Simple Algorithm Research Articles

Step Length Estimation for Blind Walkers.

Wayfinding systems using inertial data recorded from a smartphone carried by the walker have great potential for increasing mobility independence of blind pedestrians. Pedestrian dead-reckoning (PDR) algorithms for localization require estimation of the step length of the walker. Prior work has shown that step length can be reliably predicted by processing the inertial data recorded by the smartphone with a simple machine learning algorithm. However, this prior work only considered sighted walkers, whose gait may be different from that of blind walkers using a long cane or a dog guide. In this work, we show that a step length estimation network trained on data from sighted walkers performs poorly when tested on blind walkers, and that retraining with data from blind walkers can dramatically increase the accuracy of step length prediction.

Revising Failed Reverse Total Shoulder Arthroplasty: Comprehensive Techniques for Precise Explantation of Well-Fixed Implants.

With the increased utilization of reverse total shoulder arthroplasty (RTSA), there has been a corresponding increase in the incidence of and demand for revision RTSA3. In cases in which the patient has undergone multiple previous surgeries and presents with well-fixed shoulder implants, even the most experienced shoulder surgeon can be overwhelmed and frustrated. Having a simple and reproducible treatment algorithm to plan and execute a successful revision surgery will ease the anxiety of a revision operation and avoid future additional revisions. The extraction techniques described here strive to preserve the humeral and glenoid anatomy, hopefully facilitating the reimplantation steps to follow. The main principles of implant removal include several consistent, simple steps. In order to revise a well-fixed humeral implant, (1) identify the old implants; (2) create a preoperative plan that systematically evaluates the glenoid and humeral deficiencies; (3) prepare consistent surgical tools, such as an oscillating saw, osteotomes, and/or a tamp; (4) follow the deltoid; (5) dissect the soft tissue with a sponge; (6) dissect the bone with use of an osteotome; and (7) remove the humeral stem in rotation. In cases in which there is also a well-fixed glenoid implant, the surgical procedure will require additional steps, including (8) exposure of the anteroinferior glenoid, (9) disengagement of the glenosphere, and (10) removal of the glenoid baseplate in rotation. Alternatives to revision RTSA include nonoperative treatment, implant retention with conversion of modular components, extensile revision surgical techniques, and/or mechanical implant removal. With the advent of modular humeral and glenoid components, surgeons may choose to change the implant components instead of removing the entire humeral and glenoid implants; however, repeat complications may occur if the previous implant or implant position was not completely revised. When confronted with a tough humeral explantation, an extensile surgical approach involves creating a cortical window or humeral osteotomy to expose the humeral implant. This approach can compromise the humeral shaft integrity, leading to alternative and less ideal reconstruction implant choices, the use of cerclage wires, and/or the use of a strut graft, all of which may complicate postoperative mobilization. If glenoid implant removal is necessary, the glenosphere is removed first, followed by the underlying baseplate component(s). If the glenosphere is stuck or if screws are cold-welded, the use of a conventional mechanical extraction technique with a burr or diamond saw may be required; however, this may lead to additional metal debris and intraoperative sparks. Revision RTSA can lead to high complication rates, ranging from 12% to 70%2, which will often require additional revision surgeries4. The first steps to all revision RTSA procedures include careful surgical exposure and component explantation. A simplified approach to expose the humerus and glenoid, coupled with a systematic and atraumatic approach to remove the implants without inadvertent injuries, will prevent surgical complications and the need for re-revision. The proposed comprehensive technique hopefully will allow precise removal of the humeral and glenoid implants while also preserving the remnant humerus or glenoid for future reconstruction. Few studies have evaluated postoperative patient outcomes in revision RTSA. Chalmers et al. performed a meta-analysis and found that patients were able to achieve a mean elevation of 106°, a mean American Shoulder and Elbow Surgeons score of 63, and a mean Single Assessment Numeric Evaluation score of 522. Boileau showed very similar outcomes, with a mean elevation of 107° and a mean adjusted Constant score of 621. These outcomes are slightly inferior to those of primary RTSA, but patients remain satisfied with their improvement from their preoperative function. Preoperative preparation will reduce intraoperative reparation. Know the existing implant and the unique features of its design, understand the patient anatomy including bone defects, and anticipate all of the potential tools that may be needed.Know your anatomy. The anteromedial deltoid edge will help you identify the scarred-in humeral shaft.Da Vinci said that simplicity is the ultimate sophistication. Some of the most common surgical tools and instruments can be more effective than custom-designed ones.The implant should be removed in rotation.There are some company-specific explantation instruments that can be very helpful. Give the appropriate ones a try, but be prepared to consider alternative solutions. RTSA = reverse total shoulder arthroplastyCT = computed tomographyFE = forward elevation.

