TOEIC機械学習メモ



| 日本語        | 英語                    | ML文脈例文                                                        |
| ---------- | --------------------- | ------------------------------------------------------------- |
| 散らばっている    | scattered             | Data points are scattered across the feature space.           |
| 調節する、調整する  | adjust                | Adjust the learning rate to prevent overfitting.              |
| 拍手を送る      | applaud               | The team applauded the breakthrough in model accuracy.        |
| 芸術作品       | artwork               | The AI generated an artwork based on the prompt.              |
| 聴衆         | audience              | The model was fine-tuned to suit a general audience.          |
| 講堂         | auditorium            | The keynote was streamed from the main auditorium.            |
| 日よけ        | awning                | Sensors were tested under the awning for shadow effects.      |
| ～に立てかけられる  | be propped up against | The tablet was propped up against the monitor during testing. |
| 乗り込む       | board                 | Engineers boarded the autonomous vehicle prototype.           |
| レンガ        | brick                 | Brick walls were detected using a convolutional filter.       |
| （店内を）見て回る  | browse                | Users can browse the dataset catalog online.                  |
| 天窯         | canopy                | The drone flew under the forest canopy for data collection.   |
| 貨物、積み荷     | cargo                 | The model predicts cargo weight from images.                  |
| レジ係        | cashier               | A neural network was trained to detect the cashier's actions. |
| 影を落とす      | cast a shadow         | The light source cast a shadow on the training sample.        |
| 調理器具       | cookware              | Images of cookware were part of the kitchen dataset.          |
| 中庭         | courtyard             | The robot navigated the courtyard using SLAM.                 |
| 木箱、ケース     | crate                 | Items were detected in wooden crates during sorting.          |
| 横断歩道       | crosswalk             | Crosswalk detection is key in autonomous driving.             |
| 食器棚        | cupboard              | The object detector identified plates in the cupboard.        |
| 縁石         | curb                  | The model failed to recognize the curb in low light.          |
| 食事客        | diner                 | The segmentation model labeled each diner in the image.       |
| 波止場、埠頭     | dock                  | The vehicle parked near the dock for loading data.            |
| 戸口         | doorway               | The robot paused at the doorway before entering.              |
| 引き出し       | drawer                | Items were retrieved from a labeled drawer.                   |
| 詳しく調べる     | examine               | We examined the heatmap for irregularities.                   |
| 出る         | exit                  | The agent exits the state once training completes.            |
| 向く         | face                  | The camera must face the object for proper detection.         |
| 結ぶ、留める     | fasten                | Ensure the lid is fastened to avoid data loss.                |
| 噴水         | fountain              | Drones captured real-time footage near the fountain.          |
| ゴミ箱        | garbage can           | Anomaly detection flagged the garbage can as irrelevant.      |
| じっと見る      | gaze                  | The model tracks where users gaze on the interface.           |
| ちらっと見る     | glance                | The engineer glanced at the training curve.                   |
| 手袋         | glove                 | The model classifies the material of each glove.              |
| 挨拶する       | greet                 | The avatar was programmed to greet new users.                 |
| 廊下         | hallway               | The robot mapped the hallway during the test run.             |
| 扱う         | handle                | This API can handle real-time inference requests.             |
| 掛かる        | hang                  | The poster hangs behind the training station.                 |
| 向かう        | head                  | The bot headed toward the charging dock.                      |
| 遠くに        | in the distance       | An object appeared blurry in the distance.                    |
| いない        | unattended            | Unattended objects were flagged for review.                   |
| 楽器         | instrument            | The sound classifier detected a musical instrument.           |
| 交差点        | intersection          | Traffic prediction accuracy drops near intersections.         |
| ひざをつく      | kneel                 | The robot kneeled to access the low shelf.                    |
| はしご        | ladder                | A ladder detection model aids in workplace safety.            |
| 街灯柱        | lamppost              | Street view images include multiple lampposts.                |
| 広げる        | lay out               | Lay out the input features before model fitting.              |
| ～に通じる      | lead to               | Poor initialization can lead to slow convergence.             |
| 寄りかかる      | lean                  | The structure leaned due to misaligned joints.                |
| ふた         | lid                   | The camera failed to detect the jar's lid.                    |
| 灯台         | lighthouse            | The lighthouse served as a landmark for the drone.            |
| 積み込む       | load                  | Load the data into memory for preprocessing.                  |
| 顕微鏡        | microscope            | Microscopic images were used for classification.              |
| 据える、固定する   | mount                 | Cameras were mounted on the rig.                              |
| 刈る         | mow                   | The drone monitored areas recently mowed.                     |
| 見下ろす       | overlook              | The summary overlooks edge case behavior.                     |
| ページをめくる    | page through          | Page through logs to check the validation loss.               |
| テラス        | patio                 | Training footage was captured on the patio.                   |
| 歩行者        | pedestrian            | Pedestrian detection is vital for safety systems.             |
| 桟橋         | pier                  | Object recognition was tested on the pier environment.        |
| 置く         | place                 | Place the object in the region of interest.                   |
| 台、プラットフォーム | platform              | This platform supports distributed training.                  |
| 広場         | plaza                 | Surveillance AI scanned the plaza continuously.               |
| 指さす        | point                 | The cursor points to the misclassified region.                |
| 磨く         | polish                | The surface must be polished before capturing data.           |
| 肖像画        | portrait              | GANs generated realistic portraits from noise.                |
| 位置に置く      | position              | Position the camera for optimal angle.                        |
| 深鍋、植木鉢     | pot                   | Segment the image to identify the pot.                        |
| 鉢植え        | potted plant          | The model missed one potted plant in the frame.               |
| 陶器、瀬戸物     | pottery               | AI reconstructed damaged pottery digitally.                   |
| 注ぐ         | pour                  | Simulate the act of pouring to train the robot.               |
| 防護用の       | protective            | Workers wear protective gear in the dataset.                  |
| 手すり        | railing               | The railing was labeled as part of the walkway.               |
| 手を伸ばす      | reach                 | The arm reaches for the target object.                        |
| 映す、反映する    | reflect               | The chart reflects recent training improvements.              |
| 取り出す       | remove                | Remove outliers before training the model.                    |
| 列、舟をこぐ     | row                   | A row of items must be aligned correctly.                     |
| 縫う         | sew                   | Classify fabric patterns before sewing.                       |
| 棚          | shelf                 | Recognize shelf positions in warehouse images.                |
| 並んで        | side by side          | Compare models side by side in evaluation.                    |
| 向き合って座る    | sit opposite          | Two subjects sit opposite each other in the study.            |
| 傾斜した       | slanted               | Text detection failed on slanted labels.                      |
| 観客、見物客     | spectator             | Spectator behavior was tracked using sensors.                 |
| 積み重ねる      | stack                 | Stack frames to improve temporal resolution.                  |
| 手すり階段      | staircase             | Detect humans ascending a staircase.                          |
| 駅、配置する     | station               | Devices were stationed across the room.                       |
| 背もたれの無い椅子  | stool                 | The AI classified stool shapes with high accuracy.            |
| ぶらぶら歩く     | stroll                | A subject strolled through the smart city simulation.         |
| 掃く         | sweep                 | Sweep actions were learned through reinforcement learning.    |
| 刈り揃える      | trim                  | Trim long tails in the distribution.                          |
| 使われていない    | unoccupied            | Detect unoccupied seats in public transport footage.          |
| 調理器具       | utensil               | Utensils were segmented for inventory.                        |
| 花瓶         | vase                  | A vase was detected on the table.                             |
| 歩道         | walkway               | Detect obstacles on the walkway in real time.                 |
| 水をかける      | water                 | Simulate watering plants with a robotic arm.                  |
| 着用する       | wear                  | Track how many wear masks in the video.                       |
| 動かす        | wheel                 | Wheel the cart to the next test station.                      |
| 手押し一輪車     | wheelbarrow           | Wheelbarrows were detected in rural datasets.                 |
| 窓ガラス       | window pane           | Window pane reflections interfered with detection.            |
| 仕事場、作業場    | workstation           | The workstation was equipped with dual GPUs.                  |






