Search Step Research Articles

Automated retinal boundary segmentation of optical coherence tomography images using an improved Canny operator

Retinal segmentation is a prerequisite for quantifying retinal structural features and diagnosing related ophthalmic diseases. Canny operator is recognized as the best boundary detection operator so far, and is often used to obtain the initial boundary of the retina in retinal segmentation. However, the traditional Canny operator is susceptible to vascular shadows, vitreous artifacts, or noise interference in retinal segmentation, causing serious misdetection or missed detection. This paper proposed an improved Canny operator for automatic segmentation of retinal boundaries. The improved algorithm solves the problems of the traditional Canny operator by adding a multi-point boundary search step on the basis of the original method, and adjusts the convolution kernel. The algorithm was used to segment the retinal images of healthy subjects and age-related macular degeneration (AMD) patients; eleven retinal boundaries were identified and compared with the results of manual segmentation by the ophthalmologists. The average difference between the automatic and manual methods is: 2–6 microns (1–2 pixels) for healthy subjects and 3–10 microns (1–3 pixels) for AMD patients. Qualitative method is also used to verify the accuracy and stability of the algorithm. The percentage of “perfect segmentation” and “good segmentation” is 98% in healthy subjects and 94% in AMD patients. This algorithm can be used alone or in combination with other methods as an initial boundary detection algorithm. It is easy to understand and improve, and may become a useful tool for analyzing and diagnosing eye diseases.

A Fault Signal Processing Method Based on An Improved Prony Algorithm

The fault of power systems introduces a severe challenge in terms of fault recording analysis, and the traditional Prony method cannot perform satisfactorily in the process of signal recordings fitting caused by faults. Therefore, an improved adaptive Prony algorithm is proposed in this article to study the characteristics of fault recordings. Specifically, the search step size is taken as an adaptive variable, and the mean square relative fitting error (MSRFE) is set as the criterion. Then, a large step is employed to rapidly determine an approximate segmentation point in the initial stage of the searching process, and its horizon is gradually reduced to establish an accurate subsegment point. Finally, the Prony algorithm is deployed to analyze the subsegment fitting original signal. The proposed approach has been simulated on an assumed fault signal, and the results validate the accuracy and efficiency of the method.

Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM).

In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). The research steps are as follows: Firstly, in order to obtain the sparse solution of LSSVM, a more reliable prediction model is realized by weighting the error value. Secondly, the chaotic sequence is introduced into the artificial bee colony algorithm to find a better initial honey source, which increases the diversity of the population, and combines the Levy flight to update the search step to avoid falling into the trap of local optimum. At the same time, the improved artificial bee colony algorithm is used to optimize the kernel width σ and regularization parameter λ of WLSSVM, which improves the prediction accuracy and convergence rate of WLSSVM. Finally, the quantitative analysis model of WLSSVM is reconstructed by using the optimized parameters, and the nine parameters of buried depth of coal seam, gas content of coal seam, coal thickness, interlayer lithology, production rate of working face, length of working face, inclination of coal seam, gas content of adjacent layer, and thickness of adjacent layer are used as the main influencing factors. After normalization, the nonlinear prediction model of gas emission is established. The simulation results based on the three indicators of determination coefficient, root mean square error, and average relative variance show that the IABC-WLSSVM prediction model proposed in this paper can not only overcome the local optimization to obtain the global optimal solution but also has faster convergence speed and higher prediction accuracy. This prediction model has obvious advantages compared with the other three improved prediction models in terms of fitting, accuracy, and generalization ability, which can provide a reliable theoretical basis for the prediction of gas emission in coal mining face under complex factors and propose a new idea for the application of artificial intelligence in the construction of intelligent mines. At the same time, the prediction model can also be applied to other fields.

