清水_backup の履歴(No.50)

https://dic515s2.pu-toyama.ac.jp/teaching-learning_system/

	月曜日	火曜日	水曜日	木曜日	金曜日
1-2
3-4		電波工学特論		人間情報工学
昼休み
5-6					科学技術論
7-8
9-10
11-12

トップページ

修士論文まとめる用

個人情報系

https://www.kikn.fms.meiji.ac.jp/paper/2018/master/ito/hakuron_ITO.pdf

https://www.mext.go.jp/component/a_menu/education/micro_detail/__icsFiles/afieldfile/2018/08/22/1408402_1.pdf

暗号化データベース上のk-匿名化技術の提案

https://www.jstage.jst.go.jp/article/jjsai/24/2/24_283/_pdf

https://www.soumu.go.jp/main_content/000462276.pdf

https://shinmeisha.co.jp/newsroom/2020/02/03/%E3%82%B2%E3%83%BC%E3%83%88%E3%82%A6%E3%82%A7%E3%82%A4%E3%81%A8%E3%81%AF/

https://www.jstage.jst.go.jp/article/mjir/7/0/7_100/_pdf/-char/ja

https://qiita.com/gg_hatano/items/cf87fe5e1166a2ba80b2

https://dspace.jaist.ac.jp/dspace/bitstream/10119/17089/5/paper.pdf

https://www.jstage.jst.go.jp/article/isciesci/63/2/63_51/_pdf/-char/ja

https://qiita.com/saltcooky/items/3b5537170576c917a4b9#%E5%B7%AE%E5%88%86%E3%83%97%E3%83%A9%E3%82%A4%E3%83%90%E3%82%B7%E3%83%BC

差分プライバシー

https://www.jstage.jst.go.jp/article/jjet/41/3/41_42028/_pdf

https://www.stat.go.jp/training/2kenkyu/research/pdf/resear39.pdf

https://www.nstac.go.jp/sys/files/static/services/society_paper/30_08_02.pdf

https://www.slideshare.net/NobuhikoKondo/ir-79402108

https://www.ism.ac.jp/editsec/toukei/pdf/51-2-321.pdf

内部モデルの信頼度の算出
import numpy as np
from numpy.random import *
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
import codecs
import os
import pandas as pd
import csv
import scipy
import warnings
import sympy
import matplotlib.patches as patches
import math
from scipy.linalg import eig
from scipy.sparse.linalg import eigs
from sklearn import linear_model
from pandas import Series,DataFrame

np.set_printoptions(suppress = True)
warnings.filterwarnings('ignore')

# Axesを配置する領域を作成
#fig = plt.figure(facecolor = "lightgray")
#fig = plt.figure(figsize=(5,5))

# FigureにAxesを追加
#ax = fig.add_subplot(111)

#r = patches.Rectangle(xy=(120, 120), width=-240., height=-240., ec='#000000', fill=False)
#ax.add_patch(r)

dt = 0.016 #計測間隔
epsi = 0.
#Beta = 1000.

# a = MK
a1 = 0.
a2 = 0.
a3 = 0.
a4 = 0.
# b = MB
b1 = 0.
b2 = 0.
b3 = 0.
b4 = 0.
# c = M
c1 = 0.
c2 = 0.
c3 = 0.
c4 = 0.
#Hの中身をかく
h1 = 1.
h2 = 0.
h3 = 0.
h4 = 1.

x = np.array([
    [120.],
    [120.],
    [0.],
    [0.],
    [0.],
    [0.]
]) # 初期位置と初期速度を代入した「6次元状態」 6*1
      
G = np.array([
    [1, 0., 0., 0., 0., 0.],
    [0., 1, 0., 0., 0., 0.],
    [0., 0., 1, 0., 0., 0.],
    [0., 0., 0., 1, 0., 0.],
    [0., 0., 0., 0., 1, 0.],
    [0., 0., 0., 0., 0., 1]
]) #  6*6

Q = np.array([
    [0.00001, 0., 0.,  0., 0., 0.],
    [0., 0.00001, 0.,  0., 0., 0.],
    [0., 0., 0.00001,  0., 0., 0.],
    [0., 0., 0.,  0.00001, 0., 0.],
    [0., 0., 0.,  0., 0.00001, 0.],
    [0., 0., 0.,  0., 0., 0.00001]
]) #ノイズ 6*6

