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作者： Xiao, Ling Meng, Yu Tian, Xiaobing Luo, Haibo College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Science Guangdong University of Science And Technology Dongguan China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China College of Computer and Control Engineering Minjiang University Fujian China

Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low-power. Energy allocation is essential for motion-based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra-low-power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real-world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices. © 2021 John Wiley & Sons, Ltd.

关键词： Kinetic energy

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Triad Photosensitizers Based on Iodo-Aza-Bodipy and Naphthalenediimides (Ndi) with Broadband Absorption: Study of Resonance Energy Transfer and Electron Transfer

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SSRN 2024年

作者： Ma, Jie Zhao, Fangming Yin, Yanbing Li, Yingying Wu, Di Yin, Guangming Gao, Xiaohui Heilongjiang Provincial Key Laboratory of Catalytic Synthesis for Fine Chemicals School of Chemistry and Chemical Engineering Qiqihaer University Qiqihaer161006 China College of Computer and Control Engineering Qiqihaer University Qiqihaer161006 China

Two organic photosensitizers based on resonance energy transfer (A-1 and A-2) are prepared. Naphthalenediimides was used as intramolecular energy donor and iodo-aza-Bodipy was used as intramolecular energy acceptor/spin converter. A-1(Ε = 42800 at 618 nm and Ε = 59600 at 674 nm) and A-2 (Ε = 33300 at 514 nm and Ε = 68300 at 674 nm) give the two strong absorption in visible region. Both the photosensitizers show broadband absorption in visible region. The photophysical properties of the compounds were studied with steady state and nanosecond transient absorption and emission spectroscopy in detail. Quantum yield of singlet oxygen of A-1 and A-2 is 39.7% and 40.9%, respectively. The efficiency of intramolecular energy transfer is estimated to be 15.9% and 27.7% for A-1 and A-2 based on fluorescence lifetime quenching. PET is responsible for the relatively low efficiency, that is identified by the calculation of kcs / kFRET and the Gibbs free energy change(G0cs). With steady state and time-resolved spectroscopy, we found that photoexcitation into the energy donor was followed by singlet energy transfer, then via the intersystem crossing (ISC) of the energy acceptor. By nanosecond time-resolved transient difference absorption spectroscopy, triplet excited states is localized on the iodo-aza-Bodipy. A-1 and A-2 were used for singlet oxygen (1O2) mediated photooxidation of 1,5-dihydroxylnaphthalene and the photosensitizing ability are more efficient than the triplet photosensitizers with the mono-chromophore photosensitizer. © 2024, The Authors. All rights reserved.

关键词： Photosensitizers

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Development of a Soft Hand and Wrist Rehabilitation Robot Utilizing Lattice Structure Actuators

Development of a Soft Hand and Wrist Rehabilitation Robot Ut...

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World Rehabilitation Robot Convention (WRRC)

作者： Jianwei Lai Aiguo Song Wenbin Zhang Ye Lu Jiajin Wang Ye Li State Key Laboratory of Digital Medical Engineering Jiangsu Key Laboratory of Remote Measurement and Control School of Instrument Science and Engineering Southeast University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Soft robots offer promising solutions for rehabilitation due to their inherent attributes such as suppleness, lightweight construction, and safety features. However, prevalent soft actuators face limitations in output force and bending angle, impeding their effective application in rehabilitation. This paper proposes a novel design approach employing a two-layer lattice structure actuator, comprising flexion, guiding, and extension layers. This innovative design facilitates supple and efficient bi-directional rehabilitation training for fingers and wrists. Experimental results demonstrate the efficacy of the proposed dual-layer lattice structure actuator. At an air pressure of 150 kPa, it achieves an end force of 14.88 N, marking a remarkable 906% increase compared to its single-layer counterpart. Additionally, the dual-layer structure exhibits a minimum bending radius of 2.77 cm under 60 kPa, representing a 38.5% reduction relative to the single-layer actuator. Leveraging these actuators, a hand and wrist rehabilitation robot was developed and subjected to a clinical trial involving two healthy subjects. The trial outcomes reveal significant improvements in finger grasping ability and wrist range of motion with the assistance of the rehabilitation robot. These results underscore the practical usability and effectiveness of the soft hand and wrist rehabilitation robot, thereby holding promise for enhancing patient rehabilitation outcomes.

关键词： Wrist Training Actuators Force Lattices Bending Soft robotics Assistive robots Usability Periodic structures

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Multi-hop Routing Optimization of Transmission-Line Monitoring Based on the LoRa Technology 10

Multi-hop Routing Optimization of Transmission-Line Monitori...

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10th IEEE International Conference on Smart City and Informatization, iSCI 2022

作者： Zhao, Yimei Song, Junjie Che, Xin Anshun Power Supply Bureau China Southern Power Grid Anshun China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350396317

As the power transmission network connects the power generation side and power consumption side, the transmission line plays a vital role in the overall stable operation of the power grid. However, because power transmission lines are often distributed in a very harsh environment, they are extremely susceptible to being interfered with by dynamic scenarios and face transmission failures. Therefore, to maintain the power transmission, it is urgent to monitor the status of transmission lines in real time. Traditional communication technologies, such as GPRS, optical fiber, ZigBee, etc. are widely used in power monitoring systems, but their limitations make it difficult to meet the remote monitoring of transmission lines. The long-range (LoRa) technology has the characteristics of wide coverage and low energy consumption, providing an opportunity to make up for the 'last-mile' gap in transmission-line monitoring. The challenges to monitoring the transmission lines are: 1) the nodes far away from the base station need to transmit the data with considerable transmission power;2) the nodes outside the communication range of the base station cannot successfully send the data to the base station. To address these problems, this paper proposed a multi-hop communication strategy to monitor the status of transmission lines. First, we use a graph to represent the LoRa nodes and transfer the 3-phase line monitoring structure to the 1-phase line monitoring. Second, we construct an energy consumption model for each LoRa node in the multi-hop scenario. Third, we develop a max-min optimization problem to compute a multi-hop scheme for minimizing the overall energy consumption for the transmission-line monitoring and maximizing the transmission range for the base station. Finally, we conduct extensive simulations to analyze the bottleneck of the energy consumption of the overall monitoring system with LoRa and the maximum range that the base station can cover. © 2022 IEEE.

