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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digitalization and automation of technological processes are two of the leading trends in the development of information technology. The industrial Internet of Things (IOT) is the future of every field with complex and multicomponent technological processes. The possibility of full automation implies the interaction of a large number of different technologies and methods, one of which is mathematical modeling. The mathematical model allows formalizing the representation of any object, which is essential in the development of control systems. One of the most effective options for using this method is the mathematical description of natural objects. An important feature is that with the use of mathematics, it is possible to describe not only the geometric parameters of the object but also the dynamics of changes in the parameters of the object with respect to the input influences. This paper proposes to develop a conceptual model of an oil field that can reflect and predict changes in the reservoir under the conditions of changes in the parameters of the field operation mode. The study presents the process of determining and selecting the most significant parameters for modeling and presents a draft mathematical model of the oil field as a system of equations for the most significant parameters of the oil field. [ABSTRACT FROM AUTHOR]

This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynamic loads acting on the vehicle for ground and flight testing, as well as guidance, navigation, and control applications. Specifically, the approach targets hypersonic flight conditions where direct measurement of the surface pressures is challenging due to the harsh aerothermal environment. For problems employing a linear elastic structural model, the inference problem can be posed as a least-squares problem with a linear constraint arising from a finite element discretization of the governing elasticity partial differential equation. Due to the linearity of the problem, an explicit solution is given by the normal equations. Precomputation of the resulting inverse map enables rapid evaluation of the surface pressure and corresponding integrated quantities, such as the force and moment coefficients. The inverse approach additionally allows for uncertainty quantification, providing insights for theoretical recoverability and robustness to sensor noise. Numerical studies demonstrate the estimator performance for reconstructing the surface pressure field, as well as the force and moment coefficients, for the Initial Concept 3.X (IC3X) conceptual hypersonic vehicle. [ABSTRACT FROM AUTHOR]

The potential of nuclear power in tackling the global energy crisis hinges on the development of effective decommissioning strategies. While the use of human workforce is still common practice, robotics has the potential to revolutionise decommissioning tasks. However, the restricted access, crammed environments, and harsh environmental conditions of nuclear power plants are not suited for conventional robots. In this scenario, the slender design and overall length of long-reach manipulators represent a promising solution. Yet, the same features that make these manipulators a suitable candidate, also introduce significant engineering challenges in design and control. The key challenges and hazards of nuclear decommissioning are summarized using the decommissioning of the Fukushima Daiichi nuclear power plant as a representative use case, in which a manipulator over 20 m long with 18 degrees of freedom is planned to be used for fuel debris retrieval. Critical research gaps on the state-of-the-art of long-reach manipulators for nuclear decommissioning are presented to summarise the focused key research areas ongoing and foster further research engagement within the community. [ABSTRACT FROM AUTHOR]

This article explores the critical challenges and limitations of modern communication-based train control (CBTC) systems, particularly focusing on the dynamic and uncertain nature of train–ground communication. The concept of the Age of Information (AoI) is introduced, highlighting the discrepancies between the actual and derived states used for control, which can compromise system performance and safety. Existing solutions, such as wireless local area networking technologies and reinforcement learning handoff schemes, are discussed, emphasizing their contributions to reducing handoff latency and improving the freshness of information between the train and access points. However, their limitations in adequately addressing issues related to the AoI are critically examined. The article argues for the development of novel control approaches that consider these effects to minimize the information gap and enhance the overall performance of CBTC systems. [ABSTRACT FROM AUTHOR]

