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While control barrier functions provide a powerful tool to endow controllers with formal safety guarantees, robust control barrier functions (RCBF) can be used to extend these guarantees for systems with model inaccuracies. This paper presents a generalized RCBF framework that unifies and extends existing notions of RCBFs for a broad class of model uncertainties. Main results are conditions for robust safety through generalized RCBFs. We apply these generalized principles for more specific design examples: a worst-case type design, an estimation-based design, and a tunable version of the latter. These examples are demonstrated to perform increasingly closer to an oracle design with ideal model information. Theoretical contributions are demonstrated on a practical example of a pendulum with unknown periodic excitation. Using numerical simulations, a comparison among design examples are carried out based on a performance metric depicting the increased likeness to the oracle design.

This paper considers initial undershoot in the step response of discrete-time, input-output Hammerstein (DIH) systems, which have linear unforced dynamics and nonlinear zero dynamics (ZD). Initial undershoot occurs when the step response of a system moves initially in a direction that is opposite to the direction of the asymptotic response. For DIH systems, the paper investigates the relationship among the existence of initial undershoot, the step height, the height-dependent delay, and the stability of the ZD. For linear, time-invariant systems, the height-dependent delay specializes to the relative degree. The main result of the paper provides conditions under which, for all sufficiently small step heights, initial undershoot in the step response of a DIH system implies instability of the ZD. Several examples of DIH systems are presented to illustrate these results.

Cardiac data modeling remains challenging in emerging nations across Asia and Africa. This research proposes an ensemble classification method leveraging machine learning (ML) to predict cardiac problems, providing physicians with actionable insights for personalized diagnoses and treatments. An ensemble classification method for modelling cardiac temporal data is presented in this research. The minimax scalar transform is applied to first denoise the input data, and then the ENN-smote approach is applied to address the issue of an imbalanced dataset. Secondly, we employ a standard deep learning (DL) methodology. To identify the irregularities in the cardiac data pattern, a gated recurrent unit (GRU) classifier and a one-dimensional convolutional neural network (1D-CNN) are introduced. A typical genetic algorithm (GA) is used to optimize the suggested GRU network in order to pass over the local minima. This aids with 1D-CNN weight training. GA methodically optimizes the model’s GRU parameters. The data processed were finally used by the hybrid 1D-CNN-Optimized GRU network to predict cardiovascular illness. The suggested method attained a training accuracy of over 97% and a test accuracy of over 96% on the dataset. The proposed model’s overall accuracy is 99%. This is completely evaluated against other deep learning algorithms.

Abstract In recent years, IoT technologies have made our daily lives more convenient and comfortable. In particular, these technologies are being actively utilized in our living environments through the development of various smart home appliances. As the pace of Digital Transformation (DX) quickens and digital platforms become integrated into diverse residential settings, the notion of the home we inhabit is taking on greater significance beyond merely a place for rest. Therefore, this paper explains the development of user-friendly interfaces for smart home systems used in our living rooms. An appropriate service can be provided accordingly if the user’s intent is specifically understood. Therefore, this paper explores the relationship between the user’s gaze and the control targets of the smart home using Mixed Reality (MR) devices. In this study, we introduce a novel smart home control system from a range of options. Our proposed system enables user analysis and remote control through the utilization of an MR device capable of tracking a user’s gaze. In addition, we investigate the perception of user interfaces and analyze the survey results conducted after using the interface system, discussing the validity of the proposed system.

This paper focuses on cyber-security issues of networked control systems in closed-loop forms from the perspective of quantized sampled-data systems. Quantization of control inputs adds quantization error to the plant input, resulting in certain variation in the plant output. On the other hand, sampling can introduce non-minimum phase zeros in discretized systems. We consider zero-dynamics attacks, which is a class of false data injection attacks utilizing such unstable zeros. Although non-quantized zero-dynamics attacks are undetectable from the plant output side, quantized attacks may be revealed by larger output variation. Our setting is that the attack signal is applied with the same uniform quantizer used for the control input. We evaluate the attack stealthiness in the closed-loop system setting by quantifying the output variation. Specifically, we characterize the cases for static and dynamic quantization in the attack signal, while keeping the control input statically quantized. Then we demonstrate that the attacker can reduce such output variation with a modified approach, by compensating the quantization error of the attack signal inside the attack dynamics. We provide numerical examples to illustrate the effectiveness of the proposed approaches. We show that observing the quantized control input value by a mirroring model can reveal the zero-dynamics attacks.

Abstract We are currently developing a suit that assists human running motion based on the Smart Suit (SS). The SS is a wearable assistive device that intervenes in trunk kinetic chain movements by linking trunk rotation to hip flexion and extension through elastic belts. By intervening in the trunk kinetic chain, our goal is to enhance stiffness, thereby improving running economy and speed. The purpose of this study is to acquire fundamental insights into the changes in leg and trunk rotational stiffness induced by the SS, as well as the underlying mechanisms. We defined the SS torque intervention rate $$p_{ss}$$ p ss , which focuses on the magnitude of force, and the peak time difference index, $$e_{lag}$$ e lag , between SS and humans, which focuses on the timing of force, and analyzed the relationship between the stiffness change rate due to SS wearing. The results of the analysis suggest that SS affects human running sensitively rather than mechanically. We also confirmed that the degree of adaptation to SS, assessed by the timing gap between SS exertion and human exertion, contributes significantly to changes in trunk rotation stiffness.