A REVIEW OF TREATMENT, RISK FACTORS, AND INCIDENCE OF COLORECTAL CANCER

Colorectal cancer (CRC) is considered as the third most frequent cancer in the world and the incidence increases with increasing age. CRC accounts for nearly 9 % of all cancer incidence, with an estimated 1.4 million cases happening in 2012. The aim of this paper is to provide a review of incidence, risk factors, screening strategies, and treatment of colorectal cancer. We searched the studies in five English databases, including Web of Science, PubMed, Scopus, EMBASE, and Google Scholar with no limitation in publication time to find all papers regarding colorectal cancers. Papers with any language were included in the first step of search if they had an English abstract. We used the following words and terms including colorectal cancer, treatment, risk factor, diagnosis, chemotherapy, radiotherapy, surgery. Geographical variations and different time courses in the CRC incidence indicate that environmental factors and lifestyle are major factors in the development of this disease. The main preventable risk factors for CRC are nutrition, a high-fat diet, a low-fiber diet, obesity and physical inactivity, smoking and alcohol consumption, aspirin and nonsteroidal anti-inflammatory drugs, and some non-preventable risk factors such as age, gender, race, and diabetes mellitus. Colonoscopy remains the study of choice to diagnose colorectal cancer. Prior to any treatment, CT imaging of chest, abdomen and pelvis with contrast is needed for staging the patient’s CRC. The preferred option for localized colorectal cancer is surgery (etc, laparoscopic surgery, colostomy for rectal cancer); whereas the adjuvant chemotherapy is generally recommended for patients with lymph node metastases. Targeted treatment of colorectal cancer by monoclonal antibodies are important bioengineered proteins that can help the body's natural immune response to detect, attack, and kill cancer cells. Monoclonal antibodies may be used alone or in combination with other treatments such as chemotherapy. CRC accounts an important health problem worldwide that is estimated to increase because of the growth and aging of the population, and because of the adoption of at-risk manners and lifestyles, particularly in economically less developed countries. Screening has been confirmed to significantly decrease mortality and can prevent the onset of the disease. More international efforts are required to situate into practice targeted prevention approaches that might reduce the burden of CRC worldwide.

Pigeon-inspired optimisation-based cooperative target searching for multi-UAV in uncertain environment

In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon-inspired optimisation (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilising the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimised safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.

Role of Organic and Inorganic Nanoparticles in the Drug Delivery System for Hypertension Treatment: A Systematic Review.

The present investigation was designed to systematically review the antihypertensive effects of all the organic and inorganic nanoparticles in the in vitro, in vivo, and clinical trials. The current study was carried out using 06-PRISMA guideline and registered in the CAMARADES- NC3Rs Preclinical Systematic Review and Meta-analysis Facility (SyRF) database. The search was performed on five English databases, including Scopus, PubMed, Web of Science, EMBASE, and Google Scholar, without time limitation for publications worldwide related to the anti-hypertensive effects of all the organic and inorganic nanoparticles without date limitation, so as to identify all the published articles (in vitro, in vivo, clinical, and case-control). Studies in any language were entered in the search step if they had an English abstract. Out of 3602 papers, 60 including 25 werein vitro (41.7%), 17 in vitro / in vivo (28.3%), 16 in vivo (26.7%), and 2 in vitro / ex vivo (3.3%) up to 2020 met the inclusion criteria for discussion in this systematic review. The most widely used nanoparticles were organic nanoparticles such as polylactic acid, poly lactic-co-glycolic acid (PLGA), lipid, chitosan, etc., followed by inorganic nanoparticles such as silver and palladium nanoparticles. This review demonstrated the anti-hypertensive effects of some organic and inorganic nanoparticles alone or in combination with the available anti-hypertensives. We found that organic nanoparticles such as PGLA and chitosan can be considered as preferred options in nanomedicine for treating high blood pressure. The results also showed these nanoparticles displayed antihypertensive effects through some mechanisms such as sustained release forms via increasing bioavailability, increasing oral bioavailability and improving oral and non-oral absorption, counteracting excessive superoxide, decreasing blood pressure, etc. However, further investigations are required to prove these effects, particularly in clinical settings, as well as their accurate possible mechanisms and toxicity.

Proactive Monitoring via Jamming With Finite Blocklength Transmissions

Considering suspicious finite blocklength transmissions, we propose a proactive monitoring via jamming system. Our aim is to maximize the average monitoring rate, whose expression involves the integral of Q function. Although the formulated optimization problem is non-convex, we theoretically prove that the problem has a unique optimal solution, which can be found by the bisection search. Due to high complexity of integral in each step of bisection search, we propose a proactive monitoring scheme based on a linear approximation, which does not involve integral. Furthermore, by using the Riemann integral approximation, we also propose a closed-form near optimal solution to the average monitoring rate maximization problem. Numerical results demonstrate that our proposed schemes outperform the passive monitoring without jamming and proactive monitoring with constant jamming power schemes.

The Effect of Learning Conditions on Collocation Gains: A Case Study of Task-based Dictionary Use Instruction

To examine the effect of learning conditions on collocation gains, 88 Chinese EFL students were assigned randomly to one of three different learning conditions, i.e. dictionary use with prior instruction, dictionary use without training, or explicit collocation teaching. They were asked to fill in the missing verb in ten V + N target collocations embedded in sentences. A screen recorder was used to keep track of the students' lookup behaviour in the two conditions involving dictionary use such as every input of the searched word, every move and click of the mouse, every step of collocation search and the time length of dictionary consultation. After completion of the assignment all the students were given corrective feedback to the collocation task and marked their corrections on the test paper. Two weeks later, an unexpected retention test was administered. The study showed that dictionary use following a five-step training session reaped significantly more collocation gains than dictionary use without prior instruction and explicit collocation teaching. Compared with those who used the dictionary without prior training, the students with prior dictionary instruction employed more effective strategies concerning the selection of lookup words and the location and identification of collocation information. They took a more conscientious approach to dictionary use and retrieved more correct target verbs. Apart from lending new support to the continuous appeal for a position of dictionary use instruction in the EFL pedagogy, the study also provides a detailed demonstration of task-based dictionary training applicable to classroom practice.

