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In this paper, a method is introduced for probabilistic power flow calculations based on arbitrary polynomial chaos. For the polynomial chaos, orthogonal polynomial sets are used to represent the uncertainties of renewable power generation, and these orthogonal polynomials are generated from actual data. The aforementioned method is applied to probabilistic power flow calculations, and its applicability is confirmed in application to an actual transmission network. The calculation time and accuracy achieved using the arbitrary polynomial-chaos method are compared with those achieved using the popular Monte Carlo method. The results show that the calculation speed is 246-680 times greater than that with the direct Monte Carlo method, while the accuracy is almost same.

SummaryCancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented direct comparison and accumulation of large-scale datasets. Here we show that deep texture representations (DTRs) produced by a bilinear Convolutional Neural Network, express cancer morphology well in an unsupervised manner, and work as a universal encoder for cancer histology. DTRs are useful for content-based image retrieval, enabling quick retrieval of histologically similar images from optimally area selected datasets of 7,175 cases from The Cancer Genome Atlas. Via comprehensive comparison with driver and clinically actionable gene mutations, we have successfully predicted 309 combinations of genomic features and cancer types from hematoxylin and eosin-stained images at high accuracy (AUC > 0.70 and q < 0.02). With its mounting capabilities on accessible devices such as smartphones, DTR-based encoding for cancer histology has a potentially strong impact on global equalization for cancer diagnosis and targeted therapies.

Motivation The aim of precision medicine is to harness new knowledge and technology to optimize the timing and targeting of interventions for maximal therapeutic benefit. This study explores the possibility of building AI models without precise pixel-level annotation in prediction of the tumor size, extrathyroidal extension, lymph node metastasis, cancer stage and BRAF mutation in thyroid cancer diagnosis, providing the patients’ background information, histopathological and immunohistochemical tissue images. Results A novel framework for objective evaluation of automatic patient diagnosis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2017— A Grand Challenge for Tissue Microarray Analysis in Thyroid Cancer Diagnosis. Here, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the data repository of tissue microarrays; the creation of the clinical diagnosis classification data repository of thyroid cancer; and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, three automatic methods for predictions of the five clinical outcomes have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic patient diagnosis is still a challenging and unsolved problem. Availability and implementation The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/cvmi/ISBI2017/). Supplementary information Supplementary data are available at Bioinformatics online.

This paper presents a FPGA-based analog-digital hybrid real-time simulator (HRTS)with a new state space representation method. In the proposed method, electro-magnetic transient models with switches are represented by parallel ordinary differential equation (ODE)based on a block-diagonal decomposition of matrix. This paper also proposes a new update rule of state space form in order to accurately calculate with limited resources. The proposed method can be implemented with low costs of computation, and enables accurate computation with a less size fixed point of Field-Programmable Gate Array (FPGA). We implemented a doubly fed induction generator (DFIG)based wind turbine model on the FPGA using the proposed method and connected it to the analog simulator via a current source. By comparing with MATLAB/Simulink references, the effectiveness of the proposed method is illustrated.

BackgroundAs a large number of digital histopathological images have been accumulated, there is a growing demand of content-based image retrieval (CBIR) in pathology for educational, diagnostic, or research purposes. However, no CBIR systems in digital pathology are publicly available.ResultsWe developed a web application, the Luigi system, which retrieves similar histopathological images from various cancer cases. Using deep texture representations computed with a pre-trained convolutional neural network as an image feature in conjunction with an approximate nearest neighbor search method, the Luigi system provides fast and accurate results for any type of tissue or cell without the need for further training. In addition, users can easily submit query images of an appropriate scale into the Luigi system and view the retrieved results using our smartphone application. The cases stored in the Luigi database are obtained from The Cancer Genome Atlas with rich clinical, pathological, and molecular information. We tested the Luigi system by querying typical cancerous regions from four cancer types, and confirmed successful retrieval of relevant images.ConclusionsThe Luigi system will help students, pathologists, and researchers easily retrieve histopathological images of various cancers similar to those of the query image.

When an action does not relate to the speech, the speech may connect any action unrelated to the speech. In such case, a speech animation can be easily produced by choosing a suitable action from the data base containing actions captured beforehand.

In recent years, the number of vacant houses is increasing in Japan, as the population decline in local communities. In this context, there is an urgent need to develop smart socio-technical systems that enable effective use and management of vacant houses, thereby preventing them from having negative impacts on local communities. In this paper, we propose an approach to support community-centric use and management of vacant houses based on an integrated crowdsourcing platform. We describe our ongoing project in Kashiwa City, and argue for an open, inclusive, and community-centric distributed platform to cater for some of the important needs in the community.

