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This paper describes the development and evaluation of a newly developed end-effector type rehabilitation robot named DDgo Pro. In order to induce normal walking pattern, a five-link mechanism was designed to follow normal walking trajectory. In order to implement the rehabilitation training feature, the core technologies of the drive unit consisting of freewheel and BLDC motor, the construction of the robot control system, and the algorithms of the three training modes in DDgo Pro were addressed in detail. Among the three training modes, Passive Mode makes the robot to fully guide normal walking pattern, and to help patients gain muscle strength in their lower body. In Active Assisted Mode, patients who have learned Passive Mode are able to perform active rehabilitation training for a long time while receiving muscle assistance from the robot in proportion to their walking intentions. Active mode was designed for patients to perform rehabilitation training for themselves with minimal help from the robot. In order to test muscle assisted performance of the newly developed rehabilitation robot, muscle activity in each training mode was measured using Electromyography for healthy people. In addition, gait dynamics was analyzed to check whether DDgo Pro help stroke patients correct their walking patterns.

This paper gives two graph-based algorithms for radioactive decay computation. The first algorithm identifies the connected components of the graph induced from the given radioactive decay dynamics to reduce the size of the problem. The solutions are derived over the precalculated connected components, respectively and independently. The second algorithm utilizes acyclic structure of radioactive decay dynamics. The algorithm evaluates the reachable vertices of the induced system graph from the initially activated vertices and finds the minimal set of starting vertices populating the entire reachable vertices. Then, the decay calculations are performed over the reachable vertices from the identified minimal starting vertices, respectively, with the partitioned initial value over the reachable vertices. Formal arguments are given to show that the proposed graph inspired divide and conquer calculation methods perform the intended radioactive decay calculation. Empirical efforts comparing the proposed radioactive decay calculation algorithms are presented.

Gait-training rehabilitation machines (MGTR) are contraptions used for the motor rehabilitation of patients with movement disorders resulting from stroke and Parkinson’s disease. This study was aimed at implementing a walking pattern similar to the normal gait. Background: Immersion and motivation are important factors in repetitive rehabilitation exercises. This was addressed by synchronizing walking speed and virtual reality (VR) visons to provide a sense of immersion in a convergence environment of MGTR and VR. Methods: The difference in joint angle and gait event was confirmed when the step length was adjusted in this system to control the joint movement. Results: It was confirmed that the joint range of motion also increased significantly as the step length increased. Conclusions: The possibility of developing a more immersive MGTR system that feedback the actual gait state in the VR system was confirmed by applying that the joint movement varies according to the step length. It will be possible to provide an immersive feeling more similar to the actual walking by modifying the gait trajectory of the MGTR.

Self-powered wireless sensor systems have emerged as an important topic for condition monitoring in nuclear power plants. However, commercial wireless sensor systems still cannot be fully self-sustainable due to the high power consumption caused by excessive signal processing in a mini-electronic computing system. In this sense, it is essential not only to integrate the sensor system with energy-harvesting devices but also to develop simple data processing methods for low power schemes. In this paper, we report a patch-type vibration visualization (PVV) sensor system based on the triboelectric effect and a visualization technique for self-sustainable operation. The PVV sensor system composed of a polyethylene terephthalate (PET)/Al/LCD screen directly converts the triboelectric signal into an informative black pattern on the LCD screen without excessive signal processing, enabling extremely low power operation. In addition, a proposed image processing method reconverts the black patterns to frequency and acceleration values through a remote-control camera. With these simple signal-to-pattern conversion and pattern-to-data reconversion techniques, a vibration visualization sensor network has successfully been demonstrated.