Unsupervised Land-Cover Segmentation Using Accelerated Balanced Deep Embedded Clustering

In this letter, we address the issue of the automatic labeling of remote sensing datasets using a novel deep learning clustering algorithm. The proposed algorithm addresses the inherent susceptibility of the deep embedded clustering (DEC) algorithm to data imbalance using additional search and extraction steps. Furthermore, the proposed algorithm is highly parallelizable. A graphics processing unit (GPU) implementation is shown to achieve 40X to 2600X of performance speedup and improved clustering accuracy with respect to DEC and other clustering approaches.

Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation

Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments. In particular, the search direction is chosen as an unbiased estimate of the gradient which is obtained from a single experiment, regardless of the size of the MIMO system. The approach is illustrated using a simulation example, in which it is shown to be superior to a deterministic method in terms of convergence speed and thus experimental cost.

Improved Artificial Bee Colony Algorithm with Adaptive Parameter for Numerical Optimization

ABSTRACT The problem that ABC (Artificial Bee Colony) algorithm is good at exploration but poor at exploitation for the numerical optimization is investigated in this paper. PA-ABC (Parameter Adaptive ABC) algorithm is proposed, which adopts different search equations with different search abilities for the employed bee and the onlooker bee. Firstly, the best-so-far solution is introduced into each search equation to enhance exploitation; secondly, the employed bee uses two random solutions to search, so as to keep high ability of exploration; thirdly, the onlooker bee searches around a random solution to keep population diversity; most importantly, adaptive parameter computed by fitness function is introduced in the search equation of the onlooker bee, which makes the search step adjust according to the search process. So the search equation of the employed bee has balanced abilities of exploration and exploitation, while the search equation of the onlooker bee can make the search focus transfer from exploration to exploitation adaptively. The experiment results on benchmark functions show that the search performance of PA-ABC is higher than or at least comparable to basic ABC and typical improved ABCs. In addition, compared to the performance of the state-of-the-art ABC variants under their original parameter configuration, PA-ABC is verified to have similar performance to them.

A New Instantaneous Method for Attitude Determination Using GPS Phase Measurement

This article proposes a new algorithm for attitude determinations using GPS phase signals. The proposed method is an instantaneous method along with the small-angle assumption. Different from the existing methods, this method uses a newly defined performance index “BDOP” to determine search sequences for different satellite signals, phase measurement noise, and baseline geometry to define the search space and ambiguity candidates’ properties determined in previous search steps to reduce the search space for the subsequent searches. Consequently, the proposed method reduces the computation time and improves the attitude determination accuracy. The proposed method is verified both by numerical simulations and experiments. Simulation results indicate that the computation time of the proposed method is 0.01 s, the success rate 85%, and the angle estimation accuracy less than 1° when three antennas are installed 1 m apart, receiving GPS signals from five satellites, and the standard deviation of the phase measurement noise of 0.5 cm. In experiments, we verify the algorithms using a homemade three-antenna GPSR and a three-axis gimbal platform. The three antennas are installed 0.5 m apart and receive GPS signals from six satellites. The angle estimation error is less than 2° when the platform rotates in three axes simultaneously. In that experimental setup, the angle estimation degenerates at the large platform angles due to the signal interference from the ground effect and gimbal platform geometry.

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.

SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.

Spectral variable selection based on least absolute shrinkage and selection operator with ridge-adding homotopy

The least absolute shrinkage and selection operator (LASSO) is an established sparse representation approach for variable selection, and its performance relies on finding a good value for the regularization parameter, typically through cross-validation. However, cross-validation is a computationally intensive step and requires a properly determined search range and step size. In the present study, the ridge-adding homotopy (RAH) algorithm is applied with LASSO to overcome the aforementioned shortcomings. The homotopy algorithm can fit the entire solution of the LASSO problem by tracking the Karush-Kuhn Tucker (KKT) conditions and yields a finite number of potential regularization parameters. Considering the singularities, a M×1 random ridge vector will be added to the KKT conditions, which ensures that only one element is added to or removed from the active set. Finally, we can select the optimal regularization parameter by traversing the potential parameters with modelling and evaluation metrics. The selected variables are the nonzero elements in the sparse regression coefficient vector derived by the optimal regularization parameter. The proposed method has been demonstrated on three near-infrared (NIR) datasets with regard to wavelength selection and calibration. The results suggested that the “RAH-LASSO ​+ ​PLS” outperforms “LASSO ​+ ​PLS” and “full-wavelength PLS” in most cases. Importantly, the RAH method provides a systematic, as opposed to trial-and-error, procedure to determine the regularization parameter in LASSO.

