Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. A large number of numerical computations are performed then, which are required to model the involved neuronal dynamics in software.Collision avoidance is one of the most basic tasks in mobile robotics that ensures safety of the robotic platform, as well as the objects and users around it. Biological neural processing systems, including relatively small ones such as those of insects, are impressive in their ability to avoid obstacles robustly at high speeds in complex dynamical environments. Relatively simple neuronal architectures have already been proposed to implement robust obstacle avoidance   and the ROLLS neuromorphic processor. A miniature computing board Parallella is used to direct the flow of events between the robot and the ROLLS. Figure The Pushbot communicates with the Parallella board via a wireless interface for receiving motor commands and for sending address-events produced by the DVS. Using a dedicated WiFi network, we achieve communication latency below 10 ms, which was enough to demonstrate functionality of our system at speeds, possible with the Pushbot.The ROLLS device is interfaced to the Parallella board using an embedded FPGA, which is used to configure the neural network connectivity on the chip and to direct stimulating events to neurons and synapses in real time. The Parallella board runs a simple program that manages the stream of events between the neuromorphic processor and the robot.The ROLLS is a mixed signal analog/digital neuromorphic chip . This means that in a neuronal architecture, several different populations might have to share weights, which limits the complexity of the architecture. ROLLS consumes ~4 mW of power in typical experiments, run here. The ROLLS parameters (biases) used in this work are listed in the Appendix  is an event-based camera, inspired by the mammalian retina . Each event e is a vector: e = , where x and y define the pixel location in retinal reference frame, ts is the time stamp, and p is the polarity of the event. The event polarity encodes whether the luminance of the pixel increased (an “on” event) or decreased (an “off” event). All pixels share a common transmission bus, which uses the Address Event Representation (AER) protocol to transmit the address-events off chip.Each pixel of the DVS is sensitive to a relative temporal contrast change. If such change is detected, each pixel sends out an The AER representation and asynchronous nature of communication makes this sensor low power, low latency, and low-bandwidth, as the amount of data transmitted is very small . Indeed, if there is no change in the visual scene, no information is transmitted off the camera. If a change is detected, it is communicated instantaneously, taking only a few microseconds to transfer the data off-chip.For the obstacle avoidance scenario, important properties of the DVS are its low data rate, high dynamic range, and small sensitivity to lighting conditions  camera . The predominant component on the small robot is an eDVS (Section 2.2), which acquires and provides sensory information and controls actuator output, including the robot's motors, through its embedded microcontroller. The sensor's integrated 9 DOF IMU reports changes of velocity and orientation. The robot actuators include a buzzer, two parallel, horizontal forward laser pointers and an LED on top, which all can show arbitrary activation patterns. The Pushbot is powered by 4 AA-batteries, which ensure ~2 h operation time.The robot communicates through WLAN at up to 12 Mbps, which allows remote reading of sensory data (including events from the eDVS) and setting velocities with a latency <10 ms. The Pushbot robot is too small to carry the current experimental hardware setup. In principle, however, it is possible to place the ROLLS chip directly on a robot, removing the WiFi latency.The core of the system presented here is a simple neural network architecture that is realized in the ROLLS device and allows the robot to avoid obstacles and approach a simple target. The “connectionist” scheme of the obstacle avoidance part of the architecture is shown in Figure For obstacle avoidance, we configured two neuronal populations of 16 neurons each to represent a sensed obstacle to the right  and to the left  from the robot's heading direction. Each neuron in the OL and OR populations receives a spike for each DVS pixel that produces an event in the left (right) part of the sensor, respectively . The spiking neurons in the two obstacle populations sum up the camera events according to their neuronal integrate-and-fire dynamics . Consequently, if an obstacle is detected on the right, the drive left population is stimulated, and vice versa. The drive populations inhibit each other, implementing a winner-take-all dynamics. Thus, a decision about the direction of an obstacle-avoiding movement is taken and stabilized at this stage by the dynamics of neuronal populations on the chip.drive populations, in their turn, inhibit both obstacle detecting populations, since during a turning movement of the robot, many more events are generated by the DVS, compared to those generated during translational motion. This inhibition compensates for this expected increase in the input rate, similar to the motor re-afferent signals in biological neural systems , and, second, the forward velocity, calculated based on the number of spikes per neuron emitted by the speed population (Equation 2):The control signals sent to the robot are, first, the angular velocity, cturn and cspeed are turn- and speed-factors (user-defined constants), respectively.where Thus, we used neural population dynamics to represent angular and translational velocities of the robot and used the firing rate of the respective populations of neurons as the control variable.To represent targets of the navigation dynamics, we use a Dynamic Neural Fields (DNFs) architecture as defined in Bicho et al. . DNFs arIn our architecture, we select 128 neurons on the ROLLS chip to represent visually perceived targets. Each neuron in this population receives events from the upper half of each column of the 128 × 128 sensor frame from the eDVS and integrates these events according to its neuronal dynamics: only events that consistently are emitted from the same column lead to firing of the neuron. The nearby neurons support each other's activation, while inhibiting further neurons in the WTA population Figure .This connectivity stabilizes localized blobs of most salient sensory events, filtering out sensor noise and objects that are too large (inhibition starts to play role within object representation) or too small . Thus, the WTA connectivity stabilizes the target representation. The target in our experiments was a blinking LED of the second robot, which was detected in the DNF realized on the ROLLS. While this target could be easily detected since the blinking LED produces many events, more sophisticated vision algorithms are being developed to pursue an arbitrary target  sum-up contributions from the obstacle-representing populations and the target-representing WTA population Figure . The obsFigure This connectivity matrix is sent to the ROLLS device to configure the neuronal architecture on the chip, i.e., to “program” the device.We verified the performance of our system in a number of demonstrations, reported next. Overall, over 100 runs were performed with different parameter settings. In the following, we will provide an overview for the experiments and describe a few of them in greater detail to provide intuition of how the neural architecture works. For most experiments, we let the robot drive in a robotic arena with a white background and salient obstacles. We used a tape with a contrastive texture to make the walls of the arena visible to the robot. In four runs, we let the robot drive for several minutes freely in the office.In the first set of experiments, we let the robot drive straight toward a single object (a colored block 2.5 cm wide and 10 cm high) and measured the distance from the object at which the robot crossed a virtual line perpendicular to the robot's initial heading direction, on which the object is located , especially for red and yellow objects, due to an insufficient number of DVS events to drive the neuronal populations on ROLLS. Second, there is a trade-off between this effect and the expected decay in performance (in terms of the decreasing distance to the obstacle) with increasing speed. Thus, at a turning factor 0.5, best performance is achieved for the blue object at speed factor 0.5 and for the red object at speed factor 1. Distance to the obstacle can be further increased by increasing the turn factor. Thus, at turn factor 1 and speed factor 1 best performance  can be achieved for both the blue and red objects. Yellow object provides too little contrast to be reliably perceived by the DVS in our set-up.Figure We have performed several further trials, varying the lighting conditions  and parameters of the architecture. Since the architecture uses the difference in spiking activity, induced by sensory events from the two halves of the visual space, avoiding a single obstacle works robustly, although the camera might miss objects with a low contrast . More advanced noise filtering would improve performance. While more extended version of the performed tests will be reported elsewhere, Figure We repeated the controlled obstacle avoidance experiment with two and three blocks in different positions. Each configuration was tested twenty times without crashes at speed 0.35 m/s (speed factor 0.5).how the robot avoids a pair of obstacles. This example is important, since in the attractor dynamics approach to navigation, distance between the two objects determines a decision to move around or between the objects.Figure Snapshots from the overhead camera are shown on the left of Figure drive left population and now the obstacle on the right becomes visible, providing a strong signal to the right obstacle population and, consequently, to the drive left population (second and third row). Eventually, the obstacle on the right dominates and the robot drives past both obstacles on the left side (forth row).At the moment, depicted in the top row of Figure Thus, with the chosen parametrization of the neuronal network architecture, the robot tends to go around a pair of objects, avoiding the space between