Suchergebnisse

In this study, localization and environment mapping application is aimed with an autonomous robot. A new algorithm is presented to scan a larger area, to produce faster and more accurate results. The mapping process is intended not to be affected by environmental movements. It can be used in military areas to gain manpower in the mine area or to model the environment in virtual reality applications. In autonomous robot design, the horizontal and vertical angle values of the Lidar Lite V3 are provided by two servo motors. A fourwheeled car model was used. Ultrasonic sensors are placed on the front, right and left surfaces of the robot, Raspberry Pi 3 and Pi Camera was placed on top. It is seen that the moving average filter removes the noise generated on the map. The Lidar Lite V3 was able to take measurements at longer distances. Noise generation is prevented by motion detection algorithm. It can be used in interior space mapping, environment modeling, virtual reality applications, military areas, mining sector and graphic applications. In outdoor mapping, it can be used to create a map of an area of 40 meters in diameter. The mapping process was performed as close to the actual values by using the moving average filter and the Lidar Lite V3. The mapping process with the motion detection system is paused and actual position data are obtained using GPS. ; Bu çalışmada otonom bir robot ile çevre haritalaması ve konum takibi yapılması amaçlanmıştır. Daha geniş bir alanın taranması, daha hızlı ve doğru sonuçların üretilmesi amacıyla yeni bir algoritma sunulmuştur. Haritalama işleminin ortam hareketlerinden etkilenmemesi amaçlanmıştır. Askeri alanlarda, maden alanında insan gücünden kazanç sağlamak veya sanal gerçeklik uygulamalarında ortam modeli çıkarmak amacıyla kullanılabilmektedir. Otonom robot tasarımında iki adet servo motor ile Lidar Lite V3'e yatay ve düşey açı değerleri verilmiştir. Dört tekerlekli bir araba modeli kullanılmıştır. Robotun ön, sağ ve sol yüzeylerine birer ultrasonik sensör ve üzerine ...

The goal of this study is to present the mixed Tukey exponentially weighted moving average-modified exponentially weighted moving average control chart (MEME-TCC) for monitoring process location with symmetric and skewed distributions in an attempt to significantly improve detection ability. With the benefits of nonparametric assumption robustness. The average and median run lengths are supporting measurements for assessing the performance of a monitoring scheme using Monte Carlo simulation. Furthermore, the average extra quadratic loss (AEQL), relative mean index (RMI), and performance comparison index (PCI) can all be used to evaluate overall performance criteria. The proposed chart is compared with existing charts such as; EWMA, MEWMA, TCC, MEME, MMEE, and MMEE-TCC. The comparison result shows that the proposed chart is the best control chart for detecting small to moderate shifts among all distributional settings. Nevertheless, the EWMA chart detects large shifts more effectively than other charts, except in the case of the gamma distribution, where MEWMA performs best. The results of adapting the proposed control chart to two sets of real data corresponded to the research findings. Doi: 10.28991/ESJ-2023-07-03-014 Full Text: PDF

Are social (occupational) classes coherent, distinct entities? While they reflect an underlying reality, they are more fragmented than theory suggests. It is hypothesised that skill mismatches mean that each class includes a substantial proportion of poorly paid people who could be in the class below and highly paid people who could be in the class above, or in a class alone. This is tested for the service classes using the British Labour Force Survey. It is then shown using the British Household Panel Study that people within the service classes have differing class backgrounds, different class perceptions, and different political views depending on their hourly pay.

Technical analysis is based on the assumption that market patterns do not change frequently over time and that these patterns may be repeated and predicted in the future. Its foundation is on the negation of the theory of market efficiency to which the fundamental analysis is based on, considering that all market changes are included in the prices. In relation to this theory, technical analysis assumes that the market is adjusting according to the trends that are determined by the changing attitudes of investors with respect to a number of economic, monetary, political and psychological forces. Market changes cannot be immediately expressed by the price which is the basis for investors to achieve greater profits. Refined version of technical analysis is based on the assertion that there are patterns of stock prices trends that can be explained, but after these patterns have been determined by investors and after their attempts to make profit on them, investors' trading activity affects prices leading to changes in pricing patterns. A moving average is the most reliable and most widely used technical indicator representing an average value in a given time interval. It indicates the signal to buy when price rises above the moving average and the signal to sell when the price falls below the moving average. .

AbstractMemory-type control charts are widely used for monitoring small to moderate shifts in the process parameter(s). In the present article, we present an exponentiated exponentially weighted moving average (Exp-EWMA) control chart that weights the past observations of a process using an exponentiated function. We evaluated the run-length characteristics of the Exp-EWMA chart via Monte Carlo simulations. A comparison study versus the CUSUM, EWMA and extended EWMA (EEWMA) charts under similar in-control (IC) run-length properties demonstrates that the Exp-EWMA chart is more effective for detecting small and, under certain circumstances, moderate shifts for both the zero-state and steady-state cases. Moreover, the Exp-EWMA chart has better zero-state out-of-control (OOC) performance than an EWMA chart with smoothing parameter equal to the limit to the infinity of the exponentiated function, while the two charts perform similarly for the steady-state case. Finally, it is shown that the Exp-EWMA chart is more IC robust than its competitors under several non-normal distributions. Two examples are provided to explain the implementation of the proposed chart