GANN: Graph Alignment Neural Network for Semi-Supervised Learning

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of oversmoothing. To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique learning algorithm with three alignment rules is proposed to thoroughly explore hidden information for insufficient labels. Firstly, to better investigate attribute specifics, we suggest the feature alignment rule to align the inner product of both the attribute and embedding matrices. Secondly, to properly utilize the higher-order neighbor information, we propose the cluster center alignment rule, which involves aligning the inner product of the cluster center matrix with the unit matrix. Finally, to get reliable prediction results with few labels, we establish the minimum entropy alignment rule by lining up the prediction probability matrix with its sharpened result. Extensive studies on graph benchmark datasets demonstrate that GANN can achieve considerable benefits in semi-supervised node classification and outperform state-of-the-art competitors

An Energy Efficient Technique Using Electric Active Shielding for Capacitive Coupling Intra-Body Communication

Capacitive coupling intra-body communication (CC-IBC) has become one of the candidates for healthcare sensor networks due to its positive prevailing features of energy efficiency, transmission rate and security. Under the CC-IBC scheme, some of the electric field emitted from signal (SIG) electrode of the transmitter will couple directly to the ground (GND) electrode, acting equivalently as an internal impedance of the signal source and inducing considerable energy losses. However, none of the previous works have fully studied the problem. In this paper, the underlying theory of such energy loss is investigated and quantitatively evaluated using conventional parameters. Accordingly, a method of electric active shielding is proposed to reduce the displacement current across the SIG-GND electrodes, leading to less power loss. In addition, the variation of such loss in regard to frequency range and positions on human body was also considered. The theory was validated by finite element method simulation and experimental measurement. The prototype result shows that the receiving power has been improved by approximate 5.5 dBm while the total power consumption is maximally 9 mW less using the proposed technique, providing an energy efficient option in physical layer for wearable and implantable healthcare sensor networks

Air-conditioning use behaviors when elevated air movement is available

Many experimental studies demonstrated that elevating indoor temperatures to 28 or 30°C is comfortable if fans are used, while this elevation often is not applied in practice. This study aims to investigate AC thermostat use behaviors when elevated air movement is available, as well as to provide insights at guiding occupants to select warmer environments without reducing their comfort. 20 subjects participated in a series of tests at three initial ambient temperatures (26, 28, and 30°C) with different settings of an AC thermostat and a ceiling fan: (1) a free-controlled thermostat without a fan, (2) a free-controlled thermostat with a free-controlled fan, (3) a free-controlled thermostat with an always-on fan (controllable but not allowed to turn off), (4) a limited thermostat (no lower than 28°C) with a free-controlled fan, and (5) a limited thermostat with an always-on fan. The results show that without the ceiling fan, subjects used AC to lower indoor temperatures to 25.7°C. When the fan was available (free-controlled), they did not only rely on lowering the AC set-point alone but also turned on the fan immediately when starting using AC. This behavior ended a final ambient temperature up to 27.4°C. Under always-on fan conditions, the fan was running at a high speed at the beginning of using AC. Although subjects turned down the fan speed, the final fan speeds were about doubled the speeds as compared with free-controlled fan conditions. This higher initial speed by the always-on fan made subjects select higher indoor temperatures: at the 28°C initial temperature, there was no further lowering of ambient temperatures; at the 30°C initial temperature, the final temperatures were 28.6 and 28.9°C for the free-controlled and limited AC test conditions, about a 3-K set-points extension compared with the no-fan condition. Further, despite different conditions, more than 90% of subjects had comfortable feelings, which indicates that people with adjustable AC prefer a constant thermal perception rather than a constant ambient temperature. Therefore, the strategy of using fans before AC should be adopted to guide people actively to select higher AC set-point temperatures

Air-conditioning use behaviors when elevated air movement is available

Many experimental studies demonstrated that elevating indoor temperatures to 28 or 30°C is comfortable if fans are used, while this elevation often is not applied in practice. This study aims to investigate AC thermostat use behaviors when elevated air movement is available, as well as to provide insights at guiding occupants to select warmer environments without reducing their comfort. 20 subjects participated in a series of tests at three initial ambient temperatures (26, 28, and 30°C) with different settings of an AC thermostat and a ceiling fan: (1) a free-controlled thermostat without a fan, (2) a free-controlled thermostat with a free-controlled fan, (3) a free-controlled thermostat with an always-on fan (controllable but not allowed to turn off), (4) a limited thermostat (no lower than 28°C) with a free-controlled fan, and (5) a limited thermostat with an always-on fan. The results show that without the ceiling fan, subjects used AC to lower indoor temperatures to 25.7°C. When the fan was available (free-controlled), they did not only rely on lowering the AC set-point alone but also turned on the fan immediately when starting using AC. This behavior ended a final ambient temperature up to 27.4°C. Under always-on fan conditions, the fan was running at a high speed at the beginning of using AC. Although subjects turned down the fan speed, the final fan speeds were about doubled the speeds as compared with free-controlled fan conditions. This higher initial speed by the always-on fan made subjects select higher indoor temperatures: at the 28°C initial temperature, there was no further lowering of ambient temperatures; at the 30°C initial temperature, the final temperatures were 28.6 and 28.9°C for the free-controlled and limited AC test conditions, about a 3-K set-points extension compared with the no-fan condition. Further, despite different conditions, more than 90% of subjects had comfortable feelings, which indicates that people with adjustable AC prefer a constant thermal perception rather than a constant ambient temperature. Therefore, the strategy of using fans before AC should be adopted to guide people actively to select higher AC set-point temperatures

