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Titlebook: Netzplantechnik; Willi Küpper,Klaus Lüder,Lothar Streitferdt Book 1975 Springer-Verlag Berlin Heidelberg 1975 Critical Path Method.Darstel

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發(fā)表于 2025-3-21 19:08:50 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Netzplantechnik
編輯Willi Küpper,Klaus Lüder,Lothar Streitferdt
視頻videohttp://file.papertrans.cn/664/663037/663037.mp4
圖書封面Titlebook: Netzplantechnik;  Willi Küpper,Klaus Lüder,Lothar Streitferdt Book 1975 Springer-Verlag Berlin Heidelberg 1975 Critical Path Method.Darstel
出版日期Book 1975
關(guān)鍵詞Critical Path Method; Darstellung; Entwicklung; Erfolg; Evaluation; Konzeption; Methoden; Netzplantechnik; O
版次1
doihttps://doi.org/10.1007/978-3-662-12568-7
isbn_softcover978-3-7908-0139-2
isbn_ebook978-3-662-12568-7
copyrightSpringer-Verlag Berlin Heidelberg 1975
The information of publication is updating

書目名稱Netzplantechnik影響因子(影響力)




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書目名稱Netzplantechnik網(wǎng)絡(luò)公開度




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Willi Küpper,Klaus Lüder,Lothar Streitferdtor decades. We have been developing professional and DIY real remote labs for 15?years. Despite our efforts, real remote experiments are unfortunately not so easily feasible as traditional hands-on ones. Experimenting at schools split into three isolated approaches: traditional hands-on labs (includ
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發(fā)表于 2025-3-22 01:00:26 | 只看該作者
Willi Küpper,Klaus Lüder,Lothar Streitferdtor decades. We have been developing professional and DIY real remote labs for 15?years. Despite our efforts, real remote experiments are unfortunately not so easily feasible as traditional hands-on ones. Experimenting at schools split into three isolated approaches: traditional hands-on labs (includ
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Willi Küpper,Klaus Lüder,Lothar Streitferdtder. This scenario appears in the context of the automotive industry, where the objective is to determine operational profiles that comprise frequently recurring driving behavior patterns. This problem can be solved by clustering, however, standard distance measures such as the dynamic time warping
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ey issues in designing violence detection algorithms are the choice of discriminative features and learning effective models. We employ low and mid-level audio-visual features and evaluate their discriminative power within the context of the MediaEval Violent Scenes Detection (VSD) task. The audio-v
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