This article develops an energy-efficient 3D multi-band I/O interface to meet the demand for high computation and improve battery life for future mobile memory interfaces exploiting 3D integration. The proposed multi-band I/O (MBI) interface utilizes 3D integration, for two radio frequency band transceivers, and a CMOS driver with resistive feedback for the baseband (BB) transceiver. The proposed design implements a band-selective transformer to enable transmitting three bands, baseband and 2 RF bands (10&30 GHz), simultaneously. To enable simultaneous transmission of three bands, a band-selective transformer is utilized that reduces signal interference and minimizes component area and distortion for high-quality transmission and design compactness. A narrowband low-noise amplifier combined with the 30 GHz radio frequency band to improve the signal quality and minimize distortion. Furthermore, a low-dropout voltage regulator (LDO) is utilized to raise the frequency and lower the supply voltage for the I/O interface more especially, the connection between the memory and the CPU. The aggregate bandwidth and energy efficiency are greatly increased by this architecture, raising the aggregate data rate and enhancing energy efficiency. The 3D MBI system was implemented in the 130 nm CMOS process technology. It obtains a higher aggregate data rate of 14.4 Gb/s and an energy efficiency of 2.6 pJ/b compared with prior works.

In large language models (LLMs), the translation quality has limitations in the translation when translated into different languages. This study compares Chinese allusions in human and machine translated corpora translated by OpenAI GPT-3.5, Volctrans, and human translated texts. The framework innovatively combines two automated evaluation metrics, BLEU and METEOR, with a translation quality assessment method derived from Fuzzy Mathematics and Optimality Theory. The findings of the study indicate that the GPT-3.5 translated version exhibits higher quality than the Volctrans version when evaluated by a machine. Similarly, human evaluations indicate that among the three versions, Volctrans is of the lowest quality, while the human version exceeds the GPT-3.5 version in terms of quality. Thus, the study further reveals that Volctrans version is deemed to have the lowest translation quality from both human and machine evaluation perspectives. Finally, this study not only introduces but also validates a novel framework for assessing machine translation quality.

This paper investigates the system identification problem for linear discrete-time systems under adversaries and analyzes two lasso-type estimators. We examine non-asymptotic properties of these estimators in two separate scenarios, corresponding to deterministic and stochastic models for the attack times. We prove that when the system is stable and attacks are injected periodically, the sample complexity for exact recovery of the system dynamics is linear in terms of the dimension of the states. When adversarial attacks occur at each time instance with probability $p$, the required sample complexity for exact recovery scales polynomially in the dimension of the states and the probability $p$. This result implies almost sure convergence to the true system dynamics under the asymptotic regime. As a by-product, our estimators still learn the system correctly even when more than half of the data is compromised. We emphasize that the attack vectors are allowed to be correlated with each other in this work. This paper provides the first mathematical guarantee in the literature on learning from correlated data for dynamical systems in the case when there is less clean data than corrupt data.

The toxic emissions from combustion engine cars and businesses using fossil fuels have steadily increased over time in today's world of expanding needs and urbanization. Due to some advantages over vehicles with traditional engines, using electric vehicles is the most well-liked and successful alternative. The performance of electric cars is significantly influenced by an energy management system (EMS). In this study, a multifunctional power electronic interface capable of using two sources simultaneously throughout the charging process monarch butterfly optimization (MBO) based EMS for plug-in electric vehicles (PHEV) has been developed. The battery can be charged from the grid or solar photovoltaic (SPV), depending on the need. This study designs an electric vehicle (EV) charging system that uses an interleaved landsman converter followed by a flyback converter. As a result, the suggested converter helps in improving battery safety as well as user safety for the vehicle. The proposed control technique is simulated using system models created with MATLAB/Simulink. The outcomes of the simulation demonstrate that the devised technique offers better control in terms of vehicle stability. A number of simulations are run to evaluate the efficiency and performance of the proposed approach.

This research focuses on Wireless Sensor Networks (WSNs) and proposes a three-phase approach to achieve energy-efficient routing. The approach consists of node deployment using Voronoi diagrams, clustering, and Cluster Head (CH) selection using energy-efficient game theory, and a routing strategy based on Improved Pelican Optimization (ImPe) segment routing. Random deployment of sensor nodes in WSNs can lead to coverage issues, and hence, in order to address this, Voronoi-based node deployment is employed to ensure uniform and balanced coverage of the monitoring area. An energy-efficient game theory-based approach is used for CH selection by considering the energy levels to select CHs for enhancing network longevity. The proposed routing mechanism utilizes segment routing, which provides deterministic routing paths from CHs to the sink (Base Station). Segment routing eliminates the need for route discovery and maintenance, making it energy-efficient. The ImPe algorithm that works on the characteristics of pelican search agents is employed to choose the optimal segment path for information sharing. The assessment based on delay, network lifetime, packet delivery ratio, residual energy, throughput, communication overhead, and energy utilization acquired the values of 2.57, 98.59, 98.29, 0.98, 238.51, 7.71, and 0.02 respectively.