Objectives: To develop a prediction algorithm for soft tissue changes after orthognathic surgery that would result in accurate predictions (1) regardless of types or complexity of operations and (2) with a minimum number of input variables. Materials and Methods: The subjects consisted of 318 patients who had undergone the surgical correction of Class II or Class III malocclusions. Two multivariate methods—the partial least squares (PLS) and the sparse partial least squares (SPLS) methods—were used to construct prediction equations. While the PLS prediction model included 232 input variables, the SPLS method included a reduced number of variables generated by a handicapping algorithm via the sparsity control. The accuracy between the PLS and SPLS models was compared. Results: There were no significant differences in prediction accuracy depending on surgical movements, the sex of the subjects, or additional surgeries. The predictive performance with a reduced set of 34 input variables chosen using the SPLS method was statistically indistinguishable from the full set of variables with the original PLS prediction model. Conclusions: The prediction method proposed in the present study was accurate for a wide range of orthognathic surgeries. A reduced set of input variables could be selected through the SPLS method while simultaneously maintaining a prediction level that was as accurate as that of the original PLS prediction model.

The prototype machine of gait training and rehabilitation (MGTR) with a five-bar linkage structure was designed to improve the common end-effector type. Additionally, the study was conducted to evaluate the joint angle and muscle activity during walking for the evaluation of prototype: (1) Background: The gait rehabilitation systems are largely divided into exoskeletal type and end-effector type. The end-effector type can be improved a gait trajectory similar to normal gait according to this prototype. Therefore, a new design of prototype MGTR is proposed in this study. (2) Methods: The gait experience was conducted with thirteen healthy male subjects using an inertial measurement unit (IMU) sensor and electromyography (EMG). It was compared that the hip and knee joints and the muscle activity between the normal gait and MGTR. (3) Results: The results showed that there was a high correlation between the knee joint angle for normal gait and MGTR. The range of motion (RoM) was small for the MGTR. The EMG results showed that the activation of the rectus femoris muscle was most similar to the normal gait and MGTR. (4) Conclusions: The characteristics of the kinematic variables of the subjects varied widely. It is necessary to modify the machine so that the link length can be adjusted in consideration of various segment lengths of patients.

Reaching hand movement is an important motor skill investigated in brain-computer interface (BCI). Among the various components of movement analyzed is the hand's trajectory, which describes the hand's continuous position in three-dimensional space. While many studies have investigated the decoding of real movements and the possibility of reconstructing real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of imagined hand movement. In order to develop BCI systems for patients with motor dysfunction, systems need to achieve movement-free control of external devices, and this may only be possible through successful decoding of purely imagined hand movement. To make a thorough investigation on this issue, we analyzed electrocorticograms (ECoG) of eighteen epilepsy patients who performed imaginations of hand movement. We tested two experimental paradigms to induce imaginations of reach-and-grasp action and evaluated the performances of decoding models on their ability to make continuous predictions on the trajectory of imagined hand movement.

The successful memory process produces specific activity in the brain network. As the brain activity of the prestimulus and encoding phases has a crucial effect on subsequent memory outcomes (e.g., remembered or forgotten), previous studies have tried to predict the memory performance in this period. Conventional studies have used the spectral power or event-related potential of specific regions as the classification feature. However, as multiple brain regions work collaboratively to process memory, it could be a better option to use functional connectivity within the memory-related brain network to predict subsequent memory performance. In this study, we acquired the EEG signals while performing an associative memory task that remembers scene–word pairs. For the connectivity analysis, we estimated the cross–mutual information within the default mode network with the time–frequency spectra at the prestimulus and encoding phases. Then, we predicted the success or failure of subsequent memory outcome with the connectivity features. We found that the classifier with support vector machine achieved the highest classification accuracy of 80.83% ± 12.65% (mean ± standard deviation) using the beta (13–30 Hz) connectivity at encoding phase among the multiple frequency bands and task phases. Using the prestimulus beta connectivity, the classification accuracy of 72.45% ± 12.52% is also achieved. Among the features, the connectivity related to the dorsomedial prefrontal cortex was found to contribute to successful memory encoding. The connectivity related to the posterior cingulate cortex was found to contribute to the failure of memory encoding. The present study showed for the first time the successful prediction with high accuracy of subsequent memory outcome using single-trial functional connectivity.

