Guidance Method Research Articles

Planning and Construction Strategies for Infrastructure in Traditional Villages of the Grand Canal from the Perspective of Villager Needs

The traditional villages along the Jiangsu section of the Grand Canal are facing the threat of decline and disappearance due to the decline of the canal transportation function and the advancement of urbanization, and it is urgent to study the protection and renewal of these villages. Based on the needs of villagers, this study aims to explore the planning and construction strategies of traditional village infrastructure in the Jiangsu section of the Grand Canal. Through a literature review, field investigation, and qualitative and quantitative analysis methods, this paper conducts in-depth research and data analysis on the status quo of six types of infrastructure in 29 traditional villages, including road traffic, water supply and drainage, garbage sanitation, energy utilization, postal communication, and ecological disaster prevention. The research results show that, although the infrastructure construction has made some progress, there are still problems such as unreasonable planning, low construction quality, and the lack of cultural characteristics, which cannot fully meet the actual needs of villagers. Through a regional comparative analysis, it is found that the infrastructure condition of the Jiangnan Canal section is better than that of the Huaiyang Canal section and Middle Canal section, but they all face challenges of protection and renewal. Based on the needs of villagers, this study puts forward corresponding protection and optimization strategies, which provide theoretical guidance and practical methods for the sustainable development of traditional village infrastructure, and it has important reference significance for the infrastructure protection and development of traditional villages in other regions.

Reinforcement learning-adaptive fault-tolerant IGC method for a class of aircraft with non-affine characteristics and multiple uncertainties

Abstract In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

Weighted Aggregated Sum Product Assessment Method Based on Aczel–Alsina T-Norm and T-Conorm Under Bipolar T-Spherical Fuzzy Information: Design Scheme Selection Application

Selecting optimal design solutions is inherently complex due to multiple criteria encompassing users’ uncertain needs, experiences, and costs. This process must manage uncertainty and ambiguity, making developing a scientific, rational, and efficient guidance method imperative. Bipolar T-spherical fuzzy sets (BTSFS), a hybrid of bipolar fuzzy sets and T-spherical fuzzy sets, effectively handle the bipolarity inherent in all elements. In this work, we propose a Weighted Aggregated Sum Product Assessment (WASPAS) method based on BTSFS and the Aczel–Alsina T-norm (AATN) and T-conorm (AATCN) to address the problem of selecting conceptual design solutions. We first establish operational rules for BTSFS using AATN and AATCN and introduce weighted aggregation operators (BTSFAAWA) and geometric aggregation operators (BTSFAAWG) while examining fundamental properties, such as idempotency, boundedness, and monotonicity. Subsequently, we propose a two-stage BTSFS-based WASPAS method; criterion weights are calculated using the BTSFAAWA operator, and final rankings are obtained through comprehensive calculations using both the weighted sum method (WSM) based on BTSFAAWA and the weighted product method (WPM) based on BTSFAAWG. Finally, we validate the effectiveness of our method through a case study of the selection of cultural and creative products. Sensitivity and comparative analyses are conducted to demonstrate the advantages of our approach.

Pengaruh Teknik Modeling Simbolik terhadap Peningkatan Motivasi Belajar Peserta Didik Kelas X TKI 1 SMK Negeri 3 Madiun

Learning motivation is one of the things that needs to be considered in learning because there are still many who think that motivation is not that important to improve learning achievement. The aim of this research is to study the effect of symbolic modeling techniques in guidance and counseling services on increasing students' learning motivation. This technique is a method in guidance and counseling services that uses pictures, videos or other stories to help students understand the concept of learning motivation. This research uses a guidance and counseling action research approach which involves several cycles, namely planning, action, observation and reflection. The research subjects were class X TKI 1 students at SMK Negeri 3 Madiun with low learning motivation. Data was collected from observations, interviews and special questionnaires regarding learning motivation before and after the intervention.The research results showed that students learning motivation improved significantly after intervention with symbolic modeling techniques.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

Isokinetic Force Modeling in Sports Based on Embedded System of Motion Recognition Model