Intelligent Bladder Volume Monitoring for Wearable Ultrasound Devices: Enhancing Accuracy Through Deep Learning-Based Coarse-to-Fine Shape Estimation.

Accurate and continuous bladder volume monitoring is crucial for managing urinary dysfunctions. Wearable ultrasound (US) devices offer a solution by enabling noninvasive and real-time monitoring. Previous studies have limitations in power consumption and computation cost or quantitative volume estimation capability. To alleviate this, we present a novel pipeline that effectively integrates conventional feature extraction and deep learning (DL) to achieve continuous quantitative bladder volume monitoring efficiently. Particularly, in the proposed pipeline, bladder shape is coarsely estimated by a simple bladder wall detection algorithm in wearable devices, and the bladder wall coordinates are wirelessly transferred to an external server. Subsequently, a roughly estimated bladder shape from the wall coordinates is refined in an external server with a diffusion-based model. With this approach, power consumption and computation costs on wearable devices remained low, while fully harnessing the potential of DL for accurate shape estimation. To evaluate the proposed pipeline, we collected a dataset of bladder US images and RF signals from 250 patients. By simulating data acquisition from wearable devices using the dataset, we replicated real-world scenarios and validated the proposed method within these scenarios. Experimental results exhibit superior improvements, including +9.32% of IoU value in 2-D segmentation and -22.06 of RMSE in bladder volume regression compared to state-of-the-art (SOTA) performance from alternative methods, emphasizing the potential of this approach in continuous bladder volume monitoring in clinical settings. Therefore, this study effectively bridges the gap between accurate bladder volume estimation and the practical deployment of wearable US devices, promising improved patient care and quality of life.

Uniformly monotone partitioning of polygons

We study the problem of partitioning a simple polygon P with n vertices (including R reflex vertices) and no holes into a minimum number of uniformly monotone subpolygons using diagonals that lie inside P, each connecting two distinct vertices of P. A set of polygons is uniformly monotone if there exists a line such that all of the polygons are monotone with respect to the line. We present an O(nRlog⁡n+R5)-time algorithm for the problem. When Steiner points can be placed on the boundary of P and each Steiner point is considered as a vertex of P, we present an O(n+R5)-time algorithm for the problem. We present an O(n+R4)-time algorithm when Steiner points can be placed anywhere in P. Our algorithms improve upon the previously best ones. We also present simple and efficient 2-approximation algorithms.

Weakly anelliptical traveltime analysis: Ambiguity between subsurface and elasticity

The building of a subsurface and (anisotropic) velocity model from a single gather of reflection traveltime (kinematic) data is inherently ambiguous because the processing of such data can only determine a horizontal slowness component, not a vertical one. Based thereon, I derive a simple algorithm that generates an infinite series of combinations of the subsurface-velocity models, all of which will show nearly the same seismic kinematic response, as further demonstrated by simulating wave propagation through a model with different interface dips. This algorithm assumes, first, that all interface dips remain constant over the distance considered and, second, that an approximation of the elasticity model — that is, the linearization of a phase velocity — valid for weak anisotropy can be used. Furthermore, when applied to the classic and analytically solvable case of traveltime analysis for a stack of flat layers with weak transverse isotropy, the algorithm theoretically explains the combination of anisotropy parameters that govern the nonhyperbolic term of a traveltime series: the established [Formula: see text] and its new counterpart [Formula: see text] for a [Formula: see text] and [Formula: see text] wave, respectively.

Thermal optimization of buoyancy driven radiative engine-oil based viscous hybrid nanofluid flow observing the micro-rotations in an inclined permeable enclosure

Thermal enhancement of engine oil plays an important role in a variety of industrial applications including heat exchangers, automotive cooling systems, renewable energy systems, biomedical devices, and advanced manufacturing processes. Considering the high demand in industry, present investigation explores thermal optimization of engine oil contained in a porous inclined cavity involving dispersion of hybrid nanoparticles along with inclusion of micropolar fluid flow phenomena. Numerical analysis is performed on the micropolar nanofluid containing iron oxide and molybdenum disulfide while considering the effect of thermal radiation, buoyancy force, viscous dissipation, magnetic field’s strength, porosity, and vortex viscosity. Numerical solutions of the dimensionless governing equations are provided through utilization of FVM. The coupling of velocity and pressure terms was achieved through implementation of SIMPLE algorithm. The results are illustrated through streamlines, contours, velocity, temperature profiles and heat flux. The outcomes revealed that an augment in Hartmann number and thermal radiation parameter results in higher heat flux rate. The incrementation in Hartmann number disrupted the flow resulting a reduction in the size of major vortices and similar trend is noted in case of temperature profile for the said parameters. The outcomes complement the continuing efforts to improve the thermal management of various applications and offer insightful information for optimizing engine oil compositions.

