Shared Control Research Articles

Chemical and genetic basis of orange flavor.

Sweet orange (Citrus sinensis) exhibits limited genetic diversity and high susceptibility to Huanglongbing (HLB). Breeding HLB-tolerant orange-like hybrids is in dire need. However, our understanding of the key compounds responsible for orange flavor and their genetic regulation remains elusive. Evaluating 179 juice samples, including oranges, mandarins, Poncirus trifoliata, and hybrids, distinct volatile compositions were found. A random forest model predicted untrained samples with 78% accuracy and identified 26 compounds crucial for orange flavor. Notably, seven esters differentiated orange from mandarin flavor. Cluster analysis showed six esters with shared genetic control. Differential gene expression analysis identified C. sinensis alcohol acyltransferase 1 (CsAAT1) responsible for ester production in orange. Its activity was validated through overexpression assays. Phylogeny revealed the functional allele was inherited from pummelo. A SNP-based DNA marker in the coding region accurately predicted phenotypes. This study enhances our understanding of orange flavor compounds and their biosynthetic pathways and expands breeding options for orange-like cultivars.

Current Sharing Control Modeling and Design for Power Supplies in Nuclear Fusion Applications

Current Sharing Control Modeling and Design for Power Supplies in Nuclear Fusion Applications

Human-autonomy collaboration in supervisory risk control of autonomous ships

This paper presents a method for developing and testing a risk-based control system, as a first step towards including the human supervisor explicitly in the design of the system. The result is a control system with improved decision-making capabilities compared to existing control systems. The methodology presented in the paper uses the Systems Theoretic Process Analysis (STPA) to analyse the risks of an autonomous ship within its concept of operations (CONOPS), and a Human-STPA (H-STPA) is used to analyse human responsibilities and involvement. The STPA results are then used to construct a Bayesian belief network (BBN)-based risk model to assess the operational risk of the ship. This is represented as a risk cost, describing the expected cost of consequences caused by potential hazardous events. This cost is combined with fuel costs, operations costs, and the potential loss of income if new missions are not undertaken using a supervisory risk controller (SRC). The SRC is capable of making decisions about how the ship should be safely operated and notifies the human supervisor in due time when it is necessary for them to take control. The last part of the methodology presented in this paper is testing the control system using a set of verification objectives based on results from the STPA and H-STPA. A case study involving an autonomous cargo ship with a human supervisor located in a remote operation center (ROC) is included; it shows that the proposed control system can operate the ship safely in different conditions and situations. By designing the SRC to notify the human supervisor before it reaches its operational limit, the ship is able to operate in a wider range of conditions compared to when just the autonomous control system is in charge. Hence, the proposed methodology shows promising results and provides useful insights related to shared control for autonomous ships.

Performance Shortfall, Executive Social Relations and the Choice Between Shared and Full Control for New Overseas Entries

ABSTRACT Integrating the behavioral theory of the firm and social capital theory, this study examines how performance shortfalls and executive social relations jointly affect firms’ trade-offs between the shared-control entry mode and full-control entry mode. This study argues that, for new overseas entries, performance shortfalls cause firms to prefer the shared-control entry mode. This is attributed to the fact that performance shortfalls increase firms’ resource constraints and reduce their information search scope. This, in turn, leads decision-makers to prefer the shared-control entry mode, because that mode requires fewer resources, less information collection, and lower processing capabilities. In addition, guided by social capital theory, this study also finds that the positive effect of performance shortfalls on the shared-control entry mode is weakened when executives have social relations, either overseas or in banks. Further analysis shows that, compared with less-developed economies, firms facing a performance shortfall are more inclined to adopt the shared-control mode to enter developed economies. By clarifying the joint effect of performance shortfalls and executive social relations on firms’ overseas equity entry modes, this study deepens the understanding of firms’ choices of overseas equity entry modes.

Augmented-reality based brain-computer interface of robot control

Brain Computer Interface (BCI) is a new approach to human-computer interaction. It can control the external devices directly with the brain without words and body movements. Brain-controlled robot is a major research area in the field of BCI, which organically integrates BCI with robotic systems to achieve safe and effective real-time control of robots using the user's electroencephalogram (EEG). Currently, there are two types of control methods for brain-controlled robots. One is direct control and the other is shared control. Direct brain control has its shortcomings, namely, low control efficiency and easy user fatigue. Shared control technique can effectively improve the control of brain-controlled robots and reduce the thinking ability of brain-controlled robots, thus making it the main control method of brain-controlled robots. The brain-computer collaborative control system based on augmented reality (AR) technology studied in this paper is a human-computer shared control method. In the experimental analysis of virtual reality (VR) systems and AR systems, this paper processes polylines through a series of control vertices with specific coordinates, using the relative distance measured between each point and the starting point as the relative coordinates, and calculates the operational errors of the two types of systems. In the system error of machining broken lines, when the relative coordinates are (10, 20), (40, 50), and (70, 80), the error values of the VR system are 0.17 mm, 0.36 mm, and 0.55 mm, respectively, while the error values of the AR system are 0.11 mm, 0.24 mm, and 0.41 mm, respectively. Therefore, the studies have illustrated the importance of AR systems for the study of brain-computer collaborative control of robots.