them. This behavior could be changed, making the connections between the obstacle representing populations and drive populations stronger. However, for a robot equipped with a DVS, such strategy is safer, since for homogeneous objects, the DVS can only sense the edges, where a temporal contrast change can be induced by the robot's motion. The robot thus might miss the central part of an object and avoiding pairs of close objects is a safer strategy. Adaptive connectivity that depends on the robot speed is also feasible.In these experiment, the robot is driving straight in the arena while we move an obstacle (a coffee mug) into its path. We repeat this experiment six times with varying speed factors (0.1–2) of the robot. The robot was capable to avoid collisions in all tested cases. In fact, avoiding a moving obstacle is more robust than avoiding a static obstacle because the moving obstacle produces more DVS events than a static one at the same robot speed.cspeed = 0.5 (0.35 m/s) here, the cup was moved at ~0.20 m/s.Figure In the following set of experiments, we randomly placed obstacles (8–12 wooden pieces) in the arena and let the robot drive around at an average speed (0.35 m/s). We analyzed the performance of the architecture, suggesting a number of modifications to cope with its limitations.Figure To improve behavior in a cluttered environment, we modified the architecture, adding two more populations on the ROLLS chip, which receive input from the inertia measurement unit of the Pushbot and which suppress obstacle populations when the robot is turning. Moreover, we replaced the homogeneous connections between the obstacle and the drive populations with graded connections that become stronger for obstacles detected in the center than in the periphery of DVS field of view. This allows the robot to make shorter avoidance maneuvers and avoid obstacles in a denser configuration at a higher speed. Figure Since behavior of our robot is controlled by activity of neuronal populations, implemented in analog neuromorphic hardware, the behavior of the robot has some variability, even when exactly the same parameters of the architecture and the same hardware biases are used. Despite this variability, the robot's goal—avoiding obstacles—remains fulfilled. Such variability of behavior can be used as a drive for exploration, which may be exploited in learning scenarios in more complex architectures, built on top of our elementary obstacle avoidance system.Figure Finally, we tried our architecture outside of the arena as well. The robot was placed on the floor in the office and drove around avoiding both furniture and people. The high amount of background activity compared to the arena did not diminish the effectiveness of the architecture: in four 0.5–1.5-min long trials, the robot only crashes once after it maneuvered itself into a dark corner under a table where the DVS sensor could not provide sufficient information to recognize obstacles.ms around the moment in time in the corresponding snapshot on the left, and respective histograms of events from the center region, used for obstacle avoidance. Events above the mid-line of the eDVS field-of-view are shown with transparency to emphasize that they were not used for obstacle avoidance: only events from the region of the eDVS field-of-view between the two vertical lines in Figure Figure Histograms below the eDVS plots show the events from this region of the field of view, summed over the eDVS columns. These events drive the obstacle left (red colored part of the histogram) and obstacle right (blue part of the histogram) neuronal populations on the ROLLS chip.The right column shows activity of the neuronal populations on the ROLLS chip over time, as in the previous figures. Black vertical lines mark time moments that correspond to the three snapshots in the left column. These plots allow to see that although the left and right obstacle populations are often activated concurrently, only one of the drive populations (either left or right) is active at any moment, leading to a clear decision to turn in either direction in the presence of perceived obstacles. The speed plot shows that movement of the robot is not very smooth—it slows down and accelerates often based on the sensed presence of obstacles. This behavior is improved in the modified architecture, briefly described in Section 3.4.When driving around the office, robot faced very different lighting conditions, as can be seen already in the three snapshots presented here. This variation in lighting conditions did not effect obstacle avoidance in most cases, since the DVS is sensitive to relative change of each pixel's intensity, which varies less than the absolute intensity when the amount of ambient light changes. However, in an extreme case, shown in the lower snapshot in Figure In addition to obstacle avoidance we also tested target acquisition in ten experiments using a second robot with a blinking LED as target. The robot successfully turns and drives toward the target every time (at speed and turn factors = 0.5). In 8 out of 10 experiments the target is recognized as an obstacle when approached and is avoided; in two experiments, the robot failed to recognize target as obstacle after approaching it.Obviously, the simple visual preprocessing that we used did not allow us to distinguish the target from obstacles (other than through their position in the upper or lower part of the field-of-view of the DVS). Moreover, we would need an object detection algorithm to detect the target and segregate it from the background. This vision processing is outside the scope of our work, but there is a multitude of studies going in this direction . The leading robot was covered with white paper to reduce interference from the obstacle avoidance dynamics as the robots get close to each other .In the second row in Figure nexc population here). We can see that the target DNF (WTA) successfully selects the correct target in most cases, only loosing it from sight twice, as the robot receives particularly many DVS events from the background during turning. The lower part of this raster plot shows activity of the obstacle populations, the drive populations, and the speed population, thus the dynamics of the whole architecture can be seen here.This last plot shows spiking activity of 215 neurons of the ROLLS chip, used to drive the robot  and power-efficient (on the order of milliwatts) solutions, analogous to the ones used by insects. In contrast, more conventional obstacle-avoidance systems require a substantial amount of computing resources to process and store sensory data, detect obstacles, and compute motor commands. Neuromorphic implementation of such low-level processing will allow to use analog sensory signals directly, avoiding their digital representation and storage, while at the same time allowing to build complex neural-network based computing architectures, that could be used for solving cognitive tasks, such as task planning, map building, or object recognition.We consider the work proposed as a first feasibility study, which still has a number of limitations that we will address in our future work. The main limitation is variability of neuronal behavior because of parameter drift on the analog hardware: the parameters of the hardware neural network change the network properties as the experimental setup conditions  change. This is a serious limitation of the hardware used, which makes in challenging to implement complex architectures that have to balance contributions of different behavioral modules . We are currently working on algorithms and methods for automatically re-tuning these parameters in a principled fashion with optimization and machine learning techniques. In addition, we are designing new versions of the neuromorphic hardware with on-board stabilization of the chip parameters, and more resources for simplifying the fine-tuning process of the architectures. However, approach employed here—use of populations of artificial neurons in place of single nodes in the architecture—allowed us to generate behavior with the state of the art analog neuromorphic hardware.Apart from the hardware limitations, our simple architecture currently allows robust obstacle avoidance at moderate speeds (~0.35 m/s). Since the robot slows down when an obstacle is detected, movement appears to be “jerky.” Although the smoothness of the robot movement could be improved by tuning the coupling strength between the obstacle and drive populations, the best solution would involve improving the visual pre-processing stages. In our setup, the DVS detects local contrast changes and produces different amount of events depending on the objects in the environment, but also modulated by the robot translational and rotational movements. Currently we ignore about 80% of all DVS events to remove both noise and to reduce bandwidth. This very basic strategy improves the signal to noise ratio, because the architecture enhances the spatially and temporally coherent inputs and suppresses the effect of random inputs. However, we plan to study a more principled approach to pre-processing and noise reduction, and to investigate other biologically inspired architectures for obstacle avoidance, for example inspired by the fly's EMD (Elementary Motion Detector)  population will allow to stabilize the target representation, allowing it to form a “working memory,” which will support target acquisition behavior in cluttered environments. To still make the system reactive and allow it to follow the visible target, control of the strength of lateral interactions will be introduced, increasing their strength when target is being lost from view and decreasing their strength when the target is visible. Detecting the target based on its features perceived with a DVS is a separate topic of ongoing research both in our lab and worldwide (e.g., Lagorce et al., neural network implementations using the conventional computing architecture are typically time- and energy consuming, implementation of neuronal architecture using analog neuromorphic hardware approaches the efficiency of biological neural networks. Building neuronal models for higher cognitive function using, for instance, the framework of Dynamic Neural Fields (Sandamirskaya, Despite of this list of necessary improvements, our neuromorphic architecture is an important stepping stone toward robotic controllers, realized directly in neurally inspired hardware, being the first architecture for closed-loop robot navigation that uses analog neuromorphic processor and minimal preprocessing of visual input, obtained with a silicon retina DVS. Such neuromorphic controllers may become an energy efficient, fast, and adaptive alternative to conventional digital computers and microcontrollers used today to control both low-level and cognitive behaviors of robots. While MM: conceptualization of the model, analysis of the results, writing up. HB and AD: implementation of combined obstacle avoidance and target acquisition, experiments, results analysis, writing up; DS: implementation of first version of obstacle avoidance, parameter tuning on the chip, state of the art analysis; JC: support with robotic hardware and middleware, analysis of the results, writing up; GI: support with neuromorphic hardware, and state of the art and result analysis, writing up; YS: conceptualization of the model, development of the architecture, experiment design, analysis of the results, embedding in the literature, discussion of the results, writing, and overall supervision of the project.