Predictive Controller Strategies for Electrical Drives System using Inverter System

Advanced control strategies in power electronics include Predictive controller of current (P CURRENT CONTROL) and Predictive controller of torque (P TORQUE CONTROL). In order to operate a SRM or an induction machine, the Predictive controller of torque (P TORQUE CONTROL) approach analyses the stator flux and electromagnetic torque in the cost function (IM), and the Predictive controller of current (P CURRENT CONTROL) method [1,2] takes errors between the current reference and the measured current into account in the cost function. The switching vector selected for usage in IGBTs reduces the error between the references and the predicted values. The system restrictions are easy to include [4, 5]. The weighting component is not required. Together with the P TORQUE CONTROL and P CURRENT CONTROL systems, the SRM method is the most practicable direct control technique since it doesn't require a modulator and offers 10% to 30% more power than an induction motor [3]. With the same current, an induction motor can only generate between 70 and 90 percent of the force generated by an SRM due to its lagging power factor. SRM approach decreases 23% more THD in torque, speed, and stator current when P CURRENT CONTROL and P TORQUE CONTROL method with 15-level H-bridge multilevel inverter is compared to P CURRENT CONTROL and P TORQUE CONTROL method with 15-level H-bridge multilevel inverter utilising induction motor [21]. The transistors are only swapped when necessary to maintain the limits of torque and flux, which minimises switching losses. To improve the efficiency of a multilevel inverter, semiconductor switches are switched in a specific pattern. In contrast to the P TORQUE CONTROL and P CURRENT CONTROL approaches using a 2-level voltage source inverter, the 15-level H-bridge multilevel inverter employed in this study, coupled with SRM and IM, gives outstanding torque and flux responses and achieves robust and stable operation. This unique strategy quickly caught the interest of academics due to its simple algorithm and high performances in both steady and transient modes [8].

Estimation of Evapotranspiration in South Eastern Afghanistan Using the GCOM-C Algorithm on the Basis of Landsat Satellite Imagery

This study aims to assess the performance of the Global Change Observation Mission—Climate (GCOM-C) ETindex estimation algorithm to estimate the actual evapotranspiration (ETa) in southeastern Afghanistan. Here, the GCOM-C ETindex algorithm was adopted to estimate the monthly ETa for the period from November 2016 to October 2017 using a series of Landsat 8, Thermal Infrared Sensor (TIRS) Band 10 satellite imagery. The estimation accuracy was evaluated by comparing the results with other estimates of ETa, namely the mapping evapotranspiration with the internalized calibration (METRIC) model, the MODIS Global Evapotranspiration Project (MOD16), the surface energy balance system (SEBS) tools, and with the crop evapotranspiration under standard conditions (ETc) as estimated by the FAO-56 procedure. The evaluation was made for irrigated wheat, maize, rice, and orchards and for non-irrigated bare soil land. The comparison of ETa values showed good correlation among the GCOM-C, METRIC, and FAO-56, while the MOD16 and SEBS showed significantly lower values of ETa. The agreement with the METRIC ETa implies that the simple GCOM-C algorithm successfully estimated the ETa in the region and that the precision was similar to that of the METRIC. This study provides the first high-quality evapotranspiration data with the spatial resolution of Landsat Band 10 data for the southeastern part of Afghanistan. The estimation procedure is straightforward, and its results are anticipated to enhance the understanding of regional hydrology.

Pressure space: The key subsurface commodity for CCS

Storage resource estimates are part of the foundation for Carbon Capture and Storage (CCS) policy, project development and pipeline routing. Multiple generations of such estimates have found that hundreds and even thousands of gigatons of storage are available in basins around the world. However, these estimates have largely been based on basin-scale static capacity calculations that assume pore space is accessible to CO2, because basins have open boundaries that allow water to be displaced and avoid pressure build-up. However, as we now consider large-scale injection that stresses the system capacity, limitations to the flow of CO2 and displaced brine must be considered. These limits include pressure interactions among multiple projects, physical lateral discontinuities such as faults and facies changes, overpressure, juxtaposition with impermeable basement, and regulatory requirements that require protection of freshwater. We present here a simple algorithm to estimate the total pressure-limited storage resource. Applying it to the example of the Texas coastal Miocene results in a significantly lower storage resource over our previous static capacity estimates (assuming no water production). Based on this assessment, we find that CCS is still unlikely to be capacity-limited, but effective regulation, land valuation and project development will require recalibration and consideration of all projects in pressure communication. We conclude that depth-dependent and geomechanically-limited pressure space, not pore space, is the key subsurface commodity.

