IMPLEMENTASI LAOD BALANCING PADA WEB SERVER MENGGUNAKAN APACHE DENGAN SERVER MIRROR DATA SECARA REAL TIME

ABSTRAKPT.Trimitra Data Teknologi adalah perusahaan yang yang bergerak dalam bidang teknologi dan informasi, website menjadi salah satu cara jembatan komunikasi antara client dan perusahaan. Banyaknya client yang mengakses membuat beban sebuah web server dalam perusahaan menjadi berat dan menimbulkan masalah yaitu down nya server yang membuat client sulit untuk mengakses website perusahaan. Untuk membantu mengatasi masalah yang terjadi diterapkannya metode load balancing dengan algoritma request counting algorithm dimana bertujuan untuk membagi beban secara merata dalam web server dan memperkecil waktu respon antara client dan server, beban terbagi dengan anggota server yang terdaftar dalam server load balancing. Dengan penerapan metode load balancing maka kerja server akan menjadi lebih maksimal karena adanya sistem high availability dimana saat salah satu server mati maka kerja server akan diambil alih oleh server yang lain. Selain metode load balance penerapan sistem dengan server mirror yang dilakukan dapat membantu memaksimalkan metode load balance karena adanya replikasi otomatis antara web server yang menjadi anggota load balance baik konten website ataupun database. Hasil yang terjadi adalah web server perusahaan akan menjadi sistem yang mampu bekerja secara baik saat melayani client dalam hal layanan web server karena beban terbagi dengan baik dan kecilnya waktu respon sehingga tidak adanya kesulitan client untuk mengakses website perusahaan.Kata kunci : Load Balance, Web Server, Mirror Server.

Video-Based Rope Skipping Repetition Counting with ResNet Model

Video Repetition Counting is one of the important research areas in computer vision. It focuses on estimating the number of repeating actions. In this paper, we propose a method for video-based rope skipping repetition counting that combines the ResNet Model and a counting algorithm. Each frame in the given video is first classified into two categories: upward and downward, describing its current motion status. Then the classification sequence of the video is processed by a statistical counting algorithm to obtain the final repetition number. The experiments on real-world videos show the efficiency of our model.

Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study (Preprint)

BACKGROUND Older adults who engage in physical activity can reduce their risk of mobility and disability. Short amounts of walking can improve their quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (e.g., Fitbit) are proprietary, often are not tailored to the movements of older adults and have been shown to be inaccurate in clinical settings. Few studies have developed step-counting algorithms for smartwatches – but only using data from younger adults and often validating them only in controlled laboratory settings. OBJECTIVE In this work, we sought to develop and validate a smartwatch step-counting app targeting older adults that has been evaluated in free-living settings over a long period of time (24 weeks) with a large sample (N=42). METHODS the steps of older adults. The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm with a total of 42 older adults in the lab (counting from a video recording, N= 20) and in free-living conditions — one 2-day field study (N=6) and two 12-week field studies (using the Fitbit as ground truth, N=16). During system development, we evaluated four kinds of walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field study, we evaluated various values for algorithm parameters, and subsequently evaluated the method’s performance using correlations and error rates. RESULTS The results from the evaluation showed that our step-counting algorithm performs well, highly correlated with the ground truth and with low error rate. For the lab study, there was stronger correlation for normal walking R2=0.5; across all activities, the Amulet was on average 3.2 (2.1%) steps lower (SD = 25.9) than video-validated steps. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2 of 0.989) and 3.1% (SD=25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 of 0.669. CONCLUSIONS Our findings demonstrate the importance of an iterative process in algorithm development in advance of field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step-counter). Our app could potentially be used to improve the physical activity among older adults through accurate tracking of their step counts and in-app daily step-count goals.

Step-Counting Function of Adolescent Physical Training APP Based on Artificial Intelligence

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.