Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userتصميم نموذج ذاتي التحكم يحاكي تقاطع سكة حديدية مع طريق بمستو واحد " مع توضيح توضع العبارات ومقاطع الحفر والردم وتحسس وجود عوائق على الممر " رابط الفديو" https://youtu.be/zUh7FaE2m3M
1148
0 10
0
(
0
)
Created by
محمود أحمد
Ask ChatGPT about the research
المشروع المقدم عبارة عن مجسم يوضح الممرات السطحية بشيء من التفصيل وملحقاتها وفكرة تطويرها لتكون ألية وذاتية التحكم مع تطبيق الدارة ضمن المجسم بالإضافة لتفصيل مكونات الخط الحديدي وتجسيدها ضمن المشروع المنفذ , كتوضيح القسم السفلي للخط والقسم العلوي ,مع وجود عبارة ضمن جسم الخط ومقاطع الحفر والردم وملحقات السكة يحوي النموذج نموذج يحاكي الخط الحديدي وملحقاته من مقاطع الحفر والردم وبعض المنشأت الثابتة كالعبارات ونقاط العلام بالإضافة للمرات السطحية المتقاطعة مع السكة مع تصميم دارة تجعل من الممرات ذاتية التحكم وآلية بالكامل مع تحسس لوجود عائق على بلاطة الممر أمام القطار . ملاحظة : لمشاهدة الفديو موجود على الرابط https://youtu.be/zUh7FaE2m3M
No English abstract
References used
المعهد الهندسي للخطوط الحديدية السورية – بحلب
المؤسسة العامة للخطوط الحديدية السورية مديرية فرع طرطوس - دائرة المنشأت الثابتة
كتاب تعليمات تشغيل وصيانة الممرات السطحية ( د.م عدنان الياس )
كتاب هندسة السكك الحديدية (د.م جندب محمد زعرور )
كتاب الطرق 1 (د.م شذى إبراهيم أسعد )
rate research
Read More
Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to p
rovide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of asking clarifying questions in open-domain dialogues': (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.
In the context of neural passage retrieval, we study three promising techniques: synthetic data generation, negative sampling, and fusion. We systematically investigate how these techniques contribute to the performance of the retrieval system and ho
w they complement each other. We propose a multi-stage framework comprising of pre-training with synthetic data, fine-tuning with labeled data, and negative sampling at both stages. We study six negative sampling strategies and apply them to the fine-tuning stage and, as a noteworthy novelty, to the synthetic data that we use for pre-training. Also, we explore fusion methods that combine negatives from different strategies. We evaluate our system using two passage retrieval tasks for open-domain QA and using MS MARCO. Our experiments show that augmenting the negative contrast in both stages is effective to improve passage retrieval accuracy and, importantly, they also show that synthetic data generation and negative sampling have additive benefits. Moreover, using the fusion of different kinds allows us to reach performance that establishes a new state-of-the-art level in two of the tasks we evaluated.
The rapid rise of online social networks like YouTube, Facebook, Twitter allows people to express their views more widely online. However, at the same time, it can lead to an increase in conflict and hatred among consumers in the form of freedom of s
peech. Therefore, it is essential to take a positive strengthening method to research on encouraging, positive, helping, and supportive social media content. In this paper, we describe a Transformer-based BERT model for Hope speech detection for equality, diversity, and inclusion, submitted for LT-EDI-2021 Task 2. Our model achieves a weighted averaged f1-score of 0.93 on the test set.
Speech separation is a problem in the field of speech processing that has been studied in full swing recently. However, there has not been much work studying a multi-accent speech separation scenario. Unseen speakers with new accents and noise arouse
d the domain mismatch problem which cannot be easily solved by conventional joint training methods. Thus, we applied MAML and FOMAML to tackle this problem and obtained higher average Si-SNRi values than joint training on almost all the unseen accents. This proved that these two methods do have the ability to generate well-trained parameters for adapting to speech mixtures of new speakers and accents. Furthermore, we found out that FOMAML obtains similar performance compared to MAML while saving a lot of time.
Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process. However, deciding whether to read more input words or start to translate is d
ifficult for language pairs with different word orders such as English and Japanese. Motivated by the concept of pre-reordering, we propose a couple of simple decision rules using the label of the next constituent predicted by incremental constituent label prediction. In experiments on English-to-Japanese simultaneous translation, the proposed method outperformed baselines in the quality-latency trade-off.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