With the gradual deepening of sports research, sports training methods integrating more scientific and technological means can greatly improve athletes’ professional ability. Combined with the isokinetic strength training method, it can effectively alleviate muscle recovery and body flexibility, which requires an analysis of the level of sports body promotion, and a more efficient training program for various sports parameters. In this study, taking strength enhancement as an example, the convolutional neural network is innovatively integrated into isokinetic strength training. Using the motion data obtained by wearable sensors in the training, we trained the neural network model by real-time analysis of data samples through an embedded system. Further, we optimized the network parameters to realize the extraction and recognition of EMG depth features. Compared with the traditional isokinetic strength classification model, this model lays a foundation for further constructing personalized strength training guidance methods. After the simulation test, the difference of the model’s accuracy requirement before and after the pre-training is 4.6%, and the difference of the test set before and after the pre-intensive training is [Formula: see text]1%. It is 16.2% higher than the TCAM algorithm, and the gap with STA-LSTM remains between [Formula: see text]. It is verified that the attention concentration control scheme can not only reduce the error rate in visual recognition tasks, but also improve the complexity of the neural network model. The optimized lightweight data structure is especially suitable for embedded systems with limited computing power. All kinds of test projects verify its rationality and make full use of the control ability of the embedded system and the characteristics of parallel computing and programmability, which has great practical application value for the development of the Internet of Things system network. The research results have made a thorough study of the physiological guidance of existing strength training, the identification and evaluation methods of exercise-induced muscle fatigue, and the current situation of the existing isokinetic equipment.

Tracer Study on the Bachelor of Science in Law Enforcement Administration (LEA) Graduates of Isabela State University Cabagan

Tracer study is a retrospective evaluation of graduates conducted via a standardized survey sometime after graduation. The study aimed to trace the employability status of BS LEA graduates from 2017- 2019. It utilized the descriptive research design and a survey questionnaire in gathering data. The result revealed that majority of the graduates are twenty-five years old, male, single, Ibanag, bachelor’s degree graduate, and AFPSAT passers. Moreover, majority of the graduates are temporary/casual, connected to security agencies, able to hunt for a job within one to two years, delayed in their employment for reason of absence of immediate vacancy, earning a monthly salary within the range of thirty thousand to thirty-four thousand nine hundred ninety-nine, and employed due to their educational qualifications. They realized that their employment is directly related to their degree and claimed that communication skills developed at the college aided in their employment. It was also discovered that the quality of Education at ISU is excellent in terms of curriculum/course content, methods of instruction, faculty, physical plant, career guidance, job placement, and academic counseling.

Landing Guidance Control Combining Powered Descending with Nonlinear Optimization and Vertical Descending with Model Prediction

In this study, we propose a landing guidance control method for the Moon using two control methods. The landing of a spacecraft on the Moon is divided into two phases: the powered descending phase and the vertical descending phase. The powered descending phase aims to optimize the entire trajectory to satisfy the termination constraint, instead of allowing for a relatively long control period. This phase performs feedback control using trajectory updates via nonlinear optimization. The vertical-descending phase aims to achieve accurate control over a short control period. This phase applies nonlinear model predictive control for fast optimization on finite time intervals. First, the landing of a spacecraft on the Moon is modeled as a two-body problem of the Moon and spacecraft, and equations of motion are derived. Subsequently, we formulated an optimization problem for each phase and developed the proposed landing guidance method by combining the two control methods. Furthermore, numerical simulations based on the derived equations of motion were performed to confirm the effectiveness of the proposed method and to compare its performance with another optimization method, the successive convexification (SCvx) algorithm.

Three six-degree-of-freedom dynamic descent convex optimization guidance methods

A key technology for soft landing of powered descent of aircraft is to solve the six-degree-of-freedom powered descent guidance problem in real time, which can be described as the fuel-saving trajectory optimization problem under multiple constraints. Three optimization models are established by selecting three independent variables: flight time, time substitution variable, and trajectory height. The original trajectory optimization problem is transformed into a solvable form of sequential convex optimization for iterative solution, forming three online guidance methods. The three guidance methods are compared in terms of convergence, real-time performance, optimality, and solution accuracy. The results show that all three guidance methods can solve the six-degree-of-freedom powered descent problem. The guidance method with flight time as the independent variable has the shortest calculation time and the least fuel consumption, but the powered descent flight time needs to be determined in advance. The guidance methods based on the other two types of independent variables can optimize the powered descent flight time, but they are all suboptimal solutions, and the calculation time is significantly increased. The solution accuracy of the three methods is similar under the same number of discrete points. If sequential convex optimization is used as the online guidance scheme for powered descent, how to determine the optimal flight time, approach the optimal solution for fuel, and further shorten the calculation time still needs to be further studied.

