Self-Scheduled LPV Control of Asymmetric Variable-Span Morphing UAV.

In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme.

Parameterized Convex Universal Approximators for Decision-Making Problems.

Parameterized max-affine (PMA) and parameterized log-sum-exp (PLSE) networks are proposed for general decision-making problems. The proposed approximators generalize existing convex approximators, namely max-affine (MA) and log-sum-exp (LSE) networks, by considering function arguments of condition and decision variables and replacing the network parameters of MA and LSE networks with continuous functions with respect to the condition variable. The universal approximation theorem (UAT) of PMA and PLSE is proved, which implies that PMA and PLSE are shape-preserving universal approximators for parameterized convex continuous functions. Practical guidelines for incorporating deep neural networks within PMA and PLSE networks are provided. A numerical simulation is performed to demonstrate the performance of the proposed approximators. The simulation results support that PLSE outperforms other existing approximators in terms of a minimizer and optimal value errors with scalable and efficient computation for high-dimensional cases.

A Pilot Study on the Evaluation of Physicians' Laser Delivery Performance Using a Laser Beam Detection Kit.

BACKGROUND AND OBJECTIVE: Many laser devices have been developed over the past decades for various skin conditions. However, variations in the technical skill of physicians for laser skin treatment delivery have not yet been evaluated. This study evaluates the differences in omission and overlap percentages during simulated laser hair removal treatments among physicians at two clinics. MATERIALS AND METHODS: A laser beam detection kit was developed to record and collect laser irradiation from a diode laser device. Eight physicians (primary private clinic 4, tertiary referral hospital 4) were recruited to perform 80 trials of laser delivery simulation. The simulation process was captured in video frames by a camera built inside of the detection kit. The laser distribution map was reconstructed, and each physician's performance result was determined by a computer calculation. RESULTS: Various assumption tests showed that each physician had different laser delivery skills. Four physicians from clinic A had an average omission rate of 13.4%, and four physicians from clinic B had an average omission rate of 19.7%. Regarding the average overlap rate of the two clinics, clinic A had a higher rate than clinic B (26.1% vs. 14.6%). CONCLUSIONS: The study's findings confirmed the differences of the technical skills among the physicians and between the two clinics. The proposed computer-assisted evaluation of technical skill is useful for assessing physicians' performance during laser skin treatments.

Development of a Novel Automated Hair Counting System for the Quantitative Evaluation of Laser Hair Removal.

OBJECTIVE: We aimed to develop and validate a novel computer-assisted automated hair counting system for the quantitative evaluation of laser hair removal (LHR). METHODS: We developed a computer-aided image processing system to count hairs on shaved skin and validated its performance through clinical trials. Five volunteers of Fitzpatrick skin type III-IV volunteered and were tested on both thighs. The system automatically detects hair and places a "+" sign on each hair site for every positive detection. This method allows clinicians to check whether a hair has been counted or not. We analyzed the difference in the hair counts between the proposed system (automatic) and those by human observers (manual). RESULTS: The hair counts from the proposed system and the manual counts were compared. The percentage error between automatic and manual counting was <5% in each subject. The data of the two groups were statistically verified with Student's independent t-test. The averages were statistically equivalent between the two groups. The proposed system showed significant time saving in terms of counting. CONCLUSIONS: A dependable, accurate, and fast method of counting hairs on shaved skin through a computer-aided image processing system was developed and validated. The "+" signs on the image to indicate detection allows clinicians to compare with the original image and detect any omission or redundancy. The proposed system is expected to be reliable in analyzing the results of multiple skin-related treatments, including LHR and hair transplantation. Further, it is expected to be widely applicable for use in the clinic.

Improvement in Laser-Irradiation Efficiency of Robot-Assisted Laser Hair Removal Through Pose Measurement of Skin Surface.

OBJECTIVE: This study aims to improve the performance of an automatic laser hair removal (LHR) system by applying an algorithm that considers the curve and slant of the skin surface. BACKGROUND DATA: In an earlier research, a robot-assisted LHR system has been developed and validated for an almost flat skin or a relatively smooth curved part of the skin. For practical clinical applications, the feature of the robot-assisted LHR system is extended for real curved skins. METHODS: A novel pose-measurement algorithm is developed and applied to the LHR system. This system detects a six-degree of freedom (DOF) pose of the skin surface using the pose-measurement algorithm. The main principle of this algorithm is finding the equation of a plane using three noncollinear points, which are obtained by sequential movement of a one dimensional laser sensor. RESULTS: Evaluation of the proposed system was conducted. During the test, we demonstrated that the LHR device automatically and completely contacted the targets along the curved surface. The contact-accuracy test produced satisfactory outcome. The averages of the root mean square (RMS) of the position error and the RMS of the rotation were 1.4437 mm and 1.0982 degrees, respectively. The curvature measurement test produced a satisfactory average result of 0.0006 mm RMS error. CONCLUSIONS: Using the proposed six-DOF pose-measurement algorithm, the performance of the robot-assisted LHR system could be significantly improved from the clinical point of view because most real skins have curved shapes.

