Guidance System Research Articles

International Humanitarian Law’s Impact on the Development of Artificial Intelligence in Weapon Guidance Systems

Abstract There is a growing interest in developing artificial intelligence (AI) weapon guidance systems in the military domain. Current guidance systems employ various methods to guide a weapon to its target. This target accuracy is a critical factor for its effectiveness. Artificial intelligence capabilities could enable these systems to perform tasks that usually require human intelligence, such as identifying potential threats or targets on the battlefield while continually improving the system’s performance at its assigned task.

Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach.

Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. We aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians' experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use. At the "discovery" phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the "define" phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical "how might we" research questions to guide model development and implementation. We identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

Influence of Building Layout and Weather Conditions on Air Traffic Services

I Gusti Ngurah Rai International Airport is one of the busiest airports in Indonesia that serves a variety of flights, ranging from scheduled, unscheduled, cargo, and military, to recreational flights. All flights are regulated by the Air Traffic Controller which is divided into two units, namely the Aerodrome Control Tower and Approach Control Surveillance. The Aerodrome Control Tower has the responsibility of regulating flight traffic in the "vicinity of aerodrome" area. From the tower, ATC must be able to monitor all movements of flight operations in the area of responsibility. However, at the I Gusti Ngurah Rai Airport tower, several things can affect ATC's view in monitoring the movement of flight operations, namely airport buildings that block several parking stands, dark night conditions in the maneuvering area, and extreme weather. Of course, these problems will also affect the performance of ATC in providing flight traffic services. In addition, it can cause doubts in ATC in making decisions for aircraft that are being handled. This research aims to analyze the problem of ATC's limited view in monitoring the movement of flight operations in its area of responsibility. The research method used is qualitative with data collection techniques in the form of observation, literature study, interviews, and documentation. The development of Advanced Surface Movement Guidance and Control System that can monitor the movement of flight operations in various conditions can be one solution to the problem. In addition, the installation of CCTV in several areas that are not visible from the tower can also assist ATC in monitoring the movement of flight operations in the area. The results of this study are expected to assist ATC in monitoring the movement of flight operations both in dark night conditions and extreme weather. The author also hopes that the results of this study can be useful in improving organized and efficient flight traffic services at I Gusti Ngurah Rai International Airport.

Mazor X Robotic Guidance System

Mazor X Robotic Guidance System

Career Guidance System

The project aims to develop a career recommendation system for computer engineers, leveraging Python and AI/ML algorithms. It will consider students' academic performance and personal details, culminating in personalized career suggestions and salary estimations. The system collects and preprocesses data, engineer’s relevant features, and trains machine learning models for career and salary prediction. An intuitive user interface enables students to input their information. The AI component analyzes students' data, utilizing NLP and sentiment analysis to understand their interests. It then provides tailored career recommendations and salary insights. The system is continuously updated and maintained, and a feedback loop is integrated for user improvement suggestions. Stringent privacy and security measures are in place to protect student data, ensuring compliance with data protection regulations. Keywords: A.I, M.L, Career, Engineering

Abort Guidance During Lunar Powered Descent

During powered descent of a crewed lunar landing mission, the ability to abort and ascend into a clear pericynthion orbit is critical in case any contingency renders the landing infeasible or excessively risky. In such a case, it is incumbent upon the guidance system to determine autonomously a safe abort trajectory and the associated guidance command. To accomplish this task reliably, a novel two-phase abort guidance strategy is proposed in this paper where a pull-up maneuver redirects the velocity vector to help mitigate any ground collision risk and achieve an appropriate initial ascent condition, followed by a fuel-optimal ascent guidance algorithm to insert the spacecraft into a predefined safe orbit around the moon. Development of the pull-up guidance laws and a description of the fuel-optimal ascent guidance based on the indirect method of optimal control are presented. In-depth investigation is conducted to provide a full understanding of the validity of the abort solutions. The solutions and fuel efficiency of the proposed guidance strategy are independently verified by using a direct method of optimal control. Monte Carlo closed-loop simulations demonstrate the effectiveness and robustness of this method throughout the entire powered descent.