AbstractAgency is action aimed at goals selected by an agent. A deterministic world view leaves scant room for agency. To reconcile the arguments, we represent action as nested control systems, ranging from clearly deterministic to clearly volitional. Negative feedback minimizes deviations from setpoints (goals). Goals are determined by higher modules in a hierarchy of systems, ranging from gamma-efferent spindles through reflexes to operant responses; these last, and the larger system that contains them, called the Self, comprise volitional agents. When operants become habitual they descend to closed teleonomic systems—automaticity. Change in emotional states, and unpredicted changes in the context–raise them back to full volitional systems. At the highest level is the Self—the brain’s model of the agent. When aroused out of open teleonomic functioning, it must reconsider means and ends. It does so by simulating action plans, using the same neural systems it uses to effect them. The simulated stimuli and responses are conscious, approximating their perceptions as experienced in real time; this verisimilitude gives them their hedonic value. Positive feedback plays a key role in these complex adaptive systems, as it focuses and holds attention on the most salient percepts and goals, permitting the self-organization of action plans. The Self is not a separate entity, but a colloquy of command modules wearing the avatar of the agent. This system is put into correspondence with Grossberg’s Adaptive Resonance Theory. Free will and determinism emerge not as binary opposites, but the modulating inputs to a spectrum of systems. [ABSTRACT FROM AUTHOR]

In the face of increasing environmental challenges, carbon emissions from industrial parks have become a global focal point, particularly as electricity consumption serves as a major source of carbon emissions that requires effective management. Despite proactive efforts by governments and industry stakeholders to transition industrial parks toward cleaner production methods, traditional energy management systems exhibit significant limitations in data collection, real-time monitoring, and intelligent analysis, making it difficult to meet the urgent demands for carbon reduction. To address these challenges, this study proposes a carbon data management approach for industrial parks based on digital twin technology and develops an intelligent system that integrates monitoring, environmental surveillance, energy management, and carbon emission monitoring. The system supports efficient energy-saving and carbon-reducing decision making by real-time collection of energy consumption data. By incorporating Building Information Modeling (BIM) and Internet of Things (IoT) technologies, the system facilitates the integration and visualization of multi-source data, significantly enhancing the transparency of carbon data. The results of the carbon reduction validation system demonstrate that the application of this platform and its associated facilities can significantly reduce carbon emissions in the park, providing robust support for the transition of industrial parks toward low-carbon and sustainable development. [ABSTRACT FROM AUTHOR]

The data rate theorem of topological version for discrete-time control systems says that the topological feedback entropy characterizes the smallest data rate of a communication channel connecting a coder-controller to render a compact set strongly invariant. In the present paper, we prove the data rate theorem of measurable version for discrete-time control systems. To be precise, we define a new quantity named as μ -feedback entropy to measure the smallest data rate of a communication channel connecting a coder-controller to render a compact set strongly invariant except for a subset with μ -measure zero. Besides, the data rate theorem for topological feedback pressure is also established. [ABSTRACT FROM AUTHOR]

We examine the problem of reducing the control set in a dynamical system so that the solution set and the attainable sets remain essentially unchanged. We cite some classical results to exhibit the problem and promote a set-valued approach. A necessary condition is formulated by using a concept of set-valued derivative, which can be regarded as an extension of the classical Relaxation theorem. [ABSTRACT FROM AUTHOR]

Large-scale neuromorphic architectures consist of computing tiles that communicate spikes using a shared interconnect. We propose ADIONA, a dynamic segmented bus interconnect to address design scalability while reducing energy and latency of spike traffic. ADIONA consists of parallel bus lanes arranged in a ladder-shaped structure that allows any tile to connect to another, offers multiple routing options for communication links, and provides a high level of customization for different mapping scenarios and use cases. Each lane in the ladder bus is partitioned into segments using lightweight bufferless switches. Based on compile-time communication information, these switches can be dynamically reconfigured at runtime to execute the target application. Our dynamic segmented bus interconnect substantially enhances hardware utilization, improves fault tolerance, and offers adaptability to execute different applications on a single hardware platform. We evaluate ADIONA using three synthetic and three realistic machine learning workloads on a cycle-accurate neuromorphic simulator. Our results show that ADIONA reduces energy consumption by $2.1\times $ , latency by $40\times $ , and interconnect area by $2\times $ , compared to a state-of-the-art interconnect for neuromorphic systems. [ABSTRACT FROM AUTHOR]

Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true optimum. We investigate the reduction in both bias and variance errors attributed to the simplex gradient estimated from noisy function measurements, in favor of the finite-differences gradient, when both are used for black-box optimization methods. Regardless of the simplex orientation, while reducing the gradient bias error owned to several factors such as truncation, numerical or measurement noise, we claim and verify that, under relaxed assumptions about the underlying function’s differentiability, the estimated gradient by the simplex method has at most half the variance of the finite-difference gradient. The findings are validated with two comprehensive and representative case studies, one related to the minimization of a nonlinear feedback control system cost function and the second related to a deep machine learning classification problem whose hyperparameters are tuned. We conclude that in up to medium-size practical black-box optimization problems with unknown variable domains and where the noisy function measurements are expensive, a simplex gradient-based search is an attractive option.

