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This paper proposed a real-time fault diagnostic method for hydraulic systems using data collected from multiple sensors. The method is based on a proposed multi-sensor convolutional neural network (MS-CNN) that incorporates feature extraction, sensor selection, and fault diagnosis into an end-to-end model. Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. First, the sensor selection can reduce the redundant information and improve the diagnostic performance of the model. Secondly, the reduced number of sensors simplifies the model, reducing communication burden and computational complexity. These two advantages make the MS-CNN suitable for real-time hydraulic system fault diagnosis, in which the multi-sensor feature extraction and the computation speed are both significant. The proposed MS-CNN approach is evaluated experimentally on an electric-hydraulic subsea control system test rig and an open-source dataset. The proposed method shows obvious superiority in terms of both diagnosis accuracy and computational speed when compared with traditional CNN models and other state-of-the-art multi-sensor diagnostic methods. [ABSTRACT FROM AUTHOR]

As a new cyber-attack method in power cyber physical systems, false-data-injection attacks (FDIAs) mainly disturb the operating state of power systems by tampering with the measurement data of sensors, thereby avoiding bad-data detection by the power grid and threatening the security of power systems. However, existing FDIA detection methods usually only focus on the detection feature extraction between false data and normal data, ignoring the feature correlation that easily produces diverse data redundancy, resulting in the significant difficulty of detecting false-data-injection attacks. To address the above problem, we propose a multi-source self-attention data fusion model for designing an efficient FDIA detection method. The proposed data fusing model firstly employs a temporal alignment technique to integrate the collected multi-source sensing data to the identical time dimension. Subsequently, a symmetric hybrid deep network model is built by symmetrically combining long short-term memory (LSTM) and a convolution neural network (CNN), which can effectively extract hybrid features for different multi-source sensing data. Furthermore, we design a self-attention module to further eliminate hybrid feature redundancy and aggregate the differences between attack-data features and normal-data features. Finally, the extracted features and their weights are integrated to implement false-data-injection attack detection using a single convolution operation. Extensive simulations are performed over IEEE14 node test systems and IEEE118 node test systems; the experimental results demonstrate that our model can achieve better data fusion effects and presents a superior detection performance compared with the state-of-the-art. [ABSTRACT FROM AUTHOR]

For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Cooking at home is a critical survival skill. We propose a new cooking assistance system in which a user only needs to wear an all-in-one augmented reality (AR) headset without having to install any external sensors or devices in the kitchen. Utilizing the built-in camera and cutting-edge computer vision (CV) technology, the user can direct the AR headset to recognize available food ingredients by simply looking at them. Based on the types of the recognized food ingredients, suitable recipes are suggested accordingly. A step-by-step video tutorial providing details of the selected recipe is then displayed with the AR glasses. The user can conveniently interact with the proposed system using eight kinds of natural hand gestures without needing to touch any devices throughout the entire cooking process. Compared with the deep learning models ResNet and ResNeXt, experimental results show that the YOLOv5 achieves lower accuracy for ingredient recognition, but it can locate and classify multiple ingredients in one shot and make the scanning process easier for users. Twenty participants test the prototype system and provide feedback via two questionnaires. Based on the analysis results, 19 of the 20 participants would recommend others to use the proposed system, and all participants are overall satisfied with the prototype system. [ABSTRACT FROM AUTHOR]

Nowadays, web designers are forced to have an even deeper perception of how users approach their products in terms of user experience and usability. Remote Usability Testing (RUT) is the most appropriate tool to assess the usability of web platforms by measuring the level of user attention, satisfaction, and productivity. RUT does not require the physical presence of users and evaluators, but for this very reason makes data collection more difficult. To simplify data collection and analysis and help RUT moderators collect and analyze user's data in a non-intrusive manner, this research work proposes a low-cost comprehensive framework based on Deep Learning algorithms. The proposed framework, called Miora, employs facial expression recognition, gaze recognition, and analytics algorithms to capture data about other information of interest for in-depth usability analysis, such as interactions with the analyzed software. It uses a comprehensive evaluation methodology to elicit information about usability metrics and presents the results in a series of graphs and statistics so that the moderator can intuitively analyze the different trends related to the KPI used as usability indicators. To demonstrate how the proposed framework could facilitate the collection of large amounts of data and enable moderators to conduct both remote formative and summative tests in a more efficient way than traditional lab-based usability testing, two case studies have been presented: the analysis of an online shop and of a management platform. Obtained results suggest that this framework can be employed in remote usability testing to conduct both formative and summative tests. [ABSTRACT FROM AUTHOR]

With the rapid development in the field of computer vision, the vision-based approach to unmanned aerial vehicle (UAV) tracking and landing technology in weak global positioning system (GPS) or GPS-free environments has become prominent in military and civilian missions. However, this technique still suffers from problems such as interference by similar targets in the environment, low tracking accuracy, slow processing speed, and poor stability. To solve these problems, we propose the designated target anti-interference tracking (DTAT) method, which integrates YOLOv5 and SiamRPN, and built a system to achieve UAV tracking and the landing of a designated target in an environment with multiple interference targets. The system consists of the following parts: first, an image is acquired by a monocular camera to obtain the pixel position information of the designated target. Next, the position of the UAV relative to the target is estimated based on the pixel location information of the target and the known target size information. Finally, the discrete proportion integration differentiation (PID) control law is used to complete the target tracking and landing task of the UAV. To test the system performance, we deployed it on a robot operating system (ROS) platform, conducted many simulation experiments, and observed the real-time trajectories of the UAV and the target through Gazebo software. The results show that the relative distance between the UAV and the target during the tracking process when the target was moving at 0.6 m/s does not exceed 0.8 m, and the landing error of the UAV during the landing process after the target is stationary does not exceed 0.01 m. The results validate the effectiveness and robustness of the system and lay a foundation for subsequent research. [ABSTRACT FROM AUTHOR]

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively. [ABSTRACT FROM AUTHOR]

Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]