Understanding how the brain controls movements is a critical issue in neuroscience. The role of brain changes rapidly according to movement states. To elucidate the motor control mechanism of brain, it is essential to investigate the changes in brain network in motor-related regions according to movement states. Therefore, the objective of this study was to investigate the brain network transitions according to movement states. We measured whole brain magnetoencephalography (MEG) signals and extracted source signals in 24 motor-related areas. Functional connectivity and centralities were calculated according to time flow. Our results showed that brain networks differed between states of motor planning and movement. Connectivities between most motor-related areas were increased in the motor-planning state. In contrast, only connectivities with cerebellum and basal ganglia were increased while those of other motor-related areas were decreased during movement. Our results indicate that most processes involved in motor control are completed before movement. Further, brain developed network related to feedback rather than motor decision during movements. Our findings also suggest that neural signals during motor planning might be more predictive than neural signals during movement. They facilitate accurate prediction of movement for brain-machine interfaces and provide insight into brain mechanisms in motor control.

Prediction of successful memory encoding is important for learning. High-frequency activity (HFA), such as gamma frequency activity (30–150 Hz) of cortical oscillations, is induced during memory tasks and is thought to reflect underlying neuronal processes. Previous studies have demonstrated that medio-temporal electrophysiological characteristics are related to memory formation, but the effects of neocortical neural activity remain underexplored. The main aim of the present study was to evaluate the ability of gamma activity in human electrocorticography (ECoG) signals to differentiate memory processes into remembered and forgotten memories. A support vector machine (SVM) was employed, and ECoG recordings were collected from six subjects during verbal memory recognition task performance. Two-class classification using an SVM was performed to predict subsequently remembered vs. forgotten trials based on individually selected frequencies (low gamma, 30–60 Hz; high gamma, 60–150 Hz) at time points during pre- and during stimulus intervals. The SVM classifier distinguished memory performance between remembered and forgotten trials with a mean maximum accuracy of 87.5% using temporal cortical gamma activity during the 0- to 1-s interval. Our results support the functional relevance of ECoG for memory formation and suggest that lateral temporal cortical HFA may be utilized for memory prediction.

In this paper, we apply an advanced safeguards approach and associated methods for process monitoring to a hypothetical nuclear material processing system. The assessment regarding the state of the processing facility is conducted at a system-centric level formulated in a hybrid framework. This utilizes architecture for integrating both time- and event-driven data and analysis for decision making. While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis. By integrating process- and operation-related information and methodologies within a unified framework, the task of anomaly detection is greatly improved. This is because decision-making can benefit from not only known time-series relationships among measured signals but also from known event sequence relationships among generated events. This available knowledge at both time series and discrete event layers can then be effectively used to synthesize observation solutions that optimally balance sensor and data processing requirements. The application of the proposed approach is then implemented on an illustrative monitored system based on pyroprocessing and results are discussed.

There are many attempts to improve the problem of the progressive billing system of the power rate of households. Among them, the TOU (Time-Of-Use) pricing is emerging as a reasonable alternative that is payment system varies depending on the season and time of day. KEPCO (Korea Electric Power Corporation) is considering the TOU pricing plans for household by utilizing its AMI (Advanced Meter Infrastructure) system. However, in the case of complex apartment, they are using private electric meters, which makes it difficult to adopt rate plans such as TOU, CPP (Critical Peak Pricing). In this paper, we propose a software-based GAP-Filler system that supports the application of TOU pricing in the private AMR (Automatic Meter Reading) environment built in complex apartment. This will show how to provide a variety of energy services, including various power rate plans, while minimizing additional investment costs on the previous AMR or AMI. We will apply results of this study to demonstration sites and present the performance analysis for effects and optimization.

There are different types of brain-computer interfaces (BCIs). The different type of the BCI has different strengths and weaknesses. Therefore, different type BCI is used depending on the applications. The BCI system will be powerful if different type of the BCI can be applied to the one system according to a user-state. To implement the BCI system, prediction of the user state is required. In this paper, we investigated the change of brain networks according to the user states using mutual information. Our results showed that the brain networks were changed according to the user states. The result implies that multi-mode BCI system will be possible by predicting user state using brain connectivity.

