The role of memory and perspective shifts in systematic biases during object location estimation

Our previous research highlighted a systematic bias in a spatial memory task, with participants correctly detecting object movements in the same direction as the perspective shift, whilst misjudging the direction of object movements if those were in the opposite direction to the perspective shift. The aim of the current study was to investigate if the introduction of perspective shifts results in systematic biases in object location estimations. To do so, we asked participants to encode the position of an object in a virtual room and to then estimate the object's sposition following a perspective shift. In addition, by manipulating memory load (perception and memory condition) we investigated if the bias in object position estimates results from systematic distortions introduced in spatial memory. Overall, our results show that participants make systematic errors in estimating object positions in the same direction as the perspective shift. This bias was present in both the memory and the perception condition. We propose that the systematic bias in the same direction as the perspective shift is driven by difficulties in understanding the perspective shifts that may lead participants to use an egocentric representation of object positions as an anchor when estimating the object location following a perspective shift, thereby giving rise to a systematic shift in errors in the same direction as the perspective shift.

A Computer Vision-Based Real-Time Load Perception Method for Belt Conveyors

Real-time load detection method for belt conveyors based on computer vision is the research topic of this paper. A belt conveyor system equipped with cameras and a laser generator is used as the test apparatus. As the basis for conveyor intelligent speed regulation, two methods from different angles to perceive the load of conveyor belt were proposed, applied, and compared in this paper. Method 1 is based on the area proportion and method 2 is the detection based on laser-based computer vision technology. Laboratory experiments show that both methods can well detect the load on the conveyor belt. Method 2 is more economical and practical under the background of existing technology, also compared to the method 1, which provides a new idea and theoretical basis for the energy-saving control and intelligent development of the conveyor.

Analysis of Training Load Perception of Young Soccer Players Based on Some Variables

The aim of this study is to examine the perception of training load of young soccer players during a five-week preparation period, based on their positions of play, VO2 max and years licensed and to compare the perceptions of the players with the training load planned by their coach. 17 young soccer players of Beşiktaş Football Club’s U16 team who participated in the pre-season training prior to the 2018-2019 season volunteered to participate in this study. The height of the players is 175.38±4.83 cm, body weight is 64.24±4.59 kg, body mass index is 20.91±1.54 kg/m2 and body fat percentage is 8.45±3.39. During the 5-week preparation period, the players assessed the rate of perceived exertion of the sections of each training session by using the Borg Scale, on a scale from 1 to10. The SPSS package program (SPSS 24) was used for statistical analysis of the research data. The Shapiro-Wilk test was used to assess normality and Levene’s test was used to assess homogeneity. It was determined that the data distribution was normal. An independent t-test was implemented for comparison of the two groups, and a one-way ANOVA test was implemented for the comparison of multiple groups. The statistical results were evaluated at p < 0.05 significance level. As a result, the players’ VO2 max values and the years of licensed soccer play may have an influence on their perception of the training load. The coaches’ and young soccer players’ perception levels of preparation period training load are compatible.

Finger Posture and Finger Load are Perceived Independently

The ability to track the time-varying postures of our hands and the forces they exert plays a key role in our ability to dexterously interact with objects. However, how precisely and accurately we sense hand kinematics and kinetics has not been completely characterized. Furthermore, the dominant source of information about hand postures stems from muscle spindles, whose responses can also signal isometric force and are modulated by fusimotor input. As such, one might expect that changing the state of the muscles – for example, by applying a load – would influence perceived finger posture. To address these questions, we measure the acuity of human hand proprioception, investigate the interplay between kinematic and kinetic signals, and determine the extent to which actively and passively achieved postures are perceived differently. We find that angle and torque perception are highly precise; that loads imposed on the finger do not affect perceived joint angle; that joint angle does not affect perceived load; and that hand postures are perceived similarly whether they are achieved actively or passively. The independence of finger posture and load perception contrasts with their interdependence in the upper arm, likely reflecting the special functional importance of the hand.

A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

Electrical energy consumption is an important component of energy consumption for internal combustion engine vehicle, which directly affects the fuel economy. A bus-based electrical energy management system is built, and an electrical energy management strategy based on driving cycle recognition and electrical load perception is presented to achieve the refined management of vehicle energy. Six typical driving cycles are selected to establish an improved learning vector quantization neural network model for driving cycle recognition. The corresponding model training algorithm is designed by utilizing a similar driving cycle classification and the gradient optimization so that the better recognition accuracy and the less computation intensity can be obtained. An online recognition mechanism based on sliding time window is devised, and the optimal time window length is determined. To minimize fuel consumption, a dynamic optimal regulation strategy for the output power of the alternator and battery, which considers driving cycle recognition and electrical load perception, is proposed. Experimental results show that the strategy can effectually improve the vehicle fuel economy according to the driving cycle and the electrical load change and decrease the fuel consumption per 100 miles of vehicle.