Boundary effect on the soil strength estimation from T-bar penetration tests

The T-bar penetration tests have gained widespread use in assessing the undrained shear strength of marine clays. However, when dealing with model tests or sampled soil with a small diameter of the tube, the reliability of T-bar test results is influenced by both the boundary effect and the soil flow mechanism around the T-bar. In this paper, a comprehensive investigation into the boundary effect on T-bar penetration resistance was conducted by using the large deformation finite element (LDFE) method in conjunction with laboratory tests. The study reveals that the boundary conditions significantly impact the T-bar resistance when the lateral boundary dimension is smaller than 12D. Specifically, the T-bar penetration resistance is underestimated for smooth boundaries and overestimated for rough boundaries. For soils with higher strength, a larger boundary size is required. The bottom boundary also affects the T-bar resistance until the full-flow mechanism is established. However, once the full-flow mechanism is achieved, the boundary effect on T-bar penetration resistance diminishes, even for small lateral boundary sizes (2D). Considering the fact that it is challenging to attain the full-flow mechanism, especially for higher strength soils, a simple algorithm is proposed to estimate soil strength based on a shallow penetration depth (2D). This algorithm is validated through laboratory tests. To address the difficulty in achieving the full-flow mechanism, by applying the overburden pressure during the T-bar test, a novel T-bar test method is suggested. This innovative approach promises to resolve the issue effectively.

A new point cloud simplification algorithm based on V-P container constraint and normal vector angle information entropy

Abstract Most point cloud simplification algorithms use k-order neighborhood parameters, which are set by human experience; thus, the accuracy of point feature information is not high, and each point is repeatedly calculated simultaneously. The proposed method avoids this problem. The first ordinal point of the original point cloud file was used as the starting point, and the same spatial domain was then described. The design method filters out points located in the same spatial domain and stores them in the same V-P container. The normal vector angle information entropy was calculated for each point in each container. Points with information entropy values that met the threshold requirements were extracted and stored as simplified points and new seed points. In the second operation, a point from the seed point set was selected as the starting point for the operation. The same process was repeated as the first operation. After the operation, the point from the seed point set was deleted. This process was repeated until the seed point set was empty and the algorithm ended. The simplified point set thus obtained was the simplified result. Five experimental datasets were selected and compared using the five advanced methods. The results indicate that the proposed method maintains a simplification rate of over 82% and reduces the maximum error, average error, and Hausdorff distance by 0.1099, 0.074, and 0.0062 (the highest values among the five datasets), respectively. This method has superior performance for single object and multi object point cloud sets, particularly as a reference for the study of simplified algorithms for more complex, multi object and ultra-large point cloud sets obtained using terrestrial laser scanning and mobile laser scanning.

Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting

Within the geophysical exploration utilising seismic methods, it is well known that if the explored distances are much greater than the wavelength of the seismic waves with which the exploration is carried out, the ray approach of the wave theory can be used. In this way, when the rays travel through an inhomogeneous medium, they follow curved trajectories, which is imperative to determine the geological features that produce reflection and refraction phenomena. In this paper, a simple algorithm for the calculation of the trajectory of a seismic beam through an inhomogeneous stratum is presented. For this, the construction of a pseudo-Riemannian metric is required from the function of P-wave velocities of the geological stratum. Thus, the problem is inverted because instead of finding the curved trajectory of the seismic beam in a background with a Euclidean metric, it is proposed that the beam follows a geodesic of a curved space-time specific to each stratum, becoming a simple and automatic process using the differential geometry apparatus. For the reader to gain insight into this tool, different geological setups from idealised ones up to a salt dome are presented.

