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Data flow of a smart city.

Data flow of a smart city.

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In our current study, we are aiming to explore data management methods in Smart City systems. In data management, AI (Artificial Intelligence) can be used as well. Solutions for the usage of AI and integration into Smart City concept will be researched as well. Main motivation of the study is to draw attention to one of the most important element...

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... In the medical field, technologies are used to improve hospital inpatient care. Smart city data management systems provide the collected data and generate revenue, but the system should also maintain people's trust while doing so [318]. Managing and building people trust is a key challenge in sustainable smart city development. ...
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Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation man-agement, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key chal-lenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established.
... In the medical field, technologies are used to improve hospital inpatient care. Smart city data management systems provide the collected data and generate revenue, but the system should also maintain people's trust while doing so [318]. Managing and building people trust is a key challenge in sustainable smart city development. ...
Article
Full-text available
Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.
... On the court, long-term muscle contraction and ball extension, such as fast movement, kicking, swinging, and wrist strength, are different from the periodic endurance of other sports. Athletes must have special endurance quality, special strength quality, special speed quality, etc. that change with the change of competition intensity [16]. ...
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With the rapid development of the information age, Internet and other technologies have been making progress, people’s fitness awareness has been gradually enhanced, and sports fitness app has emerged as the times require. This paper mainly studies the step-counting function of physical training app for teenagers based on artificial intelligence. This paper uses the modular development method to achieve the functional requirements of the system as the goal, respectively, for parameter management, website configuration, system log, interface security settings, SMS configuration, WeChat template message and several functional modules to achieve system configuration. In this paper, three types of sensors are used to analyze the data changes in the process of walking through three types of data, and different weights are given as the results of step-counting. When the peak value of sensor data is measured, only the peak value of the primary axial data of each sensor is analyzed, which should be determined according to the actual axial value of the sensor. In this paper, the users’ evaluation indexes of sports fitness app are divided into two groups: importance and satisfaction, so the obtained data are directly divided into two groups: importance and satisfaction of user experience indexes of sports fitness app, and the two groups of data are matched with the sample t test to ensure the scientific conclusion. Finally, the advantages and disadvantages of the user experience of college students’ sports fitness app are analyzed through IPA analysis. Heuristic evaluation is carried out on the step app to score the second-level usability index of the app. The first-level usability index score and the total usability score of the step app are obtained by calculation. There is not much difference between male and female students who use sports apps. Among them, 288 are male students, accounting for 58.2% of the total and 16.4% are female students. The results show that the use of artificial intelligence technology can reduce the overall energy consumption of step-counting algorithm, so as to achieve an energy-saving step-counting algorithm. 1. Introduction With the increase of the strength of the youth physical confrontation, in order to have a place in the world basketball, it is necessary to make the overall ability of the team outstanding, and the basis of each ability is the good physical quality of the players. Therefore, for teenagers, scientific fitness and reasonable avoidance of competitive risk events are particularly important for the participation, development, and breakthrough of competitive sports. The virtual technology used by cloud computing technology isolates system resources, allowing users to perform artificial intelligence model training operations in their own unique virtualized systems, so that they can be adjusted for virtual environments with low resource utilization. It can avoid the unavailability of the system environment due to human factors. Artificial intelligence technology can improve resource utilization. Din et al. believe that, due to the existence of various pollutants produced by human, agricultural, and industrial activities, the quality of surface water has decreased. Therefore, plot the concentration of different surface water quality parameters. He tried to develop an artificial intelligence modeling method for drawing concentration maps of optical and nonoptical SWQP. For the first time, he developed a remote sensing framework based on a back-propagation neural network to quantify the concentration of different SWQP in Landsat8 satellite images. Compared with other methods (such as support vector machine), the developed Landsat8-based BPNN model is used to obtain an important measurement