Assembly method for curved porous circular workpieces based on multidimensional information fusion: Grid electrode component

To tackle the problems related to the complexities of obtaining depth information for circular workpiece with arc surface and holes, a novel vision guidance method has been introduced to precisely gauge interlayer spacing. The process includes the acquisition and preprocessing of point cloud depth information, along with matching and edge processing. The depth coordinates of the point cloud are subsequently determined. Two-dimensional image processing technology is employed to extract the coordinates of the center of the surface circular hole in the curved porous circular workpiece. The 3D coordinates are generated for guiding the assembly by integrating these datasets. Experiments on grid parts in aviation propulsion systems demonstrate that this method maintains dot matrix spacing within 0.5–1.2 mm, ensures the coaxiality of the center hole, and meets assembly standards. The results confirm the method’s effectiveness for automated assembly of curved porous round workpieces.

A Study on the E-C Translation of Daily Chemical Advertisements from the Perspective of Communicative Translation Theory

Advertising can not only build the image of a brand, but also increase the visibility of the product. Today, as the global economy becomes more and more interconnected, more and more products are pouring into the international market, in which advertising translation plays an irreplaceable role. Due to the differences between Chinese and Western cultures, this paper mainly studies from the perspective of communicative translation theory, and selects naturalization strategies as theoretical guidance, including paraphrasing, additive translation, transcreation, imitation translation and retranslation methods to conduct research on the Chinese translation of advertising slogans for daily chemical products. It is found that when translating daily chemical product advertisements, translators should not only follow the three principles of advertising slogan translation, but also flexibly adopt different translation methods for different texts, so as to continuously increase the cultural value and commercial value of daily chemical product advertising. The purpose of this paper is to provide inspiration for the translation of advertising slogans in the future, and to better promote cross-cultural business communication.

Doses of Botulinum Toxin in Cervical Dystonia: Does Ultrasound Guidance Change Injection Practices?

Cervical dystonia is widely understood to benefit from botulinum toxin injections. The injection practices may be influenced by specific factors, including the method of injection. Three main guidance methods can be used: palpation of anatomical landmarks, ultrasound, and electromyography. We investigated how target muscles and doses of botulinum toxin were modified after the transition from surface anatomy (non-guided) to ultrasound (US-guided), in patients with cervical dystonia. We also determined the long-term dose trend. We studied a group of 82 patients, who received non-guided injections (median: 16.5 cycles/5.1 years) followed by US-guided injections (median: 12.0 cycles/3.8 years). More muscles, and especially deep muscles, were injected during the US-guided period. The total dose and number of injected muscles were higher when US guidance was used, but the mean dose per muscle was lower. Over the long term, the total dose stabilized, and the mean dose per muscle decreased during the US-guided period. According to our results, the guidance method has a strong impact on the botulinum toxin injection strategy in cervical dystonia (target muscles and dose). Also, the treatment appeared more stable when using US guidance; this could be explained by the good precision of such injections.

The impact of internet-based cognitive behavioral therapy on mental health outcomes and life in breast cancer patients: a systematic review and meta-analysis.