Path Following Model Predictive Control of a Coupled Autonomous Underwater Vehicle

The operation of an autonomous underwater vehicle (AUV) faces challenges in following predetermined waypoints due to coupled motions under environmental disturbances. To address this, a 3D path following guidance and control system is developed in this work based on the line-of-sight (LOS) guidance method. Conventionally, the 3D path following problem is transformed into heading and depth control problems, assuming that the motion of the vehicle is decoupled in horizontal and depth coordinates. The proposed control system design avoids this simplifying assumption by transforming the problem into a 3D position and orientation tracking problem. This design is achieved by computing a 2D horizontal coordinate based on the desired heading and then computing a corresponding LOS depth coordinate. A model predictive controller (MPC) is then implemented using the 3D LOS coordinate and the computed orientation vector. The MPC obtains a robust control by solving a minimax optimisation problem considering the effects of unknown ocean disturbances. The effectiveness of the proposed guidance and control system is demonstrated through the simulation of a prototype AUV system. Numerical results show that the AUV can follow predetermined waypoints in the presence of time-varying disturbances, and the system is steered at a constant surge speed that is proportional to the radius of the circle of acceptance used to implement the guidance system.

Performance enhancement of Savonius wind turbine through partially deformable blades

In this study, we employ partially deformable blades to elevate the performance of Savonius wind turbines. The Bucket is constructed with rigid components equipped with a guidance system, resulting in continuous changes in its shape during turbine rotation. As the trailing edge of the advancing blade expands, it creates an active slot, effectively correcting the Bucket’s pressure distribution and enhancing the positive torque generated by the turbine. We employ a two-dimensional (2D) numerical model, implemented using the commercial software ANSYS-Fluent 23.0, with the governing motion equation executed through a user-defined function (UDF). This investigation explores the mechanism of performance enhancement by varying expansion amplitudes Our results, obtained at a Tip-speed ratio (TSR) of 1, reveal that when the amplitude of deformation exceeds one-quarter of the Bucket radius, the partially deformable blade outperforms the rigid blade, leading to a remarkable 32% improvement in the torque coefficient. These findings signify a promising path toward enhancing Savonius turbine efficiency.

Sample-Observed Soft Actor-Critic Learning for Path Following of a Biomimetic Underwater Vehicle

This paper addresses a learning-based path following control scheme for a biomimetic underwater vehicle (BUV) driven by undulatory fins. A dynamic line-of-sight (DLOS) guidance system is designed, which uses a virtual ball with a dynamic radius to detect the reference path. This DLOS system guides our BUV in the path following control and extracts essential information for the Markov decision process (MDP) of the control task. A deep reinforcement learning (DRL) algorithm, sample-observed soft actor-critic (SOSAC) is proposed. The can train out control policy with greater cumulative reward and higher success rate by using two tricks: sample observation and sample diversification. Based on the DLOS system, the MDP of the control task, and a multilayer perceptron (MLP) trained by the SOSAC, our control scheme is established. Experiments show that our BUV can successfully achieve path following control in an indoor pool environment by using this control scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to design a practical end-to-end path following control scheme for the BUV driven by undulatory fins, and verify this scheme in a real-world environment. Unlike common autonomous underwater vehicles (AUVs) using axial propellers, the BUVs apply biomimetic propellers such as the undulatory fin. Multimodel wave patterns can be implemented by the undulatory fin, which generates nonlinear thrust and lateral force simultaneously. This propulsive feature makes the driving force on different directions of the BUV to be strong coupled, and it is complicated to convert the outputs of a common controller into waveform parameters of the undulatory fins to control the BUV. Therefore, in this paper, we proposed an end-to-end learning-based path following controller, which observes environmental information and directly generates waveform parameters to control our BUV. Experiments suggest that our control scheme is practical and valid.

A high-sensitivity SnSe/Si heterojunction position-sensitive detector for ultra-low power detection.