In this paper we consider a general control system involving a spatially heterogeneous dynamics. This means that the state space is partitioned into several disjoint regions and that each region has its own (smooth) control system. As a result, the dynamics discontinuously changes whenever the trajectory crosses an interface between two regions. In that spatially heterogeneous setting (and in contrast with the usual smooth case), a needle-like perturbation of the control may generate a perturbed trajectory that does not uniformly converge towards the nominal one, and may lead to the absence of a corresponding first-order variation vector. The first contribution of this paper is to illustrate this issue by means of a simple counterexample. Our second and main contribution is to provide a modified needle-like perturbation of the control (adapted to the spatially heterogeneous setting) which generates a perturbed trajectory that uniformly converges towards the nominal one, and leads to a corresponding first-order variation vector (which has the particularity of admitting a discontinuity jump at each interface crossing). This is made possible under several assumptions (including transverse crossing conditions), by introducing new tools such as auxiliary trajectories and auxiliary controls and by using a conic version of the implicit function theorem. [ABSTRACT FROM AUTHOR]

There’s a new trojan on the block, one that specifically targets network appliances and internet of things (IoT) devices running the open-source Linux operating system. FortiGuard Labs has identified https://www.fortinet.com/blog/threat-research/analyzing-elf-sshdinjector-with-a-human-and-artificial-analyst, [...]

The driving risk is assessed using the theory of unascertained measures to determine the presence of a conditional switch in the control system of a human-machine codriving vehicle. Relevant risk indicators for driving are selected, including five driver-related indicators and three vehicle-related indicators. Subsequently, each indicator's threshold range and associated risk level are analyzed and defined. The methodologies for establishing unascertained measure and their corresponding functions for both single and multiple indicator unascertained measure are then elucidated. A game theory-based weighting method is proposed, employing ordinal relationship analysis (ORA) and entropy weighting (EW) to determine indicator weights while utilizing confidence identification criteria to ascertain risk levels. Finally, experimental analyses are conducted on the driving risk assessment model, and the simulation results demonstrated the model's ability to distinguish between normal and risky driving. In a continuous driving simulation, the model successfully identified a peak risk period (Level V) and, following a system alert, driving behavior returned to normal risk levels within 5min. The model demonstrated utility for control switching decisions in human-machine codriving scenarios, identifying instances where driver risk (Level IV) significantly exceeded vehicle risk (Level II), indicating a need to transfer control to the vehicle system. Consequently, the study's findings can provide theoretical support for control switching mechanisms in human-machine codriving vehicles., Author(s): Lingyu Zhang [1]; Dehui Sun [1]; Lili Zhang (corresponding author) [2]; Li Wang [1] 1. Introduction The widespread adoption of automobiles has significantly impacted human productivity and well-being, while [...]

Semiactive control of rotor motion in actively lubricated radial hydrostatic–dynamic bearings is considered. Open semiactive control of shaft motion in the bearing is based on neural network approximation of data regarding the relation between positions of rotor equilibrium and the pressure in the lubrication channels. The precision of rotor positioning in the bearing is determined, and the influence of setpoint selection on the energy parameters of the bearing is established, taking account of the rotor speed. Energy parameters considered include the frictional torque in the lubricant layer and the power consumption in lubricant delivery. [ABSTRACT FROM AUTHOR]