Among various control methods, model predictive control (MPC) is one of the major control strategies and has many successful applications. This paper presents an automatic tuning method for MPC using particle swarm optimization (PSO). One of the challenges in MPC is how control parameters can be tuned for various target plants, and the use of PSO for automatic tuning is one of the solutions. The MPC tuning problem is formulated as an optimization problem and PSO is applied as the optimization technique. PSO is one of the metaheuristic methods which are known to seek a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 178(1): 40–49, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21188

Recently there has been a great deal of research on using mobility in sensor networks to assist their sensing tasks. In this paper, we propose a policy-based session control protocol for Multi-Robot Sensor Networks (MRSNs) called Billiards. In a MRSN, all messages are transported by the physical motion of participants (mobile nodes) in the network. When a large volume of data or continuous data is required to be transferred, there exists a problem determining how the data is fragmented and how the mobile nodes are formed for carrying the data to the destination. To overcome the issues, we propose a suitable method of session control which is determined based on a state of surrounding mobile nodes such as number, maximum-velocity and buffer-size. Billiards also takes a system policy of delay minimization into consideration. In this paper, we describe the protocol and model of Billiards and analyze the model. We evaluated the performance of Billiards utilizing mobile robots which are equipped with MICA2 mote and comparing with non optimized method. The experimental results demonstrate that Billiards achieves less delay than non optimized method at every velocity and buffer-size of each robot.

We present a video annotation system called ``AnnoTone', which can embed various contextual information describing a scene, such as geographical location. Then the system allows the user to edit the video using this contextual information, enabling one to, for example, overlay with map or graphical annotations. AnnoTone converts annotation data into high-frequency audio signals (which are inaudible to the human ear), and then transmits them from a smartphone speaker placed near a video camera. This scheme makes it possible to add annotations using standard video cameras with no requirements for specific equipment other than a smartphone. We designed the audio watermarking protocol using dual-tone multi-frequency signaling, and developed a general-purpose annotation framework including an annotation generator and extractor. We conducted a series of performance tests to understand the reliability and the quality of the watermarking method. We then created several examples of video-editing applications using annotations to demonstrate the usefulness of Annotone, including an After Effects plug-in.

Collecting real-world information is an important task for humanitarian applications. To alleviate this cumbersome task, we have presented Choco, a low-power, end-to-end reliable collection protocol for wireless sensor networks. Sensor nodes with Choco can construct multi-hop wireless networks without awareness of network topology thanks to simultaneous flooding, which is a recently proposed flooding scheme exploiting constructive interference phenomena. In this paper, we present two field experiments of Choco in a data center, and 400 m straight road, and show the efficiency in these fields.

This paper introduces an input and output device that enables illumination, bi-directional data communication, and position sensing on a soft cloth. This "LightCloth" is woven from diffusive optical fibers. Since the fibers are arranged in parallel, the cloth has one-dimensional position information. Sensor-emitter pairs attached to bundles of contiguous fibers enable bundle-specific light input and output. We developed a prototype system that allows full-color illumination and 8-bit data input by infrared signals. We present as an application a chair with a LightCloth cover whose illumination pattern is specified using an infrared light pen. Here we describe the implementation details of the device and discuss possible interactions using the device.

We develop a visualization tool for designing a topology-aware communication algorithm. This tool visualizes the communication behavior from the logs of a network simulator or an existing parallel computer. Using multiple views, filtering functions, and an animation function, the tool affords users an intuitive understanding of communication behavior and provides statistical information. The topology view represents the spatial load distribution of the network topology in 3D space. The user can analyze the communication behavior on a specific network topology. A distinction between the behaviors of the new all-to-all communication algorithm and the conventional one is drawn clearly by the tool. In addition to the poster presentation, we are going to present a communication algorithm behavior on PC using our visualization tool.

This paper introduces optimal operational planning of energy plants via the e constrained differential evolution. In order to generate optimal operational planning for energy plants, startup/shutdown status and/or input/output values of the facilities at each control interval should be determined. The problem can be formulated as a large-scale mixed-integer nonlinear problem (MINLP). Metaheuristics (MHs) is one of the solutions for MINLP. If the formulated MINLP has various equality and inequality constraints, it remains difficult to solve it without parameters tuning. In this paper, to overcome the difficulty, we propose an improved differential evolution approach using the e constrained method for MINLP. Results show the effectiveness of the proposed method compared with conventional methods.