During the electrometallurgical pyroprocessing of used metallic nuclear fuel, the chopper operation cuts the fuel element into segments and distributes these segments between the anode basket and plenum can. This paper discusses a method of systematically assessing the mass balance of partitioned materials between the anode basket and plenum can. The proposed method fully accommodates the mass closeout measurements from the chopper operation and automates the assessment of chopper-induced material partitioning. With a set of reasonable assumptions, the proposed approach allows a formal uncertainty analysis on the partitioned masses as well. An illustrative calculation example adopted from the ongoing fissium fuel processing campaign is given.

In establishing nuclear fuel input accountancy for electrometallurgical pyroprocessing treatment of used nuclear oxide fuel, a representative sampling method has been considered as a suitable alternative of methods pursuing homogenization of used nuclear oxide fuel feed such as mixing the pulverized fuels. In principle, the representative sampling method involves random particle selection from size-classified nuclear fuel materials to give quantified and controllable sampling uncertainty. For this purpose, the rotary riffling method is known to be superior in realizing sample randomization. To qualify the rotary riffling method as a tool to establish used nuclear fuel input accountancy, this paper first derives theoretical randomization performance tailored to the rotary riffling method and used nuclear fuel feed. The performance of an off-the-shelf rotary riffler was tested and validated with mixed alumina particle feeds of different sizes.

Due to the development of the network, modules can control the home appliances by using the applications on smart devices and also monitor the condition of houses by using the built-in camera in smart devices. And, power control, physical control, natural ecosystem can be controlled on the smart device applications by using the network communications. The skills to do so are implemented to technologies. But since the smart appliances use different protocols, there has been a problem in a matter of product compatibility. In this paper, we design An XML schema on devices for collaboration between devices and implement the device manager, which defines the relationships to enable collaboration between devices. When the device’s relationship is redefined by the manager, we used the compact embedded system in Arduino and OpenWRT while designed and implemented the prototype system that enables collaboration between devices through an XML schema.

Recently the demand for high-bandwidth graphic DRAM, for game consoles and graphic cards, has dramatically increased due to the development of virtual reality, artificial intelligence, deep learning, autonomous driving cars, etc. These applications require greater data transfer speeds than pervious devices, GDDR5 [1] and GDDR5X [2], which are limited to 12Gb/s/pin. This paper introduces an 8Gb GDDR6 operating at up to 16Gb/s/pin. To exceed the prior speed limit various bandwidth extension techniques are proposed. WCK is driven with a dividing scheme to overcome speed limitations and to reduce power consumption. In addition, a dual-band architecture with different types of nibble drivers is proposed in order to cover stability of CML-to-CMOS in all frequency regions; CML nibble is used for high-speed, while CMOS nibble is used for low-speed. A DC-split scheme is implemented for duty-cycle correction and skew compensation. The bandwidth of the high-frequency divider is extended by using a proposed mode-changed flip-flop. The receiver uses a loop-unrolled one-tap decision-feedback equalizer (DFE) designed to eliminate channel inter-symbol interference (ISI). A two-stage pre-amplifier is also used for bandwidth extension. The transmitter uses a 4:1 multiplexer using a half-rate sampler, where a 1UI pulse is unnecessary to minimize the full-rate operation. To secure on-chip signal transmission characteristic, the bandwidth limitation of transistor in a DRAM process is extended by adopting an on-chip feedback EQ filter.

The use of various renewable energy sources is increasing with the 2015 United Nations Climate Change Conference (Conference Of the Parties 21). In case of unstable wind power and photovoltaic power generation, analysis and optimal maintenance of operation status through remote monitoring system are required. In this paper, we describe the implementation of monitoring system for renewable energy generation facilities with the system architecture, implementation method, and analysis program. We use various open IoT platform such as Arduino, Raspberry Pi and low-cost LoRa network. In the future, we will carry out research result on the performance analysis and improvement solutions after operating on the testbed site for a long time.

