Natural disasters are public emergencies characterized by suddenness, universality, and nonconventionality. Realizing the early warning, monitoring, and intervention of natural disasters and their derivative social impacts is significant for reducing the disasters' damage and benefits the maintenance of social stability. Social sensors are ubiquitous sensors based on social network platforms. It uses the concepts and methods of physical space to mine social signals that integrate human perception and intelligence in cyberspace. Compared with traditional physical sensors, social sensors represent a crucial data acquisition channel in the emergency management of natural disasters and have the advantages of real time, comprehensive coverage, low cost, and flexible deployment. This article reviews the application of social sensors in natural disasters emergency management. We summarize the application functions of social sensors into three categories: natural disaster situation awareness and event detection, disaster information dissemination and communication, and disaster sentiment analysis and public opinion mining. Based on the above functions, this article analyzes the research status, data, technical methods, and application systems. Finally, this article proposes a research trend of applying social sensors in natural disaster emergency management according to the requirements of real scenarios.
Digital human in cyberspace can help provide humanized services in specific applications, such as question & answer systems, recommender systems, chatter robots, and intelligent assistants. While most researches focus on behavior analytics, few of them integrate the personality that is also a closely related factor. As a classic indicator for personality representation, Myers–Briggs type indicator (MBTI) categorizes an individual into mutually exclusive types from four dichotomous axes (extraversion versus introversion, sensing versus intuition, thinking versus feeling, judging versus perceiving). Traditional recognition method using MBTI simply measures the user's preference frequency in each axis through questionnaires, treating the dominant value as the identified result. Such a paradigm, however, represents all the people with only 16 types and cannot distinguish heterogeneous users clearly. This article proposes a novel personality recognition method using fuzzy logic. Different from previous classifications, our new method categorizes the individual in a continuous space and represents one's personality in a more fine-grained level. We have designed comparative psychological tests for 77 people. The validation experiments on such tests indicate that the fuzzy-logic-based method is not only consistent with the classic MBTI tests (in the sense of defuzzification) but also provides the uncertainty for each personality type. Therefore, it can be viewed as a generalization of the classic MBTI tests and promotes the representation of individual's heterogeneity for fine-grained analytics of digital human.
We discussed the overall design of Strapped-down Inertial Navigation System(SINS) of air-to-air missile,and indicated some problems that should be considered in design of the system.From the view of overall design,we analyzed the environments of performance simulation and the algorithmic models for SINS,and established the block diagram for system simulation. On which basis we also carried out system simulation and algorithm optimization.
In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction.
Category-Agnostic Pose Estimation (CAPE) aims to localize keypoints on an object of any category given few exemplars in an in-context manner. Prior arts involve sophisticated designs, e.g., sundry modules for similarity calculation and a two-stage framework, or takes in extra heatmap generation and supervision. We notice that CAPE is essentially a task about feature matching, which can be solved within the attention process. Therefore we first streamline the architecture into a simple baseline consisting of several pure self-attention layers and an MLP regression head -- this simplification means that one only needs to consider the attention quality to boost the performance of CAPE. Towards an effective attention process for CAPE, we further introduce two key modules: i) a global keypoint feature perceptor to inject global semantic information into support keypoints, and ii) a keypoint attention refiner to enhance inter-node correlation between keypoints. They jointly form a Simple and strong Category-Agnostic Pose Estimator (SCAPE). Experimental results show that SCAPE outperforms prior arts by 2.2 and 1.3 PCK under 1-shot and 5-shot settings with faster inference speed and lighter model capacity, excelling in both accuracy and efficiency. Code and models are available at https://github.com/tiny-smart/SCAPE
Although an indirect method is widely used to evaluate oil sorption capacity of fiber materials for remediating oily water, a direct method has recently been proposed. To compare two methods, we first studied the effect of extraction time, the accuracy and precision for the direct method. Similarly, precision of the indirect method and the underlying sources of error were also investigated. The minimal extraction time required for the direct method was found to be 180 min. The relative error of direct method was less than 2.61 %, its standard deviation was less than 1.60 %o, and its coefficient of variation was less than 0.313 %. The standard deviation of the indirect method was more than 55.1 %, and its coefficient of variation was more than 16 %. The error in the indirect method mainly came from the heterogeneity of oily water, which led to large, random differences between samples that could not be eliminated.
The miniaturized total analytical system (μTAS) which indirectly detected the concentration of hydroquinone was established by means of flow injection analysis (FIA) technique and the inhibited reaction of hydroquinone to Luminol-H_2O_2-Co~(2+) chemiluminescence on designed PMMA microchip. Different factors affecting the inhibited CL reaction, especially concentration of hydroquinone, were optimized. In order to design a microchip suitable to this reaction system, the design of chip configuration and the process of these reactions were studied. The chemiluminescence intensity had a linear relation ship with the concentration of hydroquinone in water samples, and the detection limit of this method was 2.0×10~(-9) mol/L and the linear range was between 2.0×10~(-7) and 2.0×10~(-8) mol/L.
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