coefficient between Landsat8 surface reflectivity and SWQP concentration. Although his research is innovative, it lacks certain experimental data [1]. Kulkarni and Padmanabham used the extended waterfall and agile models to model the entire process of software (SW) development. They integrate AI activities such as intelligent decision making, ML, Turing test, search, and optimization into the agile model. They evaluated two indicators in five independent software projects, such as the usability target achievement indicator and the integration index. Once the SW project is developed using these models, feedback queries will be formally collected, and the collected data will be extensively analyzed to identify the various characteristics of the product, thereby determining the product’s related behavior in terms of models and indicators. Although their research is relatively comprehensive, the test content is not accurate enough [2]. Goyache et al. developed a method to use artificial intelligence to improve the design and implementation of linear morphological systems for beef cattle. The process they proposed involves an iterative mechanism, in which knowledge engineering methods are used to continuously define and calculate type features, scored by a group of well-trained human experts, and finally performed by four famous machine learning algorithms’ analysis. The results obtained in this way can be used as feedback for the next iteration to improve the accuracy and effectiveness of the proposed evaluation system. Although his research sample is relatively complete, it is not innovative enough [3]. In this paper, user demands were obtained through user interviews and analysis of competing products. Then, questionnaire survey was adopted to determine the importance of teenagers’ demands for mobile health applications. Then, the weight of demands was calculated through data analysis. In view of the difference in use motivation caused by gender, the users are classified by gender and age from the beginning of registration, and different user groups are pushed with different content of exercise knowledge. Combined with APP, this paper carries out professional evaluation on the exercise ability of users before exercise, quantifies and grades the evaluation results, gives scientific and reasonable exercise suggestions, promotes the formation of exercise habits, and provides a reference for sports and fitness enthusiasts to reasonably choose their own exercise projects. 2. Youth Physical Training 2.1. Artificial Intelligence Technology The classic sigmoid-based ESN state update equation is composed of N storage pool units, K input layer units, and L output layer units. Among them, is an N-dimensional reserve pool [4]. The output result obtained from the extended system can be expressed as Among them, is the activation function of an output layer. The expression of the hidden layer is as follows: Normally, the form of the GARCH model is as follows: When the actual output of the network model is inconsistent with the expected output, an output error E will be generated. The expression is as follows: Expand the error to the hidden layer; there are When the weight and threshold iterations corresponding to the neurons in each layer are over, the learning and training phase of the neural network enters the forward propagation link again [5, 6]. As a basic platform, in order to overcome the occurrence of the above situation, it must have basic isolation to ensure the independence of the service execution environment and hardware resources of each user. Containerized virtualization technology can provide system isolation for the platform, from the operating system to the software services, which are all defined by users, so as to provide users with a more flexible service execution environment [7]. Since the nodes in the cluster sometimes stop for various reasons, the cluster management tool usually automatically migrates all the containers running on this node to other nodes in the cluster. However, if some containers use local data volumes, data loss will occur when the containers are migrated. Using network storage disks or distributed storage disks will be a viable choice [8]. The calculation formula of the autocorrelation coefficient of the current period and the previous period data is as follows: The FFT calculation formula is as follows: Based on the above analysis, it can be seen that compared with the step-counting algorithm in the frequency domain and the time domain, the calculation cost of the former is obviously higher than that of the latter. Although the step-counting algorithm in the frequency domain has a higher computational cost, compared with most step-counting algorithms in the time domain, the step-counting algorithm in the frequency domain usually achieves higher step-counting accuracy [9, 10]. 2.2. Physical Training Between functional physical training and traditional physical training, they are interrelated and complement each other. Specialization and integrity are the most prominent features of the former. But in the traditional physical training, it can not achieve these two points. In the physical training system, the traditional physical training is the most important foundation. At the beginning, the traditional physical training can be carried out first, and then the functional training can lay a good foundation for the body, so as to prevent the defects of strength training from causing unnecessary sports injury [11]. Functional physical training is not unitary. It needs to integrate and improve the advantages of traditional physical training. We can not ignore the traditional physical training, nor can we just carry out a kind of functional