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We present a comprehensive analysis of estimation of fisheries Maximum Sustainable Yield (MSY) reference points using an ecosystem model built for Mille Lacs Lake, the second largest lake within Minnesota, USA. Data from single-species modelling output, extensive annual sampling for species abundances, annual catch-survey, stomach-content analysis for predatory-prey interactions, and expert opinions were brought together within the framework of an Ecopath with Ecosim (EwE) ecosystem model. An increase in the lake water temperature was observed in the last few decades; therefore, we also incorporated a temperature forcing function in the EwE model to capture the influences of changing temperature on the species composition and food web. The EwE model was fitted to abundance and catch time-series for the period 1985 to 2006. Using the ecosystem model, we estimated reference points for most of the fished species in the lake at single-species as well as ecosystem levels with and without considering the influence of temperature change; therefore, our analysis investigated the trophic and temperature effects on the reference points. The paper concludes that reference points such as MSY are not stationary, but change when (1) environmental conditions alter species productivity and (2) fishing on predators alters the compensatory response of their prey. Thus, it is necessary for the management to re-estimate or re-evaluate the reference points when changes in environmental conditions and/or major shifts in species abundance or community structure are observed. Management based on single-species models could tend to ignore the broader context in terms of species interactions and ecological and environmental processes within which the fishery operates , 2. SingA comparison of management reference points derived from single-species versus ecosystem approaches will allow a systematic comparison of management advice generated from adopting one or the other approach for management. Over the last half-century, MSY, the reference point we estimated, evolved from a target to a limit reference point and continues to be a popular metric . There hFew works have actually compared single-species (SS) and ecosystem (ES) based estimates of the MSY metric. After a comparison of SSmsy and ESmsy estimates, Walters et al.  using anTemperature is one of the most significant abiotic components of ecosystems because it plays dominant roles in determining the rate of the physiological process of organisms, especially cold-water species in the northern hemisphere , and theOur four objectives with the analysis were: (1) to estimate the biological reference points, MSY and Fmsy, for the principal fished species present in the study area under two frameworks—(a) Ecosystem MSY (ESmsy), where food-web dynamics were accounted for while estimating the MSY of the target species and (b) traditional single-species MSY (SSmsy), where it was assumed that the population dynamics of non-target species is unaffected as a result of fishing on the target species; (2) to explore the reasons for different values, if any, obtained for SSmsy and ESmsy i.e., the compensation that resulted in higher estimates of ESmsy or the costs, for example competition, that resulted in higher SSmsy levels; (3) to explore the influence of temperature fluctuations on the SSmsy and ESmsy estimates when the temperature forcing-function was applied to the dynamics of three species, cisco, burbot and smallmouth bass, and (4) to conduct a sensitivity analysis on the MSY estimates.Mille Lacs Lake (MLL), the second largest lake within Minnesota, is located in Aitkin, Crow Wing, and Mille Lacs counties in the east-central Minnesota  . A largeAn assessment of the impact of climate change on freshwater species in the USA found that “suitable thermal habitat” for cold-water species would decrease leading to a shrinkage in the distribution of these species and in-turn reduce opportunities for recreational fisheries . Maximumwhere, subscript i and j indicates prey and predator group respectively; B stands for Biomass, P for production, Y for total fishery catch, Q for consumption, and E for net migration rate; DCji is the fraction of prey i in the diet of predator j; BA accounts for biomass accumulation; and EE explains ecotrophic efficiency i.e. fraction of group mortality explained in the model.Our Mille Lacs Lake ecosystem model was developed using the Ecopath with Ecosim approach , 29. DetThe second equation explains the energy balance within a functional group as :Consumpi denotes net growth efficiency (production/consumption) of group I; Qji is the consumption by group I while Qij is for group I consumed by predators; Ii is the immigration rate; MOi explains other mortality excluding fishing and predation and Fi is the fishing mortalitywhere, dBi/dt is the biomass growth rate of group I in time-interval dt; g.Mille Lacs Lake ecosystem was represented by fifty functional groups in the Ecopath model. The tropho-dynamic model represented the ecosystem state in 1985 and was fitted in Ecosim with the historic time-series data of catch and abundance over a 22-year period from 1985–2006. Ecosim is a dynamic component of EwE suite which keeps track of changes in the biomass of species  as a function of temporal changes in their catch patterns, food-web (predators-prey interaction), and environmental conditions. The dynamic change is assessed using Functional groups details and important aspects of model parameterization and temporal simulation are included .Forcing function routines in Ecosim are used to incorporate environmental effects such as the effects of changes in temperature patterns. The shape of the function follows the changes in the given environmental variable. Temperatures in July and August of 2000–2005 were on average higher than the temperatures observed in 1985, the year Ecopath model was initialized to. To represent a regime of high temperatures in the forcing function, we used the average July-August temperatures from the year 2000 to 2005. We assumed that temperatures in the remaining months were within the tolerance limits for both species and hence did not require to be included in the forcing function. Setting up the forcing function this way allowed us to simulate the ecosystem at the higher temperature level observed in recent years.Optimum temperature and tolerance ranges were provided for both cisco and burbot, and the temperature forcing function was applied to the tolerance ranges. For cisco and burbot, previous studies have indicated that higher temperatures decrease the suitable thermal habitat and thereby affect the productive capacity of the fish in the lake. The option available in Ecosim to model this effect is to apply the temperature forcing function on P/B ratio for the group. At the optimum temperature, the P/B of the groups are equal to their Ecopath base P/B, and any changes in temperature (increase or decrease) away from the optimum force the P/B to decline; therefore, P/B responds dynamically to the given temperature profile (forcing function). The rate at which the P/B declines is reflected by the slope of the curves—P/B declines sharply for the species with small temperature tolerance range and vice versa . The armAs mentioned before, the phase of high temperature has a positive influence on the growth of smallmouth bass  especialThe base Ecosim model with 50 functional groups was tuned with historic time-series data of abundance from 1985 to 2006 in order to reconstruct the historical trends; the process is termed model-fitting. The first step in the fitting process was to identify the most critical vulnerability interactions in the model. The sensitivity of the sum of squared differences between model predictions and time-series data points with changes in vulnerability values were analysed in Ecosim. The fitting process involved calibrating the vulnerability interactions which highly influenced and reduced the sum of squared differences between the observed and predicted time-series were selected. The fitting exercise can be performed while including the environmental forcing functions in Ecosim. When a forcing function is applied, the fitting process accounts for the change in the associated parameter (P/B or vulnerability) according to the corresponding value for the forcing function in the time series. First, we attempted to fit the model without including the temperature forcing function, then we included the temperature forcing function and examined if there was an improvement in fit following the addition of the forcing function.There is a regular stocking program for muskellunge in the lake; hence, the resultant biomass every year is not entirely dependent on the biomass changes due to food-web and fisheries interactions. Therefore, for muskellunge, the biomass was forced using absolute biomass values. The biomass vector for cormorants and other piscivorous birds were also forced in the model. The bird population was composed of birds settled on the two islands of Mille Lacs Lake and birds migrating from other areas around the lake; their relative composition cannot be captured in the functional group dynamics of the model; except in the case where the lake experiences a constant inflow every year in which case a constant rate of immigration could be used to model the group. This was the reason behind forcing the biomass of bird functional groups in the simulation.Though EwE has a built-infunction to estimate SSmsy and ESmsy, it lacks capabilities needed to determine uncertainties around those estimates. The function works under two different conditions depending on biomass state of non-target species :SSmsy: while estimating the MSY of the target species, the biomass of non-target species  were forced to be static at their base ecopath value (1985 biomass).ESmsy: on the contrary to SSmsy, biomasses of non-target species were allowed to respond dynamically to changing target species biomass.