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仮想ネットワークの設定 不要サービスを停止する [コントロールパネル] [管理ツール] [サービス] [VMware DHCP Server] [プロパティ] スタートアップの種類 手動 OS上からのNAT Switch(VMnet8)の設定 [コントロールパネル] [ネットワーク接続] [VMware Network Adapter VMnet8] [プロパティ] [TCP/IP] [プロパティ] IPアドレス 192.168.1.253 サブネットマスク 255.255.255.0 デフォルトゲートウェイ 192.168.1.254 VMware上からのNAT Switch(VMnet8 *Publicネットワーク)の設定 [VMware] [Edit] [Virtual Network Settings...] [ホスト仮想ネットワークの割当て] [VMnet8] [ ] [サブネット] IPアドレス 192.168.1.0 サブネットマスク 255.255.255.0 VMnet7 (PrivateネットワークSwitch)の設定 [VMware] [Edit] [Virtual Network Settings...] [ホスト仮想ネットワークの割当て] [VMnet7] [ ] [サブネット] IPアドレス 192.168.10.0 サブネットマスク 255.255.255.0 DHCPサーバの停止と設定の確認 [VMware] [Edit] [Virtual Network Settings...] [DHCP] [VMnetX] [削除] [VMware] [Edit] [Virtual Network Settings...] [DHCP] [DHCPサービス] [停止] 仮想マシンの作成 VMwareを起動する 。 New Virtual Machine を選択し、以下の設定で仮想マシンを作成する。 Virtual machine configuration Custom Virtual machine format New - Workstation 5 Guest operationg system Linux Version Red Hat Enterprise Linux 3 Virtual machine name oracle1 location C \vmware\oracle1\ Number of processors One Memoery 320MB Network connecion Use network address translation(NAT) SCSI Adapters BusLogic Disk Create a new virtual disk Virtual Disk Type SCSI Disk size 8.0 Allocate all disk space now Yes Split disk into 2GB files No OSインストール VMware を起動する。 [oracle1]タブ [Devices] [CD-ROM1(IDE 1.0)] [Use ISO image]でisoファイルを選択する。 [Commands] [Start this virtual machine] 下記以外はデフォルト設定でインストールする。 Language Japanese キーボード 日本語 パーティション 自動パーティション設定 IP Address 192.168.1.1 NetMask 255.255.255.0 ホスト名 oracle01.jp.oracle.com ゲートウェイ 192.168.1.254 1番目のDNS 192.168.0.1 rootパスワード password インストールするパッケージを選択 すべて 色の深さ True Color(24Bit) 画面の解像度 800*600 ログインの種類 テキスト 2枚目のCDを求められたら、[VM] [Removable Devices] [CD-ROM1(IDE 1.0)] [Edit] [Use ISO image] [Browse]で交換する。 VMware Tools のインストール [VM] [Install VMware Tools...]を選択し、以下のコマンドを実行する。 # kon # mount /mnt/cdrom # cd /tmp # tar zxf /mnt/cdrom/vmware-linux-tools.tar.gz # umount /mnt/cdrom # cd /tmp/vmware-tools-distrib ← ここに移動してからインストールしないと失敗する。 # ./vmware-install.pl 共有フォルダを作成する [VM] [Settings...] [Options] [Shared Folders] [Add]で以下の通り設定する。 Name Share Host folder C \vmware\share フォルダを共有できていることを確認する # ls /mnt/hgfs Share 仮想マシンのコピーインストール 「D \vmware\oracle1」以下の全ファイルを「D \vmware\oracle2」以下にコピーする。 「D \vmware\oracle2\Red Hat Enterprise Linux 3.vmx」を以下の通り変更する。 displayName = “oracle02” oracle1のEth1の設定 [oracle1]タブ [Commands] [Edit virual machine setings] [Hardware] [Add] [Ethernet Adapter] [Next] [Custom] [VMnet7] [oracle1]タブ [Commands] [Start this virtual machine] 「Press any key to continue」と表示されたら、任意のキーを押す。 [Eth1]を以下の通り設定する。 IP Address 192.168.10.1 Netmask 255.255.255.0 Defaul gateway 設定しない Primary Nameserver 設定しない oracle2のNICの取り外し [File] [Open]で「C \vmware\oracle2\Red Hat Enterprise Linux 3.vmx」を選択する。 [oracle2]タブ [Commands] [Edit virual machine setings] [Hardware] [Ethernet]を選択し、[Remove]を押下する。 [oracle2]タブ [Commands] [Start this virtual machine] What do you want to do? create 「Press any key to continue」と表示されたら、任意のキーを押す。 [Remove Configuration]を選択する。 root でログインしてシャットダウンする。 # shutdown -h now oracle2のNICの取付け [oracle2]タブ [Commands] [Edit virual machine setings] [Hardware] [Add] [Ethernet Adapter] [Next] [NAT] [完了] [oracle2]タブ [Commands] [Edit virual machine setings] [Hardware] [Add] [Ethernet Adapter] [Next] [Custom] [VMnet7] [oracle2]タブ [Commands] [Start this virtual machine] 「Press any key to continue」と表示されたら、任意のキーを押す。 [Configure]を選択する。 [Eth0]を以下の通り設定する。 IP Address 192.168.1.2 Netmask 255.255.255.0 Defaul gateway 192.168.1.254 Primary Nameserver 192.168.0.1 [Eth1]を以下の通り設定する。 IP Address 192.168.10.2 Netmask 255.255.255.0 Defaul gateway 設定しない Primary Nameserver 設定しない ホスト名を変更する # vi /etc/sysconfig/network HOSTNAME=oracle02.jp.oracle.com リモートシェル設定 sshクライアント設定 # mv /etc/ssh/ssh_config /etc/ssh/ssh_config.bkp # vi /etc/ssh/ssh_config RSAAuthentication no PubkeyAuthentication no HostbasedAuthentication yes PasswordAuthentication no preferredAuthentications hostbased,publickey,password EnableSSHKeysign yes sshサーバ設定 # mv /etc/ssh/sshd_config /etc/ssh/sshd_config.bkp # vi /etc/ssh/sshd_config PermitRootLogin yes RSAAuthentication no PubkeyAuthentication no IgnoreRhosts no HostbasedAuthentication yes PasswordAuthentication no ChallengeResponseAuthentication no sshサーバを再起動する # /etc/init.d/sshd restart /root/.shostsの設定 # vi /root/.shosts (oracle01では以下の通り記述する) oracle02.jp.oracle.com (oracle02では以下の通り記述する) oracle01.jp.oracle.com 接続確認 (oracle01) # ssh oracle02.jp.oracle.com (oracle02) # ssh oracle01.jp.oracle.com リモートXの設定 Xサーバ側の設定 #vi /etc/X0.hosts oracle01.jp.oracle.com oracle02.jp.oracle.com Xクライアント側の設定 # vi /root/.bash_profile (oracle01では以下の通り記載する) export DISPLAY=oracle02.jp.oracle.com 0.0 (oracle02では以下の通り記載する) export DISPLAY=oracle01.jp.oracle.com 0.0 # . /root/.bash_profile 動作確認 # startx (oracle01で実行) # xclock (oracle02で実行) → oracle01 に時計が表示されればOK。 # startx (oracle02で実行) # xclock (oracle01で実行) → oracle02 に時計が表示されればOK。 OSのカーネルパラメータチューニング #vi /etc/sysctl.conf net.core.rmem_default = 262144 net.core.wmem_default = 262144 net.core.rmem_max = 262144 net.core.wmem_max = 262144 #/sbin/sysctl ‒p ユーザ設定 # groupadd -g 500 oinstall # groupadd -g 501 dba # useradd ‒u 500 ‒g oinstall ‒G dba ‒d /home/oracle oracle # passwd oracle 環境変数設定 #vi /home/oracle/rac.env export LANG=ja_JP export NLS_LANG=Japanese_Japan.JA16EUC export ORACLE_BASE=/home/oracle export ORA_CRS_HOME=$ORACLE_BASE/product/10.1.0/crs # vi /home/oracle/.bash_profile (oracle01では以下の通り記述する) export DISPLAY=oracle02.jp.oracle.com 0.0 (oracle02では以下の通り記述する) export DISPLAY=oracle01.jp.oracle.com 0.0 . ./rac.env
