Fundamental Investigation of Effects of Surrounding Conditions on Measurement Uncertainty in Temperature of Surface-Mounted Components by Thermocouples

This study describes the development of an evaluation method of uncertainty in temperature measurement of surface-mounted components by several types of thermocouples. When thermocouples conduct the temperature measurement of the surface-mounted components, the components’ temperature decreases because the thermocouples dissipate heat like a pin fin. Temperature measurement techniques for miniaturized electrical components are strongly needed to ensure the operation’s guaranteed temperature. In this report, a heat transfer experiment around thermocouples installed on the surface of the PCBs was conducted while changing the type and the mounting angle of thermocouples. Through the temperature measurement, the decrease of the temperature around the thermocouples was confirmed.

The Ecological Task Dynamics of Learning and Transfer in Coordinated Rhythmic Movement

Research spanning 100 years has revealed that learning a novel perception-action task is remarkably task-specific. With only a few exceptions, transfer is typically very small, even with seemingly small changes to the task. This fact has remained surprising given previous attempts to formalise the notion of what a task is, which have been dominated by common-sense divisions of tasks into parts. This article lays out an ecologically grounded alternative, ecological task dynamics, which provides us with tools to formally define tasks as experience from the first-person perspective of the learner. We explain this approach using data from a learning and transfer experiment using bimanual coordinated rhythmic movement as the task, and acquiring a novel coordination as the goal of learning. 10 participants were extensively trained to perform 60° mean relative phase; this learning transferred to 30° and 90°, against predictions derived from our previous work. We use recent developments in the formal model of the task to guide interpretation of the learning and transfer results.

Two-Level Domain Adaptation Neural Network for EEG-Based Emotion Recognition

Emotion recognition plays an important part in human-computer interaction (HCI). Currently, the main challenge in electroencephalogram (EEG)-based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network (TDANN) to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are then passed through TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy (MMD) to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network (DANN) to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness (84%), anger (87.04%), and fear (85.32%) on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness (83.79%), anger (84.13%), and fear (81.72%) on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition.

Bubble Nucleation in Core-Shell Structure Molten Oxide-Based Membranes with Combined Diffusion-Bubbling Oxygen Mass Transfer: Experiment and Theory

Oxygen-selective membranes are likely to play a leading part in the future separation processes relevant to the energy engineering. A newly developed molten copper and vanadium oxide-based diffusion-bubbling membrane with...

Application of PLC and HMI in the measurement and control platform of single-tube heat transfer experiment rig

The single-tube heat transfer experiment rig is composed of various equipment and devices connected through pipelines. This paper adopts Siemens PLC as the main controller and cooperates with WEINVIEW HMI MT6000 series HMI to design the experimental measurement and control platform of a single-tube heat transfer experiment rig. In the single-tube heat transfer experiment measurement and control platform, the PLC communicates with the HMI through an RS-485 cable, and the HMI can display the experimental data changes in real-time and has a separate control interface. The-single tube heat transfer experiment measurement and control platform is safe and reliable, with functions such as real-time monitoring and acquisition, real-time fault alarm, parameter change, and remote control, which simplifies the steps of data acquisition, reduces the difficulty of equipment control, and realizes the automatic acquisition and control.