Artificial Neural Networks as a Level-2 Trigger for the H1 Experiment: Status of the Hardware Implementation

1995 ◽  
Vol 06 (04) ◽  
pp. 541-548
Author(s):  
D. Goldner ◽  
H. Getta ◽  
M. Kolander ◽  
T. Krämerkämper ◽  
H. Kolanoski ◽  
...  
Keyword(s):  
Trigger System ◽  
Intrinsic Parallelism ◽  
Fast Processing ◽  
Level 1 ◽  
Artificial Neural ◽  
Trigger Decision ◽  
Artificial Neural Network Ann ◽  
Level 2 ◽  
Dedicated Hardware ◽  
Made In

Triggering at the HERA ep collider is challenging because of the high bunch crossing rate and an expected large background. In the H1 experiment, a trigger decision is made in four steps (level 1–4), stepwise decreasing the event rate and allowing for more sophisticated trigger decisions. The time available for L2 is about 20 μs. We have proposed to use an artificial neural network (ANN) for the L2 trigger based on the CNAPS-1064 chip available from Adaptive Solutions, (Oregon, USA). The intrinsic parallelism of the ANN algorithm together with the dedicated hardware offers fast processing of the trigger informations. The trigger system uses up to 10 decision units, each consisting of a Pattern Recognition Module (PRM) and a Data Distribution Board (DDB). A DDB receives the L2 data stream and generates the network inputs used by the algorithms on the PRM. A PRM is a commercial VME board carrying the CNAPS processors.

scholarly journals Efficient Level 2 Trigger System Based on Artificial Neural Networks

2009 ◽  
Author(s):  
Sonia Khatchadourian ◽  
Jean-Christophe Prevotet ◽  
Lounis Kessal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Trigger System ◽  
Efficient Level ◽  
Artificial Neural ◽  
Level 2

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

ANALISIS KEMAMPUAN KOMUNIKASI MATEMATIS DALAM MENYELESAIKAN MASALAH STATISTIKA

2018 ◽  
Vol 5 (2) ◽  
pp. 7 ◽  
Author(s):  
Ahmad Aunur Rohman
Keyword(s):  
Level 1 ◽  
Level 2

Penelitian ini dimaksudkan untuk mengetahui bagaimana kemampuan komunikasi matematis mahasiswa terhadap pemahaman statistika. Data dalam penelitian ini berupa hasil pekerjaan tes tertulis tentang kemampuan komunikasi matematis dan wawancara terhadap subjek penelitian. Pengumpulan data diperoleh dengan tes dan wawancara. Uji keabsahan data yang digunakan adalah triangulasi. Data penelitan yang terkumpul dianalisis dengan analisis data non statistik yang terdiri dari tiga alur, yaitu reduksi data, penyajian data, dan penarikan kesimpulan/verifikasi data. Hasil penelitian menunjukkan bahwa 1) Terdapat 5 mahasiswa yang berada pada level 0 (sangat kurang baik); 2) 24 mahasiswa berada pada level 1 (kurang baik); 3) 6 mahasiswa berada pada level 2 (cukup baik); Penelitian ini diharapkan dapat memacu individu lain untuk melakukan penelitian yang lebih baik dan mendalam tentang kemampuan komunikasi matematis.

EVALUASI PELATIHAN DI PT. PUPUK KUJANG CIKAMPEK

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Lania Muharsih ◽  
Ratih Saraswati
Keyword(s):  
Training Evaluation ◽  
Work Productivity ◽  
Learning Behavior ◽  
Level 3 ◽  
Level 1 ◽  
Four Levels ◽  
A Company ◽  
Level 2 ◽  
Evaluation Sheet ◽  
Assessment Factor

This study aims to determine the training evaluation at PT. Kujang Fertilizer. PT. Pupuk Kujang is a company engaged in the field of petrochemicals. Evaluation sheet of PT. Fertilizer Kujang is made based on Kirkpatrick's theory which consists of four levels of evaluation, namely reaction, learning, behavior, and results. At level 1, namely reaction, in the evaluation sheet is in accordance with the theory of Kirkpatrick, at level 2 that is learning should be held pretest and posttest but only made scale. At level 3, behavior, according to theory, but on assessment factor number 3, quantity and work productivity should not need to be included because they are included in level 4. At level 4, that is the result, here is still lacking to get a picture of the results of the training that has been carried out because only based on answers from superiors without evidence of any documents.   Keywords: Training Evaluation, Kirkpatrick Theory.    Penelitian ini bertujuan mengetahui evaluasi training di PT. Pupuk Kujang. PT. Pupuk Kujang merupakan perusahaan yang bergerak di bidang petrokimia. Lembar evaluasi PT. Pupuk Kujang dibuat berdasarkan teori Kirkpatrick yang terdiri dari empat level evaluasi, yaitu reaksi, learning, behavior, dan hasil. Pada level 1 yaitu reaksi, di lembar evaluasi tersebut sudah sesuai dengan teori dari Kirkpatrick, pada level 2 yaitu learning seharusnya diadakan pretest dan posttest namun hanya dibuatkan skala. Pada level 3 yaitu behavior, sudah sesuai teori namun pada faktor penilaian nomor 3 kuantitas dan produktivitas kerja semestinya tidak perlu dimasukkan karena sudah termasuk ke dalam level 4. Pada level 4 yaitu hasil, disini masih sangat kurang untuk mendapatkan gambaran hasil dari pelatihan yang sudah dilaksanakan karena hanya berdasarkan dari jawaban atasan tanpa bukti dokumen apapun.   Kata kunci: Evaluasi Pelatihan, Teori Kirkpatrick.

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