Search Results (4)

Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy. When a certain level of data accuracy is sacrificed, lossy compression technologies can achieve better compression ratios. However, different applications may have different requirements for data accuracy. Instead of keeping multiple compressed versions of a time series w.r.t. different error bounds, HIRE hierarchically maintains a tree, where the root records a constant function to approximate the whole time series, and each other node records a constant function to approximate a part of the residual function of its parent for a particular time period. To retrieve data w.r.t. a specific error bound, it traverses the tree from the root down to certain levels according to the requested error bound and aggregates the constant functions on the visited nodes to generate a new bounded error compressed version dynamically. However, the number of nodes to be visited is unknown before the tree traversal completes, and thus the data size of the new version. In this paper, a time series is progressively decomposed into multiple piecewise linear functions. The first function is an approximation of the original time series w.r.t. the largest error bound. The second function is an approximation of the residual function between the original time series and the first function w.r.t. the second largest error bound, and so forth. The sum of the first, second, …, and m-th functions is an approximation of the original time series w.r.t. the m-th error bound. For each iteration, Swing-RR is used to generate a Bounded Error Piecewise Linear Approximation (BEPLA). Resolution Reduction (RR) plays an important role. Eight real-world datasets are used to evaluate the proposed method. For each dataset, approximations w.r.t. three typical error bounds, 5%, 1%, and 0.5%, are requested. Three BEPLAs are generated accordingly, which can be summed up to form three approximations w.r.t. the three error bounds. For all datasets, the total data size of the three BEPLAs is almost the same with the size used to store just one version w.r.t. the smallest error bound and significantly smaller than the size used to keep three independent versions. The experiment result shows that the proposed method, referred to as PBEPLA-RR, can achieve very good compression ratios and provide multiple approximations w.r.t. different error bounds. Full article

The efficiency of internal combustion engines is gaining increased interest due to the impact of fuel consumption on greenhouse gas emissions and the goals of countries to minimize emissions. Thermal barrier coatings (TBCs) have shown great potential in improving the efficiency of internal combustion engines. The TBCs, applied on the surface of the piston, apart from thermal isolation, should also follow the surface temperature variations in the combustion chamber, reducing the energy loss and not affecting volumetric efficiency, and thus accomplish a raise in fuel efficiency. This characteristic of the TBC can be associated with the thermal properties, but the best performance test for TBCs is the single cylinder engine test. The single cylinder engine test is an expensive and time demanding procedure, making it not easily accessible. The purpose of this work was to develop a thermal swing test method to evaluate the applicability of TBCs in the combustion chamber of an internal combustion engine. This was carried out by measuring the temperature variation on the surface of the coating (thermal swing response) exposed to heat pulses from a high velocity air fuel (HVAF) spray torch. The TBCs were tested as sprayed (AS) and after grinding them to reduce roughness (RR) in order to ensure similar thickness and roughness along the different TBCs. Characterization of the coating microstructure was carried by scanning electron microscopy (SEM) together with image analysis techniques, and the thermal properties were measured by laser flash analysis (LFA). By correlating the thermal swing response with the microstructure and thermal properties of the coatings, it was determined that the coatings with large open pores exhibited the highest thermal swing response, which was as high as 200 °C. Full article

Music has been reported as a positive intervention for improving psychophysiological conditions and exercise performance. However, the effects of music intervention on golf performance in association with psychophysiological responses have not been well examined in the literature. The purpose of the study was to investigate the acute effects of self-selected music intervention on golf swing and putting performance, heart rate (HR), HR variability (HRV), and anxiety. Twenty collegiate golfers voluntarily participated in this study (age = 20.2 ± 1.4 years, height = 171.7 ± 8.0 cm, body weight = 69.5 ± 14.6 kg, golf experience = 7.5 ± 2.1 years). A cross-over and within-subject design was used in this study. Participants performed a non-music trial (T1), pre-exercise music trial (T2), and simultaneous music trial (T3) in a randomized order with 48–72 h apart. The participants were attached to a HR monitor to record the HR and HRV during the measurement. The golf swing and putting performance was assessed by using the Golfzon golf simulator system. The state-trait anxiety inventory-state questionnaire (STAI-S) was used to evaluate anxiety state. All measurements were taken during baseline (phase one) and after resting or music intervention (phase two). Repeated measurement of analysis of variance (ANOVA) and Cohen’s effect size (ES) were used for statistical analyses. The results show no significant differences in golf swing and putting performance (p > 0.05). However, significant decrease in STAI-S score was found in T2 (p = 0.047, ES = 0.32). A significant increase in the standard deviation of normal R-R interval (SDNN), low-frequency power spectrum (LF), standard deviation of along the line-of-identity (SD2) in T2 and T3 were observed (p < 0.05). In conclusion, a single pre-exercise or simultaneous self-selected music intervention contributes minor effects to golf performance in collegiate golfers. The positive benefits of self-selected music intervention on the psychological condition and cardia-related modulation while practicing golf is warranted. Full article

Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product quality. Generally, sensor data provides abundant information for artificial intelligence (AI) engines to make decisions for these smart devices to collect more data or activate some required activities. However, this also consumes a lot of energy to transmit the sensor data via networks and store them in data centers. Data compression is a common approach to reduce the sensor data size so as to lower transmission energies. Literature indicates that many Bounded-Error Piecewise Linear Approximation (BEPLA) methods have been proposed to achieve this. Given an error bound, they make efforts on how to approximate to the original sensor data with fewer line segments. In this paper, we furthermore consider resolution reduction, which sets a new restriction on the position of line segment endpoints. Swing-RR (Resolution Reduction) is then proposed. It has O(1) complexity in both space and time per data record. In other words, Swing-RR is suitable for compressing sensor data, particularly when the volume of the data is huge. Our experimental results on real world datasets show that the size of compressed data is significantly reduced. The energy consumed follows. When using minimal resolution, Swing-RR has achieved the best compression ratios for all tested datasets. Consequently, fewer bits are transmitted through networks and less disk space is required to store the data in data centers, thus consuming less data transmission and storage power. Full article