Sky-Worker: a heterogeneous dual-arm robot with dynamic authority assignment for live-line working

Purpose The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems. Design/methodology/approach The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization. Findings Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes. Practical implications The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks. Originality/value For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.

Control of the robotic arm system with an SSVEP-based BCI

Recent studies on brain–computer interfaces (BCIs) implemented in robotic systems have shown that the system’s effectiveness in assisting individuals with movement disorders to enhance their human–computer interaction skills. However, achieving precise and rapid online completion of tasks remains a challenge for manipulators with multiple degrees of freedom (DOFs). In this paper, we explore a time-sharing control strategy for studying motion control of a robotic arm based on steady-state visual evoked potentials. The signals are generated by the joint frequency-phase modulation method, analyzed with the filter-bank canonical correlation analysis algorithm, and identified to control the six-DOF robotic arm for task execution. The shared control strategy not only reduces user’s cognitive fatigue but also enhances system in practical environments. The use of high-frequency stimuli significantly improves user comfort, and hybrid coding increases the universality of the BCI system. Additionally, by setting multiple locations and actions randomly, the robotic arm can adaptively program the optimal path. The online results showed that BCI instructions of the proposed system could be accurately chosen from six options within 6.45 s. Subjects used an average of 12 commands for the robotic arm to achieve the proposed task with an average accuracy of 98.21%. These findings validate the feasibility and effectiveness of applying the system to robotic control. The control strategy proposed in this study exhibits versatility in controlling robots to perform various complex tasks across different domains.

Open Voice or Private Message? The Hidden Tug-of-War on Social Media Customer Service

Firms use social media as a great marketing tool and a convenient platform to deliver customer service. However, due to its public and social nature, social media tends to amplify a brand's successes as well as failures. Reluctant to subject their customer service to public scrutiny, firms are increasingly turning to private messaging on their social media channels for customer service conversations, which amounts to a reincarnation of traditional customer service in the social media era. Nonetheless, whether customers are willing to relinquish their newfound power is unclear. In this paper, we analyze a natural experiment where the inconvenience of the private channel with the treated firm is suddenly eliminated, and we find evidence that customers prefer to complain through the public channel. A randomized survey experiment further confirms this insight. Overall, firms’ and customers’ diverging preferences toward public or private channel reveal a hidden tug-of-war between the traditional mode of customer service featuring firm control and the recently emerged mode of customer service featuring shared control. These findings have important implications for firms’ customer service operations.

A Closed-Loop Shared Control Framework for Legged Robots

Shared control, as a combination of human and robot intelligence, has been deemed as a promising direction toward complementing the perception and learning capabilities of legged robots. However, previous works on human–robot control for legged robots are often limited to simple tasks, such as controlling movement direction, posture, or single-leg motion, yet extensive training of the operator is required. To facilitate the transfer of human intelligence to legged robots in unstructured environments, this article presents a user-friendly closed-loop shared control framework. The main novelty is that the operator only needs to make decisions based on the recommendations of the autonomous algorithm, without having to worry about operations or consider contact planning issues. Specifically, a rough navigation path from the operator is smoothed and optimized to generate a path with reduced traversing cost. The traversability of the generated path is assessed using fast Monte Carlo tree search, which is subsequently fed back through an intuitive image interface and force feedback to help the operator make decisions quickly, forming a closed-loop shared control. The simulation and hardware experiments on a hexapod robot show that the proposed framework gives full play to the advantages of human–machine collaboration and improves the performance in terms of learning time from the operator, mission completion time, and success rate than comparison methods.

