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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Few-Shot Transmission Line Foreign Object Detection Based on Transfer Learning 23

Few-Shot Transmission Line Foreign Object Detection Based on...

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7th International Conference on computer science and Application engineering, CSAE 2023

作者： Liang, Zhijia Zhang, Hui Cao, Yihong Du, Rui Biekezat, Baheti Huang, Zhihong School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China School of Robotics Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China State Grid Hunan Electric Power Company Limited Research Changsha China

ISBN: (纸本)9798400700590

Presence1 of foreign objects on power transmission lines can jeopardize the safe operation of the transmission lines. However, foreign objects on transmission lines are small in size and the available data for collection is limited. When using few-shot object detection algorithms for detection, there is a high risk of misclassifying of few-shot data and network bias, making the task of foreign object detection on transmission lines challenging. To address this, we propose a method for detecting foreign objects on transmission lines based on few-shot object detection. Firstly, we introduce an attention mechanism in transfer learning to enhance the network's focus on few-shot data and mitigate network bias. Secondly, we propose a method for online difficult sample selection to address the issues of few-shot data classification and the occurrence of false positives and false negatives due to small object sizes. Additionally, we used involution to improve the accuracy of foreign object detection. Furthermore, we construct the finely labeled Transmission Line Foreign Object Dataset (TLFOD). Our method achieves an average precision of 98.4% in foreign object detection on the challenging TLFOD dataset, surpassing existing state-of-the-art methods and demonstrating outstanding performance in classification regression and position regression. © 2023 ACM.

关键词： Object detection

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges

arXiv

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arXiv 2025年

作者： Shin, Ukcheol Park, Jinsun Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States School of Computer Science and Engineering Pusan National University Busan Korea Republic of

Achieving robust and accurate spatial perception under adverse weather and lighting conditions is crucial for the high-level autonomy of self-driving vehicles and robots. However, existing perception algorithms relying on the visible spectrum are highly affected by weather and lighting conditions. A long-wave infrared camera (i.e., thermal imaging camera) can be a potential solution to achieve high-level robustness. However, the absence of large-scale datasets and standardized benchmarks remains a significant bottleneck to progress in active research for robust visual perception from thermal images. To this end, this manuscript provides a large-scale Multi-Spectral Stereo (MS2) dataset that consists of stereo RGB, stereo NIR, stereo thermal, stereo LiDAR data, and GNSS/IMU information along with semi-dense depth ground truth. MS2 dataset includes 162K synchronized multimodal data pairs captured across diverse locations (e.g., urban city, residential area, campus, and high-way road) at different times (e.g., morning, daytime, and nighttime) and under various weather conditions (e.g., clear-sky, cloudy, and rainy). Secondly, we conduct a thorough evaluation of monocular and stereo depth estimation networks across RGB, NIR, and thermal modalities to establish standardized benchmark results on MS2 depth test sets (e.g., day, night, and rainy). Lastly, we provide in-depth analyses and discuss the challenges revealed by the benchmark results, such as the performance variability for each modality under adverse conditions, domain shift between different sensor modalities, and potential research direction for thermal perception. Our dataset and source code are publicly available at https://***/view/multi-spectral-stereo-dataset and https://***/UkcheolShin/SupDepth4Thermal. © 2025, CC BY-NC-SA.

关键词： Depth perception

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Research Progress and Application of Multifunctional Piezoelectric Hydrogels 14

Research Progress and Application of Multifunctional Piezoel...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Guo, Jiaduo Zhang, Qi Gai, Dongze Yang, Lianchao Liang, Wenfeng Zhang, Chuang School of Mechanical Engineering Shenyang Jianzhu University Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9798331506056

Piezoelectric hydrogels are unique materials endowed with piezoelectric properties, rendering them highly versatile for various applications. Characterized by excellent flexibility, biocompatibility, adjustability, rapid response capability, heightened sensitivity, and environmental adaptability, it exhibits a remarkable ability to convert mechanical energy into electrical energy and vice versa when subjected to external forces. This distinctive feature makes it an ideal candidate for integration into bio-syncretic robots. Despite its potential, the literature primarily focuses on singular applications of piezoelectric hydrogels, neglecting their broader scope. Therefore, this review aims to systematically introduce and classify piezoelectric hydrogels, while comprehensively exploring their diverse applications in sensing, energy conversion, and biomedicine within the realm of bio-syncretic robotics. Furthermore, this paper sheds light on the current limitations of piezoelectric hydrogels and proposes avenues for their future development, thereby offering valuable insights into advancing this promising field. © 2024 IEEE.