Information sharing has undeniably become ubiquitous in the Internet age. The global village created on the Internet provides people with instant access to information and news on events occurring in a remote area, including access to video content on websites such as YouTube . Thus, the Internet has helped us overcome barriers to information. However, we cannot conceive an event happening in a remote area and respond to it with relevant actions in a real-time fashion. To overcome this problem, we propose a system called Askus , a mobile platform for supporting networked actions. Askus facilitates an extension of the conceivable space and action by including humans in the loop. In Askus , a person's request is transmitted to a suitable person who will then act in accordance with the request at a remote site. Based on a diary study that led to detailed understanding about mobile assistance needs in everyday life, we developed the Askus platform and implemented the PC-based and mobile phone-based prototypes. We also present the results from our preliminary field trial.

Sensor networks using mobile robots have recently been proposed to provide more flexible and efficient sensing. However, mobile robots in a practical world have many accidents with actuator or communication devices especially in their traveling to the destination. Moreover, in the practical world, there are many obstacles and bad conditioned area for both wireless and movement of mobile robots. In this paper, we introduce a practical scheme for delivering data with concerning a practical world issues.

Environment monitoring in urban areas is becoming important to provide information for safety and relief for daily lives. Wireless sensor devices are regarded as means of such monitoring. However, it is not efficient to deploy the sensors more than the necessity in the city. It is efficient to deploy the sensors on the place where many people move around. Therefore, we install sensors to walking people and acquire information to complement the embedded sensors. Based on the idea, we have extended WINFO, human probe, a previously developed system, to accommodate reading peoplepsilas walking states obtained by pressure signals at the peoplepsilas feet. Our goal is to create a map utilizing the information obtained by a collection of the walking signals.

We propose an RFID-based human probe positioning system for urban sensing. We call a person who has various sensors on his/her body human probe (HP). Our goals are (1) to estimate a position for HP which is based on location information of RFIDs and other HPs which hold position information with estimated error and (2) to manage and visualize sensor data considering a degree of position accuracy.

Sensor networks using mobile robots have been proposed to provide more flexible and efficient sensing. One of the key challenges under this paradigm is a management of transferring a huge amount of data. In this paper, we propose a model and protocol for session control exploiting controlled mobility in multi-robot sensor networks (MRSNs) to transfer the huge amount of data efficiently. Since a communication in MRSNs is mainly provided by a physical movement of the mobile node, the performance of the session heavily depends on this movement. However, if a node initiating the session can utilize sufficient amount of nodes, the performance of the session significantly depends on the wireless communication. In this paper, we propose a suitable method of session control which is determined the balance between movement and wireless communications based on local information such as the location, number, maximum-velocity and buffer-size of surrounding nodes. WISER/s also takes a system policy of either delay minimization or energy efficiency into consideration. In this paper, we describe a method of how to initiate, terminate and manage the session including dynamic selection of optimal nodes' formation. We evaluate the performance of WISER/s using mobile robots which are equipped with MICA2 mote and comparing with non-optimized method. The experimental results demonstrate that WISER/s achieves better performance than non-optimized method.

Recently there has been a great deal of research on using mobility in sensor networks to assist their sensing tasks. In this paper, we propose a policy-based session control protocol for disruption tolerant sensor networks (DTSNs) called Billiards. In a DTSN, all messages are transported by the physical motion of participants (mobile nodes) in the network. When a large volume of data or continuous data is required to be transferred, there exists a problem determining how the data is fragmented and how the mobile nodes are formed for carrying the data to the destination. To overcome these issues, we propose a suitable method of session control which is determined based on a state of surrounding mobile nodes such as number, maximum-velocity and buffer-size. Billiards also takes a system policy of either delay minimization or energy efficiency into consideration. In this paper, we describe the protocol and model of Billiards and analyze the each model. We also show its effectiveness through experiments with utilizing mobile robots.

This paper presents an automatic tuning method of model predictive control (MPC) using particle swarm optimization (PSO). Although conventional PID is difficult to treat constraints and future plant dynamics, MPC can treat this issues and practical control can be realized in various industrial problems. One of the challenges in MPC is how control parameters can be tuned for various target plants and usage of PSO for automatic tuning is one of the solutions. The numerical results show the effectiveness of the proposed PSO-based automatic tuning method