-1) of the target species were incremented step by step (25 steps) and the ecosystem was allowed to equilibrate for 20 years (25*20 = 500) at each incremental F value. The F for the non-target species were held at their base ecopath value . The F value that resulted in maximum yield was recorded as Fmsy and the corresponding yield as MSY. This method was applied to estimating ESmsy under the temperature forcing function. In summary, we estimated the reference points with and without including the temperature forcing function, and this allowed us to estimate how much the lake ecosystem and the fisheries reference points were affected by the changes in the temperature pattern.Uncertainties in the estimation may arise from a number of sources such as uncertainties in the data, input parameters, and complexity of models’ structure simulating the natural process. Monte Carlo (MC) simulation is a widely used method to assess uncertainties around the estimates . In ordeSensitivity analysis: Sensitivity of the results to the Ecopath input parameters  was explored. For each run, MC simulation randomly selected a set of parameters within a 10% coefficient of variation (CV = 0.1) for the B, P/B, Q/B, EE parameters. Some sets of parameters did not satisfy Ecopath mass-balance ; in that case, the trial was considered unsuccessful, and the MC routine drew another set of parameters values. We present results from 80 successful-runs of Ecosim MC simulations for each MSY estimation.For clarity, the following abbreviations were used to refer different versions of MSYs in the manuscript:SSmsy for single-species MSY without temperature forcing function,SSmsyT for single-species MSY with temperature forcing function,ESmsy for ecosystem MSY without temperature forcing function, andESmsyT for ecosystem MSY with temperature forcing.The Ecosim model was able to reasonably predict  the dynaSSmsy and ESmsy for 13 species with and without the temperature forcing function were estimated , Fig 4. The key reason for obtaining higher ESmsy corresponding to SSmsy was a consequence of gradual increases in the availability of the prey to the target species as the target species was fished down; the biomass of the target species decreased, and this released predation pressure from their prey. We also noticed several other trophic effects propagating through ecosystem: in some cases, trophic interactions enhanced compensation in the targeted species while in others trophic interactions weakened the compensation.In the following section, we first describe the differences in the estimations because of trophic effects, i,e. between SSmsy and ESmsy; then we describe the differences due to the combined effect of trophic and temperature, i, e. between SSmsy and ESmsyT. The sensitivity analyses are presented as the lower and upper grey dashed MSY curves Figs  and 6. AThe ESmsy estimate of walleye was higher than the SSmsy . The higFishing was a major component of mortality on northern pike; thus, the ESmsy estimated was merely 2% higher than the SSmsy. This small advantage was probably due to an increase in prey species such as rock bass and yellow perch.The combined trophic and temperature effect resulted a slightly lower (5%) ESmsyT estimation  due to tYellow perch is the most important prey of the Mille Lacs ecosystem; it constitutes a major portion in the diet of almost all the piscivores. Much differences were not observed between the SSmsy and ESmsy estimates for yellow perch . A decliFor cisco, the trophic only effect did not make much difference to the estimates; therefore, ESmsy and SSmsy were very close . Cisco bOn the other hand, the effect of temperature caused cisco to decline excessively (biomass was nearly one-third of the SSmsy or ESmsy level). We also estimated the temperature-influenced single-species MSY (SSmsyT) for cisco and found the estimate was within the range of the estimates from the state-space modelling in Kumar et al. . CompareFor smallmouth bass, the ESmsy was 16% lower than SSmsy ; the decTrophic compensation from its prey such as juvenile yellow perch and crayfish caused a slightly higher (6%) ecosystem MSY estimate for burbot. Incorporating temperature negatively affected burbot, resulting a nearly one-fifth decline in ESmsyT ; the conFor white sucker, we obtained very similar estimates across all the cases of MSY estimations—differences were no more than 5% . Burbot For rock bass, the SSmsy and ESmsy estimates were very similar . CrayfisThe higher ESmsy estimates for Bullhead and Largemouth bass were the result of direct prey-predator interactions. Bullhead benefitted from invertebrates and decrease in biomass of its main predator, largemouth bass. Largemouth bass was benefitted from an increase in its prey, bullhead. Not much difference was observed between ESmsy and ESmsyT estimates for bullhead and largemouth bass .The ESmsy for Bowfin was slightly lower (6%) than SSmsy. Bowfin predates on black crappie and on trace amounts of largemouth bass along with other prey. When bowfin was fished heavily, a small improvement in largemouth bass was observed, but this had a large negative impact on black crappie. The reduction in black crappie resulted in a lower ESmsy for Bowfin. Under temperature forcing, the negative impacts were not as strong and the ESmsyT for bowfin was slightly higher compared to ESmsy. For black crappie, the ESmsy was greater than the SSmsy due to losses in its predators’ abundance such as largemouth bass, northern pike, bowfin and smallmouth bass. The ESmsy and ESmsyT estimates for black crappie were very similar. For common carps, there was less than 5% difference between the different MSY estimations.The sensitivity analysis allowed four of every functional group parameters in the 50 functional group model to vary within the Monte Carlo routine. We believe our results are quite encouraging. In many cases, especially when temperature forcing was not in effect, the SSmsy estimates were more conservative than the ESmsy estimates. When the temperature effects were accounted for, the model predicted a mixed response , Table 1In a similar analysis, Walters et al.  found thAs mentioned, yellow perch is the leading prey species in the lake and therefore the explanation of food-web dynamics of the lake without understanding perch dynamics is inconceivable. The major factors regulating its dynamics were walleye and cannibalism within the yellow perch population. When walleye was under-exploited, yellow perch was controlled by direct predatory pressure from walleye. However, the effects were not always straightforward: when walleye biomass was reduced by fishing at MSY, contrary to expectations, yellow perch biomass declined due to the increased predation from species which were released from walleye predation. Direct compensation was not observed indicating that walleye-perch relationship is highly complicated ; walleyeThis analysis enhances the understanding of interspecies interaction in the system and provides reference points which could serve to improve the current management plan that is largely based on single-species studies. Previous to this analysis, reference points for catch/stock status for Mille Lacs Lake were limited to a few species only, such as walleye , northerOur approaches allow the integration of temperature and trophic effects. We were able to identify sources of ecosystem pressure that caused lower ESmsy for cisco, rock bass and other species under temperature forcing. Temperature has a strong influence on the decline of cisco . Kumar eSeveral studies reported instances where an increase in temperature favored smallmouth bass . The preIn our paper, we assumed that P/B ratios followed the temperature tolerance curve . The senThe diet matrix parameters help in calculating the mass-balance constraint of the functional groups in Ecopath, and the Monte Carlo analysis can be criticized for not allowing the diet matrix parameters to vary. However, the strength of the predatory-prey relationship  and the consumption varies depending on the biomass, P/B, and Q/B values of the predator and the prey species. Therefore, all the important predator-prey linkages will be affected by the Monte Carlo routine on the 4*n  parameters in the model (~200 parameters in the model). Further, adding Monte Carlo on diet preferences has not yet been tested within the EwE software on account of the complexity involved. In this Mille Lacs Lake EwE model, which attempts to model 50 functional groups, there are more than 2,000 diet interactions. Allowing a sensitivity analysis on each of these interactions, while constraining for mass-balance, will be a highly computationally intensive task .In terms of structural uncertainty, a direct temperature effect was included for three species, but it might be possible that temperature had direct effects on other species too that we did not explore. So, there is a scope to investigate other effects of temperature in future. Moreover, other sources of structural uncertainty related to spatial complexity  could beWhen temperature effects were incorporated, the SSmsyT estimates for cisco and burbot were much lower than when temperature effects were not included (SSmsy). We found that the SSmsyT estimates were similar to the values obtained from the surplus production modelling . HoweverSeveral modelling studies have explored the influence of environmental changes on dynamics of individual species , 50, andS1 Table(PDF)Click here for additional data file.S1 FigThe dots are the observations and the lines are the Ecosim predictions. Prediction lines that exactly follow the dots are forced biomasses.(TIF)Click here for additional data file.


