nyuuzyou/kompy huggingface dataset:
O'qish-Yozish qo'li - 16-Mavzu: Qattiq disklar bilan ishlash

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16-Mavzu: Qattiq disklar bilan ishlash
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bet | 3/4 | Sana | 09.01.2024 | Hajmi | 109,65 Kb. | | #132848 |
O'qish-Yozish qo'li
O'qish-yozish qo'li o'qish-yozish boshini plastinaning yuzasida oldinga va orqaga siljitadigan harakatlantiruvchi mexanizm vazifasini bajaradi.
Markaziy shpindel
Plastinaning markaziga biriktirilgan mil qurilmani joyida ushlab turadi va diskning o'qi bo'ylab sezilarli darajada yuqori tezlikda bir tekis aylanishiga yordam beradi.
Magnit lagan
Magnit plastina odatda alyuminiy yoki shishadan iborat bo'lgan va uning yuzasida magnit qoplama qo'llaniladigan dumaloq diskdir. Bu qattiq diskning eng muhim qismlaridan biridir. Qattiq diskning magnit plitasi asosan magnitlanish jarayoni yordamida ma'lumotlarni saqlash uchun javobgardir. Qattiq disk bir-birining ustiga qo'yilgan va umumiy shpindelga o'rnatilgan bir qancha magnit plastinka disklaridan iborat. Plastinaning bir daqiqada aylanishi qurilmaning ishlash tezligiga to'g'ridan-to'g'ri proportsionaldir.
vilka
Qattiq diskning vilkasi qattiq diskni kompyuter tizimiga ulashga yordam beradi.
O'qish-Yozish boshi
O'qish-yozish boshi - qattiq diskdan ma'lumotlarni o'qish va ma'lumotlarni plastinka yuzasiga yozishning asosiy vazifasini bajaradigan o'qish-yozish qo'lining yuqori
qismiga biriktirilgan magnitning kichik qismi. Qattiq disk odatda har bir disk uchun ikkita o'qish va yozish kallagidan iborat. Boshlardan biri laganning yuqori qismiga biriktirilgan, ikkinchisi esa uning poydevori bilan yaqin aloqada joylashgan. Bu
qurilmaga plastinaning har ikki tomonida mavjud ma'lumotlarga kirish imkonini
beradi. Qurilmani haddan tashqari eskirish va yirtishdan himoya qilish uchun o'qish- yozish boshi sirt ustida harakatlanadi va o'qish-yozish boshi va plastinka orasiga suyuqlik yoki havo qatlami kiritiladi.
Elektron plata
Qattiq diskning elektron platasi odatda alyuminiy, shisha yoki keramik materialdan iborat. U qattiq diskning ichki sxemasiga o'rnatilgan va magnitlangan plastinaga va undan chiqadigan ma'lumotlar oqimini boshqarish orqali diskdagi ma'lumotlarni aylantirish uchun ishlatiladi.
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Qattiq diskning ishlash printsipi

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Qattiq diskning ishlash printsipi
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bet | 2/4 | Sana | 09.01.2024 | Hajmi | 109,65 Kb. | | #132848 |
Qattiq disk odatda ma'lumotlar va ma'lumotlarni saqlash uchun oddiy magnitlanish printsipi asosida ishlaydi. Qattiq disk odatda magnit materialdan iborat bo'lgan va plastinka sifatida tanilgan katta plastinkadan iborat. Plastinka odatda dumaloq shaklda qurilgan. Magnit plitaning yuzasi milliardlab mayda bo'limlarga bo'lingan. Kichkina joylarning magnitlanishi mustaqil ravishda amalga oshirilishi mumkin. Plitaning magnitlangan kichik maydoni ikkilik balandlikni bildiradi va ikkilik qiymat biriga teng; holbuki, demagnetizatsiyalangan kichik maydon ikkilik pastni bildiradi va ikkilik qiymat nolga teng. Bu qattiq diskda saqlanadigan harflar, raqamlar va boshqa ma'lumotlar shakllari ikkilik qiymatlar, ya'ni nollar yoki birliklar kombinatsiyasi ekanligini ko'rsatadi. Qattiq diskda saqlanadigan ma'lumotlarning eng kichik qismi bit deb nomlanadi. Materiallarni magnitlash jarayoni odatda disklarda ma'lumotni saqlash uchun afzaldir, chunki u elektr ta'minotini o'chirishga ta'sir qilmaydi. Uzoq vaqt davomida quvvat manbaiga ulanmagan bo'lsa ham, ma'lumotlar haydovchi tomonidan saqlanadi. Qattiq diskning magnitlangan qismi tashqi demagnetizatsiya qilinmaguncha magnitlangan bo'lib qoladi, bu esa ma'lumotlarni ishonchli saqlash imkonini beradi.
Qattiq diskning komponentlari
Qattiq disk odatda 9 ta asosiy komponentdan, ya'ni aktuator, o'qish-yozish qo'li, markaziy shpindel, magnit plastina, vilka, o'qish-yozish boshi, elektron plata, ulagich va kichik shpindeldan iborat.
Aktuator
Aktuator - bu elektr energiyasini mexanik energiyaga aylantirishga yordam beradigan oddiy qurilma. Bu erda aktuatorning asosiy maqsadi o'qish-yozish qo'lini boshqarishdir. Dastlab, step motorlar odatda qattiq disklar tomonidan o'qish-yozish qo'lini harakatlantirish uchun ishlatilgan, lekin tez orada ular aktuatorlar bilan almashtirildi, chunki step motorlar nisbatan sekinroq harakat qiladi, kamroq ishonchli, aniqlik yo'q va juda sezgir. harorat, bosim va boshqalar kabi muhitning jismoniy parametrlari. Qattiq disklarning eng so'nggi versiyalarining ba'zilarida aktuatorlar o'rnida ovozli bobinlar qo'llaniladi.
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Yakuniy Davlat Attestasiyasi sinovida talabalarning

