The University of Tokyo · Computer Science
Professor Shi Chen's research lab specializes in advanced optical communication systems, particularly free-space optical transmission under challenging environmental conditions such as atmospheric turbulence. The lab also focuses on intelligent systems for industrial safety, including vision-based monitoring of protective equipment and behavior recognition for insider threat detection in nuclear facilities. Additionally, the lab develops data-efficient AI methods for natural language processing and spectral analysis in remote sensing, emphasizing knowledge integration and automated labeling. These interdisciplinary efforts bridge photonics, artificial intelligence, and nuclear safety.
Figures are computed from collected data and may differ slightly.
By mapping traditional amplitude modulation to spatial modulation and employing adaptive optics compensation technique, we propose and experimentally demonstrate a high-speed Bessel beam encoding/decoding free-space optical link through atmospheric turbulence. The Bessel beam encoding/decoding speed is not limited by the conventional slow switching response of a spatial light modulator (SLM) but is fully determined by the modulation rate of an intensity modulator, which easily supports tens of g
In this paper, with the help of knowledge base, we build and formulate a semantic space to connect the source and target languages, and apply it to the sequence-to-sequence framework to propose a Knowledge-Based Semantic Embedding (KBSE) method. In our KB-SE method, the source sentence is firstly mapped into a knowledge based semantic space, and the target sentence is generated using a recurrent neural network with the internal meaning preserved. Experiments are conducted on two translation task
Decommissioning of the Fukushima Daiichi nuclear power station (NPS) is challenging due to industrial and chemical hazards as well as radiological ones. The decommissioning workers in these sites are instructed to wear proper Personal Protective Equipment (PPE) for radiation protection. However, workers may not be able to accurately comply with safety regulations at decommissioning sites, even with prior education and training. In response to the difficulties of on-site PPE management, this pape
The lack of labeled data is one of the main challenges when building a task-oriented dialogue system. Existing dialogue datasets usually rely on human labeling, which is expensive, limited in size, and in low coverage. In this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue intents and slots. In this framework, we collect a set of context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for inte
Spectral unmixing is the process of decomposing the measured spectrum of a mixed pixel into a set of pure spectral signatures called endmembers and their corresponding abundances, which indicate the fractional area coverage of each endmember present in the pixel. A substantial number of spectral unmixing studies rely on a spectral mixture model which assumes that spectral mixing only occurs within the extent of a pixel. However, due to adjacency effect, the spectral measurement of the pixel may
Following the Fukushima Daiichi nuclear accident, fears have arisen that terrorists can cause a similar accident by acts of sabotage against nuclear facilities; as a result, the importance of nuclear security has increased. In particular, sabotage by insiders is a distinct threat to nuclear power plants. In response to limitations of physical protection system (PPS) in nuclear facilities, in this paper, we propose a behavior recognition method that is based on hand motion time-series data analys
In smart grid, Advanced Metering Infrastructure (AMI) systems feed the metering data to Energy Supplier (ES) every 15 minutes. Using this information, ES can offer incentives to reduce the total power consumption during the peak hours through the real-time pricing mechanism. However, it also brings privacy concerns. Since the metering data is collected by the ES nearly in real-time, the customer's behavior and habits can be disclosed via the statistical patterns. The privacy problem will hamper
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