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Market Scenario In its research report, Market Research Future (MRFR), asserts that the AI in Construction Market Research 2020 is slated to grow exponentially over the review period, securing a considerable market valuation of USD 2.01 billion, and a healthy 35% CAGR over the review period. Novel coronavirus has actually AI in Construction Market Research to open new avenues for those firms that are on the lookout for solutions that are reliable, efficiently managed, scalable, and are subscription-based, to remain more focused on the core business. The AI in Construction Market is bearing lesser impact of the COVID-19 outbreak compared to most other segments of the tech world. In a nutshell, COVID-19 impact on managed services has been fruitful, with the market growth enhanced than before. Given the prevalent lockdown situation, managed services vendors are now investing heavily in remote-centric worker solutions, which can make the market highly resilient in the coming years, even as the world is currently rushing to achieve a COVID-19 breakthrough. Request a Free Sample @ https //www.marketresearchfuture.com/sample_request/6035 Segmentation The AI in construction market is differentiated by component, technology, organization size, deployment, stage, and application. On the basis of stage, the market is segmented into construction stage, pre-construction, and post-construction. Based on the component, the AI in construction market is bifurcated as solutions and services. The solution segment is categorized as demand forecasting, virtual assistant, revenue estimation, design planning, predictive maintenance, and others. The service sub-segment comprised implementation services, training consulting, and other support services. In terms of technology, the market is segregated into machine learning deep learning, neural networks, and natural learning programming (NLP). Based on the deployment, the market is divided into on-cloud and on-premises. Based on the organization size, the market is bifurcated into large enterprises, and small medium enterprise (SMEs). On the basis of application, the market is categorized as, project management, schedule management, risk management, equipment management, building information management, and supply chain management. Competitive Outlook The major market players operating in the global market as identified by MRFR are Oracle Corporation (U.S), IBM Corporation (U.S.), SAP SE (Germany), Alice Technologies.(U.S.), Microsoft Corporation (U.S.), Autodesk (U.S.), Aurora Computer Services(U.K), eSUB (U.S.), Smartvid.io(U.S.),and Building System Planning (U.S.). Some other market players who are involved in AI construction market are Jaroop, Deepomatic, Lili.Ai, Predii, Assignar, Coins Global, Beyond Limits, Doxel Askporter, Bentley Systems, Plangrid, and Renoworks Software Regional Analysis The geographical overview of the global market has been analyzed in four major regions, comprising the Asia Pacific, North America, Europe, and the rest of the world. On the building industry, North America is believed to have substantial growth in the AI, with the U.S. and Canada being the sector leading countries. Regional domination is due to increased investment by companies such as IBM Corporation, Oracle Corporation and many others, which invest directly in the advancement of technologies such as neural networks and machine learning in research and development. However, Asia Pacific is also expected to experience a strong market growth rate. The leading countries in this field are China, Japan, South Korea and India. The market growth is due to rise in demand by the region to improve smart city projects which require better facilities that boost the real estate sector. Table of Contents 1Executive Summary 2Scope of the Report 2.1Market Definition 2.2Scope of the Study 2.2.1Research objectives 2.2.2Assumptions Limitations 2.3Markets Structure Continued…. Browse Full Report Details @ https //www.marketresearchfuture.com/reports/ai-in-construction-market-6035 List of Tables Table1 Global AI In Construction Market By Region, 2020-2027 Table2 North America AI In Construction Market By Country, 2020-2027 Table3 Europe AI In Construction Market By Country, 2020-2027 Continued… List of Figures FIGURE 1 Global AI In Construction Software Market Segmentation FIGURE 2 Forecast Methodology FIGURE 3 Porter’s Five Forces Analysis of Global AI In Construction Software Market Continued… Trending #MRFR Report** https //ictmrfr.blogspot.com/2022/04/geofencing-market-companies-growth-with.html https //blogfreely.net/pranali004/telecom-expense-management-market-size-impressive-cagr-changing-business-scope https //postheaven.net/pranali004/financial-app-industry-impressive-cagr-changing-business-needs-scope-of https //market-research-future.tribe.so/post/openstack-service-market-research-impressive-cagr-changing-scope-of-current--6263de46791566c10c79891e https //www.scutify.com/articles/2022-04-24-infrastructure-as-a-service-industry-cagr-changing-business-scope-of-current-and-future-industry- About Market Research Future Market Research Future (MRFR) has created a niche in the world of market research. It is counted among the top market research companies that offer well-researched and updated market research reports and insights to businesses of all sizes. What sets us apart is our super-responsive team that offers quality work keeping clients abridged of the prospective challenges and opportunities in various markets. Our team is adept in their space as well as patiently listens to every client. The best part is they know their work inside out and possess the expertise