The foot-mounted inertial navigation system is an important method of pedestrian navigation as it, in principle, does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial pedestrian navigation aided with both ZUPT (zero velocity update) and the range decomposition constraint performs better than those in their own respective methods. This paper recommends that the separation distance between the position estimates of the two foot-mounted inertial navigation systems be restricted by an ellipsoidal constraint that relates to the maximum step length and the leg height. The performance of the proposed method is studied by utilizing experimental data, and the results indicate that the method can effectively correct the dual navigation systems’ position over the traditional spherical constraint. Positioning and tracking systems have developed over several decades in various applications, ranging from tracking of pedestrians to autonomous vehicles . A high-In real-life application, however, the low-cost inertial navigation systems suffer from the accumulation of errors while calculating the traveled distance of the objects. These errors cause the trajectories to drift away from the actual path as time grows. An effective technique is to bind the error growth using ZUPT . In 6],,6], the When we use the ZUPT-aided INS to track pedestrian location, the heading drift of navigation information is unobservable , so it iIn , the autThe experiments in ,9 are twThe Kalman filter addresses the general problem of estimating the state equation ,11:(1)XIn practice, the process noise covariance matrices Equations (4)–(7) are the basic equations of Kalman filtering. If the initial values about The inequality Kalman filter appears in solving practical problems between state variables where there are inequality relationships . The ineThe inequality-constrained Kalman optimal solution  is expreg matrix ,15. As sg matrix . ExpandiThus, the inequality constrained problem can be further simplified asThe output of MIMU can be expressed asThe detection probability is The mathematical sensor model can be expressed as Since the sensor measurement can be obtained from the joint probability density asGLRT is determined by the hypothesis In practice, ZUPT can effectively aid inertial navigation system to remove long-time accumulated errors ,17. The The state error vector is defined asThe zero-velocity correction Kalman filter model isWhen the MIMU is stationary, the speed is zero, in theory; thus, the ZUPT speed measurement equation isEach foot are fixedly mounted by a MIMU. For regular human kinematics, the separation distance between the right and left feet cannot be larger than a quantity known as foot-to-foot maximum separation ,9. The mbX axis is parallel to the surface of the MIMU, in the forward direction, and the bZ axis is perpendicular to the MIMU surface, in the upward direction. In this dual-MIMU integrated navigation system, the navigation coordinate system’s nX axis is forward, the nY axis points to the right, and the nZ axis perpendicular to the nOYnX plane, upwards. The coordinates of the navigation subsystem bound to the feet are defined in the same way.According to the coordinate system identified of the MTI-G-700 units , the carrier coordinate system, as shown in For two MIMU navigation systems, the The joint state vector is defined asXOY plane, and is confined within the circle with leg-related radius XOZ and YOZ planes, both centered at the other foot (Letting the maximum step size of the pedestrian be given by llipsoid . The posher foot .k (XOY plane (in navigation coordinate system):Assuming that left foot is on the ground and the right foot is in movement at moment k , then weAs we can see from Therefore, the ellipsoidal constraint correction algorithm between the feet can be defined asnX axis, so nY axis, so nZ axis, so Defining the matricies,We assume that two navigation systems attitude is accurate in the current moment when the decompose step size constraint. When Defining The covariance matrix of the process measurement noise of the dual-MIMU integrated navigation system isThe covariance matrix of the measurement noise of the dual-MIMU integrated navigation system isThe sampling rate of the filter is 400 Hz.(1)In a complex 2D environment: some closed trajectory containing a straight line path and turning eight times (turning angle: 90°).(2)In a complex 3D environment: a six-story staircase, and parts of corridors in the Sheng-Hua building at the Central South University. The walk strats at the first floor and ends at the sixth floor.To compare the performance of the proposed algorithm with the existing in   as an accuracy measure in this work. This is used to measure the difference between the actual values and the output of an estimator. For quantitative comparison, we only checked the starting and final positions, both in 2D and 3D, in all walking tests. We assume the starting position of the left foot and right foot as  and , hence, only the final estimates are inserted into the RMSE formula. Both of these quality indicators are given in comparison In the 3D upstairs experiment, the result shows the maximum step ellipsoidal constraint method reduces the error accumulation in the nZ axis can be accurately measured, so, in this experiment, we only analyze the root mean square error in then Z axis direction. The numerical results are given in In the indoor upstairs test, because of the objective factors of the irregular staircase, we are unable to accurately know the horizontal coordinate of the end point. However, the position in the Low-cost inertial pedestrian navigation aided with both ZUPT and the range decomposition constraint performs better than those in their own respective method. In this paper, we decompose the maximum step length along the navigation coordinate axes in real time, and establish an ellipsoidal constraint more suitable for actual walking situations. Each sub-constraint changes along with different times and makes the aiding scheme of the step size more specific and accurate. Experimental tests on different paths show that the proposed ellipsoidal constraint method can effectively improve the position accuracy of pedestrian navigation.


Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR)-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD) model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND), normalized mutual information (NMI), Weber local descriptor (WLD), and the sum of squared differences on entropy images (ESSD), the proposed method provides better registration performance in terms of target registration error (TRE) and subjective human vision. Nonrigid multimodal image registration is very important for medical image processing and analysis. Due to the different principles of various imaging technologies, such as ultrasound (US), computed tomography (CT), magnetic resonance (MR) imaging, and positron emission tomography (PET), they have their own advantages in reflecting anatomical or functional information of the human body. Multimodal image fusion can combine information of different modalities to facilitate disease diagnosis and treatment. Image registration, which aims to find the correct spatial alignment between the corresponding structures in images, is a prerequisite process for effective image fusion and has been widely studied. For example, the registration of the transrectal ultrasound image (TRUS) with the preoperative MR image has been widely studied for the guidance of prostate biopsies ,2. In crFor image registration, the similarity metrics play an important role in ensuring accurate registered results. However, it is a challenge to find an effective similarity metric for multimodal image registration. There are many widely used similarity metrics, such as sum of squared differences (SSD) and cross-correlation , but sucTo overcome the drawback of the above-mentioned similarity metrics, structural representation (SR) methods have been investigated by firstly transforming a multimodal registration into a mono-modal one using the SR method and then computing the SSD between the SR results. The local gradient orientation is utilized to find correspondences across image modalities ,15. HoweLearning the features automatically from data of interest provides a plausible solution to accurate structural image representation. Deep learning, as a popular machine learning method, is well-suited for automatic feature learning. The various deep learning models, such as deep belief network (DBN), convolutional neural network (CNN), and stacked autoencoder (SAE), can extract intrinsic features from a large number of data using multilayer linear and nonlinear transformation. Recently, deep learning has been utilized for image registration using two kinds of methods. The first kind of method extracts the image features using such networks as CNN and SAE and uses them in traditional registration methods to produce the registered images ,23,24. ITo address these problems, this paper has proposed to realize the structural representation for image registration based on PCANet. PCANet, proposed by Chan et al. , involveThis paper is organized as follows: For the traditional PCANet, binary hashing is used in the output layer, where the output values are set to be 1 and 0 for positive entries and other entries, respectively. However, the negative entries also carry the structural information of the image. Therefore, the loss of some image features may be involved in the output of the traditional PCANet. The improved PCANet network addresses this problem by using the absolute values of the outputs at the second layer and the sigmoid function for nonlinear processing. The improved PCANet model with two hidden layers is illustrated in i-th image of size N training images j-th patch in For each pixel in the PCA is utilized to remove the redundant information of j-th eigenvalue. The map is a function for rearranging the vector of Let the number of selected filters at the first layer be At the first stage, PCANet will produce To capture higher-level features, the multiple stages of PCA will be stacked as similar to DBN. Similar to the first stage, a The output of this stage is: For the second stage, each input image will produce All of the outputs at the second stage will be transformed using the sigmoid function as: Inspired by the