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Yakuniy Davlat Attestasiyasi sinovida talabalarning
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bet | 6/7 | Sana | 22.02.2024 | Hajmi | 36,93 Kb. | | #160686 |
Bog'liq H. B. Mirzoqulov Muhammad al-Xorazmiy nomidagi tatu samarqand fi-fayllar.orgYakuniy Davlat Attestasiyasi sinovida talabalarning
«Mutaxassislik fanlari» dan imtihonlarini baholash
MEZONLARI
Savollarga javoblar quyidagi mezonlarga tayanib baholanadi:
“Mutaxassislik fanlari” bo‘yicha o‘tkaziladigan Yakuniy Davlat Attestasiyasida talabalar javobi 100 ballik tizim asosida ya’ni % larda baholanadi. Test topshirig‘ida 50 ta savol bo‘lib, savollarning har biri uchun 2 balldan belgilanadi.
1. «A’lo» (45- 50)
1. Talaba to’gri javob bergan savollari soni 45 tadan 50 tagacha bo’lishi zarur.
2. Javoblar varaqasida javoblar aniq a, b, c, d, (yoki A, B, C, D) lotin harflarida keltirilishi lozim.
3. Talaba tomonidan belgilanmagan javoblar 0 ballga baholanadi.
4. Javoblar varaqasida ko’rsatilgan barcha javoblar ko’k oddiy sharikli ruchkada aniq yozilishi shart (bo’yalgan javoblarga 0 ball beriladi).
2. «Yaxshi» (35 - 44)
1. Talaba to’gri javob bergan savollari soni 35 tadan 44 tagacha bo’lishi zarur.
2. Javoblar varaqasida javoblar aniq a, b, c, d, (yoki A, B, C, D) lotin harflarida keltirilishi lozim.
3. Talaba tomonidan belgilanmagan javoblar 0 ballga baholanadi.
4. Javoblar varaqasida ko’rsatilgan barcha javoblar ko’k oddiy sharikli ruchkada aniq yozilishi shart (bo’yalgan javoblarga 0 ball beriladi).
3. «O‘rta» (30 - 34)
1. Talaba to’gri javob bergan savollari soni 30 tadan 34 tagacha bo’lishi zarur.
2. Javoblar varaqasida javoblar aniq a, b, c, d, (yoki A, B, C, D) lotin harflarida keltirilishi lozim.
3. Talaba tomonidan belgilanmagan javoblar 0 ballga baholanadi.
4. Javoblar varaqasida ko’rsatilgan barcha javoblar ko’k oddiy sharikli ruchkada aniq yozilishi shart (bo’yalgan javoblarga 0 ball beriladi).
4. «Qoniqarsiz» (0-29)
1. Talaba to’gri javob bergan savollari soni 29 tadan kam bo’lganda.
2. Javoblar varaqasida javoblar aniq a, b, c, d, (yoki A, B, C, D) lotin harflarida keltirilishi lozim.
3. Talaba tomonidan belgilanmagan javoblar 0 ballga baholanadi.
4. Javoblar varaqasida ko’rsatilgan barcha javoblar ko’k oddiy sharikli ruchkada aniq yozilishi shart (bo’yalgan javoblarga 0 ball beriladi).
Ushbu 100 ball baholash turlari bo‘yicha quyidagicha taqsimlanadi:
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Microsoft software license terms

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Microsoft software license terms
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bet | 1/14 | Sana | 30.03.2021 | Hajmi | 198,13 Kb. | | #13751 |
MICROSOFT SOFTWARE LICENSE TERMS
MICROSOFT WINDOWS SOFTWARE DEVELOPMENT KIT FOR WINDOWS 8
These license terms are an agreement between Microsoft Corporation (or based on where you live, one of its affiliates) and you. Please read them. They apply to the software named above, which includes the media on which you received it, if any. The terms also apply to any Microsoft
updates,
supplements,
Internet-based services, and
support services
for this software, unless other terms accompany those items. If so, those terms apply.
By using the software, you accept these terms. If you do not accept them, do not use the software.
As described below, using some features also operates as your consent to the transmission of certain standard computer information for Internet-based services.
If you comply with these license terms, you have the perpetual rights below.
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Asosiy adabiyotlar - Naqd pulsiz hisob-kitob operatsiyalari hisobi

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Naqd pulsiz hisob-kitob operatsiyalari hisobi
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bet | 4/4 | Sana | 20.05.2024 | Hajmi | 151,5 Kb. | | #246790 |
Bog'liq Naqd pulsiz hisob-kitob operatsiyalari hisobi
«O’zbekiston Respublikasi Markaziy banki to’g’risida»gi O’zbekiston Respublikasi Qonuni. 1995 y. 26 dekabr.
«Banklar va bank faoliyati to’g’risidagi»gi O’zbekiston Respublikasi Qonuni. 1996 y. 25 aprel.
O’zbekiston Respublikasi Prezidentining «Hisob-kitoblar mexanizmini takomillashtirish va respublika hamda mahalliy byudjetlarga to’lovlarning o’z vaqtida kelib tushishini rag’batlantirish chora-tadbirlari to’g’risida»gi Farmoni. 1998 y. 4 fevral.
O’zbekiston Respublikasi Markaziy bank buxgalteriya hisobining hisobvaraqlar rejasi.
O’zbekiston Respublikasi tijorat banklarining buxgalteriya hisobining hisobvaraqlar rejasi.
O’zbekiston Respublikasida «Naqd pulsiz hisob-kitoblarni tashkil qilish haqida»gi 60-sonli Nizom. 2002 y. 12 yanvar.
O’zbekiston Respublikasi banklarida ochiladigan bank hisobvaraqlari haqidagi 1-sonli yo’riqnoma. 2001 y. 6 oktyabr.
O’zbekiston Respublikasi banklarida buxgalteriya hisobi va hisoboti to’g’risidagi 61-sonli yo’riqnoma. 1997 y. 5 sentyabr.
Abdullaeva Sh.Z. «Pul, kredit va banklar» Toshkent 2000 y.
10.Abdullaeva Sh.Z, Navro’zova K.N. «Banklarda buxgalteriya hisobi asoslari» O’quv qo’llanma T. 2001 y.
11.Murugova I.A. Navro’zova K.N. «Osnova buxgalterskogo ucheta bankax» T. 2001 g.
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Ball 
  

Baho - Muxammad al-xorazmiy nomidagi

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Muxammad al-xorazmiy nomidagi
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bet | 7/7 | Sana | 22.02.2024 | Hajmi | 36,93 Kb. | | #160686 |
Bog'liq H. B. Mirzoqulov Muhammad al-Xorazmiy nomidagi tatu samarqand fi-fayllar.orgBall
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Baho
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Baxo (raqamda)
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90-100
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A’lo
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5
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70-89
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Yaxshi
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4
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60-69
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Qoniqarli
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3
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0-59
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Qoniqarsiz
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2
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http://fayllar.org
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Transfer Learning - Age and Gender Prediction using Deep cnns and Transfer Learning

