aimed at identifying the auditor of the joint stock company, and the statement of the concept, conditions, rights and obligations of the auditor, and the problem of the study was the effectiveness of the auditor of the joint stock company, and the st
udy was divided into two requirements of the first requirement - the concept and conditions of the auditor of the public shareholding company, and the second demand the rights and obligations of the auditor of the public shareholding company, and to achieve the objective of the study was used the analytical approach, and the study indicated the effectiveness of the auditor through monitoring and informing him of the records of companies and preparing the budget and statement Profits and losses.
The presented paper deals with an implemented speed sensorless direct vector control (DVC) technique based on stator flux orientation algorithm to estimate and control rotor speed of three phase induction motor (IM). The driver system is presented in
this paper has a three phase bridge uncontrolled rectifier and space vector pulse width modulated (SV-PWM) inverter to drive and control the speed of the (IM) at different mechanical load conditions. The rotor speed has been obtained by slip frequency estimation instead of use feedback speed sensor. The structure of Volt/Freq via PI-controller is used to control and adjust speed accuracy between required and estimation rotor speed to precise speed response. The proportional and time integration controller values obtain by Ziegler-Nichols closed-loop tuning method. Computer simulation results with Matlab/Simulink program show an acceptable speed control response, minimum error at steady state and dynamic condition, a results show also the stator voltage, current and flux estimated in direct-quadrate of stationary and synchronous reference frame at variation in load up to (10 N.m) beside the synchronous, slip and rotor speed estimation are varied between (250-1486 rpm).
Keywords: Induction Motor Drive System, Direct Vector Control, Speed Sensorless.
The European Quality Model that characterizes this edition is based on the following premise: satisfaction of customers, employees and positive impact on society can all be achieved through leadership, strategic policy, correct management of personne
l, effective use of available resources, and correct definition of operations, which ultimately result in For excellence in results.
This approach attempts to provide a broad perspective on the concepts of management concerned, which cover areas such as strategic management, or information systems and human resources.
Hence, these standards are closely related to the major resources of the institutions and the basic capabilities that control and manage them.
Approaches to improving the performance of both business and operations have developed during the last decades, starting with management by objectives and results, passing through total quality control, then total quality management, then six sigma, then the theory of constraints, then re-engineering, the methodology of exclusion of waste, then knowledge management, then electronic supply chain management, and then the integration between the six methodology Sigma and LSS and finally High Performance Organizations.
These curricula, some of them focus on performance efficiency, others focus on performance effectiveness, and some focus on developing the knowledge capabilities of the organization and then developing its intellectual capital in order to achieve self-sustainable development.
The book focuses on the Six Sigma methodology as a quality measure and improvement program, which was developed by Motorola, which focused on process control to the point of one sigma year or 3.4 defects per million units produced, and this includes identifying the most important factors for quality that are determined by the customer. Through this, process changes are reduced, capabilities are improved, stability is increased, and auxiliary systems are designed, which may be Design for Six Sigma (DFSS) to help achieve the Year Sigma goal.
The process of transfer a speech signal by high confidentially and as quickly as possible through the Internet needs to develop compression and encryption technology for a speech signal, so as, to reduce its size and make it understandable to persons
not authorized to listen to.
A system was designed to encrypt the voice over Internet Protocol (VoIP) and use compression technique for the purpose of reducing the size of data and send it over the network, (A_law PCM) algorithm was used the to compress audio data. Then algorithms of Triple Data Encryption Standard (TDES) and Advanced.
Encryption Standard (AES) were applied. A new encryption algorithm was proposed based in its work on the block cipher encryption system called the Direct and Reverse algorithm, which based on three basic steps, firstly expand the initial key, secondly direct the encryption of each round in one direction, and finally substitute (Bytes) as used in the Compensation Box in AES algorithm by making it moving. In general compression ratio was calculated and it was (50%) and the results of the correlation coefficient for the proposed algorithm was compared with the results of (AES, TDES) algorithms.
