OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.
OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.
OBJECTIVE: In this article, we study the robotic kitting system with a Robotic Mounted Rail Arm System (RMRAS), which travels narrowly to choose the elements.
RESULTS: The objective is to evaluate the efficiency of a robotic kitting system in cycle times through modeling of the elementary kitting operations that the robot performs (pick and room, move, change tools, etc.). The experimental results show that the proposed method enhances the performance and efficiency ratio when compared to other existing methods.
CONCLUSION: This study with the manufacturer can help him assess the robotic area performance in a given design (layout and picking a policy, etc.) as part of an ongoing project on automation of kitting operations.
OBJECTIVES: This paper discusses RISAPI of our original work in the field, which shows how probabilistic planning and system theory algorithms in workplace robotic systems that work with people can allow for that reasoning using a security robot system. The problem is a general way as an incomplete knowledge 2-player game.
RESULTS: In this general framework, the various hypotheses and these contribute to thrilling and complex robot behavior through real-time interaction, which transforms actual human subjects into a spectrum of production systems, robots, and care facilities.
CONCLUSION: The models of the internal human situation, in which robots can be designed efficiently, are limited, and achieve optimal computational intractability in large, high-dimensional spaces. To achieve this, versatile, lightweight portrayals of the human inner state and modern algorithms offer great hope for reasoning.
OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.
RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.
CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.
OBJECTIVE: In this paper, Non-linear Adaptive Heuristic Mathematical Model (NAHMM) has been proposed for the prevention of workplace violence using security Human-Robot Collaboration (HRC). Human-Robot Collaboration (HRC) is an area of research with a wide range of up-demands, future scenarios, and potential economic influence. HRC is an interdisciplinary field of research that encompasses cognitive sciences, classical robotics, and psychology.
RESULTS: The robot can thus make the optimal decision between actions that expose its capabilities to the human being and take the best steps given the knowledge that is currently available to the human being. Further, the ideal policy can be measured carefully under certain observability assumptions.
CONCLUSION: The system is shown on a collaborative robot and is compared to a state of the art security system. The device is experimentally demonstrated. The new system is being evaluated qualitatively and quantitatively.
METHODS: ACLF patients recruited from the APASL-ACLF Research Consortium (AARC) were followed up till 30 days, death or transplantation, whichever earlier. Clinical details, including dynamic grades of HE and laboratory data, including ammonia levels, were serially noted.
RESULTS: Of the 3009 ACLF patients, 1315 (43.7%) had HE at presentation; grades I-II in 981 (74.6%) and grades III-IV in 334 (25.4%) patients. The independent predictors of HE at baseline were higher age, systemic inflammatory response, elevated ammonia levels, serum protein, sepsis and MELD score (p