H = np.array([
    [h1, h2, 0., 0., 0., 0.],
    [h3, h4, 0., 0., 0., 0.]
])  # 観測行列 2*6

W = np.array([
    [0.01, 0., 0.,  0., 0., 0.],
    [0., 0.01, 0.,  0., 0., 0.],
    [0., 0., 0.01,  0., 0., 0.],
    [0., 0., 0.,  0.01, 0., 0.],
    [0., 0., 0.,  0., 0.01, 0.],
    [0., 0., 0.,  0., 0., 0.01]
]) #ノイズ 6*6

R = np.array([
    [0.00001, 0.],
    [0., 0.00001]
]) #ノイズ 2*2

#P0 = np.array([
#    [1., 0., 0., 0., 0., 0.],
#    [0., 1.,  0., 0., 0., 0.],
#    [0., 0., 1.,  0., 0., 0.],
#    [0., 0., 0., 1.,  0., 0.],
#    [0., 0., 0., 0., 1.,  0.],
#    [0., 0., 0., 0., 0., 1.]
#]) # 共分散行列 6*6

I = np.identity((len(x)))    # 6次元単位行列
#I = np.identity(6)

mu_D = np.array([
    [0],
    [0],
    [0],
    [0],
    [0],
    [0]
]) #ノイズ 3*1

mu_O = np.array([
    [0],
    [0]
]) #ノイズ 2*1

#global a1,a2,a3,a4,b1,b2,b3,b4,c1,c2,c3,c4
#global E
#global a1,a2,a3,a4,b1,b2,b3,b4,c1,c2,c3,c4
#E=0
IMdKd = 0
IMdBd = 0
IMd = 0
IIMd = 0
Md = 0
Bd = 0
Kd = 0
#beta = 0
#P0 = P

def kalman_filter():
    global a1,a2,a3,a4,b1,b2,b3,b4,c1,c2,c3,c4
    global E
    global IMdKd , IMdBd , IMd, IIMd, Md, Bd, Kd
    global i
    global P0
    global beta
    global j
    global P
    global K
    global S
    global e
  
    x = np.array([
        [points[0][0]],
        [points[0][1]],
        [0.],
        [0.],
        [0.],
        [0.]
    ])

y = np.array([
        [points[0][0]],
        [points[0][1]]
    ])

for n in range(len(points)):

if n == 0:  #0番目は問題なし
            ud = np.array([
                [points[n+1][0]],
                [points[n+1][1]],
                [0],
                [0],
                [0],
                [0],
                [1.],
                [1.]
            ])
            #ud = np.array([
            #    [points[n+1][0]],
            #    [points[n+1][1]],
            #    [points[n+1][2]],
            #    [points[n+1][3]],
            #    [points[n+1][4]],
            #    [points[n+1][5]],
            #    [1.],
            #    [1.]
            #])
            # 外部要素 8*1
        elif n == len(points) - 1: 
            ud = np.array([
                [points[n][0]],
                [points[n][1]],
                [0],
                [0],
                [0],
                [0],
                [1.],
                [1.]
            ])
            #ud = np.array([
            #    [points[n][0]],
            #    [points[n][1]],
            #    [points[n][2]],
            #    [points[n][3]],
            #    [points[n][4]],
            #    [points[n][5]],
            #    [1.],
            #    [1.]
            #])
        else:
            ud = np.array([
                [points[n+1][0]],
                [points[n+1][1]],
                [0],
                [0],
                [0],
                [0],
                [1.],
                [1.]
            ])
            #ud = np.array([
            #    [points[n+1][0]],
            #    [points[n+1][1]],
            #    [points[n+1][2]],
            #    [points[n+1][3]],
            #    [points[n+1][4]],
            #    [points[n+1][5]],
            #    [1.],
            #    [1.]
            #])

xp = float(x[0])
        yp = float(x[1])
        xv = float(x[2])
        yv = float(x[3])
        xa = float(x[4])
        ya = float(x[5])

xpd = float(ud[0])
        ypd = float(ud[1])
        xvd = float(ud[2])
        yvd = float(ud[3])
        xad = float(ud[4])
        yad = float(ud[5])
        Fd = float(ud[6])
        Fi = float(ud[7])

pnx = float(points[n][0])
        pny = float(points[n][1])

