Suffering from dirty strong supersonic attacks (39ﾚｽ)
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1: supersonic attacked 2017/11/29(水)01:34 ID:Yul9QZKXc(1) AAS
I'm suffering from dirty strong supersonic attacks!! Supersonic terrorisms!!
The supersonic attacker is also in Yamaguchi city.
20: YAMAGUTIseisei 2019/04/24(水)09:54 ID:5ZbN1Z79Q(19/32) BE AAS
The numbers in bold are significantly higher than the others, p<0.01.
Finally, the results of the MA are used as VPs to vary the moving speed of the robot.
The controller worked alternatively between the two states according to the experimental protocol for the increasing and decreasing speed periods, using the following rules:
An increasing speed period: the current velocity value would be updated if the incoming value was higher, otherwise the value remains stable.
A decreasing speed period: the current velocity value would be updated if the incoming value was lower, otherwise the value remains stable.
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The aforementioned experimental protocol was used to set up the brain-controlled robotic simulators to explore the performance of the proposed SSVEP-BCI in Studies I and II, as shown in the following subsections.
1)
Study I:
Trade-off between processing window length and smooth movement: The aim of this study is to find the most suitable processing window length for the speed controller to help users move the object smoothly at an acceptable speed.
The experiment protocol is shown in Figure 4.
The robot was supposed to move following the outputs of the speed controller when completing a transportation task.
To create the outputs, the brain signals were converted into velocity profiles (VPs) using both the RF poly2 predictive model and the simple MA in accordance with two rules, as explained earlier.
The researchers considered both the ability to maintain stability in the carrying of objects (or the box in this experiment) and the average moving speed.
The stability of the box was measured by the deviation of a central mass in the box on the robotic plate space (2D plane).
The deviation of the box in this study was the Euclidean distance between the original and final positions of the box on the 2D plane.
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2)
Study II:
Comparison of a brain-controlled robotic simulator using conventional and proposed SSVEP-BCIs: To demonstrate that the proposed SSVEP-BCI outperforms the conventional case in smooth control applications, the same protocol was performed as in Study I.
The RF poly2 based predictive model, with a one-second non-overlapped window (optimal window length from Study I) was applied to obtain the VPs of the proposed SSVEP-BCI.
Whereas the conventional SSVEP-BCI immediately changed to a constant speed in both the increasing and decreasing periods.
The VP of each subject in the conventional case was set using the maximum and minimum values of the same subjects VP from the proposed SSVEP-BCI.
To evaluate the performance of the robotic control task, the average speed and the deviation of the box were used as the measures.
Finally, the experimental results from the two cases were compared using the standard t-test.
IV.
RESULTS
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A.
Result I:
Predictive Model for SSVEP Magnitude Variation The purpose of this study is to select the most appropriate model to predict SSVEP magnitude variation.
Six predictive models are compared here; Poly poly2, Poly poly3, RF poly2, RF poly3, NN(GRUs) poly2 and NN(GRUs) poly3.
As shown in Table I, the mean of MSE from all predictive models is not significantly different.
However, there is a statistical difference in the mean comparison of the computational-time prediction.
One-way repeated measures ANOVA with the Greenhouse-Geisser correction reported F(1.377, 12.395) = 383.877, p<0.01.
Moreover, the Bonferroni correction and pairwise comparison presented the computational-time prediction for both textitRF poly2 and RF poly3 models as significantly lower than the other models, p<0.01.
However, RF poly2 was selected for the rest of the study since it has less complexity in polynomial degrees.
In order to obtain qualitative results, the predicted signals were plotted per time step for each experiment condition as shown in Figure 5.
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(a) (b) (c) (d)
Fig.5:
Demonstration of raw SSVEP responses for the predictive model, RF poly2.
The bottom row shows a comparison of actual (SSVEP inputs) and predicted signals.
