scholarly journals Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm

2020 ◽  
Vol 8 (11) ◽  
pp. 843
Author(s):  
Yuan Liu ◽  
Min Wang ◽  
Zhou Su ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Autonomous Underwater Vehicle ◽  
Learning Algorithm ◽  
Test Results ◽  
Reward Function ◽  
Collaborative Search ◽  
New Type ◽  
System Robustness ◽  
Mine Countermeasures ◽  
Tailored Design

As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.

An E/H Control Design for a Hydraulic Variable Displacement Axial Piston Motor

2011 ◽  
Author(s):  
Hongliu Du
Keyword(s):  
Adaptive Learning ◽  
Lower Cost ◽  
Learning Algorithm ◽  
Control Design ◽  
Test Results ◽  
Satisfactory Performance ◽  
Control Scheme ◽  
Variable Displacement ◽  
System Uncertainties ◽  
Axial Piston

A simple and novel speed control scheme for variable displacement motors has been developed under the consideration of some system uncertainties. Theoretical analysis and experimental test results have shown that the proposed control strategy is capable of driving the swashplate to track its desired trajectory with robust stability and satisfactory performance. An adaptive learning algorithm enables the controls to automatically adjust for uncertainties in the control bias current. Compared with its hydro-mechanical counterpart, the provided E/H control results in a hydraulic variable displacement motor with lower cost and better performance.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

scholarly journals Chip Attach Scheduling in Semiconductor Assembly

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Zhicong Zhang ◽  
Kaishun Hu ◽  
Shuai Li ◽  
Huiyu Huang ◽  
Shaoyong Zhao
Keyword(s):  
Objective Function ◽  
Objective Function Value ◽  
Learning Algorithm ◽  
Parallel Machine Scheduling ◽  
Volume Index ◽  
Production Volume ◽  
Learning Problem ◽  
Reward Function ◽  
Unrelated Parallel Machine Scheduling ◽  
Target Production

Chip attach is the bottleneck operation in semiconductor assembly. Chip attach scheduling is in nature unrelated parallel machine scheduling considering practical issues, for example, machine-job qualification, sequence-dependant setup times, initial machine status, and engineering time. The major scheduling objective is to minimize the total weighted unsatisfied Target Production Volume in the schedule horizon. To apply Q-learning algorithm, the scheduling problem is converted into reinforcement learning problem by constructing elaborate system state representation, actions, and reward function. We select five heuristics as actions and prove the equivalence of reward function and the scheduling objective function. We also conduct experiments with industrial datasets to compare the Q-learning algorithm, five action heuristics, and Largest Weight First (LWF) heuristics used in industry. Experiment results show that Q-learning is remarkably superior to the six heuristics. Compared with LWF, Q-learning reduces three performance measures, objective function value, unsatisfied Target Production Volume index, and unsatisfied job type index, by considerable amounts of 80.92%, 52.20%, and 31.81%, respectively.

Experimental Study on Shear Behavior of Perfobond Connector with Boot Shaped Slots

2019 ◽  
Author(s):  
Zhanchong Shi ◽  
Qingtian Su ◽  
Xinyi He ◽  
Quanlu Wang ◽  
Kege Zhou ◽  
...  
Keyword(s):  
Failure Modes ◽  
Shear Stiffness ◽  
Shear Capacity ◽  
High Shear ◽  
Test Results ◽  
Precise Location ◽  
Construction Problem ◽  
Circular Holes ◽  
New Type ◽  
Push Out

<p>In order to solve the construction problem of perforating rebars’ precise location and it’s getting through the circular holes for the the conventional perfobond connector, a new type of perfobond connector with boot shaped slots was proposed. This new type perfobond connector has the advantage of convenient construction and pricise location. Three groups of push-out tests with nine specimens were carried out to study the shear capacity of the new type perfobond connector. The effect of the number and the spacing of boot shaped slots on failure modes, shear capacity, peak slip and shear stiffness were mainly studied. The test results show that the new type of perfobond connector with boot shaped slots has a high shear capacity and a good ductility, it could be widely applied on the connection between the steel and the concrete structures.</p>

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

scholarly journals DUKUNGAN KELUARGA TERHADAP TINGKAT KESEMBUHAN PASIEN COVID-19 DI RUANG ISOLASI RUMAH SAKIT AMINAH 2021

2021 ◽  
Vol 7 (2) ◽  
pp. 164-169
Author(s):  
Agah Nugraha ◽  
Rostime Hermayerni Simanullang
Keyword(s):  
Family Support ◽  
Virus Disease ◽  
Sampling Technique ◽  
Statistical Test ◽  
P Value ◽  
Test Results ◽  
Raising Children ◽  
The Family ◽  
New Type

 Corona virus Disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is a new type of coronavirus that has never been previously identified in humans. Family support is an activity oriented to improve family functions on the basis of raising children and other family activities. in a system and resources that support. This study aims to identify family support for the healing rate of Covid-19 patients in the Isolation Room at Aminah Hospital, Tangerang in 2021. Method: observational analytic used in this research and 23 covid-19 participantn,  in this study using the Total Sampling technique. Statistical test used is the Spearman Rank statistical test. Results: The results of study obtained p value = 0.000 <0.05. Conclusion: there is a relationship between family support and the healing of  Covid-19 patients.  The role of the family is very important. and breaking chain of spread of the Covid-19.

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