In the 3-D space, this paper implements vehicle convoy and interception by using guidance strategy. Based on the differential equations for the range, the pitch angle and the yaw angle between successive vehicles, the deviated pursuit and the proportional navigation are proposed to derive decentralized control strategy for vehicle convoy and interception control. Guidance laws are used for convoy and interception, and this method presents a family of navigation laws resulting in a rich behaviour for different parameters. Convoy and interception performance of the proposed guidance laws is superior to the pure proportional navigation (PPN). Simulations are proposed to demonstrate the feasibility and effectiveness of the proposed control method.

Iron Deficiency Anaemia (IDA) is the most prevalent form of anaemia, affecting 24.8% of the global population. An examination of the complete blood count (CBC) is performed to determine general health and the presence of illnesses. Accurate and timely diagnosis of IDA is essential for proper treatment, yet traditional methods can be time-consuming and costly. This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. Two approaches were implemented: first, a ResNet50 model was used to classify PBS images as Normal or IDA; second, the YOLOv7 object detection model was employed to localize hypochromic microcytes within the images. The YOLOv7 model was tested on 17 images containing 425 instances of hypochromic microcytes and demonstrated superior performance, achieving a test mean Average Precision (mAP) of 89% with faster inference times than ResNet50. By providing localized detection of hypochromic microcytes, YOLOv7 enhances diagnostic accuracy and speed compared to image-level classification. This study highlights the potential of object detection models for improving automated anaemia diagnosis, with implications for faster and more cost-effective healthcare solutions.

Abstract Pipelines are crucial infrastructure supporting human life, yet their maintenance costs are substantial, necessitating the development of time-efficient robotic systems. Therefore, this study proposes an in-pipe mobile robot powered by wind generated using a ducted fan rotor, enabling faster movement within pipelines than conventional methods, e.g., inchworm. Deriving the propulsion force from wind requires analyzing airflow within the pipeline, which is challenging due to the confined space and complexity, especially when considering the presence of robots. Hence, we developed a prototype of a ducted-fan type pipeline robot (DPR) and experimentally investigated duct shapes that enhance the propulsion of the DPR within pipelines. As a result, we identified duct shapes that amplify the propulsion force generated by the ducted fan within pipelines. Additionally, we also experimentally elucidated the relationship between the distance from the pipe’s end and the propulsion force of the robot. Furthermore, we demonstrated the capabilities of the DPR by traversing a pipeline with a diameter of 110 mm, achieving speeds of 1500 mm/s in horizontal pipes and 700 mm/s while ascending vertical pipes. The results show that DRP has the potential for in-pipe inspection.

Abstract This paper describes a graph-based SLAM approach using wall detection and floor plan constraints without relying on loop closure. In SLAM, loop closure is widely used to address cumulative errors. Although loop closure helps maintain the map’s relative consistency, it does not ensure the accuracy of absolute positions. Therefore, we focus on floor plans that accurately depict the environmental geometry and propose a SLAM method that leverages this information. However, floor plans do not depict semi-static objects such as bookshelves and other fixtures. Thus, our study aims to build accurate maps based on floor plans and represent actual environments. The proposed method achieves this goal by integrating wall detection and floor plan constraints within the framework of graph-based SLAM. We evaluated the proposed method based on qualitative assessments of mapping results and quantitative evaluations of robot trajectories and processing time. Experiments were conducted using datasets obtained from both simulation and real-world environments. The results demonstrate that the proposed method can build a map with accurate absolute positions in a low processing time by leveraging wall detection and floor plan constraints.

To enable machines to understand human-centric images and videos, they need the capability to detect human–object interactions. This capability has been studied using various approaches, but previous research has mainly focused only on recognition accuracy using widely used open datasets. Given the need for advanced machine-learning systems that provide spatial analysis and services, the recognition model should be robust to various changes, have high extensibility, and provide sufficient recognition speed even with minimal computational overhead. Therefore, we propose a novel method that combines the skeletal method with object detection to accurately predict a set of $ \langle $ human, verb, object $ \rangle $ triplets in a video frame considering the robustness, extensibility, and lightweight of the model. Training a model with similar perceptual elements to those of humans produces sufficient accuracy for advanced social systems, even with only a small training dataset. The proposed model is trained using only the coordinates of the object and human landmarks, making it robust to various situations and lightweight compared with deep-learning methods. In the experiment, a scenario in which a human is working on a desk is simulated and an algorithm is trained on object-specific interactions. The accuracy of the proposed model was evaluated using various types of datasets.

Control valve stiction is prevalent in the process industry, leading to oscillations in process variables and degrading unit performance. Stiction is usually repaired during scheduled plant maintenance, but until then, compensation methods are required to reduce oscillations. This study presents a novel approach to address this problem by identifying control valve and process models and optimizing control parameters. The method combines a stiction model, a process model, and a controller to achieve optimal control with stiction. Additionally, the method incorporates a controller output filter and considers undershooting during the step response for control performance assessment. The proposed method is designed to handle different types of valve stiction models, providing a versatile solution. The results show that our method significantly improves the control loop performance of all valves tested.