A deep reinforcement learning based searching method for source localization

The localization of hazardous sources (e.g. poisonous gas sources) is an important task regarding the security of human society. To find the unknown source in time, various autonomous source searching methods have mushroomed and been employed over the past decade. This paper designs a fresh source searching approach, namely particle clustering-deep Q-network, PC-DQN, which applies the deep reinforcement learning (DRL) techniques as a source searching approach for the first time. Specifically, the search process is formulated as the partially observable Markov decision process, then converted into the Markov decision process based on the belief state (represented by the particle filter). PC-DQN leverages the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the feature of belief state, and employ the deep Q-network (DQN) algorithm to find the optimal policy for the source searching task. Through the comparison with two baseline methods (i.e. RANDOM and Entrotaxis algorithm) under various experimental conditions, the viability of our proposed PC-DQN is testified. Results explicitly reveal that the success rate of the PC-DQN maintains at a high level (beyond 99.6%) in all scenarios in this paper, and the mean search step shows evident superiority over baseline methods in most scenarios. Significantly, we also introduce the transfer learning concept to reuse the well-trained Q-network into new scenarios. These findings show important implications of the DRL-based approach as an alternative and more effective source searching approach.

Homo psychotherapeuticus, or psychotherapy in the reflection of contemporary philosophy

Psychotherapeutic practices are always based on personality theories. The century-old experience of psychotherapy has shown relatively equal effectiveness of various directions. Can philosophical anthropology help in the study of the equivalence paradox? Can specialists in their search step beyond the usual psychotherapeutic discourse in understanding the subject of psychotherapy? The article examines the connection between the fate of the subject in the philosophy of the XX century and the issues of personology in psychotherapy. The article is addressed to philosophers, psychologists, educators, those specialists who implement a personalistic approach in their practice.

Regulation Characteristics and Load Optimization of Pump-Turbine in Variable-Speed Operation

In order to improve the operating and regulation characteristics of the hydropower unit and to stabilize the load fluctuations, variable-speed pumped storage technology based on converters has been proposed and given more attention recently. However, different from the conventional units, due to the variability of operation conditions, variable-speed units need to develop a load optimization strategy in terms of operating parameter identification to ensure state matching for operation. Therefore, this paper proposes an optimization search step based on the model test curve, and the process of parameter optimization search is elaborated and calculated in the turbine operating condition and pump operating condition, respectively. A mathematical model of the turbine regulation system is established to analyze the influence of speed and guide vane related parameters on the regulation characteristics, and the achievable operating range and regulation capacity in the variable-speed condition is pointed out based on pump-turbine model test, as well as the advantages over the fixed-speed operation. The results show that by applying the load optimization method, the variable-speed unit can be significantly improved in terms of operating efficiency, especially at low head and low power conditions. Meanwhile, a certain range of active power regulation can be realized by the decoupling control of the converter and measuring the guide vane opening in both modes. The analysis of the model test verifies the effectiveness of the variable-speed regulation operation of pump-turbine and provides a reference for the design and operation of the variable-speed hydropower units.

A Modified Cuckoo Search Algorithm and Its Applications in Function Optimization

A modified Cuckoo search algorithm (MCS) is proposed in this paper to improve the accuracy of the algorithm’s convergence by implementing random operators and adapt the adjustment mechanism of the Levy Flight search step length. Comparative experiments reveal that MCS can effectively adjust the search mechanism in the high-dimensional function optimization and converge to the optimal global value.

Hierarchical Satellite System Graph for Approximate Nearest Neighbor Search on Big Data

Approximate nearest neighbor search is a classical problem in data science, which is widely applied in many fields. With the rapid growth of data in the real world, it becomes more and more important to speed up the nearest neighbor search process. Satellite System Graph (SSG) is one of the state-of-the-art methods to solve the problem. However, with the further increase of the data scale of problems, SSG still needs a considerable amount of time to finish the search due to the limitation of step length and start point locations. To solve the problem, we propose Hierarchical Satellite System Graph (HSSG) and present its index algorithm and search algorithm. The index process can be distributed deployed due to the good parallelism of our designed hierarchical structure. The theoretical analysis reveals that HSSG decreases the search steps and reduces the computational cost and reduces the search time by searching on the hierarchical structure with a similar indexing time compared with SSG, hence reaches a better search efficiency. The experiments on multiple datasets present that HSSG reduces the distance computations, accelerates the search process, and increases the search precision in the real tasks, especially under the tasks with large scale and crowded distributions, which presents a good application prospect of HSSG.