Split Point Research Articles

Design of reduced-current overcurrent protection circuit based on GBDT model

Abstract To address the problems of slow response and insufficient protection sensitivity of existing overcurrent protection techniques, this paper verifies the feasibility of the design of reduced-current overcurrent protection circuits by using the GBDT model. Firstly, the regression tree is used as a circuit learner for iteration, and the CART algorithm is applied to calculate circuit splitting characteristics and optimal splitting points to obtain the gain of splitting. Then the line loss rate is calculated using the differential evolution method to derive the objective function of the minimized line and the optimal value of the circuit. Finally, the mean value is extracted by calculating the optimal line’s statistical characteristics, the regression tree nodes are calculated using the GBDT model, and the root means the square error is used to verify the effectiveness of the circuit protection design proposed in this paper. The experimental results show that the overcurrent protection value of the circuit sample is around 1.35A and the current limit protection time is around 13ms by verifying the current reduction protection function and indication function of the circuit in the full temperature range. This shows that the GBDT model enables the reduced-current overcurrent protection circuit to perform timely and accurate overcurrent detection actions, and the circuit can be reliably protected under different overcurrent conditions.

Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning.

Cancer is a broad term that refers to a wide range of diseases that can affect any part of the human body. To minimize the number of cancer deaths and to prepare an appropriate health policy on cancer spread mitigation, scientifically supported knowledge of cancer causes is critical. As a result, in this study, we analyzed lung cancer risk factors that lead to a highly severe cancer case using a decision tree-based ranking algorithm. This feature relevance ranking algorithm computes the weight of each feature of the dataset by using split points to improve detection accuracy, and each risk factor is weighted based on the number of observations that occur for it on the decision tree. Coughing of blood, air pollution, and obesity are the most severe lung cancer risk factors out of nine, with a weight of 39%, 21%, and 14%, respectively. We also proposed a machine learning model that uses Extreme Gradient Boosting (XGBoost) to detect lung cancer severity levels in lung cancer patients. We used a dataset of 1000 lung cancer patients and 465 individuals free from lung cancer from Tikur Ambesa (Black Lion) Hospital in Addis Ababa, Ethiopia, to assess the performance of the proposed model. The proposed cancer severity level detection model achieved 98.9%, 99%, and 98.9% accuracy, precision, and recall, respectively, for the testing dataset. The findings can assist governments and non-governmental organizations in making lung cancer-related policy decisions.

Variational inequalities over the solution sets of split variational inclusion problems

We study variational inequalities over the solution sets of split variational inclusion problems with multiple output sets. In order to find a solution to such problems, we introduce a new parallel iterative algorithm by combining the inertial forward–backward splitting method with the steepest descent method. Our algorithm does not depend on the norm of the transfer mappings. Some of its applications to solving split common null point, split minimum point, and split common fixed point problems are also given.

Fronthaul latency and capacity constrained cost-effective and energy-efficient 5G C-RAN deployment

The third-generation partnership project (3GPP) has identified different new functional split points, defined as options 1–7, between the baseband unit (BBU) and the remote unit (RU) for the 5G centralized radio access networks (C-RANs). The functional splits facilitate lower fronthaul line rate and latency requirements while imposing technical complexity and cost-efficiency trade-offs. Among all the functional split options, the standardization groups such as 3GPP, Open RAN (O-RAN), and small cell forum have selected option-7 as the most suitable choice for the 5G C-RAN deployment that can meet the operator’s requirements. One of the biggest challenges for designing option-7 functional split-based C-RAN is the capacity and latency requirement fulfillment as specified by 3GPP, while minimizing the deployment cost. In this regard, we propose an optimization model for cost-effective and energy-efficient option-7 functional split-based C-RAN architecture using wavelength division multiplexed passive optical network (WDM-PON) in the fronthaul. For the given locations of the next-generation core (NGC) and the set of RUs, the proposed optimization model explores the most suitable locations for the placement of BBU pools and arrayed waveguide gratings (AWGs). It provides complete interconnection details among NGC, BBU pools, AWGs, and RUs. It also provides the optimal number of digital units in each selected BBU pool, the splitting ratio of each AWG, and maximum fronthaul reach, satisfying the fronthaul latency, capacity, and power budget constraints. To the best of our knowledge, for the first time, we estimate the worst-case queuing delay for the upstream traffic flow using the G/G/1 queuing model and investigate its impact on fronthaul latency and deployment cost for different RU configurations. We compare the performance of the proposed option-7 functional split-based C-RAN with the traditionally used option-8 functional split-based C-RAN in terms of the deployment cost and fronthaul latency improvement.

