Manipulation Scheme Research Articles

Unconventional magnon blockade in a dissipative photon–magnon coupling system

We investigate unconventional magnon blockade (UMB) in a dissipative photon–magnon coupling cavity, where the coherent and dissipative coupling between photon and magnon can be adjusted by the position of the yttrium iron garnet sphere in the cavity. An approximate analytical solution for the statistical property of magnon is provided, which is in good agreement with the results obtained by numerically solving the Lindblad master equation. Our results indicate that UMB can be achieved in the case of dissipative photon–magnon coupling, but not in the case of coherent photon–magnon coupling. Secondly, by adjusting the amplitude of the external laser field, dynamic control of UMB can be achieved. Finally, by solving the Lindblad master equation containing the occupation number of thermal magnon and photon, the statistical properties of magnons will transition from nonclassical to classical. It is shown that the above phenomena originate from the interference between different quantum paths. Our research may provide theoretical possibilities for quantum manipulation schemes at the single-magnon level and open up a new avenue for designing single-magnon sources in a dissipative photon–magnon coupling system.

An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance.

Hybrid Orientation and Position Collaborative Motion Generation Scheme for a Multiple Mobile Redundant Manipulator System Synthesized by a Recurrent Neural Network.

To enable distributed multiple mobile manipulator systems to complete collaborative tasks safely and stably, this article investigates and presents a motion generation scheme that considers both orientation and position coordination based on a distributed recurrent neural network. Moreover, physical limits are also considered. Specifically, the orientation and position coordination constraints and physical limits are modeled separately as equality and inequality constraints with coupled variables. Subsequently, a motion generation scheme for multiple mobile manipulators based on quadratic programming is established. Finally, a distributed linear variational inequality-based primal-dual neural network is constructed to solve the motion generation scheme and obtain the motion trajectories of all the mobile manipulators. The simulation results demonstrate that the hybrid orientation and position collaboration motion generation scheme effectively addresses the position and orientation coordination problem for multiple mobile manipulator systems. Compared to other schemes, the proposed scheme based on a distributed computing structure greatly enhances the stability of the system. Additionally, the proposed approach introduces orientation coordination and physical limits, which increases the practicality of the system.

A Data-Driven Image-Based Visual Servoing Scheme for Redundant Manipulators With Unknown Structure and Singularity Solution

A Data-Driven Image-Based Visual Servoing Scheme for Redundant Manipulators With Unknown Structure and Singularity Solution

Cerebellum-Inspired Learning and Control Scheme for Redundant Manipulators at Joint Velocity Level.

Redundant manipulators, as mechanical equipments imitating human arms, have been applied to various areas in recent years from the perspective of control. Different from pure control technologies, the motion capability of a human arm is achieved by a complex and efficient neural system, with the cerebellum playing a pivotal role. Motivated by this fact, we design a cerebellum model based on an echo state network (ESN) for the learning and control of redundant manipulators. In addition, to simulate the skillful control ability of the cerebellum over movements of human arms, the proposed model is constructed at the joint velocity level. Furthermore, to improve the accuracy and applicability, we propose an ESN-based Kalman-filter-incorporated and cerebellum-inspired (KFICI) scheme for the learning and control of redundant manipulators with Kalman filter incorporated. The proposed scheme enables a redundant manipulator to track the desired trajectory at the velocity level and tolerate noises. Finally, simulations and experiments based on a physical redundant manipulator are performed to verify the effectiveness of the proposed control scheme.