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Network Architecture. The tasks tackled using this transfer learning approach include
age estimation and gender classification. Following is the network architecture we used
in our models to train on top of features extracted.
For the gender classification, for convenience, we chose custom model names
VGG_f_gender, ResNet50_f_gender and SENet50_f_gender whose design as follows.
VGG_f_gender comprises of 2 blocks, each containing layers in order of batch normal-
ization, spatial dropout with drop probability of 0.5, separable convolutions layers with
512 filters of size 3x3 with keeping padding same to reduce loss of information during
convolution operations followed by max pooling with kernel size 2x2. The fully con-
nected system consisted of batch norm layers, followed by alpha dropout, and 128 neu-
rons with ReLU activation and He uniform initialized followed by another batch norm
layer and finally the output layer with 1 neuron with sigmoid activation. Batch size
chosen was 64. ResNet50_f_gender comprises of just the fully connected system with
batch norm, dropout with probability of 0.5, and followed by 128 units with exponential
linear units (ELU) activation, with He uniform initialization and having max norm
weight constraint of magnitude 3. The output layer had single neuron with sigmoid
activation. The batch size we chose for this was 128. For, SENet50_f_gender we kept
the same model as for ResNet50_f_gender.
For the age estimation the models have been named VGG_f_age, ResNet50_f_age
and SENet50_f_age. VGG_f_age consists of 2 convolution blocks each containing in
order, a batch norm layer, spatial dropout with keep probability of 0.8 and 0.6 respec-
tively, separable convolution layer with 512 filters of size 3x3, padding same so that
dimension doesn’t change (and information loss is curtailed), with ReLU activation
function and He initialization. Each convolution block was followed by max pooling
with kernel size 2x2. The fully connected system consisted of 3 layers with 1024, 512,
128 neurons respectively, with a dropout keep probability of 0.2, 0.2, and 1. Each layer
had ELU activation function with He uniform initialization. The output layer had one
unit, ReLU activation function with He uniform initialization and batch normalization.
Batch size of 128 was chosen. ResNet50_f_age consists of a fully-connected system of
5 layers with 512, 512, 512, 256, 128 units with dropout with keep probability of 0.5,
0.3, 0.3, 0.3 and 0.5 respectively. Each of the layers contains batch normalization and
has Scaled Exponential Linear Unit (SELU) as the activation function. Like previously,
for SENet50_f_age we kept the same model as for ResNet50_f_age.
Age and Gender Prediction using Deep cnns and Transfer Learning

Age and Gender Prediction using Deep cnns and Transfer Learning
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12-maruza Energiya samarali yarim o‘tkazgichli boshqariladigan to‘g‘rilagichlar

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12-maruza Energiya samarali yarim o‘tkazgichli boshqariladigan to‘g‘rilagichlar
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bet | 1/2 | Sana | 05.12.2023 | Hajmi | 0,76 Mb. | | #111924 |
Bog'liq 12-maruza
12-maruza
Energiya samarali yarim o‘tkazgichli boshqariladigan to‘g‘rilagichlar
Reja:
1. Energiya samarali bir va uch fazali yarim o‘tkazgichli boshqariluvchi to‘g‘rilagichlarning turlari, vaqt diagrammalari, solishtirma tahlili.
2. Bir va uch fazali yarim o‘tkazgichli boshqariluvchi to‘g‘rilagichlarning energiya samaradorligini oshirish usullari.
1. Energiya samarali bir va uch fazali yarim o‘tkazgichli boshqariluvchi to‘g‘rilagichlarning turlari, vaqt diagrammalari, solishtirma tahlili.
Boshqariluvchi to‘g‘rilagichlar boshqarilmaydigan to‘g‘rilagichlar singari turlarga bo‘linadi:
─ Bir fazali bitta yarim davrli to‘g‘rilagich;
─ Bir fazali ikkita yarim davrli to‘g‘rilagich;
─ Bir fazali kuchlanishni ikkilantiruvchi to‘g‘rilagich;
─ Bir fazali ko‘prik sxemali to‘g‘rilagich;
─ Uch fazali o‘rta nuqtali to‘g‘rilagich;
─ Uch fazali ko‘prik sxemali to‘g‘rilagich;
─ Ko‘p fazali to‘g‘rilagich sxemalari.
1-rasm. Ikkita yarim davrli to‘g‘rilagich sxemasi va vaqt diagrammalari
Bir fazali, ko‘prik chizmali to‘g‘rilagichlar, uning tuzilishi va vaqt diagrammalari.
2-rasm. Bir fazali ko‘prik chizmali to‘g‘rilagich sxemasi va vaqt diagrammasi
Kuchlanishni ikkilantirib ko‘paytirish sxemasi va vaqt diagrammalari
3-rasm. Kuchlanishni ikkilantirib ko‘paytirish sxemasi va vaqt diagrammasi
Rostlashning eng ko‘p tarqalgan usuli ─ bu to‘g‘rilagichlarning ventil elementlariga bevosita ta’sir etish usulidir. Shu maqsadda ventil elementlari sifatida boshqariluvchi ventillar, masalan tiristorlardan foydalaniladi.
«Boshqariluvchi to‘g‘rilagichlar» nomi odatda bevosita boshqariluvchi ventillardan tuzilgan to‘g‘rilagichlarga tegishli bo‘ladi. Boshqariluvchi va boshqarilmaydigan ventilli to‘g‘rilagichlar sxemalari ishlash prinsiplari bo‘yicha ko‘p tomondan o‘xshaydi. Misol uchun bir fazali boshqariluvchi bir yarim davrli to‘g‘rilash sxemasida to‘g‘rilash jarayonini ko‘rib chiqamiz.
To‘g‘rilash jarayoni to‘g‘rilash sxemasining bevosita ventil elementlari tomonidan amalga oshiriladi. Mazkur sxema texnik adabiyotda ba’zan bir fazali bir taktli sxema deyiladi. Sxema transformator, ventil (boshqariluvchi ventil ─ tiristor), aktiv yuklama bo‘lgan rezistor ─ Rd (4-rasm). Transformatorning birlamchi chulg‘amiga oziqlantirish tarmog‘idan o‘zgaruvchan sinusoidal kuchlanish beriladi. Transformatorning ikkilamchi chulg‘amida ham sinusoidal kuchlanish bo‘ladi.
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References - Age and Gender Prediction using Deep cnns and Transfer Learning