Recently, the effectiveness of image-based sensors, which are based on image-processing techniques, has been analysed in terms of occupancy lighting control in comparison with a passive infrared (PIR) sensor, which monitors changes in infrared radiation from moving occupants. However, the image-based sensor has two shortcomings: occupants cannot be accurately detected in a dark environment, and the overall energy consumption from the light and sensor is excessive. Therefore, we evaluated a combined approach that employs both the PIR sensor and the image-based sensor simultaneously. Because the combined sensor uses the image-based sensor only in a sufficiently bright environment, the first shortcoming can be overcome. In addition, an experiment, was conducted at a single office, demonstrating that the combined sensor's overall energy consumption decreased by 3.59% or more compared with either of its constituent sensors individually. Thus, the combined sensor resolves the major shortcomings of the image-based sensor's performance. Further discussion and careful analysis are included in this paper.

The safety of railways is ensured by the regular maintenance of tracks, signals and rolling stocks. This mainly involves manual inspection by skilled maintainers and automated inspection systems are not commonly used. The development of an automated inspection system is crucial for maintaining good rail conditions. In this study on automatic surface defect detection systems, we focused on wheels as the target because they are critical components of rolling stocks, and any wheel defects can cause severe safety problems. Wheel-tread-profile dimensions have been measured using laser instruments in previous studies. In this research, to validate the accuracy of the measurement equipment, special test pieces with surface defects are prepared. We propose a combined inspection system using laser sensors, and a machine vision technique to confirm the effectiveness of the proposed system. An artificial intelligence (AI)-based surface defect detection system is employed to improve the proposed system. Combining edge detection through image transformation with an AI-based surface defect detection system offers remarkable benefits. The effectiveness of the system is experimentally confirmed using test pieces that simulate different materials and passing speeds of wheels.

For autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan avoidance motions in consideration of the time series variation in the images. For this purpose, an LSTM block is applied to the CNN. The policy of the motion planner represented by CNN with LSTM is trained through imitation learning. In this regard, however, it is difficult for the robot to recognize unknown objects as obstacles. For obstacle recognition, a perception process is further provided between the image inputs and CNN with LSTM in the motion planner. Moreover, the robot plans different avoidance motions depending on the velocity of the dynamic obstacle. For this purpose, an obstacle state classifier based on CNN is used ahead of the motion planner. A depth-difference image generated from two depth images is fed as the input to the classifier. A classified state that indicates the velocity of the obstacle is fed as the input to the following motion planner. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward a destination autonomously while avoiding standing and walking persons, respectively. Furthermore, we show that the robot based on the motion planner with the obstacle state input is able to plan different avoidance motions for a person walking slowly or fast using the obstacle state classifier.

In this study, we analyse the local identifiability of parameters in linear systems whose state matrix is given by a graph Laplacian. Graph Laplacian is a matrix given by a graph and the weights of its edges, where the weights represent the parameters in those systems. There are cases in which parameter estimation has to be conducted with a single trajectory data. In this case, detecting whether the parameter is locally unidentifiable (non-locally identifiable (LI)) from a single-state trajectory a priori is important. This is because we need conditions to avoid estimating non-LI parameters. Therefore, we address a problem to find the condition which implies that the parameter is non-LI from any single-state trajectory. Then, we obtain a sufficient condition for the parameter to be non-LI from any single-state trajectory. The condition is given based on the number of vertices and edges of the graph and the number of distinct eigenvalues of the graph Laplacian. This paper also presents an example that satisfies the condition.

As a metaheuristic method for the continuous optimization problem, the grey wolf optimizer (GWO) has attracted much attention from researchers because the method is reported to be superior to other methods. However, some works show that the GWO is too specialized only to problems having the zero-optimal solution, which can lead to a significant deterioration of the efficiency for other problems. In this paper, we, first, theoretically prove the shift-dependence of the GWO, which is the underlying cause of the over-specialization of the GWO, and we experimentally analyze the property by using a larger number of problems. Secondly, we propose am shift-invariant GWO, GWO-SR, and, modify the GWO-SR by adding two methods: an adjustment technique the size of the search area and a mutation process to enhance the diversity of the search (GWO-AS) Finally, we show advantages of two proposed GWOs by comparing them with other metaheuristic methods.

Efficiently recognizing Activities of Daily Living (ADLs) requires overcoming challenges in collecting datasets through innovative approaches. Simultaneously, it involves adapting to the demand for interpreting human activities amidst temporal sequences of actions and interactions with objects, considering real-life scenarios and resource constraints. This study investigates the potential of generating synthetic training data for ADLs recognition using the VirtualHome2KG framework. Furthermore, we investigate the transformative potential of simulating activities in virtual spaces, as evidenced by our survey of real-world activity datasets and exploration of synthetic datasets in virtual environments. Our work explicitly simulates activities in the 3D Unity platform, affording seamless transitions between environments and camera perspectives. Furthermore, we meticulously construct scenarios not only for regular daily activities but also for abnormal activities to detect risky situations for independent living, ensuring the incorporation of critical criteria. We incorporate one contemporary method for abnormal activity detection to demonstrate the efficacy of our simulated activity data. Our findings suggest that our activity scenario preparation accomplishes the intended research objective while paving the way for an interesting research avenue.

In tunnel inspections, it is important to check for the progression of cracks and other deformations. The point clouds of tunnels are not reproducible due to accumulated errors in mobile mapping systems using GNSS/IMU navigation. Therefore, it has been impossible to confirm the progression of deformations with the original point cloud because the shapes of the acquired point clouds do not match. Therefore, the position of the point cloud has been corrected by surveying or by manual alignment of the point cloud. This was time-consuming and costly. In this paper, we propose a new positioning method for two-time difference comparisons based on a scan-matching technique that matches tunnel features and describe the evaluation results. The results of evaluation experiments show that the average error before alignment was 0.831 m. The proposed method achieves alignment with an average error of less than 0.040 m, which is unprecedentedly accurate.

