Miracle-dz/UniFAR-FTUs huggingface dataset:
How important is the timing of vibrotactile stimuli for the excitation of an emotional response for audio-visual content?

In this study, we investigated the effect of the timing of vibrotactile stimuli in the context of audio-visual content, on feeling of viewers. We used videos of figure skating performances, and vibrotactile stimuli were supplied to the upper body of the viewer using voicecoil motors, according to the jump scenes in the videos. The increase in skin conductance of the palm in response to the experience was measured, and the experimental participants scored their level of excitement with a questionnaire. We performed experiments under conditions with and without the vibrotactile stimuli. For the vibrotactile conditions, the timing of the vibrotactile stimuli was varied by 200 ms. The increases in the skin conductance and the questionnaire scores in the vibrotactile conditions were significantly high compared to those in the non-vibrotactile condition. However, the timing of the vibrotactile stimuli had no marked effect on these values.
Prediction of Angle Deflection of Smart Car Based on Neural Network

This paper designs a model based on convolutional neural network to simulate the deflection angle of a driverless car while running. The model is first trained by extracting the features in the images of the training set, and then the simulated car sends the images taken by the on-board camera into the trained model, so that the driverless car can predict the direction and angle according to the real-time road conditions. After testing, the model's judgment loss on the input road condition images during the training process is reduced to 0.3, which enables the driverless car to predict the direction in which it should deflect based on the actual road conditions in the virtual environment.
Brain activity associated with graphic emoticons. The effect of abstract faces in communication over a computer network

In this paper, we describe the brain activities that are associated with graphic emoticons by using functional MRI (fMRI). We use various types of faces, from abstract to photorealistic, in computer network applications. A graphics emoticon is an abstract face in communication over a computer network. In this research, we created various graphic emoticons for the fMRI study and the graphic emoticons were classified according to friendliness and level of arousal. We investigated the brain activities of participants who were required to evaluate the emotional valence of the graphic emoticons (happy or sad). The experimental results showed that not only the right inferior frontal gyrus and the cingulate gyrus, but also the inferior and middle temporal gyrus and the fusiform gyrus, were found to be activated during the experiment. Furthermore, it is possible that the activation of the right inferior frontal gyrus and the cingulate gyrus is related to the type of abstract face. Since the inferior and middle temporal gyrus were activated, even though the graphic emoticons are static, we may perceive graphic emoticons as dynamic and living agents. Moreover, it is believed that text and graphics emoticons play an important role in enriching communication among users. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 177(3): 36–45, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21162
Social responsibility inspiration in the fourth party logistics service supply chain: A game-theoretic approach

Corporate Social Responsibility (CSR) is considered in a fourth-party logistics service supply chain, consisting of a Fourth Party Logistics supplier (4PL) and a Third Party Logistics provider (3PL). In this research, a transportation price contract is designed to effectively improve the CSR performance, which is mainly determined by the 3PL's output of carbon dioxide ($CO_{2}$) emission. The associated cost for improving CSR performance level of the fourth-party logistics service supply chain is measured by a variable, which is only bring by the 3PL with an expectation of being shared with the 4PL via the transportation price contract. As such, the key issue is how the 4PL can identify the contract parameters to effectively transfer the associated cost of the 3PL in the 4PL service supply chain dominated by 4PL, and to encourage the 3PL to improve the performance level of CSR. A game-theoretical analysis is carried out on two games, bringing about different interaction plans between the 4PL and the 3PL, to derive their corresponding equilibria. Through the numerical analysis on the equilibria, we find that the 4PL service supply chain can achieve a win-win situation for both parties when the parameter values can be properly identified by the 4PL in designing the transportation price contract. Moreover, a well-designed contract can effectively motivate the 3PL to improve the performance level of CSR.
Negative binomial factor regression with application to microbiome data analysis

The human microbiome provides essential physiological functions and helps maintain host homeostasis via the formation of intricate ecological host-microbiome relationships. While it is well established that the lifestyle of the host, dietary preferences, demographic background, and health status can influence microbial community composition and dynamics, robust generalizable associations between specific host-associated factors and specific microbial taxa have remained largely elusive. Here, we propose factor regression models that allow the estimation of structured parsimonious associations between host-related features and amplicon-derived microbial taxa. To account for the overdispersed nature of the amplicon sequencing count data, we propose Negative Binomial reduced rank regression (NB-RRR) and Negative Binomial co-sparse factor regression (NB-FAR). While NB-RRR encodes the underlying dependency among the microbial abundances as outcomes and the host-associated features as predictors through a rank-constrained coefficient matrix, NB-FAR uses a sparse singular value decomposition of the coefficient matrix. The latter approach avoids the notoriously difficult joint parameter estimation by extracting sparse unit-rank components of the coefficient matrix sequentially. To solve the non-convex optimization problems associated with these factor regression models, we present a novel iterative block-wise majorization procedure. Extensive simulation studies and an application to the microbial abundance data from the American Gut Project demonstrate the efficacy of the proposed procedure. In the American Gut Project data, we identify key factors that strongly link dietary habits and host life style to specific microbial families.
Secure State Estimation for Discrete CPSs with State Delay and Adversarial Attacks

