., Wada S., Wakamatsu T., Watanabe Y., Xu W. -R., Yamada T., Yamamoto A., Yamamoto K., Yamamoto K., Yamamoto S., Yamamoto T., Yokogawa K., Yokoyama J., Yokozawa T., Yoon T. H., Yoshioka T., Yuzurihara H., Zeidler S., Zhu Z. -H.   +197 more
openaire   +8 more sources

Throughput and resolution with a next-generation direct electron detector

IUCrJ, 2019
Direct electron detectors (DEDs) have revolutionized cryo-electron microscopy (cryo-EM) by facilitating the correction of beam-induced motion and radiation damage, and also by providing high-resolution image capture.
Joshua H. Mendez, Atousa Mehrani, Peter Randolph, Scott Stagg   +3 more
doaj   +2 more sources

IDG-SemiAD: An Immune Detector Generation-Based Collaborative Learning Scheme for Semi-supervised Anomaly Detection in Industrial Cyber-physical Systems

International Journal of Computational Intelligence Systems
Anomaly detection is a critical line of defense to ensure the network security of industrial cyber-physical systems. However, a significant issue in the anomaly detection is the insufficient labels of anomaly classes.
Meng Huang, Wenshan Li, Junjiang He, Xiaolong Lan, Tao Li, Nian Zhang   +5 more
doaj   +2 more sources

Generating detectors from anomaly samples via negative selection for network intrusion detection [PDF]

Scientific Reports
Negative selection algorithms (NSAs), which simulate the human immune mechanism for network anomaly detection, represent a promising approach in this field.
Zhiyong Li, Xiang Wei, Chunyan Li, Jianhong Sun   +3 more
doaj   +2 more sources

Progress and future prospect of the CRAFFT project for the next generation UHECR observatory [PDF]

EPJ Web of Conferences, 2023
The next generation of ultra-high energy cosmic ray observations will require large detector arrays to achieve large statistics. In order to realize next-generation large-scale detector arrays, the Cosmic Ray Air Fluorescence Fresnel lens Telescope ...
Tameda Yuichiro, Tomida Takayuki, Ikeda Daisuke, Yamazaki Katsuya, Kim Jihyun, Nakamura Yuya, Kubota Yuto, Shibata Norimichi, Nishio Eiji, Murakami Miyato, Ishimoto Yasuki, Katayama Tomoki, Kobayashi Yuga   +12 more
doaj   +1 more source

Simulation and Analysis of the 3He Detector for Thermal Neutron Detection [PDF]

مجلة التربية والعلم, 2023
This research took advantage of the new features of the MCNPX 6.2 algorithm to model the 3He (n,p) nuclear reaction. The neutron source for the simulation is Am-Be, which emits neutrons with an average energy of 5 MeV.
Rabee Behnam Alkhayat
doaj   +1 more source

Olfactory Generalization in Detector Dogs [PDF]

Animals, 2019
Generalizing to target odor variations while retaining specificity against non-targets is crucial to the success of detector dogs under working conditions. As such, the importance of generalization should be considered in the formulation of effective training strategies.
Ariella Y. Moser, Lewis Bizo, Wendy Y. Brown   +2 more
openaire   +3 more sources

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection [PDF]

International Journal of Computational Intelligence Systems, 2011
A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance.
Wen Chen, Xiao-Jie Liu, Tao Li, Yuan-Quan Shi, Xu-Fei Zheng, Hui Zhao   +5 more
doaj   +1 more source

Neighborhood Detector Generation Algorithm Based on Clustering [PDF]

Jisuanji gongcheng, 2016
Neighborhood Negative Selection(NNS) algorithm needs to traverse the whole self-samples and leads to large amount of calculation.At the same time there are phenomena about overlap rate higher at matching stage.To address this issue,making in-depth study ...
ZHANG Fengbin,YANG Ze,GE Haiyang
doaj   +1 more source

Long-wave bilayer graphene/HgCdTe based GBp type-II superlattice unipolar barrier infrared detector

Results in Optics, 2023
Type-II superlattice (T2SL) material is used to design highly efficient next-generation infrared (IR) detectors. The flexibility of the material allows the integration of unipolar barrier IR detector architecture to suppress generation-recombination ...
Shonak Bansal
doaj   +1 more source