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References
1. H. Han, C. Otto, and A. K. Jain, “Age estimation from face images: Human vs. machine per-
formance,” in Proc. Int. Conf. BTAS, Jun. 2013, pp. 1–8.
2. UTKFace. (n.d.). Retrieved July 14, 2020, from http://aicip.eecs.utk.edu/wiki/UTKFace
3. IMDB-WIKI – 500k+ face images with age and gender labels. (n.d.). Retrieved July 14, 2020,
from https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
4. Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep Face Recognition. Procedings of the
British Machine Vision Conference 2015. doi:10.5244/c.29.41
5. Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018). VGGFace2: A Dataset for
Recognising Faces across Pose and Age. 2018 13th IEEE International Conference on Auto-
matic Face & GestureRecognition (FG 2018). doi:10.1109/fg.2018.00020
6. Akhand, M. A., Sayim, M. I., Roy, S., & Siddique, N. (2020). Human Age Prediction from
Facial Image Using Transfer Learning in Deep Convolutional Neural Networks. Proceedings
of International Joint Conference on Computational Intelligence Algorithms for Intelligent Sys-
tems, 217-229. doi:10.1007/978-981-15-3607-6_17
7. A. Fariza, Mu’arifin and A. Z. Arifin, "Age Estimation System Using Deep Residual Network
Classification Method," 2019 International Electronics Symposium (IES), Surabaya, Indonesia,
2019, pp. 607-611,doi: 10.1109/ELECSYM.2019.8901521.
8. Angulu, R., Tapamo, J. R., & Adewumi, A. O. (2018). Age estimation via face images: A sur-
vey. EURASIP Journal on Image and Video Processing, 2018(1). doi:10.1186/s13640-018-
0278-6
9. Hu, L., Li, Z., & Liu, H. (2015). Age Group Estimation on Single Face Image Using
Blocking ULBP and SVM. Proceedings of the 2015 Chinese Intelligent Automation Confer-
ence Lecture Notes in Electrical Engineering, 431-438. doi:10.1007/978-3-662-46469-4_46
10. G. Guo, Y. Fu, T. S. Huang and C. R. Dyer, "Locally Adjusted Robust Regression for Human
Age Estimation," 2008 IEEE Workshop on Applications of Computer Vision, Copper Moun-
tain, CO, 2008, pp. 1-6, doi: 10.1109/WACV.2008.4544009.
11. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to
document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791
12. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep
convolutional neural networks." Advances in neural information processing systems. 2012.
13. Rothe, Rasmus, Radu Timofte, and Luc Van Gool. "Dex: Deep expectation of apparent age
from a single image." Proceedings of the IEEE International Conference on Computer Vision
Workshops. 2015.
14. X. Wang, R. Guo, and C. Kambhamettu, “Deeply-learned feature for age estimation,” in Proc.
IEEE Winter Conf. Appl. Comput. Vision, 2015, pp. 534–541.
15. Reddi, S. J., Kale, S., & Kumar, S. (2018). On the convergence of Adam and beyond. ICLR
2018.
16. Viola, P., & Jones, M. (n.d.). Rapid object detection using a boosted cascade of simple
features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision
andPattern Recognition. CVPR 2001. doi:10.1109/cvpr.2001.990517
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Introduction - Age and Gender Prediction using Deep cnns and Transfer Learning

2
do better? Can we automate this problem in a bid to reduce human dependency and to
simultaneously obtain better results?
One must acknowledge that aging of face is not only determined by genetic factors
but it is also influenced by lifestyle, expression, and environment [1]. Different
people
of similar age can look very different due to these reasons. That is why predicting age
is such a challenging task inherently. The non-linear relationship between facial images
and age/gender coupled with the huge paucity of large and balanced datasets with cor-
rect labels further contribute to this problem.
Very few such datasets exist,
majority
datasets available for the task are highly imbalanced with a huge chunk of people lying
in the age group of 20 to 75 [3]-[5] or are biased towards one of the genders. Use of
such biased datasets is not prudent as it would create a
distribution mismatch when
deployed for testing on real-time images, thereby giving poor results.
This field of study has a huge amount of underlying potential. There has been an
ever-growing interest in automatic age and gender prediction because of the huge po-
tential it has in various fields of computer science such as HCI (Human Computer In-
teraction). Some of the potential applications include forensics, law enforcement [1],
and security control [1]. Another very practical application involves incorporating these
models into IoT. For example, a restaurant can change its theme by estimating the av-
erage age or gender of people that have entered so far.
The remaining part of the paper is organized as follows. Section 2 talks about the
background and work done before in this field and how it inspired us to work. Section
3 contains the exact technical details of the project and is
further divided into three
subsections. Section 4 talks about the evaluation metric used. Section 5 presents the
various experiments we performed along with the results we obtained, and finally sec-
tion 6 wraps up the paper with conclusion and future work.
Matlab simullik dasturiy kompleksi constant, scope, gain, itegrator bloklari bilan ishlash

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Matlab simullik dasturiy kompleksi constant, scope, gain, itegrator bloklari bilan ishlash
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bet | 1/5 | Sana | 10.10.2024 | Hajmi | 1,02 Mb. | | #274404 |
Bog'liq 1-LAB. Raximovdocx
1-LABORATORIYA ISHI
MATLAB SIMULLIK DASTURIY KOMPLEKSI CONSTANT, SCOPE , GAIN, ITEGRATOR BLOKLARI BILAN ISHLASH
Ishning maqsadi:
Ushbu laboratoriya ishining maqsadi Simulink yordamida oddiy dinamik tizimni yaratish va tahlil qilish, shuningdek, signalning vaqt bo'yicha o'zgarishlarini kuzatishdir.
MATLAB Simulink dasturida Constant, Scope, Gain, va Integrator bloklari signal va tizim modellari bilan ishlash uchun muhim hisoblanadi.
1. Constant bloki
Constant bloki Simulink modelida doimiy qiymat (konstant) chiqaradi. Bu blokdan statik (o'zgarmas) qiymatlarni boshqa bloklarga uzatish uchun foydalaniladi.
Qo'llanilishi:
Modelga doimiy signal manbai sifatida ulash.
Har qanday turdagi signallarni olish uchun foydalanish mumkin (masalan, 1, 5, yoki biron-bir aniq qiymat).
2. Scope bloki
Scope bloki kiruvchi signalni real vaqt rejimida kuzatish imkonini beradi. Bu blok grafik ko'rinishda signalning qanday o'zgarishini ko'rsatadi.
Qo'llanilishi:
Modeldagi signallarni visualizatsiya qilish.
Modeldagi signal o'zgarishlarini tahlil qilish.
3. Gain bloki
Gain bloki signalni biror koeffitsientga ko'paytirish yoki kuchaytirish uchun ishlatiladi.
Masalan, agar signal u(t) bo'lsa, Gain bloki natijani k⋅u(t)k \cdot u(t) ko'rinishida hosil qiladi, bu yerda k — koeffitsient.
Qo'llanilishi:
Tizim parametrlari bilan signalni moslashtirish.
Signal kuchini yoki amplitudasini oshirish yoki kamaytirish.
4. Integrator bloki
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Methodology - Age and Gender Prediction using Deep cnns and Transfer Learning