WALDATA: Wavelet transform based adversarial learning for the detection of anomalous trading activities

Detecting manipulative activities in stock market trading poses a significant challenge due to the complex temporal correlations inherent to the dynamically changing stock price data. This challenge is further exacerbated by the limited availability of labelled anomalous trading data instances. Stock price manipulations, which consist of infrequent anomalies in stock price trading data, are challenging to capture due to their sporadic occurrence and dynamically evolving nature. This scarcity and inherent complexity significantly complicate the creation of labelled datasets hence hinders the development of robust detection of different stock price manipulation schemes through supervised learning methods. Overcoming these challenges is crucial for enhancing our understanding of market dynamics and implementing robust market surveillance systems. To address these challenges, we introduce a novel stock price manipulation detection approach called WALDATA (Wavelet Transform based Adversarial Learning for the Detection of Anomalous Trading Activities). We leverage the Wavelet Transform (WT) to decompose non-stationary stock price time series into informative features and capture multi-scale dynamics within the data. We encode stock price data by transforming it into scalogram images through the Continuous Wavelet Transform, effectively converting stock price time series data into a 2D image representation. Subsequently, we employ a Generative Adversarial Network (GAN) architecture, originally applied to computer vision, to learn the underlying distribution of normal trading behaviour from the encoded images. We then train the discriminator as an anomaly detector for identifying manipulative trading activities in the stock market. The efficacy of WALDATA is rigorously evaluated on diverse real-world stock datasets using 1-level tick data from the LOBSTER project and the experimental results demonstrate the significant performance of our approach achieving an average AUC of 0.99 while maintaining low false alarm rates across various market conditions. These findings not only validate the effectiveness of the proposed WALDATA approach in accurately identifying stock price manipulations but also provide investors and regulators alike with valuable insights for the development of advanced market surveillance systems. This research demonstrates the promising potential of combining wavelet-based feature extraction and stock price time series to image representation with generative adversarial learning frameworks for anomaly detection in financial time series data. The successful implementation of WALDATA contributes to the development of advanced market surveillance systems and paves the way for further advancements in market surveillance, contributing towards a more efficient and robust financial system and a fair market environment.

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

Simulation Study of Localized, Multi-Directional Continuous Dynamic Tailoring for Optical Skyrmions

The topological properties of optical skyrmions have enormous application value in fields such as optical communication and polarization sensing. At present, research on optical skyrmions focuses primarily on the topological principles of skyrmions and their applications. Nonetheless, extant research devoted to skyrmion-array manipulation remains meager. The sole manipulation scheme has a limited effect on the movement direction of the whole skyrmion array. Based on the interference principle of the surface plasmon polariton (SPP) wave, we propose an upgraded scheme for the tailoring of electric-field optical skyrmions. A distributed Gaussian-focused spots array is deployed. Unlike the existing manipulation, we customize the phase of the light source to be more flexible, and we have discovered optical-skyrmion tailoring channels and shaping channels. Specifically, we move the skyrmions within the channel in both directions and manipulate the shape of the topological domain walls to achieve customized transformation. This work will evolve towards a more flexible regulatory plan for tailoring optical-skyrmion arrays, and this is of great significance for research in fields such as optical storage and super-resolution microimaging.

Sequential Optimal Trajectory Planning Scheme for Robotic Manipulators along Specified Path Based on Direct Collocation Method

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets.

Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach

With the rise of electric vehicles, autonomous driving, and valet parking technologies, considerable research has been dedicated to automatic charging solutions. While the current focus lies on charging robot design and the visual positioning of charging ports, a notable gap exists in addressing safety aspects during the charging plug-in process. This study aims to bridge this gap by proposing a collision classification scheme for robot manipulators in automatic electric vehicle charging scenarios. In situations with minimal visual positioning deviation, robots employ impedance control for effective insertion. Significant deviations may lead to potential collisions with other vehicle parts, demanding discrimination through a global visual system. For moderate deviations, where a robot’s end-effector encounters difficulty in insertion, existing methods prove inadequate. To address this, we propose a novel data-driven collision classification method, utilizing vibration signals generated during collisions, integrating the robust light gradient boosting machine (LightGBM) algorithm. This approach effectively discerns the acceptability of collision contacts in scenarios involving moderate deviations. Considering the impact of passing vehicles introducing environmental noise, a noise suppression module is introduced into the proposed collision classification method, leveraging empirical mode decomposition (EMD) to enhance its robustness in noisy charging scenarios. This study significantly contributes to the safety of automatic charging processes, offering a practical and applicable collision classification solution tailored to diverse noisy scenarios and potential contact forms encountered by charging robots. The experimental results affirm the effectiveness of the collision classification method, integrating LightGBM and EMD, and highlight its promising prediction accuracy. These findings offer valuable perspectives to steer future research endeavors in the domain of autonomous charging systems.