Comparison of Efficacy Between Novel Robot-Assisted Laser Hair Removal and Physician-Directed Hair Removal.

OBJECTIVE: This study aimed to evaluate the number of laser irradiation sessions, process duration, and hair removal rate required for robot-assisted automatic versus physician-directed laser hair removal. BACKGROUND DATA: This research group previously developed and tested an automatic laser hair removal (LHR) system to provide uniform laser treatment distribution. METHODS: Six subjects 20-40 years of age, with skin types III-IV completed this study. A home-use LHR device with an 810 nm diode laser was used to treat equal-sized areas of both upper thighs; a random computer generator determined the use of a robot-assisted automatic LHR system or physician-directed LHR on the right or left thigh. The treatment schedule comprised five visits; subjects were photographed and shaved, and received LHR during the first through the fourth visits at 2-week intervals. The fifth visit occurred 1 month after the fourth, and only involved photography. RESULTS: All subjects successfully completed the clinical trial with no noticeable or permanent side effects. The average hair removal rates were 49.0% (standard error of the mean [SEM]: 4.0) and 29.5% (SEM: 4.0) for robot-assisted and physician-directed LHR, respectively. The average treatment duration and number of irradiation shots were 18 min, 30 sec (SEM: 33 sec) and 260 (SEM: 5.7) for robot-assisted LHR and 3 min, 11 sec (SEM: 15 sec) and 73 (SEM: 5.9) for physician-directed LHR. CONCLUSIONS: This clinical study successfully demonstrated the safety and effectiveness of robot-assisted LHR. The proposed novel system will benefit both patients and clinicians.

Computational Discrimination of Breast Cancer for Korean Women Based on Epidemiologic Data Only.

Breast cancer is the second leading cancer for Korean women and its incidence rate has been increasing annually. If early diagnosis were implemented with epidemiologic data, the women could easily assess breast cancer risk using internet. National Cancer Institute in the United States has released a Web-based Breast Cancer Risk Assessment Tool based on Gail model. However, it is inapplicable directly to Korean women since breast cancer risk is dependent on race. Also, it shows low accuracy (58%-59%). In this study, breast cancer discrimination models for Korean women are developed using only epidemiological case-control data (n = 4,574). The models are configured by different classification techniques: support vector machine, artificial neural network, and Bayesian network. A 1,000-time repeated random sub-sampling validation is performed for diverse parameter conditions, respectively. The performance is evaluated and compared as an area under the receiver operating characteristic curve (AUC). According to age group and classification techniques, AUC, accuracy, sensitivity, specificity, and calculation time of all models were calculated and compared. Although the support vector machine took the longest calculation time, the highest classification performance has been achieved in the case of women older than 50 yr (AUC = 64%). The proposed model is dependent on demographic characteristics, reproductive factors, and lifestyle habits without using any clinical or genetic test. It is expected that the model could be implemented as a web-based discrimination tool for breast cancer. This tool can encourage potential breast cancer prone women to go the hospital for diagnostic tests.

Formation Flight of Multiple UAVs via Onboard Sensor Information Sharing.

To monitor large areas or simultaneously measure multiple points, multiple unmanned aerial vehicles (UAVs) must be flown in formation. To perform such flights, sensor information generated by each UAV should be shared via communications. Although a variety of studies have focused on the algorithms for formation flight, these studies have mainly demonstrated the performance of formation flight using numerical simulations or ground robots, which do not reflect the dynamic characteristics of UAVs. In this study, an onboard sensor information sharing system and formation flight algorithms for multiple UAVs are proposed. The communication delays of radiofrequency (RF) telemetry are analyzed to enable the implementation of the onboard sensor information sharing system. Using the sensor information sharing, the formation guidance law for multiple UAVs, which includes both a circular and close formation, is designed. The hardware system, which includes avionics and an airframe, is constructed for the proposed multi-UAV platform. A numerical simulation is performed to demonstrate the performance of the formation flight guidance and control system for multiple UAVs. Finally, a flight test is conducted to verify the proposed algorithm for the multi-UAV system.

A grip force model for the da Vinci end-effector to predict a compensation force.

A torque transfer system (TTS) that measures grip forces is developed to resolve a potential drawback of the current da Vinci robot system whose grip forces vary according to the different postures of its EndoWrist. A preliminary model of EndoWrist Inner Mechanism Model (EIMM) is also developed and validated with real grip force measurements. EndoWrist's grip forces, posture angles, and transferred torque are measured by using TTS. The mean measured grip forces of three different EndoWrist for 27 different postures were very diverse. The EndoWrist exerted different grip forces, with a minimum of 1.84-times more and a maximum of 3.37-times more in specific posture even if the surgeon exerted the same amount of force. Using the posture angles as input and the grip forces as output, the EIMM is constructed. Then, expected grip force values obtained from EIMM are compared with actual measurements of da Vinci EndoWrist to validate the proposed model. From these results, surgeons will be beneficial with the understandings of actual grip force being applied to tissue and mechanical properties of robotic system. The EIMM could provide a baseline in designing a force-feedback system for surgical robot. These are significantly important to prevent serious injury by maintaining a proper force to tissue.

