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2023 7th International Conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Wenxue Li Chi Cheng Peihua Xu Renfeng Liu Yun Zhang Huabing Zhou College of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Meteorological Service Center Wuhan China Faculty of Artificial Intelligence Education School of Educational Information Technology Central China Normal University Wuhan China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models.

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Overview of Dialogue robot Competition 2022

arXiv

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arXiv 2022年

作者： Minato, Takashi Higashinaka, Ryuichiro Sakai, Kurima Funayama, Tomo Nishizaki, Hiromitsu Nagai, Takayuki Hiroshi Ishiguro Laboratories ATR Kyoto Japan Guardian Robot Project RIKEN Kyoto Japan Graduate School of Informatics Nagoya University Nagoya Japan Graduate Faculty of Interdisciplinary Research University of Yamanashi Yamanashi Japan Graduate School of Engineering Science Osaka University Osaka Japan

Although many competitions have been held on dialogue systems in the past, no competition has been organized specifically for dialogue with humanoid robots. As the first such attempt in the world, we held a dialogue robot competition in 2020 to compare the performances of interactive robots using an android that closely resembles a human. Dialogue robot Competition 2022 (DRC2022) was the second competition, held in August 2022. The task and regulations followed those of the first competition, while the evaluation method was improved and the event was internationalized. The competition has two rounds, a preliminary round and the final round. In the preliminary round, twelve participating teams competed in performance of a dialogue robot in the manner of a field experiment, and then three of those teams were selected as finalists. The final round will be held on October 25, 2022, in the robot Competition session of IROS2022. This paper provides an overview of the task settings and evaluation method of DRC2022 and the results of the preliminary round. Copyright © 2022, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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Localization of mobile robot based on multi-sensor fusion

Localization of mobile robot based on multi-sensor fusion

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Chinese Control and Decision Conference, CCDC

作者： Yu Gao Fei Wang Jinghong Li Yuqiang Liu College of Information Science and Engineering Northeast University Shenyang China Faculty of Robot Science and Engineering Northeast University Shenyang China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to realize the precise positioning of mobile robot, the data information of multiple sensors needs to be collected by the localization system to enhance the state estimation ability of robot. Due to the need for accurate calibration and initialization of sensor groups, as well as the processing of measurement errors with different rates and delays, multi-sensor fusion still needs to solve many problems and faces many challenges. A method of multi-sensor fusion, which can analyze and screen the signal quality of sensors is proposed in this paper. Based on the extended Kalman filter, UWB data, IMU data and Lidar data are fused. By filtering and processing the sensor information, the impact of noise can be reduced. At the same time, the results of various sensor information fusion are closer to the actual results, and the mobile robot can be located.

关键词： Mobile robots Laser radar robot sensing systems Sensor systems Mathematical model

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A New Non-Line-of-Sight Localization Algorithm for Wireless Sensor Network

A New Non-Line-of-Sight Localization Algorithm for Wireless ...

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International Conference on Computer and Communications (ICCC)

作者： Zhibo Fan Hao Chu Fei Wang Jian Lu College of Information Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In recent years, node localization technology based on Wireless sensor network (WSN) plays an increasingly important role in production and life. The accuracy of node localization largely depends on the distance measurement between the target node and the beacon node. When there are many obstacles, the signal transmission is greatly disturbed, and the positioning accuracy is reduced. To solve this problem, a new non-line-of-sight (NLOS) localization method is proposed. Firstly, a statistical method based on Anderson-Darling (AD) test is employed to test the signal propagation state. After that, we construct the location function with constraints by LOS measurements and NLOS measurements. Next, we propose an improved particle swarm optimization (PSO) algorithm to determine the target location. Simulation results show that the proposed algorithm can effectively improve the positioning accuracy of the target node in complex environment.

关键词： Location awareness Wireless sensor networks Atmospheric measurements Statistical analysis Computational modeling Simulation Particle measurements

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Unsupervised learning for monocular depth and motion with real scale

Unsupervised learning for monocular depth and motion with re...