Free-electron interaction with nonlinear optical states in microresonators.

The short de Broglie wavelength and strong interaction empower free electrons to probe structures and excitations in materials and biomolecules. Recently, electron-photon interactions have enabled new optical manipulation schemes for electron beams. In this work, we demonstrate the interaction of electrons with nonlinear optical states inside a photonic chip-based microresonator. Optical parametric processes give rise to spatiotemporal pattern formation corresponding to coherent or incoherent optical frequency combs. We couple such "microcombs" to electron beams, demonstrate their fingerprints in the electron spectra, and achieve ultrafast temporal gating of the electron beam. Our work demonstrates the ability to access solitons inside an electron microscope and extends the use of microcombs to spatiotemporal control of electrons for imaging and spectroscopy.

Multitask control of aerial manipulator robots with dynamic compensation based on numerical methods

This paper presents a control scheme for aerial manipulators which allows to solve different motion problems: end-effector position control, end-effector trajectory tracking control and path-following control. The scheme has two cascaded controllers: i) the first controller is a minimum norm controller based on numerical methods, it solves the three motion control problems just by modifying the controller references. Also, since the aerial manipulator robot is a redundant system, i.e., it has extra degrees of freedom to accomplish the task, it is possible to set other control objectives in a hierarchical order. As a secondary objective of the control it is proposed to maintain a desired configuration for the robotic arm during the task. ii) The second cascade controller is designed to compensate the dynamics of the system which main objective is to drive the velocity errors to zero. The coupled dynamic model of the robotic system (hexarotor and robotic arm) is presented. This model is usually developed as a function of the forces and torques. However, in this work, it is written as a function of reference velocities which are usual references for these vehicles. The proposed control algorithms are given with the corresponding stability and robustness analysis. Finally, to validate the control scheme, experimental tests are performed in a partially structured environment with an aerial manipulator conformed by an aerial platform and a 3DOF robotic arm.

An Obstacle Avoidance Scheme for Manipulators Aided by Noise-Tolerant Neural Dynamics

An Obstacle Avoidance Scheme for Manipulators Aided by Noise-Tolerant Neural Dynamics

Reconfigurable Manufacturing Systems through Intelligent Workpiece Handling and Artificial Intelligence

In recent years, businesses have strived to align their manufacturing goods with the unique needs and preferences of their customers. However, the mass customization paradigm introduces challenges mostly associated to the manipulation of small product batches and knowledge management. This paper proposes a framework for intelligent reconfigurable manufacturing systems, that leverages robotic automation supported by a collaborative dual-arm manipulator, cognitive mechatronic devices, and machine-learning algorithms. In more detail, pioneer end-effectors and machinery provide the capacity of grasping and handling a variety of products without time-consuming hardware modifications. The backbone of this manipulation scheme is a set of machine learning tools for: a) the planning and synchronization of handling agents, b) identification and localization of workpieces, c) the generation of optimal packaging configurations based on customer orders and identified workpieces, and d) the allocation of the respective handling operations to robots, operators, and machinery. The proposed framework is evaluated through its implementation on a use case deriving from the metal industry. The particularities of this use case, as expressed in product variety, workpiece complexity, customer orders and intralogistics, provide a fertile ground for the validation of the discussed technologies.