Classification of non-referenced continuous-wave terahertz reflection spectra for remote material identification

Commonly, terahertz spectra (both continuous-wave and pulsed) are deconvoluted by reference spectra to remove the water vapor absorption lines and other system related responses. However, in real-life applications obtaining reference spectra can be problematic and adds to the complexity of the system. Thus, a reference-free method for classification of terahertz spectra could be a welcomed advance for remote sensing applications. In this paper, we study how simple machine learning algorithms perform as a reference-free method for terahertz stand-off identification of materials. The algorithms are trained using spectra measured under controlled humidity conditions and tested by a completely independent data set measured under ambient conditions. We apply three different classification algorithms; namely a Gaussian Bayes model, the k nearest neighbors, and a support vector machine. We found that, if the terahertz spectra are processed using a supervised algorithm (Regularized Linear Discriminant Analysis), very high classification scores (¿98.6%) can be retained for the non-referenced spectra. Moreover, the high accuracy is obtained meanwhile the dimensionality is reduced by a factor larger than 160, which further reduces the computational requirements. Hence, we have demonstrated that simple supervised machine learning algorithms can serve as a highly accurate reference-free method for THz material identification. This could be of great importance for real-world remote sensing applications based on terahertz spectroscopy.

EventLFM: event camera integrated Fourier light field microscopy for ultrafast 3D imaging

Ultrafast 3D imaging is indispensable for visualizing complex and dynamic biological processes. Conventional scanning-based techniques necessitate an inherent trade-off between acquisition speed and space-bandwidth product (SBP). Emerging single-shot 3D wide-field techniques offer a promising alternative but are bottlenecked by the synchronous readout constraints of conventional CMOS systems, thus restricting data throughput to maintain high SBP at limited frame rates. To address this, we introduce EventLFM, a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy (LFM), a state-of-the-art single-shot 3D wide-field imaging technique. The event camera operates on a novel asynchronous readout architecture, thereby bypassing the frame rate limitations inherent to conventional CMOS systems. We further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by EventLFM. Experimental results demonstrate that EventLFM can robustly reconstruct fast-moving and rapidly blinking 3D fluorescent samples at kHz frame rates. Furthermore, we highlight EventLFM’s capability for imaging of blinking neuronal signals in scattering mouse brain tissues and 3D tracking of GFP-labeled neurons in freely moving C. elegans. We believe that the combined ultrafast speed and large 3D SBP offered by EventLFM may open up new possibilities across many biomedical applications.

Managing power balance and reserve feasibility in the AC unit commitment problem

Incorporating the AC power flow equations into unit commitment models has the potential to avoid costly corrective actions required by less accurate power flow approximations. However, research on unit commitment with AC power flow constraints has been limited to a few relatively small test networks. This work investigates large-scale AC unit commitment problems for the day-ahead market and develops decomposition algorithms capable of obtaining high-quality solutions at industry-relevant scales. The results illustrate that a simple algorithm that only seeks to satisfy unit commitment, reserve, and AC power balance constraints can obtain surprisingly high-quality solutions to this AC unit commitment problem. However, a naive strategy that prioritizes reserve feasibility leads to AC infeasibility, motivating the need to design heuristics that can effectively balance reserve and AC feasibility. Finally, this work explores a parallel decomposition strategy that allows the proposed algorithm to obtain feasible solutions on large cases within the two hour time limit required by typical day-ahead market operations.

Enhanced thermal effectiveness of square duct with V-type double-baffles: Numerical study

The article puts forward three-dimensional computational research on heat transmission augmentation within a square channel containing 45o V-type double-baffles positioned on the lower and top parts at regular intervals in the turbulence zone for Reynolds numbers (Re) that vary from 3000 to 20,000. The primary goal of this research is to increase the thermal effectiveness and relative Nusselt number (Nu/Nu0), in order to conserve energy and reduce the size of the heating or cooling system. The simulations utilize a finite volume approach in common with the SIMPLE algorithm, whereas the turbulent model used is the realizable k–ε. The baffles are designed to be separated vertically for reducing pressure loss. Both single V-baffles and double V-baffles have four relative pitches (PR = 0.4, 0.5, 0.6, and 1.0) and height/blockage ratios (BR = 0.05, 0.1, 0.15, and 0.2), with a fixed attack angle (α) of 45o. The computational findings show that both V-baffles are capable of producing the primary vortices, but only the double V-baffles have the ability to provide the impinging streams onto the wall, cooling the region behind the baffles. This suggests that the double V-baffles not only boost heat transmission but also reduce frictional loss. When compared to a single V-baffle, the double ones enhance heat transfer by an average of 1.04–9.94% while decreasing frictional loss by an average of 9.88–31.73%. The thermal effectiveness factor (TEF) of the double V-baffles ranges from 1.03 to 3.21, and its peak value of around 3.21 is for PR = 0.4, BR = 0.05, at lower Re.

