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Description
ESSER Paket 1 für Brandmeldecomputer IQ8Control CMit einem Mikromodulsteckplatz, VdS und Feuerwehroption. VdS Anerkennung: G 205129 Leistungsmerkmale: Max. zwei Mikromodule Max. zwei Ringmodule esserbus Kurzschluss und unterbrechungstoleranter Ringleitungsbetrieb mit Stichleitungsabgngen Ringbus Installation ber Fernmeldekabel I Y(ST)Y 0,8 mm bis zu einer max. Lnge von 3,5 km Bis zu 127 esserbus Teilnehmer (Brandmelder bzw. Handmelder) Meldergruppen pro Ringbus Bis zu 32 esserbus Koppler pro Ringbus
Mit einem Mikromodulsteckplatz, VdS- und Feuerwehroption.
VdS-Anerkennung: G 205129
Leistungsmerkmale:
- Max. zwei Mikromodule
- Max. zwei Ringmodule esserbus®
- Kurzschluss- und unterbrechungstoleranter Ringleitungsbetrieb mit Stichleitungsabgängen
- Ringbus-Installation über Fernmeldekabel I-Y(ST)Y 0,8 mm bis zu einer max. Länge von 3,5 km
- Bis zu 127 esserbus®-Teilnehmer (Brandmelder bzw. Handmelder)/Meldergruppen pro Ringbus
- Bis zu 32 esserbus®-Koppler pro Ringbus/Betrieb von Funkkomponenten (siehe Kapitel 15)
- Betriebsarten TM und PM nach DIN VDE 0833-2 zur Vermeidung von Falschalarmen
- Feuerwehrbedienfeld- und AÜE-Interface auf dem Peripheriemodul integriert
- Drei Sammelrelais, frei programmierbar, überwacht, potenzialfrei bis 24 V DC (auf dem Peripheriemodul)
- TTY oder RS485-Schnittstelle, RS 232 optional
- Vernetzbar im kurzschluss- und unterbrechungstoleranten essernet® mit bis zu 30 weiteren BMZ
- Anschluss an grafische Managementsysteme
- Bedienteil mit alphanumerischer Anzeige
- Ereignisspeicher für 10.000 Ereignisse
- Alle Mikromodule des Systems 8000 kompatibel
- Interface für internen Drucker
- Zwei Akkumulatoren überwacht anschließbar
- Überwachungseingang für externes Netzteil
- Integrale Notredundanz für Überwachungsflächen bis 48.000 m² oder mehr als 512 Brandmelder
Zusätzliche Leistungsmerkmale für esserbus®-PLus:
- Max. 2 Ringmodule esserbus®-PLus
- Busversorgte, synchron gesteuerte, akustische Alarmierungseinrichtungen nach DIN EN 54-3 mit Alarmton gemäß DIN 33404
- Optische Alarmgeber gem. EN 54-23
- Busversorgte Warntongeber und Signalgeber pro Ringbus (Konfiguration gemäß Projektierungsbeispiel esserbusPLus im Kataloganhang)
- Wiedereinschaltung der Signalgeber im Alarmfall nach einem Kurzschluss innerhalb von 5 Sekunden von VdS-Schadenverhütung geprüft und bestätigt
Technische Daten:
| Ausgangsspannung | 12 V DC |
| Akkukapazität | 2 x 12 Ah, 2 x 24 Ah im Erweiterungsgehäuse |
| Rel. Luftfeuchte | < 95 % |
| Farbe | blau, ähnlich Pantone 546 |
| Gewicht | ca. 6,5 kg |
| Abmessungen | B: 450 mm H: 320 mm T: 185 mm |
| Leistungserklärung | DoP-20827130701 |
Zusätzliche Informationen:
Aufnahme für max. 2 Akkus 12V/12 Ah (Art.-Nr. 018011).
Die Bedienteilfront muss gesondert bestellt werden, ist aber im Preis enthalten.
Lieferumfang:
- 1 x Zentrale im Gehäuse ohne Front 808003
- 1 x Bedienteilfront 7860xx
- 1 x Peripheriemodul 772479
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4.1 ★★★★★
Based on 7 reviews
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Product Reviews
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book.
For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch.
For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min).
From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures.
I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications.
Several textbook editions later, what is different about this new edition?
First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills.
Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like.
Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
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