to guide the client in the right direction and achieve results on a tight deadline. We are a one-stop solution for all your data research needs. Our team does not believe in the “one size fits all” approach to creating a report that is detailed and concise. We handle 13 industry verticals including Healthcare, Chemicals and Materials, Information and Communications Technology, Semiconductor and Electronics, Energy and Power, Food, Beverages Nutrition, Automobile, Consumer and Retail, Aerospace and Defense, Industrial Automation and Equipment, Packaging Transport, Construction, and Agriculture. With our unique approach for every market report, we aim to reach the zenith in qualitative business intelligence and syndicated market research. Contact Market Research Future (Part of Wantstats Research and Media Private Limited) 99 Hudson Street, 5Th Floor New York, NY 10013 United States of America 1 628 258 0071 (US) 44 2035 002 764 (UK) Email sales@marketresearchfuture.com Website https //www.marketresearchfuture.com
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Market Analysis The artificial intelligence AI in Supply Chain Market Growth is predicted to touch USD 3-4 billion at a whopping 37% CAGR between 2018- 2023, states the recent Market Research Future (MRFR) analysis. Artificial intelligence in the supply chain industry has gained rapid traction. The use of AI in the supply chain is enabling companies to automate decision making, offer better human resource utilization, improve efficiencies, improve supply chain management productivity, helps to engage with customers in the digital world. Various factors are propelling the global artificial intelligence in supply chain market share. According to the recent MRFR report, such factors include the increasing adoption of computer vision technology in different industries, favorable initiatives by the government towards digitalization, growing awareness about big data analytics and artificial intelligence, perpetually growing smart devices and internet users across the globe, growing adoption of artificial intelligence to improve consumer satisfaction and services, and burgeoning demand for higher transparency greater visibility in supply chain data processes. On the contrary, lack of infrastructure, high procurement operating price, shortage of artificial intelligence technology experts, and the ongoing COVID-19 impact are factors that may impede the global artificial intelligence in supply chain market growth in the region. Market Segmentation The MRFR report highlights an inclusive segmental analysis of the global based on artificial intelligence in supply chain market based on end users, application, deployment, technology, and component. By component, the global artificial intelligence in supply chain market is segmented into software and hardware. The hardware segment is again segmented into network, memory, and processors. The processors segment is further segmented into ASIC, FPGA, GPU, and CPU. Of these, the software segment is predicted to lead the market over the forecast period. By technology, the global artificial intelligence in supply chain market is segmented into computer vision, context-aware computing, natural language processing, and machine learning. Of these, the computer vision segment will lead the market over the forecast period. By deployment, the global artificial intelligence in supply chain market is segmented into hybrid, on-cloud, and on-premise. By application, the global artificial intelligence in supply chain market is segmented into risk management, supplier relationship management, logistics and shipping, supply chain planning, warehouse management, fleet management, and others. Of these, the supply chain segment will dominate the market over the forecast period. By end users, the artificial intelligence in supply chain market is segmented into consumer electronics, food and beverage, manufacturing, retail, automotive, aerospace, and others. Of these, the retail sector will have a major share in the market over the forecast period. Request a Free Sample @ https //www.marketresearchfuture.com/sample_request/7233 Regional Analysis Based on the region, the global artificial intelligence (AI) in supply chain industry report covers the recent trends and growth opportunities across the Asia Pacific (APAC), North America, Europe, the Rest of the World (RoW). Of these, North America will spearhead the market over the forecast period. Advances in technology, presence of highly developed infrastructure, increasing technical expertise in the artificial intelligence technology, booming retail and e-commerce industry, demand for advanced solutions for warehouse and logistics management, and the presence of top industry players are adding to the global artificial intelligence in supply chain market growth in the region. The United States, Mexico, Canada hold the utmost share in the market. The artificial intelligence (AI) in supply chain market in the APAC region is anticipated to have a notable growth during the forecast period. Rising market penetration of e-commerce, the growing trend of online purchase, developing digital technology and network infrastructure, and rising adoption of natural language processing and machine learning technology by technology developers to cater to the growing and custom needs of the automotive, retail, and manufacturing industry applications are adding to the artificial intelligence (AI) in supply chain market growth in the region. The artificial intelligence (AI) in supply chain market in Europe is predicted to have a healthy growth during the forecast