Automatics and Robotics students usually engage in some interdisciplinary approaches on their courses at universities, joining the areas of computer science and control theory. During the limited duration of these courses, some knowledge must be acquired by the students. Remote education, temporarily enforced by the COVID-19 pandemic in recent years, has posed new challenges related to the limited contact that students have with academic staff, as well as the limited access students have to hardware facilities. This paper discusses the motivations of students after the COVID-19 pandemic based on a case study of student projects completed at the University of Zielona Gora (Poland). The conducted study was student-centered and highlighted some important aspects of motivation from the point of view of the students, focusing mostly on the emerging challenges, how they have been overcome, and what the students have learned from them. The results indicate that the students, minimally supported by an academic teacher, were able to plan everything by themselves and bring the project to completion according to their own intuition. In comparison to before the COVID-19 pandemic, students are now more independent and self-motivated. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Internet of Things is currently one of the fastest-growing branches of computer science. The development of 5G wireless networks and modern data transmission protocols offers excellent opportunities for rapid development in this field. The article presents an overview of monitoring and control systems based on the Internet of Things. The authors discuss various aspects of these systems, including their architecture, applications, and challenges. We focus on analyzing the latest achievements in this field, considering technological innovations and practical applications in various sectors. Also, we emphasize the importance of integrating data from multiple sources and developing data analysis algorithms to ensure the effectiveness and precision of IoT-based monitoring and control systems. The article provides a valuable overview of the current state of knowledge in this dynamic area, inspiring further research and technological development. It also includes case studies showing various IoT device applications and energy consumption management. [ABSTRACT FROM AUTHOR]

Improving the efficiency and performance of control systems in food processing remains a significant challenge for engineers and researchers. In this paper, Proportional, Integral, and Derivative (PID) control; Model Predictive Control (MPC); and Adaptive Model Predictive Control (AMPC) were implemented on a Continuous Flow Ohmic Heater (CFOH) pilot plant to process tomato basil sauce. The sauce, composed of tomato puree, basil, spices, and other ingredients, was used to assess the effectiveness of these advanced control strategies. This research presents a case study on the pilot-scale heating of tomato basil sauce, with applications in the broader food industry. The performances and energy efficiencies of the different control techniques were compared, demonstrating significant improvements in controlling the CFOH process. The results highlight the industrial practicality of using CFOH technology with advanced process controls for food processing. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of digital technologies has driven a profound transformation in both individual lives and business operations. The integration of Industry 4.0 with advanced mechatronic systems is at the forefront of this digital transformation, reshaping the landscape of smart manufacturing. This article explores the convergence of digital technologies and physical systems, with a focus on the critical role of mechatronics in enabling this transformation. Using technologies such as advanced robotics, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, industries are developing intelligent and interconnected systems capable of real-time data exchange, distributed decision making, and automation. The paper further explores two case studies: one on a smart plastic injection moulding machine and another on soft robots. These examples illustrate the synergies, benefits, challenges, and future potential of integrating mechatronics with Industry 4.0 technologies. Ultimately, this convergence fosters the development of smart factories and products, enhancing manufacturing efficiency, adaptability, and productivity, while also contributing to sustainability by reducing waste, optimising resource usage, and lowering the environmental impact of industrial production. This marks a significant shift in industrial production towards more sustainable practices. [ABSTRACT FROM AUTHOR]

This research presents a pioneering approach in virtual control systems, focusing on developing a highly intuitive Graphical User Interface (GUI) for real-time PID control of LED brightness using Arduino IDE. By embracing a robust pedagogical framework, the study meticulously outlines specific learning objectives that adhere to the SMART criteria, ensuring precise educational outcomes. The key to this research is the tailored design of the GUI, which considers diverse audience needs, prior knowledge, and learning styles. The interfaces are meticulously crafted for optimal user experience, ensuring seamless navigation and engagement. Interactive elements, encompassing simulations, virtual experiments, problem-solving scenarios, and quizzes, foster active participation and hands-on learning. Multimedia elements, such as text, images, videos, animations, and audio, are thoughtfully integrated to cater to varied learning preferences. This paper underscores the critical role of collaborative learning facilitated through discussion forums, chat functionalities, and collaborative virtual environments. The study emphasizes continuous improvement through regular assessments and detailed user feedback analysis. Notably, the research highlights the significant educational impact of the designed GUI. By offering a practical platform where students can dynamically adjust PID parameters and observe immediate effects on LED brightness, the GUI enhances the learning experience in classroom settings. This approach equips students with a tangible understanding of complex PID principles, preparing them for real-world applications. In summary, this research showcases the creation of engaging, practical virtual control system tools, fostering profound and meaningful learning experiences for users within educational contexts. [ABSTRACT FROM AUTHOR]