In this paper, an observer is designed for cyber-physical systems (CPSs) modeled by a class of discrete-time systems with state delay and sparse actuator and sensor attacks. In the design, the original system is rewritten into a new form, which provides the basis for estimating the actuator and sensor attacks. And then a Projection Operator combined with the notion of orthogonal complement matrix is utilized to determine the correct attack sets. In addition, based on the Krasovskii Stability Theorem, a Lyapunov-Krasovskii functional is chosen and a sufficient condition for the existence of the observer together with its design method is determined. Finally, a numerical example is given to verify the effectiveness of the designed observer.
A piezoelectric bistable plate for nonlinear broadband energy harvesting

Recently, the idea of using nonlinearity to enhance the performance of vibration-based energy harvesters has been investigated. Nonlinear energy harvesting devices have been shown to be capable of operating over wider frequency ranges delivering more power than their linear counterparts, rendering them more suitable for real applications. In this paper, we propose to exploit the rich nonlinear behavior of a bistable composite plate with bonded piezoelectric patches for broadband nonlinear energy harvesting. The response of the structure is experimentally investigated revealing different large amplitude oscillations. Substantially large power is extracted over a wide frequency range achieving broadband nonlinear energy harvesting.
Theoretical Analysis of the Efficiency of a 7 × 1 End-Pumped Power Combiner

An analytical formulation for the power transmission efficiency of a 7 × 1 end-pumped combiner has been developed and is reported in this paper. The formulation is supported by the Coupled-Mode Theory, and considers the modal coupling in a straight, circular cross section, optical fiber whose radius decreases monotonically. Within this scope, the power efficiency for each input fiber is determined for different tapers shapes.
Transient thermal analysis of an ACF package assembly process

Transient thermal simulation was performed to analyze thermal response of the assembly process for a package using anisotropic conductive film (ACF). The main purpose of the study is to simulate the actual assembly and manufacturing process, in order to provide a first-hand approximation and insight of the thermal behavior of the package and ACF film during the process. Two assembly processes were modeled: a simplified process where the package was fixed at two different temperatures during assembly, and a detailed process where the package experienced a ramping heating process, followed by a constant temperature curing process. A full convection-conduction case was conducted first. The results indicate a weak hydrodynamic field and radiation effects, hence for computational purposes (reduced CPU time), it was decided to model the process using a conduction-only investigation. Results from the detailed process modeling indicated that during the initial ramping, within 0.02 s, the die and nozzle head experienced a small temperature drop due to the cooling effect of the ACF material and substrate. The ACF material also displayed a steep increase in temperature after contacting the die, followed by a short decay, then ramped up again. At the end of the 10-s ramping process, the ACF reached a temperature of almost 203/spl deg/C, while the die was at 206/spl deg/C. During the 5 s of curing, all parts reached steady state in less than 2 s.
An Adaptive DFE Using Pattern-Dependent Data-Level Reference in 28 nm CMOS Technology

The sign-sign least-mean-squares (SS-LMS) algorithm has been widely used to adapt both decision feedback equalizer (DFE) tap-coefficient and data-level reference. However, the adaptation process suffers from insufficient adaptation in the presence of pre-cursor inter-symbol-interference (ISI), resulting in eye-diagram destruction. A novel pattern-dependent data-level reference (PD-DLR) adaptation scheme is proposed to alleviate the problem. The PD-DLR is acquired by averaging the received signal when the adjacent two digitized data are both "1". With only an extra pattern filter working at low speed, the proposed scheme can be easily extended from existing adaptive DFE. Simulation results show about 20%, 43%, and 80% improvement in recovered eye height with normalized pre-cursor ISI of 0.14, 0.23, and 0.31 respectively and better noise immunity over its conventional counterparts. The effectiveness of the scheme is also verified in a 28 Gbps non-return to zero modulation adaptive DFE designed in 28 nm CMOS technology.
Study on High-Speed-to-Hovering Back-Transition Control of Ducted Fan UAV

