Smart Technologies for Visually Impaired: Assisting and conquering infirmity of blind people using AI Technologies
فناوری های هوشمند برای افراد دارای اختلال بینایی : کمک و تسخیر ناتوانی افراد نابینا با استفاده از فناوری های هوشمند مصنوعی-2020
Physical disability has affected many people’s lives across the world. One of these disabilities that strongly affected some large category of people is visual lose. Blind people often face difficulties in moving around freely such as: in crossing the street, in reading, driving or socializing. They often rely on using certain aid devices to reach certain places or perform any other daily activities such as walking sticks. There are ongoing scientific researches in the area of rectifying blindness, but it has to go long way to achieve the solution. Also, there are research unleashes the ideas of assisting the blind people deficiency but lacks in technological aspects of implementation. This research project aims at helping blind people of all categories to achieve their day to day tasks easier through the use of a smart device. By using artificial intelligent and image processing, this smart device is able to detect faces, colors and deferent objects. The detection process is manifested by notifying the visually impaired person through either a sound alert or vibration. Additionally, this study presents a palpable survey that entails visually impaired people from the local community. Subsequently, the project uses both Open CV and Python for programming and implementation. The exertion of this project prototype investigates the algorithms which are used for detecting the objects. Also, it demonstrates how this smart device could detects certain physical object and how it could send a warning signal when faced by any obstacles. Overall, this research will be a positive addition in the world of health care sector by supporting blind people with the use of smart technology.
Keywords: Artificial Intelligent | Open CV | Python | Face Recognition | Object Detection | Health Care Introduction
Energy saving based lighting system optimization and smart control solutions for rail transportation: Evidence from China
بهینه سازی سیستم روشنایی مبتنی بر صرفه جویی در انرژی و راه حل های کنترل هوشمند برای حمل و نقل ریلی: شواهدی از چین-2020
As the natural resources are becoming exhausted, energy consumption by metro systems dominates internal transportation resources in urban areas. The comprehensive exploration of energy improvements in lighting system energy is necessary. To evaluate the energy-saving potential and identify the efficiency improvement opportunities for lighting operations in metro systems, an intelligent energy management system for metro stations is examined through a case study in Nanchang city of China. First, the study explores the main factors influencing the lighting energy consumption of metro systems and analyses the lighting distribution in different station regions. Second, DIALux software is employed to optimize and monitor the best illuminating effect for hall lighting in the selected station. Third, an intelligent model is proposed for the lighting system based on the energy-saving scheme and solution using BECH energy analysis software combined with DIALux software. A thermal model is proposed to verify the energy and load performances. Results show that (1) the proper layout by means of DIALux software, can not only meet the functional demands of lighting but also reduce energy consumption; (2) intelligent lighting control system can improve the lighting energy-saving design, and the lighting control framework is capable of refined control; and (3) based on the performance analyses, the solution with the adopted DALI digital light adjustment is helpful for increasing passengers comfort and realizing the goals reduction.The novelty is to integrate the lighting energy saving solution with software within an intelligent management and verified its valuable application, it is practical for construction emission control
Keywords: Lighting system | Influence factors | Artificial intelligent control technology | Energy management system
Mining discriminative spatial cues for aerial image quality assessment towards big data
استخراج نشانه های مکانی تبعیض آمیز برای ارزیابی کیفیت تصویر هوایی نسبت به داده های بزرگ-2020
Evaluating massive-scale aerial/satellite images quality is useful in computer vision and intelligent applications. Traditional local features-based algorithms have achieved impressive performance. However, spatial cues, i.e., geometric property and topological structure, have not been exploited effectively and explicitly. Thus, in this paper, we propose a novel method for image quality assessment towards aerial/satellite images, where discriminative spatial cues are well encoded. More specifically, in order to mine inherent spatial structure of aerial images, each image is segmented into several basic components such as buildings, airport and playground. Afterwards, a weighted region adjacency graph (RAG) is built based on the basic components to represent the spatial feature of each aerial image. We integrate the spatial feature with other transform domain features, and train a support vector regression model to achieve image quality assessment. Experiments demonstrate that our method shows competitive or even better performance compared with several state-of-the-art algorithms.
Keywords: Big data | Artificial intelligent | Data mining | Image quality assessment
Design and Accomplishment of AI Control Platform for Reactive Power Cloud Compensation System
طراحی و تحقق بستر کنترل هوش مصنوعی برای سیستم جبران ابر برای توان راکتیو-2020
The balance of active and reactive power in the power system is very important for the normal operation of the whole system, the correct method is to inject the corresponding reactive power where much of the reactive power is consumed to maintain the balance. it is of great positive significance to develop a device with integrated new switching technology that can realize non-impact switching of capacitor banks and be controlled by better algorithms. In this paper ???? an artificial intelligent (AI) control platform for reactive power cloud compensation system is designed and achieved, by switching capacitors on the load side, the requirements of capacitor switching conditions are analyzed, the requirements of capacitor bank and capacitor controller are put forward, and the theoretical analysis is carried out. Results of the installation operation in site show high performance of the designed system.
Keywords: control platform | reactive power | artificial intelligent | cloud compensation system
Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel
یادگیری ماشین با هدایت متالورژی فیزیکی و طراحی هوشمند مصنوعی از فولاد ضد زنگ قوی-2019
With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including highend steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its effects have not been demonstrated and systematically analysed nor experimentally validated with prototype alloys. To address this issue, a physical metallurgy (PM) -guided ML model was developed, wherein intermediate parameters were generated based on original inputs and PM principles, e.g., equilibrium volume fraction (Vf) and driving force (Df) for precipitation, and these were added to the original dataset vectors as extra dimensions to participate in and guide the ML process. As a result, the ML process becomes more robust when dealing with small datasets by improving the data quality and enriching data information. Therefore, a new material design method is proposed combining PM-guided ML regression, ML classifier and a genetic algorithm (GA). The model was successfully applied to the design of advanced ultrahigh-strength stainless steels using only a small database extracted from the literature. The proposed prototype alloy with a leaner chemistry but better mechanical properties has been produced experimentally and an excellent agreement was obtained for the predicted optimal parameter settings and the final properties. In addition, the present work also clearly demonstrated that implementation of PM parameters can improve the design accuracy and efficiency by eliminating intermediate solutions not obeying PM principles in the ML process. Furthermore, various important factors influencing the generalizability of the ML model are discussed in detail.
Keywords: Alloy design | Machine learning | Physical metallurgy | Small sample problem | Stainless steel