previous work in , the mulFor the output stage, the fused feature image is generated in the same way as in the first stage:Finally, the multilevel features are combined using the exponential function to construct the PSR as:The decay parameters are locally adaptive for each image pixel at B of a pixel at Generally, in nonlocal means image denoising, the decay parameter is proportional to the noise variance . BorrowiPSR can be used to convert the multimodal image registration into a mono-modal one, which means that the similarity of multimodal images can be evaluated by a simple similarity metric. In this paper, the similarity metric The FFD model is used as the transformation model. In order to find the optimal spatial transformation, a cost function which is associated with the spatial transformation parameters is defined. The cost function consists of the similarity metric and the smoothness constraint, and it is expressed as:The cost function can be optimized using the Limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization algorithm to produce the optimal parameters of the FFD transformation The implementation details of the proposed method can be summarized as the following five steps.Step 1: Train the PCANet using a large amount of training data and obtain the convolution kernels of the two hidden layers;Step 2: Calculate the structural representation result Step 3: Construct the cost function based on Step 4: Minimize the cost function using the L-BFGS optimization algorithm to find the optimal spatial transformation.Step 5: Output the optimal transformation http://brainweb.bic.mni.mcgill.ca/brainweb/, a real brain image dataset Atlas at http://www.med.harvard.edu/aanlib/home.html, and the CT and MR image dataset RIRE at http://www.insight-journal.org/midas/community/view/16. The parameter settings of the proposed method are firstly discussed based on the Atlas dataset. Then, the performance of the proposed method is compared with that of such state-of-the-art methods as MIND, ESSD, WLD, and NMI based on the BrainWeb and RIRE datasets.In this section, several experiments have been performed on different datasets, including a simulated brain image dataset BrainWeb at For the appreciation of registration accuracy, the target registration error (TRE), which is the average Euclidean distance of these anatomical landmarks in the images to be registered, is used and it is defined as: The main parameters of the PCANet include the number of stages, the number of filters in each stage, the patch size, and the decay parameters. In our experiments, it is found that two-layer PCANet is good enough for image registration, and a deeper network does not lead to much better performance. As regards the number of filters in each stage, it is set to  filters  with eigTo explore the effect of patch size on image registration, The coefficients To evaluate the influence of the coefficients objectively, In order to demonstrate the superiority of our method, we compare PSR with the structural representations obtained by other methods on the BrainWeb dataset. In addition to the comparison of the structural representations, a comparison of the registration accuracy between the proposed method and the state-of-the-art methods has also been made based on the BrainWeb dataset and the RIRE dataset. For the proposed method, we set the penalty parameter t-test is performed between the PSR method and each comparison method. The test results show that there exists a significant difference between the proposed registration method and any other compared method (p < 0.05). The reason for the superiority of the PSR method can be explained in this way. Medical images are complicated and it is difficult for human-designed descriptors to represent their complex features. However, due to the strong feature learning abilities of PCANet in the proposed method, more intrinsic features can be extracted automatically and effectively, thereby leading to better structural representation results.In order to evaluate and compare the registered results objectively, 50 registration experiments have been conducted. In order to explore the robustness of the registration methods, different levels (1–10%) of Rician noise have been added to the MR images. To further test the registration performance of the proposed method, experiments have been conducted on the RIRE dataset. In this paper, a novel PCANet-based structural representation method has been proposed for multimodal medical image registration. Compared with the human-designed feature extraction methods, the PCANet can automatically learn the intrinsic features from a large number of medical images through multilevel linear and nonlinear transformations. Distinctively, the proposed method can provide effective structural representations for multimodal images by utilizing the multilevel image features extracted in the various layers of the PCANet. Extensive experiments on Atlas, BrainWeb, and RIRE datasets demonstrate that the proposed method can provide lower TRE values and more satisfactory registered results in terms of human vision than the MIND, ESSD, WLD, and NMI methods. Our future work will focus on extending the proposed approach to more challenging data, in particular 3D ultrasound data. The extension of our method is an arduous task due to the serious noise and the unclear anatomical edge in ultrasound images, which pose a challenge for the construction of structural representation. To ensure that the proposed method can be used for the registration of challenging data, the training of the PCANet and the construction of the structural representation will be modified and more clinical samples will be collected to facilitate the training and testing of the PCANet-based registration method.


An evaluation is made of possible differences in treatment effects between labial and lingual fixed appliances.A comprehensive search was made of the PubMed-Medline, Cochrane Library and LILACS databases, with an additional manual search covering the period up until April 2017. There were no restrictions in terms of year of publication or language. Agreement between the authors was quantified by the Cohen kappa statistic. A random-effect model was applied to calculate weighted mean differences with 95% confidence intervals.p = 0.101) and reduce the angle between the major axis of upper central incisor and the sellar-nasion plane - though statistical significance was not reached .A total of 249 patients corresponding to four eligible studies were included in the systematic review. Among the six angles and distances entered in the meta-analysis, a tendency was observed in lingual appliances to increase the interincisal angle (95% CI −0.80-8.99; The results obtained indicate that treatment with lingual appliances favors incisor tipping by exerting lingual crown torque, but there are no differences in cephalometric values between labial and lingual fixed appliances. Because of the small number of included studies, the results of this meta-analysis should be interpreted with caution. Future research should focus on the generation of a consensus document allowing selection of the type of orthodontic approach not only conditioned to the esthetic requirements of the patient but also considering the characteristics of the malocclusion. On the other hand, standardized international guidelines are lacking; the measurements of angles and distances therefore have to be unified with a view to future investigations. Lingual orthodontics were introduced over three decades ago , and in Cephalometric analysis is used to study the craniofacial structures of the patient, and its results have an impact on treatment planning. Cephalometry is not a direct method for diagnosing the patient conditions, yet it offers details on the craniofacial structures of the patient and thus yields diagnostic information that is helpful in defining the orthodontic treatment strategy . With thApart from the undeniable esthetic advantages of lingual versus labial appliances, other biomechanical advantages have been described referred particularly to expansion, open bite or mass retraction , torque,This meta-analysis was based on the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement  and the On the basis of the acronym “PICO” , the question that guided this review was: Are there differences in treatment effects between labial and lingual fixed appliances?We searched the PubMed-Medline, Cochrane Library and LILACS databases, covering the period up until April 2017. The specific search strategies are shown in Table Studies comparing changes in the cephalometric values of patients with malocclusions requiring orthodontic treatment, in which one group of subjects received treatment with the lingual technique while the other group received treatment with the buccal technique.Randomized controlled trials (RCTs), prospective controlled trials (CCTs) and retrospective studies.A specific protocol was conducted in advance. The following inclusion criteria were established:Patients with previous phase 1 treatment or surgical treatment, patients with continuing dental growth and in vitro or animal studies were excluded. Any obscure or missing data were obtained by contacting the authors. The electronic search included all articles, with no restrictions in terms of year of publication or language. In those cases involving more than one publication with the same group of patients and the same follow-up period, we only included the study that came closest to the objectives of the present review or which comprised the largest sample. A translation was arranged for one article in Chinese . All theTwo of the authors (F.A.A. and J.A.A.) independently assessed the risk of bias of the included randomized controlled trials using the Cochrane Collaboration tool for assessing risk of bias of RCTs . The criThe risk of bias of the included non-randomized studies was independently assessed by the same authors using the Newcastle-Ottawa Scale (NOS) . The NOSp-value of the null effect contrast of the factor “type of orthodontic treatment” (WMD = 0) for solution of the meta-analysis. Graphic representation using Forest plots was made for WMD. Due to the diversity of the analyzed measurements, and taking into account that the quantitative analysis was made independently for each of the measurements, we obtained