This research deals with the cooperative control of CAVs (Connected and Automated Vehicles) at signal-free intersection. CAVs communicate with each other and adjust their speeds to safely pass through the intersection without having to stop. This is expected to improve intersection throughput and reduce fuel consumption. In order to execute cooperative control, a two-stage control structure is considered. In the first stage, the merging time, defined for each CAV as the time at which the CAV reaches the intersection, is obtained by solving mixed integer linear programming (MILP). In the second stage, each CAV solves an optimal control problem to determine the control input that allows it to reach the intersection at the merging time obtained in the first stage. In this research, a nonlinear model that considers air resistance, rolling resistance, and slope resistance is used to take into account the real environment. We propose a method using a control barrier function to guarantee the safety of CAVs even with nonlinear models , and an input correction method using a disturbance observer to suppress modelling errors caused by complex dynamics. We also propose a method that consists of establishing a speed regulation zone to avoid the possibility of passing through an intersection at an unsafe speed to improve throughput. Finally, a comparison with previous studies shows the superiority of the proposed method in reducing fuel consumption.

Ge'ez has been used in Ethiopian churches for centuries to read and interpret the Bible. As a part of the liturgy and religious ceremonies, it is also utilized in prayer and chanting. Since the language is ancient, plenty of handwritten physical documents are generated. Recognizing handwritten Ge'ez numerals poses significant challenges due to variations in handwriting styles, incomplete or overlapping strokes, noise, and distortion. This study introduces an end-to-end approach for handwritten Ge'ez multiple numeral recognition employing deep neural networks. The proposed method streamlines recognition by eliminating manual feature extraction stages. To enable end-to-end training without explicit alignment, the model uses attention mechanisms and a connectionist temporal classification-based loss function. The proposed model is evaluated on 120,000 handwritten Ge'ez multiple numeral images, which is a syntactically generated dataset. The developed recognition model achieved a character error rate (CER) of 2.81% and a word error rate (WER) of 18.13%. These results highlight the effectiveness of the approach in accurately and reliably recognizing handwritten Ge'ez multiple numerals.

One of the most underdiagnosed medical conditions worldwide is depression. It has been demonstrated that the current classical procedures for early detection of depression are insufficient, which emphasizes the importance of seeking a more efficient approach to overcome this challenge. One of the most promising opportunities is arising in the field of Artificial Intelligence as AI-based models could have the capacity to offer a fast, widely accessible, unbiased and efficient method to address this problem. In this paper, we compared three natural language processing models, namely, BERT, GPT-3.5 and GPT-4 on three different datasets. Our findings show that different levels of efficacy are shown by fine-tuned BERT, GPT-3.5, and GPT-4 in identifying depression from textual data. By comparing the models on the metrics such as accuracy, precision, and recall, our results have shown that GPT-4 outperforms both BERT and GPT-3.5 models, even without previous fine-tuning, showcasing its enormous potential to be utilized for automated depression detection on textual data. In the paper, we present newly introduced datasets, fine-tuning and model testing processes, while also addressing limitations and discussing further considerations for future research.

Portfolio optimization that allows the borrowed money from a loan to be invested in risk assets has been formulated as a chance constrained problem. In this paper, in order to reflect investor preferences called “values” in the solution, or the portfolio, prospect theory is used to expand the chance constrained problem that maximizes the profit. The new portfolio optimization problem maximizes the value instead of the profit. In order to get a deeper understanding of the characteristics of the new portfolio optimization problem, the fitness landscape analysis method using Convex-Hull Mapping (CHM) is employed. Furthermore, since the new portfolio optimization problem is usually non-convex, the multi-start local search method with CHM is used to find the solution. Finally, numerical experiments on artificial and actual asset data sets show that the solution of the new portfolio optimization problem is more acceptable to investors than the solution of the conventional one.

Video Assistant Referee (VAR) is used for line judging in tennis to improve the fairness of a game. However, currently, popular VAR systems are based on three-dimensional trajectories and do not take actual ball bounce marks into account, which may lead to misjudgment due to misalignment. Therefore, in this paper, we propose Apollon Mark, a bounce mark visualization system with preceding mirror control based on high-speed drop location prediction. Based on high-speed measurement of three-dimensional position and motion prediction, the proposed system enables a high-resolution camera to capture the ground texture both before and after the ball drops on the ground by directing the angle of view of the camera to the predicted drop location of the ball before it drops. The system then enables bounce mark visualization by simple differential processing between the before and after images with a template-matching image processing algorithm. We have quantitatively evaluated the validity of the proposed system through the simulation of drop location prediction for real-scale tennis ball trajectories and validated the bounce mark visualization algorithm under a dynamic angle of view using galvanometer mirrors.