关键词： Piezoelectricity

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Research Progress in Cell Classification Detection Based on Deep Learning 14

Research Progress in Cell Classification Detection Based on ...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Gai, Dongze Zhang, Chuang Zhang, Yiwei Guo, Jiaduo Zhang, Qi Qin, Hengshen Dang, Dan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China School of Mechanical Engineering Shenyang Jianzhu University Shenyang110168 China School of Science Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9798331506056

This paper reviews the application of deep learning in cell classification detection, highlights the importance of convolutional neural networks in image analysis, and discusses the application of image preprocessing, feature extraction and deep learning models. Despite the progress made by deep learning techniques in improving cell classification accuracy, challenges such as data quality, model generalization and interpretability remain. This article suggests future research directions, including combining medical knowledge with deep learning and developing multimodal learning to improve the accuracy and efficiency of cell detection. Meanwhile, this article also explores the potential of label-free cell classification methods combining optically induced optically-induced dielectrophoresis (ODEP) techniques with deep learning to achieve cell classification without labeling, providing a new perspective on cell analysis. optically induced. © 2024 IEEE.

关键词： Contrastive Learning

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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2023 International Conference on Networking, Electrical engineering, computer science, and Technology, IConNECT 2023

作者： Sirait, Tamsir Hasudungan Lumban Gaol, Ford Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Universitas Indonesia Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology. © 2023 IEEE.

关键词： Information management

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Human-Robot Interaction Studies with Adults in Health and Wellbeing Contexts - Outcomes and Challenges 15th

Human-Robot Interaction Studies with Adults in Health and...

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15th International Conference on Social robotics, ICSR 2023

作者： Ghafurian, Moojan Dautenhahn, Kerstin Teka, Arsema Chandra, Shruti Rasouli, Samira Baliyan, Ishan Hutchinson, Rebecca Social and Intelligent Robotics Research Laboratory Faculty of Engineering University of Waterloo WaterlooON Canada David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada Davis Centre Library University of Waterloo WaterlooON Canada

ISBN: (纸本)9789819987146

Social robots have great potential to support individuals’ health and wellbeing. We did a follow up study based on previous work of a 2021 systematic review following PRISMA guidelines that identified 443 research articles evaluating social robots in health/wellbeing contexts with adult participants. In this paper we performed a new analysis that showed that while the vast majority of the articles reported positive outcomes related to the use of social robots in these contexts, only half of those articles supported the results by statistical tests and comparisons, highlighting a need for future studies with robust methodologies that can further study and inform the use of social robots for supporting adults in health/wellbeing. We discuss that different qualitative and quantitative methodologies are equally valuable, as long as the conclusions are based on the data collected. We also encourage publication of studies with well designed and executed methodologies that lead to neutral or negative outcomes. This is in line with other scientific research fields that emphasize the need to report on such results to avoid needless replication of studies that have already been done and could provide important lessons for the field, but never got published. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Human robot interaction

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Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator

Preprocessing-based Kinodynamic Motion Planning Framework fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ramkumar Natarajan Hanlan Yang Qintong Xie Yash Oza Manash Pratim Das Fahad Islam Muhammad Suhail Saleem Howie Choset Maxim Likhachev The Robotics Institute School of Computer Science Carnegie Mellon University USA Department of Engineering Science University of Oxford UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile’s future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator’s dynamic limits and extreme time constraints (≤ 350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot’s joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

关键词： Service robots Projectiles Pipelines Robot vision systems Trajectory Planning Time factors

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Change of Electrical and Transport Properties of Nickel Oxide by Carrier Concentration and Temperature through First‑Principle Calculations

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Nanomanufacturing and Metrology 2023年第4期6卷 109-118页

作者： Nayem Md.Reza Shah Chang‑Dong Yeo Minyeong Choi Yang‑Ki Hong Jeong H.You Department of Mechanical Engineering Texas Tech UniversityLubbockTX 79409USA Department of Electrical and Computer Engineering and Material Science Ph.D.Program The University of AlabamaTuscaloosaAL 35487USA Department of Mechanical Engineering School of EngineeringUniversity of St.ThomasSt.PaulMN 55105USA

Nickel is typically used as one of the main components in electrical contact devices or *** oxide(NiO)is usually formed on the surfaces of electrodes and can negatively impact system performance by introducing electrical contact *** thermal,electrical,and transport properties of NiO,as a Mott insulator or a p-type semiconductor,can be altered by operating and environmental conditions such as temperature and stress/strain by *** this study,we inves-tigate the fundamental material properties of NiO through the first-principle ***,we obtain and compare the lattice parameter,magnetic moment,and electronic structure for NiO via the WIEN2K simulations with four different poten-tials(i.e.,GGA,GGA+U,LSDA,and LSDA+U).Then,using the WIEN2K simulation results with LSDA+U potential that produces a highly accurate bandgap for NiO,we calculate the electrical conductivity and electrical part of the thermal conductivity of nickel and NiO as a function of temperature and carrier concentration through the BoltzTraP *** simulation results revealed that the electrical conductivity relative to the relaxation time for NiO increases with the carrier concentration,while it shows a slightly decreasing trend with temperature under a fixed carrier *** contrast,the electrical part of the thermal conductivity shows an increasing trend considering carrier concentration and temperature.

关键词： Nickel oxide Magnetic moments Density of states Seebeck coefcient Electrical conductivity

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