Deep learning technology establishes advanced methods for enhancing Human-computer Interaction systems by providing machines with the ability to sense and understand human emotional states. The capability's value ...
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Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing In...
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The proceedings contain 27 papers. The topics discussed include: the evaluation and application of the capacity measurement;IMSMON: a low overhead monitor for IMS systems;performance evaluation of a minicomputer-based...
The proceedings contain 27 papers. The topics discussed include: the evaluation and application of the capacity measurement;IMSMON: a low overhead monitor for IMS systems;performance evaluation of a minicomputer-based data collection system;waste is not a four-letter word;gaining an awareness of the performance of COBOL programs;meaningful data processing performance measures for management;REALUTL - a system for profiling user resource usage;a comparison of the workload on several computersystems;sampling design considerations for benchmarking;software product performance simulation in multi environments;contention analysis of serially reusable programs;two-class finite memory analytic models for non-pages computersystems;and a structured approach for evaluating UNIVAC 1100 series computersystems.
In response to issues of low detection accuracy and poor robustness caused by missed detections of small objects and occlusions among dense objects in road scenes, a YOLOv8-RC object detection algorithm is proposed. F...
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This paper presents an integrated approach to evalu-ating the interplay between localization and path-tracking performances in autonomous vehicle navigation systems. Considering stochastic uncertainties in motion and ...
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This study proposes the novel real-time electroencephalogram (EEG) signal decoding algorithm based on deep learning from the theoretical modeling level. The original intention of the design of this algorithm is to opt...
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A state-constrained optimal control problem is considered in which the starting position is fixed and the terminal position is free. The endpoints are strictly included within the set shaped by the given state constra...
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As a new communication technology for Industry 4.0, Time-Sensitive Networking (TSN) provides reliable support for real-time data amidst massive data transmissions. However, manually verifying the overall performance m...
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Due to a rising number of entities and more advanced systems, modern air combat engagements are increasing in complexity. Therefore, to ensure success in pilot training and perform accurate threat evaluation using sim...
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ISBN:
(纸本)9783031713965;9783031713972
Due to a rising number of entities and more advanced systems, modern air combat engagements are increasing in complexity. Therefore, to ensure success in pilot training and perform accurate threat evaluation using simulations, it is substantial to not only replicate and simulate the physical properties of the computer-Generated forces (CGFs) to an adequate degree but also to provide them with sufficiently realistic, coordinated and situation-adaptive behavior. Additionally, most air combat research assumes that all aircraft information is known, however, in real-world scenarios, multiple factors, such as sensor performance limitations, can lead to missing or incorrect information about the position, altitude, or velocity of adversary aircraft. In this paper, we propose a Tactical Planning Process as part of an overarching CGF Team Behavior Agent Function utilizing information such as threat risk and the enemy's intent from a Situation Analysis created with realistically available data. This process is partitioned into two stages, Team Planning and Maneuver Selection. Team Planning consists of deciding whether the mission itself should be commenced or aborted, selecting Tactics to counter the threats, as well as performing a Targeting in which threat aircraft are assigned to the individual CGFs. Further, in Maneuver Selection, the own current risks are assessed and used to continuously decide the current task for each CGF with respect to its target. Following, the maneuver command itself is being selected and sent to the simulated aircraft. This is done within an evaluation of the own chances and risks by, in a first step, identifying suitable tactical maneuver types, in a second step, narrow down their parameters, so that, in the final step, predicted risks from the Situation Analysis can be incorporated in the selection process as well. We employ Behavior Trees to guide the CGFs through these different tasks, while repeatedly assessing the developing risks to be ab
Analysis of Markov Decision Processes (MDP) is often hindered by state space explosion. Abstraction is a well-established technique in model checking to mitigate this issue. This paper presents a novel lazy abstractio...
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ISBN:
(纸本)9783031707520;9783031707537
Analysis of Markov Decision Processes (MDP) is often hindered by state space explosion. Abstraction is a well-established technique in model checking to mitigate this issue. This paper presents a novel lazy abstraction method for MDP analysis based on adaptive simulation graphs. Refinement is performed only when new parts of the state space are explored, which makes partial exploration techniques like Bounded Real-Time Dynamic Programming (BRTDP) retain more merged states. Therefore, we propose a combination of lazy abstraction and BRTDP. To evaluate the performance of our algorithm, we conduct initial experiments using the Quantitative Verification Benchmark Set.
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