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Age and Gender Prediction using Deep cnns and Transfer Learning3
Methodology
3.1
Dataset
In this paper, we use the UTKFace dataset [2] (aligned and cropped) consists of over
20,000 face images with annotations of age, gender, and ethnicity. It has a total of 23708
images of which 6 were missing age labels. The images cover large variations in facial
expression, illumination, pose, resolution and occlusion. We chose this dataset because
of its relatively more uniform distributions, the diversity it has in image characteristics
such as brightness, occlusion and position and also because it involves images of the
general public.
Some sample images from the UTKFace dataset can be seen in Fig. 1. Each image
is labeled with a 3-element tuple, with age (in years), gender (Male-0, Female-1) and
races (White-0, Black-1, Asian-2, Indian-3 and Others-4) respectively.
Fig. 1. Sample images from the UTKFace dataset.
For both our approaches (custom CNN and transfer learning based models), we used
the same set of images for training, testing and validation, to have standardized results.
This was done by dividing the data sets into train, test and validation in 80 : 10 : 10
ratios. This division was done while ensuring that the data distribution in each division
remains roughly the same, so that there is no distribution mismatch while training and
testing the models. The Table 1 and Table 2 show the composition of training, valida-
tion and test data with respect to gender and age respectively.
4
Table 1. Composition of sets by gender
Gender
Training
Validation
Test
Total
Male
9900
1255
1234
12389
Female
9061
1115
1137
11313
Total
18961
2370
2371
23702
Table 2. Composition of sets by age
Age Group
Training
Validation
Test
Total
0-10
2481
303
278
3062
11-20
1222
150
158
1530
21-30
5826
765
753
7344
31-40
3618
462
456
4536
41-50
1767
223
254
2244
51-60
1858
214
226
2298
61-70
1057
137
122
1316
71-80
577
57
65
699
81-90
413
45
46
504
91-100
114
11
12
137
101-116
28
3
1
32
Total
18961
2370
2371
23702
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Related Work - Age and Gender Prediction using Deep cnns and Transfer Learning

3
to extract features. This happens by convolving over the given image to generate invar-
iant features which are passed onto the next layer in a sequential fashion. It
is this con-
tinual passing of information from one layer to the next that leads to CNNs being so
robust and supple to occlusions, brightness changes etc.
The first application of CNNs was the Le-Net-5 [11]. However, the actual boom in
using CNNs for age and gender prediction started after D-CNN [12] was introduced for
image classification tasks. Rothe et al. [13] proposed DEX: Deep EXpectation of Ap-
parent Age for age classification using an ensemble of 20 networks on the cropped faces
of IMDB-Wiki dataset. Another popular work includes combining features
from Deep
CNN to features obtained from PCA done by Wang et al. [14].
Training & Testing - Age and Gender Prediction using Deep cnns and Transfer Learning

6
(a) (b) (c)
Fig. 2. Training plots depicting loss by the epoch of a) Age Estimation b) Age Classification
c) Gender Classification
Table 4 shows the lowest loss value to which our model could converge while training.
Table 4. Minimum loss value
Age
Estimation
Age
Classification
Gender
Classification
Train
Validation
Train
Validation
Train
Validation
56.8346
56.7397
0.3886
0.4009
0.1037
0.1864
The next subsection explores our work using transfer learning.
Experimentation and Results

9
It is apparent that separable convolution coupled with spatial dropout (in the convolu-
tional layers) helped the model in converging faster and generalize better. This is be-
cause, separable convolutions consist of first performing a depth wise spatial convolu-
tion (which acts on each input channel separately) followed by a pointwise convolution
which mixes the resulting output channels. Basically, separable convolutions factorize
a kernel into 2 smaller kernels,
leading to lesser computations,
thereby helping the
model to converge faster.
Then we experimented with other arguments associated with the namely the type of
weight initialization and weight constraints which determine the final weights of our
model and hence its performance. Table 6 summarizes the results of this experiment.
Table 6. Comparison of models based on the arguments of the best performing layer
Layers
Configuration
Age Estimation
(MAE)
Age Classification
(Accuracy)
Gender Classification
(Accuracy)
Separable Conv2D +
Spatial dropout + Xavier
uniform
initialization
6.08
78.279
91.269
Separable Conv2D +
Spatial dropout + He uni-
form initialization
Training & Testing - Age and Gender Prediction using Deep cnns and Transfer Learning

8
for better convergence. The choice of optimizer was based on the experiments carried
out while training our custom CNN architecture and theory [15].
4
Evaluation
The performance of the age estimation algorithms is evaluated based on the closeness
of the predicted value to the actual value. The metrics widely used for the age estimation
as a regression task is the mean absolute error or MAE
which captures the average
magnitude of error in a set of predictions. MAE calculates the absolute error between
actual age and predicted age as defined by the equation (1).
𝑀𝐴𝐸 =
1
𝑛
∑
|𝑦
𝑗
− 𝑦
𝑗
̂ |
𝑛
𝑗=1
(1)
Where n is the number of testing samples,
𝑦
𝑗
denotes the ground truth age and
𝑦
𝑗
̂ is the
predicted age of the j-th sample.
For classification tasks (age and gender), the evaluation metric used was accuracy
which denotes the fraction of correctly classified samples over the total number of sam-
ples.
Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»

Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»
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Bosh sahifa
Aloqalar Bosh sahifa Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»
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Tizimingizni qanday xavfsiz qilish kerak