New Algorithms for Solving the Split Common Zero Point Problem in Hilbert Space

We introduce new self-adaptive algorithms for solving the split common zero point problem with multiple output sets in Hilbert space. We also apply our main results to solving split feasibility problems with multiple output sets.

Bone biomarkers and subsequent survival in men with bone-metastatic hormone sensitive prostate cancer (HSPC): Results from the phase III SWOG S1216 trial of androgen deprivation +/- orteronel.

5048 Background: We previously reported that circulating serum biomarkers of bone metabolism were significantly associated with overall survival (OS) in men with advanced prostate cancer, either in the hormone sensitive (Lara, et al. ASCO 2022) or castration resistant state (Lara, et al. JNCI 2016). In S1216, we showed that elevated bone biomarkers were significantly associated with an increased risk of death in HSPC regardless of bone metastases (mets). We also identified three risk groups (low, intermediate, and poor) with differential OS outcomes (median OS: 8.2, 5.1, and 2.1 years, respectively) based on combinations of bone biomarkers in these patients. Here we report the association of bone biomarkers with OS in men with HSPC and documented skeletal mets as part of a planned analysis of S1216. Methods: Bone resorption [C-telopeptide (CTx) and Pyridinoline (PYD)] and bone formation markers [C-terminal collagen propeptide (CICP) and bone alkaline phosphatase (BAP)] were assessed from patient sera. Patients were randomly divided into training (n=238) and validation (n=475) sets. In the training set, recursive partitioning that maximizes discrimination of OS was used to identify the dichotomous cut-point for each biomarker and for a combination of biomarker split points to define prognostic groups. In the validation set, Cox proportional hazards models were used to assess the impact of biomarkers on OS, adjusted for patient and tumor characteristics. Results: Of 1,279 men in S1216, 713 had both baseline bone mets and evaluable bone biomarkers. Patient characteristics were similar between the overall population and the subset with bone mets. Elevated levels of CICP, CTX, and PYD were strongly prognostic for OS, but not for BAP. Hazard ratios (95% CI) for OS adjusted for treatment arm and baseline clinical variables were: BAP – 1.31 (0.93, 1.84), p=0.12; CICP – 1.58 (1.09, 2.29), p<0.02; CTx – 1.55 (1.12, 2.15), p=0.008; and PYD – 1.66 (1.27, 2.217), p=0.0002. There was no evidence of interaction between elevated biomarkers and treatment (all p>0.2). Recursive partitioning algorithms identified four groups of patients with differential OS outcomes based on bone biomarkers, adjusted for baseline clinical variables, with median OS ranging from 2.3 years (highest risk group) to 7.5 years (lowest risk group). Conclusions: In this subset analysis of men with HSPC and bone mets in S1216, elevated serum markers of bone metabolism were significantly associated with worse OS. Bone biomarker levels alone and in combination with patient and tumor characteristics identify unique subsets of men with differential OS outcomes. Clinical trial information: NCT01809691 .

Wireless Channel Adaptive DNN Split Inference for Resource-Constrained Edge Devices

Split inference facilitates deep neural network (DNN) inference tasks at resource-constrained edge devices. However, a pre-determined split configuration of a DNN limits the inference performance in time-varying wireless channels. To accelerate the inference, we propose a two-stage wireless channel adaptive split inference method by considering memory and energy constraints on the edge device. The proposed scheme is able to offer the privacy of the edge device and improves inference performance in time-varying wireless channels by leveraging a U-shaped DNN splitting framework and adaptively determining the splitting points of a DNN in real-time according to time-varying wireless channel gains.

A Fast Parallel Random Forest Algorithm Based on Spark

To improve the computational efficiency and classification accuracy in the context of big data, an optimized parallel random forest algorithm is proposed based on the Spark computing framework. First, a new Gini coefficient is defined to reduce the impact of feature redundancy for higher classification accuracy. Next, to reduce the number of candidate split points and Gini coefficient calculations for continuous features, an approximate equal-frequency binning method is proposed to determine the optimal split points efficiently. Finally, based on Apache Spark computing framework, the forest sampling index (FSI) table is defined to speed up the parallel training process of decision trees and reduce data communication overhead. Experimental results show that the proposed algorithm improves the efficiency of constructing random forests while ensuring classification accuracy, and is superior to Spark-MLRF in terms of performance and scalability.