Position-sensitive detectors (PSDs) based on the lateral photovoltaic effect are crucial components in non-contact distance measurement, process control, guidance systems, and other related applications. However, PSDs are challenging due to the narrow spectral range and low sensitivity, limiting further practical application. Here, we present an ultra-sensitive SnSe/Si PSD device. A large-area uniform SnSe nanorod (NR) array film was grown on Si using a glancing-angle magnetron sputtering deposition technique and a SnSe/Si heterojunction PSD device was fabricated. PSDs exhibit an excellent photoresponse in a wide spectral range of 405-980 nm, showing an ultrahigh position sensitivity of 1517.4 mV mm-1 and an excellent spectral sensitivity of 4 × 104 V W-1. More importantly, the detection limit power of the device is as low as 10 nW, indicating the outstanding potential for weak light detection. Based on the unique structural features and interface effect, the mechanisms for the remarkable performance of the fabricated SnSe/Si PSD device are clarified. This work indicates the large potential of SnSe/Si heterojunctions as a promising material for ultrasensitive optical position-sensitive devices.

AI Assist User Interface: To Achieve Personal and Professional Goals

This study proposes an AI-based career guidance app. The app will use machine learning algorithms to predict job suitability based on student education and skills. The application collects student data, preprocesses it, and uses advanced machine learning to train a predictive model. The model helps explain how education, skills, and job eligibility relate. Guidance systems have many academic and business uses. Guidance system evaluation has traditionally focused on precision. Most students worldwide are confused after high school. They must choose a career path now. Students' immaturity hinders their ability to understand career selection criteria. As we progress through the stages, we realize that each student has a series of post-high school planning uncertainties or cognitive processes. This question is difficult. Then they must decide if they're cut out for the chosen field. Based on skill set, our computerized career counseling system can match an individual to the right department. An objective exam will assess these skills, taking into account the individual's selected areas of interest and their 12th-grade grades in their top two subjects. After completing our online assessment, a person will be guided to a suitable course. Incorrect career choice failures are reduced by this process.

Analysis of Hot Topics Regarding Global Smart Elderly Care Research - 1997-2021.

With the assistance of the internet, big data, cloud computing, and other technologies, the concept of smart elderly care has emerged. This study presents information on the countries or regions that have conducted research on smart elderly care, as well as identifies global hotspots and development trends in this field. The results of this study suggest that future research should focus on fall detection, health monitoring, and guidance systems that are user-friendly and contribute to the creation of smarter safer communities for the well-being of the elderly.

Electromagnetic Modelling and Dynamic Simulation of Combined Propulsion, Levitation and Guidance System in EDS

Electromagnetic Modelling and Dynamic Simulation of Combined Propulsion, Levitation and Guidance System in EDS

A POMDP model-based online risk mitigation method for autonomous inland vessels

Autonomous surface vessels (ASVs) increasingly gain appeal in the maritime industry for their high efficiency and improved navigational capabilities. However, risks originating from various internal and external factors such as faults, traffic, harsh weather conditions, etc., can affect their guidance and control capabilities and impact nominal vessel operations. The existing risk mitigation methods mainly focus on the vessel's guidance system and do not consider unsafe actions due to the control system. In this paper, we propose a new method based on a partially observable Markov decision process (POMDP) model for the online risk mitigation of autonomous inland vessels. The POMDP model-based method utilizes information about situational awareness to assist the vessel's planning and control system in real-time decision-making during hazardous situations, thereby ensuring that the vessel remains in a minimum-risk condition. Based on the identified risk-influencing factors (RIFs), the transition probabilities are updated by a Bayesian belief network (BBN). A case study of an autonomous inland vessel navigating in a confined waterway is presented to demonstrate the capability of the proposed method.

An Automated On-The-Go Unloading System Reduces Harvest Operator Stress Relative to Manual Operation