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## ■ 家電製品 / Home Appliances

| 日本語     | 英語              | ML関連英文例文                                                                              | 日本語訳                              |
| ------- | --------------- | ------------------------------------------------------------------------------------- | --------------------------------- |
| 冷蔵庫     | refrigerator    | The smart refrigerator uses a machine learning model to optimize storage temperature. | スマート冷蔵庫は、機械学習モデルを使って保存温度を最適化する。   |
| 電子レンジ   | microwave oven  | A camera-equipped microwave uses image recognition to detect food type.               | カメラ付き電子レンジは、画像認識で食品の種類を検出する。      |
| コンロ     | stove           | A fire detection model monitors gas stove usage via IoT sensors.                      | ガスコンロの使用状況をIoTセンサーで監視する火災検知モデル。   |
| エアコン    | air conditioner | AI-based air conditioners learn user preferences for temperature control.             | AI搭載のエアコンは、ユーザーの好みに基づいて温度制御を学習する。 |
| 暖房・ストーブ | heater          | A predictive model controls the heater based on weather data.                         | 天気データを元に暖房を制御する予測モデル。             |
| 掃除機     | vacuum cleaner  | Vacuum noise patterns are analyzed to detect operational anomalies.                   | 掃除機の騒音パターンから異常を検出する。              |
| 洗濯機     | washing machine | Machine learning enables smart washing machines to classify fabric types.             | 機械学習により、生地の種類を分類するスマート洗濯機。        |