The ducted fan unmanned aerial vehicle (UAV) can transit from hovering to high-speed flight by continuously pitching down, reaching a high-speed and high-efficiency status. However, during the back-transition process from high speed to hovering, unstable environmental airflow and improper velocity planning could lead to serious oscillation, or even divergence of the aircraft states. In this paper, we address this control problem of a ducted fan UAV by utilizing an expected velocity planning method and a robust controller. The expected velocity curve is planned to ensure the dynamic effect of the deceleration process and decrease the computational complexity in solving the numerical optimization problem. The robust controller aims to reduce the tracking error of velocity and angular velocity considering modeling uncertainty and disturbance. Furthermore, the unit quaternion is selected to represent the attitude to avoid the singularity problem. Theoretical analysis verifies the stability of the proposed controller based on unit quaternion. Moreover, the simulation and experimental results collectively indicate the effectiveness of our methods.
Evaluation of rule-based, CountVectorizer, and Word2Vec machine learning models for tweet analysis to improve disaster relief

Social media platforms, such as Twitter, are being increasingly used by people as a means of requesting help during disaster events. Machine learning techniques can be used to identify tweets containing requests for help and classify them based on the type of aid that is being requested. In this paper, we build and compare three different models to classify tweets requesting aid into the categories “food,” “water,” “energy,” and “medical” by using the tweet dataset from Hurricane Sandy, which took place in 2012. Our first model uses a rule-based classifier. Our second model is based on the Scikit-learn toolkit's CountVectorizer, and the third model uses the Word2Vec based classifier. We found that the machine learning models based on CountVectorizer and Word2Vec have higher accuracy than the rule-based classifier model. We also show that the model based on Word2Vec provides the highest accuracy among the three models.
Non-Isolated Single-Switch Step Up/Down Converter with Wide Conversion Range

A non-isolated single-switch step up/down converter with wide conversion range is proposed in this paper. In the porposed converter, a boost converter and a single-ended primary inductor converter (SEPIC) are connected in cascade. Due to the cascade connection of a boost converter and SEPIC, wide conversion range is achieved. By utilizing a single switch, a complex control circuitry problem of conventional cascade converters is solved. The boost converter at the input stage can provide a continuous input current. The operation principle and steady-state analysis of the proposed converter are provided. A prototype of the proposed converter is developed, and its experimental results are presented for validation
Effective Repair Grouting Methods and Materials for Filling Voids in External Posttensioned Tendons

Recent inspections of segmental posttensioned (PT) bridges reported the presence of large voids in ducts that contain PT strands. Corrosion of the strands was observed in these voids, which are believed to be a result of poor grouting. Corrosion of the PT strands can result in structural failure. Controversy exists on how best to protect PT tendons from corrosion; filling these voids with grout is one option. An optimized grouting procedure for repairing these voids is needed to protect the strands from corrosive environments. This research investigates three grouting methods for efficiently repairing the voids in PT duct systems: vacuum grouting (VG), pressure grouting (PG), and pressure–vacuum grouting (PVG). Each method is evaluated for filling capability, filling performance, and economic feasibility. Also, three prepackaged grouts for repair are assessed in this research to propose the most suitable material for repairing voids in the PT ducts. The results indicate that the PG and PVG methods are more constructible and likely to be more economical than the VG method. However, the PVG and VG methods seem to be more effective than the PG method in filling the voids. As a result of these tests, the PVG method is recommended for filling voids in tendons. The results also show that Classes C-1 and C-2 grouts have better filling capability than Class C-3 grout.
Research on the Ecological Niche Evaluation of High-tech Industry in China: Empirical research based on provincial panel data of China

In innovation ecosystem stage, competition between industries has evolved into competition between industrial innovation ecosystems. Ecological niche is a vital indicator to evaluate competitiveness of a certain innovation ecosystem. Based on the ecological niche theories, this paper constructed an evaluation model of niche of the high-tech industry innovation ecosystem, and used provincial panel data in China from 2007 to 2017 to calculate the niche of innovation ecosystem to which a specific regional high-tech industry belongs through three aspects: output capacity, resource input abundance and overall competitiveness. The research found that: regional high-tech industry innovation ecosystems in China mainly show a weak output capacity, low resource input abundance, and concentrated overall competitiveness; niches of high-tech industry innovation ecosystems in the eastern area are more advanced than those in the western part; niches in Guangdong and Jiangsu have shown strong superiority in all dimensions; niche strength is in some degree positively correlated to niche breadth.
Neural Optimal Control for Discrete-Time Asymmetric Constrained Nonlinear Systems via DHP Technique

For discrete-time nonlinear affine systems with asymmetric constraints, an adaptive optimal control algorithm is established in this paper, which is implemented through dual heuristic dynamic programming (DHP). A novel non-quadratic performance functional is presented to overcome the problem caused by asymmetric constrained inputs. Then, under the adaptive critic framework, three neural networks are constructed for implementing the DHP algorithm, which are designed to approximate the nonlinear system, the costate function, and the control law, respectively. Furthermore, by conducting a simulation example with randomly given initial state vectors, the excellent applicability of the present method is demonstrated.
GYROTRON INTRODUCTION FOR ECRIS 2008