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Tizimingizni qanday xavfsiz qilish kerak
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bet | 2/4 | Sana | 22.07.2024 | Hajmi | 20,56 Kb. | | #268182 |
Bog'liq Xushyorov Asadbekning Ingliz tiliTizimingizni qanday xavfsiz qilish kerak:
Tizim ma'lumotlarini xavfsiz va xavfsiz saqlash uchun siz quyidagi choralarni ko'rishingiz kerak:
1. Har doim ma'lumotlaringizning zaxira nusxasini saqlang.
2. Xavfsizlik devori dasturini o'rnating va uni har safar yangilab turing.
3. Kuchli va buzish qiyin parollardan foydalaning (bosh va kichik alifbolar, raqamlar va maxsus belgilarga ega).
4. Antivirus/anti-josus dasturlarni o'rnating va uni har safar yangilab turing.
5. To'liq tizimingizni o'z vaqtida skanerlang.
6. Har qanday dasturni o'rnatishdan oldin uni o'rnatish xavfsiz yoki yo'qligini tekshiring (Antivirus dasturidan foydalangan holda).
7. Qo'shimchalarni o'z ichiga olgan elektron pochta xabarlarini o'qishda ehtiyot bo'ling.
8. Tizimingizni doimo yangilab turing.
sKOMPYUTER VIRUSI NIMA?
Virus - bu sizning bilimingiz yoki ruxsatingizsiz ishlash usulini o'zgartirish orqali kompyuteringizga salbiy ta'sir ko'rsatish uchun ishlab chiqilgan va yozilgan dasturiy ta'minot. Odatda virus ikki xil funktsiyaga ega:
Bir fayldan ikkinchi faylga sizning kiritishingiz yoki bilimingizsiz tarqaladi. Texnik jihatdan, bu o'z-o'zini ko'paytirish va ko'paytirish deb nomlanadi.
Jinoyatchi tomonidan rejalashtirilgan simptom yoki zararni amalga oshiradi. Bunga diskni o'chirish, dasturlarni buzish yoki shunchaki kompyuteringizni buzish kiradi.
Virusli koddagi noto'g'ri dasturlash va aniq xatolar tufayli zarar etkazadigan sezilarli miqdordagi viruslar mavjud. Zararli virus bir yoki bir nechta dasturlarni o'zgartirishi mumkin, shunda u kerak bo'lganidek ishlamaydi. Infektsiyalangan dastur g'ayritabiiy tarzda tugashi, hujjatlaringizga noto'g'ri ma'lumot yozishi mumkin.
Viruslar nima qiladi
Ba'zi viruslar dasturlarni buzish, fayllarni o'chirish yoki butun qattiq diskni o'chirish orqali kompyuteringizdagi ma'lumotlarga zarar etkazish uchun maxsus dasturlashtirilgan. Hozirda ma'lum bo'lgan Macintosh viruslarining aksariyati hech qanday zarar etkazish uchun mo'ljallanmagan. Biroq, virus ichidagi xatolar (dasturlash xatolari) tufayli infektsiyalangan tizim tartibsiz harakat qilishi mumkin.
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Viruslar nima qilmaydi - Abaut my self My name is Rivojiddin. I am Wakeddin

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Abaut my self My name is Rivojiddin. I am Wakeddin
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Bog'liq Xushyorov Asadbekning Ingliz tiliViruslar nima qilmaydi
Kompyuter viruslari yozishdan himoyalangan disklardagi fayllarga zarar etkazmaydi va hujjatlarga zarar yetkazmaydi, faqat Word 6.0 yoki undan yuqori versiyalarida yozilgan hujjatlar va shablonlarni zararlovchi Word makro viruslari bundan mustasno. Ular siqilgan fayllarni ham yuqtirmaydi. Biroq, siqilgan fayl ichidagi ilovalar siqilishdan oldin zararlangan bo'lishi mumkin. Viruslar, shuningdek, monitor yoki kompyuter chiplari kabi kompyuter uskunasiga ham zarar yetkazmaydi; ular faqat dasturiy ta'minotni yuqtirishadi.
Hozirgi kunda viruslar soni 55 000 ga yaqin. U doimiy ravishda ortib bormoqda. Viruslarning yangi noma'lum turlari paydo bo'ladi. Ularni tasniflash tobora qiyinlashib bormoqda. Umumiy holda ularni uchta asosiy belgi bo'yicha ajratish mumkin: joylashish joyi, foydalanilgan operatsion tizim va ish algoritmlari.
Top 5 eng keng tarqalgan IT muammolari
1. Eskirgan IT infratuzilmasi
Hech kimga sir emaski, innovatsiyalar har qanday biznes muvaffaqiyatining kalitidir. Biroq, ko'p hollarda muammo yangi tizimlar va vositalarni joriy etishdir. Bu vazifa IT bo'limlari uchun yanada qiyinroq. Agentlar ko'pincha kundalik talablarga e'tibor qaratadilar va yangi vositalarni amalga oshirish uchun etarli vaqtga ega emaslar.
Bundan tashqari, ular juda cheklangan resurslar bilan nosozliklarni bartaraf etishlari va bir nechta biznes bo'linmalariga xizmat ko'rsatishlari kerak. Bu ular tomonidan ko'p vaqt va kuch talab qiladi, bu esa innovatsiyalarni kechiktiradi.
2. Kutish vaqtini oshirish
Kompaniyada qanchalik zarur texnologiya bo'lsa, so'rovlarga tezda javob berishga ehtiyoj kuchayadi. Muammo tegishli aloqa vositalari mavjud bo'lmaganda va olingan so'rovlar ko'p bo'lganda paydo bo'ladi. Jarayon sekinlashadi, ma'lumotlar yo'qoladi va ba'zi so'rovlar hatto e'tibordan chetda qolishi mumkin.
AT muammolarini kuzatish uchun tuzilgan tizimsiz IT bo'limi takroriy nosozliklarni aniqlashga yordam beradigan ma'lumotlarni to'play olmaydi. Bu javob vaqtini ko'paytirish va sifatsiz xizmat ko'rsatishga olib keladi.
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Inson iste’dodining yetishmasligi