关键词： Electric lines

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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A predictive model of death from cerebrovascular diseases in intensive care units

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Intelligent Medicine 2023年第4期3卷 267-279页

作者： Mohammad Karimi Moridani Seyed Kamaledin Setarehdan Ali Motie Nasrabadi Esmaeil Hajinasrollah Department of Biomedical Engineering Faculty of HealthTehran Medical SciencesIslamic Azad UniversityTehranIran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer EngineeringCollege of EngineeringUniversity of TehranTehranIran Departments of Biomedical Engineering Shahed UniversityTehranIran Loghman Medical Center Shahid Beheshti University of Medical SciencesTehranIran

Objective This study aimed to explore the mortality prediction of patients with cerebrovascular diseases inthe intensive care unit(ICU)by examining the important signals during different periods of admission in theICU,which is considered one of the new topics in the medical *** approaches have been proposed forprediction in this *** of these methods has been able to predict mortality somewhat,but many of thesetechniques require recording a large amount of data from the patients,where recording all data is not possiblein most cases;at the same time,this study focused only on heart rate variability(HRV)and systolic and diastolicblood *** The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring inIntensive Care II(MIMIC-II)Clinical *** proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients,48 men and 40 women,during their first 48 hours of ICU *** electrocardiogram(ECG)signals are related to lead II,and the sampling frequency is 125 *** time of admission and time ofdeath are labeled in all *** this study,the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood *** the patient’s future condition,the combination of features extracted from the return mapping generatedby the HRV signal,such as angle(𝛼),area(A),and various parameters generated by systolic and diastolic bloodpressure,including DBPMax−Min SBPSD have been ***,to select the best feature combination,the geneticalgorithm(GA)and mutual information(MI)methods were *** sample t-test statistical analysis was usedto compare the results of two episodes(death and non-death episodes).The P-value for detecting the significancelevel was considered less than *** The results indicate that the new approach presented in this paper can be compared with other methodsor l

关键词： Death prediction Cerebrovascular diseases Intensive care unit Heart rate variability Systolic and diastolic blood pressure Return map

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GrasOpen: Biometric Authentication via Reach-and-Grasp for Smart Door Access Using Smartwatch

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IEEE Internet of Things Journal 2025年

作者： Ding, Lei Shi, Jiale Wu, Yuan Hu, Xinrong Bi, Hongliang Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments. © 2014 IEEE.

关键词： Biometrics

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Representing Scenes as Compositional Generative Neural Feature Fields Based on GIRAFFE for 3D Reconstruction of Classroom Scenes 18th

Representing Scenes as Compositional Generative Neural Feat...

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Fu, Tianlin Deng, Renyu Gao, Yue Zhang, Fuquan Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China School of Art and Design East University of Heilongjiang Harbin150066 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China

ISBN: (纸本)9789819901043

Traditional online classroom learning through two-dimensional video cannot meet the interactivity and immersive class experience. Providing immersive teaching scenes through 3D reconstruction technology is the future trend of education. We use the GIRAFFE method to reconstruct classroom scenes that can be synthesized as a combination of multiple "feature fields" for the task of synthesizing controlled images. The reconstruction of complete and realistic 3D scenes is a prerequisite and basis for the realization of new forms of online courses. This experiment aims to restore a real classroom scene consisting of multiple objects, elements and movable elements such as desks and chairs, teacher images and classroom scenes to build a new model of online course. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

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computers, Materials & Continua 2021年第11期69卷 2295-2312页

作者： Mohamed Esmail Karar Omar Reyad Mohammed Abd-Elnaby Abdel-Haleem Abdel-Aty Marwa Ahmed Shouman College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer Science Faculty of ScienceSohag UniversitySohag82524Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Physics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 ***,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 *** this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US *** to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware *** main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US *** image dataset(POCUS)was used to validate our proposed COVID-LWNet framework *** classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this *** results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,*** paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

关键词： Coronavirus medical image processing artificial intelligence ultrasound

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Machine-learning-based detection for quantum hacking attacks on continuous-variable quantum-key-distribution systems

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Physical Review A 2023年第6期107卷 062422-062422页

作者： Chao Ding Shi Wang Yaonan Wang Zijie Wu Jingtao Sun Yiyu Mao College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha 410082 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China

Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum hacking attacks due to imperfect devices and insufficient assumptions. In this paper, we propose a universal defense strategy called a machine-learning-based attack detection scheme (MADS). Leveraging the combined advantages of density-based spatial clustering of applications with noise (DBSCAN) and multiclass support vector machines (MCSVMs), MADS demonstrates remarkable effectiveness in detecting quantum hacking attacks. Specifically, we first establish a set of attack-related features to extract feature vectors. These vectors are then utilized as input data for DBSCAN to identify and remove any noise or outliers. Finally, the trained MCSVMs are employed to classify and predict the processed data. The predicted results can immediately determine whether or not to generate a final secret key. Simulation results show that the proposed MADS can efficiently detect most quantum hacking attacks and revise the overestimated secret key rates caused by a CVQKD system without any defense strategy to obtain a tighter security bound.

关键词： Quantum cryptography

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