Leave-one-person-out cross validation (one out of ten) is used to evaluate the model in Figure 5 (a)-(d) are examples of the actual and predicted signals from experimental cond.1, cond.2, cond.3, and cond.4, respectively.
(a) (b)
Fig.6:
The means of average speed and deviations of the box in varying window lengths from one to five seconds, (a) is the increasing speed period and (b) is the decreasing speed period.
TABLE II:
Comparison of the conventional and proposed measures for SSVEP-BCIs in maintaining a box on the moving robotic.
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GENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2017
(a) (b) (c) (d) (e) (f)
Fig.7:
Comparison of the VPs from three subjects.
The upper row (a)-(c) shows the VPs from the proposed SSVEP-BCI and the lower row (d)-(f) the VPs from the conventional SSVEP-BCI.
(a) (b)
Fig.8:
An example of a Pioneer P3DX robot movement with different paradigms.
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The one-way repeated measures ANOVA with the Greenhouse-Geisser correction reported a significant difference in average speed across the processing window lengths for the increasing and decreasing period of F2 and F1 , respectively.
In pairwise comparison, the mean of the average speed from the one-second processing window is significantly higher than that from the three, four, and five seconds, p<0.05.
For the decreasing period, the mean of the average speeds from the one-second processing window is significantly higher than that from the two and three seconds, p<0.05.
Although the average speed was significantly higher on the one-second processing window length, the deviation in the box results did not differ across processing window length as shown in Figure 6.
According to the experimental environment of the brain-controlled robotic simulator, it can be inferred that the one-second processing window length can have a higher information transfer rate with acceptable accuracy compared to the other lengths.
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The one-way repeated measures A with the reported a significant difference in speed across the processing window lengths for the increasing and decreasing period of F(2.421, 21.792) = 21.633, p<0.01 and F(1.387, 12.483) = 3.687, p=0.068, respectively.
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2)
Comparison of brain-controlled robotic simulator using conventional and proposed SSVEP-BCIs: Table II demonstrates the merits of the proposed SSVEP-BCI over the conventional.
Even if the mean of the average speed from the proposed BCI is close to that of the conventional, the deviation of the box during the robotic movement in the proposed model is significantly lower than that of the conventional for increasing periods t9 .
Although the results for the decreasing periods had no statistical difference, the proposed model gave a lower box deviation.
The qualitative results in Figure 7 indicate that three out of ten subjects perform a comparison of the VPs from the proposed and conventional SSVEP-BCIs.
Capture of the brain-controlled robotic simulator in the delivery task is presented in Figure 8.
The speed of the robot varies according to the experimental protocol.
V.
DISCUSSIONS
According to the experimental results, three main issues arise.
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The variation of visual stimulus intensity can help subjects to manipulate the SSVEP response magnitude.
A state-of-the-art machine learning approach, namely Random Forest Regression (RF) was proposed as the predictive model for handing SSVEP magnitude variation.
Leave-one-subject-out cross validation using the RF model showed the highest performance in the prediction of varying SSVEP magnitude compared to polynomial regression and neural networks models.
Thus, the RF model is promising for the further development of the proposed SSVEP-BCI in this study.
Even if an experiment has not yet been conducted on the proposed SSVEP-BCI system in an actual online mode,
the environmental and practical scenario demonstrated in the simulator, streaming back the actual brain signals from ten subjects, ensures that the proposed system is feasible and novel.
Through an online-like simulation, the system is evaluated in terms of speed, error, and smoothness from the brain-controlled robot in carrying the box to the destination.
Furthermore, the conceptual design of SSVEP stimulation is simple and user-friendly.
There are only three frequencies for the flickered stimuli on the screen with a single EEG channel, (Oz), for the measured brain signals.
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As the research findings on the predictive model for SSVEP magnitude variation indicate, the predictive SSVEP magnitude paradigm can now be integrated into the frequency recognition paradigm to achieve a novel online SSVEP-BCI.