period and that in the RoW is predicted to have steady growth over the forecast period. Key Players Eminent contenders profiled in the global artificial intelligence in supply chain market report include LLamasoft, Inc., Amazon, Oracle Corporation, Logility, Inc., SAP SE, Micron Technology, Microsoft Corporation, Samsung Electronics, Xilinx, Inc., Intel Corporation, IBM Corporation, and Nvidia Corporation, among others. Browse Complete Report @ https //www.marketresearchfuture.com/reports/artificial-intelligence-in-supply-chain-market-7233 Table of Contents 1 Executive Summary 2 Scope of The Report 2.1 Market Definition 2.2 Scope of The Study 2.2.1 Research Objectives 2.2.2 Assumptions Limitations 2.3 Market Structure Continued… Similar Report B2B Telecommunication Market Information by Solution (Unified Communication and Collaboration), Deployment (Fixed, Mobile), Organization Size (Large, Enterprise), Application (Industrial, Commercial) and regions Trending #MRFR Report** https //ictmrfr.blogspot.com/2022/04/geofencing-market-companies-growth-with.html https //blogfreely.net/pranali004/telecom-expense-management-market-size-impressive-cagr-changing-business-scope https //postheaven.net/pranali004/financial-app-industry-impressive-cagr-changing-business-needs-scope-of https //market-research-future.tribe.so/post/openstack-service-market-research-impressive-cagr-changing-scope-of-current--6263de46791566c10c79891e https //www.scutify.com/articles/2022-04-24-infrastructure-as-a-service-industry-cagr-changing-business-scope-of-current-and-future-industry- About Market Research Future At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research Consulting Services. 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基本情報 名前 sora64 名前 riku64(忘れたい過去とのこと) ジャンル ゲーム 性別 男 年齢 30 身長・体重 171㌢・67㌔ マイク あり webカメ あり(きれいきれいしてないと嫌よ♪) 配信時間帯 主に深夜 sora配信者ページ こちらから riku配信者ページ こちらから sora配信通知登録 こちらから riku配信通知登録 こちらから ブログ 更新されないヘタレsoraの気持ち悪いブログ 合計 - 人が激昂カージャンに潰され、現在-人がカージャンと戦っています。 乙神は盛岡を、日本、いや世界を救う＾＾ｂ 実況作品名 ゲーム名 ハード 配信者曰く 情報 Monster Hunter Portable 2nd G PSP 配信者曰く、「ヘタレ配信」 Xlink Kai/アドパ使用金冠配信まだー？ 初音ミク -Project DIVA- PSP 配信者曰く、「後悔はしてる最中かもしれない・・・」 VALKYRIE PROFILE -LENNETH- PSP 配信者曰く、「続けるかどうかはわかりませんｗ」 NormalMode 打開 Hard Mode 打開 Monster Hunter Frontier PC 配信者曰く、「誰かMHFのことwikiに書いて」 MHF2鯖,HR213 休止中 Monster Hunter P3rd PSP 配信者曰く、「お守りのせいでやる気なくなります」 kai、アドパ寄生いけます Final Fantasy 13 PS3 配信者曰く、「悪評の程ではなかった」 riku64なんてなかった Demon s Souls PS3 配信者曰く、「おじさんヌルゲーがいいよ」 賭け事の為一時売却 俺の屍を越えてゆけ PS3 配信者曰く、「おじさん飽きちゃって600円損したお」 後処理に困ってます P3Pペルソナ３ポータブル PSP 配信者曰く、「ペルソナ４がやりたいです」 映画までには打開したい 特徴 嫁は逢坂大河なんやで( ｰ`дｰ´)ｷﾘｯ モンハンの配信の際は名前がsora64でその他のゲームの時はriku64になるらしい 誕生日は配信者名からもわかる６月４日。よく６月９日と混同されがちなのが少々悩みの種である。 岩手県の森丘在住の天才ハンター あまり遅くまで配信していると激昂したカージャンに睨まれて3乙するのであまり遅くまで配信できない。 （激昂したカージャンはデンプシーロールで扉の部位破壊を行うので鍵をかけても意味がない。※カージャン：天災。いつかsoraがリアル３乙しないか懸念されている） 黒グラビは鬼嫁(喧嘩をふっかけてきたsoraでさんざん遊んだあげく３乙させる。彼女にはダイダラボラスも毒を吐くことを拒否する) 驚異のフラグ回収率 もはやその存在自体がフラグ。 「伝説の指」を極めており、その官能的な指使いでＰＳＰもＰＳ２コントローラーもヌルヌル。 ロリ調教物が好き。 ヤリマン、パイパン(お尻はチクチクやで)、イケメン、腐男子 2010年の抱負は「生々しいのは嫌」 しゃこ神 モンペ→モンスターペニス 英語が苦手であり、外国人の客を拒否ってしまったことがある。 ティガとナルガの立ち回りには自信がある。(自称) ヤったら逃げるのは基本です。 男に自身のポークビッツ(通常時も怒り時も最小金冠！)をしゃぶられたことがある。 謎のま○こ事件(午前８時03分にスカイプにて謎の「ま○こ」と発言) 配信中 東北かつや軍団（かずま うんぢぃ じゅん）によく呼び出される 緊張するとお腹を下す習性があり、以前エイリアンが侵入したことがある 29歳になって皮○ナ卒業したら先っちょが赤くなっちった(´･ω･｀) 好きだったAV女優は堤さやか、五十嵐こころ、笠木忍 久しぶりに配信をする度にリスナーから「仕事やめたの？」と言われる風潮あり 配信中の名言・迷言 +卑猥なsoraさん 俺はロリコンだ！！ 真実はいつも一つ！おっぱいはいつも二つ！！ 俺がミクをイカセテやるよ！ あぁ～ものすごいHしたいわ～ ※220 おカージャンといっしょポロリもあるよ 中学のときパ○パンにしたけど、その後チクチクして大変だった かなりの数の女をモノにしている。 一度抱いた女は２ヶ月で捨てる 幼女って言うのはねえ、そんな手をだすもんじゃないの。見て愛でるものなの。わかる？(1月16日 21時14分頃の発言) 僕三次元派じゃないんで・・・ 俺は中学生の時に３時間で８発抜いたことがある( ｰ`дｰ´)ﾄﾞﾔｯ +その他 Ｇ級訓練で採取とかゆとりのすること･･･プロはそんな事しない （キリッ 笑いすぎて顎が痛い…ﾊﾊﾊ グーテンモルゲン！ （マガティガで散々乙り2時間以上リスナーを閉鎖空間に閉じ込めたあと）ねっ？簡単でしょ？ 3乙ってなぁ～↑にぃ～↓？×数回 （マガティガでハットトリック＠配信11回目（このとき3乙3回）） モンハンの世界に行きたい・・・ ウンコはチャンス！！！Yes we can！！！ 乙るぜ！チョー乙るぜ！ しぬがよい 主が行ったネ申プレイ・m9プレイ +乙神様の御利益 武神1人1殺にて黒グラビと対峙、30分近く使ったあげく3乙してしまう。後日リベンジを果たしてくれることを期待だ。 マガティガ挑戦前に３乙を予言され、電撃的に１人で３乙する。 ラスクエですと言ってマガティガを4回連続失敗。5回目にして成功するもその間soraは10回乙る。午前4時から5時半にかけての出来事。 ↑の中で、攻撃を1回くらいピンチになった状況でなぜかいきなり肉を食べ始め（スタミナ減っていない）、その肉を食べているsoraのところでMIKEがロマン切りを狙い、見事成功。が、ティガは止まったのにもかかわらずなぜかsoraだけ被弾しそのままクエスト終了。 ↑↑でラストと言っていたにもかかわらず冷凍本マグロ縛りのマガティガを7時半からラストクエを始め、結局9時まで配信を続ける。 2010年3月9日、乙に飢える彼はついに4画面配信による伝説の1クエ12乙を達成！ sora「この記録は誰にも抜けないと思います!(記録は)大事にしていきたいです！･･･反省してます(^q^ )」 2010年3月12日、裸ウカム挑戦中にて、2乙状態で怒り移行の咆哮に対し何をトチ狂ったか後ろ足に突撃、soraさん死亡。当然この乙でクエストは失敗した。 2010年5月12日、マガティガ挑戦中に強噛みつきを食らい瀕死に、その後足の削りダメージを食らい３乙目。乙神様はあらゆる手段で乙を稼ぐのだ。 初韋駄天期間中に4分03秒93から１秒ジャスト更新するという神技を披露(2010年10月12日午前３時５５分の奇跡) 剛クシャ中に「クシャって、毒ると龍風圧なくなるんすね・・・」と今更の発言(第３６回配信より) P3配信中に「ティガって、高耳なのね・・・」とまたも今更の発言(6/12配信より) Ｆベル戦中にともにベルキックで２乙し、さらにはフリーズし、２乙離脱になってしまう 乙神タグ1000回記念のカラオケ配信において、大都会のリクに挑戦するも歌いだしに失敗、やり直しを要求されるもことごとく同じところを失敗、take5の末クリアする +3乙せずとも・・・ 武神一人一殺にて時間を35分残し黒グラビに挑むが毒ハンマーが拗ねて毒を出してくれず、 長丁場になりいにしえの秘薬を調合中に薙ぎ払われ１乙。25分消費して倒す。 ケルビの角とマンドラゴラを持っていき、活力剤は忘れてくる。 配信14回目神ハンター4人 SoRa Saketu YUKI Yuki 武神1人1殺35分針11分残し（sora2乙）クリア 他配信に凸した際、捕獲勲章狙いにもかかわらず問答無用でモンスを殺しまくった sora「スタンプおいしいです(^q^ ) 」 6月4日深夜、縛り武神をハンマーで打開。残り時間ちょうど1分。その時流れていたBGMは『エビはシャコに似ている』 +お茶目なsoraさん 2009年12月5日 罰ゲームにより、チルノのパーフェクト算数教室（sora/MIKE/Draco/disk ver）を熱唱！ 2009年12月5日同日、同じく罰ゲームにより、無職童貞ニジウラセブン（sora独唱ver）を熱唱！ またリクエストしようね＾＾ｂ 2010年2月19日、念願のマガティガソロをクリアしたにもかかわらずリスナにほめてもらえずすねる。 同日、ハンマーでモンスターハンターを1乙でクリアするもリスナにほめてもらえずすねる。 2010年3月16日、酒に酔っぱらった挙げ句意味不明な暴走を開始。 2010年3月20日、凸者と1対2でナルガ2頭の競争を行い、（soraさんは1人） 縦3が決まって「残り1頭」の表示に高らかな勝利宣言をするも倒したのは相手でとても恥ずかしい思いを味わう。 2011年6月20日、21時55分いつもの通り勝手にメシに行き、再開予定時間を22時15分とするも、なかなか帰って来ず、 13分遅れで帰っては来たものの腹痛に襲われ、謎の外人喘ぎ「Oh,yesyes...」をした後、トイレ宣言し、22時35分突如配信終了。 その後、あなりーＤＱ５配信において「ごめんね腹からエイリアン出そうな勢いで痛かったの(´・ω・｀)」という名言を残す。 その２時間後には早くも「エイリアンは宇宙に帰ったお（ ＾ω＾）」 中１の頃、簡易トイレでう○こすｒのが嫌でマウンテンバイクを全力でこぐもあと50ｍのところで間に合わず漏らしてしまい、学校のジャージに具が見えていた。 