Earnings Management in Business Groups during the SARS-CoV-2 Pandemic

Abstract This paper aims to assess the influence of the SARS-CoV-2 pandemic on the accrual-based earnings management patterns in non-financial business groups in the Warsaw Stock Exchange (WSE). This study contributes to the existing literature by considering earnings manipulation behaviours in assessing the reporting data quality, both in consolidated financial statements of business groups and individual financial statements of parent undertakings. The research methods are based on the Modified Jones Model with the simultaneous separation of individual subcategories of accruals via cross-sectional analysis and time-series data approach (separately for each business group). Empirical findings supported the presumption that earnings manipulation schemes in the SARS-CoV-2 transitional (2020) and crisis (2021) periods differed statistically compared to the previous 2019 and 2020 years, respectively. Moreover, this article proved that the company‘s potential financial distress could imply accrual-based earnings management behaviours and moderate the association between the SARS-CoV-2 pandemic and earnings manipulations activities. Finally, the obtained results confirmed that although business groups have an expanded set of balance sheet policy instruments, the quality of earnings reported in the individual financial statements of parent undertakings and the consolidated financial statements of business groups were similar during the SARS-CoV-2 pandemic.

Frequency-bin photonic quantum information

Discrete frequency modes, or bins, present a blend of opportunities and challenges for photonic quantum information processing. Frequency-bin-encoded photons are readily generated by integrated quantum light sources, naturally high-dimensional, stable in optical fiber, and massively parallelizable in a single spatial mode. Yet quantum operations on frequency-bin states require coherent and controllable multifrequency interference, making them significantly more challenging to manipulate than more traditional spatial degrees of freedom. In this mini-review, we describe recent developments that have transformed these challenges and propelled frequency bins forward. Focusing on sources, manipulation schemes, and detection approaches, we introduce the basics of frequency-bin encoding, summarize the state of the art, and speculate on the field’s next phases. Given the combined progress in integrated photonics, high-fidelity quantum gates, and proof-of-principle demonstrations, frequency-bin quantum information is poised to emerge from the lab and leave its mark on practical quantum information processing—particularly in networking where frequency bins offer unique tools for multiplexing, interconnects, and high-dimensional communications.

Generic fabrication solution of freeform Fresnel optics using ultra-precision turning.

Freeform Fresnel optics represent an emerging category of modern optics that reproduces powerful optical functionalities while maintaining an ultra-compact volume. The existing ultra-precision machining (UPM) technique faces technical challenges in meeting the fabrication requirements for freeform Fresnel optics because of the absence of appropriate geometry definition and corresponding tool path planning strategy to overcome the extreme asymmetry and discontinuity. This study proposes a new scheme for ultra-precision machining using four axes (X, Y, Z, C) to fabricate freeform Fresnel optics, including a general geometry description for freeform Fresnel optics, the quasi-spiral tool path generation strategy to overcome the lack of rotary symmetry, and the adaptive tool pose manipulation method for avoiding tool interference. In addition, the tool edge compensation and the adaptive timestep determination are also introduced to enhance the performance and efficiency of the proposed scheme. The machining of two exemplary freeform Fresnel lenses is successfully demonstrated. Overall, this study introduces a comprehensive routine for the fabrication of freeform Fresnel optics and proposes the adaptive tool pose manipulation scheme, which has the potential for broader applications in the ultra-precision machining of complex or discontinuous surfaces.

Field Manipulations in On-Chip Micro/Nanoscale Lasers Based on Colloid Nanocrystals

Owning to merits such as bandgap tunability, solution processability, large absorption coefficients, and high photoluminescence quantum yields, colloidal quantum dots (CQDs) emerged as a promising gain material to make on-chip micro/nanoscale lasers with high silicon compatibility. In this paper, we review the recent progress in CQD on-chip micro/nanoscale lasers, with a special focus on the physical properties achieved through field manipulation schemes in different types of cavities. Key aspects include manipulating and engineering wavelength, polarization, and direction as well as coupling and light extraction. Finally, we give our prospects for future research directions toward the integration of robust CQD nano/microscale lasers with photonic integrated circuits.

Scalable Motion Planning and Task-Oriented Coordination Scheme for Mobile Manipulators in Smart Manufacturing

Scalable Motion Planning and Task-Oriented Coordination Scheme for Mobile Manipulators in Smart Manufacturing