A study on the development of a robot-assisted automatic laser hair removal system.

UNLABELLED: Abstract Background and Objective: The robot-assisted automatic laser hair removal (LHR) system is developed to automatically detect any arbitrary shape of the desired LHR treatment area and to provide uniform laser irradiation to the designated skin area. METHODS: For uniform delivery of laser energy, a unit of a commercial LHR device, a laser distance sensor, and a high-resolution webcam are attached at the six axis industrial robot's end-effector, which can be easily controlled using a graphical user interface (GUI). During the treatment, the system provides real-time treatment progress as well as the total number of "pick and place" automatically. RESULTS: During the test, it was demonstrated that the arbitrary shapes were detected, and that the laser was delivered uniformly. The localization error test and the area-per-spot test produced satisfactory outcome averages of 1.04 mm error and 38.22 mm(2)/spot, respectively. CONCLUSIONS: RESULTS showed that the system successfully demonstrated accuracy and effectiveness. The proposed system is expected to become a promising device in LHR treatment.

Pneumatic-type surgical robot end-effector for laparoscopic surgical-operation-by-wire.

BACKGROUND: Although minimally invasive surgery (MIS) affords several advantages compared to conventional open surgery, robotic MIS systems still have many limitations. One of the limitations is the non-uniform gripping force due to mechanical strings of the existing systems. To overcome this limitation, a surgical instrument with a pneumatic gripping system consisting of a compressor, catheter balloon, micro motor, and other parts is developed. METHOD: This study aims to implement a surgical instrument with a pneumatic gripping system and pitching/yawing joints using micro motors and without mechanical strings based on the surgical-operation-by-wire (SOBW) concept. A 6-axis external arm for increasing degrees of freedom (DOFs) is integrated with the surgical instrument using LabVIEW® for laparoscopic procedures. The gripping force is measured over a wide range of pressures and compared with the simulated ideal step function. Furthermore, a kinematic analysis is conducted. To validate and evaluate the system's clinical applicability, a simple peg task experiment and workspace identification experiment are performed with five novice volunteers using the fundamentals of laparoscopic surgery (FLS) board kit. The master interface of the proposed system employs the hands-on-throttle-and-stick (HOTAS) controller used in aerospace engineering. To develop an improved HOTAS (iHOTAS) controller, 6-axis force/torque sensor was integrated in the special housing. RESULTS: The mean gripping force (after 1,000 repetitions) at a pressure of 0.3 MPa was measured to be 5.8 N. The reaction time was found to be 0.4 s, which is almost real-time. All novice volunteers could complete the simple peg task within a mean time of 176 s, and none of them exceeded the 300 s cut-off time. The system's workspace was calculated to be 11,157.0 cm3. CONCLUSIONS: The proposed pneumatic gripping system provides a force consistent with that of other robotic MIS systems. It provides near real-time control. It is more durable than the existing other surgical robot systems. Its workspace is sufficient for clinical surgery. Therefore, the proposed system is expected to be widely used for laparoscopic robotic surgery. This research using iHOTAS will be applied to the tactile force feedback system for surgeon's safe operation.

Tool to visualize and evaluate operator proficiency in laser hair-removal treatments.

BACKGROUND: The uniform delivery of laser energy is particularly important for safe and effective laser hair removal (LHR) treatment. Although it is necessary to quantitatively assess the spatial distribution of the delivered laser, laser spots are difficult to trace owing to a lack of visual cues. This study proposes a novel preclinic tool to evaluate operator proficiency in LHR treatment and applies this tool to train novice operators and compare two different treatment techniques (sliding versus spot-by-spot). METHODS: A simulation bed is constructed to visualize the irradiated laser spots. Six novice operators are recruited to perform four sessions of simulation while changing the treatment techniques and the presence of feedback (sliding without feedback, sliding with feedback, spot-by-spot without feedback, and spot-by-spot with feedback). Laser distribution maps (LDMs) are reconstructed through a series of images processed from the recorded video for each simulation session. Then, an experienced dermatologist classifies the collected LDMs into three different performance groups, which are quantitatively analyzed in terms of four performance indices. RESULTS: The performance groups are characterized by using a combination of four proposed indices. The best-performing group exhibited the lowest amount of randomness in laser delivery and accurate estimation of mean spot distances. The training was only effective in the sliding treatment technique. After the training, omission errors decreased by 6.32% and better estimation of the mean spot distance of the actual size of the laser-emitting window was achieved. Gels required operators to be trained when the spot-by-spot technique was used, and imposed difficulties in maintaining regular laser delivery when the sliding technique was used. CONCLUSIONS: Because the proposed system is simple and highly affordable, it is expected to benefit many operators in clinics to train and maintain skilled performance in LHR treatment, which will eventually lead to accomplishing a uniform laser delivery for safe and effective LHR treatment.

Adaptive support vector regression for UAV flight control.

This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model.