Educational data mining aims to study the available data in the educational field and extract the hidden knowledge from it in order to benefit from this knowledge in enhancing the education process and making successful decisions that will improve th
e student’s academic performance. This study proposes the use of data mining techniques to improve student performance prediction. Three classification algorithms (Naïve Bayes,J48, Support Vector Machine) were applied to the student performance database, and then a new classifier was designed to combine the results of those individual classifiers using Voting Method. The WEKA tool was used, which supports a lot of data mining algorithms and methods. The results show that the ensemble classifier has the highest accuracy for predicting students' levels compared to other classifiers, as it has achieved a recognition accuracy of 74.8084%. The simple k-means clustering algorithm was useful in grouping similar students into separate groups, thus understanding the characteristics of each group, which helps to lead and direct each group separately.
دور رأس المال الفكري القائم على العلاقات في تحقيق الميزة التنافسية بالتطبيق علي شركة زهراء المعادى للاستثمار والتعمير
The researcher prepared materials and tools for his study , he prepared guide for teachers to guide them how they teach with some cognitive load strategies and developed a lot of dependent variables and behavior in various domain such as cognitive an
d affective and psychomotor domain , researcher prepared too two tools to measure two dependent variables , he prepare test in
skills of futuristic thinking , and also prepare questionnaire to
measure successful cognitive academic administration , he used
secondary student , the sample of study contained 30 student.
The researcher had controlled the tools of his study he used
various statistical methods , he used co efficient for measure
validity and used alpha crookback to measure stability of tools of
its study , study used one sample experimental design , study due
to that there effective of using of some cognitive load strategies in
teaching psychology for developing skills of futuristic thinking and
successful cognitive academic administration to secondary stage
students, and study due to that there was different between pre
and post application for post one in developing skills of futuristic
thinking and successful cognitive academic administration
Following the success of dot-product attention in Transformers, numerous approximations have been recently proposed to address its quadratic complexity with respect to the input length. While these variants are memory and compute efficient, it is not
possible to directly use them with popular pre-trained language models trained using vanilla attention, without an expensive corrective pre-training stage. In this work, we propose a simple yet highly accurate approximation for vanilla attention. We process the queries in chunks, and for each query, compute the top-*k* scores with respect to the keys. Our approach offers several advantages: (a) its memory usage is linear in the input size, similar to linear attention variants, such as Performer and RFA (b) it is a drop-in replacement for vanilla attention that does not require any corrective pre-training, and (c) it can also lead to significant memory savings in the feed-forward layers after casting them into the familiar query-key-value framework. We evaluate the quality of top-*k* approximation for multi-head attention layers on the Long Range Arena Benchmark, and for feed-forward layers of T5 and UnifiedQA on multiple QA datasets. We show our approach leads to accuracy that is nearly-identical to vanilla attention in multiple setups including training from scratch, fine-tuning, and zero-shot inference.
Training large language models can consume a large amount of energy. We hypothesize that the language model's configuration impacts its energy consumption, and that there is room for power consumption optimisation in modern large language models. To
investigate these claims, we introduce a power consumption factor to the objective function, and explore the range of models and hyperparameter configurations that affect power. We identify multiple configuration factors that can reduce power consumption during language model training while retaining model quality.
Language models pretrained on vast corpora of unstructured text using self-supervised learning framework are used in numerous natural language understanding and generation tasks. Many studies show that language acquisition in humans follows a rather
structured simple-to-complex pattern and guided by this intuition, curriculum learning, which enables training of computational models in a meaningful order, such as processing easy samples before hard ones, has been shown to potentially reduce training time. The question remains whether curriculum learning can benefit pretraining of language models. In this work, we perform comprehensive experiments involving multiple curricula strategies varying the criteria for complexity and the training schedules. Empirical results of training transformer language models on English corpus and evaluating it intrinsically as well as after fine-tuning across eight tasks from the GLUE benchmark, show consistent improvement gains over conventional vanilla training. Interestingly, in our experiments, when evaluated on one epoch, the best model following a document-level hard-to-easy curriculum, outperforms the vanilla model by 1.7 points (average GLUE score) and it takes the vanilla model twice as many training steps to reach comparable performance.