その後、泣きながらパンツを洗ったらしい。 2012年5月1日配信中にシャワーを借りに来たマーくん(まさひろとの情報も)にライフガードを買わせ、しかも配信に登場することを強要、マーくんは怯えているものと思いきやまさかの満面のダブルピースで登場(やはりイケメン)、リスナーの好感度は上々であった 2013年初配信において、マガティガで案の定３乙し、罰ゲームとしてＬＯ朗読を行った。照れながらもがんばるかっちゃんにアツイ無言の顔文字・ＡＡが多数投稿された。 その他配信まとめ +推奨コメント おじゃす！ 激天で＾＾ｂ soraさんはグラビ担当ですよね？ おめでとう チッ soraさんお肉美味しい？スタミナ減っていないのに美味しい？ （Screamさんの勇姿を称えつつ語尾に）ブロック！ 用意しておいてよかった ｍ９ 凸っていいですか？（soraさんに） パイパンなだけに、おツルんですね（第13回※198） しぬがよい かみがみしいね！（noelお兄ちゃんの頬を赤らめることが出来る呪文） +soraさんの日常 +見たら後悔するよ！それでも見るの！？ +じゃあ見やがれ＞＜ __Ｏ)二)))(・ω・｀) sora「邪魔すんな！ババコンガのア×ルが狙えないだろ！」 0二━━ )＿＿＿＿）┐ノヽ A ||ﾐ|＼ くく +こんなイベクエも・・・ ┌───────────────┐│ 狩猟クエスト ││ おかん激天 ││報酬金 ：６００００ｚ ││契約金 ：０ｚ ││制限時間：５０分 ││指定地 ：自宅＜夜＞ ││主なモンスター： /| ││カージャン |/＿＿ ││ ヽ| l l│＜ﾊｰｲ ││特殊条件： ┷┷┷ ││なし ││ ││ クエスト詳細 □１／３ │└───────────────┘ +AA ／.⌒ヽ ／ .＼ ../ ヽ. ＼ (./ ヽ. ） i r-ｰ-┬-‐､i | |,,＿ ＿,{| Ｎ| "ﾟ ` {"ﾟ｀lﾘ ﾄ.i ,__ _ ! くせー！ soraイカの匂いがぷんぷんするぜぇー！＞ ／i/ l＼ ー .ｲ|､ . 丿ﾉ ﾉ 丁丁￣l＼ . く_(__（＿（_._」____）ノ 【グラビモス】 凹凹 凹凹凹 凸 凹凹凹 ( ﾟ∀ﾟ) 上手に焼いてやんよ～♪ □□凹凹凹∪(≡≡) +soraさんの華麗なる乙写真集 マガティガにおける姿 imageプラグインエラー ご指定のURLはサポートしていません。png, jpg, gif などの画像URLを指定してください。 GJテオにおける姿 imageプラグインエラー ご指定のURLはサポートしていません。png, jpg, gif などの画像URLを指定してください。 この時、報酬で古龍の大宝玉獲得!おめでとうsoraお兄ちゃん!! imageプラグインエラー ご指定のURLはサポートしていません。png, jpg, gif などの画像URLを指定してください。 そしてsoraは乙の美しさを追求し始めるのであった・・・ imageプラグインエラー ご指定のURLはサポートしていません。png, jpg, gif などの画像URLを指定してください。 主の迷コメ集 +お茶目なかつやのコメント 71 sora64 6/20 13 15 53 いつも全裸体育座りで拝見させて頂いております＾＾(あなりーDQ5配信にてコメント) 80 sora64 6/20 13 18 05 まだ2歳だから理解してないお（ ＾ω＾） 2041 sora64 6/20 22 49 07 あなりぃぃぃぃぃぃいいいい腹痛いぃぃぃぃぃいいいおおおおおおいえすいえす ＼ﾀｲｶﾞｰｱｯﾊﾟｶｯ／ おじゃす 2045 sora64 6/20 22 51 13 ごめんね腹からエイリアン出そうな勢いで痛かったの(´・ω・｀) 2341 sora64 6/21 1 18 24 エイリアンは宇宙に帰ったお（ ＾ω＾） 626 sora64 8/23 2 06 43 ちん「こ」まん「こ」あそ「こ」うん「こ」(あなりーお絵描き配信にてコメント) 263 sora64 9/4 18 09 56 おんちぽみるく(自身の配信にてピンクコメテストしようとして誤爆) リンク 他のMHP2G配信者のページはこちら らいつべモンハン配信掲示板はこちら 主のステータス 喋り Ｍ 思考力 Ｍ 判断力 Ｍ アクションスキル ドＭ 読み子さんとの夫婦仲 重婚中 ノリの良さ Ｍ 性知識 Ｍ 歌唱力 Ｍ 配信環境 OS Windows 7 Professional CPU AMD PhenomⅡ×4 955 Processor 3.20GHz Memory DDR2 8.00G(物理約3.00GとRAM Disc約5.00G) VGA Nvidia GeForce GTS 250 Sound Sound Blaster X-Fi Titanium Professional Audio コメント wiki作っていただいてありがとうございます。( ｡・ω・｡)ﾉｼこれからもドンドン恥をさらして逝きますので、よろしくお願いしますm(_ _)m -- sora64 名前 コメント
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Computer-Aided Design Market Overview Market Research Future (MRFR), in its latest report on the Global Electrical Computer-Aided Design (ECAD) Market reveals that different forces that are likely to act on it. MRFR conducted a complete analysis on the impact of COVID 19 on the electrical computer-aided design (ECAD) market. MRFR study shows that the electrical computer-aided design market can rise at 9.22% CAGR across 2018 to 2024. The electrical computer-aided design market value can escalate from exhibiting USD 1,710.1 million in 2018 to USD 2,860.3 Million by 2024. Increase in awareness about Electrical computer-aided design (ECAD) benefits, such as easy-to-use, real-time standalone, and multi-user electrical design tool that efficiently resolve electrical interconnections for complex electrical systems is expected to support the rise of the ECAD market across the analysis period. Features, such as designing circuits accurately, quickly, and with optimization of the overall design process can promote the rise of the electrical computer-aided design market in the years to come. The increase in the application of ECAD in diagram making, concurrent designing, 3D assembly, and generation of manufacturing and documentation can favor the market rise. Request a Free Sample @ https //www.marketresearchfuture.com/sample_request/8235 Competitive Analysis ALPI International Software, EPLAN Software Service, Dassault Systèmes SE, Siemens PLM, Trimble, Inc., Autodesk, Inc., Bentley Systems, Inc., IGE+XAO, and Nemetschek SE among others are some reputed providers of global electrical computer-aided design (ECAD) solutions, as listed by MRFR. Segmental Analysis The segment study of the Global Electrical Computer-Aided Design (ECAD) Market is done by Industry Type, application, component, and vertical. The Component based segments of the electrical computer-aided design market are Software and Services. The Industry Type based segments of the electrical computer-aided design market are Process Industries and Discrete Industries. The Application based segments of the electrical computer-aided design market are Plant Design, Water Treatment and Distribution System Control, Switchgear Design, Industrial Machine Controls, Rail Signaling, Mining Equipment Control, and Others. The Vertical based segments of the electrical computer-aided design market are Equipment and Machinery, Construction, Automotive, Aerospace Defense, Power Generation and Energy, Railway, Oil Gas, Equipment and Machinery, and Food Beverage among others. Regional Analysis The trends of the electrical computer-aided design (ECAD) market is studied across APAC, North America, EU, and the rest of the world. MRFR regional evaluation reveals that the electrical computer-aided design market in North America is expected to be the largest market value. North America electrical computer-aided design market can thrive at 7.94% CAGR across the forecast period and reach a valuation of 0USD 620.4 million by 2024. The rise in the adoption of electrical computer-aided design (ECAD) technology across the growing number of discrete industries is expected to support the expansion of the regional market. In addition, the rise in industrial manufacturing investments can also promote the regional market growth. In Europe, the mounting complexities of manufacturing process are creating growth scope for the electrical computer-aided