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## ■ 日用品 / Daily Goods

| 日本語     | 英語                   | ML関連英文例文                                                                  | 日本語訳                             |
| ------- | -------------------- | ------------------------------------------------------------------------- | -------------------------------- |
| 電球      | (light) bulb         | Smart bulbs adjust brightness based on usage patterns learned by ML.      | スマート電球は、使用パターンを学習して明るさを調整する。     |
| 本棚      | bookcase / bookshelf | Object recognition detects book placement on the bookshelf.               | 物体認識で本棚上の本の配置を検出する。              |
| 洗剤      | (laundry) detergent  | Detergent levels are monitored using image classification in smart homes. | スマートホームで洗剤の残量を画像分類で監視する。         |
| 照明器具    | light fixture        | Light fixtures report malfunctions via anomaly detection.                 | 異常検知によって照明器具の故障を報告する。            |
| ホッチキス   | stapler              | Office supply monitoring tracks stapler usage with sensors.               | オフィス用品の監視によりホッチキスの使用をセンサーで追跡。    |
| 日焼け止め   | sunscreen            | Sunscreen reminders are personalized using skin tone recognition models.  | 肌色認識モデルで日焼け止めのリマインダーをパーソナライズする。  |
| 封筒      | envelope             | Envelope detection helps sort scanned documents using OCR.                | OCRでスキャンした文書を封筒の有無で仕分ける。         |
| 秤       | scale                | Scales log weight readings in real time for health tracking ML models.    | 秤は健康管理用MLモデルにリアルタイムで体重を記録する。     |
| 食器棚     | cupboard             | Smart cupboards detect item presence using RFID and classification.       | RFIDと分類で食器棚内の物品の有無を検出する。         |
| はさみ     | scissors             | Smart drawer systems detect scissor usage with vibration sensors.         | スマート引き出しシステムで、はさみの使用を振動センサーで検出。  |
| 文房具     | stationery           | Stationery inventory is tracked using object detection algorithms.        | 文房具の在庫を物体検出アルゴリズムで管理する。          |
| ビニール袋   | plastic bag          | Plastic bags are detected in waste classification systems.                | ごみ分類システムでビニール袋を検出。               |
| 折りたたみ椅子 | folding chair        | Folding chair usage is logged in event-space planning ML tools.           | 折りたたみ椅子の使用状況がイベントスペース管理MLに記録される。 |
| 段ボール箱   | cardboard box        | Box sizes are predicted in logistics using regression models.             | ロジスティクスで箱のサイズを回帰モデルで予測。          |
| マフラー    | scarf                | ML models classify scarf materials for washing instructions.              | 洗濯表示分類のためにスカーフ素材をMLモデルで分類する。     |

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## ■ IT機器 / Digital Devices

| 日本語   | 英語                   | ML関連英文例文                                                         | 日本語訳                              |
| ----- | -------------------- | ---------------------------------------------------------------- | --------------------------------- |
| ノートPC | laptop               | We trained a model directly on laptops using federated learning. | フェデレーテッドラーニングで、ノートPC上で直接モデルを訓練した。 |
| 携帯電話  | mobile / cell phone  | Mobile apps use on-device ML for offline speech recognition.     | 携帯アプリは、端末内MLでオフライン音声認識を実行する。      |
| コピー機  | photocopier          | ML is used to detect paper jams in copiers with sensor data.     | コピー機の紙詰まりをセンサーデータで検出するMLが使われている。  |
| 充電池   | rechargeable battery | Battery charge levels are predicted with LSTM models.            | バッテリー残量をLSTMモデルで予測する。             |

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## ■ その他 / Miscellaneous

| 日本語       | 英語               | ML関連英文例文                                                              | 日本語訳                              |
| --------- | ---------------- | --------------------------------------------------------------------- | --------------------------------- |
| 書類かばん     | briefcase        | Smart briefcases use sensors to log access events.                    | スマート書類かばんはセンサーでアクセスを記録。           |
| 回転ドア      | revolving door   | Person detection at revolving doors helps monitor traffic flow.       | 回転ドアの人物検出は人の流れの監視に役立つ。            |
| 貨物用エレベーター | freight elevator | Freight elevator usage is predicted for building energy optimization. | 貨物エレベーターの使用を予測してビルのエネルギー最適化に利用する。 |
| ランニングマシン  | treadmill        | Treadmill speed is adjusted via user gait analysis models.            | ランニングマシンの速度は歩行解析モデルで調整される。        |

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