One of the most challenging issues in recent BCI research is not only achieving high performance, but also creating a sense of ownership of artificial devices. To investigate this issue, sensory-motor integrated BMI system should be considered. In this study, we attempted to predict the somatosensory property of tactile stimulus as well as the movement trajectory and type using elctrocorticography (ECoG) signals. We showed that 1) single-trial 3-D movement trajectory can be estimated from low-frequency ECoG signals with relatively high performance, 2) high-gamma activity can be a robust feature for movement type classification, and 3) the location of pressure stimulation can be classified by macro ECoG signals from sensory-related cortical areas. These results might be applied to the closed-loop BMBI systems which simultaneously encode sensory information during movement decoding.

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user’s intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

From 2009, the market of smart devices has been rapidly increasing. These devices provide various services to users. The cloud messaging service, especially, is applied to many various services, and sends messages asynchronously. In the cloud messaging service, there are two methods for message transmission, message transmission based on an IP address and a publish/subscribe technique. Each technique uses basic messages in order to send messages to mobile devices. In this paper, the hybrid message transmission, based on user-customized analysis to reduce basic messages, is proposed. The hybrid message transmission uses Exponential Moving Average (EMA) and K-means algorithms for user-customized analysis, and determines the message transmission techniques in each timeslot.

As the IoT environment, in which all objects are connected and information is shared, is being expanded, new services are being applied to the traditional energy services gradually. In this paper, the PhotoVoltaic(PV) monitoring system, which is required to develop efficient management service for Solar power generation system, was developed by applying IoT technology and it shows that it is possible to construct an efficient monitoring system at low cost. For the IoT based PV monitoring system, we developed IoT gateway based on inexpensive Raspberry pi hardware and adopted the Message Queuing Telemetry Transfer (MQTT) protocol at IoT gateway and Smart Phone. And also we can down the cost by using the Smart Utility Network(SUN) communication of the license-exempt band which is sub-1Ghz band. In the future, we are going to develop optimal database system and data analysis tools for this IoT Gateway and its performance will be verified.

Over the last several decades, there have been lots of BMI studies. However, it is still difficult to use BMI system in real life. Here, we introduce our three BMI studies to overcome these problems. First, we predicted continuous movement trajectory from non-invasive MEG signals. Second, we proposed a new BMI prediction model to increase the prediction accuracy using external stereo camera. Finally, we showed that modes of the BMI system can be changed according to the user's brain state. Based on our results, we expect that practical and high accuracy BMI system will be possible by combining brain states and feedback information.

Cloud computing uses virtualization technology to segment mass computing resources into various service types, such as Infrastructure as a Service, Platform as a Service, and Software as a Service. The system manager does not need to manage hardware, resource expansion and reduction are convenient, and management is not restricted by time or space. The initial cost for installing mass servers and establishing service infrastructure can be effectively reduced. Amazon, IBM, and Microsoft are the main global vendors that provide the cloud computing service. They globally distribute and manage high-availability cloud service resources. However, when resources generated by users are in different management regions, they cannot be integrated and monitored in the management page provided by the service vendor. In this study, a resource-monitoring service using an API provided by the Amazon Web Service (AWS) was implemented; however, the reception latency time occurred continuously because information is received through the API every time the page changes from the actual service step. To solve these problems, the current condition collector that collects resource information was linked with database management system to enhance the performance by reducing unnecessary requests of calling the AWS API.

A Photovoltaic (PV) system needs to monitor individual PV panels to maintain the performance. In a high-dense large-scale PV system, two characteristics can limit the use of communication technology. First, high density can degrade wireless communication performance due to the interference between densely deployed nodes. Second, a large-scale area can limit the communication distance in power line communication (PLC) due to cable loss. This paper proposes a hybrid network using PLC in a local group of PV panels and SUN in a long distance link. A pilot field test shows the feasibility of the proposed hybrid network.