Deep Graph Clustering via Dual Correlation Reduction

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github

Detailed measured air speed distribution in four commercial buildings with ceiling fans

The layout of ceiling fans in buildings is challenging because of the need to co-ordinate with other elements in the ceiling space, and because the resulting airflows within the occupied space interact with furniture. This study conducted detailed air speed measurements in four buildings with different room sizes, furniture configurations, ceiling fan types, and ceiling-fan-to-floor-area ratios. We measured air speeds across the occupied spaces at four heights while varying ceiling fan operation modes such as fan rotational speed, operating direction, and the number of operating fans. In total, we collected 207,080 air speed samples at 343 sites under 20 test conditions. This paper presents the magnitude and distribution of air speeds, cooling effects, and their influencing factors. The Airspeed Coverage Index (ACI= (Fan air speed (SF)× Fan diameter (D))/√(Average area served per ceiling fan (A))) describes the combined effects of multiple influencing factors on the magnitude of air speed. ACI is employed to predict the average air speed and occupant cooling effect, yielding regression confidences higher than 0.95. When designing a space to a target airspeed or cooling effect, the ACI can help to determine parameters such as fan density required for fan choices. The measured data are compared with predictions from the CBE fan tool that had been developed from laboratory tests under simplified conditions. The comparison displays the blocking effects of the furniture, lowering the average air movement in the space, as well as reducing the air movement at the ankle level while increasing it at higher heights. The blocking effect increases with the density of the furniture. We also visually present fan interactions in which triplets of fans are arranged linearly or diagonally, showing that the diagonal layout of ceiling fans increases average air speed and improves its uniformity

Detailed measured air speed distribution in four commercial buildings with ceiling fans

The layout of ceiling fans in buildings is challenging because of the need to co-ordinate with other elements in the ceiling space, and because the resulting airflows within the occupied space interact with furniture. This study conducted detailed air speed measurements in four buildings with different room sizes, furniture configurations, ceiling fan types, and ceiling-fan-to-floor-area ratios. We measured air speeds across the occupied spaces at four heights while varying ceiling fan operation modes such as fan rotational speed, operating direction, and the number of operating fans. In total, we collected 207,080 air speed samples at 343 sites under 20 test conditions. This paper presents the magnitude and distribution of air speeds, cooling effects, and their influencing factors. The Airspeed Coverage Index (ACI= (Fan air speed (SF)× Fan diameter (D))/√(Average area served per ceiling fan (A))) describes the combined effects of multiple influencing factors on the magnitude of air speed. ACI is employed to predict the average air speed and occupant cooling effect, yielding regression confidences higher than 0.95. When designing a space to a target airspeed or cooling effect, the ACI can help to determine parameters such as fan density required for fan choices. The measured data are compared with predictions from the CBE fan tool that had been developed from laboratory tests under simplified conditions. The comparison displays the blocking effects of the furniture, lowering the average air movement in the space, as well as reducing the air movement at the ankle level while increasing it at higher heights. The blocking effect increases with the density of the furniture. We also visually present fan interactions in which triplets of fans are arranged linearly or diagonally, showing that the diagonal layout of ceiling fans increases average air speed and improves its uniformity

An Improved Double-Branch Network for Estimation of Crater Ages Based on Semisupervised Learning and Multi-Source Lunar Data

While various methods have been developed to estimate the age of impact craters, such as the crater size frequency distribution and morphology methods. Accurately and efficiently estimating the ages of lunar craters using traditional techniques is challenging due to their complex morphology and large number. As a result, the accuracy of age estimation algorithms for meteorite craters based on deep learning is restricted by factors such as a scarcity of age-labeled data and the complex morphology of these craters. To address these issues, this article presents an enhanced double-branch network for estimating crater ages via semisupervised learning and multisource lunar data. The algorithm consists of three steps: semisupervised training data augmentation, adaptive two-branch feature extraction, and a two-stage crater age classification process. The effectiveness of the improved approach was validated through ablation experiments, resulting in an overall accuracy of 83.7% on the test set of meteorite craters. This is 5.2% higher than the accuracy achieved by the previous deep learning method