中等技術科教育の本質と課題について、材料と加工に関する授業実践の観点からまとめる。初めに、学習指導要領について技術論と技術科教育の視点から考察する。次に、材料と加工に関する授業実践例を検討する。最後にこれらを受けて授業現場の視点から、課題に関するいくつかの具体的提案をする。

エネルギーは我々の生活において欠かせないもののひとつであり、現代社会においては、その資源や用途は多様で、利用方法は高度化されてきている。このようなことから新学習指導要領ではエネルギー教育を環境教育の一つに位置付け、様々な学年で複数の教科の中で取り扱われるようになっている。本稿では各教科、学年におけるエネルギー教育の目的と意義を確認し、中学校技術科における教育分野の一つである「エネルギーの変換と利用」を中心とした教科を横断するエネルギー教育の可能性や問題点について述べる。

技術分野の生物育成分野では生育技術と育成技術に関する内容を扱っている。本稿では生物育成分野の中の植物の栽培における現状について学習指導要領や教科書の内容を整理し、生物育成分野の特徴や課題について考察する。さらに、技術分野が基礎から応用まで扱っているために学生の技術への関心を誘発する可能性が高いので、広範な知識を必要とする技術分野教員をサポートする仕組みが必要であることを指摘した。加えて附属農場で行われた実習についても考察した。

2017年度前期の「生活」の講義で行われた和歌山大学附属農場におけるトウモロコシ栽培について、講義内容を振り返るとともに、本講義を受講した学生からの生活科における植物栽培に関するレポートを集約・整理し、生活科における植物栽培について考察した。

<p>Some organisms using undulatory locomotion show the transition of the swimming frequency and the wavelength of the undulation. This transition of swimming behavior is achieved by sensory feedback, and may have the role to cope with the environmental change. Especially, one of the nematode: <i>Caenorhabditis elegans</i> can modulate its frequency from approximately 0.3 to 2 Hz, and vary the wavelength of undulating body from less than a single body length to almost twice the body length. With this modulation, <i>C. elegans</i> achieves the movement under the conditions from in water to on the surface of an agar. Inspired by this finding, we propose the adaptive control for the multilink swimming robot because the environmental change demands a mobile robot the change of the pattern of the locomotion. Proposed adaptive control employs the sensory feedback from joint angles to inputs of actuators at joints to generate the self-excited oscillation. At first, we construct the multilink model in fluid with sensory feedback. After that, we construct the linearized model to obtain the relationship among the external resistive force, parameters of the multilink model and the swimming frequency by using the eigenvalue analysis. We confirm whether the proposed adaptive control simulates the modulation of undulation like <i>C. elegans</i>. Moreover, we specify parameters of the multilink model which are related to the frequency and wavelength of undulation significantly.</p>

<p>This paper reports numerical simulations of the energy harvester utilizing the flutter phenomenon of a flexible sheet. Especially, we focus on the energy harvester that an electric generator is at the leading edge of a flexible sheet, which is proposed by a preceding study and a simpler device than conventional power generators using flutter. We employ the simple numerical model which is modeled by the finite element method with Absolute Nodal Coordinate Formulation: ANCF and Unsteady Vortex Lattice Method: UVLM to design this energy harvester easily. The equation of motion for a flexible sheet has the Kelvin-Voigt-type material damping and the rotational damping at the leading edge of a sheet to emulate the effect of the coupling between a sheet and the energy harvesting circuit. This model was verified by comparing among experimental results and numerical results obtained by CFD analysis. After that, we searched the appropriate damping coefficient which corresponds to the load of a generator circuit to achieve high harvesting efficiency by the proposed model. As a result, we confirmed that the power obtained from fluid by a weak rotational damper decreases as increase of material damping when inlet flow velocity is large, and increases as increase of material damping when inlet flow velocity is small.</p>

In this paper, a quantification method of the carbohydrate is proposed for samples of spot tests using the thin-layer chromatography (TLC) based on the scanned image analysis compensating lack of overlap between each spot. The color density of image data is modeled by 2-dimensional Gaussian function. Parameters in the Gaussian function are estimated by calculating marginal distribution and using least squares (LS) method. Sample image of the spots on the TLC plate are quantified as calculating the volume of Gaussian function. A numerical example is then carried out for quantifying the glucose which is an important and commonly unit of the carbohydrate. The efficiency of our method is pointed out comparing with two non-compensated methods. In addition, in order to investigate the performance of proposed method, calibrated values are also compared with results using the high performance liquid chromatography (HPLC).

