Adaptive Event-Triggered Predictive Control for Agile Motion of Underwater Vehicles by Bo Wang, Junchao Peng, Jing Zhou, Liming Zhao

“…This paper focuses on the design and agile motion control of autonomous underwater vehicles (AUVs) operating in subsea environments, addressing key issues such as structural design, system modeling, and control algorithm development. An optimization model for the arrangement of propellers is formulated and solved using a Sequential Quadratic Programming (SQP) algorithm. …”
Get full text

New Diagnostic Candidate Molecular Biomarkers of Acute Respiratory Distress Syndrome by V. V. Moroz, A. M. Golubev, A. N. Kuzovlev, V. M. Pisarev

Get full text

Security-Constrained Multi-Stage Robust Dynamic Economic Dispatch with Bulk Storage by Li Dai, Renshi Ye, Dahai You, Xianggen Yin

Get full text

Invariant set theory for predicting potential failure of antibiotic cycling by Alejandro Anderson, Matthew W. Kinahan, Alejandro H. Gonzalez, Klas Udekwu, Esteban A. Hernandez-Vargas

Get full text

A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning by Huihui XU, Yunfei TIAN, Yuyang ZHAO, Jing PENG, Qingxin SHI, Rui CHENG

Get full text

A Heuristic Approach for Two-Dimensional Rectangular Cutting Stock Problem considering Balance for Material Utilization and Cutting Complexity by Dianjian Wu, Guangyou Yang

Get full text

Energy-Optimized Path Planning for Fully Actuated AUVs in Complex 3D Environments by Shuo Liu, Zhengfei Wang, Tao Wang, Shanmin Zhou, Yu Zhang, Pengji Jin, Guanjun Yang

Get full text

Deep Reinforcement Learning-Assisted Teaching Strategy for Industrial Robot Manipulator by János Simon, László Gogolák, József Sárosi

“…This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. …”
Get full text

Automated cell structure extraction for 3D electron microscopy by deep learning by Jin Kousaka, Atsuko H. Iwane, Yuichi Togashi

Get full text

Leveraging Dual Timestamps to Enhance Process Model Construction Using Time-Infused Inductive Miner by Yutika Amelia Effendi, Minsoo Kim

“…The proposed approach also demonstrates the capability to effectively minimize dummy activities when discovering process models containing both sequential and parallel activities. Experimental results highlight the improved performance of the proposed algorithm, with metrics showing a fitness score of 0.815, precision of 0.827, generalization of 0.846, and simplicity of 0.834. …”
Get full text

Preliminary Development of a Database for Detecting Active Mounting Behaviors Using Signals Acquired from IoT Collars in Free-Grazing Cattle by Miguel Guarda-Vera, Carlos Muñoz-Poblete

“…The algorithm achieves an average F1 Score of 88.6% for the World Frame of reference, showing a significant improvement compared to the algorithm trained with Body Frame (78.6%) when both are trained with the same 112 features. …”
Get full text

Joint Data Hiding and Partial Encryption of Compressive Sensed Streams by Cristina-Elena Popa, Constantin-Cristian Damian, Daniela Coltuc

Get full text

Action Recognition, Tracking, and Optimization Analysis of Training Process Based on SVR Model and Multimedia Technology by Xuejiao Zhong

“…Describing the introduction of regression can effectively improve the performance of sequential action proposals and event description algorithms, and compared with the current mainstream methods, it has certain performance advantages.…”
Get full text

Energy Efficient Indivisible Workload Distribution in Geographically Distributed Data Centers by Muhammad Imran Khan Khalil, Iftikhar Ahmad, Abdulwahab Ali Almazroi

“…In this paper, we propose a green geographical load balancing (GreenGLB) online algorithm based on the greedy algorithm design technique for the interactive and indivisible workload distribution. …”
Get full text

Joint Optimization of Resource Allocation and Task Offloading Strategies in Multi-Cell Dynamic MEC Systems Using Multi-Agent DRL by Yuntao Hu, Ming Chen, Haowen Sun, Yinlu Wang, Yihan Cang

Get full text

Survey of FPGA based recurrent neural network accelerator by Chen GAO, Fan ZHANG

“…Recurrent neural network(RNN) has been used wildly used in machine learning field in recent years,especially in dealing with sequential learning tasks compared with other neural network like CNN.However,RNN and its variants,such as LSTM,GRU and other fully connected networks,have high computational and storage complexity,which makes its inference calculation slow and difficult to be applied in products.On the one hand,traditional computing platforms such as CPU are not suitable for large-scale matrix operation of RNN.On the other hand,the shared memory and global memory of hardware acceleration platform GPU make the power consumption of GPU-based RNN accelerator higher.More and more research has been done on the RNN accelerator of the FPGA in recent years because of its parallel computing and low power consumption performance.An overview of the researches on RNN accelerator based on FPGA in recent years is given.The optimization algorithm of software level and the architecture design of hardware level used in these accelerator are summarized and some future research directions are proposed.…”
Get full text

Integrated OVMD-BiGRU-SMAC Framework for Forecasting Construction Accidents in the Kingdom of Saudi Arabia by Badr T. Alsulami, Afaq Khattak

“…These IMFs are individually forecasted using Bidirectional Gated Recurrent Unit (BiGRU) models, which are capable of learning sequential patterns in both temporal directions. To enhance the predictive accuracy, the hyperparameters of each BiGRU model are optimized using the Sequential Model-based Algorithm Configuration (SMAC) technique. …”
Get full text

Two-Step Screening for Depression and Anxiety in Patients with Cancer: A Retrospective Validation Study Using Real-World Data by Bryan Gascon, Joel Elman, Alyssa Macedo, Yvonne Leung, Gary Rodin, Madeline Li

“…Further research on optimal screening approaches for anxiety in cancer is warranted.…”
Get full text

APPLICATION OF MODIFIED UNSCENTED KALMAN FILTER AND UNSCENTED PARTICLE FILTER TO SOLVING TRACKING PROBLEMS by I. A. Kudryavtseva, M. V. Lebedev

Get full text

Resource Adaptive Automated Task Scheduling Using Deep Deterministic Policy Gradient in Fog Computing by Prashanth Choppara, S. Sudheer Mangalampalli

“…This paper presents a novel adaptive scheduling framework based on the DDPG algorithm for task scheduling optimization in fog computing environments. …”
Get full text