the proceedings contain 97 papers. the topics discussed include: sequential frequency estimation using auxiliary particle filter;gamification of donation app: what affect donators’ decision?;a self-augmentation trans...
ISBN:
(纸本)9781665489126
the proceedings contain 97 papers. the topics discussed include: sequential frequency estimation using auxiliary particle filter;gamification of donation app: what affect donators’ decision?;a self-augmentation transfer learning network for image deraining;towards employee-driven idea mining: concept, benefits, and challenges;the classification of edible-nest swiftlets using deep learning;disambiguation of web search terms based on clustering using page rank and distance between words;adaptive noise cancellation using a fully connected network: a lesson learned;research on self-driving based on dynamic recognition of traffic signs;data clustering using particle swarm optimization for pairwise microarray bioinformatics data;system design of a toxic waste management system empirical study for Chiang Rai province;shape recognition using unconstrained pill images based on deep convolution network;beauty face: an android application for cosmetic consumers to try on and receive product recommendation;and in-memory synchronization platform for electronic toll collection via computer network of the expressway in thailand.
Withthe advancement of deep learning (DL) technologies, applying DL methods to processing surface electromyographic (sEMG) signals for movement intent recognition has gained increasing interest in the research commun...
详细信息
ISBN:
(纸本)9781665462921
Withthe advancement of deep learning (DL) technologies, applying DL methods to processing surface electromyographic (sEMG) signals for movement intent recognition has gained increasing interest in the research community. Compared to conventional non-DL methods commonly used for EMG patternrecognition (PR), DL algorithms have the advantage of automatically extracting sEMG features without the cumbersome manual feature engineering step and are especially effective in processing sEMG signals collected from 1-dimentional (1D) or 2D sensor arrays. However, a key challenge to the deployment of DL methods in sEMG-controlled neural-machine interface (NMI) applications (e.g., myoelectric controlled prostheses) is the high computational cost associated with DL algorithms (e.g., convolutional neural network (CNN)) since most NMI applications need to be implemented on resource-constrained embedded computer systems and have real-time requirements. In this paper, we designed and implemented EffiE - an efficient CNN for real-time EMG PR system on edge devices. the development of the EffiE system integrated several strategies including a deep transfer learning strategy to adaptively and quickly update the pre-trained CNN model based on the user's newly collected data on the edge device, and a deep learning quantization method that can dramatically reduce the memory consumption and computational load of the CNN model without sacrificing the model accuracy. the proposed EffiE system has been implemented on a Sony Spresense 6-core microcontroller board as a working prototype for real-time NMIs. the embedded NMI prototype has integrated input/output interfaces as well as efficient memory management and precise timing control schemes to achieve real-time DL-based myoelectric control of a bionic arm using hand gestures. We released all the source code at: https://***/MIC-Laboratory/EffiE
this paper addresses a critical challenge hindering the widespread adoption of smart manufacturing and Condition-Based Maintenance: the contextualization step for behavioral indicator extraction. Contextualization is ...
详细信息
Traffic sign recognition plays a significant role in intelligent transportation system. therefore, in this paper, I propose a traffic sign recognition algorithm based on Convolutional Neural Network (CNN). the dataset...
详细信息
In this paper, the basic theory of neural network introduces the basic concept, development process and application of neural network, analyzes the basic working principle of the network and the relationship between n...
详细信息
Medical image segmentation plays a pivotal role in modern healthcare applications, significantly assisting healthcare professionals in making accurate diagnosis. However, limited annotated data hinders the development...
详细信息
Facial expressions are one of the fundamental ways that people convey their feelings. Perhaps the most remarkable, consistent people can quickly express their feelings and intentions by making facial expressions. ther...
详细信息
In the relatively short history of machinelearning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. the most recent wave of AI has brought to the IR communi...
详细信息
ISBN:
(纸本)9781450391320
In the relatively short history of machinelearning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. the most recent wave of AI has brought to the IR community powerful techniques, particularly for patternrecognition. While many benefits from the burst of ideas as numerous tasks become algorithmically feasible, the balance is tilting toward the application side. the existing theoretical tools in IR can no longer explain, guide, and justify the newly-established methodologies. With no choices, we have to bet our design on black-box mechanisms that we only empirically understand. the consequences can be suffering: in stark contrast to how the IR industry has envisioned modern AI making life easier, many are experiencing increased confusion and costs in data manipulation, model selection, monitoring, censoring, and decision making. this reality is not surprising: without handy theoretical tools, we often lack principled knowledge of the patternrecognition model's expressivity, optimization property, generalization guarantee, and our decision-making process has to rely on over-simplified assumptions and human judgments from time to time. Facing all the challenges, we started researching advanced theoretical tools emerging from various domains that can potentially resolve modern IR problems. We encountered many impactful ideas and made several independent publications emphasizing different pieces. Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems. We emphasize systematicity because IR is a comprehensive discipline that touches upon particular aspects of learning, causal inference analysis, interactive (online) decision-making, etc. It thus requires systematic calibrations to render the actual usefulness of the imported theoretical tools to serve IR problem
Driven by increasing customer demands, manufacturing processes now encompass increasingly intricate workflows. the industry uses computer-aided process planning to manage these complex manufacturing processes effectiv...
详细信息
Imbalanced class distribution is common issue in machinelearning and datamining. It affects various applications like fraud detection, medical diagnosis, and network intrusion detection. the mentioned problem occurs...
详细信息
暂无评论