Human Activity Recognition Based on Frequency-Modulated Continuous Wave and DenseNet

With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.

Biological behaviors of two novel syngeneic human osteosarcoma cell lines

Objective To characterize and compare the different biological behaviors of two novel human osteosarcoma cell lines,Zos and Zos-M,established respectively from the primary site and the skip metastasis of an osteosarcoma patient.Methods Two

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

APPLICATION OF HUMAN COGNITIVE MODEL FOR TIME DEPENDENT OPERATOR BEHAVIOR IN CHINESE NPP

This paper discusses some issues on human reliability model of time dependent human behavior. Some results of the crew reliability experiment on Tsinghua training simulator in China are given, Meanwhile, a case of calculation for human error probability during anticipated transient without scram (ATWS) based on the data drew from the recent experiment is offered.

An Event Alarm System Based on Single and Group Human Behavior Analysis

Due to the increasing demand for security, the development of intelligent surveillance systems has attracted considerable attention in recent years. This study aims to develop a system that is able to identify whether or not the people need help in a public place. Different from previous work, our work considers not only the behaviors of the target person but also the interaction between him and nearby people. In the paper, we propose an event alarm system which can detect the human behaviors and recognize the happening event through integrating the results generated from the single and group behavior analysis. Several new effective features are proposed in the study. Besides, a mechanism capable of extracting one-to-one and multiple-to-one relations is also developed. Experimental results show that the proposed approach can correctly detect human behaviors and provide the alarm messages when emergency events occur.

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.

Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition

In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.

On Mechanisms of Human Behavior： The ＂Mind Blindness Phenomenon＂ in Philosophy, Religion, Science, and Medicine

The phenomenon described here has no scientific title, but occurs frequently in daily living, from science to philosophy, religion, and medicine. In every field of human endeavor, when a view is expressed, sharp and profound differences of opinion ensue. Initially, we coin this phenomenon as ＂understanding blindness＂ or ＂mind＇s awareness.＂ Thereafter, we decide to refer to it as ＂mind blindness,＂ a concept introduced to science by Professor Simon Baron-Cohen, who coins it for a cognitive disorder associated with autism, Asperger＇s syndrome, and schizophrenia. Baron-Cohen＇s usage has subsequently been extended to dementia, bi-polar disorders, antisocial personality disorders, and even normal aging. In our view, definition and identification of ＂mind blindness＂ in philosophy, religion, science, medicine, and at end-of-life care can help mankind to better understand mechanisms of human behavior, and the causes of conflicts, controversies, contradictions, and sharp differences of opinion in human life, and even to solve some of them.

Human Body Behavior as Response on Autonomous Maneuvers, Based on ATD and Human Model

In the near future, active safety systems will take more control over the vehicle driving, even up to introducing fully autonomous vehicles. Nowadays, it is expected that the active safety systems will aid avoiding collisions much more efficiently than human drivers. These systems can protect not only the passengers, but also other road users. To mitigate collision, certain maneuvers （e.g., sudden braking, lane change, etc.） need to be done in a reasonably quick time. However, this may lead to low-g energy pulses. The latter fact, may cause unexpected and, in some cases, unwanted occupant body motion resulting even in OOP （out of position） postures. New patterns of occupant reactions in such cases are, to some extent, confirmed experimentally [1-3]. This paper evaluates the limits of standard ATDs （anthropometric test devices） and chosen human models in well established maneuver scenarios. Obtained results are compared with experimental data available in the literature. Drawbacks identify new challenges for the near future simulation based safety engineering. One scenario with combined conditions of emergency braking during lane change has been used as an example of OOP posture after maneuver.

Origin of Traditional Chinese Medicine in Cuba in the 19th Century from Its Main Exponents and Some Notable Medical Descendants in the 20th Century

The Cuban people are made up of three major migratory currents, the Chinese are one of them. They brought their culture, the methods, and procedures of traditional Chinese medicine(TCM) in the 19th century. Few were able to return and so they created families in Cuba;some of their descendants dedicated themselves to medicine. In order to investigate the practices that were predecessors of TCM in Cuba in the 19th century, a qualitative phenomenological research was carried out, reviewing what was published by various sources, applying documentary analysis, logical historical analysis, abstraction, synthesis, and systematization of the results on the regularities of the work and human behavior of Chinese doctors in the Cuban 19th and 20th centuries. This made it possible to identify six Chinese doctors in the 19th century in Cuba who gave rise to the beginning of some practices of TCM in Cuba, and five from the 20th century, descendants of coolies who dedicated themselves to other specialties of medicine. It was found that despite their geographical and time disperse, they were all notorious for their outstanding professional and human behavior, with a trail of accumulated successes in achieving “almost the impossible” with the patient. They have left their mark on Cuban culture.