A unified scheme for solving split inclusions with applications

In this article, we propose an iterative scheme for solving a generalized split common null point problem. The iterative method uses self‐adaptive stepsizes that do not depend on the Lipschitz constants of the underlying operators. Instead, the stepsizes are updated using a simple updating formula. We prove the strong convergence theorems of the sequence generated by the iterative scheme under some mild assumptions. Furthermore, we derive iterative methods for solving split generalized variational inequality, convex feasibility, and minimization problems. In addition, some numerical illustrations are provided to support the main result.

PO-01-195 PHENOTYPING PERSISTENT AF WITH ELECTROGRAPHIC FLOW MAPPING

There is broad consensus that differentiating AF into the broad categories of “paroxysmal AF” vs “persistent AF (PerAF)” vs “LS-PerAF” AF is a relatively crude means to differentiate AF types. Clearly, there is a pressing need for better strategies to phenotype AF. Electrographic Flow (EGF) mapping is designed to identify not only active sources of excitation, but also to estimate the consistency of observed atrial wavefront propagation. Electrographic flow consistency (EGFC) is computed from the Euclidean length of vector field estimates over time and may provide additional insight into an individual patient’s atrial fibrillation (AF) disease that in combination with EGF-identified sources may enable the phenotyping of PerAF patients. To 1) determine the relationship between EGFC and AF recurrence, and 2) propose a phenotyping framework for the treatment and prognosis based on functional mechanisms as described by EGF. In the randomized FLOW-AF trial, patients prospectively underwent PVI and subsequent EGF-guided source ablation in all cases when such sources were identified, and PVI-only when no sources were seen. In each patient, a series of 1-min EGF recordings were taken in multiple locations per atrium post-PVI but pre- any adjunctive or source ablation. Mean EGFC from these recordings were averaged for each basket position and these averages were averaged to determine an overall EGFC across all locations. A decision tree classifier was employed to determine the optimal splitting point below which EGFC scores were more commonly associated with recurrence within 12 months. Per FLOW-AF’s design, patients were divided into no sources (n=32) vs with sources above threshold randomized to EGF-guided ablation (n=21). The decision tree classifier identified 0.63 as EGFC above which recurrence is least common and below which recurrence is most common. Patients with high EGFC and no sources had 100% (10/10) freedom from AF (FFAF); patients with high EGFC and at least 1 ablated source had 88% (7/8) FFAF; patients with low EGFC and no sources had 50% (11/22) FFAF; and patients with low EGFC and at least 1 ablated source had 46% (6/13) FFAF. Using EGF mapping algorithms to detect the presence of functional mechanisms of AF, the clinically heterogeneous population of PerAF patients can be stratified into distinct EGF phenotypes that guide the minimum-required ablation strategy, and also provide post-ablation prognosis. A larger prognostication study is warranted.

Denoising method based on CNN-LSTM and CEEMD for LDV signals from accelerometer shock testing

The laser Doppler velocimeter (LDV) is commonly used in high-G accelerometer shock testing to provide high-precision reference velocity measurements. However, noise inevitably interferes with LDV signals, reducing the measurement accuracy. A novel denoising method based on convolutional neural network with long short-term memory (CNN-LSTM) and complementary ensemble empirical mode decomposition (CEEMD) is proposed to improve the measurement accuracy of reference velocity. First, the weights were obtained by training the constructed CNN-LSTM neural network. CEEMD was then used to process the training signals, and the resulting IMF was partially zeroed. Furthermore, the splitting points were evaluated and optimized. Finally, the weights and optimal splitting points were applied to the test signals. Simulation and experimental results show that the proposed method outperforms wavelet thresholding and CNN-LSTM in denoising performance. The results show that the proposed method can improve the accuracy of the demodulated velocity and thus contribute to accelerometer shock testing.