This paper presents a method of switching from position to stiffness control for a two-degree-of-freedom robot arm driven by three pairs of antagonistic actuators by applying the position and stiffness controller with the aim of developing a robot arm that can perform tasks involving a change from free to constrained motion. The control law consists of a feedback position controller and a feedforward stiffness controller in joint space. The position controller is a traditional PD feedback. The stiffness controller is designed using a newly developed method for solving the robot redundancy and adjusts joint stiffness through the coactivation of all the actuators. The solution to the redundancy expresses the actuator force space by a direct sum of its three subspaces and gives two independent relationships between actuator forces to joint torques and to joint stiffness. The position and stiffness controller is expected to control the position and the stiffness simultaneously and seamlessly perform tasks that involve changing from free to constrained motion. However, it has been revealed that the PD feedback controller affects the joint stiffness and that the stiffness controller reduces energy efficiency during position control. Therefore, this paper attempts to solve the problem by controlling the free motion with the position controller and by controlling the stiffness after the robot reaches the desired posture with the stiffness controller. Simulation results demonstrate that the proposed method can bring the robot to the desired posture, switch the control smoothly, and generate the desired stiffness.

This paper presents a novel control strategy for drone networks to improve the quality of 3D structures reconstructed from aerial images by drones. Unlike the existing coverage control strategies for this purpose, our proposed approach simultaneously controls both the camera orientation and drone translational motion, enabling more comprehensive perspectives and enhancing the map's overall quality. Subsequently, we present a novel problem formulation, including a new performance function to evaluate the drone positions and camera orientations. We then design a QP-based controller with a control barrier-like function for a constraint on the decay rate of the objective function. The present problem formulation poses a new challenge, requiring significantly greater computational efforts than the case involving only translational motion control. We approach this issue technologically, namely by introducing JAX, utilizing just-in-time (JIT) compilation and Graphical Processing Unit (GPU) acceleration. We finally conduct extensive verifications through simulation in ROS (Robot Operating System) and show the real-time feasibility of the controller and the superiority of the present controller to the conventional method.

This paper presents a novel angle-aware coverage control method to enhance monitoring efficiency for large-scale 3D map reconstruction using drone networks. The proposed method integrates a Voronoi-based coverage control as the nominal input into the quadratic programming problem required to solve in the original angle-aware coverage control. This approach provides a practical solution, ensuring diverse viewing angles to improve the quality of 3D map reconstruction and offers effective coverage of distant unobserved areas, particularly for large-scale area missions. We then implement the present method and validate its effectiveness through simulation on Robot Operating System as well as experiments conducted on a robotic testbed. The comparative analysis demonstrates the advantages of our proposed approach, resulting in increased monitoring efficiency for large-scale 3D map reconstruction.

In this paper, we introduce a strategic model for users and a manager in a community, considering investments in photovoltaic (PV) panels and a shared battery. We also analyse how their strategic behaviour influences the overall social welfare within the community. We first formulate the investment optimization problems for the users and the manager explicitly considering electricity exchanges, where users invest in PV panels and the manager invests in a battery. Considering interactions of information and electricity, the strategic model, which combines the investment optimization problems mentioned above, can be formulated as a game. Then, we point out that the formulated game does not always guarantee the existence of a generalized Nash equilibrium (GNE). To overcome the problem, we present new utility functions that ensure the existence of an equilibrium through potential game theory. Finally, we conduct a case study with actual electricity data to numerically analyse the impact of the strategic behaviour on social welfare as well as the advantages of battery sharing in the presence of such strategic behaviour.

Unlike ballistic movements such as reaching, little is known about how aging affects tracking performance. The current study initially aimed at confirming the differences in tracking performance between younger and older people. The experiment included 14 young and 10 older subjects who were instructed to perform a circular tracking task on the fronto-parallel and sagittal planes with a visible target in the first half interval (called the feedback (FB) interval) and an invisible target disappearing and reappearing in the remaining interval (called the feedforward (FF) interval). The results demonstrated that the aging effect was sufficient to deteriorate tracking performance, regardless of the environment. However, the older group’s performance was not significantly influenced by the depth movement, whereas the younger group’s performance significantly deteriorated during the FB interval. Finally, the differences in errors between the FF and FB intervals for each movement plane were investigated, demonstrating the vulnerability of the internal model used for each plane to the visibility of the target. The younger group demonstrated a significant difference between the fronto-parallel and sagittal planes; however, the older group exhibited no significant difference, indicating that the younger group acquired an internal model sensitive to visual information. We confirmed aging-induced tracking performance degradation and highlighted the impact of aging on tracking performance, resulting in an increased understanding of cerebellar motor control rather than conventional ballistic movements.

When hexapod robots walk on uneven terrain, they need to avoid the legs from collisions with obstacles, their own legs, and the ground, while also considering joint limits and reaching their feet to a destination. To address these issues, several local controls are defined in this paper for the swing legs in the Follow-the-Contact-Point (FCP) gait, which are integrated with Riemannian Motion Policies (RMPs). RMPs allow for multiple local control policies to be considered simultaneously for different points on the leg frames. For the local controls, a range sensor is installed on the foot to prevent dragging and contacting the ground firmly, even if the destination is in the air. Additionally, linear position sensors are equipped around the tibia to detect and avoid obstacles. The effectiveness of the proposed method is confirmed in walking experiments on flat and uneven terrain.

