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The construction of knowledge graph of dangerous goods (KGDG) is with great significance of inferring relative information of dangerous goods, developing corresponding policy for its storage and transport, preventing disaster caused by dangerous goods(DG), and providing emergency plan when the disaster happens. Since distributed representation of natural language is an effective method for knowledge...
Recently, a state-of-the-art algorithm, called deep deterministic policy gradient (DDPG), has achieved good performance in many continuous control tasks in the MuJoCo simulator. To further improve the efficiency of the experience replay mechanism in DDPG and thus speeding up the training process, in this paper, a prioritized experience replay method is proposed for the DDPG algorithm, where prioritized...
Industrial equipment, such as bulldozers, excavators, and cranes, requires human operation. Construction machines with better operability are necessary in order to improve productivity. The evaluation of such operational equipment is performed in order to obtain better operability, at the design stage, by sensory evaluation from a specific evaluator. Therefore, the design and evaluation of operational...
The scale of the modern city has been expanding, which leads to a lot of serious environmental pollution problems. Among them, the air pollution problem is the most prominent. In order to control the air pollution in urban cities, the government has deployed a lot of air pollutant monitoring equipment which produce massive multi-dimensional time series data. Through the motif discovery and analysis...
This paper investigates the uncertainty of the day-ahead distribution system scheduling considering the random variations of both Photovoltaic-based distributed generator (PV-DG) output power and load. Instead of Monte-Carlo simulation (MCS), a two-point estimation method (2PEM) is applied to obtain accurate and computation-efficient analysis results. Based on the two-year real-world hourly weather...
Many novel physical assistance devices are beginning to incorporate intelligent robotic systems and mechatronic components. In terms of a human-centered design it is crucial to assess the perceived subjective usability and acceptance of these systems. A questionnaire was thus designed to evaluate novel physically assisting devices in order to support developers in their design decisions as well as...
Accurately predicting driving service orders in different regions is an essential task for service companies, in order to improve the service quality. In this paper, a specific ensemble multi-view prediction framework is proposed to address this task. It ensembles several different multi-view-based models with a weighted linear combination. Specifically, we have designed three specific multi-view-based...
Autonomous driving is on the horizon. Vehicles with partially automated driving capabilities are already in the market. Before the widespread adoption however, human factors issues regarding automated driving need to be addressed. One of the key issues is how much drivers trust in automated driving systems and how they calibrate their trust and reliance based on their experience. In this paper, we...
Long monitoring tasks without regular actions, are becoming increasingly common from aircraft pilots to train conductors as these systems grow more automated. These task contexts are challenging for the human operator because they require inputs at irregular and highly interspaced moments even though these actions are often critical. It has been shown that such conditions lead to divided and distracted...
Feature ranking from video-wide temporal evolution brings reliable information for complex action recognition. However, a video may contain similar features in the sequence of frames which deliver unnecessary information to the ranking function. This paper proposes a method to improve the rank-pooling strategy which captures the optimized latent structure of the video sequence data. The optimization...
Human brain determines the ability of perceiving and behaving. Previous works have introduced network science and control theory to stimulate the brain states and functions. In this work, network controllability theory is utilized to study the structural controllability of both temporal and static functional brain networks. We find that only a few nodes are the required driver nodes to structurally...
This paper takes advantages from probability theory and fuzzy modeling. We use probability theory to overcome some common problems in data based modeling methods. A probability based clustering method is proposed to partition the hidden features, and extract fuzzy rules with probability measurement. An optimization method are applied to train the consequent part of the fuzzy rules and the probability...
Clustering is a popular method to deal with the problem for mode identification of multimode processes. Unlike traditional distance-based clustering methods, in this paper, a new correlation-based bi-partition hierarchical clustering (CBHC) method is proposed, which classifies the observations according to their correlation relationships rather than their distances. Motivated by an existing correlation-based...
Matrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not...
As technology evolves, the Internet of Things (IoT) is gaining more importance for constituting a foundation to reach better connectivity between people and things. For this to happen, certain strategies and processes are considered to enhance and grant optimal interoperability between the heterogenous devices of a typical IoT network. Two major key aspects of these networks are autonomous error recovery...
In the paper, we propose an effective long-term real-time tracking method to address the problem of robustness and tracking failure in visual tracking with UAVs. Most existing trackers only consider short-term tracking, therefore are unable to cope with partial and complete occlusion, which finally leads to object drifting or loss. Our method still follows the tracking-by-detection framework. However,...
The performance of classification of various mental states using Electroencephalography (EEG) is often limited by the lack of information regarding the most discriminative channels and frequency bands. The paper proposes a Canonical Correlation Analysis (CCA) of EEG recorded during bilateral imagined hand movement. CCA determines linear transformation of EEG that is maximally correlated with a transformed...
TrapFetch is trained by monitoring the read requests issued by an application. It detects bursts of disk reads, determines the appropriate addresses at which breakpoints should be inserted in the application and library codes prior to the bursts of reads, and then logs this information with the data requested during the interval between each consecutive pair of breakpoints. When the application and...
Functional connectomes (FCs) are powerful in characterizing brain conditions. Temporal FC metrics can index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior. However, time-varying properties of temporal brain networks in general mental disorders have been less investigated. In this paper, FCs derived from resting-state fMRI (R-fMRI) data are temporally...
In this study, correlation of heart rate variability (HRV) results in different postures between ECG-based and PPG-based cardiac measurement devices is explored. Electrocardiogram (ECG) is one of the best indicators for the assessment of physical health and heart function, while photoplethysmography (PPG) uses light sensor to detect the change of blood volume in the vessel, and is thus less susceptible...
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