Internet-based cognitive behavioral therapy(ICBT) improves the impact of breast cancer through online platforms, modular learning, goal setting, relaxation exercises, and other techniques. Compared to traditional cognitive behavioral therapy (CBT), ICBT offers advantages such as the convenience of flexible time and location choices and reduced manpower requirements. In recent years, research exploring the impact of ICBT on breast cancer patients has been increasing, with conflicting results across different studies. Therefore, the purpose of this study was to comprehensively examine the impact of ICBT on the psychological health and quality of life of breast cancer patients through a systematic review and meta-analysis. We searched ten databases in both English and Chinese, with the search period extending from the inception of the databases to December 30, 2023. Literature screening, bias risk assessment, data extraction, and evidence level evaluation were independently conducted by two researchers. All the data were analyzed using RevMan 5.4 and Stata 17.0 software. A total of 2079 breast cancer patients were included in this study, of which 1171 patients received ICBT treatment. The results show that ICBT can reduce anxiety [SMD=-0.19, 95%CI (-0.37, -0.01), P=0.0008] and depression [SMD=-0.20, 95%CI (-0.37, -0.02), P=0.001], alleviate fatigue [SMD=-0.34, 95%CI (-0.67, -0.01), P=0.04], and improve quality of life [SMD=0.20, 95% CI (0.03, 0.38), P=0.02] in breast cancer patients. However, the intervention effects of ICBT on insomnia [SMD=-0.44, 95%CI (-0.93, 0.06), P=0.08] and sleep quality [SMD=-0.14, 95%CI (-0.30, 0.01), P=0.06] in breast cancer patients are not significant. The subgroup analysis showed that when the intervention period is longer than 8 weeks, the number of intervention modules exceeds 6, and a waitlist control group is included, there is a significant effect on reducing patients' anxiety and depression. However, the method of guidance and whether the intervention period exceeds 12 weeks are not related. ICBT can alleviate anxiety and depression in breast cancer patients, with the intervention effects being independent of the guidance method. Significant results were obtained when the intervention period was >8 weeks and the number of modules was >6. ICBT can reduce fatigue and improve quality of life in breast cancer patients, but its impact on sleep quality was not significant. More high-quality research is needed in the future. PROSPERO, identifier CRD42024494744.

Olfactory-based powered descent guidance for Mars methane plume exploration in long-time-average wind environment

This paper investigates an olfactory-based powered descent guidance method that enable a lander to autonomously locate and target the vent source of any methane plume in long-time-average wind environment on Mars. The episodic methane plumes emanating from the Martian surface invite the possibility of direct access to the subsurface methane reservoir to acquire pristine organic materials. Constrained by the insufficient knowledge of the accurate plume vent location, Mars landers must infer the target location — the plume source — autonomously during the powered descent. However, existing powered descent guidance methods require the target location as a terminal constraint, rendering them ineffective in plume exploration missions. We propose an olfactory-based powered descent guidance method that could steer a lander to target the methane vent source by tracking the gradient of methane concentration in long-time-average wind environment, imposing sub-optimal fuel consumption. A novel olfactory navigation method based on tracking the negative logarithmic concentration gradient, and the corresponding gradient measurement method, is proposed. By implementing the negative logarithmic gradient field, gradient-dependent dynamic equations for the powered descending lander are proposed. The gradient-dependent equations provide a way to transform the original optimal guidance problem, which is non-convex and terminal-free, into a convex and terminal-fixed problem. A suboptimal guidance law, similar to E-guidance, is then derived by solving this problem. The proposed guidance could target the vent source of any methane plume in long-time-average wind environment, imposing modest fuel consumption by feeding back the concentration gradient. Numerical simulations illustrate that, in comparison to the terminal-fixed E-guidance and the open-loop fuel-optimal guidance, the lander imposes additional fuel consumption of 2.57% and 12.15% respectively, using the proposed guidance law.

Finite-Time Line-of-Sight Guidance-Based Path-Following Control for a Wire-Driven Robot Fish.

This paper presents an adaptive line-of-sight (LOS) guidance method, incorporating a finite-time sideslip angle observer to achieve precise planar path tracking of a bionic robotic fish driven by LOS. First, an adaptive LOS guidance method based on real-time cross-track error is presented. To mitigate the adverse effects of the sideslip angle on tracking performance, a finite-time observer (FTO) based on finite-time convergence theory is employed to observe the time-varying sideslip angle and correct the target yaw. Subsequently, classical proportional-integral-derivative (PID) controllers are utilized to achieve yaw tracking, followed by static and dynamic yaw angle experiments for evaluation. Finally, the yaw-tracking-based path-tracking control strategy is applied to the robotic fish, whose motion is generated by an improved central pattern generator (CPG) and equipped with a six-axis inertial measurement unit for real-time swimming direction. Quantitative comparisons in tank experiments validate the effectiveness of the proposed method.