Energy usage optimization in homes is an important issue because over 30% of the energy consumption of the world occurs in the residential sector. One of key points for home energy management is energy monitoring and control through low-power and low-cost wireless communication. The block-coded group PPM (BC-GPPM) is a scheme with low complexity typically proportional to power and cost because the BC-GPPM can support coding gain through modulation. This paper proposes a design method of BC-GPPM modem. From implementation results, the BC-GPPM achieves the improved complexity by about 6 times and the enhanced power efficiency by about 17 times.

Accurate observation results of dynamic displacements are essential to the protection of civil structures. In this study, we evaluated the optimal methods of the RTK/GPS Accelerometer integration through comparison and analysis of several experiments results. Two methods will be used to calculate the dynamic displacements from the results of the acceleration data as well as two integration methods for measuring the dynamic, static, and quasi-static displacements by incorporating the displacement results from the RTK-GPS and Accelerometer. By using a Cantilever Beam and LVDT measurement, we were able to ensure that the different displacement comparisons would be reliable and accurate. As a results from experiments, the accelerometer processing method applied by use of accelerometers data was filtering with the double integral using FIR band-pass filter which is most optimal for assessing the dynamic displacements. Also, the integrated method using extracting substitution displacements is suitable for measuring synthetically the dynamic static and quasi-static displacements of civil structures with RTK-GPS and accelerometer.

Interactive media are applied to inner and outer space of small public space to enhance for the interaction between the spectator and works, also widely used in the exterior wall by LED and projector for advertisement. In the other hand, case studies of interactive media for large space have not less accomplished than those of buildings and public spaces. Thus, these studies analyzed 11 large structure examples by display, content, interface, and technology device by comparison of those cases of ordinary building public space. First interactive media for large space are found to be contents(39%), and next one is play(27%), advertisement(17%) and message(17%).

We investigated the reliability of nodal network metrics of functional connectivity (FC) networks of magnetoencephalography (MEG) covering the whole brain at the sensor level in the eyes-closed (EC) and eyes-open (EO) resting states. Mutual information (MI) was employed as a measure of FC between sensors in theta, alpha, beta, and gamma frequency bands of MEG signals. MI matrices were assessed with three nodal network metrics, i.e., nodal degree (Dnodal), nodal efficiency (Enodal), and betweenness centrality (normBC). Intraclass correlation (ICC) values were calculated as a measure of reliability. We observed that the test-retest reliabilities of the resting states ranged from a poor to good level depending on the bands and metrics used for defining the nodal centrality. The dominant alpha-band FC network changes were the salient features of the state-related FC changes. The FC networks in the EO resting state showed greater reliability when assessed by Dnodal (maximum mean ICC = 0.655) and Enodal (maximum mean ICC = 0.604) metrics. The gamma-band FC network was less reliable than the theta, alpha, and beta networks across the nodal network metrics. However, the sensor-wise ICC values for the nodal centrality metrics were not uniformly distributed, that is, some sensors had high reliability. This study provides a sense of how the nodal centralities of the human resting state MEG are distributed at the sensor level and how reliable they are. It also provides a fundamental scientific background for continued examination of the resting state of human MEG.

—Tracking human facial expression within a video image has many useful applications, such as surveillance and teleconferencing, etc. Initially, the Active Appearance Model (AAM) was proposed for facial recognition; however, it turns out that the AAM has many advantages as regards continuous facial expression recognition. We have implemented a continuous facial expression recognition system using the AAM. In this study, we adopt an independent AAM using the Inverse Compositional Image Alignment method. The system was evaluated using the standard Cohn-Kanade facial expression database, the results of which show that it could have numerous potential applications. Keywords —Active Appearance Model, Facial Expression Recognition, Image Alignment Method 1. I NTRODUCTION Facial expression recognition is a crucial method of inferring human emotions. Facial emo-tions are basically categorized into six facial expressions (surprise, fear, sadness, anger, disgust, happiness). The process flow of the present system is shown in Figure 1. Facial-expression im-ages are captured from a web camera. The active appearance model includes various parameters of shape and appearance. With these images, an AAM