Behavior Recognition of the Elderly in Indoor Environment Based on Feature Fusion of Wi-Fi Perception and Videos

With the intensifying aging of the population,the phenomenon of the elderly living alone is also increasing.Therefore,using modern internet of things technology to monitor the daily behavior of the elderly in indoors is a meaningful study.Video-based action recognition tasks are easily affected by object occlusion and weak ambient light,resulting in poor recognition performance.Therefore,this paper proposes an indoor human behavior recognition method based on wireless fidelity(Wi-Fi)perception and video feature fusion by utilizing the ability of Wi-Fi signals to carry environmental information during the propagation process.This paper uses the public WiFi-based activity recognition dataset(WIAR)containing Wi-Fi channel state information and essential action videos,and then extracts video feature vectors and Wi-Fi signal feature vectors in the datasets through the two-stream convolutional neural network and standard statistical algorithms,respectively.Then the two sets of feature vectors are fused,and finally,the action classification and recognition are performed by the support vector machine(SVM).The experiments in this paper contrast experiments between the two-stream network model and the methods in this paper under three different environments.And the accuracy of action recognition after adding Wi-Fi signal feature fusion is improved by 10%on average.

Image Semantic Segmentation Approach for Studying Human Behavior on Image Data

Image semantic segmentation is an essential technique for studying human behavior through image data.This paper proposes an image semantic segmentation method for human behavior research.Firstly,an end-to-end convolutional neural network architecture is proposed,which consists of a depth-separable jump-connected fully convolutional network and a conditional random field network;then jump-connected convolution is used to classify each pixel in the image,and an image semantic segmentation method based on convolu-tional neural network is proposed;and then a conditional random field network is used to improve the effect of image segmentation of hu-man behavior and a linear modeling and nonlinear modeling method based on the semantic segmentation of conditional random field im-age is proposed.Finally,using the proposed image segmentation network,the input entrepreneurial image data is semantically segmented to obtain the contour features of the person;and the segmentation of the images in the medical field.The experimental results show that the image semantic segmentation method is effective.It is a new way to use image data to study human behavior and can be extended to other research areas.

E-Mobility in Urban Areas and the Impact of Parking Organisation

In this paper we explore the preconditions and requirements in order to enable the renewal of the vehicle fleet towards e-cars without weakening eco-mobility(public transport,biking,walking).We follow a linked approach of arranging charging infrastructure and regulating the parking spaces.We analyze the results of this approach by modeling different scenarios for the case study city of Vienna with the LUTI(land-use transport interaction)model MARS(Metropolitan Activity Relocation Simulator).Four different policy scenarios are modeled and the results compared.We look at changes in transport behavior(modal split and vehicle kilometers),the emissions and the impact on public transport ridership.

Economic Models and Human Behavior:The Tension Between Mathematics and Reality

The use of mathematical models in economics has long been a subject of debate,particularly regarding the tension between mathematical precision and the unpredictability of human behavior.This paper critically examines the limitations of over-relying on mathematical models to predict economic outcomes,arguing that such models often fail to account for the social,cultural,and psychological factors that drive economic activities.The analysis highlights how attempts to quantify complex economic behaviors may lead to oversimplifications and inaccurate forecasts,as illustrated by past financial crises and market disruptions.The paper concludes by advocating for a more balanced approach that combines mathematical rigor with an understanding of human behavior to enhance the accuracy and effectiveness of economic predictions.

Simulation of Police Actions and Operations

The paper presents modern perception of crisis management and its importance for security in the context of non-military threats. It shows the role of the police as a part of a governmental system, performing their tasks in all stages of crisis management. And it is about simulator crisis situation.

Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

Cognitive work analysis to comprehend operations and organizations in the mining industry

Complex industrial systems, including mining, have a prominent challenge in understanding the interrelationship among the cognitive processes, working environment and available equipment. The concept of cognitive work analysis(CWA) transcends the traditional analytic methods of evaluating human tasks solely based on perceptual and physical traits, and rather implements the notions of behavioral and cognitive awareness indispensable for the intricacy of modern technology. In the last few decades, academic and industrial settings employ this type of analysis to set a suitable standard for a system's safety feasibility, and as a result reduce human-based errors. This research paper analyzes current CWA methods and proposes a five-level quantification model portraying the overall cognitive quality of a mining operation.

Our mindset, our behavior and our future

To illustrate how the universality of climate change is exhibited in radically different specifics,Kalamazoo,Michigan’s“100-year flood plain”which has been flooded three or four times in the past several years is offered as an immediate example.The county’s general topography and very complex watersheds are described,noting the similarity between this microcosm and giant riparian systems.China’s enormous data collection and analysis system founded on a magnificent recursive feedback loop is described.The parallel structure of human cognition as an inherited psycho-biological recursive feedback loop as the structure of all human cognition and learning is described with reference to how infants actually learn their native language.A brief summary of the critical role of China’s“Three Teachings”(Confucianism,Daoism and Buddhism)in fostering adaptation to nature is proposed in contrast to the Western preference for manipulating nature to fit human comfort.Practicing traditional modes of“meditation”is urged as a pathway towards a brighter future for both humanity and the nature.Coopting specialists in publicizing and advertising is required to help change the human narrative.

Architectural Requirements for Improving Social Interactions in Subway Stations： An Evidence Based Design Approach

Environmental design is concerned with the function of people within an environment and their interactions. Subway stations spaces are important examples of public spaces that are in close connection with the social life of people. Also, the fact that the social interactions and relations between people and city environments are becoming indispensable for subway station space indicates that these spaces play an essential role in urban life. This paper addresses the result of a study conducted by the authors on the influential elements pivotal to the improvement of social interactions in subway stations. The theory of John Lang in Urban design with consideration of social interaction was considered in this study. The objective of the study was to identify the architectural requirements in improving social interaction between people and environment in subway station spaces according to an evidence based design approach. To do so, relevant literature in different disciplines, architecture, urban design, social sustainability and so on, were reviewed Next, the most important environmental factors which contribute to human behavior in public spaces were derived and analyzed. Data of the study were collected via questionnaires filled out by the users of Tehran＇s subway stations. The collected data were analyzed and the architectural elements/requirements for improving social interactions were classified. The results of the study indicate that in the case of architectural requirements, contribution to the improvement of social interactions, design considerations for physical and mental safety, accessibility and lighting are the most important factors.

A Theoretical Approach to the Effective Decision-Making Process

This research develops and elaborates studies done for a contribution to the 2019 PIC International Conference 2019 in Malta, about the decision-making process. Decision-making is the act of choosing between two or more courses of action. In the wider process of problem-solving, decision-making involves choosing between possible solutions to a problem, and these decisions can be made through either an intuitive or reasoned process, or a combination of the two. The study of decision-making processes, to be understood as the role of human factors, becomes particularly interesting in complex organizations. This research aims to analyze how an effective team, within organizations, can develop a more correct and effective decision-making, in order to get an optimal solution, overcoming the typical uncertainty. The paper describes the point of departure of decision in complex, time-pressured, uncertain, ambiguous and changing environments. The use of a leading case (the Tenerife air accident, 1977), will lead us to the desired results, i.e. to demonstrate how an effective decisional process, including team dynamics, can be useful to reduce the risk, present in all decisions, and reduce errors. The case of Tenerife air disaster, confirm our research. In that case, in fact, the group dynamics prove not to have worked. Thus, we can state that if a team approach had been followed instead of a more individual one, the results would probably have been different. The central belief of the research, is that classic decision theory could benefit from a team approach, which reduces the risk that a decision may lead to undesirable consequences. As demonstrated with the case study, within organizations, the decision-making is not a solitary action. Decisions, in fact, are made within a team and in order to be able to function effectively in a group, and manage group situations, there are essential skills. The team can then become a resource for the decisional process and problem solving, but it is necessary to understand the dynamics.