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The use of three-dimensional (3D) light detection and ranging (Lidar) to assist measurement and perception is feasible. LoRa-Based Internet of Things Secure Localization System and Application (I)ĭepartment of Computer Science and Information Engineering, HungĪbstract: This paper presents a self-driving technology that relies on a built-in sensing system to detect traffic objects. On the other hand, the evaluation of HCI sessions impeded by obstacles but supported with different UI adaptations shows that LSTM results well match the subjective assessment as a plausible detector of behaviour changes. Results show advantages of the proposed sequential LSTM model: on the one hand, the LSTM outperforms the baseline random guess and also a baseline static model LDA in the detection of visual obstacles with 70.6% as an average accuracy. Furthermore, we also investigate the influence of different adaptation mechanisms on performance and subjective assessment. We investigate the classification performance on data from a user study with 17 participants. UI adaptations for both types of obstacles are discussed and analyzed. A sequential model based on Long-Short Term Memory (LSTM) is suggested for such a detection of HCI obstacles. In this paper, we discuss the detection of two main HCI obstacles: memory-based and visual obstacles. Cognitive adaptive systems should dynamically detect such obstacles and compensate them with suitable User Interface (UI) adaptation. Keywords: Human-Computer Interaction, Human Performance Modeling, Human-Machine InterfaceĪbstract: Human Computer Interaction (HCI) performance can be impaired by several HCI obstacles. Visual and Memory-Based HCI Obstacles: Behaviour-Based Detection and User Interface Adaptations Analysis Thus, the proposed control strategy is advantageous at various speed commands and has improved dynamic responses.
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The results show that the proposed AFCRC exhibits the robustness against external disturbances.
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The experimental results reveal that the root mean square error (RMSE) is used as a performance index for comparing of the traditional CMAC, FCMAC, and AFCRC, respectively. This study uses the proposed AFCRC to control the direct torque control drive system of a switched reluctance motor (SRM), and compares it with the traditional CMAC and FCMAC. The AFCRC system contains an integrated error function, a TSK fuzzy compensator, and a novel cerebellar model articulation controller (CMAC), which is developed based on the concept of a recurrent neural networks (RNNs) and a functional coupling NN (FCNN). Keywords: Human-Computer Interaction, Brain-based Information Communications, Multi-User InteractionĪbstract: This paper proposes the adaptive functional coupling recurrent cerebellar model articulation controller (AFCRC). National Chin-Yi University of Technology Simulation studies for identifying a time-varying system and tracking a chaotic trajectory are performed to validate the effectiveness and superiority of the proposed method.ĭesign of Adaptive Function Coupling Recurrent Cerebellar Model Articulation Controller for Switched Reluctance Motor Drive Systems (I) The gradient descent technique is used to find the adaptive laws to online tune the parameters of the system effectively. The sub-structures include a prefrontal cortex, an amygdala, and a new dual function-link network, then it can efficiently reduce the identification and tracking errors, and obtain good performance. The WDFLFBELS consists of three sub-structures and a fuzzy inference system. The proposed wavelet dual function-link fuzzy brain emotional learning system (WDFLFBELS) is used as an identifier to identify the system and to track the trajectory of nonlinear systems. Keywords: Interactive Design Science and EngineeringĪbstract: This paper proposes a new efficient identification system for nonlinear systems.