up to six independent meta-analyses. The Bessel correction was used to integrate a part of the meta-analysis .i et al. : All meaFour studies were included in this systematic review, and the angles and distances measured in each study are summarized in Table The only RCT included in the systematic review was considered as having high risk of bias for inadequate allocation sequence, concealment and selective outcome reporting. Following the norms of the Cochrane Handbook for Systematic Reviews of Interventions (version 5.1.0) , we exclWith regard to the three non-randomized studies –9, two pp-value, and the I2 index. The corresponding forest plots in these analyses  and reduce the SN-U1 angle . The results of the meta-analysis for each measure are shown in Figs. The two articles included in the meta-analysis summed a total of 85 patients subjected to labial orthodontic treatment and 84 patients treated with lingual brackets. Meta-analysis was performed of five angles  and a linear measurement (Me-N). A nonsignificant tendency was observed in lingual orthodontics to increase the U1-L1/IIA angle (95% CI −0.80-8.99; The present study offers a systematic review and exhaustive meta-analysis of the scientific literature, with the aim of answering the following question: Are there differences in treatment effects between labial and lingual fixed appliances? Cephalometric analysis confirmed that there was minimal change in the mandibular plane angle in the lingual group, though there was a slight tendency for the maxillary molars to extrude . AlthougSome studies –22 have Although it is more difficult to control incisor torque with the lingual orthodontic technique, a study  has founWith a prevalence of malocclusion of 45.6% among males between 18 and 21 years of age, it is easy to intuit that almost one-half of all evaluated patients will require orthodontic treatment –9, 31 evAlthough the results of our study must be viewed with caution, they could represent a starting point for future research leading to the generation of a consensus document allowing selection of the type of orthodontic approach not only conditioned to the esthetic requirements of the patient but also considering the characteristics of the malocclusion. On analyzing the parameters measured by the different studies, only some of them appear to show some coincidence, for example referred to SNA and ANB , 31, SNBOur study has several strengths. A particular strength is that in order to minimize publication bias, we performed a systematic search of different international medical databases. Moreover, no language limitation or publication date was set in order to ensure inclusion of as many data as possible from appropriate studies. Two reviewers independently chose, extracted, and evaluated data quality in order to reduce bias and transcription errors. In addition, the statistical power of the study was 90%. To our knowledge, this is the first systematic review and meta-analysis to evaluate possible differences in treatment effects between labial and lingual fixed appliances. However, our systematic review also has certain limitations. A first limitation is the small number of articles available for review, which may cause penalization by a degree of type β error. Secondly, since fewer than 10 studies were included, funnel plots and Begg’s rank correlation test were not performed , 36. LasThe present systematic review found no statistically significant cephalometric differences between the lingual and labial orthodontic techniques. However, there was a tendency to increase the interincisal angle and reduce the angle between the major axis of the upper central incisor and the sellar-nasion plane. These findings indicate that treatment with lingual appliances favors incisor tipping by exerting lingual crown torque. However, because of the small number of included studies, the results of this meta-analysis should be interpreted with caution. Future research is advisable, leading to the generation of a consensus document allowing selection of the type of orthodontic approach not only conditioned to the esthetic requirements of the patient but also considering the characteristics of the malocclusion. On the other hand, standardized international guidelines are lacking; the measurements of angles and distances therefore have to be unified with a view to future investigations.


As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes. The engine is regarded as the heart of the vehicle, and its bearings’ working status is directly related to the stability. To prevent failure, especially in harsh environments or critical moments, it is of great significance to monitor and evaluate the health state of bearings since their failure in general can cause injuries and financial losses when faults are poorly diagnosed. The early fault diagnosis of bearings has attracted lots of attention . VibratiThe lubricating oil is just like the “blood” of the engine, and its online monitoring has been a research focus in recent years. Wear debris analysis is becoming the most powerful oil analysis strategy for assessing machine condition . The BBRTo sum up, it is of great importance to establish a new and accurate evaluation scheme based on oil lubricating multi-parameters and explore a correspondence between oil parameters features and the changing of bearing wear states. Based on the above background research, we studied the technique of online oil monitoring and evaluation for bearing wear states based on lubrication multi-parameters obtained through the online oil multi-parameters monitoring system (OOMS). Through monitoring oil multi-parameters by multiple sensors, we aimed at obtaining the characteristics comprising oil quality indications, oil metal debris, pollutants, and so on. Furthermore, the wear states of bearings in this system could be evaluated by analyzing these characteristics and their changing trends. We proposed a novel model optimized k-means clustering model to evaluate bearing wear states. The specific research and implementation route are as follows. A testing platform was built for carrying out the bearings’ whole life test. During the experiment, oil multi-parameters such as micro-metal debris, pollution, viscosity, water, density indications, and vibration signals were monitored and acquired. Then, an approach for analyzing the relationship between oil parameters and wear states was proposed based on an improved k-means clustering model (KCM) optimized by a gray correlation degree model (GCM). First, a correlation degree distance matrix was generated using GCM to express the relationship of samples of oil multi-parameters of different times. Then the matrix was clustered using KCM and the oil multi-parameter samples were clustered into different clusters. The experimental results show that lubricating oil components contain vital information about the bearing wear states and there is an obvious correlation between the clusters and the bearing wear states. To verify the state evaluation results, bearing states were also identified using vibration signals, which are basically consistent with that of oil multi-parameters. Furthermore, we found that the time points for bearing wear states changing through oil multi-parameters were earlier than the time points obtained through vibration signals, which indicates that lubricating oil multi-parameters have the advantage over vibration signals in bearing early wear failure prediction.The rest of this article is organized as follows: In general, the wear states curve of bearings through their whole life is shown in Due to the fact that the composition of lubricating oil is affected by the bearing wear states, in theory, there could be three different stages for online oil samples which can reflect the different wear states of bearings. Therefore, aimed at the problem about data aggregation or wear modes classification, the appropriate clustering algorithm is considered for solving such problems. If we can obtain online oil monitoring samples of bearings through the whole lifetime, the samples can be classified into three clusters, and each cluster in the time domain would correspond to a different wear stage of bearings.The k-means clustering model algorithm takes distance as the evaluation indication of similarity, which means that the closer the two objects are, the greater their similarity is. KCM classifies samples close to each other into the same cluster. The schematic diagram of the algorithm is shown in As for the deficiency of the KCM method, on the one hand, according to the bearings’ whole life testing, their wear states go through three stages from beginning to failure as shown in Gray system theory proposes a gray correlation degree model to find the correlations between subsystems or sub-factors in the system ,24. BefoTherefore, after the data is preprocessed, we firstly calculate the correlation degree of the data samples using GCM. Then KCM is used to cluster the obtained correlation matrix. When analyzing the data samples by GCM, the dimensionless processing should be performed for measurement quantities of different dimensions.Assuming The gray correlation coefficient fined as Before ented in . The graThe specific implementation of the gray k-mean clustering algorithm is as follows.Step 1: Calculate the initial value  of each sequence.Step 2: Obtain the absolute value sequence of the difference between Note Step 3: Find the maximum and minimum value of Step 4: Calculate the correlation coefficient according to Equation (2)Step 5: Calculate the average of Step 6: Repeat above steps 2–5 to calculate all the gray correlation degrees. And the gray correlation degree matrix R can be represented as Step 7: Input the matrix Step 8: Select the initial center point, like As for m as the number of Step 9: Set Step 10: Repeat steps 8–9 until all the changes of the In order to realize the bearing wear state evaluation based on online lubricating oil multi-parameters, the whole lifetime test of rolling bearings was performed on a bearing fatigue test machine. During the test, multiple online oil parameters such as metal debris and physical and chemical properties, etc., were recorded using monitoring sensors. The experimental data acquisition system has been built up to collect the multiple oil parameters and vibration signals from new bearings to complete