A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TVAR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (ILS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. Simulation studies illustrate the advantages and the limits of the approach.

A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TVAR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (ILS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. Simulation studies illustrate the advantages and the limits of the approach.

Estimating the autoregressive parameters from noisy observations has been addressed by various authors for the last decades. Although several on-line or off-line approaches have been proposed when the additive noise is white, few papers deal with the additive moving average noise. In this paper, we suggest estimating the model parameters by using the prediction error method. Despite its high computational cost, the method has the advantage of being efficient in the Gaussian case. A comparative study with existing methods is then carried out and points out the efficiency of our approach especially when the number of samples is small.

A great deal of interest has been paid to autoregressive parameter estimation in the noise-free case or when the observation data are disturbed by random noise. Tracking time-varying autoregressive (TVAR) parameters has been also discussed, but few papers deal with this issue when there is an additive zero-mean white Gaussian measurement noise. In this paper, one considers deterministic regression methods (or evolutive methods) where the WAR parameters are assumed to be weighted combinations of basis functions. However, the additive white measurement noise leads to a weight-estimation bias when standard least squares methods are used. Therefore, we propose two alternative blind off-line methods that allow both the variance of the additive noise and the weights to be estimated. The first one is based on the errors-in-variable issue whereas the second consists in viewing the estimation issue as a generalized eigenvalue problem. A comparative study with other existing methods confirms the effectiveness of the proposed methods. (C) 2011 Elsevier B.V. All rights reserved.

The multichannel time-varying autoregressive (M-TVAR) model is used in various applications such as radar processing, mobile communications and econometric time series. In that case, the M-TVAR parameter matrices are usually estimated by extending the evolutive method, initially proposed by Grenier in the 80ies for the scalar case. In that case, the time-varying coefficients of each matrix are expressed as weighted combinations of pre-defined basis functions. However, the estimation of the M-TVAR parameter matrices from noisy observations is a still key issue to be addressed. In this paper, we propose an effective evolutive method to estimate M-TVAR matrices from noisy observations. We suggest viewing the weight estimation from noisy observations as an errors-invariables issue. Despite its high computational cost, the approach has the advantage of also providing the covariance matrix of both the driving process and the additive noise. Simulation results point out the relevance of the approach. © 2011 EURASIP.

A great deal of interest has been paid to the estimation of time-varying autoregressive (TVAR) parameters. However, when the observations are disturbed by an additive white measurement noise, using standard least squares methods leads to a weight-estimation bias. In this paper, we propose to jointly estimate the TVAR parameters and the measurement-noise variance from noisy observations by means of a generalized eigenvalue decomposition. It extends to the TVAR case an off-line method that was initially proposed for AR parameter estimation from noisy observations. A comparative study is then carried out with existing methods such as the recursive errors-in-variable approach and Kalman based algorithms.

The multichannel time-varying autoregressive (M-TVAR) model is used in various applications such as radar processing, mobile communications and econometric time series. In that case, the M-TVAR parameter matrices are usually estimated by extending the evolutive method, initially proposed by Grenier in the 80ies for the scalar case. In that case, the time-varying coefficients of each matrix are expressed as weighted combinations of pre-defined basis functions. However, the estimation of the M-TVAR parameter matrices from noisy observations is a still key issue to be addressed. In this paper, we propose an effective evolutive method to estimate M-TVAR matrices from noisy observations. We suggest viewing the weight estimation from noisy observations as an errors-in variables issue. Despite its high computational cost, the approach has the advantage of also providing the covariance matrix of both the driving process and the additive noise. Simulation results point out the relevance of the approach.