引用

Chinese Automation Congress (CAC)

作者： Sibo Liu Lijin Fang College Information Science and Engineering Northeastern university Shenyang China Faculty of Robot Science and Engineering Northeastern university Shenyang China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In Computer Vision, the method of using a single image to learn depth information through the CNNs has gradually improved, especially for the unsupervised learning methods which trains without the ground-truth are based on the image process and vision. However, the depth map generated from these methods are relative depth values instead of absolute depths, therefore these methods can't be well applied to practical *** this paper, we use the monocular video sequence as input, and get partial true depth value by using a prior conditions, so as to constrain the depth and the relative pose of the entire sequence with the "real" scale. Here we solve the previous scale ambiguous problem by fusing the geometry information, which correct the scale of the entire sequence by our rescaled module. This paper only uses monocular sequences to accomplish these ***, our method is specifically effective in the field of automated guided vehicles with only a camera as the requirement which reducing the dependence on other devices but improved the quality.

关键词： Cameras Geometry Three-dimensional displays Vehicle dynamics Estimation robot sensing systems Image sequences

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Clustering Multimodal Ensemble Learning for Predicting Gastric Cancer Neoadjuvant Chemotherapy Efficacy

Clustering Multimodal Ensemble Learning for Predicting Gastr...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jianning Chi Huixuan Wu Yujin Shi Zelan Li Xiaohu Sun Lu Wang Zitian Zhang Yuehua Gong Faculty of Robot Science and Engineering Northeastern University Shenyang China Tumor Etiology and Screening Department of Cancer Institute and General Surgery Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province The First Hospital of China Medical University Shenyang China Department of Radiotherapy Cancer Hospital of China Medical University Liaoning Cancer Hospital & Institute Cancer Hospital of Dalian University of Technology Shenyang PR China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting the response to neoadjuvant chemotherapy (NCT) is crucial for gastric cancer treatment planning. Multimodal diagnostic methods enhance prediction accuracy by integrating images and clinical features, but they often overlook intra-class differences, causing feature entanglement, and single models struggle to capture dataset diversity. To address these issues, we propose Clustering Multimodal Ensemble Learning (CMEL) framework, which aligns and fuses image features from different domains with clinical features and performs decision fusion using an ensemble model. Specifically, we design the clustering process using Siamese network and memory bank to partition images in feature space and introduce contrastive clustering loss to enhance clustering properties and reduce feature entanglement across domains. We design Hierarchical Feature Alignment Fusion (HFAF) module to generate multi-level image features at different depths and then separately align and fuse them with clinical features, providing diverse features to cover variations in the dataset. We design Dynamic Multi-Classifier Decision Fusion (DMDF) module to predict on fused features using a gating mechanism supervised by pseudo-labels for dynamic weights, enhancing prediction across different domains. On our collected the Gastric Cancer Chemotherapy Response (GCCR) dataset, CMEL achieves an AUC of 76.09%, accuracy of 72.00%, PPV of 86.02%. These results demonstrate the feasibility of joint image and clinical data in predicting NCT response and provide valuable insights for gastric cancer treatment. The code is available at https://***/WHX0259/CMEL.

关键词： Chemotherapy Accuracy Fuses Biological system modeling Cancer treatment Predictive models Feature extraction Planning Ensemble learning Cancer

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Correction to: Person search via class activation map transferring

引用

Multimedia Tools and Applications 2022年第6期82卷 9553-9553页

作者： Li, Ruilong Zhang, Yunzhou Zhu, Shangdong Liu, Shuangwei College of Information Science and Engineering Northeastern University of China Shenyang China Faculty of Robot Science and Engineering Northeastern University of China Shenyang China

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Post-Stimulus Time-Dependent Event Descriptor

Post-Stimulus Time-Dependent Event Descriptor

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IEEE International Conference on Image Processing

作者： S. Harrigan S. Coleman D. Kerr P. Yogarajah Z. Fang C. Wu Faculty of Computing Engineering and Built Environment Ulster University Northern Ireland UK Faculty of Robot Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Event-based image processing is a relatively new domain in the field of computer vision. Much research has been carried out on adapting event-based data to comply with established techniques from frame-based computer vision. On the contrary, this paper presents a descriptor which is designed specifically for direct use with event-based data and therefore can be considered to be a pure event-based vision descriptor as it only uses events emitted from event-based vision devices without transforming the data to accommodate frame-based vision techniques. This novel descriptor is known as the Post-stimulus Time-dependent Event Descriptor (P-TED). P-TED is comprised of two features extracted from event data which describe motion and the underlying pattern of transmission respectively. Furthermore a framework is presented which leverages the P-TED descriptor to classify motions within event data. This framework is compared against another state-of-the-art event-based vision descriptor as well as an established frame-based approach.