Taking into consideration both the frequency and magnitude of the steady brain responses, a continuous SSVEP-BCI can be provided, allowing the users to smoothly control devices (e.g. a mobile robot).
Furthermore, we are planning to integrate the proposed SSVEP-BCI to handle robotic arms in an online mode using sparse EEG channels.
The continuous increase or decrease of the predicted signal from the predictive SSVEP magnitude can be mapped into command functions, for instance, by accelerating or decelerating the velocity of the robotic arm.
To improve performance of the proposed SSVEP-BCI, a robustness to noises for the continuous magnitude prediction is an important.
To overcome this issue, one approach is a simple adaptive algorithm which measures magnitude information from not only target SSVEP frequency, but also from the neighbor frequencies.
Using relative values instead of an absolute value from target frequency, we hypothesize that the predictive model possibly classifies weather magnitude variation is an effect from noises or actual varying in SSVEP response.
Another approach, we are going to apply distributed recurrent neural forward model which behaves as a temporal memory module to maintain continuous information as closed as uncorrupted SSVEP responses [19].
Hence, the contribution of this work can act as a gateway to future BCI-based control.
VI.
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This is the first study on SSVEP magnitude prediction toward a novel SSVEP-BCI.
We created datasets from experiments on varying SSVEP magnitude responses.
The Random Forest Regression was then proposed as the algorithm for instantaneous SSVEP magnitude prediction.
The experimental results were obtained from ten subjects using leave-one-subject-out cross validation seem promising.
The instantaneous changes in predicted SSVEP magnitude can be mapped into the speed controller for brain-controlled applications (e.g. robot control).
Here, an online-like system was conducted using a simulated mobile robot.
The experiments involved streaming back the real SSVEP responses of varying magnitudes to control the moving speed of the robot.
For practical purposes, a single (Oz) EEG channel was used through all the experiments.
The advantage of the SSVEP magnitude prediction is that it has an ability to maintain stability when controlling the robotic.
In the near future, the outcomes from this work will be implemented in other smooth brain-controlled applications such as accelerating or decelerating the speed of a mobile robot or a robotic arm.
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REFERENCES
[1]
J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan,
`` Braincomputer interfaces for communication and control, ''
Clinical neurophysiology, vol. 113, no. 6, pp. 767791, 2002.
[2]
B. Allison, T. Luth, D. Valbuena, A. Teymourian, I. Volosyak, and A. Graser,
`` Bci demographics: How many (and what kinds of) people can use an ssvep bci? ''
IEEE transactions on neural systems and rehabilitation engineering, vol. 18, no. 2, pp. 107116, 2010.
[3]
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[8]
E. Yin, Z. Zhou, J. Jiang, F. Chen, Y. Liu, and D. Hu,
`` A novel hybrid bci speller based on the incorporation of ssvep into the p300 paradigm, ''
Journal of neural engineering, vol. 10, no. 2, p. 026012, 2013.
[9]
E. Yin, Z. Zhou, J. JIang, F. Chen, Y. Liu, and D. Hu,
`` A speedy hybrid bci spelling approach combining p300 and ssvep, ''
IEEE Transactions on Biomedical Engineering, vol. 61, no. 2, pp. 473483, 2014.
[10]
Y. Wang, X. Chen, X. Gao, and S. Gao,
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[15]
K. Cho, B. Van Merri boer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio,
`` Learning phrase representations using rnn encoder-decoder for statistical machine translation, ''
arXiv preprint arXiv:1406.1078, 2014.
[16]
F. Chollet et al., `` Keras, ''
外部ﾘﾝｸ:github.com
, 2015.
[17]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay,
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>>8
To develop a novel SSVEP-BCI paradigm for a brain-machine control system which allows users to continuously increase/decrease the moving speed of the application , this study hypothesizes that magnitude variation would help attain the goal.