In football, transitioning from first-person to a bird’s-eye view and its relation to positional memory remains unclear. To investigate this, we developed and validated a system using immersive head-mounted displays (HMD) to assess football players’ abilities, thus potentially enhancing positional memory. We assessed experienced and inexperienced players in a virtual football setting with randomly placed players. We measured the time and perspective shifts required to reconstruct player positions from a bird’s-eye view. Further, both groups observed players in a virtual environment and then reconstructed their positions for accuracy. Compared to inexperienced players, experienced players showed higher precision in reconstructing the spatial arrangement of player objects, suggesting the feasibility of evaluating the transition from first-person to bird’s-eye view. The system enables us to assess ball control, memory of teammate and opponent positions, and recall of key player positions for future plays. This demonstrated the value of the immersive HMD system, which can quantitatively manipulate VR variables, in comprehensively understanding cognitive relationships. Expanding this system for evaluating player attributes and training football players holds great promise.

We have developed a novel hand sensor device designed to mitigate sensor malfunction caused by palm bending and to adjust output values by considering palm hardness. The primary goal of this device is not to acquire accurate hand load values but rather to gather pertinent information for estimating the lumbar load. Therefore, leveraging the load and posture data captured by this sensor, we endeavoured to estimate the electromyography (EMG) value – specifically the muscle action potentials in the lumbar region – using myoelectric potential sensors and adopting deep learning methodologies. The estimated values closely matched the EMG results and demonstrated a strong correlation with actual measurements of vertical luggage movement. Additionally, the usefulness of the hand sensor device was validated through simulations conducted with varying levels of information, thereby elucidating the impact of explanatory variables used in the estimation process.

This study aims to develop an encrypted motion-copying system using homomorphic encryption for secure motion preservation and reproduction. A novel concept of encrypted motion-copying systems is introduced, realizing the preservation, edition, and reproduction of the motion over encrypted data. The developed motion-copying system uses the conventional encrypted four-channel bilateral control system with robotic arms to save the leader's motion by a human operator in the ciphertext in a memory. The follower's control system reproduces the motion using the encrypted data loaded from the secure memory. Additionally, the developed system enables us to directly edit the motion data preserved in the memory without decryption using homomorphic operation. Finally, this study demonstrates the effectiveness of the developed encrypted motion-copying system in free motion, object contact, and spatial scaling scenarios.

Model-based control methods play an important role in control theory. Such controllers are designed via system identification; however, this approach is not applicable to cases where it is difficult to execute system identification owing to the large amount of data or experimental construction. To address these problems, data-driven control methods, in which controllers are designed by directly using target input–output data, have attracted attention for their effectiveness. Thus a framework has been proposed to reevaluate the aforementioned approaches and consider them in terms of data informativity. Herein, we use this framework to address the design problem of data-driven control for dissipativity. Using an algebraic approach, we derive necessary and sufficient conditions that data must satisfy to render a system dissipative via feedback control.

Developing remote control and supervision technology can help in addressing current and future safety issues at sea owing to human errors resulting from an individual’s watch-keeping. This paper presents a remote navigational watch-keeping system that uses virtual reality (VR) to reduce the risk of human errors and alleviate officers’ workload. The system creates a VR environment that replicates the state and navigational instruments of a ship, with limited connectivity via satellite, enabling an operator at an office onshore to have a comprehensive understanding of the surroundings as through the operator is on the navigational bridge. Two navigation experiences were used to evaluate the system. The results demonstrate that the proposed system is effective for double-checking purposes.

Flexibility provided by Combined Heat and Power (CHP) units in district heating networks is an important means to cope with increasing penetration of intermittent renewable energy resources, and various methods have been proposed to exploit thermal storage tanks installed in these networks. This paper studies a novel problem motivated by an example of district heating and cooling networks in Japan, where high-temperature steam is used as the heating medium. In steam-based networks, storage tanks are usually absent, and there is a strong need to utilize thermal inertia of the pipeline network as storage. However, this type of use of a heating network directly affects the operating condition of the network, and assuring safety and supply quality at the use side is an open problem. To address this, we formulate a control problem to utilize CHP units in frequency regulation market while satisfying physical constraints on a steam network described by a nonlinear model capturing dynamics of heat flows and heat accumulation in the network. Furthermore, a Model Predictive Control (MPC) framework is proposed by consistently combining several nonlinear control techniques, and its closed-loop behaviour is analysed through a two-site benchmark example. The proposed MPC controller is computationally efficient and shown to work in real time.

Image denoising is an extensively researched problem in computer vision that is commonly used as a benchmark for many imaging tasks. Noise distorts an image throughout the acquisition and transmission cycle. The elimination of noise from the original image is a significant challenge for scientists and this work considers Gaussian, Salt and pepper noises, as medical images are prone to it. Hence, this work presents ways for mitigating noise, while preserving the relevant image information. In this study, a Hybrid Wavelet Transform with Non-Local Mean (Hybrid WT-NLM) filter is proposed for reducing noise in lung CT images. The final denoised images are formed by the summation of NLM-filtered images and wavelet coefficients. The proposed study offers a straightforward method for removing noise to attain better visual quality in a reasonable period. The efficacy of the proposed denoising approach is proven by comparing it with traditional denoising approaches such as Morphological filtering, Mean Filter, Median filter, Adaptive median filter and Non-Local Means (NLM) upon two public datasets such as Public LIDC database and in-house clinical ICCN database. The average PSNR of the proposed work is 25.47 dB at a time consumption of 1.03 s.