关键词： Support vector machines Vision sensors Lattices Feature extraction Machine learning Microsoft Windows

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Diversity amplification and data generation of Chinese Sign Language based on Generative Adversarial Network

Diversity amplification and data generation of Chinese Sign ...

引用

IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Fei Wang Zhen Zeng Shizhuo Sun Yanjun Liu Faculty of Robot Science and Engineering Northeastern University Professer Shenyang China School of Computer Science and Engineering Northeastern University Bachelor Shenyang China

ISBN: (数字)9781728190105

ISBN: (纸本)9781728190112

There are many factors affecting the effectiveness and accuracy of Sign Language Recognition (SLR) based on wearable device combing sEMG and IMU signals. Among them, the diversity of sign language signals caused by various factors will greatly affect the effect of SLR in the process of sign language acquisition and the use of SLR system. In the algorithm design stage, such diversity and difference should be taken into account, so that the designed algorithm can be more robust to these factors. Therefore, it is necessary to design some schemes for data amplification and data generation to solve the problem of sign language data diversity. In this paper, a random core extraction method is proposed for fast data amplification according to the characteristics of time migration without deformation. And considering that a lot of manpower and time are often consumed in the actual data acquisition process, the data volume is not sufficient. We proposed a method of sign language data generation based on Generative Adversarial Networks (GAN). This data amplification method can not only solve the problem of temporal diversity of sign language data set, but also effectively prevent model overfitting by increasing the sample numbers of sign language data set. In the experimental part, the effectiveness of the method is proved by comparing and analyzing the corresponding experimental results in the process of data generation and amplification.

关键词： Assistive technology Gesture recognition Generative adversarial networks Data models Gallium nitride Data mining Interference

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Gaits generation from a mimetic word based on sound symbolism

引用

Transactions of the Japanese Society for Artificial Intelligence 2021年第5期36卷

作者： Kato, Hirotaka Hirayama, Takatsugu Doman, Keisuke Ide, Ichiro Kawanishi, Yasutomo Deguchi, Daisuke Murase, Hiroshi Graduate School of Informatics Nagoya University Japan Faculty of Human Environment University of Human Environments Japan School of Engineering Chukyo University Japan Mathematical and Data Science Center Japan Guardian Robot Project R-IH RIKEN Japan

The Japanese language is known to have a rich vocabulary of mimetic words, which have the property of sound symbolism;Phonemes that compose the mimetic words are strongly related to the impression of various phenomena. Especially, human gait is one of the most commonly represented phenomena by mimetic words expressing its visually dynamic state. Sound symbolism is useful for modeling the relation between gaits and mimetic words intuitively, but there has been no study on their intuitive generation. Most previous gait generation methods set specific class labels such as "elderly" but have not considered the intuitiveness of the generation model. Thus, in this paper, we propose a framework to generate gaits from a mimetic word based on sound symbolism. This framework enables us to generate gaits from one or more mimetic words. It leads to the construction of a generation model represented in a continuous feature space, which is similar to human intuition. Concretely, we train an encoder-decoder model conditioned by a "phonetic vector", a quantitive representation of mimetic words, with an adaptive instance normalization module inspired by style transfer. The phonetic vector is a dense description of the intuitive impression of a corresponding gait and is calculated from many mimetic words in the HOYO dataset, which includes gait motion data and corresponding mimetic word annotations. Through experiments, we confirmed the effectiveness of the proposed framework. © 2021, Japanese Society for Artificial Intelligence. All rights reserved.

关键词： Linguistics

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