Toward Consumer Acceptance of Cooperative Driving Systems: A Human-Centered Shared Steering Control Approach Within a Hierarchical Framework

Toward Consumer Acceptance of Cooperative Driving Systems: A Human-Centered Shared Steering Control Approach Within a Hierarchical Framework

Research on Human-robot Shared Control of Throat Swab Sampling Robot Based on Intention Estimation

Research on Human-robot Shared Control of Throat Swab Sampling Robot Based on Intention Estimation

Online Advertising, Data Sharing, and Consumer Control

Invoking a property rights approach, we examine how competition between advertising exchanges influences the targeting options that these exchanges make available to advertisers. When advertisers have strong property rights over data regarding consumers’ active purchase interests, competition between ad exchanges leads to too little sharing of data. This may harm consumers, who receive too few pertinent ads, and advertisers themselves can also be harmed due to a situation resembling a prisoner’s dilemma. We find that reallocating property rights to consumers, that is, giving them the right to opt out of tracking may also benefit consumers who allow tracking by altering the incentives of ad exchanges to offer improved targeting options. In addition, we show that initiatives by Apple and Google to limit third-party tracking and to introduce alternative tracking systems such as Google’s Topics might benefit consumers by weakening the data property rights of advertisers. Because more data are shared by default under such systems, this can be true even if these systems are less accurate than third-party tracking systems. This paper was accepted by Joshua Gans, business strategy.

Lateral Evasive Maneuver with Shared Control Algorithm: A Simulator Study.

Shared control algorithms have emerged as a promising approach for enabling real-time driver automated system cooperation in automated vehicles. These algorithms allow human drivers to actively participate in the driving process while receiving continuous assistance from the automated system in specific scenarios. However, despite the theoretical benefits being analyzed in various works, further demonstrations of the effectiveness and user acceptance of these approaches in real-world scenarios are required due to the involvement of the human driver in the control loop. Given this perspective, this paper presents and analyzes the results of a simulator-based study conducted to evaluate a shared control algorithm for a critical lateral maneuver. The maneuver involves the automated system helping to avoid an oncoming motorcycle that enters the vehicle's lane. The study's goal is to assess the algorithm's performance, safety, and user acceptance within this specific scenario. For this purpose, objective measures, such as collision avoidance and lane departure prevention, as well as subjective measures related to the driver's sense of safety and comfort are studied. In addition, three levels of assistance (gentle, intermediate, and aggressive) are tested in two driver state conditions (focused and distracted). The findings have important implications for the development and execution of shared control algorithms, paving the way for their incorporation into actual vehicles.

Power Sharing Control for a Microgrid with PV Power Plants, Batteries and Quasi-Z-source Cascaded H-bridge Multilevel Inverter

In recent years, Quasi-Z-source cascaded H-bridge multilevel inverters (qZS-CHBMLIs) have become an interesting solution for integrating renewable energy into the utility grid. The possibility of performing power conversion in a single stage, without an additional DC/DC converter, and a higher voltage gain, are their main advantages over traditional inverters. In addition, individual control of the maximum power point tracking (MPPT) can be achieved for each PV plant. Owing to the intermittent nature of PV power plants, battery energy storage systems (BESS) are commonly used to smooth out PV power fluctuations. This paper presents a control system for the active and reactive power delivered to the grid according to the system operator references and an EMS for an ES-qZS-CHBMLI. The BESS is coordinated through an energy management system (EMS) based on the state of charge (SOC) The system is evaluated under two different operation modes. One of them, where the PV power plants operate according to their MPP and the other in which the MPPT faults and thus, the PV power is decreased. A MATLAB-Simulink simulation is used to validate the proposed control system for a grid-connected single-phase configuration based on a qZS-CHBMLI with three cascade qZSI, each connected to a 4.8 kW PV power plant and a BESS.

Unfair Business Competition in Bankruptcy Institutions and Suspension of Debt Payment Obligations in Indonesia

Unfair business competition in the business world is not uncommon even though there has been a law on the prohibition of monopolistic practices and unfair business competition, namely Law Number 5 of 1999. Basically, in carrying out business activities, it is natural for business actors to compete with other business actors, but must comply with legal signs governing such competition. Along with the increasingly complex development of the business world and the ever older anti-monopoly and unfair business competition laws, unfair business competition practices are also developing. On this occasion, unfair business competition practices will be discussed by bankrupting competing business actors. The study in this paper uses the normative juridical research method which prioritizes literature studies through the applicable laws and regulations in Indonesia as well as several related book references and uses an analytical descriptive approach. Based on the studies carried out, along with the results of his research, competition for market share control for business actors is a very strategic matter as a result of which business actors often use unhealthy methods as stipulated in the anti-monopoly and unfair business competition law. Unfair business competition in the perspective of bankruptcy law as mentioned above can occur due to weaknesses in regulatory patterns regarding bankruptcy requirements as stated in Article 2 paragraph (1) UUK & PKPU.
 Keywords: unfair business competition practices, bankruptcy

Shared control for human–machine co-driving vehicles based on constraint-following approach