A self-adaptive inertial algorithm for solving split null point problems and common fixed point problems

A self-adaptive inertial algorithm for solving split null point problems and common fixed point problems

Spectral Splitting Sensing Using Optical Fiber Bragg Grating for Spacecraft Lateral Stress Health Monitoring

Optical fiber sensing is a promising detection method for spacecraft health monitoring, since optical fiber sensors are lightweight, small in size, easy to integrate and immune to electromagnetic interference. As a significant optical sensor, fiber Bragg gratings (FBG) are widely used for force sensing because of their axial strain characteristics. However, it is necessary to detect not only one-dimensional strain but also plane strain and its deformation in order to comprehensively evaluate the condition of the structure. Therefore, it is very important to analyze the reflection spectrum of FBG under lateral stress. When FBG are subjected to lateral stress, the refractive index of the waveguide in the x and y directions changes, resulting in a birefringence phenomenon. This result causes the reflection spectrum of FBG to split into two peaks. In this paper, a transverse stress detection method based on spectral split sensing for the fiber Bragg grating is proposed, intended for monitoring spacecraft–small particle collisions. The FBG local lateral stress detection system is designed and verified by experiments. The wavelength pressure correlation is established in the experiment by adjusting the number of weights to change the lateral pressure on the FBG. The loading range of FBG lateral pressure is 4.0–7.0 N, the step size is 0.5 N, and round-trip measurement is carried out four times. The wavelengths of the peak and split point of the FBG reflection spectrum are recorded. The experimental results show that FBG’s split point and right peak pressure sensitivities are 16.57 pm/N and 45.14 pm/N, respectively. The spectral splitting phenomenon can be applied in spacecraft structure health monitoring systems and has certain reference value for the simplification of sensor systems.

ClaSP: parameter-free time series segmentation

The study of natural and human-made processes often results in long sequences of temporally-ordered values, aka time series (TS). Such processes often consist of multiple states, e.g. operating modes of a machine, such that state changes in the observed processes result in changes in the distribution of shape of the measured values. Time series segmentation (TSS) tries to find such changes in TS post-hoc to deduce changes in the data-generating process. TSS is typically approached as an unsupervised learning problem aiming at the identification of segments distinguishable by some statistical property. Current algorithms for TSS require domain-dependent hyper-parameters to be set by the user, make assumptions about the TS value distribution or the types of detectable changes which limits their applicability. Common hyper-parameters are the measure of segment homogeneity and the number of change points, which are particularly hard to tune for each data set. We present ClaSP, a novel, highly accurate, hyper-parameter-free and domain-agnostic method for TSS. ClaSP hierarchically splits a TS into two parts. A change point is determined by training a binary TS classifier for each possible split point and selecting the one split that is best at identifying subsequences to be from either of the partitions. ClaSP learns its main two model-parameters from the data using two novel bespoke algorithms. In our experimental evaluation using a benchmark of 107 data sets, we show that ClaSP outperforms the state of the art in terms of accuracy and is fast and scalable. Furthermore, we highlight properties of ClaSP using several real-world case studies.

Optimization method of linear barrier coverage deployment for multistatic radar

To address the problem of building linear barrier coverage with the location restriction, an optimization method for deploying multistatic radars is proposed, where the location restriction splits the deployment line into two segments. By proving the characteristics of deployment patterns, an optimal deployment sequence consisting of multiple deployment patterns is proposed and exploited to cover each segment. The types and numbers of deployment patterns are determined by an algorithm that combines the integer linear programming (ILP) and exhaustive method (EM). In addition, to reduce the computation amount, a formula is introduced to calculate the upper threshold of receivers' number in a deployment pattern. Furthermore, since the objective function is non-convex and non-analytic, the overall model is divided into two layers concerning two suboptimization problems. Subsequently, another algorithm that integrates the segments and layers is proposed to determine the deployment parameters, such as the minimum cost, parameters of the optimal deployment sequence, and the location of the split point. Simulation results demonstrate that the proposed method can effectively determine the optimal deployment parameters under the location restriction.