design regional market. Along with, the rise in focus on environmental concerns and industrial safety can impel the expansion of the market across the review period. The rise of EU ECAD market can be attributed to increase in viability of new energy sources, and gradual rise in cost of products as fresh set of competitive designers and engineers are focused on innovation. In APAC, the rise in popularity of ECAD and other effective 3D solution to enhance manufacturing and design processes is expected to drive the regional market. Controls development costs, accelerates time-to-market, aids in increasing cash flow, improves product quality, and profit margins can boost the expansion of APAC ECAD market. Access Report Details @ https //www.marketresearchfuture.com/reports/electrical-computer-aided-design-market-8235 Table of Content 1 Executive Summary 2 Market Introduction 2.1 Definition 17 2.2 Product Considered 17 2.3 Scope of The Study 18 2.4 Market Structure 19 Continued…. Similar Report Application Management Services Market By Service-Type (System Integration, Consulting Services, Modernization Services, And Others), By Organization Size, By Deployment, And By End-Users Open Source Intelligence (OSINT) Market By Security Type (Human Intelligence, Content Intelligence, Dark Web Analysis, Link/Network Analysis, Data Analytics, Text Analytics, Artificial Intelligence, Big Data, Others), Technology (Bid Data Software, Video Analytics, Text Analytics, Visualization Tool, Cyber Security, Web Analysis, Social Media Analysis, Others), Application (Military Defense, Homeland Security, Private Sector, Public Sector, National Security, Others) List of Tables Table 1 List of Assumptions 26 Table 2 Global Electrical Computer-Aided Design (Ecad) Market, By Component, 2020–2027 (Usd Million) 35 Table 3 Software Electrical Computer-Aided Design (Ecad) Market Estimates Forecast, By Region 36 Table 4 Global Electrical Computer-Aided Design (Ecad) Market, By Services, 2020–2027 (Usd Million) 36 Table 5 Support Electrical Computer-Aided Design (Ecad) Market Estimates Forecast, By Region 37 Continued….. List of Figures Figure 1 Market Synopsis 16 Figure 2 Market Structure Global Electrical Computer-Aided Design (Ecad) Market 19 Figure 3 North America Market Size Market Share By Country (2020 Vs 2027) 20 Figure 4 Asia-Pacific Market Size Market Share By Country (2020 Vs 2027) 20 Figure 5 Europe Market Size Market Share By Country (2020 Vs 2027) 21 Continued…. About Market Research Future At Market Research Future (MRFR), we enable our clients to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research Consulting Services. MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by Solutions, Application, Logistics and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions. Contact Market Research Future (Part of Wantstats Research and Media Private Limited) 99 Hudson Street, 5Th Floor New York, NY 10013 United States of America 1 628 258 0071 (US) 44 2035 002 764 (UK) Email sales@marketresearchfuture.com Website https //www.marketresearchfuture.com #market #research #industry #data #growth #trend #report #analyis #share #marketing #forecast #digital #geographic #demographic #gnews Plugin Error キーワードを入力してください。 #tech #researchreport #marketreport #futrue
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On Lawyers and Localization, Phoenix Wright Ace Attorney Justice for Allinterview http //www.siliconera.com/index.php/2007/01/18/on-lawyers-and-localization-phoenix-wright-ace-attorney-justice-for-all-interview/ Phoenix Wright Ace Attorney quickly found a fanbase with its clever writingand unique courtroom setting. This week Capcom is released the next installmentof the Phoenix Wright series entitled, Phoenix Wright Ace Attorney Justice forAll with a slightly more mature lawyer. Minae Matsukawa the producer of Justicefor All and Janet Hsu explain what is new in the sequel and speak about thegame’s localization. Phoenix Wright AceAttorney（海外版逆転裁判）は、その面白い文章と独特の法廷システムで、多くのファンを引き込みました。今週カプコンは、AceAttorneyシリーズとして、少し成長した弁護士と共にPhoenixWright Ace Attorney Justice forAll（海外版逆転裁判2）を発売しました。Justice forAllプロデューサーの松川美苗氏と翻訳スタッフのJanetHsu氏が、ゲームの翻訳について語りました。 In “Justice for All”, Phoenix is a seasoned lawyer. What has changedbetween the events in the first game to now? Justice forAllでは、Phoenix（成歩堂）は成長をとげた弁護士です。1作目と2作目の間で何か変化はありましたか？ Minae Matsukawa (producer) Various things happened between the two games, butnothing important, per se. More than anything, we wanted to start “Justicefor All” at a point where the player would feel that Phoenix has grown andmatured a little since the first game. So while he’s not a rookie anymore, hestill doesn’t always know what to do. 松川氏：当然2つのゲームの間には、重要でないことも含めて、色々なことが起こっています。何よりも私たちは、Phoenixが成長したことをプレーヤーが感じるような雰囲気が、JusticeforAllの始まりにあると見せたかったのです。彼はもはや新米弁護士でない、いつも何をしたらよいかを知っています。 Phoenix gains a new skill in the series with help from the Fey family, theability to break “psyche locks”. How does this enhance the gameplay? Phoenixは、Fey（綾里）から、「サイコ・ロック」を破る新しい能力を得ます。これによって、ゲームはどのように進行しますか？ Minae Matsukawa Gameplay-wise, the Psyche-Lock system adds to theinvestigation segments by providing players with a chance to interact withcharacters to obtain different kinds of information that Phoenix couldn’totherwise. It also gave the investigation segments a sense of direction and apurpose. I think this enhances the investigation segments and makes them morebalanced with the courtroom portion. 松川氏：サイコ・ロックシステムは、プレーヤーにPhoenixが今まで得ることが出来なかった、異なる種類の情報を得るために登場人物と接する機会を提供することによって、探偵パートをより面白くします。探偵パートに進行方向と目的を与える役目を果たしています。これが探偵パートを強化して、法廷パートとより釣り合うようにしています。 