The fields of telemedicine and aided medical diagnostics are benefiting greatly from medical imaging. For healthcare professionals to view patient medical images, a secure online transmitting technique is essential. The work introduces a DICOM image copyright protection by extended data encryption and watermarking technique with several potential applications in the medical field. Encrypting watermark images before embedding them into a DICOM cover images ensures the confidentiality of medical data, allowing for high-security access. Encryption here makes use of enhanced chaos with fruit fly optimization. The chaotic encryption technique makes use of the Lorenz map and a Customized Deep Learning Model (CDLM) based on Convolution Neural Networks (CNNs) are presented for watermarking. Multiple DICOM images with varying numbers of watermarks were used to evaluate the proposed model, and the resulting output was subjected to qualitative analysis using metrics such as MSE, SNR, PSNR, NC, and Q. Additionally, the requirements are satisfied for many assaults. In comparison to the state-of-the-art model, the suggested model performs better in every respect. Moreover, the approach provides impressive metrics such as MSE at 1.41E-06, SNR at 59.9876, PSNR at 60.3456, NC at 0.9989, and Q at 0.9992, showcasing its outstanding performance and reliability.

The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of – 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms.

Abstract Efficient route planning in transportation networks, particularly under stochastic conditions like severe weather (i.e. snow or hail), poses a significant computational challenge. This article addresses this challenge by modeling the route planning problem as a Markov decision process (MDP) problem, establishing reachability criteria, and identifying the minimum‐weight arborescence in the directed graph. To achieve this, the reachability determination algorithm is designed to assess the courier company's reachability to all junctions based on the queue‐typed data structure and breadth‐first search idea. Subsequently, the minimal‐cost route planning algorithm is developed to find a feasible transport route with the minimal cost of clearing obstacles by resorting to the Edmonds' algorithm and some feasible data structures. In particular, the article introduces a Fibonacci‐heap‐typed data structure to the minimal‐cost route planning algorithm, resulting in a remarkable reduction of the time complexity from O(mn) to O(m+nlogn), where m and n represent the cardinalities of the arc set and nodeset, respectively. Ultimately, the proposed method is applied to optimize route planning in a transportation network, providing a cost‐efficient solution for logistics and transportation planning.

Abstract This study focuses on a model reference adaptive control method that ensures identical orientation outputs for different prototypes of a two‐axis gimbal produced in mass production. In this method, unlike traditional MRAC structures, an MRAC structure is used in conjunction with state feedback control. First, the reasons for the need for an adaptation mechanism in gimbals and why Model Reference Adaptive Control (MRAC) alone won't be sufficient have been discussed. In the first section, various applications of MRAC have also been mentioned. Then, the mathematical foundation of the model reference adaptive controller used in this study is elaborately explained, followed by stability analyses. In the next step, an ideal reference model exhibiting desired behavior and a real system model with different dynamics are created in a simulation environment. This allows a comparison of the adaptation capabilities of only MRAC and MRAC+State Feedback controllers. Based on the information gathered in this section, the recommended approach in the article is tested on a real gimbal system, and the results are shared. The obtained results demonstrate that the MRAC+State Feedback control structure significantly reduces the error in the gimbal's orientation response compared to the reference model.

Abstract The penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances.

Abstract With the rapid development of wind power, the power performance and mechanical load characteristics of wind turbine are simultaneously considered and focused. Normally, wind turbine senses the incoming flow characteristics through the nacelle mounted anemometer, due to the inability to perceive the characteristics of wind speed in advance, the control strategy makes the wind turbine itself to be at a passive state during the operation process. In this paper, a wind turbine mechanical load optimization control strategy based on an accurate wind speed estimator with time series Broad Learning System Method (BLSM) is designed, simulated and also verified. Firstly, the basic control theory of the BLSM and also a mechanical load optimization controller is designed. Then the OpenFAST is used to conduct a full‐life cycle simulation comparison study on mechanical load characteristics of wind turbine before and after the implementation of the optimization control strategy. Finally, a field empirical mechanical load test is performed on the wind turbine, which is configured with BLSM mechanical load optimization control technology. The findings indicate that the implementation of this control strategy can significantly mitigate the ultimate and fatigue loads of wind turbines, particularly the fatigue loads of tower base tilt and roll bending moments, with a reduction rate of approximately 6.2% and 4.3%, respectively.

Abstract This article discusses finite‐time lag bipartite (FETLB) synchronization of double‐layer networks with non‐linear coupling strength and multiple time delays. The cooperative and competitive interactions between nodes are considered based on signed graphs. To address the non‐linear couplings strength, the T‐S fuzzy logic theory is used. An intermittent control approach is introduced to achieve FETLB synchronization, effectively minimizing control costs. Moreover, by the Lyapunov functional method, we derive criteria for achieving FETLB synchronization and provide estimations for the synchronization settling time. In addition, numerical simulation affirms the validity of the theoretical findings, showcasing the practical application of the synchronization results in secure communication.

Abstract Aiming at the reconnaissance task of unmanned vehicle formation under the malicious attack, a security state estimation method based on attack signal reconstruction is proposed. First the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then the attack compensation based particle filter is designed to estimate the target state for each vehicle. An information fusion strategy is designed to obtain the final reconnaissance result based on agent centrality and the number of attacks on unmanned vehicles. Finally, simulations are provided to illustrate the effectiveness of the proposed method.