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Inson iste’dodining yetishmasligi
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Bog'liq Xushyorov Asadbekning Ingliz tili3. Inson iste’dodining yetishmasligi
Texnologiya taraqqiyoti sari, kompaniyalar ularga qadam qo'yishga yordam beradigan malakali xodimlarni topishga qiynalmoqda. Ushbu iste'dod etishmasligi tashkilotlarning o'sishiga va mijozlar bilan munosabatlariga ta'sir qildi. Gartner tomonidan olib borilgan tadqiqotda, IT-menejerlar bugungi kunda ular duch kelayotgan eng katta toʻsiq bu yangi texnologiyalarni oʻzlashtira olmaslik ekanligini taʼkidladilar. Bu hatto amalga oshirish va xavfsizlik xavflari bilan bog'liq xarajatlardan ham oshib ketadigan narsadir.
4. Takroriy IT muammolari hujjatlashtirilmaydi
Bu masala oldingi nuqta bilan chambarchas bog'liq. Bunday holda, agar IT bo'limlari o'z muammolarini kuzatib bora olmasalar, kompaniyaning resurslari bir xil muammoni qayta-qayta hal qilish uchun sarflanadi. Agar voqea sodir bo'lganida ma'lumot to'plashning iloji bo'lmasa, IT bo'limi muvaffaqiyatsizliklarni bartaraf etishga va so'rovlar sonini kamaytirishga yordam beradigan harakatlar rejalarini tuza olmaydi. Bunday hollarda muammolarga ularning kelib chiqishidan hujum qilish yaxshidir.
5. AT aktivlari inventarizatsiyasi eskirgan
IT muammolari odatda bir guruh foydalanuvchilar uchun tayinlangan qurilma bilan bog'liq, shuning uchun AT mutaxassislari nosozlikni aniqlash va samarali yechimni qo'llash uchun ma'lumot to'plashlari kerak. Masalan, bir guruh foydalanuvchilar bir xil raqamli aktivga oid muammo haqida xabar berganda, AT texnik xodimlari nosozlik manbasini topish va uni tuzatishga kirishish uchun xizmat tarixini tekshirishlari kerak.
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Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз» - bet 6

Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»
Шубина С.В ТБп-1502
|
Bosh sahifa
Aloqalar Bosh sahifa Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»
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Система противопожарной защиты зданий и сооружений

19
Для передачи тревожных извещений на прибор «Рубеж – 08»
организовано 73 шлейфа пожарной сигнализации, которые подключены к
сетевым контроллерам шлейфов сигнализации (БЛ 1…БЛ 24).
При срабатывании пожарного извещателя в любом из 73 пожарных
шлейфов соответствующий сетевой контроллер передает полученную
информацию на процессор центрального блока.
ВВЕДЕНИЕ - Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»

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Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»Bog'liq Шубина С.В ТБп-1502ВВЕДЕНИЕ
Пожар – чрезвычайное происшествие, возникшее в результате
возгорания горючих веществ и материалов и представляющий угрозу для
людей и их материальных ценностей.
Обеспечение пожарной безопасности на производстве имеет особую
актуальность, так как ежегодно пожар уносит жизнь большого количества
людей, и приносит большой материальный ущерб.
Возникновение пожара на ПАО «АВТОВАЗ» может причинить вред
жизни и здоровью сотрудникам, а также нарушить частично или полностью
конструкцию зданий.
Основными причинами возникновения пожара являются:
- не соблюдение правил пожарной безопасности;
- не обучение сотрудников пожарно-техническому минимуму;
- халатное отношение к проведению противопожарного инструктажа,
либо не проведение инструктажа;
- не проведение практических тренировок по эвакуации;
- не исправность электрической проводки;
- отсутствие первичных средств пожаротушения.
Причин, по которым может возникнуть пожар множество, но при
соблюдении элементарных требований пожарной безопасности со стороны
руководства и сотрудников, пожаров и тяжелых последствий можно
избежать. Поэтому тема данной работы актуальна и направлена на оценку и
разработку противопожарной защиты в ПАО «АВТОВАЗ»
ПАО «АВТОВАЗ» является одной из крупнейшей компании, по
производству легковых автомобилей в России и Восточной Европы,
основанный в 1966 году.
Целью данной работы является расчет пожарных рисков в корпусе 01/8
(в месте пересечения с ПОУ-7) в ПАО «АВТОВАЗ».
7
Оценка пожарного риска проводится в целях определения соответствия
объекта защиты требованиям пожарной безопасности в порядке,
установленном Федеральным законом от 22.07.2008 г. № 123-ФЗ
«Технический регламент о требованиях пожарной безопасности» и
нормативными правовыми актами Российской Федерации.
|
|
Bosh sahifa
Aloqalar
Bosh sahifa
Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз»
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Deep CNNs - Age and Gender Prediction using Deep cnns and Transfer Learning

5
Table 3. Network Architecture for Age Estimation
Layer
Filters
Output Size
Kernel Size
Activation
Image
-
180 x 180 x 3
-
-
Separable Conv1
64
180 x 180 x 64
3 x 3
ReLU
Max Pooling
-
90 x 90 x 64
2 x 2
-
Separable Conv2
128
90 x 90 x 128
3 x 3
ReLU
Max Pooling
-
45 x 45 x 128
2 x 2
-
Separable Conv3
128
45 x 45 x 128
3 x 3
ReLU
Max Pooling
-
22 x 22 x 128
2 x 2
-
Separable Conv4
256
22 x 22 x 256
3 x 3
ReLU
Max Pooling
-
11 x 11 x 256
2 x 2
-
Separable Conv5
256
11 x 11 x 256
3 x 3
ReLU
Max Pooling
-
5 x 5 x 256
2 x 2
-
FC1
-
128
-
ReLU
FC2
-
64
-
ReLU
FC3
-
32
-
ReLU
Output
-
1
-
ReLU
Административное здание 01/8 расположено по адресу: г. Тольятти, ул. 
Воскресенская, д.7. 
1.2 Производимая продукция или виды услуг

|
Административное здание 01/8 расположено по адресу: г. Тольятти, ул.
Воскресенская, д.7.
1.2 Производимая продукция или виды услугBog'liq Шубина С.В ТБп-1502
Административное здание 01/8 расположено по адресу: г. Тольятти, ул.
Воскресенская, д.7.
1.2 Производимая продукция или виды услуг
На 9 этаже административного здания 01/8 выполняются следующие
виды услуг:
- разработка проектов;
- разработка условий по обустройству объекта;
- хранение документов;
- разработка программ по инвестированию.
1.3 Оборудование
Административное здание 01/8 состоит из следующего оборудования:
- рабочие столы;
- стулья;
- тумбочки;
- шкафы;
- стеллажи;
- персональные компьютеры;
- принтеры;
- сканеры.
1.4 Виды выполняемых работ
На 9 этаже административного здания 01/8 находятся несколько
отделов такие, как:
- отдел отопления и вентиляции (ОВ);
15
- отдел проектирования и электротехнической части, КИПиА и связи
(ОПЭКС);
- отдел проектирования контрольно-измерительных приборов и
автоматики (КИПиА);
- отдел промышленного управления капитального строительства
(ПромУКС);
- отдел водоснабжения и канализации (ВК).
В свою очередь они выполняют различные виды работ.
Отдел ОВ выполняет проектирование систем вентиляции, отопления и
кондиционирования, согласно разработанной технической документации.
Отдел ВК выполняет проектирование сетей водоснабжения и
канализации, согласно разработанной технической документации и
спецификации оборудования, а так же разработку необходимой
документации по очистным сооружениям, технологических схем.
Также в отделах происходит организация встреч, прием посетителей,
организация совещаний и переговоров.
16
2 Технологический раздел
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Bosh sahifa
Aloqalar
Bosh sahifa
Административное здание 01/8 расположено по адресу: г. Тольятти, ул.
Воскресенская, д.7.
1.2 Производимая продукция или виды услуг
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Организация тушения пожара обслуживающим персоналом