failure states under appropriate loads. The online oil monitoring system is abbreviated as OOMS, and the fatigue experiment of a bearing’s whole life is FEOBL, which provided data support for bearing wear states analysis.Through comprehensive analysis, the oil multi-parameters that need to be monitored online include the density, temperature, kinematic viscosity, permittivity, and metal debris in the oil. In particular, kinematic viscosity and density reflect oil’s physical and chemical properties, while water content and permittivity can reflect indirectly the degree of oil pollution . In addiThe ZCMS type of metal debris sensor was developed by ourselves, and can detect ferrous debris with an equivalent diameter of no less than 70 µm. Other indicators of oil were measured by the sensors from Shenzhen Front Wave Tech as shown in The rate of water content can be measured by detecting the changing permittivity in the oil using the sensor shown in The above six oil parameters were combined as a set of sample sequences of the bearings data. After data preprocessing, the outcome, as the characteristic information of lubricating oil, was sent to G-KCM to classify the oil states. Bearing vibration signals can also be used for bearing wear states classification, which were handled by G-KCM, too. The results obtained from vibration signals were compared with those from the oil multi-parameters. The above two kinds of classification results were compared and analyzed.The implementation route of the bearing states evaluation is shown in The block diagram of the experimental system is shown in The FEOBL system is mainly composed of the bearing fatigue testing machine (ABLT-1A) and the online oil multi-parameters monitoring system (OOMS). The ABLT-1A is manufactured by the company named HBRC (Hangzhou Bearing Test & Research Center), located in Hangzhou, Zhejiang province in china. The bearing fatigue testing machine is shown in The OOMS was integrated in the ABLT-1A to collect the data of metal debris, pollution, viscosity, and water, while various oil parameters and vibration signals were also recorded using a B&K4371 accelerator. The OOMS was constituted with a hardware system including oil multi-parameter monitoring sensors, signal conditioner, data acquisition device, and software system with functions including oil data display, recording, and storage. The vibration data acquisition card is ADLINK ACL-8112PG for IBM PC or compatible computers, which combines all data acquisition functions, such as A/D, D/A, DIO, and timer/counter into a single board. The ACL-8112PG card provides a high-speed sampling rate up to 100 kS/s at all gains, and features 16 single-ended inputs and bipolar inputs. In this test, the sampling rate is set at 20 kHz to acquire vibration signals. The OOMS and the oil circulation loop are shown in The assembled test bearing test setup is shown in The steps of experimental installation is as follows.Step 1: Before installing the test head, select the large or small pistons according to the radial load of the test bearings.Step 2: The inner of the bearings housing must be washed cleanly and free of stuff. The transmission spindle should be placed in the small hole on the right side of the test head with a small square shaft. Use the hand lever cam to retract it.Step 3: Place the left and right pads in the housing to fix the test head as shown in Step 4: Connect the small square shaft and assemble the coupling as shown in At the beginning of the test, part of the data of the bearing grinding state is shown in During the experiment, in order to find a more reliable corresponding relationship between the oil monitoring parameters and bearing early wear characteristics in time, the test machine was stopped and the bearings had to be disassembled to inspect and search for signs of early wear failure of the bearings when the magnitude of bearing vibration signals increased to multiples of the initial stable value. Thus a 2 times root-mean-square value of initial vibration magnitude of the normal wear period had been set as the alarm threshold to shut down the test. Parts of the data of the bearings’ normal wear state during the test are shown in 2 after continuously working for 49 h under appropriate loads of about 24.4 MPa. Metal debris sensors had not detected metal debris with a diameter larger than 150 µm and bearings had entered the normal wear stage from the grinding stage. The vibration level of the bearings increased to 6 m/s2 after running about 110 h of the normal wear period. We stopped the machine and bearings were disassembled to check. We found that early fatigue pitting failure had occurred in the inner rings of the bearings as shown in During the test, the vibration level of the bearings raised to about 5 m/s2 more than 50 times. We disassembled and checked the bearings again. There were clear peeling failures occurring on the surfaces of the inner ring and one rolling ball, which indicated that the bearing appeared to have peeling failure. The flaking failure of the bearing is shown in The bearings were reassembled to continue testing, and afterwards, the machine stopped automatically when running to about 128 h. The recorded data showed that the vibration level had exceeded 8 m/s2. Bearings were reassembled to continue the experiment. The machine stopped automatically again at about 161 h when the vibration level had exceeded the threshold more than 50 times. The online oil monitoring data showed that the metal debris increased significantly. After the bearings had been disassembled, we found that there was a large peeling point on the roller of the previous bearing as shown in In order to study further the change trends of oil composition during the whole lifetime, we raised the vibration threshold of stopping the test to 30 m/sParts of the data of the bearings’ severe wear state during the test are shown in It can be seen from First we use KCM and G-KCM algorithms to cluster the online oil monitoring samples, respectively. We found that G-KCM is more effective than KCM. Then we used G-KCM to cluster bearing vibration signals and compared the clustering results with those of the online oil monitoring samples.It can be seen from The preprocessed data needs to be normalized prior to being analyzed directly using KCM as the control group. At the same time, the G-KCM algorithm was applied to the data without normalization as the test group. The specific calculation process is shown in k should be set to 3, and the clustering results by the KCM and G-KCM algorithms are shown in According to Equations (4) and (7), GCM has a function of dimensionless processing and distributing the data in  the same as the normalization in order to make the calculation converge faster. In Although the comparison results have worked out, its reliability needs to be verified by further analysis. All vibration signals acquired in A variety of indicators of oil parameters were picked out and calculated by G-KCM and KCM. Supposing that all data could be entirely divided into three separate groups similar to those in In The oil parameters except the temperature index and only the temperature index are recalculated, respectively, and the results are shown in Also, To further verify the effect of temperature on the results, the parameters of vibration signals except temperature index were recalculated, and the results are shown in In general, bearing vibrations are hardly affected by temperature. In this test, the RMS values of vibration signals continuously increased in whole-life testing of the bearings. For further verification that the oil parameters have the ability to reflect the wear states of bearings truly and effectively in the stationary temperature situation, all data from 7680 min to the end, while the temperature was not over 56 °C, were deleted. At the same time, the temperature from 2800 min to 3000 min was consecutive, increasing continuously. Suppose that the deleted part is the third stage of bearing wear state, severe wear, then the rest of the data is supposed to be divided into two groups. To exclude the effect of classification quantity on clustering results, the k of the clustering algorithm was set as two and three, respectively, to cluster the oil parameters. When And when k is two or three, and the first change of stage is at the 2800 min point approximately similar to the results of vibration signals except temperature in To explore the applicability, merits, and defects of the proposed model under certain conditions, it is compared with other models that are usually used to analyze for evaluating bearing health conditions. The comparison of performance of each model is shown in By means of the G-KCM algorithm, the oil multi-parameters and vibration signals of bearings through their whole lifetime were calculated and compared. The analysis based on oil parameters was set as a new test group and the vibration signals analysis as a new control group. It was possible to find the difference by comparative analysis.By comparing It can be seen from the comparison between In this paper, we built up the fatigue experiment system for bearing whole-lifetime testing and online oil multi-parameter monitoring systems. The G-KCM clustering method based on the k-means clustering model optimized by a gray correlation model was proposed. Then it was used to cluster the bearings oil data and vibration signals. As a result, the reliable correlation between lubricating oil and bearing wear states has been established, since the time for bearing wear stages conversion is basically consistent with the results based on oil multi-parameters and vibration signals by the G-KCM algorithm. Also, analysis results show that there is an obvious correspondence between the clusters and the bearing wear states that the lubricating oil multi-parameters contain vital information that can be used to find the approximate time points when the bearing wear state is to change. The comparison of the clustering results based on oil multi-parameters and on vibration signals has shown that online oil data have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health conditions and to provide an approach for early identification of bearing-related failure modes. Further research is to apply this model to the prediction of bearing residual life based on the online oil multi-parameters.