How did you come up with the name puns for the characters in Phoenix Wright AceAttorney Justice for All? あなたはどのようにPhoenix Wright Ace Attorney Justice forAllでの各キャラクターの名前の考えましたか？ Janet Hsu (localization) Some of the names were based on the character’spersonality, background or some other aspect, and some were simply visual gags.In the case of episode 3, a lot of them were the original names Mr. Takumiwrote for the Japanese version, since the names were already English puns tobegin with. JanetHsu（翻訳担当）：キャラの個性、背景、見た目、その他の面に基づいたものです。3話では、巧氏が日本用に考えた名前が、既に英語のダジャレになっていました。 Speaking of characters who was your favorite character to write dialoguefor? キャラの会話を書くに当たって、あなたの好きなキャラは？ Janet Hsu Personally, I enjoyed writing Franziska von Karma and AdrianAndrews. Franziska actually has a lot more depth than people give her creditfor, but you’ll have to get to the end to find out why. Adrian is a very deepcharacter, and I think she is one of the more realistic and human characters ofthe game, along with Acro. Jean Pierre (JP) Kellams, who wrote episode 3, sayshis favorite character is Moe by far because he is such a stooge. Janet Hsu：個人的に、Franziska von Karma（狩魔冥）と AdrianAndrews（華宮霧緒）の会話は書いていて楽しかったです。最後までプレイすればわかりますが、Franziskaは見た目よりずっと深いキャラクターです。Adrianもまた、非常に深いキャラで、Acro（アクロ）と同様、彼女はゲームの中のキャラクターでありなが、非常に現実的な人物だと思いました。JeanPierreKellams（3話翻訳担当）は、Moe（トミー）もまた深いキャラであったので、一番好きだと言っています。 What were some of your favorite jokes or lines that you put in the game?あなたがゲームを入れた好きな冗談は何でしたか？ Janet Hsu One word chewing gum. I don’t want to spoil the scene any morethan that. Look for it in episode 4. As for episode 3, JP says he enjoyedwriting pretty much everything Moe said. His exact words were, “Moe Curls ForThe Win!” On the serious front, I loved writing the scene where a certainsomeone had a discussion with Phoenix about what it is to be a lawyer. JanetHsu：一つ目はチューインガム。ネタバレになるので詳細は話しません。4話にあるので探してください。3話に関しては、JeanPierreKellams（3話翻訳担当）がMoeが言ったすべてを書くのが楽しかったと言います。確か、 MoeCurls For TheWin! 後はシリアスな場面、Phoenixが弁護士について議論する場面を書くのが興味深かった。 The Phoenix Wright series has become a pretty large hit. Was the success of theseries surprising? PhoenixWrightシリーズは、かなり大きなヒットになりました。シリーズの成功に驚きましたか？ Minae Matsukawa Gyakuten Saiban (as it’s known in Japan) started out smallhere as well. The original team that made it was no more than a handful ofdedicated people, and to see it become so popular around the world isdefinitely surprising. We’re very happy to have the opportunity to bring thisgame to fans outside of Japan. 松川氏：PhoenixWright（日本では逆転裁判）は、日本と同じく小規模なスタートでした。最初のファンはほんの少数の熱心な人々でしかありませんでした、その後、世界中で人気が出てきたことは明らかに驚くべきことです。私たちは、日本以外にも非常に熱心なファンがいることを幸せに思います。 If this game is successful too (and we hope it is) is there hope for morePhoenix Wright games in the future? このゲームがもっとヒットしたならば（そして、私たちも望んでいます）、PhoenixWrightシリーズの続きに関する要望が、将来実現しますか？ Minae Matsukawa If “Justice for All” does well, we will definitelyconsider bringing out more Ace Attorney games overseas in the future. 松川氏：Justice forAllが成功すれば、私たちは将来、続きのPhoenixWrightシリーズを海外で出すことを考えるでしょう。 ▲ページ上へ▲
https://w.atwiki.jp/api_programming/pages/154.html
Positioning and Sensors LocationLocation Events Sensors Fit ファイルへの記録 Communicating With ANT Sensors Positioning and Sensors Monkey C provides access to the wearable’s available sensors, which may include the GPS, altimeter, thermometer, and supported ANT sensors. Location A Location is an abstraction of a coordinate. It exposes the ability to retrieve the coordinates in radians or decimal degrees and then provides a method to convert to coordinate formats supported by the Garmin system. The Position module also exposes string parsing interface to convert from various coordinate formats to a Location object. // The GEO enum is used to retrieve coordinates in various String representations. enum { GEO_DEG, // Degree Format, ddd.dddddd 38.278652 GEO_DM, // Degree/Minute Format, dddmm.mmm 38 27.865 GEO_DMS, // Degree/Minute/Seconds Format, dddmmss 38 27 8" GEO_MGRS // Military Grid Reference System (MGRS) 4QFJ12345678 } // The Location object represents a position. It provides accessor // methods for retrieving the coordinates in various formats. class Location { // Constructor create a coordinate based off an options hash table // @param [Dictionary] options Hash table of options // @option options [Number] latitude The latitude // @option options [Number] longitude The longitude // @option options [Symbol] format The format of lat/long (possible // values are degrees, radians, or semicircles) function initialize( options ); // Use toDegrees() to retrieve the coordinate back as an Array of degree values. // @return [Array] An Array of the latitude and the longitude in degree format function toDegrees(); // Use toRadians() to retrieve the coordinate back as an Array of radian values. // @return [Array] An Array of the latitude and the longitude in radian format function toRadians(); // Use toGeoString() to get a String representation of the coordinate. // @param format Coordinate format to which coordinate should be // converted (GEO constant) // @return [String] Formatted coordinate String function toGeoString( format ); } Location Events To enable the GPS call the enableLocationEvents() method. The parameters are outlined below enum { LOCATION_ONE_SHOT, // One-time retrieval of Location LOCATION_CONTINUOUS, // Register for Location updates LOCATION_DISABLE // Unregister for Location updates } // Request a location event with enableLocationEvents(). // @param type LOCATION_ONE_SHOT for a single location request, // LOCATION_CONTINUOUS to enable location tracking, and // LOCATION_DISABLE to turn off location tracking // @param [Method] listener Method object to call with