Split‐flow layouts of supercritical CO2 Brayton cycle for coal‐fired power plant considering the flue gas residual heat utilization

AbstractThe utilization of residual energy of high‐temperature flue gas at the tail of coal‐fired boilers has attracted widespread attention in recent years. The thermal performances of the boiler and system in different split‐flow layouts still need to be analyzed and compared in detail. Thermodynamic models for four different splitting layouts of supercritical carbon dioxide (S‐CO2) Brayton cycles tailored for coal‐fired power plant are built and validated. By performing the process simulations, the effects of splitting ratios on the system thermal performance are comparatively analyzed when the secondary split point and the confluence point are set at different positions. The maximum system efficiency of the two‐split layout with a secondary splitting point at the inlet of the cold side of the high‐temperature recuperator (HTR) can be obtained within a range of secondary split ratio. The maximal and minimal boundaries are determined by the exhaust flue gas temperature and the heat capacity match in the recuperators, respectively. The mass flow rate of the cold stream in the low‐temperature recuperator (LTR) should be kept unchanged by constraining the sum of primary and secondary split ratios, the constant to prevent the cycle efficiency from decreasing dramatically, if the splitting layout with the split point at the outlet of the main compressor (MC) is used. The two‐splitting layout with a secondary splitting point at the inlet of the cold side of the HTR has the best performance by comparing the system efficiencies of different layouts. If there are any improved coupling approaches of the S‐CO2 Brayton cycle and boiler, this paper can provide the reference for the selections of the split ratios.

Two-step inertial method for solving split common null point problem with multiple output sets in Hilbert spaces

<abstract><p>In this paper, an algorithm with two-step inertial extrapolation and self-adaptive step sizes is proposed to solve the split common null point problem with multiple output sets in Hilbert spaces. Weak convergence analysis are obtained under some easy to verify conditions on the iterative parameters in Hilbert spaces. Preliminary numerical tests are performed to support the theoretical analysis of our proposed algorithm.</p></abstract>

The relationship between playing video games on mobile devices and well-being in a sample of Japanese adolescents and adults.

In this study, I have tested the following hypothesis: Playing a mobile game for an appropriate length of time results in a stronger sense of happiness (well-being) than not playing at all. I used the questionnaires data from 5000 participants. Well-being was measured using the three following indicators: the Positive and Negative Affect Schedule, satisfaction with life scale, and four-dimensional happiness indicator. I constructed a mathematical model for determining individual well-being. The variable of game playtime was split into two parts, and the split points were selected as the most applicable using Akaike Information Criterion to search for the appropriate length of time spent playing the mobile game. The results showed that mobile games were significantly and positively correlated with both positive and negative emotions. Playing a mobile game for 2 h or less each day was associated with a significantly higher satisfaction with life than not playing one at all. Further, well-being was significantly higher when there were no time limitations with regard to "relationship and gratitude" and when playing a mobile game for no more than 1.5 h each day with regard to "independence and my own pace" as compared to not playing one at all. Playing a mobile game for more than 2 h each day, however, resulted in lower well-being with regard to "self-realization and growth" and "positive frame of mind and optimism" as compared to not playing one at all. These findings indicate that playing a mobile game for no longer than 1.5 h each day results in higher well-being than not playing one at all. Conversely, playing a mobile game for more than 2 h each day significantly lowers some aspects of happiness but does not significantly affect the overall satisfaction with life.

A Unified Early Termination Technique for Primal-Dual Algorithms in Mixed Integer Conic Programming

We propose an early termination technique for mixed integer conic programming within branch-and-bound based solvers. Our approach generalizes previous early termination results for ADMM-based solvers to a broader class of primaldual algorithms, including both operator splitting and interior point methods. The complexity for checking early termination is O(n) for each termination check assuming a bounded problem domain. We show that this domain restriction can be relaxed for problems whose data satisfies a simple rank condition, in which case each check requires an O(n2) solve using a linear system that is factored only once at the root node. We further show how this approach can be used in hybrid model predictive control problems with bounded inputs. Numerical results show that our method leads to a moderate reduction in the computational time required for branch-and-bound conic solvers with interior-point based subsolvers.

An inertially constructed projection based hybrid algorithm for fixed point and split null point problems

<abstract><p>In this paper, we posit a framework for the investigation of the fixed point problems (FPP) involving an infinite family of $ \Bbbk $-demicontractive operators and the split common null point problems (SCNPP) in Hilbert spaces. We employ an accelerated variant of the hybrid shrinking projection algorithm for the construction of a common solution associated with the FPP and SCNPP. Theoretical results comprise strong convergence characteristics under suitable sets of constraints as well as numerical results are established for the underlying algorithm. Applications to signal processing as well as various abstract problems are also incorporated.</p></abstract>