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Таблица 7 – Табель пожарного расчета
Номер
пожарного
расчета
Должность
Действия номера
пожарного расчета при
пожаре
1
Первый
обнаруживший
пожар
Сообщает в пожарную охрану о месте
возникновения пожара,
свою фамилию,
контактный телефон
2
Персонал
Оповещает людей о пожаре, организовывает
эвакуацию людей.
Персонал
Организовывает эвакуацию материальных
ценностей
3
Электромонтер
Обесточивает здание
4
Персонал
Приступает к тушению пожара первичными
средствами пожаротушения
5
Персонал
Организовывает встречу пожарной охраны
СОДЕРЖАНИЕ - Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7)...

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5.1 Оценка антропогенного воздействия объекта на окружающую среду ..... 40
5.2 Предлагаемые
или рекомендуемые принципы, методы и средства
снижения антропогенного воздействия на окружающую среду ...................... 40
5.3 Разработка документированных процедур согласно ИСО 14000 .............. 41
6 Оценка эффективности мероприятий по
обеспечению техносферной
безопасности .......................................................................................................... 42
6.1 Разработка плана мероприятий, направленных на обеспечение пожарной
безопасности в организации ................................................................................. 44
6.2 Расчет математического ожидания потерь при возникновении пожара в
организации .......................................................................................................... 45
6.3 Определение интегрального эффекта от
противопожарных мероприятий
................................................................................................................................. 47
ЗАКЛЮЧЕНИЕ ..................................................................................................... 49
СПИСОК ИСПОЛЬЗУЕМЫХ ИСТОЧНИКОВ ................................................. 50
ПРИЛОЖЕНИЯ ..................................................................................................... 53
План размещения оборудования - Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте...

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-УФ 6 – УФ 8.
2.3 Анализ пожарной безопасности на участке
На 9 этаже административного здания 01/8 имеются в минимальном
количестве для угрозы воспламенения взрывопожарные вещества, такие как:
бумага, краска
для принтера, мебель в кабинетах, розетки, орг. техника.
Для расчета пожарного риска на производственном объекте
происходит подбор сценария пожара, в результате которого предполагаются
наихудшие последствия для работников ПАО «АВТОВАЗ».
Сценарий пожара включает в себя место возникновения,
степень
сложности и характер развития пожара, а так же учитывает количество
горючей нагрузки и сотрудников ПАО «АВТОВАЗ» на 9 этаже
административного здания 01/8.
Вероятное место возникновения пожара на 9 этаже административного
здания 01/8 представлено в Таблице 1.
Таблица 1 - Возможное место возникновения пожара.
Наименование
сценария
Очаг пожара
Расположение
очага пожара
Параметры очага пожара
Сценарий 1
Очаг пожара
1
Этаж 9, Кабинет
915
Тип горючей нагрузки:
негорючий.
Площадь: 216 кв. м.
Удельная
мощность
1582,830 кВт
Огонь может распространиться в сторону оконных проёмов,
вентиляции, лестницы.
Также возможно обрушение подвесного потолка.
Обеспечение пожарной безопасности и расчет пожарных рисков в корпусе 01/8 (в месте пересечения с поу-7) в пао «автоваз» - bet 2

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ABSTRACT
The title of the graduation work is “Fire safety and calculation of fire risks in
building 01/8 at PJSC AVTOVAZ”.
The issue of fire safety at organizational and
regional level is becoming
more and more pressing these days. Incompetence and excessive emotions can
aggravate the
process of evacuating people, which in total can lead to a large
number of deaths. The reasons for
fire break-out are numerous, such as non-
compliance with fire safety at the workplace and inappropriate use of explosive
and flammable substances.
The aim of the graduation work is to assess fire
risk in the building and
ensure fire safety at the site.
The first section of the work describes the characteristics of the object.
The second section contains all the information about the technological
process in the building.
The third section provides an assessment of the risk of fire in order to ensure
compliance with the facility’s safety requirements.
The fourth section contains documents on labor protection.
WINDOWS NT AND NETWORKING CONCEPTS

WINDOWS NT AND NETWORKING CONCEPTS
Familiarity with WINDOWS NT,WINDOWS 95 environment
Basic concept of domains and IP addressing
Creating user accounts
Implementing security on files and resources
Interpreting event log and taking backup of event log
Preventive measures against system breakdown
Backup and restoring of data
SQL SERVER
Basic RDBMS concepts
Familiarity with SQL server tools like SQL enterprise manager , isql/w
Monitoring SQL activity using performance monitor
Backup and restore of SQL databases using enterprise manager
Interpreting SQL server error log
If present staff does not have skills mentioned above, please indicate:
When is the training planned for staff : ________________________________
Duration of training : ______________________________________________
Name of the institute : _____________________________________________
How many people have access to NT Console (administration password) ?
System staff details
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Shift-1
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Shift-2
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Shift-3 *
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Name
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No.
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Name
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No.
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System Administrator
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Database Administrator
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Operators
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Shift Timings
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* Note : Shift-3 would be required in case of extended Production hours when EOD may be delayed.
Are any other applications apart from ‘NSDL Application’ installed (to be installed) on this Hardware ?
Server : __________________________________________________
Node : __________________________________________________
Which virus protection s/w you are using ?
Server : __________________________________________________
Node : __________________________________________________
Have you received following documents ? ( If received please tick mark )
Operation Manual DPM User Manual
How many DATS do you have in store?
Total No. DAT Specification
Manufacturer Name
Have you planned for fire proof cabinet for storing DATs ? Yes No
Where do you plan to store Remote backup : _____________________________
Frequency with which DATs are rotated with Remote site : ___________________
Please mention your System date and Time Zone : _________________________
Signature Date
Name
Organization
(For Office use only)
Verified by
Signature Date
Name _______________________________________
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