With recent advances in airborne weapons, modern air combats tend to be accomplished in the beyond-visual-range (BVR) phase. Multiaircraft cooperation is also required to adapt to the complexities of modern air combats. The scale of the traditional rule-based expert system will become incredible in this case. In view of this, a mixed-reward multiagent proximal policy optimization (MRMAPPO) method is proposed in this article that is used to help train cooperative BVR air combat tactics via adversarial self-play. First, a two-on-two BVR air combat simulation platform is established, and the combat game is modeled as a Markov game. Second, centralized training with decentralized execution architecture is established. Multiple actors are involved in the architecture, each corresponding to a policy that generates a specified kind of command, e.g., the maneuvering and firing command. Moreover, in order to accelerate training as well as enhance the stability of the training process, four optimization mechanisms are introduced. The experimental section discusses how the effectiveness of the MRMAPPO is verified with comparative and ablation experiments, along with several air combat tactics that emerge in the training process. [ABSTRACT FROM AUTHOR]

This paper explores nonlinear control systems within a finite dimensional Euclidean space. To guide the system towards a compact set at a specific time instant, the approach presented in this paper relies on the construction of approximate solvability sets. The Runge–Kutta methods, coupled with distance clustering, is employed to construct these approximate solvability sets. Simultaneously, the corresponding controls are recorded. The effectiveness of our theoretical results is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

In this paper, hybrid feed-forward deep neural network and ANFIS framework is developed to control an active suspension system of half vehicle. The dataset were generated from previous literature that studies driver comfort on different road profiles. The deep neural network aims to learn intricate relationships within dataset between features and output. The deep neural network was trained using back propagation algorithm and automated search method was implemented to obtain optimum network structure. The paper starts generating various road roughness profiles according to ISO 8608. Then, through comprehensive examination of rear and front body displacements and pitch angle accelerations, the study highlights system's significant contributions to ride comfort and vehicle dynamics. The proposed framework outperforms other controllers like proportional-integral-derivative (PID), demonstrating its robustness across different road profiles. The results demonstrated effectiveness of proposed control to minimize peak overshooting and settling times which improves ride comfort and stability significantly. Also, the proposed model has small root mean square error (RMSE) values which indicate smoother and less energetic responses, which are typically preferred for passenger comfort. Furthermore, the adaptive neural fuzzy inference system-deep neural network (ANFIS-DNN) has minimum crest factor (CF) which indicates that the signal has fewer peaks relative to its average. [ABSTRACT FROM AUTHOR]

Sparse rewards and sample efficiency are open areas of research in the field of reinforcement learning. These problems are especially important when considering applications of reinforcement learning to robotics and other cyber-physical systems. This is so because in these domains many tasks are goal-based and naturally expressed with binary successes and failures, action spaces are large and continuous, and real interactions with the environment are limited. In this work, we propose Deep Value-and-Predictive-Model Control (DVPMC), a model-based predictive reinforcement learning algorithm for continuous control that uses system identification, value function approximation and sampling-based optimization to select actions. The algorithm is evaluated on a dense reward and a sparse reward task. We show that it can match the performance of a predictive control approach to the dense reward problem, and outperforms model-free and model-based learning algorithms on the sparse reward task on the metrics of sample efficiency and performance. We verify the performance of an agent trained in simulation using DVPMC on a real robot playing the reach-avoid game. Video of the experiment can be found here: . [ABSTRACT FROM AUTHOR]