To develop a novel SSVEP-BCI paradigm for a system which allows users to continuously increase/decrease the moving speed of the application (i.e. speed robot movements), this study hypothesizes that magnitude variation would help attain the goal.
application (i.e. speed robot movements)
外部ﾘﾝｸ:miraitranslate.com
ユーザがアプリケーション(速度ロボットの動作)の移動速度を連続的に増加/減少させることを可能にする脳機械制御システムのための新しいSSVEP‐BCIパラダイムを開発するために,本研究では規模変動が目標の達成を助けるであろうと仮定した。
35: YAMAGUTIseisei 2019/07/12(金)20:46 ID:f5bVsPQ5K(1/3) AAS
外部ﾘﾝｸ:www.jstage.jst.go.jp
Page 1
Organ Variation 3D Model Library for Surgery Simulators
a) M. Komori, b) K. Tagawa, b) H. Tanaka, a) Y. Kurumi, a) S. Morikawa
Abstract
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II.
CURRENT STATUS
Cystic ducts (9 types), aortic arches (4 types) and renal veins (4 types) with variation were modeled in OBJ format.
Fig.2 shows examples from those cystic duct models.
The cystic duct models were merged to a surgery simulation scene using multi-resolution modeling approach and a synchronization approach for maintaining consistency of binary trees [3].
In this project, many studies have to be done, eg clinical verification of those models, optimization for faster performance of the surgical simulator with more complex organ structure and so on.
As a future work, this theme will be reported.
ACKNOWLEDGMENT
Research supported by SCOPE fund and JSPS KAKENHI Grant Number25282154.
a) Shiga University of Medical Science, Otsu, Shiga 6062192 Japan (corresponding 1st author to provide phone: +81-77-548-2641; fax: +81-77-548-2412; e-mail: kom@belle.shiga-med.ac.jp).
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REFERENCES
[1]
V. Sherman, LS Feldman, D. Stanbridge, R. Kazmi, GM Fried,
“Assessing the learning curve for the acquisition of laparoscopic skills on a virtual reality simulator”
, Surg Endosc, 19: 678?682, 2005
[2]
FH Netter,
“Atlas of Human Anatomy (Book style with paper title and editor),”
in Plastics , 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64.
[3]
省4
38: [m9(^Д^)] 2020/08/14(金)16:29 AAS
統合失調症
39: 2023/10/22(日)20:49 ID:HKomAKF4A(1) AAS
人を殺すと地獄に堕ちるとかお子ちゃまみたいなこと言ってるオトナには唖然とするよな
騒音に温室効果カ゛スにコロナにとまき散らさせて、気侯変動させて海水温上昇させてかつてない量の水蒸氣を発生させて、
土砂崩れに洪水、暴風．猛暑、大雪にと災害連発させて地球破壊して、静音が生命線の知的産業に威力業務妨害して根絶やしにして、
医療崩壊させて助かる命まで奪い取って多くの無辜の住民の生命と財産を破壊して私腹を肥やしてる斎藤鉄夫ら世界最悪の殺人テロ組織
国土破壊省だのJΑLた゛の機長殴って駆け付けた警官まで殴打して現行犯逮捕の酒気帯び運転ANAだのクソアヰヌドゥだの酒飲んで業務
してるクサイマ―クた゛のゴキブリフライヤーだのテロリストを皆殺しにしたら、人のみならず多くの生命が救われるんだから、どんな理屈を
こねたところで．明らかに天国行きだろ、税金で票買って腐敗を謳歌してきた腐敗の権化を討ち取った民主主義の教祖山上大先生も完全に
天国行き確定だし，少しは物事を論理的に理解できる人としての最低限の能カくらい身に着けよう!
(羽田)外部ﾘﾝｸ[Php]:www.call4.jpΡe=itｅms&id＝I００00062 , ttР外部ﾘﾝｸ:haneda-project.jimdofree.Com
(成田］ttps：//n-souonhigaisosyoudan.amеbaownd.сom/
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