ホウレンソウ自動収穫装置では，土中の根切り刃経路の設定は重要であり，自動収穫の成否にかかわる．これまで幾つかの方法を提案したが，収穫装置本体の上下変動により経路が変動する問題があった．本稿では，これまでの経路設定を改良し，より収穫に有利となる経路の提案を行うとともに，圃場実験によってその性能を検証する．

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It is desired to detect stenosis at an early stage to use hemodailysis shunt for longer time. Stethoscope auscultation of vascular murmurs is useful noninvasive diagnostic approach, but an experienced expert operator is necessary. Some experts often say that the high-pitch murmurs exist if the shunt becomes stenosed, and some studies report that there are some features detected at high frequency by time-frequency analysis. However, some of the murmurs are difficult to detect, and the final judgment is difficult. This study proposes a new diagnosis support system to screen stenosis by using vascular murmurs. The system is performed using artificial neural networks (ANN) with the analyzed frequency data by maximum entropy method (MEM). The author recorded vascular murmurs both before percutaneous transluminal angioplasty (PTA) and after. Examining the MEM spectral characteristics of the high-pitch stenosis murmurs, three features could be classified, which covered 85 percent of stenosis vascular murmurs. The features were learnt by the ANN, and judged. As a result, a percentage of judging the classified stenosis murmurs was 100%, and that of normal was 86%.

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We verified that arteriovenous fistula (AVF) function can be evaluated from shunt sound using a body-conducted sound sensor (BCS) with a wide-band frequency characteristic. In the BCS, we compared two types of sensors: an air-coupled microphone (ACM) and an acceleration sensor (Type 8001). Evaluation for sensors were performed for 36 patients. We also developed a shunt-sound observation system and evaluated it using the spectrogram and power spectrum values obtained using a sound spectrometer. Furthermore, we verified that two indices of ultrasonography information, namely, the resistance index (RI) and flow volume (FV), can be estimated from the shunt sound and classified through machine learning. The BCS detected the shunt sound signal in the spectrogram over a wide frequency band. The sensitivities of the BCS in the non-amplified detection of stenosis sound signals were 0.93/0.92 (below 1 kHz/above 1 kHz, respectively), surpassing those of other sensors(ACM: 0.82/0.77, Type 8001: 0.82/0.25). Furthermore, the highest machine learning accuracy was obtained using BCS in the classification segments of RI > 0.60, 0.65, and 0.70 and FV < 350 ml/min. BCS is particularly suitable for detecting stenosis sounds of over 1 kHz and estimating RI and is considered to be suitable for functional evaluations of AVF.

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