本論文では，非定常不規則雑音に埋もれた信号を検出する新しい手法を提案する．従来，信号検出問題においては，雑音は数学的に取扱いが容易であることからその統計的性質が時間的に変化しない定常確率過程として取り扱われてきた．しかし，実際に観測されるデータには，例えば海上や海底における潮力などの影響によって非定常なものが多く存在する．このような非定常雑音中の信号検出問題に対して，本論文では信号は時間的に局所的に存在し，雑音の非定常性は緩やかに変化するものと仮定して，観測雑音を時変係数を持つARMAモデルで記述できるとする．このモデルパラメータをカルマンフィルタにより推定し非定常観測データを定常とみなせる観測データに変換する手法を提案し，その有効性を数値シミュレーションにより確認する．この手法により，非定常雑音が観測データに介在する問題は，従来の定常雑音に埋もれた信号の検出問題に帰着できる．

Recently, the authors have proposed a method for the detection of signals corrupted by nonstationary random noise based on stationarization of the observation data which can be modeled by the first-order Ito stochastic differential equation. In this paper, in order to apply this method to more general situation, we propose a stationarization method incorporated with Kalman filter. To test the proposed method simulation experiments are presented.

The purpose of this paper is to estimate unknown motion parameters, acceleration and initial velocity, from the radar signal corrupted by random noise. The principal attack of the approach is to use the (pseudo)-Wigner distribution which is computed from the noisy observation data. Parameters are estimated by least-squares method for the noisy instanteanous frequency of returned signal. Numerical simulations are presented to verify the efficacy of the proposed method.

This paper proposes a method for detecting a signal embedded in nonstationary noise. In most past studies of the signal detection problem, random noise is considered as a stationary stochastic process, since it is mathematically easy to handle. However, the noise observed in practice contains many nonstationary elements with time-varying (evolutionary) statistical properties. In this study, observational noise is modeled as a probability density function with slowly evolving parameters. Then, based on the evolving spectral representation, the nonstationary observation data are transformed to a stationary process. A new method is proposed as follows. It is assumed that nonstationarity remains in the stationarized observation data in the interval containing the signal, due to the effect of the signal. Then the signal is detected by testing for stationarity. In the proposed method, Priestley's evolutionary spectrum is used in the spectral representation of the nonstationary stochastic process, and the method of Okabe and colleagues based on the KM2O-Langevin equation is used for the stationarity test. The effectiveness of the proposed method is verified by a simulation experiment. (C) 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(8): 29-38, 2007; Published online in Wiley InterScience (www.interscience.wiley.com).

本研究は非定常雑音に埋もれた信号の検出に対する一つの手法を提案したものである.従来の多くの信号検出問題においては,不規則雑音は数学的取扱いが容易であることから定常確率過程として取り扱われてきた.しかし,実際観測される雑音には時間とともに統計的性質が変化する非定常雑音が多く存在する.本研究では,観測雑音を緩やかに変化するパラメータをもつ確率密度関数としてモデル化し,その時間変化するスペクトル表現から非定常な観測データを定常過程に変換する.このとき,信号が存在する区間においてはその信号の影響により定常化された観測データに非定常性が残存すると考え,その定常性の検定により信号を検出するという新しい手法を提案する.この手法において,非定常確率過程のスペクトル表現にはPriestleyの時変スペクトル,定常性の検定には岡部らによるKM_2O-ランジュヴァン方程式に基づいた手法を用いる.提案した手法の有効性をシミュレーション実験により確認している.

In this paper, an effective method is proposed for detecting a signal which is corrupted by nonstationary random noise. First, the noisy observation data whose statistical property is nonstationary owing to the noise is modified such that they become stationary ones. Then, based on this stationarized observation process, the stationarity test is executed to detect the signal. The method is tested by simulations to show the efficacy.

Estimation of time-varying motion parameters of moving objects is considered. The motion parameters are mapped into the instantaneous frequency (IF) of a frequency modulated signal. The IF estimation is obtained by using the Wigner distribution. Generalization in the case of multiple objects and reduced interference distributions is given. A new propagation approach is proposed for joint estimation of velocity and position of objects in a video-sequence. This approach produces better accuracy than the variable mu-propagation-based one. (C) 2004 Elsevier B.V. All rights reserved.