Highlights Stress was measured in harvest operators who performed on-the-go unloading manually and with an automated system. Automated unloading reduced the average grain cart and combine operator stress rate by 18% and 12%, respectively, compared to manual operation. Harvest operators usually worked more than 9 hours and often worked more than 12 hours per workday during harvest. The use of automated unloading systems could positively affect the health of harvest operators. Abstract. On-the-go unloading improves harvest operational efficiency, but it requires skilled labor because it is challenging and stressful to balance numerous concurrent tasks. Harvest automation reduces workload, stress, and fatigue. The objective of this study was to determine if using a commercially available, automated on-the-go unloading system (Raven Cart AutomationTM, RCA, Raven Industries) would reduce operator stress compared to manual operation. Nine grain cart tractor operators and six combine operators participated in this study. Operators performed their typical harvest operation, except to alternate on-the-go unloading using RCA or operating manually. Skin conductance (electrodermal activity) was measured with an Empatica E4 wristband, and stressful events were quantified. Machine data was collected from the tractor and combine via CAN logs. Over 200 total unload events were analyzed. Grain cart and combine operators using RCA had an 18% (p = 0.022) and 12% (p = 0.18) reduction in stress rate, respectively, compared to operating the grain cart tractor manually. RCA reduced the tractor cross-track error standard deviation by 2.5 cm on straight passes (p &amp;lt; 0.0001). The use of an automated on-the-go unloading system reduces operator stress during harvest and could positively affect the health of operators, especially during the long harvest workdays. Keywords: Agricultural workers, Combine operators, Custom harvesters, Electrodermal activity, Grain cart operators, Guidance systems, Harvest, Skin conductance, Stress, Vehicle automation.

Real-Time Biomedical Imaging for Surgical Guidance: A Review of Challenges and Solutions

The domain of real-time biomedical imaging has seen remarkable technological advances, enhancing the efficacy of surgical interventions. This paper addresses the critical challenges associated with the implementation of real-time biomedical imaging systems for surgical guidance and discusses comprehensive solutions to mitigate these issues. It outlines the substantial computational demands for real-time processing and the necessity for high-fidelity image synthesis. The intricacies of integrating multimodal imaging data, ensuring minimal latency, and maintaining spatial accuracy for augmented reality applications are also examined. Solutions leveraging cutting-edge machine learning algorithms for image segmentation and enhancement, as well as the application of parallel processing architectures for expediting computational tasks, are presented. This manuscript also explores the potential of quantum computing paradigms in transcending conventional processing limitations. Also, the paper addresses the importance of interoperability standards for seamless integration of imaging systems in diverse surgical environments. It concludes with a discussion on the ethical implications and privacy considerations in deploying artificial intelligence in surgical settings. This paper highlights the importance of interdisciplinary innovations necessary for the advancement of real-time biomedical imaging for surgical guidance. The machine learning techniques such as CNNs, helps the trade-off with accuracy and computational speed. Whereas transfer learning procedures take 20 seconds and Federated Learning in 15 seconds represents the better performance.

Research on the Reform of Teaching System of Social Sports Guidance and Management Major Based on OBE Concept

Research on the Reform of Teaching System of Social Sports Guidance and Management Major Based on OBE Concept

SLAM-Based Breast Reconstruction System for Surgical Guidance Using a Low-Cost Camera

SLAM-Based Breast Reconstruction System for Surgical Guidance Using a Low-Cost Camera

Nonlinear Transient Switching Filter for Automatic Buffer Window Adjustment in Short-term Ship Response Prediction

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution.

Nonlinear PID Control for Automatic Docking of a Large Container Ship in Confined Waters Under the Influence of Wind and Currents

This study addresses the challenges of maneuvering a large container ship in confined waters under the influence of wind and currents. The proposed guidance, navigation, and control system presents a novel combination of modeling methodologies, control allocation techniques, and nonlinear control systems theory. We consider a docking scenario through a simulation of a realistic, narrow harbor accommodating area-specific currents and global wind forces. Control allocation is solved by utilizing detailed models of the actuators and hydrodynamics in a mixed-integer-like solution for optimal thrust allocation, and an inverse mapping to the control inputs. Furthermore, our approach introduces a modified Wageningen B-Series model, that is capable of modeling propellers with negative pitch dynamics. The complexity of safely docking in environments like the Antwerp harbor, where wind and currents pose significant risks, is tackled using a nonlinear PID control system integrated with Line-Of-Sight (LOS) guidance. This enables precise maneuvering of a fully actuated Roll-On/Roll-Of vessel to its docking position and orientation. Given that few studies on automated docking holistically address the intricate challenges of complex harbor environments and the concurrent impact of multiple external forces, our work proposes a comprehensive solution based on an integrated combination of guidance, navigation, and control systems.