Konzept einer automatisierten und modularen Befundungsstation in der wandlungsfähigen Produktion

Kaiser, Jan-Philipp; Mitschke, Norbert; Stricker, Nicole; Heizmann, Michael; Lanza, Gisela

doi:10.1515/zwf-2021-0070

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Kegiatan Word Games diharapkan akan membentuk pola pikir para siswa supaya didalam mengikuti seluruh proses pembelajaran di lembaga MI Nurul Mun'im, para siswa selalu mengikuti dengan gembira dan ceria sehingga para siswa mampu menangkap materi pembelajaran dengan mudah terlebih didalam proses membaca dan menulis bagi siswa kelas awal. Jan -Philips Kaiser mengatakan bahwa Pendekatan multisensori mendukung pengalaman pembelajaran yang lebih holistik, di mana siswa tidak hanya memahami konsep secara intelektual tetapi juga melibatkan aspek emosional dan fisik (Kaiser et al, 2021).…”

Section: Gambar 13 Bahan Pembelajaran Word Gamesunclassified

Gerakan Literasi melalui Metode Pembelajaran Jolly Phonics Untuk Mengembangkan Kemampuan Membaca dan Menulis Anak

Rozi,

Paputungan

2023

PJP

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Penelitian ini bertujuan untuk mengetahui bagaimana gerakan literasi untuk mengembangkan kemampuan membaca dan menulis anak di MI Nurul Mun'im dengan metode pembelajaran Jolly Phonics melalui kegiatan yang menyenangkan dan multisensori. Penelitian ini menggunakan penelitian kualitatif, penelitian ini menggambarkan secara sistematis mengenai fakta-fakta yang di temukan di lapangan. Teknik pengumpulan data yang digunakan dalam penelitian ini adalah teknik wawancara, observasi dan dokumentasi. Hasil penelitian menunjukkan bahwa model pembelajaran Jolly Phonics dinilai sesuai dengan kebutuhan belajar siswa MI Nurul Mun'im. Proses pembelajaran model Jolly Phonics yaitu melalui Pengenalan Huruf (Letter Word), Pembentukan Kata (Word Building), Membaca dan Menulis Bersama (Shared Reading and Writing), dan Permainan Kata (Games Word). Model pembelajaran Jolly Phonics menjadi model pembelajaran yang diharapakan sesuai dengan karakteristik belajar siswa saat ini dan akan menjadi model pembelajaran dasar yang akan dikembangkan sesuai dengan perkembangan global.

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Section: Gambar 13 Bahan Pembelajaran Word Gamesunclassified

Gerakan Literasi melalui Metode Pembelajaran Jolly Phonics Untuk Mengembangkan Kemampuan Membaca dan Menulis Anak

Rozi,

Paputungan

2023

PJP

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“…In the healthcare sector for the detection of disease types (Maqsood et al, 2019). Or in the context of product remanufacturing (e.g., batteries) during the initial diagnosis of products (Kaiser et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Cross-domain Transfer of Defect Features in Technical Domains Based on Partial Target Data

Schlagenhauf

Scheurenbrand

2023

IJPHM

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A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions. Following a contrastive learning approach, a CNN encoder is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.

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Sensor- and Data-Supported Sustainable Manufacturing

Fleischer,

Hansjosten,

Sawodny

et al. 2024

Encyclopedia of Sustainable Technologies

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Konzept einer automatisierten und modularen Befundungsstation in der wandlungsfähigen Produktion

Cited by 5 publications

References 9 publications

Gerakan Literasi melalui Metode Pembelajaran Jolly Phonics Untuk Mengembangkan Kemampuan Membaca dan Menulis Anak

Gerakan Literasi melalui Metode Pembelajaran Jolly Phonics Untuk Mengembangkan Kemampuan Membaca dan Menulis Anak

Cross-domain Transfer of Defect Features in Technical Domains Based on Partial Target Data

Sensor- and Data-Supported Sustainable Manufacturing

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