航空機の高度計や地中レーダーなどに使用されるFM-CWレーダーは発信信号と受信信号の位相のずれを周波数に変換することで距離を，またドップラー効果による周波数変調度により相対速度を推定する手法である．しかしその推定方法は周波数推定によるもので，雑音による影響が大きい場合には推定が困難になる．本研究ではFM-CWレーダー信号が連続波であり解像度が高いといった特徴をふまえ，雑音に埋もれた信号のパラメータ推定といった観点からすでに提案している，時間-周波数分布の一つであるウィグナー分布を用いた信号パラメータ推定法をこのFM-CWレーダー信号に応用する．またシミュレーションにより有効性を確認する．

Phase differentiation algorithms represent a powerful tool for estimation of polynomial phase wideband signals parameters. Here, they are used for development of a suboptimal algorithm for estimation of parameters of wideband signals received on a linear sensor array. The proposed suboptimal method is considered in the case of linear FM signal and compared with the chirp-beamformer approach. The proposed approach behaves slightly worse than the later. However, the chirp beamformer requires a 3D search procedure (or genetic algorithms) that is very consuming comparing to the ID search for chirp-rate parameter and 2D search for carrier frequency and angle-of-arrival estimation. This approach can be generalized to the polynomial phase signals of higher order.

不規則雑音に埋もれた信号の検定・推定問題は,音声,通信,計測など工学の分野だけでなく医療,自然信号など様々な分野で応用されている.本論文では不規則雑音に埋もれて受信される信号の遅れ時間と伸縮パラメータを同時に推定する新しい手法を提案する.信号の伸縮が受信された信号の周波数に反映されるということに着目して,時間-周波数解析の有力な手法の一つである擬似ウィグナー分布を用い,その時間-周波数領域における確率場に対して尤(ゆう)度関数を求め,それを最大にするアルゴリズムを導出することによってこれらの未知パラメータを推定する.更に,その有効性を数値シミュレーションによって示す.

Recently, authors have proposed a method to estimate parameters of signals corrupted by random noise, using pseudo-Wigner distribution. In this paper efficacy of this method is demonstrated by experiments concerning to an ultrasonic radar system.

An efficient procedure is proposed for the realization of robust short-time Fourier transform (STFT) based on the recursive realization of the standard STFT. In the initial stage it uses the robust STFT calculated by iterative or sorting procedures and, after that, the recursive relation is applied. Basic difference from: the standard STFT realization is in limiting samples in the next instants, since they can be corrupted by an impulse noise. It results in a numerically efficient form of the STFT, keeping robustness to the impulse noise influence.

Estimation of the instantaneous frequency (IF) in a high noise environment, by using the Wigner distribution (WD) and the Viterbi algorithm, is considered. The proposed algorithm combines the nonparametric IF estimation based on the WD maxima with minimization of the IF variations between consecutive points. Algorithm realization is performed recursively by using the (modified) Viterbi algorithm. Performances are compared with the IF estimation based on the WD maxima.

An effective procedure for estimation of line parameters using time-frequency (TF) representations and the SLIDE algorithm is presented. The line parameters are mapped into the instantaneous frequency (IF) of frequency modulated (FM) signals. The IF is estimated by using the TF representation. A new propagation approach is proposed. Performance of the proposed approach and the variable It-propagation are compared.

In this paper a novel method for identifying unknown parameters associated with the received signals which are corrupted by random noise is established. Our key approach is to construct a likelihood (-ratio) function for the time-frequency random field generated by the pseudo-Wigner distribution. Numerical simulations are presented to verify the efficacy of the proposed method.

A new signal detector of a chain of solitary signals corrupted by random noise is proposed by modifying the conventional Wigner distribution with wavelet. The efficacy of the proposed detector is shown by simulation studies.

Two types of wavelet-based algorithms are proposed for an online detection of a train of unknown pulse signals corrupted by random noise. The mechanism of detecting singularities hidden in the noisy observation data is analyzed, and the performance of the proposed signal detectors is evaluated. Simulation studies are provided to confirm the effectiveness of the algorithms.

The wavelet-based algorithms are proposed for an on-line detection of a train of pulse signals corrupted by random noise. The key approach is to catch the time instant at which the pulse signal changes by computing the correlation between wavelet and observation data. The effectiveness of the proposed algorithms are confirmed by numerical simulation experiments.