Ea1 = -1.0*h1*(-((dt**2)*xp)/2) + (((dt**2)*xpd)/2)*(-h1*(((Fd*c1*(dt**2))/2) + ((Fi*c2*(dt**2))/2) + ((a1*(dt**2)*xpd)/2) - ((a2*(dt**2)*yp)/2) + ((a2*(dt**2)*ypd)/2) + ((b1*(dt**2)*xvd)/2)\
                    - ((b2*(dt**2)*yv)/2) + ((b2*(dt**2)*yvd)/2) + (((dt**2)*xad)/2) + xp*(-((a1*dt**2)/2)+1) + xv*(-((b1*dt**2)/2) + dt))\
                    - h2*(((Fd*c3*dt**2)/2) + ((Fi*c4*dt**2)/2) - ((a3*(dt**2)*xp)/2) + ((a3*(dt**2)*xpd)/2) + ((a4*(dt**2)*ypd)/2) - ((b3*(dt**2)*xv)/2) + ((b3*(dt**2)*xvd)/2) + ((b4*(dt**2)*yvd)/2)\
                    + (((dt**2)*yad)/2) + yp*(-((a4*(dt**2))/2)+1) + yv*(-((b4*dt**2)/2)+dt)) + pnx) - 1.0*h3*(-((dt**2)*xp)/2 + ((dt**2)*xpd)/2)*(-h3*(((Fd*c1*dt**2)/2) + ((Fi*c2*dt**2)/2) + ((a1*(dt**2)*xpd)/2) - ((a2*(dt**2)*yp)/2) + ((a2*(dt**2)*ypd)/2) + ((b1*(dt**2)*xvd)/2)\
                    - ((b2*(dt**2)*yv)/2) + ((b2*(dt**2)*yvd)/2) + (((dt**2)*xad)/2) + xp*(-((a1*(dt**2))/2)+1) + xv*(-((b1*(dt**2))/2)+dt)) - h4*(((Fd*c3*dt**2)/2) + ((Fi*c4*dt**2)/2) - ((a3*(dt**2)*xp)/2) + ((a3*(dt**2)*xpd)/2) + ((a4*(dt**2)*ypd)/2) - ((b3*(dt**2)*xv)/2)\
                    + ((b3*(dt**2)*xvd)/2) + ((b4*(dt**2)*yvd)/2) + (((dt**2)*yad)/2) + yp*(-(a4*(dt**2)/2)+1) + yv*(-(b4*(dt**2)/2)+dt)) + pny)

Ea2 = -1.0*h1*(-((dt**2)*yp)/2) + (((dt**2)*ypd)/2

(Z.T.shape)\n \n e = Z - np.dot(H, x) # 2*1\n\n E += math.sqrt*3\n \n #Sは常にプラス\n S = R + np.dot(np.dot(H, P), H.T) # 2*2\n \n #Sの逆行列も問題なし\n K = np.dot(np.dot(P, H.T), np.linalg.inv(S)) # 6*2\n \n x = x + np.dot(K, e)\n \n P = np.dot*4, P) # 6*6\n\n if n == len(points) - 1 and j == 0 and i == 0:\n P0 = P\n\n beta = beta + (np.linalg.det(P0)/np.linalg.det(P))\n \n #if i == 9 and j == 9:\n #plt.plot(points[n][0],points[n][1],marker='>')\n #plt.plot(x[0], x[1],marker='+')\n #print(y[0],y[1])\n #plt.plot(y[0], y[1],marker='+')\n \n return \n\nfor i in range(10):\n #with codecs.open("simi"+str(i+1)+".csv","r","utf-8","ignore") as file:\n with codecs.open("kawa"+str(i+1)+".csv","r","utf-8","ignore") as file:\n reader = list(csv.reader(file))\n points = [[float(s) for s in z] for z in reader]\n \n P = np.array([\n [1., 0., 0., 0., 0., 0.],\n [0., 1., 0., 0., 0., 0.],\n [0., 0., 1., 0., 0., 0.],\n [0., 0., 0., 1., 0., 0.],\n [0., 0., 0., 0., 1., 0.],\n [0., 0., 0., 0., 0., 1.]\n ]) # 共分散行列 6*6\n\n beta = 0\n \n for j in range(10):\n E = 0\n \n kalman_filter()\n \n #print("結果"+str(i+1)+"回目の誤差", E/len(points)) \n print("結果"+str(i+1)+"回目の信頼度",beta / len(points))\n)

研究会

専門ゼミ

卒業論文テーマ

論文候補

中間発表

修士論文まとめる用