Gyrotrons are proving to be very reliable sources of high power at frequencies in the range of 28 to 170 GHz, where other sources are very limited in power capability. As a specific example for ECRIS applications, a 10 kW, 28 GHz CW gyrotron has made possible significant increases in the ion currents generated by the Venus ion source at the Lawrence Berkeley Laboratory [1]. In this paper we briefly discuss the physics and engineering aspects of the gyrotron oscillator, point out some of the issues that require special treatment in the control system and power supplies for it, review related gyro-devices, and present important applications.
Machine Learning based SET Propagation Prediction for Large Scale Integrated Circuits

This paper presents a machine learning based SET propagation prediction method. A back propagation neural network (BPNN) is implemented in the proposed method. It is used to calculate the SET propagation probability to flip-flops. Compared with the conventional SET propagation simulation method, the proposed method achieves similar SET propagation probability results with the runtime improvement. The proposed method is benefit for locating high SET propagation probability nodes in large scale integrated circuits. It is useful for circuit design, evaluation and radiation hardening.
On-line decoupled identification of transient and sub-transient generator parameters

This paper describes a method of identifying the transient parameters of a generator under normal operating conditions using on-line measurements of the generator current output or the voltage of the bus to which the generator is connected through a transformer. A decoupled method is developed to identify the parameters of the generator in any power system and under any operating conditions. The method is based on Park's two-axis model, and the parameters to be estimated have definite physical meanings, which are suitable for stability analysis and control of power systems. The continuous corrected extended Kalman filter is used as the identification algorithm, so the model does not need to be changed into a discrete form. The identification converges to within 1% of the actual parameter values in most power system structures. >
Computer-vision-based abnormal human behavior detection and analysis in electric power plant

With the increasing demand for intelligent security in power plants, the rapid and accurate processing of massive surveillance video data is urgently needed. Researches on the detection and analysis of abnormal human behaviors in power plants still focus on traditional image processing technology, and most of them lack robustness. In this article, abnormal behavior detection and analysis system based on personnel information are proposed to solve the above problems. The proposed method using an improved Y oLov3 algorithm first detects persons and extracts abnormal behavior information on this basis. In the implementation, some training tricks are introduced to improve performance. Experimental results show that the system can effectively detect abnormal human behaviors, and the improved Y oLov3 algorithm can also effectively improve model performance. The proposed abnormal behavior detection and analysis system based on personnel information prove its effectiveness through experiments, which can efficiently perform in power plants with lower computing costs.
NetSkylines: Digital Twins for Evaluating Disaster Communication

Thoroughly evaluating new communication mechanisms for emergency communication is a necessity prior to deployment. This evaluation is often heavily dependent on specialized movement patterns, ranging from purely random over highly scenario specific to real-world traces. The latter being usually the most realistic, but also hard to realize for many different scenarios. Furthermore, the generation of the movement patterns and integration in a network simulation is often a nontrivial task for experts, involving various specialized tools. Thus, professional responders, that quickly want to model a scenario relevant for them and afterwards evaluate technical solutions in this setting, will fail. In this paper, we present a novel framework to utilize an accessible city building game to build a digital twin and generate movement traces in large scale. This can be used to easily model different scenarios, what-if analyses, and also simulate different disasters. Moreover, we provide tools and easy integration of these traces into various network simulators and emulation environments. Therefore, evaluation of disruption-tolerant networking protocols or mesh setups becomes much easier.
Low Band Gap Polymer: Fullerene based Photodetector with Spectral Response from 350 nm to 850 nm

We report fabrication and characterization of high performance wideband photodetector with inverted device structure based on low bandgap polymer (poly[[4,8-bis [(2 Ethylhexyl) oxy] benzo[1,2-b:4,S-b′]dithiophene-2,6-diyl] [3-fluoro-2-[(2ethylhexyl) carbonyl] thieno [3,4-b] thiophenediyl]] (PTB7)): fullerene ([6, 6]-phenyl C61 butyric acid methyl ester (PCBM)) bulk heterojunction. ZnO nanorods are used to serve dual functions one as an electron transport layer (ETL) second as an active layer. Herein, we successfully demonstrated polymer: fullerene composites-based photodetectors with a wide spectral response from 350 nm to 850 nm. The proposed photodetector at a low external bias of −0.5V exhibits a high value of responsivity greater than ~ 1.23 A/W over the broad spectral range from 350 nm to 850 nm.