A Real-Time Beam Steering and Accurate Vital Sign Estimation Method in an Indoor Environment

A Real-Time Beam Steering and Accurate Vital Sign Estimation Method in an Indoor Environment

Explainability of artificial neural network in predicting career fulfilment among medical doctors in developing nations: Applicability and implications

BackgroundCareer fulfilment among medical doctors is crucial for job satisfaction, retention, and healthcare quality, especially in developing nations with challenging healthcare systems. Traditional career guidance methods struggle to address the complexities of career fulfilment. While recent advancements in machine learning, particularly Artificial Neural Network (ANN) models, offer promising solutions for personalized career predictions, their applicability, interpretability, and impact remain challenging. MethodThis study explores the applicability, explainability, and implications of ANN models in predicting career fulfillment among medical doctors in developing nations, considering socio-economic, psychological, and professional factors. Box plots visualized data distribution, while Heatmaps assessed data intensity and relationships. Matthew's correlation coefficient and Taylor's chart were used to evaluate model performance. Input feature contributions to ANN predictions were analyzed using permutation importance, SHAP, LIME, and Williams plots. The model was tested on a dataset tailored to medical professionals in Nigeria and China, with evaluation metrics including Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R2 Score. ResultsThe ANN model demonstrates strong predictive accuracy, capturing relationships between input factors and outcomes. For Chinese doctors, it achieved an MSE of 0.0004 and R2 of 0.9994, while for Nigerian doctors, it recorded an MSE of 0.0003 and R2 of 0.9998. Key factors for Chinese doctors' satisfaction were IF1 and IF2, while EF1 and EF3 were crucial in preventing dissatisfaction. For Nigerian doctors, IF2 and IF3 drove satisfaction, while EF1 and EF4 were significant in avoiding dissatisfaction. ConclusionThe results highlights the ANN model's effectiveness in predicting career fulfillment among medical doctors in developing nations, offering a valuable tool for career guidance, policymaking, and improving job satisfaction, retention, and healthcare quality.

BẢO HỘ NHÃN HIỆU ÂM THANH TẠI VIỆT NAM: THỰC TRẠNG VÀ GIẢI PHÁP HOÀN THIỆN QUY ĐỊNH PHÁP LUẬT

As a member of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), Vietnam has recognized and allowed the registration of sound trademark protection since 2022. However, the current regulations on audio trademarks are not yet detailed. This article aims to research current legal regulations on sound trademarks in Vietnam and evaluate their practical applicability based on comparison with regulations in Australia and the European Union (EU), thereby providing possible solutions to improve legal regulations and relevant guidance methods on sound trademark protection in Vietnam.

Designing of a Trajectory Control System for Wheelchairs with a Combination of Automatic and Manual Steering Methods

Designing of a Trajectory Control System for Wheelchairs with a Combination of Automatic and Manual Steering Methods

Design of adaptive barrier function-based backstepping finite time guidance control for interceptor-target systems

This study introduces an optimal finite-time backstepping sliding mode guidance law, leveraging an adaptive continuous barrier function to examine the interceptor-target system’s motion in cylindrical coordinates. The integration of sliding mode and backstepping controllers enhances the robustness and performance of the proposed guidance method amidst external disturbances and model uncertainties. This combination also improves transient response, reduces chattering, and lowers control efforts. The adaptive continuous barrier function employed in this method eliminates the need for prior knowledge of the upper bounds of uncertainties and disturbances. Additionally, it ensures that adaptation gains are not overestimated, leading to more accurate performance and complete elimination of chattering. The Lyapunov stability theory is used to prove the finite-time convergence of the state trajectories of the interceptor-target system to a predefined neighborhood around the origin, despite external disturbances and uncertainties. A genetic optimization algorithm is utilized to optimally select the parameters of the guidance method. The efficacy of the proposed technique is validated through simulation results and real-time experiments on the Baseline Speedgoat Real-Time Target Machine platform. The method’s effectiveness and performance are further demonstrated through analysis and simulation in the MATLAB/Simulink environment.