Many efforts have been made to correct scissors-bite and establish proper molar interdigitation for prosthetic or orthodontic treatment. The critical procedures for scissors-bite correction are intruding and palatally tipping the involved tooth when it is both extruded and buccally flared. Conventional approaches give rise to problems such as repetitive bonding failure and loss of anchorage. A newly designed spring, the dragon helix appliance, is used with an indirect skeletal anchorage system to correct scissors-bite. This spring provides effective tooth movement and the convenience of a simple and small design. We report a successful treatment with the dragon helix.

The imaging of neural sources of magnetoencephalographic data based on distributed source models requires additional constraints on the source distribution in order to overcome ill-posedness and obtain a plausible solution. The minimum l(p) norm (0 < p < or = 1) constraint is known to be appropriate for reconstructing focal sources distributed in several regions. A well-known recursive method for solving the l(p)-norm minimization problem, for example, is the focal underdetermined system solver (FOCUSS). However, this iterative algorithm tends to give spurious sources when the noise level is high. In this study, we present an algorithm to incorporate a smoothing technique into the FOCUSS algorithm and test different smoothing kernels in a surface-based cortical source space. Simulations with cortical source patches assumed in auditory areas show that the incorporation of the smoothing procedure improves the performance of the FOCUSS algorithm, and that using the geodesic distance for constructing a smoothing kernel is a better choice than using the Euclidean one, particularly when employing a cortical source space. We also apply these methods to a real data set obtained from an auditory experiment and illustrate their applicability to realistic data by presenting the reconstructed source images localized in the superior temporal gyrus.

This paper presents relation management for machine socialization. Machine socialization is making thing or machines to talk to and say what they do or need to each other follow each other, discuss with each other and collaborate with each other. Relation is an association between or among object capability within machine enabling to share or provide capability to achieve the task in collaboration. Relation includes scheduling of process between or among machines during performing the task This paper also presents relation models for machine socialization.

Modern flat panel displays, including thin-film transistor liquid crystal displays (TFT-LCDs) and organic light-emitting diodes (OLEDs), pursue more and more natural color expression. This requires the data driving system should produce very fine and accurate signal voltages or currents. Especially, the uniformity among channels of a driving system is critically important because the color or luminance differences among columns are easily noticeable. We propose a simple and efficient driving method for solving the artifacts caused by the existing nonuniformity among channels of a driving system, and confirms its impact by simulations using C-programming. The nonuniformity among channels mostly stem from the random offset of the output circuits of a driving system, which in turn is caused by the process variations. The proposed driving method shares N-output circuits between N-output channels such that the existing offsets of the channels are averaged out. Thus, the output signal error due to the offsets spreads out among the channels, improving the uniformity between the channels

According to recent advances in three-dimensional display systems, interest in stereoscopic video coding and reconstruction has been increased in various fields of video processing. Efficient video coding techniques are required to reduce the amount of data. In this paper, we propose an effective algorithm that improves the quality of moving picture experts group-2 stereoscopic sequences, in which optimal quantization parameters (QPs) are selected for pairs of corresponding macroblocks in left and right frames taken at the same time instants but from different view angles. The proposed algorithm reduces the binocular redundancy between the left and right frames. To minimize the total distortion, it adaptively determines QPs and the bandwidth for encoding of pairs of the frame sequence. The proposed algorithm is compared with the conventional video encoding methods in terms of the video quality and the use of bandwidth. Especially for video sequences containing a number of moving blocks and scene changes, the proposed algorithm shows better performance than the conventional methods in terms of the performance of disparity estimation and adaptive bandwidth control.