location updates function enableLocationEvents( type, listener ); To register a position listener, use the method() call to create a Method callback function onPosition( info ) { Sys.println( "Position " + info.position.toGeoString( Position.GEO_DM ) ); } function initializeListener() { Position.enableLocationEvents( Position.LOCATION_CONTINUOUS, method( onPosition ) ); } All of the location information will be sent in an Info object // The Location.Info class contains all information necessary for the Location. // It can be passed on the update or it can be retrieved on demand. class Info { var position; // Lat/lon var speed; // Speed in meters per second var altitude; // Altitude in meters, mean sea level var accuracy; // Accuracy - good, usable, poor, not available var heading; // Heading in radians var when; // Moment Object GPS time stamp of fix } // Use getInfo() to retrieve the current Location.Info // @return [Location.Info] The Info object containing the current information function getInfo(); Sensors センサー (Sensor) モジュールを使うと、 Garmin ANT+ センサーから情報を読み出すことができる。使うためには、センサーを有効にし、リスナー関数を割り当てなければならない。 function initialize() { Sensor.setEnabledSensors( [Sensor.SENSOR_HEARTRATE] ); Sensor.enableSensorEvents( method( onSensor ) ); } function onSensor(sensorInfo) { System.println( "Heart Rate " + sensorInfo.heartRate ); } Sensor information is packaged in the Sensor.Info object // The Sensor.Info class contains all information necessary for the Sensor. // It can be passed on the update or it can be retrieved on demand. class Info { var speed; // Speed in meters per second var cadence; // Cadence in revolutions per minute var heartRate; // HR in beats per minute var temperature; // Temperature in degrees Centigrade var altitude; // Altitude in meters var pressure; // Pressure in Pa var heading; // Heading in Radians } The simulator can simulate sensor data via the Simulation menu by selecting Fit Data Simulate Data. This generates valid but random values that can be read in via the sensor interface. For more acuate simulation, the simulator can play back a FIT file and feed the input into the Sensor module. To do this, select Simulation Fit Data Playback File and choose a FIT file from the dialog. Fit ファイルへの記録 Monkey C では watch apps にて、 FIT ファイル の記録開始・停止を行うことができる。これには数ステップが必要。 各種センサーでの sensors を有効にする Fit.createSession(options) で session オブジェクトを生成する。 start() で FIT session に有効なセンサーからのデータを記録（開始）する stop() to pause the recording save() で保存、もしくは discard() で削除 The FIT file will sync with Garmin Connect. You can use the Garmin Connect API to process the FIT file from a web service. Communicating With ANT Sensors Connect IQ provides a low level interface for communication with ANT and ANT+ sensors. With this interface, an ANT channel can be created to send and receive ANT packets. The MO2Display sample provides a sample application that implements the Muscle Oxygen ANT profile. The ANT Generic interface is not available to watch faces. Low and High priority search timeout for sensors differs from the basic ANT radio specification to allow for interoperation with native ANT behavior on devices. These are limited to a maximum timeout of 30 seconds and 5 seconds respectively.
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【登録タグ 曖昧さ回避】 曖昧さ回避のためのページ CielPの曲Sora/CielP コンピレーションCDSoRa/コンピレーションアルバム 曖昧さ回避について 曖昧さ回避は、同名のページが複数存在してしまう場合にのみ行います。同名のページは同時に存在できないため、当該名は「曖昧さ回避」という入口にして個々のページはページ名を少し変えて両立させることになります。 【既存のページ】は「ページ名の変更」で移動してください。曖昧さ回避を【既存のページ】に上書きするのはやめてください。「〇〇」という曲のページを「〇〇/作り手」等に移動する場合にコピペはしないでください。 曖昧さ回避作成時は「曖昧さ回避の追加の仕方」を参照してください。 曖昧さ回避依頼はこちら→修正依頼/曖昧さ回避追加依頼
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【Tags Luka Rin Toraboruta-P tI S】 Original Music title ソラトバズ English music title I Don t Fly Romaji music title Sora Tobazu Music Lyrics written, Voice edited by トラボルタP (Toraboruta-P) Music arranged by トラボルタP (Toraboruta-P) Singer(s) 鏡音リン (Kagamine Rin), Chorus by 巡音ルカ (Megurine Luka) Click here for the original Japanese Lyrics English Lyrics (translated by motokokusanagi2009)： The flightless bird envies the flying birds Looking up to the huge sky, The bird laments over who it is Ah, everyone else is so fortunate Why only am I with these wings Those who can gracefully fly high Are passing me one by one I don t care, that s life But I m just pretending to be strong To be honest I can t fully accept it... The flightless bird noticed Recently that all it said were complaints Depressed and bowing down its head, It stared at its feet Ah, that s right! I do have These feet to move ahead Crossing over fields and mountains Kicking up some dirt I bravely step forward Step by step Those who can fly, those who can walk fast Are yet passing me by Go ahead, don t worry about me Everybody should go at their own pace I can neither fly nor walk fast I won t stop I ve made up my mind on that Romaji lyrics (transliterated by motokokusanagi2009)： tobe nai tori wa omou toberu yatsu tte īnā ōzora o nagame te tobe nai mi o nageku ah nande minna zurui ya nande boku dake konna tsubasa de umareta nda sora toberu mono yūga ni takaku tsugitsugi boku o oi koshite iku yo ki ni shite nai sa sonna monda to tsuyogatte iru kedo hontō wa tsurai nda... tobe nai tori wa omou saikin guchi bakari dā shonbori kubi kashige ashimoto o nagameta ah sō datta nda boku datte motte ita nda mae ni susumeru kono ryōashi o no o koe yama koe daichi o kette kiri tto ī kao de ippo zutsu sora toberu mono ayumi hayai mono sore demo boku o oi koshite iku yo o saki ni dōzo boku ni kamawazu jibun no hohaba de minna ikeba ī sora tobezu tomo ayumi osoku tomo susumi tsuzuke yō to boku wa kimeta nda [Toraboruta-P, ToraborutaP]