本論文では，非定常不規則雑音に埋もれた信号を検出する新しい手法を提案する．従来，信号検出問題においては，雑音は数学的に取扱いが容易であることからその統計的性質が時間的に変化しない定常確率過程として取り扱われてきた．しかし，実際に観測されるデータには，例えば海上や海底における潮力などの影響によって非定常なものが多く存在する．このような非定常雑音中の信号検出問題に対して，本論文では信号は時間的に局所的に存在し，雑音の非定常性は緩やかに変化するものと仮定して，観測雑音を時変係数を持つARMAモデルで記述できるとする．このモデルパラメータをカルマンフィルタにより推定し非定常観測データを定常とみなせる観測データに変換する手法を提案し，その有効性を数値シミュレーションにより確認する．この手法により，非定常雑音が観測データに介在する問題は，従来の定常雑音に埋もれた信号の検出問題に帰着できる．

航空機の高度計や地中レーダーなどに使用されるFM-CWレーダーは発信信号と受信信号の位相のずれを周波数に変換することで距離を，またドップラー効果による周波数変調度により相対速度を推定する手法である．しかしその推定方法は周波数推定によるもので，雑音による影響が大きい場合には推定が困難になる．本研究ではFM-CWレーダー信号が連続波であり解像度が高いといった特徴をふまえ，雑音に埋もれた信号のパラメータ推定といった観点からすでに提案している，時間-周波数分布の一つであるウィグナー分布を用いた信号パラメータ推定法をこのFM-CWレーダー信号に応用する．またシミュレーションにより有効性を確認する．

公開講座・講演会の企画・講師等

講師(メディアの活用),日付:2018.8.15-16

公開講座・講演会の企画・講師等

講師(メディアの活用),日付:2018.8.15-16

講演講師等

講師,任期:2017年８月～2017年８月

学術雑誌等の編集委員・査読・審査員等

編集および製本を担当,任期:1年

公開講座・講演会の企画・講師等

「Arduinoを用いた自動制御」講師,日付:2017.8.11

公開講座・講演会の企画・講師等

講師(メディアの活用),日付:2018.8.22-23

講演講師等

講師,任期:2016年8月-2016年8月

公開講座・講演会の企画・講師等

「Arduinoを用いた自動制御」講師,日付:2016.8.27

公開講座・講演会の企画・講師等

コンテスト用ロボットの開発方法についての講習会主催,日付:2016.11.14

公開講座・講演会の企画・講師等

講師(メディアの活用),日付:2016.8.16-17

学術雑誌等の編集委員・査読・審査員等

論文査読委員,任期:1年

公開講座・講演会の企画・講師等

「Arduinoを用いた自動制御」講師,日付:2015.8.21

公開講座・講演会の企画・講師等

コンテスト用ロボットの開発方法についての講習会主催,日付:2015.11.14

公開講座・講演会の企画・講師等

ロボット競技大会の開催,日付:2015.11.15

公開講座・講演会の企画・講師等

講師(学校図書館メディアの構成),日付:2015.8.19-20

公開講座・講演会の企画・講師等

制御要素と情報の流れおよびメカトロニクス部品に関する講義,日付:2015.3.1

講演講師等

講師,任期:2014/08/09～2015/08/29

公開講座・講演会の企画・講師等

「Arduinoを用いた自動制御」講師,日付:2014.8.28

公開講座・講演会の企画・講師等

コンテスト用ロボットの開発方法についての講習会主催,日付:2014.12.13

公開講座・講演会の企画・講師等

ロボット競技大会の開催,日付:2014.12.14

公開講座・講演会の企画・講師等

講師(メディアの活用),日付:2014.8.19-20

公開講座・講演会の企画・講師等

コンテスト用ロボットの開発方法についての講習会主催,日付:2013.12.10

公開講座・講演会の企画・講師等

ロボット競技大会の開催,日付:2012.12.16

小・中・高校生を対象とした学部体験入学・出張講座等

実行委員長

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講師(学校図書館メディアの構成),日付:2011.8.22-23

小・中・高校生を対象とした学部体験入学・出張講座等

実行委員長

小・中・高校生を対象とした学部体験入学・出張講座等

実行委員

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コンテスト用ロボットの開発方法についての講習会主催,日付:2011. 8.27

公開講座・講演会の企画・講師等

コンテスト用ロボットの開発方法についての講習会主催,日付:2011.10.23

公開講座・講演会の企画・講師等

コンテスト用ロボットの開発方法についての講習会主催,日付:2011.12.10

公開講座・講演会の企画・講師等

ロボット競技大会の開催,日付:2011.12.11

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コンテスト用ロボットの開発方法についての講習会主催,日付:2010/1/31

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公開講座・講演会の企画・講師等

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