physical training. The two complement each other and complement each other, so as to make a special targeted and integrated training arrangement, so as to improve the athletes’ special technical level and ability [12]. Physical fitness itself is an organic whole, not the mechanical or simple addition of various parts. We should understand physical fitness with the help of system theory. Systematic method has become an important method for people to understand and analyze things in modern science. The core idea of systematic view is the overall concept of system [13]. In general, in order to improve the basic shape of sports, improve the system initiative of athletes’ organs, and give full play to the best mode of sports mechanism and effect, the physical fitness index system is taken as an important reference standard in the process of training. It belongs to the basic index of technical training and tactical training and has a positive impact on the technology, tactics, load training, physical condition, and sports life of special sports. The establishment of a reasonable physical fitness index system can be used as a powerful carrier for the selection mechanism of athletes in reality [14]. Physical fitness is the foundation of young athletes and provides strong support for their technical level. Ordinary teenagers are mostly in the system of compulsory education or secondary and higher education, and their training purposes and means are different from those of young athletes. As far as the means of physical training are concerned, athletes will be better than ordinary teenagers in terms of selection, training, competition, and other aspects, but from the perspective of physiological development characteristics, they are in the second peak of development. The stimulation of training means will have a more obvious effect on athletes’ training, which can provide training support for ordinary teenagers [15]. This will make the competition time longer and test the physical fitness of athletes. If one side’s physical condition is not strong, there will be calf muscle cramps, or even acute sports injury. On the court, long-term muscle contraction and ball extension, such as fast movement, kicking, swinging, and wrist strength, are different from the periodic endurance of other sports. Athletes must have special endurance quality, special strength quality, special speed quality, etc. that change with the change of competition intensity [16]. 2.3. Pedometer APP The primary task of the pedometer algorithm is to obtain the original three-axis acceleration data based on the sensor module and then perform data analysis and algorithm design based on the entire waveform. The actual test shows that there are many interference clutters in the acceleration signal generated by the human body when counting steps in various scenes. Therefore, it is very important to preprocess the original data before formally analyzing the motion waveform [17]. The data collection function of the pedometer is realized by the main controller reading data from the sensor, and its core is the acceleration sensor. The use of analog signal sensors requires additional analog-to-digital converters, which will increase the complexity of the circuit and the space utilization rate; the use of digital signal sensors avoids this problem while using high-precision sensors to ensure the reliability of data. In addition, it is necessary to ensure a higher speed data interface, a certain processing capacity, and lower power consumption in the selection of the main controller and the sensor [18]. The overall architecture of pedometer is shown in Figure 1. According to the function requirement analysis, the acceleration and angular velocity data selected collection of six-axis accelerometer and gyroscope inertial sensor MPU6050, master controller selects 16 ultra-low power consumption microprocessor MSP430G2553. Data transmission can use serial port transmission or wireless module transmission, and the programming of the main controller can be realized through online programmable function [19].
Chapter
Smart city is an inevitable development trend in the future, which has a strong role in promoting urban development. Promoting the construction of smart city can not only improve people’s living standards and quality of life, but also effectively promote urban development. This paper first gives an overview of smart city, then briefly introduces the characteristics of smart city, and finally analyzes the key supporting technologies and applications of smart city construction, including cloud computing technology, big data technology, Internet of things technology, artificial intelligence technology and 3D Printing technology. Smart city is a new urban form. In the process of building a smart city, the core is the key supporting technology. Therefore, it is necessary to strengthen the research on the key supporting technology and combine it with the actual situation of the city to achieve effective use.
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Our hypothesis was that the ecosystem of self-driving cars could be treated as a complex system. The proof of this was based on the definition of self-driving car ecosystem and definition of complex system. We not found definition for ecosystem of self-driving cars. That's why we made our own definition of self-driving car ecosystem. Self-driving car ecosystem is all technology and person and service that connect to the self-driving car and have effect to the self-driving car technology, self-driving car design, self-driving car traffic, self-driving car environment (infrastructure), self-driving car maintenance, self-driving car education and self-driving car law.