A theoretical framework and its practical implications for formulating and implementing model-based monitoring of discrete flow networks are discussed. Possible flows of items are described as discrete-event (DE) traces. Each trace defines the DE sequence(s) that are triggered when an entity follows a given flow-path, visiting tracking locations within the monitored system. To deal with alternative routing, creation of items, flow bifurcations and convergences are allowed. Given the set of possible discrete flows, a possible-behavior model-an interacting set of automata-is constructed, where each automation models the item discrete flow at each tracking location. In this model, which assumes total observability, event labels or symbols contain all the information required to unambiguously distinguish each discrete movement. Within the possible behavior, there is a special sub-behavior whose occurrence is required to be detected. The special behavior may be specified by the occurrence of routing events, such as faults or route violations, for example. These intermittent or non-persistent events may occur repeatedly. An observation mask is then defined, characterizing the observation configuration available for collecting item tracking data. The verification task is to determine whether this observation configuration is capable of detecting the specified special behavior. The assessment is accomplished by evaluating several observability notions, such as detectability and diagnosibility. If the corresponding property is satisfied, associated formal observers are constructed to perform the monitoring task at hand. The synthesis of observation masks may also be conducted to suggest optimal observation configurations (specifying number, type, and tracking locations of observation devices) guaranteeing the detection of special events and to construct associated monitoring agents. The developed framework, modeling methodology, and supporting techniques for defining and implementing discrete flow monitoring of entity movements are presented and illustrated with examples.

Most of the results on decentralized supervisory control are based on supervisors that make unconditional decisions: "enable" and "disable." In this paper, we introduce and study the properties of decentralized supervisory control architectures where supervisors are allowed to make conditional decisions in addition to unconditional decisions. The conditional decisions we consider are of the form: "enable if nobody disables" and "disable if nobody enables." We characterize the notion of conditional coobservability that appears in the necessary and sufficient conditions for the existence of supervisors in the context of such control architectures. This condition relaxes the previous notions of coobservability for unconditional architectures. The key properties of conditional coobservability are studied. We develop a polynomial-time algorithm for verifying the notion of conditional coobservability. A polynomial-time method of partitioning the controllable events between "enable by default" and "disable by default" is presented.

The objective of this work was to provide a new, precise registration of the cortical mantle with a non-linear transformation. Image registration is broadly classified into two types, using intensity similarity and feature similarity. Whereas the former approach has merit in global brain matching, the latter provides a fast registration centred on a region of interest. The hybrid registration proposed in this paper was achieved using a Bayesian framework, which consisted of a likelihood model including intensity similarity and a prior model including feature information and a smoothing constraint. In this approach, each voxel was spatially transformed, so that the distance between corresponding features was shortened and also so that the intensity correlation was maximised. The result of the hybrid method clearly showed a good match of global brain (r = 0.930) by including intensity similarity. Moreover, this method compensated for the approximated sulcus of the feature-based method with intensity information, so that the geometric shape and thickness of the sulcus at the feature-defined region was likely to be registered. The accuracy in the feature-defined area was improved by 33.4% and 7.5% compared with feature-based and intensity-based methods, respectively.

Tumor-induced angiogenesis is important to understand the mechanism of tumor growth. However, due to its heterogeneous microenvironment, it is challenging to elucidate the correlation between tumor growth and angiogenesis. In this talk, we develop a computational model of tumor-induced angiogenesis with blood flow. The complex steps of angiogenesis are expressed by mathematical models from diffusion of growth factor, sprouting from existing vessels, vessel growth and anastomosis. Furthermore, the blood flow in the new vessels is simulated by using one dimensional flow model with hematocrit redistribution. First of all, the computational model will be validated by comparing with experimental data. In addition, the developed model will be tested in predicting tumor-induced angiogenesis with different model parameters. Finally, the future perspectives of computational modeling in tumor-induced angiogenesis will be fully discussed. Note: This abstract was not presented at the conference. Citation Format: Tae-Rin Lee, Sung Sic Yoo, Junhong Jo, Anna Go, Hyung-Kwon Seo. Computational modeling of vessel growth and blood flow in the tumor microvasculature. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A48.