Industry Foundation Class-Based Building Information Modeling Lightweight Visualization Method for Steel Structures

The efficient extraction, storage, and visualization of geometric and semantic information is a key foundation for the operation of the building information modeling (BIM) platform. This study aims to develop a lightweight BIM system and optimize the system’s performance according to the specific characteristics of steel structures. This study proposes several novel techniques for extracting and decoupling the geometric and semantic information of components from industry foundation class (IFC) files. A redundancy removal approach combining the principal content analysis (PCA) algorithm and the Hausdorff-based comparison algorithm is proposed to identify standardized steel components, and a lightweight visualization method on Web3D for redundant instances is also presented. A loading mechanism of the level of detail (LOD) model based on a mesh simplification algorithm is presented to optimize the display efficiency. The developed system is evaluated by three steel structural models. Using the redundancy removal approach, the number of instances is decreased by 96.46% in less than 30 s and over 30 FPS (frame per second) is kept when rendering. Using the LOD loading mechanism, 95.38% of vertices and 98.46% of patches are eliminated under 50 mm precision. The experiment results indicate that users can quickly load large BIM models and fetch sufficient information from the website.

Обучаем школьников анализу языковой личности персонажа: подготовка к итоговому сочинению по литературе

The article is devoted to the problem of preparing eleventh graders to write a final essay on literature. The focus is on the methodology of working on the topic “Language and linguistic personality”. In modern humanities, the term “linguistic personality” is studied by scientists from different angles: linguodidactic, linguoritorical, communicative, linguoculturological and linguocognitive approaches. A controversy arises around the methodology for analyzing the linguistic personality, both real (native speaker) and conventional (virtual) within the framework of a literary text, which determines the relevance of the study, the novelty of which lies in the development of an algorithm for analyzing the linguistic personality of a character in a literary text based on the material, adapted for schoolchildren story by A.P. Chekhov’s “Gooseberry”. The purpose of the article is to develop, based on the research of L.N. Churilina and Yu.N. Karaulov, a simplified algorithm for analyzing linguistic personality for schoolchildren, which can be used if you choose the appropriate topic when writing a final essay on literature as part of the Unified State Exam. The research is based on linguostylistic and comparative methods, as well as methods of citation and holistic analysis of a literary work. The subject of the study is the linguistic personality of the heroes of Chekhov’s story “Gooseberry”. Based on the analysis of the story, the author demonstrates the main aspects of revealing the linguistic personality of the characters: mental and speech activity determined by the worldview of the characters, their education and intellectual development, origin and social circle, the space-time continuum in which the text production of the characters is inscribed both in the mental (thought process, objectified by internal speech), and in real discourse (speaking) taking into account the addressee and the statements of others (dialogue, direct speech, improperly direct speech). The proposed work format develops students’ skills in analyzing the linguistic personality of a character and can be successfully used by philologist teachers in preparing for the final essay on literature at school.

Competitive Model Selection in Algorithmic Targeting

We study how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face a general bias-variance trade-off when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm has a data analyst who uses the chosen algorithm to estimate demand for multiple consumer segments, based on which it devises a targeting policy to maximize estimated profits. We show that competition induces firms to strategically choose simpler algorithms that involve more bias but lower variance. Therefore, more complex/flexible algorithms may have higher value for firms with greater monopoly power. History: Anthony Dukes served as the senior editor for this article. Funding: This work was supported by Hong Kong Research Grants Council [project number 14503122].

Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection

This paper introduces an autonomous robot designed for in-pipe structural health monitoring of oil/gas pipelines. This system employs a 3D Optical Laser Scanning Technical Vision System (TVS) to continuously scan the internal surface of the pipeline. This paper elaborates on the mathematical methodology of 3D laser surface scanning based on dynamic triangulation. This paper presents the mathematical framework governing the combined kinematics of the Mobile Robot (MR) and TVS. It discusses the custom design of the MR, adjusting it to use of robustized mathematics, and incorporating a laser scanner produced using a 3D printer. Both experimental and theoretical approaches are utilized to illustrate the formation of point clouds during surface scanning. This paper details the application of the simple and robust mathematical algorithm RANSAC for the preliminary processing of the measured point clouds. Furthermore, it contributes two distinct and simplified criteria for detecting defects in pipelines, specifically tailored for computer processing. In conclusion, this paper assesses the effectiveness of the proposed mathematical and physical method through experimental tests conducted under varying light conditions.