To ensure the human–machine co-driving vehicle path-tracking accuracy, as well as to diminish the effects of vehicle underactuated characteristics and undesirable driver operation, a parallel shared control framework is proposed in this paper. By structuring a rational driving weights allocation module, the framework enables cooperative driving between the driver and automated system. First, for the front steering vehicle, a robust path-tracking controller is proposed using the constraint-following approach. The system uncertainty is decomposed into matched and non-matched uncertainties. As the non-matched portions are orthogonal to the constraint-following geometric space, they will not affect control performance. Therefore, the constraint-following control (CFC) is designed to handle the path-tracking equality constraint, initial state deviations, and matched uncertainties. Second, a fuzzy rule-based driving weights allocation module is proposed. According to the lateral deviation and the relative accuracy of the steering angle, the allocation module adjusts the driving weight of the driver and automated system to improve the vehicle condition. Finally, the simulation results show that the framework can effectively reduce the emergence caused by the driver or automated system, and significantly improve the path-tracking performance. Additionally, as the controller exhibits sufficient robustness, it can handle multiple sources of uncertainty in vehicle systems.

AI co-pilot bronchoscope robot

The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdeveloped areas. Here, we present an artificial intelligence (AI) co-pilot bronchoscope robot that empowers novice doctors to conduct lung examinations as safely and adeptly as experienced colleagues. The system features a user-friendly, plug-and-play catheter, devised for robot-assisted steering, facilitating access to bronchi beyond the fifth generation in average adult patients. Drawing upon historical bronchoscopic videos and expert imitation, our AI–human shared control algorithm enables novice doctors to achieve safe steering in the lung, mitigating misoperations. Both in vitro and in vivo results underscore that our system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. This study offers innovative strategies to address the pressing issue of medical resource disparities through AI assistance.

DESIGN AND IMPLEMENTAION OF SAFETY MEASURES CAUSE BY DROWSINESS DETECTION IN AUTOMOTIVE VEHICLE

Driver’s drowsiness is the major problem that causes road accidents. Unlike normal facial expression, drowsiness is defined to be a condition of exhaustion, where the expression of the face is different from usual. The important steps in detecting drowsiness are face detection and expression detection. When the detection occurs that time the hazard lights are activate automatically to warn other drivers that the vehicle is a temporary obstruction. Also we have proposed new safety method using Ultrasonic sensors. It is used to detect the obstacles in which by using transmitter and receiver signals. The barrier principle determines the distance from the sensor to the reflector (Retro-Reflective Sensor) or to an object (Through-Beam Sensor) in the measuring range. The distance measurement using ultrasonic sensors to detect obstacles and their distances. Once the obstacle has been detected and safe separation distance is reached, the vehicle will automatically get stopped and also shared control concept for lane keeping assist (LKA) systems of intelligent vehicles. The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Two local optimal-based controllers with two predefined objectives (i.e., lane keeping and conflict management) are designed in the operational part. The control supervisor in the tactical part aims to provide a decision making signal which allows for a smooth transition between two local controllers. The control design is based on a human-in the-loop vehicle system to improve the mutual driver-automation understanding, thus reducing or avoiding the conflict. In particular, the control design is formulated as an LMI optimization which can be easily solved with numerical solvers. The effectiveness of the proposed shared control method is clearly demonstrated through various hardware experiments with human drivers. Its controls the steering and to go on the straight road.

Learning Human Behavior in Shared Control: Adaptive Inverse Differential Game Approach.

To enhance the collaborative intelligence of a machine, it is important for the machine to understand what behavior a human may adopt to interact with the machine when performing a task in shared control. In this study, an online behavior learning method is proposed for continuous-time linear human-in-the-loop shared control systems by using the system state data only. A two-player nonzero-sum linear quadratic dynamic game paradigm is used for modeling the control interaction between a human operator and an automation that actively compensates for human control action. In this game model, the cost function representing the human behavior is assumed to have an unknown weighting matrix. Here, we want to learn the human behavior or retrieve the weighting matrix by using the system state data only. Accordingly, a new adaptive inverse differential game (IDG) method, which integrates concurrent learning (CL) and linear matrix inequality (LMI) optimization, is proposed. First, a CL-based adaptive law and an interactive controller of the automation are developed to estimate the feedback gain matrix of the human online, and second, an LMI optimization problem is solved to determine the weighting matrix of the human cost function. Finally, simulation results on a cooperative shared control driver assistance system are provided to elucidate the feasibility of the developed method.

Shared Control Up to the Limits of Vehicle Handling

Shared Control Up to the Limits of Vehicle Handling