Formula Student (FS) competitions aim to prepare and encourage engineering students to participate in the progression of automotive and motorsport industries. The built racing cars adhere to strict regulations set by competition guidelines to ensure the safety of both teams and spectators. For electric racing cars, the high-voltage (HV) battery system usually operates within a voltage range between 100 V to 600 V to supply the motor and its controller with the required electrical power. It is essential to ensure that these components are operating effectively to minimize battery and motor current as well as to ensure efficient and reliable performance throughout the race. A low-voltage control system (LVCS), usually operating at 12 V, is used to coordinate a wide array of critical operational and safety functions to control the HV system. These functions include: (1) turning on/off procedures, (2) monitoring speed, voltage, and current, (3) interfacing with pedals, (4) controlling dashboard features, (5) managing lighting, (6) facilitating data communication, and (7) implementing safety protocols. The design and operation of the LVCS are crucial for compliance with safety regulations and enhancing the FS electric racing car (FSERC) performance. This details and discusses the design procedures of the LVCS, using the Lancaster E-Racing (LER) FSERC as a case study. The LER car employs a 400 V battery system to power a 68-kW permanent manet synchronous motor (PMSM) using a three-phase voltage source inverter. Using mathematical analysis, SIMULINK/MATLAB® computer simulations, and the experimental real-data results provided by the LER FSERC, this study seeks to offer valuable insights regarding the LVCS practical implementation and optimization. [ABSTRACT FROM AUTHOR]

In research work the problem of studying the process of ozone control with the help of electronic sensors is considered. In research work, special sensors were used, which are formed around coronary electrodes in the ozonator and to monitor the concentration of ozone in the room. This is because ozone is known to adversely affect human health if its maximum permissible air concentration exceeds 0.16 mg/m3. A small system of ozonators was developed in a special laboratory, theoretical and experimental tests were carried out. In practice, the obtained data and the electric diagram of the ozonator (on the ARDUINO platform) were collected. "Prana Air" sensors and current sensors were used to accurately determine the ozone (O3) concentration around the ozone nozzle to measure the current at the electrodes.

Abstract Applications of electrospinning (ES) range from fabrication of biomedical devices and tissue regeneration scaffolds to light manipulation and energy conversion, and even to deposition of materials that act as growth platforms for nanoscale catalysis. One major limitation to wide adoption of ES is stochastic fiber deposition resulting from the chaotic motion of the polymer stream as is approaches the deposition surface. In the past, fabrication of structures or materials with precisely determined mesoscale morphology has been accomplished through modification of electrode shape, use of multi-dimensional electrodes or pins, deposition onto weaving looms, hand-held electrospinning devices that allow the user to guide deposition, or electric field manipulation by lensing elements or apertures. In this work, we demonstrate an ES system that contains multiple high voltage power supplies that are independently controlled through a control algorithm implemented in LabVIEW. The end result is what we term “multiplex ES” where multiple independently controlled high-voltage signals are combined by the ES fiber to result in unique deposition control. COMSOL Multiphysics® software was used to model the electric field produced in this novel ES system. Using the multi-power supply system, we demonstrate fabrication of woven fiber materials that do not require complex deposition surfaces. Time-varied sinusoidal wave inputs were used to create electrospun torus shapes. The outer diameter of the tori was found, through parametric analysis, to be rather insensitive to frequency used during deposition, while inner diameter was inversely related to frequency, resulting in overall width of the tori increasing with frequency. Multiplex ES has a high-frequency cutoff based on the time response of the high voltage electrical circuit. These time constants were measured and minimized through the addition of parallel resistors that decreased impedance of the system and improved the high-frequency cutoff by up to 63%.

The LAPARA System, a Philippine-made robotic surgical system, tested its control system in this paper. Three types of tests are then done to the system: PID Optimization Test, Position Checking, and Data Transfer Rate and Memory Bandwidth Testing. Results from the PID resulted in the values 2.32 for the P, 0.4 for the I, and 1.5 for the D to be chosen to ensure the system runs smoothly. The system was also able to run properly during Position Checking, though movement in the Pitch and Yaw required refinement due to the constraints. Also, the data transfer rate for the PC to Arduino Due connection yielded a 128kb/s speed, slower than the 480 Mbps rating, while the memory bandwidth testing yielded results that allowed for storage of 23,040 32_bit values. In conclusion, although minor adjustments were needed to refine the system, the LAPARA system was able to perform as intended.